%0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e69113 %T Impact of Acute Respiratory Infections on Medical Absenteeism Among Military Personnel: Retrospective Cohort Study %A M,Premikha %A Goh,Jit Khong %A Ng,Jing Qiang %A Mutalib,Adeliza %A Lim,Huai Yang %K respiratory infections %K military %K epidemiology %K public health %K surveillance %D 2025 %7 18.4.2025 %9 %J JMIR Form Res %G English %X Background: Acute respiratory infections (ARI) are a significant challenge in military settings due to close communal living, which facilitates the rapid transmission of pathogens. A variety of respiratory pathogens contribute to ARI, each varying in prevalence, severity, and impact on organizational productivity. Understanding and mitigating the impact of ARI is critical for optimizing the health of military personnel and maintaining organizational productivity. Objective: This retrospective study of surveillance data aims to identify pathogens causing ARI among servicemen and determine which pathogens contribute most to medical absenteeism, defined as the combined duration of the issued medical certificate and light duty. Methods: From September 2023 to August 2024, anonymous nasopharyngeal swabs (BioFire FilmArray Respiratory Panel) were collected from Singapore Armed Forces servicemen presenting with ARI symptoms after a doctor’s consultation at a local military camp’s medical centre. The presence of fever and duration of medical certificate and light duty were self-reported by Singapore Armed Forces servicemen. Results: A total of 1095 nasopharyngeal swabs were collected, of which 608 (55.5%) tested positive. The most common respiratory pathogen was human rhinovirus/enterovirus (HRV/HEV) in 303 (27.7%) individuals. The highest proportions of fever were observed in servicemen with influenza (62.8%, 27/43), SARS-CoV-2 (34.3%, 12/35), and parainfluenza (31.6%, 12/38). The odds of patients with influenza that have fever was 5.8 times higher than those of patients infected with HRV/HEV (95% CI 2.95‐11.40, P<.001). The median duration of medical certificate, light duty, and medical absenteeism were 0 (IQR 0), 2 (IQR 2) and 2 (IQR 0) days, respectively. The odds of patients with influenza having a medical certificate with duration ≥1 day was 5.34 times higher than those in patients with HRV/HEV (95% CI 2.63‐10.88, P<.001). No significant differences in the duration of medical absenteeism were found between HRV/HEV and other pathogens. Conclusions: Compared to HRV/HEV, influenza infections were significantly associated with longer medical certificate duration. Nonetheless, there were no significant differences in the overall duration of medical absenteeism across pathogens, as servicemen infected with other pathogens were given light duty instead. These findings emphasize the need for pathogen-agnostic ARI measures. While influenza vaccinations are already mandatory for servicemen in local military camps, encouraging additional public health measures (eg, mask-wearing among symptomatic servicemen, COVID-19 vaccinations, therapeutics) can further reduce ARI incidence, minimize the duration of medical absenteeism, and mitigate the impact on organizational productivity. %R 10.2196/69113 %U https://formative.jmir.org/2025/1/e69113 %U https://doi.org/10.2196/69113 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e68936 %T Digital Health Intervention on Awareness of Vaccination Against Influenza Among Adults With Diabetes: Pragmatic Randomized Follow-Up Study %A Fundoiano-Hershcovitz,Yifat %A Lee,Felix %A Stanger,Catherine %A Breuer Asher,Inbar %A Horwitz,David L %A Manejwala,Omar %A Liska,Jan %A Kerr,David %+ Dario Health, Ofek 8, 5 Tarshish St, Caesarea, 3079821, Israel, 972 0525296979, yifat@dariohealth.com %K digital health %K diabetes management %K influenza vaccination %K flu vaccination awareness %K mobile health %D 2025 %7 10.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Diabetes mellitus significantly increases the risk of severe complications from influenza, necessitating targeted vaccination efforts. Despite vaccination being the most effective preventive measure, coverage remains below the World Health Organization’s targets, partly due to limited awareness among patients. This study evaluated a digital health intervention aimed at improving influenza vaccination rates among adults with diabetes. Objective: This study aimed to demonstrate the effectiveness of digital health platforms in increasing vaccination rates among people with diabetes and to emphasize the impact of tailored messaging frequency on patient engagement and health behavior change. We hypothesized that digital tools providing empirical evidence of increased health risk awareness can effectively drive preventive actions. Methods: The study leveraged the Dario (Dario Health Corp) digital health platform to retrospectively analyze data from 64,904 users with diabetes assigned by the platform into three groups: (1) Group A received previously studied monthly flu nudge messages; (2) Group B received an adapted intervention with 2-3 monthly messages; (3) Group C served as the control with no intervention. Surveys were conducted at baseline, 3 months, and 6 months to assess vaccination status, awareness of influenza risks, and recollection of educational content. Statistical analyses, including logistic regression, chi-square tests, and t tests, were used to evaluate differences between groups. Results: Out of 64,904 users, 8431 completed the surveys. Vaccination rates were 71.0% in group A, 71.9% in group B, and 70.5% in group C. Group B showed significantly higher awareness of influenza risks compared with the control group odds ratio (OR; OR 1.35, 95% CI 1.12-1.63; P=.001), while group A did not (OR 1.10, 95% CI 0.92-1.32; P=.27). Recollection of educational content was also higher in groups A (OR 1.29, 95% CI 1.07-1.56; P=.008) and B (OR 1.92, 95% CI 1.59-2.33; P<.001) compared with the control. In addition, a significant correlation between awareness and vaccination rates was found only in group B (χ2(df=1)=6.12, P=.01). Conclusions: The adapted digital intervention (group B) effectively increased awareness of influenza risks and recollection of educational content, which correlated with the higher trend in vaccination rates. This study demonstrates the potential of digital health tools to enhance influenza vaccination among people with diabetes by improving risk awareness and education. Further research should focus on optimizing these interventions to achieve significant improvements in vaccination uptake and overall public health outcomes. Trial Registration: ClinicalTrials.gov NCT06840236; https://clinicaltrials.gov/study/NCT06840236 %M 40209214 %R 10.2196/68936 %U https://www.jmir.org/2025/1/e68936 %U https://doi.org/10.2196/68936 %U http://www.ncbi.nlm.nih.gov/pubmed/40209214 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e67050 %T Impact of Primary Health Care Data Quality on Infectious Disease Surveillance in Brazil: Case Study %A Florentino,Pilar Tavares Veras %A Bertoldo Junior,Juracy %A Barbosa,George Caique Gouveia %A Cerqueira-Silva,Thiago %A Oliveira,Vinicius de Araújo %A Garcia,Marcio Henrique de Oliveira %A Penna,Gerson Oliveira %A Boaventura,Viviane %A Ramos,Pablo Ivan Pereira %A Barral-Netto,Manoel %A Marcilio,Izabel %K primary health care %K data quality %K infectious disease surveillance %K Brazil %K early warning system %D 2025 %7 21.2.2025 %9 %J JMIR Public Health Surveill %G English %X Background: The increase in emerging and re-emerging infectious disease outbreaks underscores the need for robust early warning systems (EWSs) to guide mitigation and response measures. Administrative health care databases provide valuable epidemiological insights without imposing additional burdens on health services. However, these datasets are primarily collected for operational use, making data quality assessment essential to ensure an accurate interpretation of epidemiological analysis. This study focuses on the development and implementation of a data quality index (DQI) for surveillance integrated into an EWS for influenza-like illness (ILI) outbreaks using Brazil’s a nationwide Primary Health Care (PHC) dataset. Objective: We aimed to evaluate the impact of data completeness and timeliness on the performance of an EWS for ILI outbreaks and establish optimal thresholds for a suitable DQI, thereby improving the accuracy of outbreak detection and supporting public health surveillance. Methods: A composite DQI was established to measure the completeness and timeliness of PHC data from the Brazilian National Information System on Primary Health Care. Completeness was defined as the proportion of weeks within an 8-week rolling window with any register of encounters. Timeliness was calculated as the interval between the date of encounter and its corresponding registry in the information system. The backfilled PHC dataset served as the gold standard to evaluate the impact of varying data quality levels from the weekly updated real-time PHC dataset on the EWS for ILI outbreaks across 5570 Brazilian municipalities from October 10, 2023, to March 10, 2024. Results: During the study period, the backfilled dataset recorded 198,335,762 ILI-related encounters, averaging 8,623,294 encounters per week. The EWS detected a median of 4 (IQR 2‐5) ILI outbreak warnings per municipality using the backfilled dataset. Using the real-time dataset, 12,538 (65%) warnings were concordant with the backfilled dataset. Our analysis revealed that 100% completeness yielded 76.7% concordant warnings, while 80% timeliness resulted in at least 50% concordant warnings. These thresholds were considered optimal for a suitable DQI. Restricting the analysis to municipalities with a suitable DQI increased concordant warnings to 80.4%. A median of 71% (IQR 54%-71.9%) of municipalities met the suitable DQI threshold weekly. Municipalities with ≥60% of weeks achieving a suitable DQI demonstrated the highest concordance between backfilled and real-time datasets, with those achieving ≥80% of weeks showing 82.3% concordance. Conclusions: Our findings highlight the critical role of data quality in improving the EWS’ performance based on PHC data for detecting ILI outbreaks. The proposed framework for real-time DQI monitoring is a practical approach and can be adapted to other surveillance systems, providing insights for similar implementations. We demonstrate that optimal completeness and timeliness of data significantly impact the EWS’ ability to detect ILI outbreaks. Continuous monitoring and improvement of data quality should remain a priority to strengthen the reliability and effectiveness of surveillance systems. %R 10.2196/67050 %U https://publichealth.jmir.org/2025/1/e67050 %U https://doi.org/10.2196/67050 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e63881 %T InfectA-Chat, an Arabic Large Language Model for Infectious Diseases: Comparative Analysis %A Selcuk,Yesim %A Kim,Eunhui %A Ahn,Insung %+ , Department of Data-Centric Problem Solving Research, Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea, 82 42 869 1053, isahn@kisti.re.kr %K large language model %K Arabic large language models %K AceGPT %K multilingual large language model %K infectious disease monitoring %K public health %D 2025 %7 10.2.2025 %9 Original Paper %J JMIR Med Inform %G English %X Background: Infectious diseases have consistently been a significant concern in public health, requiring proactive measures to safeguard societal well-being. In this regard, regular monitoring activities play a crucial role in mitigating the adverse effects of diseases on society. To monitor disease trends, various organizations, such as the World Health Organization (WHO) and the European Centre for Disease Prevention and Control (ECDC), collect diverse surveillance data and make them publicly accessible. However, these platforms primarily present surveillance data in English, which creates language barriers for non–English-speaking individuals and global public health efforts to accurately observe disease trends. This challenge is particularly noticeable in regions such as the Middle East, where specific infectious diseases, such as Middle East respiratory syndrome coronavirus (MERS-CoV), have seen a dramatic increase. For such regions, it is essential to develop tools that can overcome language barriers and reach more individuals to alleviate the negative impacts of these diseases. Objective: This study aims to address these issues; therefore, we propose InfectA-Chat, a cutting-edge large language model (LLM) specifically designed for the Arabic language but also incorporating English for question and answer (Q&A) tasks. InfectA-Chat leverages its deep understanding of the language to provide users with information on the latest trends in infectious diseases based on their queries. Methods: This comprehensive study was achieved by instruction tuning the AceGPT-7B and AceGPT-7B-Chat models on a Q&A task, using a dataset of 55,400 Arabic and English domain–specific instruction–following data. The performance of these fine-tuned models was evaluated using 2770 domain-specific Arabic and English instruction–following data, using the GPT-4 evaluation method. A comparative analysis was then performed against Arabic LLMs and state-of-the-art models, including AceGPT-13B-Chat, Jais-13B-Chat, Gemini, GPT-3.5, and GPT-4. Furthermore, to ensure the model had access to the latest information on infectious diseases by regularly updating the data without additional fine-tuning, we used the retrieval-augmented generation (RAG) method. Results: InfectA-Chat demonstrated good performance in answering questions about infectious diseases by the GPT-4 evaluation method. Our comparative analysis revealed that it outperforms the AceGPT-7B-Chat and InfectA-Chat (based on AceGPT-7B) models by a margin of 43.52%. It also surpassed other Arabic LLMs such as AceGPT-13B-Chat and Jais-13B-Chat by 48.61%. Among the state-of-the-art models, InfectA-Chat achieved a leading performance of 23.78%, competing closely with the GPT-4 model. Furthermore, the RAG method in InfectA-Chat significantly improved document retrieval accuracy. Notably, RAG retrieved more accurate documents based on queries when the top-k parameter value was increased. Conclusions: Our findings highlight the shortcomings of general Arabic LLMs in providing up-to-date information about infectious diseases. With this study, we aim to empower individuals and public health efforts by offering a bilingual Q&A system for infectious disease monitoring. %M 39928922 %R 10.2196/63881 %U https://medinform.jmir.org/2025/1/e63881 %U https://doi.org/10.2196/63881 %U http://www.ncbi.nlm.nih.gov/pubmed/39928922 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e66072 %T Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study %A Xiong,Xin %A Xiang,Linghui %A Chang,Litao %A Wu,Irene XY %A Deng,Shuzhen %+ Department of School Health, Yunnan Provincial Center for Disease Control and Prevention, No.1177 Xianghe Street, Luolong Street, Chenggong District, Kunming, 650500, China, 86 15096624164, 461447164@qq.com %K mumps %K deep learning %K baidu index %K forecasting %K incidence prediction %K time series analysis %K Yunnan %K China %D 2025 %7 6.2.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availability of an effective vaccine, mumps outbreaks have reemerged globally, including in China, where it remains a significant public health issue. In Yunnan province, China, the incidence of mumps has fluctuated markedly and is higher than that in mainland China, underscoring the need for improved outbreak prediction methods. Traditional surveillance methods, however, may not be sufficient for timely and accurate outbreak prediction. Objective: Our study aims to leverage the Baidu search index, representing search volumes from China’s most popular search engine, along with environmental data to develop a predictive model for mumps incidence in Yunnan province. Methods: We analyzed mumps incidence in Yunnan Province from 2014 to 2023, and used time series data, including mumps incidence, Baidu search index, and environmental factors, from 2016 to 2023, to develop predictive models based on long short-term memory networks. Feature selection was conducted using Pearson correlation analysis, and lag correlations were explored through a distributed nonlinear lag model (DNLM). We constructed four models with different combinations of predictors: (1) model BE, combining the Baidu index and environmental factors data; (2) model IB, combining mumps incidence and Baidu index data; (3) model IE, combining mumps incidence and environmental factors; and (4) model IBE, integrating all 3 data sources. Results: The incidence of mumps in Yunnan showed significant variability, peaking at 37.5 per 100,000 population in 2019. From 2014 to 2023, the proportion of female patients ranged from 41.3% in 2015 to 45.7% in 2020, consistently lower than that of male patients. After excluding variables with a Pearson correlation coefficient of <0.10 or P values of <.05, we included 3 Baidu index search term groups (disease name, symptoms, and treatment) and 6 environmental factors (maximum temperature, minimum temperature, sulfur dioxide, carbon monoxide, particulate matter with a diameter of 2.5 µm or less, and particulate matter with a diameter of 10 µm or less) for model development. DNLM analysis revealed that the relative risks consistently increased with rising Baidu index values, while nonlinear associations between temperature and mumps incidence were observed. Among the 4 models, model IBE exhibited the best performance, achieving the coefficient of determination of 0.72, with mean absolute error, mean absolute percentage error, and root-mean-square error values of 0.33, 15.9%, and 0.43, respectively, in the test set. Conclusions: Our study developed model IBE to predict the incidence of mumps in Yunnan province, offering a potential tool for early detection of mumps outbreaks. The performance of model IBE underscores the potential of integrating search engine data and environmental factors to enhance mumps incidence forecasting. This approach offers a promising tool for improving public health surveillance and enabling rapid responses to mumps outbreaks. %M 39913179 %R 10.2196/66072 %U https://www.jmir.org/2025/1/e66072 %U https://doi.org/10.2196/66072 %U http://www.ncbi.nlm.nih.gov/pubmed/39913179 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e57495 %T Elevated Ambient Temperature Associated With Reduced Infectious Disease Test Positivity Rates: Retrospective Observational Analysis of Statewide COVID-19 Testing and Weather Across California Counties %A Kwok,Nicholas Wing-Ping %A Pevnick,Joshua %A Feldman,Keith %K body temperature %K BT %K fever %K febrile %K feverish %K ambient temperature %K environmental factor %K environmental context %K environmental %K environment %K COVID-19 %K SARS-CoV-2 %K coronavirus %K respiratory %K infectious %K pulmonary %K COVID-19 pandemic %K pandemic %K diagnostics %K diagnostic test %K diagnostic testing %K public health surveillance %D 2024 %7 12.12.2024 %9 %J JMIR Public Health Surveill %G English %X Background: From medication usage to the time of day, a number of external factors are known to alter human body temperature (BT), even in the absence of underlying pathology. In select cases, clinical guidance already suggests the consideration of clinical and demographic factors when interpreting BT, such as a decreased threshold for fever as age increases. Recent work has indicated factors impacting BT extend to environmental conditions including ambient temperature. However, the effect sizes of these relationships are often small, and it remains unclear if such relationships result in a meaningful impact on real-world health care practices. Objective: Temperature remains a common element in public health screening efforts. Leveraging the unique testing and reporting infrastructure developed around the COVID-19 pandemic, this paper uses a unique resource of daily-level statewide testing data to assess the relationship between ambient temperatures and positivity rates. As fever was a primary symptom that triggered diagnostic testing for COVID-19, this work hypothesizes that environmentally mediated BT increases would not reflect pathology, leading to decreased COVID-19 test positivity rates as temperature rises. Methods: Statewide COVID-19 polymerase chain reaction testing data curated by the California Department of Public Health were used to obtain the daily number of total tests and positivity rates for all counties across the state. These data were combined with ambient temperature data provided by the National Centers for Environmental Information for a period of 133 days between widespread testing availability and vaccine approval. A mixed-effects beta-regression model was used to estimate daily COVID-19 test positivity rate as a function of ambient temperature, population, and estimates of COVID prevalence, with nested random effects for a day of the week within unique counties across the state. Results: Considering over 19 million tests performed over 4 months and across 45 distinct counties, adjusted model results highlighted a significant negative association between daily ambient temperature and testing positivity rate (P<.001). Results of the model are strengthened as, using the same testing data, this relationship was not present in a sensitivity analysis using random daily temperatures drawn from the range of observed values (P=.52). Conclusions: These results support the underlying hypothesis and demonstrate the relationship between environmental factors and BT can impact an essential public health activity. As health care continues to operate using thresholds of BT as anchor points (ie, ≥100.4 as fever) it is increasingly important to develop approaches to integrate the array of factors known to influence BT measurement. Moreover, as weather data are not often readily available in the same systems as patient data, these findings present a compelling case for future research into when and how environmental context can best be used to improve the interpretation of patient data. %R 10.2196/57495 %U https://publichealth.jmir.org/2024/1/e57495 %U https://doi.org/10.2196/57495 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e54597 %T A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study %A Deady,Matthew %A Duncan,Raymond %A Sonesen,Matthew %A Estiandan,Renier %A Stimpert,Kelly %A Cho,Sylvia %A Beers,Jeffrey %A Goodness,Brian %A Jones,Lance Daniel %A Forshee,Richard %A Anderson,Steven A %A Ezzeldin,Hussein %+ Center for Biologics Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD, 20993, United States, 1 240 205 2215, hussein.ezzeldin@fda.hhs.gov %K adverse event %K vaccine safety %K interoperability %K computable phenotype %K postmarket surveillance system %K fast healthcare interoperability resources %K FHIR %K real-world data %K validation study %K Food and Drug Administration %K electronic health records %K COVID-19 vaccine %D 2024 %7 25.11.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Adverse events (AEs) associated with vaccination have traditionally been evaluated by epidemiological studies. More recently, they have gained attention due to the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several AEs of interest to ensure the safety of vaccines, including those for COVID-19. Objective: This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the US Food and Drug Administration’s postmarket surveillance capabilities while minimizing the burden of collecting clinical data on suspected postvaccination AEs. The objective of this study was to enhance active surveillance efforts through a pilot platform that can receive automatically reported AE cases through a health care data exchange. Methods: We detected cases by sharing and applying computable phenotype algorithms to real-world data in health care providers’ electronic health records databases. Using the fast healthcare interoperability resources standard for secure data transmission, we implemented a computable phenotype algorithm on a new health care system. The study focused on the algorithm's positive predictive value, validated through clinical records, assessing both the time required for implementation and the accuracy of AE detection. Results: The algorithm required 200-250 hours to implement and optimize. Of the 6,574,420 clinical encounters across 694,151 patients, 30 cases were identified as potential myocarditis/pericarditis. Of these, 26 cases were retrievable, and 24 underwent clinical validation. In total, 14 cases were confirmed as definite or probable myocarditis/pericarditis, yielding a positive predictive value of 58.3% (95% CI 37.3%-76.9%). These findings underscore the algorithm's capability for real-time detection of AEs, though they also highlight variability in performance across different health care systems. Conclusions: The study advocates for the ongoing refinement and application of distributed computable phenotype algorithms to enhance AE detection capabilities. These tools are crucial for comprehensive postmarket surveillance and improved vaccine safety monitoring. The outcomes suggest the need for further optimization to achieve more consistent results across diverse health care settings. %M 39586081 %R 10.2196/54597 %U https://www.jmir.org/2024/1/e54597 %U https://doi.org/10.2196/54597 %U http://www.ncbi.nlm.nih.gov/pubmed/39586081 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e64969 %T Public Health Data Exchange Through Health Information Exchange Organizations: National Survey Study %A Rosenthal,Sarah %A Adler-Milstein,Julia %A Patel,Vaishali %K public health informatics %K health information exchange %K health information technology %K data exchange %K health information %K national survey %K surveillance %K United States %K PHA %K HIO %K public health agency %K health information exchange organization %D 2024 %7 15.11.2024 %9 %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic revealed major gaps in public health agencies’ (PHAs’) data and reporting infrastructure, which limited the ability of public health officials to conduct disease surveillance, particularly among racial or ethnic minorities disproportionally affected by the pandemic. Leveraging existing health information exchange organizations (HIOs) is one possible mechanism to close these technical gaps, as HIOs facilitate health information sharing across organizational boundaries. Objective: The aim of the study is to survey all HIOs that are currently operational in the United States to assess HIO connectivity with PHAs and HIOs’ capabilities to support public health data exchange. Methods: Drawing on multiple sources, we identified all potential local, regional, and state HIOs that were operational in the United States as of March 1, 2022. We defined operational as HIOs that facilitated exchange between at least 2 independent entities. We fielded a survey among our census list of 135 HIOs in January-July 2023. The survey confirmed HIO status as well as captured organizational demographics and current and potential support for PHAs. We report descriptive statistics on HIO demographics and connectivity with PHAs. We also include results on services and data available to support PHAs, funding sources to support public health reporting, and barriers to public health reporting. Of the 135 potential HIOs that received the survey, 90 met our definition of an HIO, and 77 completed the survey, yielding an 86% response rate. Results: We found that 66 (86%) of HIOs in 45 states were electronically connected to at least 1 PHA, yielding 187 HIO-PHA connections across all HIOs. Among HIOs connected to PHAs, the most common type of public health reporting supported by HIOs was immunization registry (n=39, 64%), electronic laboratory result (n=37, 63%), and syndromic surveillance (n=34, 61%). In total, 58% (n=38) of HIOs connected to PHAs provided data to address COVID-19 information gaps, and an additional 30% (n=20) could do so. The most common types of data provided to PHAs were hospitalization information (n=54, 93%), other demographic data (n=53, 91%), health information (eg, chronic health conditions; n=51, 88%), and hospital laboratory results (n=51, 88%). A total of 64% (n=42) of HIOs provided at least 1 type of data analytic service to PHAs to support COVID-19 pandemic response efforts. Top HIO reported barriers to support PHA activities included limited PHA funding (n=21, 32%) and PHAs’ competing priorities (n=15, 23%). Conclusions: Our results show that many HIOs are already connected to PHAs and that they are assuming an emerging role to facilitate public health reporting. HIOs are well-positioned to provide value-added support for public health data exchange and address PHAs’ information gaps, as ongoing federal efforts to modernize public health data infrastructure and interoperability continue. %R 10.2196/64969 %U https://publichealth.jmir.org/2024/1/e64969 %U https://doi.org/10.2196/64969 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e62641 %T Early Warning Systems for Acute Respiratory Infections: Scoping Review of Global Evidence %A Patel,Atushi %A Maruthananth,Kevin %A Matharu,Neha %A Pinto,Andrew D %A Hosseini,Banafshe %+ Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada, 1 416 864 6060 ext 76148, benita.hosseini@unityhealth.to %K early warning systems %K acute respiratory infections %K early detection systems %D 2024 %7 7.11.2024 %9 Review %J JMIR Public Health Surveill %G English %X Background: Early warning systems (EWSs) are tools that integrate clinical observations to identify patterns indicating increased risks of clinical deterioration, thus facilitating timely and appropriate interventions. EWSs can mitigate the impact of global infectious diseases by enhancing information exchange, monitoring, and early detection. Objective: We aimed to evaluate the effectiveness of EWSs in acute respiratory infections (ARIs) through a scoping review of EWSs developed, described, and implemented for detecting novel, exotic, and re-emerging ARIs. Methods: We searched Ovid MEDLINE ALL, Embase, Cochrane Library (Wiley), and CINAHL (Ebsco). The search was conducted on October 03, 2023. Studies that implemented EWSs for the detection of acute respiratory illnesses were included. Covidence was used for citation management, and a modified Critical Appraisal Skills Programme (CASP) checklist was used for quality assessment. Results: From 5838 initial articles, 29 met the inclusion criteria for this review. Twelve studies evaluated the use of EWSs within community settings, ranging from rural community reporting networks to urban online participatory surveillance platforms. Five studies focused on EWSs that used data from hospitalization and emergency department visits. These systems leveraged clinical and admission data to effectively detect and manage local outbreaks of respiratory infections. Two studies focused on the effectiveness of existing surveillance systems, assessing their adaptability and responsiveness to emerging threats and how they could be improved based on past performance. Four studies highlighted the integration of machine learning models to improve the predictive accuracy of EWSs. Three studies explored the applications of national EWSs in different health care settings and emphasized their potential in predicting clinical deterioration and facilitating early intervention. Lastly, 3 studies addressed the use of surveillance systems in aged-care facilities, highlighting the unique challenges and needs of monitoring and responding to health threats in environments housing vulnerable populations. The CASP tool revealed that most studies were relevant, reliable, and of high value (score 6: 11/29, 38%; score 5: 9/29, 31%). The common limitations included result generalizability, selection bias, and small sample size for model validation. Conclusions: This scoping review confirms the critical role of EWSs in enhancing public health responses to respiratory infections. Although the effectiveness of these systems is evident, challenges related to generalizability and varying methodologies suggest a need for continued innovation and standardization in EWS development. %M 39510516 %R 10.2196/62641 %U https://publichealth.jmir.org/2024/1/e62641 %U https://doi.org/10.2196/62641 %U http://www.ncbi.nlm.nih.gov/pubmed/39510516 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e55706 %T Evaluating a WeChat-Based Intervention to Enhance Influenza Vaccination Knowledge, Attitude, and Behavior Among Chinese University Students Residing in the United Kingdom: Controlled, Quasi-Experimental, Mixed Methods Study %A Li,Lan %A Wood,Caroline E %A Kostkova,Patty %+ Centre for Digital Public Health in Emergencies, Department for Risk and Disaster Reduction, University College London, Gower Street, London, WC1E6BT, United Kingdom, 44 7529917633, lan.li.19@ucl.ac.uk %K influenza vaccination %K intervention study %K social media %K students %K health promotion %K mixed methods %D 2024 %7 24.10.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: University students, who often live in close quarters and engage in frequent social interaction, face a heightened risk of influenza morbidity. Still, vaccination rates among this group, particularly Chinese students, remain consistently low due to limited awareness and insufficient access to vaccinations. Objective: This study examines the effectiveness of a cocreated WeChat-based intervention that targets mainland Chinese university students in the United Kingdom, aiming to improve their knowledge, attitude, and behavior (KAB) toward seasonal influenza vaccination. Methods: A quasi-experimental mixed methods design was used, incorporating an intervention and comparison group, with baseline and follow-up self-reported surveys. The study was conducted from December 19, 2022, to January 16, 2023. The primary outcome is the KAB score, which was measured before and after the intervention phases. System-recorded data and user feedback were included in the analysis as secondary outcomes. A series of hypothesis testing methods were applied to test the primary outcomes, and path analysis was used to explore the relationships. Results: Our study included 596 students, of which 303 (50.8%) were in the intervention group and 293 (49.2%) were in the control group. The intervention group showed significant improvements in knowledge, attitude, and intended behavior scores over time, whereas the control group had only a slight increase in intended behavior scores. When comparing changes between the 2 groups, the intervention group displayed significant differences in knowledge and attitude scores compared to the control group, while intended behavior scores did not significantly differ. After the intervention, the actual vaccination rate was slightly higher in the intervention group (63/303, 20.8%) compared to the control group (54/293, 18.4%). Path analysis found that the intervention had a significant direct impact on knowledge but not on attitudes; knowledge strongly influenced attitudes, and both knowledge and attitudes significantly influenced intended behavior; and there was a strong correlation between intended and actual behavior. In the intervention group, participants expressed a high level of satisfaction and positive review of the content and its use. Conclusions: This study demonstrates how a WeChat intervention effectively improves KAB related to seasonal influenza vaccination among Chinese students, highlighting the potential of social media interventions to drive vaccination behavior change. It contributes to the broader research on digital health intervention effectiveness and lays the groundwork for tailoring similar interventions to different health contexts and populations. %M 39447171 %R 10.2196/55706 %U https://formative.jmir.org/2024/1/e55706 %U https://doi.org/10.2196/55706 %U http://www.ncbi.nlm.nih.gov/pubmed/39447171 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e47370 %T Changes in the Epidemiological Features of Influenza After the COVID-19 Pandemic in China, the United States, and Australia: Updated Surveillance Data for Influenza Activity %A Jiang,Mingyue %A Jia,Mengmeng %A Wang,Qing %A Sun,Yanxia %A Xu,Yunshao %A Dai,Peixi %A Yang,Weizhong %A Feng,Luzhao %+ School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, 31 Beiji Tower, Dongcheng District, Beijing, 100730, China, 86 10 65120716, fengluzhao@cams.cn %K influenza %K seasonal variation %K COVID-19 pandemic %K stringency index %D 2024 %7 9.10.2024 %9 Original Paper %J Interact J Med Res %G English %X Background: There has been a global decrease in seasonal influenza activity since the onset of the COVID-19 pandemic. Objective: We aimed to describe influenza activity during the 2021/2022 season and compare it to the trends from 2012 to 2023. We also explored the influence of social and public health prevention measures during the COVID-19 pandemic on influenza activity. Methods: We obtained influenza data from January 1, 2012, to February 5, 2023, from publicly available platforms for China, the United States, and Australia. Mitigation measures were evaluated per the stringency index, a composite index with 9 measures. A general additive model was used to assess the stringency index and the influenza positivity rate correlation, and the deviance explained was calculated. Results: We used over 200,000 influenza surveillance data. Influenza activity remained low in the United States and Australia during the 2021/2022 season. However, it increased in the United States with a positive rate of 26.2% in the 49th week of 2022. During the 2021/2022 season, influenza activity significantly increased compared with the previous year in southern and northern China, with peak positivity rates of 28.1% and 35.1% in the second week of 2022, respectively. After the COVID-19 pandemic, the dominant influenza virus genotype in China was type B/Victoria, during the 2021/2022 season, and accounted for >98% (24,541/24,908 in the South and 20,543/20,634 in the North) of all cases. Influenza virus type B/Yamagata was not detected in all these areas after the COVID-19 pandemic. Several measures individually significantly influence local influenza activity, except for influenza type B in Australia. When combined with all the measures, the deviance explained values for influenza A and B were 87.4% (P<.05 for measures of close public transport and restrictions on international travel) and 77.6% in southern China and 83.4% (P<.05 for measures of school closing and close public transport) and 81.4% in northern China, respectively. In the United States, the association was relatively stronger, with deviance-explained values of 98.6% for influenza A and 99.1% (P<.05 for measures of restrictions on international travel and public information campaign) for influenza B. There were no discernible effects on influenza B activity in Australia between 2020 and 2022 due to the incredibly low positive rate of influenza B. Additionally, the deviance explained values were 95.8% (P<.05 for measures of restrictions on gathering size and restrictions on international travel) for influenza A and 72.7% for influenza B. Conclusions: Influenza activity has increased gradually since 2021. Mitigation measures for COVID-19 showed correlations with influenza activity, mainly driven by the early stage of the pandemic. During late 2021 and 2022, the influence of mitigation management for COVID-19 seemingly decreased gradually, as the activity of influenza increased compared to the 2020/2021 season. %M 39382955 %R 10.2196/47370 %U https://www.i-jmr.org/2024/1/e47370 %U https://doi.org/10.2196/47370 %U http://www.ncbi.nlm.nih.gov/pubmed/39382955 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e55208 %T Influenza-Like Illness in Lesotho From July 2020 to July 2021: Population-Based Participatory Surveillance Results %A Greenleaf,Abigail R %A Francis,Sarah %A Zou,Jungang %A Farley,Shannon M %A Lekhela,Tšepang %A Asiimwe,Fred %A Chen,Qixuan %K surveillance %K participatory surveillance %K influenza-like illness %K COVID-19 %K cell phone %K sub-Saharan Africa %K population-based %K Lesotho %K SARS-CoV-2 %K technology %K epidemiology %K adult %K data collection %K innovation %K mobile phone %K cellphone %D 2024 %7 8.10.2024 %9 %J JMIR Public Health Surveill %G English %X Background: Participatory surveillance involves at-risk populations reporting their symptoms using technology. In Lesotho, a landlocked country of 2 million people in Southern Africa, laboratory and case-based COVID-19 surveillance systems were complemented by a participatory surveillance system called “LeCellPHIA” (Lesotho Cell Phone Population-Based HIV Impact Assessment Survey). Objective: This report describes the person, place, and time characteristics of influenza-like illness (ILI) in Lesotho from July 15, 2020, to July 15, 2021, and reports the risk ratio of ILI by key demographic variables. Methods: LeCellPHIA employed interviewers to call participants weekly to inquire about ILI. The average weekly incidence rate for the year-long period was created using a Quasi-Poisson model, which accounted for overdispersion. To identify factors associated with an increased risk of ILI, we conducted a weekly data analysis by fitting a multilevel Poisson regression model, which accounted for 3 levels of clustering. Results: The overall response rate for the year of data collection was 75%, which resulted in 122,985 weekly reports from 1776 participants. ILI trends from LeCellPHIA mirrored COVID-19 testing data trends, with an epidemic peak in mid to late January 2021. Overall, any ILI symptoms (eg, fever, dry cough, and shortness of breath) were reported at an average weekly rate of 879 per 100,000 (95% CI 782‐988) persons at risk. Compared to persons in the youngest age group (15‐19 years), all older age groups had an elevated risk of ILI, with the highest risk of ILI in the oldest age group (≥60 years; risk ratio 2.6, 95% CI 1.7‐3.8). Weekly data were shared in near real time with the National COVID-19 Secretariat and other stakeholders to monitor ILI trends, identify and respond to increases in reports of ILI, and inform policies and practices designed to reduce COVID-19 transmission in Lesotho. Conclusions: LeCellPHIA, an innovative and cost-effective system, could be replicated in countries where cell phone ownership is high but internet use is not yet high enough for a web- or app-based surveilance system. %R 10.2196/55208 %U https://publichealth.jmir.org/2024/1/e55208 %U https://doi.org/10.2196/55208 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e47879 %T Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study %A Farooq,Kamran %A Lim,Melody %A Dennison-Hall,Lawrence %A Janson,Finn %A Olszewska,Aspen Hazel %A Ahmad Zabidi,Muhammad Mamduh %A Haratym-Rojek,Anna %A Narowski,Karol %A Clinch,Barry %A Prunotto,Marco %A Chawla,Devika %A Hunter,Victoria %A Ukachukwu,Vincent %+ Roche Data & Analytics Chapter (Data Science), Wurmisweg, Kaiseraugst, 4303, Switzerland, 41 616881111, kamran.farooq@roche.com %K influenza %K influenza-like illness %K wearable sensor %K person-generated health care data %K machine learning %D 2024 %7 4.10.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Machine learning offers quantitative pattern recognition analysis of wearable device data and has the potential to detect illness onset and monitor influenza-like illness (ILI) in patients who are infected. Objective: This study aims to evaluate the ability of machine-learning algorithms to distinguish between participants who are influenza positive and influenza negative in a cohort of symptomatic patients with ILI using wearable sensor (activity) data and self-reported symptom data during the latent and early symptomatic periods of ILI. Methods: This prospective observational cohort study used the extreme gradient boosting (XGBoost) classifier to determine whether a participant was influenza positive or negative based on 3 models using symptom-only data, activity-only data, and combined symptom and activity data. Data were collected from the Home Testing of Respiratory Illness (HTRI) study and FluStudy2020, both conducted between December 2019 and October 2020. The model was developed using the FluStudy2020 data and tested on the HTRI data. Analyses included participants in these studies with an at-home influenza diagnostic test result. Fitbit (Google LLC) devices were used to measure participants’ steps, heart rate, and sleep parameters. Participants detailed their ILI symptoms, health care–seeking behaviors, and quality of life. Model performance was assessed by area under the curve (AUC), balanced accuracy, recall (sensitivity), specificity, precision (positive predictive value), negative predictive value, and weighted harmonic mean of precision and recall (F2) score. Results: An influenza diagnostic test result was available for 953 and 925 participants in HTRI and FluStudy2020, respectively, of whom 848 (89%) and 840 (90.8%) had activity data. For the training and validation sets, the highest performing model was trained on the combined symptom and activity data (training AUC=0.77; validation AUC=0.74) versus symptom-only (training AUC=0.73; validation AUC=0.72) and activity-only (training AUC=0.68; validation AUC=0.65) data. For the FluStudy2020 test set, the performance of the model trained on combined symptom and activity data was closely aligned with that of the symptom-only model (combined symptom and activity test AUC=0.74; symptom-only test AUC=0.74). These results were validated using independent HTRI data (combined symptom and activity evaluation AUC=0.75; symptom-only evaluation AUC=0.74). The top features guiding influenza detection were cough; mean resting heart rate during main sleep; fever; total minutes in bed for the combined model; and fever, cough, and sore throat for the symptom-only model. Conclusions: Machine-learning algorithms had moderate accuracy in detecting influenza, suggesting that previous findings from research-grade sensors tested in highly controlled experimental settings may not easily translate to scalable commercial-grade sensors. In the future, more advanced wearable sensors may improve their performance in the early detection and discrimination of viral respiratory infections. %M 39365646 %R 10.2196/47879 %U https://www.jmir.org/2024/1/e47879 %U https://doi.org/10.2196/47879 %U http://www.ncbi.nlm.nih.gov/pubmed/39365646 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e60319 %T Advancing Public Health Surveillance in Child Care Centers: Stakeholder-Informed Redesign and User Satisfaction Evaluation of the MCRISP Network %A Gribbin,William %A Dejonge,Peter %A Rodseth,Jakob %A Hashikawa,Andrew %K public health %K disease surveillance %K data collection %K dashboard %K child care %K child %K children %K care center %K user satisfaction %K ill %K illness %K transmission %K tracking %K tracker %K COVID-19 %K SARS-CoV-2 %K pandemic %K disease monitoring %K technology %K respiratory %K gastrointestinal %K user-centered design %K infectious disease %K visualization %D 2024 %7 24.9.2024 %9 %J JMIR Public Health Surveill %G English %X Leveraging user feedback, we redesigned a novel disease monitoring utility to allow for bidirectional data flow and in this letter offer insights into that process as well as lessons learned. %R 10.2196/60319 %U https://publichealth.jmir.org/2024/1/e60319 %U https://doi.org/10.2196/60319 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e54861 %T Kinetics of Viral Shedding for Outbreak Surveillance of Emerging Infectious Diseases: Modeling Approach to SARS-CoV-2 Alpha and Omicron Infection %A Lin,Ting-Yu %A Yen,Amy Ming-Fang %A Chen,Sam Li-Sheng %A Hsu,Chen-Yang %A Lai,Chao-Chih %A Luh,Dih-Ling %A Yeh,Yen-Po %A Chen,Tony Hsiu-Hsi %+ Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Room 533, No 17, Xu-Zhou Road, Taipei, 100, Taiwan, 886 233668033, chenlin@ntu.edu.tw %K COVID-19 %K PCR testing %K Ct values %K viral load %K kinetics of viral shedding %K emerging infectious disease %K SARS-CoV-2 variants %K infection surveillance %D 2024 %7 19.9.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Previous studies have highlighted the importance of viral shedding using cycle threshold (Ct) values obtained via reverse transcription polymerase chain reaction to understand the epidemic trajectories of SARS-CoV-2 infections. However, it is rare to elucidate the transition kinetics of Ct values from the asymptomatic or presymptomatic phase to the symptomatic phase before recovery using individual repeated Ct values. Objective: This study proposes a novel Ct-enshrined compartment model to provide a series of quantitative measures for delineating the full trajectories of the dynamics of viral load from infection until recovery. Methods: This Ct-enshrined compartment model was constructed by leveraging Ct-classified states within and between presymptomatic and symptomatic compartments before recovery or death among people with infections. A series of recovery indices were developed to assess the net kinetic movement of Ct-up toward and Ct-down off recovery. The model was applied to (1) a small-scale community-acquired Alpha variant outbreak under the “zero-COVID-19” policy without vaccines in May 2021 and (2) a large-scale community-acquired Omicron variant outbreak with high booster vaccination rates following the lifting of the “zero-COVID-19” policy in April 2022 in Taiwan. The model used Bayesian Markov chain Monte Carlo methods with the Metropolis-Hastings algorithm for parameter estimation. Sensitivity analyses were conducted by varying Ct cutoff values to assess the robustness of the model. Results: The kinetic indicators revealed a marked difference in viral shedding dynamics between the Alpha and Omicron variants. The Alpha variant exhibited slower viral shedding and lower recovery rates, but the Omicron variant demonstrated swifter viral shedding and higher recovery rates. Specifically, the Alpha variant showed gradual Ct-up transitions and moderate recovery rates, yielding a presymptomatic recovery index slightly higher than 1 (1.10), whereas the Omicron variant had remarkable Ct-up transitions and significantly higher asymptomatic recovery rates, resulting in a presymptomatic recovery index much higher than 1 (152.5). Sensitivity analysis confirmed the robustness of the chosen Ct values of 18 and 25 across different recovery phases. Regarding the impact of vaccination, individuals without booster vaccination had a 19% higher presymptomatic incidence rate compared to those with booster vaccination. Breakthrough infections in boosted individuals initially showed similar Ct-up transition rates but higher rates in later stages compared to nonboosted individuals. Overall, booster vaccination improved recovery rates, particularly during the symptomatic phase, although recovery rates for persistent asymptomatic infection were similar regardless of vaccination status once the Ct level exceeded 25. Conclusions: The study provides new insights into dynamic Ct transitions, with the notable finding that Ct-up transitions toward recovery outpaced Ct-down and symptom-surfacing transitions during the presymptomatic phase. The Ct-up against Ct-down transition varies with variants and vaccination status. The proposed Ct-enshrined compartment model is useful for the surveillance of emerging infectious diseases in the future to prevent community-acquired outbreaks. %M 39298261 %R 10.2196/54861 %U https://publichealth.jmir.org/2024/1/e54861 %U https://doi.org/10.2196/54861 %U http://www.ncbi.nlm.nih.gov/pubmed/39298261 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58704 %T From Fax to Secure File Transfer Protocol: The 25-Year Evolution of Real-Time Syndromic Surveillance in England %A Elliot,Alex J %A Hughes,Helen E %A Harcourt,Sally E %A Smith,Sue %A Loveridge,Paul %A Morbey,Roger A %A Bains,Amardeep %A Edeghere,Obaghe %A Jones,Natalia R %A Todkill,Daniel %A Smith,Gillian E %+ Real-time Syndromic Surveillance Team, UK Health Security Agency, 23 Stephenson Street, Birmingham, B2 4BH, United Kingdom, 44 1212329211, alex.elliot@ukhsa.gov.uk %K epidemiology %K population surveillance %K sentinel surveillance %K public health surveillance %K bioterrorism %K mass gathering %K pandemics %D 2024 %7 17.9.2024 %9 Viewpoint %J J Med Internet Res %G English %X The purpose of syndromic surveillance is to provide early warning of public health incidents, real-time situational awareness during incidents and emergencies, and reassurance of the lack of impact on the population, particularly during mass gatherings. The United Kingdom Health Security Agency (UKHSA) currently coordinates a real-time syndromic surveillance service that encompasses 6 national syndromic surveillance systems reporting on daily health care usage across England. Each working day, UKHSA analyzes syndromic data from over 200,000 daily patient encounters with the National Health Service, monitoring over 140 unique syndromic indicators, risk assessing over 50 daily statistical exceedances, and taking and recommending public health action on these daily. This English syndromic surveillance service had its origins as a small exploratory pilot in a single region of England in 1999 involving a new pilot telehealth service, initially reporting only on “cold or flu” calls. This pilot showed the value of syndromic surveillance in England, providing advanced warning of the start of seasonal influenza activity over existing laboratory-based surveillance systems. Since this initial pilot, a program of real-time syndromic surveillance has evolved from the single-system, -region, -indicator pilot (using manual data transfer methods) to an all-hazard, multisystem, automated national service. The suite of systems now monitors a wide range of syndromes, from acute respiratory illness to diarrhea to cardiac conditions, and is widely used in routine public health surveillance and for monitoring seasonal respiratory disease and incidents such as the COVID-19 pandemic. Here, we describe the 25-year evolution of the English syndromic surveillance system, focusing on the expansion and improvements in data sources and data management, the technological and digital enablers, and novel methods of data analytics and visualization. %M 39288377 %R 10.2196/58704 %U https://www.jmir.org/2024/1/e58704 %U https://doi.org/10.2196/58704 %U http://www.ncbi.nlm.nih.gov/pubmed/39288377 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e55613 %T Digital Gamification Tool (Let’s Control Flu) to Increase Vaccination Coverage Rates: Proposal for Algorithm Development %A Lopes,Henrique %A Baptista-Leite,Ricardo %A Hermenegildo,Catarina %A Atun,Rifat %+ NOVA Center for Global Health, NOVA Information Management School, Universidade Nova de Lisboa, Campus de Campolide, Lisbon, 1070-312, Portugal, 351 962499020, hlopes@novaims.unl.pt %K influenza %K gamification %K public health policies %K vaccination coverage rates %K health promotion %D 2024 %7 10.9.2024 %9 Proposal %J JMIR Res Protoc %G English %X Background: Influenza represents a critical public health challenge, disproportionately affecting at-risk populations, including older adults and those with chronic conditions, often compounded by socioeconomic factors. Innovative strategies, such as gamification, are essential for augmenting risk communication and community engagement efforts to address this threat. Objective: This study aims to introduce the “Let’s Control Flu” (LCF) tool, a gamified, interactive platform aimed at simulating the impact of various public health policies (PHPs) on influenza vaccination coverage rates and health outcomes. The tool aligns with the World Health Organization’s goal of achieving a 75% influenza vaccination rate by 2030, facilitating strategic decision-making to enhance vaccination uptake. Methods: The LCF tool integrates a selection of 13 PHPs from an initial set proposed in another study, targeting specific population groups to evaluate 7 key health outcomes. A prioritization mechanism accounts for societal resistance and the synergistic effects of PHPs, projecting the potential policy impacts from 2022 to 2031. This methodology enables users to assess how PHPs could influence public health strategies within distinct target groups. Results: The LCF project began in February 2021 and is scheduled to end in December 2024. The model creation phase and its application to the pilot country, Sweden, took place between May 2021 and May 2023, with subsequent application to other European countries. The pilot phase demonstrated the tool’s potential, indicating a promising increase in the national influenza vaccination coverage rate, with uniform improvements across all targeted demographic groups. These initial findings highlight the tool’s capacity to model the effects of PHPs on improving vaccination rates and mitigating the health impact of influenza. Conclusions: By incorporating gamification into the analysis of PHPs, the LCF tool offers an innovative and accessible approach to supporting health decision makers and patient advocacy groups. It enhances the comprehension of policy impacts, promoting more effective influenza prevention and control strategies. This paper underscores the critical need for adaptable and engaging tools in PHP planning and implementation. International Registered Report Identifier (IRRID): RR1-10.2196/55613 %M 39255031 %R 10.2196/55613 %U https://www.researchprotocols.org/2024/1/e55613 %U https://doi.org/10.2196/55613 %U http://www.ncbi.nlm.nih.gov/pubmed/39255031 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e45513 %T The Clinical Severity of COVID-19 Variants of Concern: Retrospective Population-Based Analysis %A Harrigan,Sean P %A Velásquez García,Héctor A %A Abdia,Younathan %A Wilton,James %A Prystajecky,Natalie %A Tyson,John %A Fjell,Chris %A Hoang,Linda %A Kwong,Jeffrey C %A Mishra,Sharmistha %A Wang,Linwei %A Sander,Beate %A Janjua,Naveed Z %A Sbihi,Hind %+ BC Centre for Disease Control, 655 W12th Avenue, Vancouver, BC, V5Z4R4, Canada, 1 604 7071400, hind.sbihi@bccdc.ca %K COVID-19 %K SARS-CoV-2 %K severity %K whole genome sequencing %K WGS %K social determinants of health %K SDOHs %K vaccination %K variants of concern %K VOCs %K Alpha %K Gamma %K Delta %K Omicron %D 2024 %7 27.8.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: SARS-CoV-2 variants of concern (VOCs) emerged and rapidly replaced the original strain worldwide. The increased transmissibility of these new variants led to increases in infections, hospitalizations, and mortality. However, there is a scarcity of retrospective investigations examining the severity of all the main VOCs in presence of key public health measures and within various social determinants of health (SDOHs). Objective: This study aims to provide a retrospective assessment of the clinical severity of COVID-19 VOCs in the context of heterogenous SDOHs and vaccination rollout. Methods: We used a population-based retrospective cohort design with data from the British Columbia COVID-19 Cohort, a linked provincial surveillance platform. To assess the relative severity (hospitalizations, intensive care unit [ICU] admissions, and deaths) of Gamma, Delta, and Omicron infections during 2021 relative to Alpha, we used inverse probability treatment weighted Cox proportional hazard modeling. We also conducted a subanalysis among unvaccinated individuals, as assessed severity differed across VOCs and SDOHs. Results: We included 91,964 individuals infected with a SARS-CoV-2 VOC (Alpha: n=20,487, 22.28%; Gamma: n=15,223, 16.55%; Delta: n=49,161, 53.46%; and Omicron: n=7093, 7.71%). Delta was associated with the most severe disease in terms of hospitalization, ICU admissions, and deaths (hospitalization: adjusted hazard ratio [aHR] 2.00, 95% CI 1.92-2.08; ICU: aHR 2.05, 95% CI 1.91-2.20; death: aHR 3.70, 95% CI 3.23-4.25 relative to Alpha), followed generally by Gamma and then Omicron and Alpha. The relative severity by VOC remained similar in the unvaccinated individual subanalysis, although the proportion of individuals infected with Delta and Omicron who were hospitalized was 2 times higher in those unvaccinated than in those fully vaccinated. Regarding SDOHs, the proportion of hospitalized individuals was higher in areas with lower income across all VOCs, whereas among Alpha and Gamma infections, 2 VOCs that cocirculated, differential distributions of hospitalizations were found among racially minoritized groups. Conclusions: Our study provides robust severity estimates for all VOCs during the COVID-19 pandemic in British Columbia, Canada. Relative to Alpha, we found Delta to be the most severe, followed by Gamma and Omicron. This study highlights the importance of targeted testing and sequencing to ensure timely detection and accurate estimation of severity in emerging variants. It further sheds light on the importance of vaccination coverage and SDOHs in the context of pandemic preparedness to support the prioritization of allocation for resource-constrained or minoritized groups. %M 39190434 %R 10.2196/45513 %U https://publichealth.jmir.org/2024/1/e45513 %U https://doi.org/10.2196/45513 %U http://www.ncbi.nlm.nih.gov/pubmed/39190434 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e43173 %T Impact of the COVID-19 Pandemic on Influenza Hospital Admissions and Deaths in Wales: Descriptive National Time Series Analysis %A Alsallakh,Mohammad %A Adeloye,Davies %A Vasileiou,Eleftheria %A Sivakumaran,Shanya %A Akbari,Ashley %A Lyons,Ronan A %A Robertson,Chris %A Rudan,Igor %A Davies,Gwyneth A %A Sheikh,Aziz %K influenza %K hospitalization %K mortality %K COVID-19 pandemic %K nonpharmaceutical interventions %K Wales %K COVID-19 %K community health %K hospital admission %K endemic virus %K public health surveillance %D 2024 %7 21.8.2024 %9 %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic and the ensuing implementation of control measures caused widespread societal disruption. These disruptions may also have affected community transmission and seasonal circulation patterns of endemic respiratory viruses. Objective: We aimed to investigate the impact of COVID-19–related disruption on influenza-related emergency hospital admissions and deaths in Wales in the first 2 years of the pandemic. Methods: A descriptive analysis of influenza activity was conducted using anonymized pathology, hospitalization, and mortality data from the Secure Anonymised Information Linkage Databank in Wales. The annual incidence of emergency hospitalizations and deaths with influenza-specific diagnosis codes between January 1, 2015, and December 31, 2021, was estimated. Case definitions of emergency hospitalization and death required laboratory confirmation with a polymerase chain reaction test. Trends of admissions and deaths were analyzed monthly and yearly. We conducted 2 sensitivity analyses by extending case definitions to include acute respiratory illnesses with a positive influenza test and by limiting admissions to those with influenza as the primary diagnosis. We also examined yearly influenza testing trends to understand changes in testing behavior during the pandemic. Results: We studied a population of 3,235,883 Welsh residents in 2020 with a median age of 42.5 (IQR 22.9–61.0) years. Influenza testing in Wales increased notably in the last 2 months of 2020, and particularly in 2021 to 39,720 per 100,000 people, compared to the prepandemic levels (1343 in 2019). The percentage of influenza admissions matched to an influenza polymerase chain reaction test increased from 74.8% (1890/2526) in 2019 to 85.2% (98/115) in 2021. However, admissions with a positive test per 100,000 population decreased from 17.0 in 2019 to 2.7 and 0.6 in 2020 and 2021, respectively. Similarly, deaths due to influenza with a positive influenza test per 100,000 population decreased from 0.4 in 2019 to 0.0 in 2020 and 2021. Sensitivity analyses showed similar patterns of decreasing influenza admissions and deaths in the first 2 years of the COVID-19 pandemic. Conclusions: Nonpharmaceutical interventions to control COVID-19 were associated with a substantial reduction in the transmission of the influenza virus, with associated substantial reductions in hospital cases and deaths observed. Beyond the pandemic context, consideration should be given to the role of nonpharmaceutical community-driven interventions to reduce the burden of influenza. %R 10.2196/43173 %U https://publichealth.jmir.org/2024/1/e43173 %U https://doi.org/10.2196/43173 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e55822 %T Increased Risk of Influenza Infection During Cold Spells in China: National Time Series Study %A Wang,Haitao %A Geng,Mengjie %A Schikowski,Tamara %A Areal,Ashtyn Tracey %A Hu,Kejia %A Li,Wen %A Coelho,Micheline de Sousa Zanotti Stagliorio %A Saldiva,Paulo Hilário Nascimento %A Sun,Wei %A Zhou,Chengchao %A Lu,Liang %A Zhao,Qi %A Ma,Wei %K influenza %K cold spell %K definition %K vulnerable population %K modification effect %K China %D 2024 %7 13.8.2024 %9 %J JMIR Public Health Surveill %G English %X Background: Studies have reported the adverse effects of cold events on influenza. However, the role of critical factors, such as characteristics of cold spells, and regional variations remain unresolved. Objective: We aimed to systematically evaluate the association between cold spells and influenza incidence in mainland China. Methods: This time series analysis used surveillance data of daily influenza from 325 sites in China in the 2014‐2019 period. A total of 15 definitions of cold spells were adopted based on combinations of temperature thresholds and days of duration. A distributed lag linear model was used to estimate the short-term effects of cold spells on influenza incidence during the cool seasons (November to March), and we further explored the potential impact of cold spell characteristics (ie, intensity, duration, and timing during the season) on the estimated associations. Meta-regressions were used to evaluate the modification effect of city-level socioeconomic indicators. Results: The overall effect of cold spells on influenza incidence increased with the temperature threshold used to define cold spells, whereas the added effects were generally small and not statistically significant. The relative risk of influenza-associated with cold spells was 3.35 (95% CI 2.89‐3.88), and the estimated effects were stronger during the middle period of cool seasons. The health effects of cold spells varied geographically and residents in Jiangnan region were vulnerable groups (relative risk 7.36, 95% CI 5.44‐9.95). The overall effects of cold spells were positively correlated with the urban population density, population size, gross domestic product per capita, and urbanization rate, indicating a sterner response to cold spells in metropolises. Conclusions: Cold spells create a substantial health burden on seasonal influenza in China. Findings on regional and socioeconomic differences in the health effects of cold spells on seasonal influenza may be useful in formulating region-specific public health policies to address the hazardous effects of cold spells. %R 10.2196/55822 %U https://publichealth.jmir.org/2024/1/e55822 %U https://doi.org/10.2196/55822 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e57349 %T A Bayesian System to Detect and Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases: Algorithm Development and Validation %A Aronis,John M %A Ye,Ye %A Espino,Jessi %A Hochheiser,Harry %A Michaels,Marian G %A Cooper,Gregory F %+ Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Suite 500, Pittsburgh, PA, 15206-3701, United States, 1 412 624 5100, jma18@pitt.edu %K biosurveillance %K outbreak %K novel disease %K natural language processing %K disease modeling %K Bayesian modeling %K influenza %K influenza-like illnesses %K novel diseases %K public health %K COVID-19 %K SARS-CoV-2 %K coronavirus %K hospital %K hospitals %K emergency department %K patient care %K NLP %K algorithm %K respiratory syncytial %K human metapneumovirus %K parainfluenza %K Pennsylvania %K enterovirus D68 %K surveillance %D 2024 %7 13.8.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background:  The early identification of outbreaks of both known and novel influenza-like illnesses (ILIs) is an important public health problem. Objective:  This study aimed to describe the design and testing of a tool that detects and tracks outbreaks of both known and novel ILIs, such as the SARS-CoV-2 worldwide pandemic, accurately and early. Methods:  This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known ILIs in hospital emergency departments in a monitored region using findings extracted from patient care reports using natural language processing. We then show how the algorithm can be extended to detect and track the presence of an unmodeled disease that may represent a novel disease outbreak. Results:  We include results based on modeling diseases like influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for 5 emergency departments in Allegheny County, Pennsylvania, from June 1, 2014, to May 31, 2015. We also include the results of detecting the outbreak of an unmodeled disease, which in retrospect was very likely an outbreak of the enterovirus D68 (EV-D68). Conclusions:  The results reported in this paper provide support that ILI Tracker was able to track well the incidence of 4 modeled influenza-like diseases over a 1-year period, relative to laboratory-confirmed cases, and it was computationally efficient in doing so. The system was also able to detect a likely novel outbreak of EV-D68 early in an outbreak that occurred in Allegheny County in 2014 as well as clinically characterize that outbreak disease accurately. %M 38805611 %R 10.2196/57349 %U https://publichealth.jmir.org/2024/1/e57349 %U https://doi.org/10.2196/57349 %U http://www.ncbi.nlm.nih.gov/pubmed/38805611 %0 Journal Article %@ 2369-2960 %I %V 10 %N %P e37625 %T A Novel Web-Based Application for Influenza and COVID-19 Outbreak Detection and Response in Residential Aged Care Facilities %A Hsiao,Kai Hsun %A Quinn,Emma %A Johnstone,Travers %A Gomez,Maria %A Ingleton,Andrew %A Parasuraman,Arun %A Najjar,Zeina %A Gupta,Leena %K web application %K digital health %K communicable disease control %K outbreak %K surveillance %K influenza %K aged care %K aged care homes %D 2024 %7 24.6.2024 %9 %J JMIR Public Health Surveill %G English %X The use of innovative digital health technologies in public health is expanding quickly, including the use of these tools in outbreak response. The translation of a digital health innovation into effective public health practice is a complex process requiring diverse enablers across the people, process, and technology domains. This paper describes a novel web-based application that was designed and implemented by a district-level public health authority to assist residential aged care facilities in influenza and COVID-19 outbreak detection and response. It discusses some of the challenges, enablers, and key lessons learned in designing and implementing such a novel application from the perspectives of the public health practitioners (the authors) that undertook this project. %R 10.2196/37625 %U https://publichealth.jmir.org/2024/1/e37625 %U https://doi.org/10.2196/37625 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e56064 %T A Prediction Model for Identifying Seasonal Influenza Vaccination Uptake Among Children in Wuxi, China: Prospective Observational Study %A Wang,Qiang %A Yang,Liuqing %A Xiu,Shixin %A Shen,Yuan %A Jin,Hui %A Lin,Leesa %+ Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom, 44 617 632 6142, Leesa.Lin@lshtm.ac.uk %K influenza %K vaccination %K children %K prediction model %K China %K vaccine %K behaviors %K health care professional %K intervention %K sociodemographics %K vaccine hesitancy %K clinic %K Bayesian network %K logistic regression %K accuracy %K Cohen κ %K prediction %K public health %K immunization %K digital age %D 2024 %7 17.6.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Predicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions. Objective: The aim of this study was to develop predictive models for influenza vaccination behavior among children in China. Methods: We obtained data from a prospective observational study in Wuxi, eastern China. The predicted outcome was individual-level vaccine uptake and covariates included sociodemographics of the child and parent, parental vaccine hesitancy, perceptions of convenience to the clinic, satisfaction with clinic services, and willingness to vaccinate. Bayesian networks, logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), naive Bayes (NB), random forest (RF), and decision tree classifiers were used to construct prediction models. Various performance metrics, including area under the receiver operating characteristic curve (AUC), were used to evaluate the predictive performance of the different models. Receiver operating characteristic curves and calibration plots were used to assess model performance. Results: A total of 2383 participants were included in the study; 83.2% of these children (n=1982) were <5 years old and 6.6% (n=158) had previously received an influenza vaccine. More than half (1356/2383, 56.9%) the parents indicated a willingness to vaccinate their child against influenza. Among the 2383 children, 26.3% (n=627) received influenza vaccination during the 2020-2021 season. Within the training set, the RF model showed the best performance across all metrics. In the validation set, the logistic regression model and NB model had the highest AUC values; the SVM model had the highest precision; the NB model had the highest recall; and the logistic regression model had the highest accuracy, F1 score, and Cohen κ value. The LASSO and logistic regression models were well-calibrated. Conclusions: The developed prediction model can be used to quantify the uptake of seasonal influenza vaccination for children in China. The stepwise logistic regression model may be better suited for prediction purposes. %M 38885032 %R 10.2196/56064 %U https://publichealth.jmir.org/2024/1/e56064 %U https://doi.org/10.2196/56064 %U http://www.ncbi.nlm.nih.gov/pubmed/38885032 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e56271 %T Defining and Risk-Stratifying Immunosuppression (the DESTINIES Study): Protocol for an Electronic Delphi Study %A Leston,Meredith %A Ordóñez-Mena,José %A Joy,Mark %A de Lusignan,Simon %A Hobbs,Richard %A McInnes,Iain %A Lee,Lennard %+ Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Woodstock Road, Oxford, OX2 6GG, United Kingdom, 44 7896980320, meredith.leston@phc.ox.ac.uk %K immunosuppressed %K immunocompromised %K COVID %K vaccines %K COVID-19 %K surveillance %K phenotype %K adult %K immunosuppression %K clinical risk %K disease surveillance %K clinical consensus %K eDelphi %K immunosuppressed patient %K immunosuppressed patients %K study design %K Delphi %K methods %K methodology %K statistic %K statistics %K statistical %K consensus %K immune %K immunity %K immunology %K immunological %D 2024 %7 6.6.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Globally, there are marked inconsistencies in how immunosuppression is characterized and subdivided into clinical risk groups. This is detrimental to the precision and comparability of disease surveillance efforts—which has negative implications for the care of those who are immunosuppressed and their health outcomes. This was particularly apparent during the COVID-19 pandemic; despite collective motivation to protect these patients, conflicting clinical definitions created international rifts in how those who were immunosuppressed were monitored and managed during this period. We propose that international clinical consensus be built around the conditions that lead to immunosuppression and their gradations of severity concerning COVID-19. Such information can then be formalized into a digital phenotype to enhance disease surveillance and provide much-needed intelligence on risk-prioritizing these patients. Objective: We aim to demonstrate how electronic Delphi objectives, methodology, and statistical approaches will help address this lack of consensus internationally and deliver a COVID-19 risk-stratified phenotype for “adult immunosuppression.” Methods: Leveraging existing evidence for heterogeneous COVID-19 outcomes in adults who are immunosuppressed, this work will recruit over 50 world-leading clinical, research, or policy experts in the area of immunology or clinical risk prioritization. After 2 rounds of clinical consensus building and 1 round of concluding debate, these panelists will confirm the medical conditions that should be classed as immunosuppressed and their differential vulnerability to COVID-19. Consensus statements on the time and dose dependencies of these risks will also be presented. This work will be conducted iteratively, with opportunities for panelists to ask clarifying questions between rounds and provide ongoing feedback to improve questionnaire items. Statistical analysis will focus on levels of agreement between responses. Results: This protocol outlines a robust method for improving consensus on the definition and meaningful subdivision of adult immunosuppression concerning COVID-19. Panelist recruitment took place between April and May of 2024; the target set for over 50 panelists was achieved. The study launched at the end of May and data collection is projected to end in July 2024. Conclusions: This protocol, if fully implemented, will deliver a universally acceptable, clinically relevant, and electronic health record–compatible phenotype for adult immunosuppression. As well as having immediate value for COVID-19 resource prioritization, this exercise and its output hold prospective value for clinical decision-making across all diseases that disproportionately affect those who are immunosuppressed. International Registered Report Identifier (IRRID): PRR1-10.2196/56271 %M 38842925 %R 10.2196/56271 %U https://www.researchprotocols.org/2024/1/e56271 %U https://doi.org/10.2196/56271 %U http://www.ncbi.nlm.nih.gov/pubmed/38842925 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e39297 %T A Profile of Influenza Vaccine Coverage for 2019-2020: Database Study of the English Primary Care Sentinel Cohort %A Hoang,Uy %A Delanerolle,Gayathri %A Fan,Xuejuan %A Aspden,Carole %A Byford,Rachel %A Ashraf,Mansoor %A Haag,Mendel %A Elson,William %A Leston,Meredith %A Anand,Sneha %A Ferreira,Filipa %A Joy,Mark %A Hobbs,Richard %A de Lusignan,Simon %+ Nuffield Department of Primary Care Health Sciences, University of Oxford, Eagle House, 7 Walton Well Road, Oxford, OX2 6ED, United Kingdom, 44 01865 289 344, simon.delusignan@phc.ox.ac.uk %K medical records systems %K computerize %K influenza %K influenza vaccines %K sentinel surveillance %K vocabulary controlled %K general practitioners %K general practice %K primary health care %K vaccine %K public health %K surveillance %K uptake %D 2024 %7 24.5.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Innovation in seasonal influenza vaccine development has resulted in a wider range of formulations becoming available. Understanding vaccine coverage across populations including the timing of administration is important when evaluating vaccine benefits and risks. Objective: This study aims to report the representativeness, uptake of influenza vaccines, different formulations of influenza vaccines, and timing of administration within the English Primary Care Sentinel Cohort (PCSC). Methods: We used the PCSC of the Oxford-Royal College of General Practitioners Research and Surveillance Centre. We included patients of all ages registered with PCSC member general practices, reporting influenza vaccine coverage between September 1, 2019, and January 29, 2020. We identified influenza vaccination recipients and characterized them by age, clinical risk groups, and vaccine type. We reported the date of influenza vaccination within the PCSC by International Standard Organization (ISO) week. The representativeness of the PCSC population was compared with population data provided by the Office for National Statistics. PCSC influenza vaccine coverage was compared with published UK Health Security Agency’s national data. We used paired t tests to compare populations, reported with 95% CI. Results: The PCSC comprised 7,010,627 people from 693 general practices. The study population included a greater proportion of people aged 18-49 years (2,982,390/7,010,627, 42.5%; 95% CI 42.5%-42.6%) compared with the Office for National Statistics 2019 midyear population estimates (23,219,730/56,286,961, 41.3%; 95% CI 4.12%-41.3%; P<.001). People who are more deprived were underrepresented and those in the least deprived quintile were overrepresented. Within the study population, 24.7% (1,731,062/7,010,627; 95% CI 24.7%-24.7%) of people of all ages received an influenza vaccine compared with 24.2% (14,468,665/59,764,928; 95% CI 24.2%-24.2%; P<.001) in national data. The highest coverage was in people aged ≥65 years (913,695/1,264,700, 72.3%; 95% CI 72.2%-72.3%). The proportion of people in risk groups who received an influenza vaccine was also higher; for example, 69.8% (284,280/407,228; 95% CI 69.7%-70%) of people with diabetes in the PCSC received an influenza vaccine compared with 61.2% (983,727/1,607,996; 95% CI 61.1%-61.3%; P<.001) in national data. In the PCSC, vaccine type and brand information were available for 71.8% (358,365/498,923; 95% CI 71.7%-72%) of people aged 16-64 years and 81.9% (748,312/913,695; 95% CI 81.8%-82%) of people aged ≥65 years, compared with 23.6% (696,880/2,900,000) and 17.8% (1,385,888/7,700,000), respectively, of the same age groups in national data. Vaccination commenced during ISO week 35, continued until ISO week 3, and peaked during ISO week 41. The in-week peak in vaccination administration was on Saturdays. Conclusions: The PCSC’s sociodemographic profile was similar to the national population and captured more data about risk groups, vaccine brands, and batches. This may reflect higher data quality. Its capabilities included reporting precise dates of administration. The PCSC is suitable for undertaking studies of influenza vaccine coverage. %M 38787605 %R 10.2196/39297 %U https://publichealth.jmir.org/2024/1/e39297 %U https://doi.org/10.2196/39297 %U http://www.ncbi.nlm.nih.gov/pubmed/38787605 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e47626 %T Persistence and Variation of the Indirect Effects of COVID-19 Restrictions on the Spectrum of Notifiable Infectious Diseases in China: Analysis of National Surveillance Among Children and Adolescents From 2018 to 2021 %A Chen,Li %A Wang,Liping %A Xing,Yi %A Xie,Junqing %A Su,Binbin %A Geng,Mengjie %A Ren,Xiang %A Zhang,Yi %A Liu,Jieyu %A Ma,Tao %A Chen,Manman %A Miller,Jessica E %A Dong,Yanhui %A Song,Yi %A Ma,Jun %A Sawyer,Susan %+ Institute of Child and Adolescent Health, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100083, China, 86 15123132408, dongyanhui@bjmu.edu.cn %K children and adolescents %K COVID-19 %K notifiable infectious diseases %D 2024 %7 15.5.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Beyond the direct effect of COVID-19 infection on young people, the wider impact of the pandemic on other infectious diseases remains unknown. Objective: This study aims to assess changes in the incidence and mortality of 42 notifiable infectious diseases during the pandemic among children and adolescents in China, compared with prepandemic levels. Methods: The Notifiable Infectious Disease Surveillance System of China was used to detect new cases and fatalities among individuals aged 5-22 years across 42 notifiable infectious diseases spanning from 2018 to 2021. These infectious diseases were categorized into 5 groups: respiratory, gastrointestinal and enterovirus, sexually transmitted and blood-borne, zoonotic, and vector-borne diseases. Each year (2018-2021) was segmented into 4 phases: phase 1 (January 1-22), phase 2 (January 23-April 7), phase 3 (April 8-August 31), and phase 4 (September 1-December 31) according to the varying intensities of pandemic restrictive measures in 2020. Generalized linear models were applied to assess the change in the incidence and mortality within each disease category, using 2018 and 2019 as the reference. Results: A total of 4,898,260 incident cases and 3701 deaths were included. The overall incidence of notifiable infectious diseases decreased sharply during the first year of the COVID-19 pandemic (2020) compared with prepandemic levels (2018 and 2019), and then rebounded in 2021, particularly in South China. Across the past 4 years, the number of deaths steadily decreased. The incidence of diseases rebounded differentially by the pandemic phase. For instance, although seasonal influenza dominated respiratory diseases in 2019, it showed a substantial decline during the pandemic (percent change in phase 2 2020: 0.21, 95% CI 0.09-0.50), which persisted until 2021 (percent change in phase 4 2021: 1.02, 95% CI 0.74-1.41). The incidence of gastrointestinal and enterovirus diseases decreased by 33.6% during 2020 but rebounded by 56.9% in 2021, mainly driven by hand, foot, and mouth disease (percent change in phase 3 2021: 1.28, 95% CI 1.17-1.41) and infectious diarrhea (percent change in phase 3 2020: 1.22, 95% CI 1.17-1.28). Sexually transmitted and blood-borne diseases were restrained during the first year of 2021 but rebounded quickly in 2021, mainly driven by syphilis (percent change in phase 3 2020: 1.31, 95% CI 1.23-1.40) and gonorrhea (percent change in phase 3 2020: 1.10, 95% CI 1.05-1.16). Zoonotic diseases were not dampened by the pandemic but continued to increase across the study period, mainly due to brucellosis (percent change in phase 2 2020: 0.94, 95% CI 0.75-1.16). Vector-borne diseases showed a continuous decline during 2020, dominated by hemorrhagic fever (percent change in phase 2 2020: 0.68, 95% CI 0.53-0.87), but rebounded in 2021. Conclusions: The COVID-19 pandemic was associated with a marked decline in notifiable infectious diseases in Chinese children and adolescents. These effects were not sustained, with evidence of a rebound to prepandemic levels by late 2021. To effectively address the postpandemic resurgence of infectious diseases in children and adolescents, it will be essential to maintain disease surveillance and strengthen the implementation of various initiatives. These include extending immunization programs, prioritizing the management of sexually transmitted infections, continuing feasible nonpharmaceutical intervention projects, and effectively managing imported infections. %M 38748469 %R 10.2196/47626 %U https://publichealth.jmir.org/2024/1/e47626 %U https://doi.org/10.2196/47626 %U http://www.ncbi.nlm.nih.gov/pubmed/38748469 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e40792 %T The Changing Landscape of Respiratory Viruses Contributing to Hospitalizations in Quebec, Canada: Results From an Active Hospital-Based Surveillance Study %A Gilca,Rodica %A Amini,Rachid %A Carazo,Sara %A Doggui,Radhouene %A Frenette,Charles %A Boivin,Guy %A Charest,Hugues %A Dumaresq,Jeannot %+ Direction des risques biologiques, Institut national de santé publique du Québec, 945 Av. Wolfe, Québec, QC, G1V5B3, Canada, 1 4186505115 ext 6278, rodica.gilca@inspq.qc.ca %K respiratory viruses %K SARS-CoV-2 %K COVID-19 %K hospitalizations %K acute respiratory infections %K children %K adults %K coinfections %K prepandemic %K pandemic %D 2024 %7 6.5.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: A comprehensive description of the combined effect of SARS-CoV-2 and respiratory viruses other than SARS-CoV-2 (ORVs) on acute respiratory infection (ARI) hospitalizations is lacking. Objective: This study aimed to compare the viral etiology of ARI hospitalizations before the pandemic (8 prepandemic influenza seasons, 2012-13 to 2019-20) and during 3 pandemic years (periods of increased SARS-CoV-2 and ORV circulation in 2020-21, 2021-22, and 2022-23) from an active hospital-based surveillance network in Quebec, Canada. Methods: We compared the detection of ORVs and SARS-CoV-2 during 3 pandemic years to that in 8 prepandemic influenza seasons among patients hospitalized with ARI who were tested systematically by the same multiplex polymerase chain reaction (PCR) assay during periods of intense respiratory virus (RV) circulation. The proportions of infections between prepandemic and pandemic years were compared by using appropriate statistical tests. Results: During prepandemic influenza seasons, overall RV detection was 92.7% (1384/1493) (respiratory syncytial virus [RSV]: 721/1493, 48.3%; coinfections: 456/1493, 30.5%) in children (<18 years) and 62.8% (2723/4339) (influenza: 1742/4339, 40.1%; coinfections: 264/4339, 6.1%) in adults. Overall RV detection in children was lower during pandemic years but increased from 58.6% (17/29) in 2020-21 (all ORVs; coinfections: 7/29, 24.1%) to 90.3% (308/341) in 2021-22 (ORVs: 278/341, 82%; SARS-CoV-2: 30/341, 8.8%; coinfections: 110/341, 32.3%) and 88.9% (361/406) in 2022-23 (ORVs: 339/406, 84%; SARS-CoV-2: 22/406, 5.4%; coinfections: 128/406, 31.5%). In adults, overall RV detection was also lower during pandemic years but increased from 43.7% (333/762) in 2020-21 (ORVs: 26/762, 3.4%; SARS-CoV-2: 307/762, 40.3%; coinfections: 7/762, 0.9%) to 57.8% (731/1265) in 2021-22 (ORVs: 179/1265, 14.2%; SARS-CoV-2: 552/1265, 43.6%; coinfections: 42/1265, 3.3%) and 50.1% (746/1488) in 2022-23 (ORVs: 409/1488, 27.5%; SARS-CoV-2: 337/1488, 22.6%; coinfections: 36/1488, 2.4%). No influenza or RSV was detected in 2020-21; however, their detection increased in the 2 subsequent years but did not reach prepandemic levels. Compared to the prepandemic period, the peaks of RSV hospitalization shifted in 2021-22 (16 weeks earlier) and 2022-23 (15 weeks earlier). Moreover, the peaks of influenza hospitalization shifted in 2021-22 (17 weeks later) and 2022-23 (4 weeks earlier). Age distribution was different compared to the prepandemic period, especially during the first pandemic year. Conclusions: Significant shifts in viral etiology, seasonality, and age distribution of ARI hospitalizations occurred during the 3 pandemic years. Changes in age distribution observed in our study may reflect modifications in the landscape of circulating RVs and their contribution to ARI hospitalizations. During the pandemic period, SARS-CoV-2 had a low contribution to pediatric ARI hospitalizations, while it was the main contributor to adult ARI hospitalizations during the first 2 seasons and dropped below ORVs during the third pandemic season. Evolving RVs epidemiology underscores the need for increased scrutiny of ARI hospitalization etiology to inform tailored public health recommendations. %M 38709551 %R 10.2196/40792 %U https://publichealth.jmir.org/2024/1/e40792 %U https://doi.org/10.2196/40792 %U http://www.ncbi.nlm.nih.gov/pubmed/38709551 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e54340 %T Responding to the Return of Influenza in the United States by Applying Centers for Disease Control and Prevention Surveillance, Analysis, and Modeling to Inform Understanding of Seasonal Influenza %A Borchering,Rebecca K %A Biggerstaff,Matthew %A Brammer,Lynnette %A Budd,Alicia %A Garg,Shikha %A Fry,Alicia M %A Iuliano,A Danielle %A Reed,Carrie %+ National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, 1600 Clifton Rd, NE MS H24-7, Atlanta, GA, 30329, United States, 1 4046395214, xhq2@cdc.gov %K disease burden %K modeling %K seasonal influenza %K surveillance %D 2024 %7 8.4.2024 %9 Viewpoint %J JMIR Public Health Surveill %G English %X We reviewed the tools that have been developed to characterize and communicate seasonal influenza activity in the United States. Here we focus on systematic surveillance and applied analytics, including seasonal burden and disease severity estimation, short-term forecasting, and longer-term modeling efforts. For each set of activities, we describe the challenges and opportunities that have arisen because of the COVID-19 pandemic. In conclusion, we highlight how collaboration and communication have been and will continue to be key components of reliable and actionable influenza monitoring, forecasting, and modeling activities. %M 38587882 %R 10.2196/54340 %U https://publichealth.jmir.org/2024/1/e54340 %U https://doi.org/10.2196/54340 %U http://www.ncbi.nlm.nih.gov/pubmed/38587882 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e52047 %T Postpandemic Sentinel Surveillance of Respiratory Diseases in the Context of the World Health Organization Mosaic Framework: Protocol for a Development and Evaluation Study Involving the English Primary Care Network 2023-2024 %A Gu,Xinchun %A Watson,Conall %A Agrawal,Utkarsh %A Whitaker,Heather %A Elson,William H. %A Anand,Sneha %A Borrow,Ray %A Buckingham,Anna %A Button,Elizabeth %A Curtis,Lottie %A Dunn,Dominic %A Elliot,Alex J. %A Ferreira,Filipa %A Goudie,Rosalind %A Hoang,Uy %A Hoschler,Katja %A Jamie,Gavin %A Kar,Debasish %A Kele,Beatrix %A Leston,Meredith %A Linley,Ezra %A Macartney,Jack %A Marsden,Gemma L %A Okusi,Cecilia %A Parvizi,Omid %A Quinot,Catherine %A Sebastianpillai,Praveen %A Sexton,Vanashree %A Smith,Gillian %A Suli,Timea %A Thomas,Nicholas P B %A Thompson,Catherine %A Todkill,Daniel %A Wimalaratna,Rashmi %A Inada-Kim,Matthew %A Andrews,Nick %A Tzortziou-Brown,Victoria %A Byford,Rachel %A Zambon,Maria %A Lopez-Bernal,Jamie %A de Lusignan,Simon %+ Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, United Kingdom, 44 01865 617 283 ext 17 283, simon.delusignan@phc.ox.ac.uk %K sentinel surveillance %K pandemic %K COVID-19 %K human influenza %K influenza vaccines %K respiratory tract infections %K vaccination %K World Health Organization %K respiratory syncytial virus %K phenotype %K computerized medical record system %D 2024 %7 3.4.2024 %9 Protocol %J JMIR Public Health Surveill %G English %X Background: Prepandemic sentinel surveillance focused on improved management of winter pressures, with influenza-like illness (ILI) being the key clinical indicator. The World Health Organization (WHO) global standards for influenza surveillance include monitoring acute respiratory infection (ARI) and ILI. The WHO’s mosaic framework recommends that the surveillance strategies of countries include the virological monitoring of respiratory viruses with pandemic potential such as influenza. The Oxford-Royal College of General Practitioner Research and Surveillance Centre (RSC) in collaboration with the UK Health Security Agency (UKHSA) has provided sentinel surveillance since 1967, including virology since 1993. Objective: We aim to describe the RSC’s plans for sentinel surveillance in the 2023-2024 season and evaluate these plans against the WHO mosaic framework. Methods: Our approach, which includes patient and public involvement, contributes to surveillance objectives across all 3 domains of the mosaic framework. We will generate an ARI phenotype to enable reporting of this indicator in addition to ILI. These data will support UKHSA’s sentinel surveillance, including vaccine effectiveness and burden of disease studies. The panel of virology tests analyzed in UKHSA’s reference laboratory will remain unchanged, with additional plans for point-of-care testing, pneumococcus testing, and asymptomatic screening. Our sampling framework for serological surveillance will provide greater representativeness and more samples from younger people. We will create a biomedical resource that enables linkage between clinical data held in the RSC and virology data, including sequencing data, held by the UKHSA. We describe the governance framework for the RSC. Results: We are co-designing our communication about data sharing and sampling, contextualized by the mosaic framework, with national and general practice patient and public involvement groups. We present our ARI digital phenotype and the key data RSC network members are requested to include in computerized medical records. We will share data with the UKHSA to report vaccine effectiveness for COVID-19 and influenza, assess the disease burden of respiratory syncytial virus, and perform syndromic surveillance. Virological surveillance will include COVID-19, influenza, respiratory syncytial virus, and other common respiratory viruses. We plan to pilot point-of-care testing for group A streptococcus, urine tests for pneumococcus, and asymptomatic testing. We will integrate test requests and results with the laboratory-computerized medical record system. A biomedical resource will enable research linking clinical data to virology data. The legal basis for the RSC’s pseudonymized data extract is The Health Service (Control of Patient Information) Regulations 2002, and all nonsurveillance uses require research ethics approval. Conclusions: The RSC extended its surveillance activities to meet more but not all of the mosaic framework’s objectives. We have introduced an ARI indicator. We seek to expand our surveillance scope and could do more around transmissibility and the benefits and risks of nonvaccine therapies. %M 38569175 %R 10.2196/52047 %U https://publichealth.jmir.org/2024/1/e52047 %U https://doi.org/10.2196/52047 %U http://www.ncbi.nlm.nih.gov/pubmed/38569175 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e50799 %T Use of Sentinel Surveillance Platforms for Monitoring SARS-CoV-2 Activity: Evidence From Analysis of Kenya Influenza Sentinel Surveillance Data %A Owusu,Daniel %A Ndegwa,Linus K %A Ayugi,Jorim %A Kinuthia,Peter %A Kalani,Rosalia %A Okeyo,Mary %A Otieno,Nancy A %A Kikwai,Gilbert %A Juma,Bonventure %A Munyua,Peninah %A Kuria,Francis %A Okunga,Emmanuel %A Moen,Ann C %A Emukule,Gideon O %+ Influenza Division, US Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30333, United States, 1 4044014398, pgv7@cdc.gov %K SARS-CoV-2 %K COVID-19 %K influenza %K sentinel surveillance %K Kenya %K epidemic %K local outbreak %K respiratory infection %K surveillance %K cocirculation %K monitoring %K respiratory pathogen %D 2024 %7 25.3.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Little is known about the cocirculation of influenza and SARS-CoV-2 viruses during the COVID-19 pandemic and the use of respiratory disease sentinel surveillance platforms for monitoring SARS-CoV-2 activity in sub-Saharan Africa. Objective: We aimed to describe influenza and SARS-CoV-2 cocirculation in Kenya and how the SARS-CoV-2 data from influenza sentinel surveillance correlated with that of universal national surveillance. Methods: From April 2020 to March 2022, we enrolled 7349 patients with severe acute respiratory illness or influenza-like illness at 8 sentinel influenza surveillance sites in Kenya and collected demographic, clinical, underlying medical condition, vaccination, and exposure information, as well as respiratory specimens, from them. Respiratory specimens were tested for influenza and SARS-CoV-2 by real-time reverse transcription polymerase chain reaction. The universal national-level SARS-CoV-2 data were also obtained from the Kenya Ministry of Health. The universal national-level SARS-CoV-2 data were collected from all health facilities nationally, border entry points, and contact tracing in Kenya. Epidemic curves and Pearson r were used to describe the correlation between SARS-CoV-2 positivity in data from the 8 influenza sentinel sites in Kenya and that of the universal national SARS-CoV-2 surveillance data. A logistic regression model was used to assess the association between influenza and SARS-CoV-2 coinfection with severe clinical illness. We defined severe clinical illness as any of oxygen saturation <90%, in-hospital death, admission to intensive care unit or high dependence unit, mechanical ventilation, or a report of any danger sign (ie, inability to drink or eat, severe vomiting, grunting, stridor, or unconsciousness in children younger than 5 years) among patients with severe acute respiratory illness. Results: Of the 7349 patients from the influenza sentinel surveillance sites, 76.3% (n=5606) were younger than 5 years. We detected any influenza (A or B) in 8.7% (629/7224), SARS-CoV-2 in 10.7% (768/7199), and coinfection in 0.9% (63/7165) of samples tested. Although the number of samples tested for SARS-CoV-2 from the sentinel surveillance was only 0.2% (60 per week vs 36,000 per week) of the number tested in the universal national surveillance, SARS-CoV-2 positivity in the sentinel surveillance data significantly correlated with that of the universal national surveillance (Pearson r=0.58; P<.001). The adjusted odds ratios (aOR) of clinical severe illness among participants with coinfection were similar to those of patients with influenza only (aOR 0.91, 95% CI 0.47-1.79) and SARS-CoV-2 only (aOR 0.92, 95% CI 0.47-1.82). Conclusions: Influenza substantially cocirculated with SARS-CoV-2 in Kenya. We found a significant correlation of SARS-CoV-2 positivity in the data from 8 influenza sentinel surveillance sites with that of the universal national SARS-CoV-2 surveillance data. Our findings indicate that the influenza sentinel surveillance system can be used as a sustainable platform for monitoring respiratory pathogens of pandemic potential or public health importance. %M 38526537 %R 10.2196/50799 %U https://publichealth.jmir.org/2024/1/e50799 %U https://doi.org/10.2196/50799 %U http://www.ncbi.nlm.nih.gov/pubmed/38526537 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e40216 %T Decreased Seasonal Influenza Rates Detected in a Crowdsourced Influenza-Like Illness Surveillance System During the COVID-19 Pandemic: Prospective Cohort Study %A Gertz,Autumn %A Rader,Benjamin %A Sewalk,Kara %A Varrelman,Tanner J %A Smolinski,Mark %A Brownstein,John S %+ Computational Epidemiology Lab, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, United States, 1 8572185188, info@outbreaksnearme.org %K participatory surveillance %K influenza %K crowdsourced data %K disease surveillance %K surveillance %K COVID-19 %K respiratory %K transmission %K detection %K survey %K sore throat %K fever %K cough %K vaccination %K diagnosis %K precautions %D 2023 %7 28.12.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Seasonal respiratory viruses had lower incidence during their 2019-2020 and 2020-2021 seasons, which overlapped with the COVID-19 pandemic. The widespread implementation of precautionary measures to prevent transmission of SARS-CoV-2 has been seen to also mitigate transmission of seasonal influenza. The COVID-19 pandemic also led to changes in care seeking and access. Participatory surveillance systems have historically captured mild illnesses that are often missed by surveillance systems that rely on encounters with a health care provider for detection. Objective: This study aimed to assess if a crowdsourced syndromic surveillance system capable of detecting mild influenza-like illness (ILI) also captured the globally observed decrease in ILI in the 2019-2020 and 2020-2021 influenza seasons, concurrent with the COVID-19 pandemic. Methods: Flu Near You (FNY) is a web-based participatory syndromic surveillance system that allows participants in the United States to report their health information using a brief weekly survey. Reminder emails are sent to registered FNY participants to report on their symptoms and the symptoms of household members. Guest participants may also report. ILI was defined as fever and sore throat or fever and cough. ILI rates were determined as the number of ILI reports over the total number of reports and assessed for the 2016-2017, 2017-2018, 2018-2019, 2019-2020, and 2020-2021 influenza seasons. Baseline season (2016-2017, 2017-2018, and 2018-2019) rates were compared to the 2019-2020 and 2020-2021 influenza seasons. Self-reported influenza diagnosis and vaccination status were captured and assessed as the total number of reported events over the total number of reports submitted. CIs for all proportions were calculated via a 1-sample test of proportions. Results: ILI was detected in 3.8% (32,239/848,878) of participants in the baseline seasons (2016-2019), 2.58% (7418/287,909) in the 2019-2020 season, and 0.27% (546/201,079) in the 2020-2021 season. Both influenza seasons that overlapped with the COVID-19 pandemic had lower ILI rates than the baseline seasons. ILI decline was observed during the months with widespread implementation of COVID-19 precautions, starting in February 2020. Self-reported influenza diagnoses decreased from early 2020 through the influenza season. Self-reported influenza positivity among ILI cases varied over the observed time period. Self-reported influenza vaccination rates in FNY were high across all observed seasons. Conclusions: A decrease in ILI was detected in the crowdsourced FNY surveillance system during the 2019-2020 and 2020-2021 influenza seasons, mirroring trends observed in other influenza surveillance systems. Specifically, the months within seasons that overlapped with widespread pandemic precautions showed decreases in ILI and confirmed influenza. Concerns persist regarding respiratory pathogens re-emerging with changes to COVID-19 guidelines. Traditional surveillance is subject to changes in health care behaviors. Systems like FNY are uniquely situated to detect disease across disease severity and care seeking, providing key insights during public health emergencies. %M 38153782 %R 10.2196/40216 %U https://publichealth.jmir.org/2023/1/e40216 %U https://doi.org/10.2196/40216 %U http://www.ncbi.nlm.nih.gov/pubmed/38153782 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45085 %T Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study %A Yang,Liuyang %A Zhang,Ting %A Han,Xuan %A Yang,Jiao %A Sun,Yanxia %A Ma,Libing %A Chen,Jialong %A Li,Yanming %A Lai,Shengjie %A Li,Wei %A Feng,Luzhao %A Yang,Weizhong %+ School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, 9 Dong Dan San Tiao, Dongcheng District, Beijing, 100730, China, 86 010 65120552, yangweizhong@cams.cn %K early warning %K epidemic intelligence %K infectious disease %K influenza-like illness %K surveillance %D 2023 %7 17.10.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. Objective: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. Methods: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. Results: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. Conclusions: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models. %M 37847532 %R 10.2196/45085 %U https://www.jmir.org/2023/1/e45085 %U https://doi.org/10.2196/45085 %U http://www.ncbi.nlm.nih.gov/pubmed/37847532 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e46644 %T Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View %A Leal Neto,Onicio %A Paolotti,Daniela %A Dalton,Craig %A Carlson,Sandra %A Susumpow,Patipat %A Parker,Matt %A Phetra,Polowat %A Lau,Eric H Y %A Colizza,Vittoria %A Jan van Hoek,Albert %A Kjelsø,Charlotte %A Brownstein,John S %A Smolinski,Mark S %+ Ending Pandemics, 870 Market Street, Suite 528, San Francisco, CA, 94102, United States, 1 5106938359, onicio@gmail.com %K participatory surveillance %K digital epidemiology %K influenza-like illness %K data transfer %K surveillance %K digital platform %K Global Flu View %K program %K data sharing %K public health %K innovative %K flu %D 2023 %7 1.9.2023 %9 Viewpoint %J JMIR Public Health Surveill %G English %X Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges influenza-like illness (ILI) data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an application program interface, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists, and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic. %M 37490846 %R 10.2196/46644 %U https://publichealth.jmir.org/2023/1/e46644 %U https://doi.org/10.2196/46644 %U http://www.ncbi.nlm.nih.gov/pubmed/37490846 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e46383 %T Reported Global Avian Influenza Detections Among Humans and Animals During 2013-2022: Comprehensive Review and Analysis of Available Surveillance Data %A Szablewski,Christine M %A Iwamoto,Chelsea %A Olsen,Sonja J %A Greene,Carolyn M %A Duca,Lindsey M %A Davis,C Todd %A Coggeshall,Kira C %A Davis,William W %A Emukule,Gideon O %A Gould,Philip L %A Fry,Alicia M %A Wentworth,David E %A Dugan,Vivien G %A Kile,James C %A Azziz-Baumgartner,Eduardo %+ Influenza Division, National Center for Immunization and Respiratory Diseases, United States Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA, 30333, United States, 1 470 998 1990, LQZ9@cdc.gov %K avian influenza %K novel influenza %K pandemic influenza %K One Health %K zoonotic influenza %K surveillance %D 2023 %7 31.8.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Avian influenza (AI) virus detections occurred frequently in 2022 and continue to pose a health, economic, and food security risk. The most recent global analysis of official reports of animal outbreaks and human infections with all reportable AI viruses was published almost a decade ago. Increased or renewed reports of AI viruses, especially high pathogenicity H5N8 and H5N1 in birds and H5N1, H5N8, and H5N6 in humans globally, have established the need for a comprehensive review of current global AI virus surveillance data to assess the pandemic risk of AI viruses. Objective: This study aims to provide an analysis of global AI animal outbreak and human case surveillance information from the last decade by describing the circulating virus subtypes, regions and temporal trends in reporting, and country characteristics associated with AI virus outbreak reporting in animals; surveillance and reporting gaps for animals and humans are identified. Methods: We analyzed AI virus infection reports among animals and humans submitted to animal and public health authorities from January 2013 to June 2022 and compared them with reports from January 2005 to December 2012. A multivariable regression analysis was used to evaluate associations between variables of interest and reported AI virus animal outbreaks. Results: From 2013 to 2022, 52.2% (95/182) of World Organisation for Animal Health (WOAH) Member Countries identified 34 AI virus subtypes during 21,249 outbreaks. The most frequently reported subtypes were high pathogenicity AI H5N1 (10,079/21,249, 47.43%) and H5N8 (6722/21,249, 31.63%). A total of 10 high pathogenicity AI and 6 low pathogenicity AI virus subtypes were reported to the WOAH for the first time during 2013-2022. AI outbreaks in animals occurred in 26 more Member Countries than reported in the previous 8 years. Decreasing World Bank income classification was significantly associated with decreases in reported AI outbreaks (P<.001-.02). Between January 2013 and June 2022, 17/194 (8.8%) World Health Organization (WHO) Member States reported 2000 human AI virus infections of 10 virus subtypes. H7N9 (1568/2000, 78.40%) and H5N1 (254/2000, 12.70%) viruses accounted for the most human infections. As many as 8 of these 17 Member States did not report a human case prior to 2013. Of 1953 human cases with available information, 74.81% (n=1461) had a known animal exposure before onset of illness. The median time from illness onset to the notification posted on the WHO event information site was 15 days (IQR 9-30 days; mean 24 days). Seasonality patterns of animal outbreaks and human infections with AI viruses were very similar, occurred year-round, and peaked during November through May. Conclusions: Our analysis suggests that AI outbreaks are more frequently reported and geographically widespread than in the past. Global surveillance gaps include inconsistent reporting from all regions and human infection reporting delays. Continued monitoring for AI virus outbreaks in animals and human infections with AI viruses is crucial for pandemic preparedness. %M 37651182 %R 10.2196/46383 %U https://publichealth.jmir.org/2023/1/e46383 %U https://doi.org/10.2196/46383 %U http://www.ncbi.nlm.nih.gov/pubmed/37651182 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e41435 %T Effect of Rapid Urbanization in Mainland China on the Seasonal Influenza Epidemic: Spatiotemporal Analysis of Surveillance Data From 2010 to 2017 %A Lei,Hao %A Zhang,Nan %A Niu,Beidi %A Wang,Xiao %A Xiao,Shenglan %A Du,Xiangjun %A Chen,Tao %A Yang,Lei %A Wang,Dayan %A Cowling,Benjamin %A Li,Yuguo %A Shu,Yuelong %+ School of Public Health, Zhejiang University, 866 Yu-hang-tang Road, Hangzhou, 310058, China, 86 0571 8707 6051, leolei@zju.edu.cn %K seasonal influenza %K attack rate %K urbanization %K urban population %K human contact %K agent-based model %K influenza %K seasonal flu %K spatiotemporal %K epidemic %K disease transmission %K disease spread %K epidemiology %K influenza transmission %K epidemics %D 2023 %7 7.7.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The world is undergoing an unprecedented wave of urbanization. However, the effect of rapid urbanization during the early or middle stages of urbanization on seasonal influenza transmission remains unknown. Since about 70% of the world population live in low-income countries, exploring the impact of urbanization on influenza transmission in urbanized countries is significant for global infection prediction and prevention. Objective: The aim of this study was to explore the effect of rapid urbanization on influenza transmission in China. Methods: We performed spatiotemporal analyses of province-level influenza surveillance data collected in Mainland China from April 1, 2010, to March 31, 2017. An agent-based model based on hourly human contact–related behaviors was built to simulate the influenza transmission dynamics and to explore the potential mechanism of the impact of urbanization on influenza transmission. Results: We observed persistent differences in the influenza epidemic attack rates among the provinces of Mainland China across the 7-year study period, and the attack rate in the winter waves exhibited a U-shaped relationship with the urbanization rates, with a turning point at 50%-60% urbanization across Mainland China. Rapid Chinese urbanization has led to increases in the urban population density and percentage of the workforce but decreases in household size and the percentage of student population. The net effect of increased influenza transmission in the community and workplaces but decreased transmission in households and schools yielded the observed U-shaped relationship. Conclusions: Our results highlight the complicated effects of urbanization on the seasonal influenza epidemic in China. As the current urbanization rate in China is approximately 59%, further urbanization with no relevant interventions suggests a worrisome increasing future trend in the influenza epidemic attack rate. %M 37418298 %R 10.2196/41435 %U https://publichealth.jmir.org/2023/1/e41435 %U https://doi.org/10.2196/41435 %U http://www.ncbi.nlm.nih.gov/pubmed/37418298 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e44970 %T Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis %A Wang,Qing %A Jia,Mengmeng %A Jiang,Mingyue %A Liu,Wei %A Yang,Jin %A Dai,Peixi %A Sun,Yanxia %A Qian,Jie %A Yang,Weizhong %A Feng,Luzhao %+ School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, No 9, Dongdan Santiao, Dongcheng District, Beijing, 100730, China, 86 10 65120716, fengluzhao@cams.cn %K COVID-19 %K influenza %K negative correlation %K seesaw effect %K respiratory infectious disease %K epidemiological trends %D 2023 %7 12.6.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Seasonal influenza activity showed a sharp decline in activity at the beginning of the emergence of COVID-19. Whether there is an epidemiological correlation between the dynamic of these 2 respiratory infectious diseases and their future trends needs to be explored. Objective: We aimed to assess the correlation between COVID-19 and influenza activity and estimate later epidemiological trends. Methods: We retrospectively described the dynamics of COVID-19 and influenza in 6 World Health Organization (WHO) regions from January 2020 to March 2023 and used the long short-term memory machine learning model to learn potential patterns in previously observed activity and predict trends for the following 16 weeks. Finally, we used Spearman correlation coefficients to assess the past and future epidemiological correlation between these 2 respiratory infectious diseases. Results: With the emergence of the original strain of SARS-CoV-2 and other variants, influenza activity stayed below 10% for more than 1 year in the 6 WHO regions. Subsequently, it gradually rose as Delta activity dropped, but still peaked below Delta. During the Omicron pandemic and the following period, the activity of each disease increased as the other decreased, alternating in dominance more than once, with each alternation lasting for 3 to 4 months. Correlation analysis showed that COVID-19 and influenza activity presented a predominantly negative correlation, with coefficients above –0.3 in WHO regions, especially during the Omicron pandemic and the following estimated period. The diseases had a transient positive correlation in the European region of the WHO and the Western Pacific region of the WHO when multiple dominant strains created a mixed pandemic. Conclusions: Influenza activity and past seasonal epidemiological patterns were shaken by the COVID-19 pandemic. The activity of these diseases was moderately or greater than moderately inversely correlated, and they suppressed and competed with each other, showing a seesaw effect. In the postpandemic era, this seesaw trend may be more prominent, suggesting the possibility of using one disease as an early warning signal for the other when making future estimates and conducting optimized annual vaccine campaigns. %M 37191650 %R 10.2196/44970 %U https://publichealth.jmir.org/2023/1/e44970 %U https://doi.org/10.2196/44970 %U http://www.ncbi.nlm.nih.gov/pubmed/37191650 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e39700 %T Comparing the Use of a Mobile App and a Web-Based Notification Platform for Surveillance of Adverse Events Following Influenza Immunization: Randomized Controlled Trial %A Bota,A Brianne %A Bettinger,Julie A %A Sarfo-Mensah,Shirley %A Lopez,Jimmy %A Smith,David P %A Atkinson,Katherine M %A Bell,Cameron %A Marty,Kim %A Serhan,Mohamed %A Zhu,David T %A McCarthy,Anne E %A Wilson,Kumanan %+ Clinical Epidemiology Program, Ottawa Hospital Research Institute, 1053 Carling Avenue, Administrative Service Building, Box 684, Ottawa, ON, K1Y 4E9, Canada, 1 6137985555 ext 17921, kwilson@toh.ca %K active participant–centered reporting %K health technology %K adverse event reporting %K mobile apps %K immunization %K vaccine %K safety %K influenza %K campaign %K apps %K mobile %K surveillance %K pharmacovigilance %D 2023 %7 8.5.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Vaccine safety surveillance is a core component of vaccine pharmacovigilance. In Canada, active, participant-centered vaccine surveillance is available for influenza vaccines and has been used for COVID-19 vaccines. Objective: The objective of this study is to evaluate the effectiveness and feasibility of using a mobile app for reporting participant-centered seasonal influenza adverse events following immunization (AEFIs) compared to a web-based notification system. Methods: Participants were randomized to influenza vaccine safety reporting via a mobile app or a web-based notification platform. All participants were invited to complete a user experience survey. Results: Among the 2408 randomized participants, 1319 (54%) completed their safety survey 1 week after vaccination, with a higher completion rate among the web-based notification platform users (767/1196, 64%) than among mobile app users (552/1212, 45%; P<.001). Ease-of-use ratings were high for the web-based notification platform users (99% strongly agree or agree) and 88.8% of them strongly agreed or agreed that the system made reporting AEFIs easier. Web-based notification platform users supported the statement that a web-based notification-only approach would make it easier for public health professionals to detect vaccine safety signals (91.4%, agreed or strongly agreed). Conclusions: Participants in this study were significantly more likely to respond to a web-based safety survey rather than within a mobile app. These results suggest that mobile apps present an additional barrier for use compared to the web-based notification–only approach. Trial Registration: ClinicalTrials.gov NCT05794113; https://clinicaltrials.gov/show/NCT05794113 %M 37155240 %R 10.2196/39700 %U https://publichealth.jmir.org/2023/1/e39700 %U https://doi.org/10.2196/39700 %U http://www.ncbi.nlm.nih.gov/pubmed/37155240 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e41050 %T Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study %A Hunter,Victoria %A Shapiro,Allison %A Chawla,Devika %A Drawnel,Faye %A Ramirez,Ernesto %A Phillips,Elizabeth %A Tadesse-Bell,Sara %A Foschini,Luca %A Ukachukwu,Vincent %+ Roche Products Limited, 6 Falcon Way, Shire Park, Welwyn Garden City, AL7 1TW, United Kingdom, 44 7785642250, vincent.ukachukwu@roche.com %K influenza %K influenza-like illness %K wearable sensor %K person-generated health care data %D 2023 %7 23.3.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: The burden of influenza-like illness (ILI) is typically estimated via hospitalizations and deaths. However, ILI-associated morbidity that does not require hospitalization remains poorly characterized. Objective: The main objective of this study was to characterize ILI burden using commercial wearable sensor data and investigate the extent to which these data correlate with self-reported illness severity and duration. Furthermore, we aimed to determine whether ILI-associated changes in wearable sensor data differed between care-seeking and non–care-seeking populations as well as between those with confirmed influenza infection and those with ILI symptoms only. Methods: This study comprised participants enrolled in either the FluStudy2020 or the Home Testing of Respiratory Illness (HTRI) study; both studies were similar in design and conducted between December 2019 and October 2020 in the United States. The participants self-reported ILI-related symptoms and health care–seeking behaviors via daily, biweekly, and monthly surveys. Wearable sensor data were recorded for 120 and 150 days for FluStudy2020 and HTRI, respectively. The following features were assessed: total daily steps, active time (time spent with >50 steps per minute), sleep duration, sleep efficiency, and resting heart rate. ILI-related changes in wearable sensor data were compared between the participants who sought health care and those who did not and between the participants who tested positive for influenza and those with symptoms only. Correlative analyses were performed between wearable sensor data and patient-reported outcomes. Results: After combining the FluStudy2020 and HTRI data sets, the final ILI population comprised 2435 participants. Compared with healthy days (baseline), the participants with ILI exhibited significantly reduced total daily steps, active time, and sleep efficiency as well as increased sleep duration and resting heart rate. Deviations from baseline typically began before symptom onset and were greater in the participants who sought health care than in those who did not and greater in the participants who tested positive for influenza than in those with symptoms only. During an ILI event, changes in wearable sensor data consistently varied with those in patient-reported outcomes. Conclusions: Our results underscore the potential of wearable sensors to discriminate not only between individuals with and without influenza infections but also between care-seeking and non–care-seeking populations, which may have future application in health care resource planning. Trial Registration: Clinicaltrials.gov NCT04245800; https://clinicaltrials.gov/ct2/show/NCT04245800 %M 36951890 %R 10.2196/41050 %U https://www.jmir.org/2023/1/e41050 %U https://doi.org/10.2196/41050 %U http://www.ncbi.nlm.nih.gov/pubmed/36951890 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e38080 %T Protecting Older Adult Residents in Care Facilities Against Influenza and COVID-19 Using the Influenza Communication, Advice and Reporting (FluCARE) App: Prospective Cohort Mixed Methods Study %A Quinn,Emma %A Hsiao,Kai Hsun %A Johnstone,Travers %A Gomez,Maria %A Parasuraman,Arun %A Ingleton,Andrew %A Hirst,Nicholas %A Najjar,Zeina %A Gupta,Leena %+ Public Health Unit, Sydney Local Health District, Royal Prince Alfred Hospital Campus, Missenden Road, Camperdown, Sydney, 2050, Australia, 61 2 95159420, emma.quinn@health.nsw.gov.au %K web app %K digital health %K influenza %K COVID-19 %K outbreak %K monitoring %K disease control %K infection spread %K infection control %K detect %K aged care %K elderly %K elderly population %K older adult %K long term care %K care home %K AFC %K LTC %K nursing home %K retirement home %K mobile application %K health application %K mHealth %K care facility %K online training %K health impact %K feasibility %K efficacy %K satisfaction %K prevention %K disease spread %K notification %D 2023 %7 13.3.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Early detection and response to influenza and COVID-19 outbreaks in aged care facilities (ACFs) are critical to minimizing health impacts. The Sydney Local Health District (SLHD) Public Health Unit (PHU) has developed and implemented a novel web-based app with integrated functions for online line listings, detection algorithms, and automatic notifications to responders, to assist ACFs in outbreak response. The goal of the Influenza Outbreak Communication, Advice and Reporting (FluCARE) app is to reduce time delays to notifications, which we hope will reduce the spread, duration, and health impacts of an influenza or COVID-19 outbreak, as well as ease workload burdens on ACF staff. Objective: The specific aims of the study were to (1) evaluate the acceptability and user satisfaction of the implementation and use of FluCARE in helping ACFs recognize, notify, and manage influenza and COVID-19 outbreaks in their facility; (2) identify the safety of FluCARE and any potential adverse outcomes of using the app; and (3) identify any perceived barriers or facilitators to the implementation and use of FluCARE from the ACF user perspective. Methods: The FluCARE app was piloted from September 2019 to December 2020 in the SLHD. Associated implementation included promotion and engagement, user training, and operational policies. Participating ACF staff were invited to complete a posttraining survey. Staff were also invited to complete a postpilot evaluation survey that included the user Mobile Application Rating Scale (uMARS) measuring app acceptance, utility, and barriers and facilitators to use. An issues log was also prospectively maintained to assess safety. Survey data were analyzed descriptively or via content analysis where appropriate. Results: Surveys were completed by 31 consenting users from 27 ACFs. FluCARE was rated 3.91 of 5 overall on the uMARS. Of the 31 users, 25 (80%) would definitely use FluCARE for future outbreaks, and all users agreed that the app was useful for identifying influenza and COVID-19 outbreaks at their facilities. There were no reported critical issues with incorrect or missed outbreak detection. User training, particularly online training modules, and technical support were identified as key facilitators to FluCARE use. Conclusions: FluCARE is an acceptable, useful, and safe app to assist ACF staff with early detection and response to influenza and COVID-19 outbreaks. This study supports feasibility for ongoing implementation and efficacy evaluation, followed by scale-up into other health districts in New South Wales. %M 36763638 %R 10.2196/38080 %U https://formative.jmir.org/2023/1/e38080 %U https://doi.org/10.2196/38080 %U http://www.ncbi.nlm.nih.gov/pubmed/36763638 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44238 %T Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation %A Yang,Liuyang %A Li,Gang %A Yang,Jin %A Zhang,Ting %A Du,Jing %A Liu,Tian %A Zhang,Xingxing %A Han,Xuan %A Li,Wei %A Ma,Libing %A Feng,Luzhao %A Yang,Weizhong %+ School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 9, Dongdan Santiao, Dongcheng District, Beijing, Beijing, 100730, China, 86 1 391 181 9068, yangweizhong@cams.cn %K influenza %K ILI %K multisource heterogeneous data %K deep learning %K MAL model %K megacity %D 2023 %7 13.2.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. Objective: The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. Methods: We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. Results: The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. Conclusions: The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population. %M 36780207 %R 10.2196/44238 %U https://www.jmir.org/2023/1/e44238 %U https://doi.org/10.2196/44238 %U http://www.ncbi.nlm.nih.gov/pubmed/36780207 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42519 %T Long Short-term Memory–Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation %A Athanasiou,Maria %A Fragkozidis,Georgios %A Zarkogianni,Konstantia %A Nikita,Konstantina S %+ School of Electrical and Computer Engineering, National Technical University of Athens, 9, Iroon Polytechniou Str., Zografos, Athens, 15780, Greece, 30 2107722149, mathanasiou@biosim.ntua.gr %K influenza-like illness %K epidemiological surveillance %K machine learning %K deep learning %K social media %K Twitter %K meteorological parameters %D 2023 %7 6.2.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources. Objective: The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases. Methods: The model’s input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models’ LSTM layers for the latter to feed a dense layer. Results: The primary model that learned from weather data yielded better forecast accuracy (root mean square error [RMSE]=0.144; Pearson correlation coefficient [PCC]=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822). Conclusions: The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics. %M 36745490 %R 10.2196/42519 %U https://www.jmir.org/2023/1/e42519 %U https://doi.org/10.2196/42519 %U http://www.ncbi.nlm.nih.gov/pubmed/36745490 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e42530 %T Influenza-Associated Excess Mortality by Age, Sex, and Subtype/Lineage: Population-Based Time-Series Study With a Distributed-Lag Nonlinear Model %A Li,Li %A Yan,Ze-Lin %A Luo,Lei %A Liu,Wenhui %A Yang,Zhou %A Shi,Chen %A Ming,Bo-Wen %A Yang,Jun %A Cao,Peihua %A Ou,Chun-Quan %+ State Key Laboratory of Organ Failure Research, Department of Biostatistics, School of Public Health, Southern Medical University, No1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China, 86 20 61649461, ouchunquan@hotmail.com %K influenza %K disease burden %K distributed-lag nonlinear model %K excess mortality %K harvesting effects %D 2023 %7 11.1.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Accurate estimation of the influenza death burden is of great significance for influenza prevention and control. However, few studies have considered the short-term harvesting effects of influenza on mortality when estimating influenza-associated excess deaths by cause of death, age, sex, and subtype/lineage. Objective: This study aimed to estimate the cause-, age-, and sex-specific excess mortality associated with influenza and its subtypes and lineages in Guangzhou from 2015 to 2018. Methods: Distributed-lag nonlinear models were fitted to estimate the excess mortality related to influenza subtypes or lineages for different causes of death, age groups, and sex based on daily time-series data for mortality, influenza, and meteorological factors. Results: A total of 199,777 death certificates were included in the study. The average annual influenza-associated excess mortality rate (EMR) was 25.06 (95% empirical CI [eCI] 19.85-30.16) per 100,000 persons; 7142 of 8791 (81.2%) deaths were due to respiratory or cardiovascular mortality (EMR 20.36, 95% eCI 16.75-23.74). Excess respiratory and cardiovascular deaths in people aged 60 to 79 years and those aged ≥80 years accounted for 32.9% (2346/7142) and 63.7% (4549/7142) of deaths, respectively. The male to female ratio (MFR) of excess death from respiratory diseases was 1.34 (95% CI 1.17-1.54), while the MFR for excess death from cardiovascular disease was 0.72 (95% CI 0.63-0.82). The average annual excess respiratory and cardiovascular mortality rates attributed to influenza A (H3N2), B/Yamagata, B/Victoria, and A (H1N1) were 8.47 (95% eCI 6.60-10.30), 5.81 (95% eCI 3.35-8.25), 3.68 (95% eCI 0.81-6.49), and 2.83 (95% eCI –1.26 to 6.71), respectively. Among these influenza subtypes/lineages, A (H3N2) had the highest excess respiratory and cardiovascular mortality rates for people aged 60 to 79 years (20.22, 95% eCI 14.56-25.63) and ≥80 years (180.15, 95% eCI 130.75-227.38), while younger people were more affected by A (H1N1), with an EMR of 1.29 (95% eCI 0.07-2.32). The mortality displacement of influenza A (H1N1), A (H3N2), and B/Yamagata was 2 to 5 days, but 5 to 13 days for B/Victoria. Conclusions: Influenza was associated with substantial mortality in Guangzhou, occurring predominantly in the elderly, even after considering mortality displacement. The mortality burden of influenza B, particularly B/Yamagata, cannot be ignored. Contrasting sex differences were found in influenza-associated excess mortality from respiratory diseases and from cardiovascular diseases; the underlying mechanisms need to be investigated in future studies. Our findings can help us better understand the magnitude and time-course of the effect of influenza on mortality and inform targeted interventions for mitigating the influenza mortality burden, such as immunizations with quadrivalent vaccines (especially for older people), behavioral campaigns, and treatment strategies. %M 36630176 %R 10.2196/42530 %U https://publichealth.jmir.org/2023/1/e42530 %U https://doi.org/10.2196/42530 %U http://www.ncbi.nlm.nih.gov/pubmed/36630176 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e41329 %T Monitoring School Absenteeism for Influenza-Like Illness Surveillance: Systematic Review and Meta-analysis %A Tsang,Tim K %A Huang,Xiaotong %A Guo,Yiyang %A Lau,Eric H Y %A Cowling,Benjamin J %A Ip,Dennis K M %+ WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong, China (Hong Kong), 852 39179715, timtsang@connect.hku.hk %K influenza %K surveillance %K school absenteeism %K monitoring %K school attendance %K influenza-like illness %K correlation %K trend %K pattern %K predict %K prediction %K influenza activity %K infection %K surveillance tolls %D 2023 %7 11.1.2023 %9 Review %J JMIR Public Health Surveill %G English %X Background: Influenza causes considerable disease burden each year, particularly in children. Monitoring school absenteeism has long been proposed as a surveillance tool of influenza activity in the community, but the practice of school absenteeism could be varying, and the potential of such usage remains unclear. Objective: The aim of this paper is to determine the potential of monitoring school absenteeism as a surveillance tool of influenza. Methods: We conducted a systematic review of the published literature on the relationship between school absenteeism and influenza activity in the community. We categorized the types of school absenteeism and influenza activity in the community to determine the correlation between these data streams. We also extracted this correlation with different lags in community surveillance to determine the potential of using school absenteeism as a leading indicator of influenza activity. Results: Among the 35 identified studies, 22 (63%), 12 (34%), and 8 (23%) studies monitored all-cause, illness-specific, and influenza-like illness (ILI)–specific absents, respectively, and 16 (46%) used quantitative approaches and provided 33 estimates on the temporal correlation between school absenteeism and influenza activity in the community. The pooled estimate of correlation between school absenteeism and community surveillance without lag, with 1-week lag, and with 2-week lag were 0.44 (95% CI 0.34, 0.53), 0.29 (95% CI 0.15, 0.42), and 0.21 (95% CI 0.11, 0.31), respectively. The correlation between influenza activity in the community and ILI-specific absenteeism was higher than that between influenza activity in community all-cause absenteeism. Among the 19 studies that used qualitative approaches, 15 (79%) concluded that school absenteeism was in concordance with, coincided with, or was associated with community surveillance. Of the 35 identified studies, only 6 (17%) attempted to predict influenza activity in the community from school absenteeism surveillance. Conclusions: There was a moderate correlation between school absenteeism and influenza activity in the community. The smaller correlation between school absenteeism and community surveillance with lag, compared to without lag, suggested that careful application was required to use school absenteeism as a leading indicator of influenza epidemics. ILI-specific absenteeism could monitor influenza activity more closely, but the required resource or school participation willingness may require careful consideration to weight against the associated costs. Further development is required to use and optimize the use of school absenteeism to predict influenza activity. In particular, the potential of using more advanced statistical models and validation of the predictions should be explored. %M 36630159 %R 10.2196/41329 %U https://publichealth.jmir.org/2023/1/e41329 %U https://doi.org/10.2196/41329 %U http://www.ncbi.nlm.nih.gov/pubmed/36630159 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 12 %P e38751 %T Examining the Use of an Artificial Intelligence Model to Diagnose Influenza: Development and Validation Study %A Okiyama,Sho %A Fukuda,Memori %A Sode,Masashi %A Takahashi,Wataru %A Ikeda,Masahiro %A Kato,Hiroaki %A Tsugawa,Yusuke %A Iwagami,Masao %+ Aillis, Inc, 1-10-1-11F, Yurakucho, Chiyoda-ku, Tokyo, 100-0006, Japan, 81 3 5218 2374, sho.okiyama@aillis.jp %K influenza %K physical examination %K pharynx %K deep learning %K diagnostic prediction %D 2022 %7 23.12.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: The global burden of influenza is substantial. It is a major disease that causes annual epidemics and occasionally, pandemics. Given that influenza primarily infects the upper respiratory system, it may be possible to diagnose influenza infection by applying deep learning to pharyngeal images. Objective: We aimed to develop a deep learning model to diagnose influenza infection using pharyngeal images and clinical information. Methods: We recruited patients who visited clinics and hospitals because of influenza-like symptoms. In the training stage, we developed a diagnostic prediction artificial intelligence (AI) model based on deep learning to predict polymerase chain reaction (PCR)–confirmed influenza from pharyngeal images and clinical information. In the validation stage, we assessed the diagnostic performance of the AI model. In additional analysis, we compared the diagnostic performance of the AI model with that of 3 physicians and interpreted the AI model using importance heat maps. Results: We enrolled a total of 7831 patients at 64 hospitals between November 1, 2019, and January 21, 2020, in the training stage and 659 patients (including 196 patients with PCR-confirmed influenza) at 11 hospitals between January 25, 2020, and March 13, 2020, in the validation stage. The area under the receiver operating characteristic curve for the AI model was 0.90 (95% CI 0.87-0.93), and its sensitivity and specificity were 76% (70%-82%) and 88% (85%-91%), respectively, outperforming 3 physicians. In the importance heat maps, the AI model often focused on follicles on the posterior pharyngeal wall. Conclusions: We developed the first AI model that can accurately diagnose influenza from pharyngeal images, which has the potential to help physicians to make a timely diagnosis. %M 36374004 %R 10.2196/38751 %U https://www.jmir.org/2022/12/e38751 %U https://doi.org/10.2196/38751 %U http://www.ncbi.nlm.nih.gov/pubmed/36374004 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 11 %P e36712 %T Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Primary Health Care Electronic Records %A Giner-Soriano,Maria %A de Dios,Vanessa %A Ouchi,Dan %A Vilaplana-Carnerero,Carles %A Monteagudo,Mònica %A Morros,Rosa %+ Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Gran Via de les Corts Catalanes 587, àtic, Barcelona, 08007, Spain, 34 934824110, mginer@idiapjgol.info %K SARS-CoV-2 %K COVID-19 %K influenza vaccines %K pneumonia %K electronic health records %K primary health care %K vaccination %K public health %K cohort study %K epidemiology %K eHeatlh %K health outcome %K mortality %D 2022 %7 11.11.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: A possible link between influenza immunization and susceptibility to the complications of COVID-19 infection has been previously suggested owing to a boost in the immunity against SARS-CoV-2. Objective: This study aimed to investigate whether individuals with COVID-19 could have benefited from vaccination against influenza. We hypothesized that the immunity resulting from the previous influenza vaccination would boost part of the immunity against SARS-CoV-2. Methods: We performed a population-based cohort study including all patients with COVID-19 with registered entries in the primary health care (PHC) electronic records during the first wave of the COVID-19 pandemic (March 1 to June 30, 2020) in Catalonia, Spain. We compared individuals who took an influenza vaccine before being infected with COVID-19, with those who had not taken one. Data were obtained from Information System for Research in Primary Care, capturing PHC information of 5.8 million people from Catalonia. The main outcomes assessed during follow-up were a diagnosis of pneumonia, hospital admission, and mortality. Results: We included 309,039 individuals with COVID-19 and compared them on the basis of their influenza immunization status, with 114,181 (36.9%) having been vaccinated at least once and 194,858 (63.1%) having never been vaccinated. In total, 21,721 (19%) vaccinated individuals and 11,000 (5.7%) unvaccinated individuals had at least one of their outcomes assessed. Those vaccinated against influenza at any time (odds ratio [OR] 1.14, 95% CI 1.10-1.19), recently (OR 1.13, 95% CI 1.10-1.18), or recurrently (OR 1.10, 95% CI 1.05-1.15) before being infected with COVID-19 had a higher risk of presenting at least one of the outcomes than did unvaccinated individuals. When we excluded people living in long-term care facilities, the results were similar. Conclusions: We could not establish a protective role of the immunity conferred by the influenza vaccine on the outcomes of COVID-19 infection, as the risk of COVID-19 complications was higher in vaccinated than in unvaccinated individuals. Our results correspond to the first wave of the COVID-19 pandemic, where more complications and mortalities due to COVID-19 had occurred. Despite that, our study adds more evidence for the analysis of a possible link between the quality of immunity and COVID-19 outcomes, particularly in the PHC setting. %M 36265160 %R 10.2196/36712 %U https://publichealth.jmir.org/2022/11/e36712 %U https://doi.org/10.2196/36712 %U http://www.ncbi.nlm.nih.gov/pubmed/36265160 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 10 %P e36211 %T Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis %A Ganser,Iris %A Thiébaut,Rodolphe %A Buckeridge,David L %+ McGill Clinical and Health Informatics, School of Population and Global Health, McGill University, 2001 McGill College Avenue, Suite 1200, Montreal, QC, H3A 1G1, Canada, 1 514 934 1934 ext 32991, david.buckeridge@mcgill.ca %K event-based surveillance %K digital disease detection %K public health surveillance %K influenza %K infectious disease outbreak %K surveillance %K disease %K outbreak %K analysis %K public health %K data %K detection %K detect %K epidemic %D 2022 %7 31.10.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed to allow timely detection of infectious disease outbreaks by using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance. Objective: The aim of this study was to assess the variation in the timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability by using the example of seasonal influenza epidemic in 24 countries. Methods: We obtained influenza-related reports between January 2013 and December 2019 from 2 EBS systems, that is, HealthMap and the World Health Organization Epidemic Intelligence from Open Sources (EIOS), and weekly virological influenza counts for the same period from FluNet as the gold standard. Influenza epidemic periods were detected based on report frequency by using Bayesian change point analysis. Timely sensitivity, that is, outbreak detection within the first 2 weeks before or after an outbreak onset was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance. Results: Overall, while monitoring the frequency of EBS reports over 7 years in 24 countries, we detected 175 out of 238 outbreaks (73.5%) but only 22 out of 238 (9.2%) within 2 weeks before or after an outbreak onset; in the best case, while monitoring the frequency of health-related reports, we identified 2 out of 6 outbreaks (33%) within 2 weeks of onset. The positive predictive value varied between 9% and 100% for HealthMap and from 0 to 100% for EIOS, and timeliness of detection ranged from 13% to 94% for HealthMap and from 0% to 92% for EIOS, whereas system specificity was generally high (59%-100%). The number of EBS reports available within a country, the human development index, and the country’s geographical location partially explained the high variability in system performance across countries. Conclusions: We documented the global variation of EBS performance and demonstrated that monitoring the report frequency alone in EBS may be insufficient for the timely detection of outbreaks. In particular, in low- and middle-income countries, low data quality and report frequency impair the sensitivity and timeliness of disease surveillance through EBS. Therefore, advances in the development and evaluation and EBS are needed, particularly in low-resource settings. %M 36315218 %R 10.2196/36211 %U https://publichealth.jmir.org/2022/10/e36211 %U https://doi.org/10.2196/36211 %U http://www.ncbi.nlm.nih.gov/pubmed/36315218 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 10 %P e37177 %T Pilot Influenza Syndromic Surveillance System Based on Absenteeism and Temperature in China: Development and Usability Study %A Yang,Zhen %A Jiang,Chenghua %+ Dongfang Hospital, Tongji University, No 1239 Siping Road, Yangpu District, Shanghai, 200092, China, 86 18917266778, jchtongji@163.com %K influenza %K syndromic surveillance system %K face recognition %K infrared thermometer %K absenteeism %K temperature %D 2022 %7 14.10.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Shortcomings of the current school-based infectious disease syndromic surveillance system (SSS) in China include relying on school physicians to collect data manually and ignoring the health information of students in attendance. Objective: This study aimed to design and implement an influenza SSS based on the absenteeism (collected by face recognition) and temperature of attending students (measured by thermal imaging). Methods: An SSS was implemented by extending the functionality of an existing application. The system was implemented in 2 primary schools and 1 junior high school in the Yangtze River Delta, with a total of 3535 students. The examination period was from March 1, 2021, to January 14, 2022, with 174 effective days. The daily and weekly absenteeism and fever rates reported by the system (DAR1 and DFR; WAR1 and WFR) were calculated. The daily and weekly absenteeism rates reported by school physicians (DAR2 and WAR2) and the weekly positive rate of influenza virus (WPRIV, released by the Chinese National Influenza Center) were used as standards to evaluate the quality of the data reported by the system. Results: Absenteeism reported by school physicians (completeness 86.7%) was 36.5% of that reported by this system (completeness 100%), and a significant positive correlation between them was detected (r=0.372, P=.002). When the influenza activity level was moderate, DAR1s were significantly positively correlated among schools (rab=0.508, P=.004; rbc=0.427, P=.02; rac=0.447, P=.01). During the influenza breakout, the gap of DAR1s widened. WAR1 peaked 2 weeks earlier in schools A and B than in school C. Variables significantly positively correlated with the WPRIV were the WAR1 and WAR2 of school A, WAR1 of school C, and WFR of school B. The correlation between the WAR1 and WPRIV was greater than that between the WAR2 and WPRIV in school A. Addition of the WFR to the WAR1 of school B increased the correlation between the WAR1 and WPRIV. Conclusions: Data demonstrated that absenteeism calculation based on face recognition was reliable, but the accuracy of the temperature recorded by the infrared thermometer should be enhanced. Compared with similar SSSs, this system has superior simplicity, cost-effectiveness, data quality, sensitivity, and timeliness. %M 36239991 %R 10.2196/37177 %U https://publichealth.jmir.org/2022/10/e37177 %U https://doi.org/10.2196/37177 %U http://www.ncbi.nlm.nih.gov/pubmed/36239991 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 10 %P e38710 %T Influence of Digital Intervention Messaging on Influenza Vaccination Rates Among Adults With Cardiovascular Disease in the United States: Decentralized Randomized Controlled Trial %A Marshall,Nell J %A Lee,Jennifer L %A Schroeder,Jessica %A Lee,Wei-Nchih %A See,Jermyn %A Madjid,Mohammad %A Munagala,Mrudula R %A Piette,John D %A Tan,Litjen %A Vardeny,Orly %A Greenberg,Michael %A Liska,Jan %A Mercer,Monica %A Samson,Sandrine %+ Evidation Health, Inc, 63 Bovet Rd #146, San Mateo, CA, 94402, United States, 1 (415) 515 1985, nmarshall@evidation.com %K influenza %K randomized trial %K public health %K cardiovascular disease %K immunization %K vaccination %K digital messaging %K digital intervention %K mobile health %K mHealth %D 2022 %7 7.10.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Seasonal influenza affects 5% to 15% of Americans annually, resulting in preventable deaths and substantial economic impact. Influenza infection is particularly dangerous for people with cardiovascular disease, who therefore represent a priority group for vaccination campaigns. Objective: We aimed to assess the effects of digital intervention messaging on self-reported rates of seasonal influenza vaccination. Methods: This was a randomized, controlled, single-blind, and decentralized trial conducted at individual locations throughout the United States over the 2020-2021 influenza season. Adults with self-reported cardiovascular disease who were members of the Achievement mobile platform were randomized to receive or not receive a series of 6 patient-centered digital intervention messages promoting influenza vaccination. The primary end point was the between-group difference in self-reported vaccination rates at 6 months after randomization. Secondary outcomes included the levels of engagement with the messages and the relationship between vaccination rates and engagement with the messages. Subgroup analyses examined variation in intervention effects by race. Controlling for randomization group, we examined the impact of other predictors of vaccination status, including cardiovascular condition type, vaccine drivers or barriers, and vaccine knowledge. Results: Of the 49,138 randomized participants, responses on the primary end point were available for 11,237 (22.87%; 5575 in the intervention group and 5662 in the control group) participants. The vaccination rate was significantly higher in the intervention group (3418/5575, 61.31%) than the control group (3355/5662, 59.25%; relative risk 1.03, 95% CI 1.004-1.066; P=.03). Participants who were older, more educated, and White or Asian were more likely to report being vaccinated. The intervention was effective among White participants (P=.004) but not among people of color (P=.42). The vaccination rate was 13 percentage points higher among participants who completed all 6 intervention messages versus none, and at least 2 completed messages appeared to be needed for effectiveness. Participants who reported a diagnosis of COVID-19 were more likely to be vaccinated for influenza regardless of treatment assignment. Conclusions: This personalized, evidence-based digital intervention was effective in increasing vaccination rates in this population of high-risk people with cardiovascular disease. Trial Registration: ClinicalTrials.gov NCT04584645; https://clinicaltrials.gov/ct2/show/NCT04584645 %M 36206046 %R 10.2196/38710 %U https://www.jmir.org/2022/10/e38710 %U https://doi.org/10.2196/38710 %U http://www.ncbi.nlm.nih.gov/pubmed/36206046 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 8 %P e38551 %T The Landscape of Participatory Surveillance Systems Across the One Health Spectrum: Systematic Review %A McNeil,Carrie %A Verlander,Sarah %A Divi,Nomita %A Smolinski,Mark %+ Ending Pandemics, 870 Market Street, Suite 528, San Francisco, CA, 94102, United States, 1 415 571 2175, carrie@endingpandemics.org %K participatory surveillance %K One Health %K citizen science %K community-based surveillance %K infectious disease %K digital disease detection %K community participation %K mobile phone %D 2022 %7 5.8.2022 %9 Review %J JMIR Public Health Surveill %G English %X Background: Participatory surveillance systems augment traditional surveillance systems through bidirectional community engagement. The digital platform evolution has enabled the expansion of participatory surveillance systems, globally, for the detection of health events impacting people, animals, plants, and the environment, in other words, across the entire One Health spectrum. Objective: The aim of this landscape was to identify and provide descriptive information regarding system focus, geography, users, technology, information shared, and perceived impact of ongoing participatory surveillance systems across the One Health spectrum. Methods: This landscape began with a systematic literature review to identify potential ongoing participatory surveillance systems. A survey was sent to collect standardized data from the contacts of systems identified in the literature review and through direct outreach to stakeholders, experts, and professional organizations. Descriptive analyses of survey and literature review results were conducted across the programs. Results: The landscape identified 60 ongoing single-sector and multisector participatory surveillance systems spanning five continents. Of these, 29 (48%) include data on human health, 26 (43%) include data on environmental health, and 24 (40%) include data on animal health. In total, 16 (27%) systems are multisectoral; of these, 9 (56%) collect animal and environmental health data; 3 (19%) collect human, animal, and environmental health data; 2 (13%) collect human and environmental health data; and 2 (13%) collect human and animal health data. Out of 60 systems, 31 (52%) are designed to cover a national scale, compared to those with a subnational (n=19, 32%) or multinational (n=10, 17%) focus. All systems use some form of digital technology. Email communication or websites (n=40, 67%) and smartphones (n=29, 48%) are the most common technologies used, with some using both. Systems have capabilities to download geolocation data (n=31, 52%), photographs (n=29, 48%), and videos (n=6, 10%), and can incorporate lab data or sample collection (n=15, 25%). In sharing information back with users, most use visualization, such as maps (n=43, 72%); training and educational materials (n=37, 62%); newsletters, blogs, and emails (n=34, 57%); and disease prevention information (n=32, 53%). Out of the 46 systems responding to the survey regarding perceived impacts of their systems, 36 (78%) noted “improved community knowledge and understanding” and 31 (67%) noted “earlier detection.” Conclusions: The landscape demonstrated the breadth of applicability of participatory surveillance around the world to collect data from community members and trained volunteers in order to inform the detection of events, from invasive plant pests to weekly influenza symptoms. Acknowledging the importance of bidirectionality of information, these systems simultaneously share findings back with the users. Such directly engaged community detection systems capture events early and provide opportunities to stop outbreaks quickly. %M 35930345 %R 10.2196/38551 %U https://publichealth.jmir.org/2022/8/e38551 %U https://doi.org/10.2196/38551 %U http://www.ncbi.nlm.nih.gov/pubmed/35930345 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 3 %P e25803 %T Adverse Events of Interest Following Influenza Vaccination in the First Season of Adjuvanted Trivalent Immunization: Retrospective Cohort Study %A de Lusignan,Simon %A Tsang,Ruby S M %A Akinyemi,Oluwafunmi %A Lopez Bernal,Jamie %A Amirthalingam,Gayatri %A Sherlock,Julian %A Smith,Gillian %A Zambon,Maria %A Howsam,Gary %A Joy,Mark %+ University of Oxford, Eagle House 7, Walton Well road, Oxford, OX2 6ED, United Kingdom, 44 7713632524, simon.delusignan@phc.ox.ac.uk %K influenza %K influenza vaccines %K adverse events of interest %K computerized medical record systems %K sentinel surveillance %D 2022 %7 28.3.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Vaccination is the most effective form of prevention of seasonal influenza; the United Kingdom has a national influenza vaccination program to cover targeted population groups. Influenza vaccines are known to be associated with some common minor adverse events of interest (AEIs), but it is not known if the adjuvanted trivalent influenza vaccine (aTIV), first offered in the 2018/2019 season, would be associated with more AEIs than other types of vaccines. Objective: We aim to compare the incidence of AEIs associated with different types of seasonal influenza vaccines offered in the 2018/2019 season. Methods: We carried out a retrospective cohort study using computerized medical record data from the Royal College of General Practitioners Research and Surveillance Centre sentinel network database. We extracted data on vaccine exposure and consultations for European Medicines Agency–specified AEIs for the 2018/2019 influenza season. We used a self-controlled case series design; computed relative incidence (RI) of AEIs following vaccination; and compared the incidence of AEIs associated with aTIV, the quadrivalent influenza vaccine, and the live attenuated influenza vaccine. We also compared the incidence of AEIs for vaccinations that took place in a practice with those that took place elsewhere. Results: A total of 1,024,160 individuals received a seasonal influenza vaccine, of which 165,723 individuals reported a total of 283,355 compatible symptoms in the 2018/2019 season. Most AEIs occurred within 7 days following vaccination, with a seasonal effect observed. Using aTIV as the reference group, the quadrivalent influenza vaccine was associated with a higher incidence of AEIs (RI 1.46, 95% CI 1.41-1.52), whereas the live attenuated influenza vaccine was associated with a lower incidence of AEIs (RI 0.79, 95% CI 0.73-0.83). No effect of vaccination setting on the incidence of AEIs was observed. Conclusions: Routine sentinel network data offer an opportunity to make comparisons between safety profiles of different vaccines. Evidence that supports the safety of newer types of vaccines may be reassuring for patients and could help improve uptake in the future. %M 35343907 %R 10.2196/25803 %U https://publichealth.jmir.org/2022/3/e25803 %U https://doi.org/10.2196/25803 %U http://www.ncbi.nlm.nih.gov/pubmed/35343907 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 3 %P e25658 %T A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barré Syndrome: Development and Validation Study %A Huang,Yun %A Luo,Chongliang %A Jiang,Ying %A Du,Jingcheng %A Tao,Cui %A Chen,Yong %A Hao,Yuantao %+ Department of Medical Statistics, Sun Yat-Sen University, No. 74 Zhongshan II Road, Guangzhou, 510080, China, 86 02087331587, haoyt@mail.sysu.edu.cn %K adverse events %K Bayesian network %K Guillain-Barré syndrome %K risk prediction %K trivalent influenza vaccine %D 2022 %7 25.3.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Identifying the key factors of Guillain-Barré syndrome (GBS) and predicting its occurrence are vital for improving the prognosis of patients with GBS. However, there are scarcely any publications on a forewarning model of GBS. A Bayesian network (BN) model, which is known to be an accurate, interpretable, and interaction-sensitive graph model in many similar domains, is worth trying in GBS risk prediction. Objective: The aim of this study is to determine the most significant factors of GBS and further develop and validate a BN model for predicting GBS risk. Methods: Large-scale influenza vaccine postmarketing surveillance data, including 79,165 US (obtained from the Vaccine Adverse Event Reporting System between 1990 and 2017) and 12,495 European (obtained from the EudraVigilance system between 2003 and 2016) adverse events (AEs) reports, were extracted for model development and validation. GBS, age, gender, and the top 50 prevalent AEs were included for initial BN construction using the R package bnlearn. Results: Age, gender, and 10 AEs were identified as the most significant factors of GBS. The posttest probability of GBS suggested that male vaccinees aged 50-64 years and without erythema should be on the alert or be warned by clinicians about an increased risk of GBS, especially when they also experience symptoms of asthenia, hypesthesia, muscular weakness, or paresthesia. The established BN model achieved an area under the receiver operating characteristic curve of 0.866 (95% CI 0.865-0.867), sensitivity of 0.752 (95% CI 0.749-0.756), specificity of 0.882 (95% CI 0.879-0.885), and accuracy of 0.882 (95% CI 0.879-0.884) for predicting GBS risk during the internal validation and obtained values of 0.829, 0.673, 0.854, and 0.843 for area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, respectively, during the external validation. Conclusions: The findings of this study illustrated that a BN model can effectively identify the most significant factors of GBS, improve understanding of the complex interactions among different postvaccination symptoms through its graphical representation, and accurately predict the risk of GBS. The established BN model could further assist clinical decision-making by providing an estimated risk of GBS for a specific vaccinee or be developed into an open-access platform for vaccinees’ self-monitoring. %M 35333192 %R 10.2196/25658 %U https://publichealth.jmir.org/2022/3/e25658 %U https://doi.org/10.2196/25658 %U http://www.ncbi.nlm.nih.gov/pubmed/35333192 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 3 %P e25532 %T Evaluating the Increased Burden of Cardiorespiratory Illness Visits to Adult Emergency Departments During Flu and Bronchiolitis Outbreaks in the Pediatric Population: Retrospective Multicentric Time Series Analysis %A Morel,Benoit %A Bouleux,Guillaume %A Viallon,Alain %A Maignan,Maxime %A Provoost,Luc %A Bernadac,Jean-Christophe %A Devidal,Sarah %A Pillet,Sylvie %A Cantais,Aymeric %A Mory,Olivier %+ Department of Pediatric Emergency, University Hospital of Saint Etienne, Saint Etienne, France, 33 477828134, aymeric.cantais@chu-st-etienne.fr %K respiratory infections %K emergency departments %K flu outbreak %K bronchiolitis outbreak %K cardiorespiratory illness %K time series analysis %K influenza %K bronchiolitis %K outbreak %K pediatrics %D 2022 %7 10.3.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Cardiorespiratory decompensation (CRD) visits have a profound effect on adult emergency departments (EDs). Respiratory pathogens like respiratory syncytial virus (RSV) and influenza virus are common reasons for increased activity in pediatric EDs and are associated with CRD in the adult population. Given the seasonal aspects of such challenging pathology, it would be advantageous to predict their variations. Objective: The goal of this study was to evaluate the increased burden of CRD in adult EDs during flu and bronchiolitis outbreaks in the pediatric population. Methods: An ecological study was conducted, based on admissions to the adult ED of the Centre Hospitalier Universitaire (CHU) of Grenoble and Saint Etienne from June 29, 2015 to March 22, 2020. The outbreak periods for bronchiolitis and flu in the pediatric population were defined with a decision-making support tool, PREDAFLU, used in the pediatric ED. A Kruskal-Wallis variance analysis and a Spearman monotone dependency were performed in order to study the relationship between the number of adult ED admissions for the International Classification of Diseases (ICD)-10 codes related to cardiorespiratory diagnoses and the presence of an epidemic outbreak as defined with PREDAFLU. Results: The increase in visits to the adult ED for CRD and the bronchiolitis and flu outbreaks had a similar distribution pattern (CHU Saint Etienne: χ23=102.7, P<.001; CHU Grenoble: χ23=126.67, P<.001) and were quite dependent in both hospital settings (CHU Saint Etienne: Spearman ρ=0.64; CHU Grenoble: Spearman ρ=0.71). The increase in ED occupancy for these pathologies was also significantly related to the pediatric respiratory infection outbreaks. These 2 criteria gave an idea of the increased workload in the ED due to CRD during the bronchiolitis and flu outbreaks in the pediatric population. Conclusions: This study established that CRD visits and bed occupancy for adult EDs were significantly increased during bronchiolitis and pediatric influenza outbreaks. Therefore, a prediction tool for these outbreaks such as PREDAFLU can be used to provide early warnings of increased activity in adult EDs for CRD visits. %M 35266876 %R 10.2196/25532 %U https://publichealth.jmir.org/2022/3/e25532 %U https://doi.org/10.2196/25532 %U http://www.ncbi.nlm.nih.gov/pubmed/35266876 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 3 %P e32364 %T United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study %A Cai,Owen %A Sousa-Pinto,Bernardo %+ Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Rua Plácido Costa s/n, Porto, 4200-450, Portugal, 351 225513622, bernardosousapinto@protonmail.com %K COVID-19 %K influenza %K surveillance %K media coverage %K Google Trends %K infodemiology %K monitoring %K trend %K United States %K information-seeking %K online health information %D 2022 %7 3.3.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends. Objective: We aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States. Methods: We retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data. Results: We observed a nonsignificant weak correlation (ρ= –0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models—for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: ρ=0.643; 2019-2020: ρ=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: ρ=0.746; 2019-2020: ρ=0.707). Conclusions: Relevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool. %M 34878996 %R 10.2196/32364 %U https://publichealth.jmir.org/2022/3/e32364 %U https://doi.org/10.2196/32364 %U http://www.ncbi.nlm.nih.gov/pubmed/34878996 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 2 %P e28268 %T Diagnostic Accuracy of an At-Home, Rapid Self-test for Influenza: Prospective Comparative Accuracy Study %A Geyer,Rachel E %A Kotnik,Jack Henry %A Lyon,Victoria %A Brandstetter,Elisabeth %A Zigman Suchsland,Monica %A Han,Peter D %A Graham,Chelsey %A Ilcisin,Misja %A Kim,Ashley E %A Chu,Helen Y %A Nickerson,Deborah A %A Starita,Lea M %A Bedford,Trevor %A Lutz,Barry %A Thompson,Matthew J %+ Department of Family Medicine, University of Washington, 4225 Roosevelt Way NE Suite 308, Seattle, WA, 98195, United States, 1 2066163961, geyerr@uw.edu %K influenza %K influenza %K rapid testing %K acute respiratory illness %K self-collection %K self-testing %K mHealth %K mobile health %K home collection %K home testing %K mobile phone %D 2022 %7 22.2.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Rapid diagnostic tests (RDTs) for influenza used by individuals at home could potentially expand access to testing and reduce the impact of influenza on health systems. Improving access to testing could lead to earlier diagnosis following symptom onset, allowing more rapid interventions for those who test positive, including behavioral changes to minimize spread. However, the accuracy of RDTs for influenza has not been determined in self-testing populations. Objective: This study aims to assess the accuracy of an influenza RDT conducted at home by lay users with acute respiratory illness compared with that of a self-collected sample by the same individual mailed to a laboratory for reference testing. Methods: We conducted a comparative accuracy study of an at-home influenza RDT (Ellume) in a convenience sample of individuals experiencing acute respiratory illness symptoms. Participants were enrolled in February and March 2020 from the Greater Seattle region in Washington, United States. Participants were mailed the influenza RDT and reference sample collection materials, which they completed and returned for quantitative reverse-transcription polymerase chain reaction influenza testing in a central laboratory. We explored the impact of age, influenza type, duration, and severity of symptoms on RDT accuracy and on cycle threshold for influenza virus and ribonuclease P, a marker of human DNA. Results: A total of 605 participants completed all study steps and were included in our analysis, of whom 87 (14.4%) tested positive for influenza by quantitative reverse-transcription polymerase chain reaction (70/87, 80% for influenza A and 17/87, 20% for influenza B). The overall sensitivity and specificity of the RDT compared with the reference test were 61% (95% CI 50%-71%) and 95% (95% CI 93%-97%), respectively. Among individuals with symptom onset ≤72 hours, sensitivity was 63% (95% CI 48%-76%) and specificity was 94% (95% CI 91%-97%), whereas, for those with duration >72 hours, sensitivity and specificity were 58% (95% CI 41%-74%) and 96% (95% CI 93%-98%), respectively. Viral load on reference swabs was negatively correlated with symptom onset, and quantities of the endogenous marker gene ribonuclease P did not differ among reference standard positive and negative groups, age groups, or influenza subtypes. The RDT did not have higher sensitivity or specificity among those who reported more severe illnesses. Conclusions: The sensitivity and specificity of the self-test were comparable with those of influenza RDTs used in clinical settings. False-negative self-test results were more common when the test was used after 72 hours of symptom onset but were not related to inadequate swab collection or severity of illness. Therefore, the deployment of home tests may provide a valuable tool to support the management of influenza and other respiratory infections. %M 35191852 %R 10.2196/28268 %U https://publichealth.jmir.org/2022/2/e28268 %U https://doi.org/10.2196/28268 %U http://www.ncbi.nlm.nih.gov/pubmed/35191852 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 2 %P e31131 %T An Association of Influenza Epidemics in Children With Mobile App Data: Population-Based Observational Study in Osaka, Japan %A Katayama,Yusuke %A Kiyohara,Kosuke %A Hirose,Tomoya %A Ishida,Kenichiro %A Tachino,Jotaro %A Nakao,Shunichiro %A Noda,Tomohiro %A Ojima,Masahiro %A Kiguchi,Takeyuki %A Matsuyama,Tasuku %A Kitamura,Tetsuhisa %+ Department of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, 2-15 Yamada-oka, Suita, Japan, 81 6 6879 5707, orion13@hp-emerg.med.osaka-u.ac.jp %K syndromic surveillance %K mobile app %K influenza %K epidemic %K children %D 2022 %7 10.2.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Early surveillance to prevent the spread of influenza is a major public health concern. If there is an association of influenza epidemics with mobile app data, it may be possible to forecast influenza earlier and more easily. Objective: We aimed to assess the relationship between seasonal influenza and the frequency of mobile app use among children in Osaka Prefecture, Japan. Methods: This was a retrospective observational study that was performed over a three-year period from January 2017 to December 2019. Using a linear regression model, we calculated the R2 value of the regression model to evaluate the relationship between the number of “fever” events selected in the mobile app and the number of influenza patients ≤14 years of age. We conducted three-fold cross-validation using data from two years as the training data set and the data of the remaining year as the test data set to evaluate the validity of the regression model. And we calculated Spearman correlation coefficients between the calculated number of influenza patients estimated using the regression model and the number of influenza patients, limited to the period from December to April when influenza is prevalent in Japan. Results: We included 29,392 mobile app users. The R2 value for the linear regression model was 0.944, and the adjusted R2 value was 0.915. The mean Spearman correlation coefficient for the three regression models was 0.804. During the influenza season (December–April), the Spearman correlation coefficient between the number of influenza patients and the calculated number estimated using the linear regression model was 0.946 (P<.001). Conclusions: In this study, the number of times that mobile apps were used was positively associated with the number of influenza patients. In particular, there was a good association of the number of influenza patients with the number of “fever” events selected in the mobile app during the influenza epidemic season. %M 35142628 %R 10.2196/31131 %U https://formative.jmir.org/2022/2/e31131 %U https://doi.org/10.2196/31131 %U http://www.ncbi.nlm.nih.gov/pubmed/35142628 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e26523 %T Novel Methods in the Surveillance of Influenza-Like Illness in Germany Using Data From a Symptom Assessment App (Ada): Observational Case Study %A Cawley,Caoimhe %A Bergey,François %A Mehl,Alicia %A Finckh,Ashlee %A Gilsdorf,Andreas %+ Ada Health GmbH, Karl-Liebknecht Strasse 1, Berlin, 10178, Germany, 49 17680765335, caoimhecawley@gmail.com %K ILI %K influenza %K syndromic surveillance %K participatory surveillance %K digital surveillance %K mobile phone %D 2021 %7 4.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Participatory epidemiology is an emerging field harnessing consumer data entries of symptoms. The free app Ada allows users to enter the symptoms they are experiencing and applies a probabilistic reasoning model to provide a list of possible causes for these symptoms. Objective: The objective of our study is to explore the potential contribution of Ada data to syndromic surveillance by comparing symptoms of influenza-like illness (ILI) entered by Ada users in Germany with data from a national population-based reporting system called GrippeWeb. Methods: We extracted data for all assessments performed by Ada users in Germany over 3 seasons (2017/18, 2018/19, and 2019/20) and identified those with ILI (report of fever with cough or sore throat). The weekly proportion of assessments in which ILI was reported was calculated (overall and stratified by age group), standardized for the German population, and compared with trends in ILI rates reported by GrippeWeb using time series graphs, scatterplots, and Pearson correlation coefficient. Results: In total, 2.1 million Ada assessments (for any symptoms) were included. Within seasons and across age groups, the Ada data broadly replicated trends in estimated weekly ILI rates when compared with GrippeWeb data (Pearson correlation—2017-18: r=0.86, 95% CI 0.76-0.92; P<.001; 2018-19: r=0.90, 95% CI 0.84-0.94; P<.001; 2019-20: r=0.64, 95% CI 0.44-0.78; P<.001). However, there were differences in the exact timing and nature of the epidemic curves between years. Conclusions: With careful interpretation, Ada data could contribute to identifying broad ILI trends in countries without existing population-based monitoring systems or to the syndromic surveillance of symptoms not covered by existing systems. %M 34734836 %R 10.2196/26523 %U https://publichealth.jmir.org/2021/11/e26523 %U https://doi.org/10.2196/26523 %U http://www.ncbi.nlm.nih.gov/pubmed/34734836 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 1 %N 1 %P e31983 %T Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study %A Benis,Arriel %A Chatsubi,Anat %A Levner,Eugene %A Ashkenazi,Shai %+ Faculty of Industrial Engineering and Technology Management, Holon Institute of Technology, Golomb St. 52, Holon, 5810201, Israel, 972 35026892, arrielb@hit.ac.il %K influenza %K vaccines %K vaccination %K social media %K social networks %K health communication %K artificial intelligence %K machine learning %K text mining %K infodemiology %K COVID-19 %K SARS-CoV-2 %D 2021 %7 14.10.2021 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines. Objective: Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications. Methods: The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. Results: We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. Conclusions: This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process. %M 34693212 %R 10.2196/31983 %U https://infodemiology.jmir.org/2021/1/e31983 %U https://doi.org/10.2196/31983 %U http://www.ncbi.nlm.nih.gov/pubmed/34693212 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e26869 %T Identifying Enablers of Participant Engagement in Clinical Trials of Consumer Health Technologies: Qualitative Study of Influenza Home Testing %A Dharanikota,Spurthy %A LeRouge,Cynthia M %A Lyon,Victoria %A Durneva,Polina %A Thompson,Matthew %+ Department of Information Systems and Business Analytics, Florida International University, 11200 SW 8th Street, Miami, FL, 33199, United States, 1 3057812536, sdhar006@fiu.edu %K consumer health care technologies %K CHTs %K smartphone-supported home tests %K Smart-HT %K premarket clinical trials %K trial engagement %K at-home diagnostic testing %K mobile phone %D 2021 %7 14.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: A rise in the recent trend of self-managing health using consumer health technologies highlights the importance of efficient and successful consumer health technology trials. Trials are particularly essential to support large-scale implementations of consumer health technologies, such as smartphone-supported home tests. However, trials are generally fraught with challenges, such as inadequate enrollment, lack of fidelity to interventions, and high dropout rates. Understanding the reasons underlying individuals’ participation in trials can inform the design and execution of future trials of smartphone-supported home tests. Objective: This study aims to identify the enablers of potential participants’ trial engagement for clinical trials of smartphone-supported home tests. We use influenza home testing as our instantiation of a consumer health technology subject to trial to investigate the dispositional and situational enablers that influenced trial engagement. Methods: We conducted semistructured interviews with 31 trial participants using purposive sampling to facilitate demographic diversity. The interviews included a discussion of participants’ personal characteristics and external factors that enabled their trial engagement with a smartphone-supported home test for influenza. We performed both deductive and inductive thematic analyses to analyze the interview transcripts and identify enabler themes. Results: Our thematic analyses revealed a structure of dispositional and situational enablers that enhanced trial engagement. Situationally, clinical affiliation, personal advice, promotional recruitment strategies, financial incentives, and insurance status influenced trial engagement. In addition, digital health literacy, motivation to advance medical research, personal innovativeness, altruism, curiosity, positive attitude, and potential to minimize doctors’ visits were identified as the dispositional enablers for trial engagement in our study. Conclusions: We organized the identified themes for dispositional and situational enablers of trial engagement with a smartphone-supported home test into a research framework that can guide future research as well as the trial design and execution of smartphone-supported home tests. We suggest several trial design and engagement strategies to enhance the financial and scientific viability of these trials that pave the way for advancements in patient care. Furthermore, our study also offers practical strategies to trial organizers to enhance participants’ enrollment and engagement in clinical trials of these home tests. %M 34519664 %R 10.2196/26869 %U https://www.jmir.org/2021/9/e26869 %U https://doi.org/10.2196/26869 %U http://www.ncbi.nlm.nih.gov/pubmed/34519664 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e28116 %T Using an Individual-Centered Approach to Gain Insights From Wearable Data in the Quantified Flu Platform: Netnography Study %A Greshake Tzovaras,Bastian %A Senabre Hidalgo,Enric %A Alexiou,Karolina %A Baldy,Lukaz %A Morane,Basile %A Bussod,Ilona %A Fribourg,Melvin %A Wac,Katarzyna %A Wolf,Gary %A Ball,Mad %+ Center for Research & Interdisciplinarity, INSERM U1284, Université de Paris, 8bis Rue Charles V, Paris, 75004, France, 33 766752149, bgreshake@googlemail.com %K symptom tracking %K COVID-19 %K wearable devices %K self-tracking %K citizen science %K netnographic analysis %K cocreation %D 2021 %7 10.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices. Objective: This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data. Methods: We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion. Results: The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa. Conclusions: As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates. %M 34505836 %R 10.2196/28116 %U https://www.jmir.org/2021/9/e28116 %U https://doi.org/10.2196/28116 %U http://www.ncbi.nlm.nih.gov/pubmed/34505836 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 5 %P e23305 %T Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study %A Jang,Beakcheol %A Kim,Inhwan %A Kim,Jong Wook %+ Department of Computer Science, Sangmyung Univerisity, 20, Hongjimun 2-gil, Jongno-gu, Seoul, 03016, Republic of Korea, 82 027817590, jkim@smu.ac.kr %K influenza %K training data extraction %K keyword %K sorting %K word embedding %K Pearson correlation coefficient %K long short-term memory %K surveillance %K infodemiology %K infoveillance %K model %D 2021 %7 25.5.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Each year, influenza affects 3 to 5 million people and causes 290,000 to 650,000 fatalities worldwide. To reduce the fatalities caused by influenza, several countries have established influenza surveillance systems to collect early warning data. However, proper and timely warnings are hindered by a 1- to 2-week delay between the actual disease outbreaks and the publication of surveillance data. To address the issue, novel methods for influenza surveillance and prediction using real-time internet data (such as search queries, microblogging, and news) have been proposed. Some of the currently popular approaches extract online data and use machine learning to predict influenza occurrences in a classification mode. However, many of these methods extract training data subjectively, and it is difficult to capture the latent characteristics of the data correctly. There is a critical need to devise new approaches that focus on extracting training data by reflecting the latent characteristics of the data. Objective: In this paper, we propose an effective method to extract training data in a manner that reflects the hidden features and improves the performance by filtering and selecting only the keywords related to influenza before the prediction. Methods: Although word embedding provides a distributed representation of words by encoding the hidden relationships between various tokens, we enhanced the word embeddings by selecting keywords related to the influenza outbreak and sorting the extracted keywords using the Pearson correlation coefficient in order to solely keep the tokens with high correlation with the actual influenza outbreak. The keyword extraction process was followed by a predictive model based on long short-term memory that predicts the influenza outbreak. To assess the performance of the proposed predictive model, we used and compared a variety of word embedding techniques. Results: Word embedding without our proposed sorting process showed 0.8705 prediction accuracy when 50.2 keywords were selected on average. Conversely, word embedding using our proposed sorting process showed 0.8868 prediction accuracy and an improvement in prediction accuracy of 12.6%, although smaller amounts of training data were selected, with only 20.6 keywords on average. Conclusions: The sorting stage empowers the embedding process, which improves the feature extraction process because it acts as a knowledge base for the prediction component. The model outperformed other current approaches that use flat extraction before prediction. %M 34032577 %R 10.2196/23305 %U https://medinform.jmir.org/2021/5/e23305 %U https://doi.org/10.2196/23305 %U http://www.ncbi.nlm.nih.gov/pubmed/34032577 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 4 %P e27433 %T Coinfection With SARS-CoV-2 and Influenza A(H1N1) in a Patient Seen at an Influenza-like Illness Surveillance Site in Egypt: Case Report %A Fahim,Manal %A Ghonim,Hanaa Abu El Sood %A Roshdy,Wael H %A Naguib,Amel %A Elguindy,Nancy %A AbdelFatah,Mohamad %A Hassany,Mohamed %A Mohsen,Amira %A Afifi,Salma %A Eid,Alaa %+ Department of Surveillance and Epidemiology, Ministry of Health and Population, 3 Magles El Shab Street, Cairo, , Egypt, 20 01222598200 ext 202, fahimmanal@yahoo.com %K influenza-like Illness %K pandemic %K SARS-CoV-2 %K COVID-19 %K influenza %K virus %K case study %K Egypt %K flu %K coinfection %K infectious disease %K surveillance %K outcome %K demographic %D 2021 %7 28.4.2021 %9 Short Paper %J JMIR Public Health Surveill %G English %X Background: Sentinel surveillance of influenza-like illness (ILI) in Egypt started in 2000 at 8 sentinel sites geographically distributed all over the country. In response to the COVID-19 pandemic, SARS-CoV-2 was added to the panel of viral testing by polymerase chain reaction for the first 2 patients with ILI seen at one of the sentinel sites. We report the first SARS-CoV-2 and influenza A(H1N1) virus co-infection with mild symptoms detected through routine ILI surveillance in Egypt. Objective: This report aims to describe how the case was identified and the demographic and clinical characteristics and outcomes of the patient. Methods: The case was identified by Central Public Health Laboratory staff, who contacted the ILI sentinel surveillance officer at the Ministry of Health. The case patient was contacted through a telephone call. Detailed information about the patient’s clinical picture, course of disease, and outcome was obtained. The contacts of the patient were investigated for acute respiratory symptoms, disease confirmation, and outcomes. Results: Among 510 specimens collected from patients with ILI symptoms from October 2019 to August 2020, 61 (12.0%) were COVID-19–positive and 29 (5.7%) tested positive for influenza, including 15 (51.7%) A(H1N1), 11 (38.0%) A(H3N2), and 3 (10.3%) influenza B specimens. A 21-year-old woman was confirmed to have SARS-CoV-2 and influenza A(H1N1) virus coinfection. She had a high fever of 40.2 °C and mild respiratory symptoms that resolved within 2 days with symptomatic treatment. All five of her family contacts had mild respiratory symptoms 2-3 days after exposure to the confirmed case, and their symptoms resolved without treatment or investigation. Conclusions: This case highlights the possible occurrence of SARS-CoV-2/influenza A(H1N1) coinfection in younger and healthy people, who may resolve the infection rapidly. We emphasize the usefulness of the surveillance system for detection of viral causative agents of ILI and recommend broadening of the testing panel, especially if it can guide case management. %M 33784634 %R 10.2196/27433 %U https://publichealth.jmir.org/2021/4/e27433 %U https://doi.org/10.2196/27433 %U http://www.ncbi.nlm.nih.gov/pubmed/33784634 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 3 %P e25977 %T Social Media Engagement and Influenza Vaccination During the COVID-19 Pandemic: Cross-sectional Survey Study %A Benis,Arriel %A Khodos,Anna %A Ran,Sivan %A Levner,Eugene %A Ashkenazi,Shai %+ Faculty of Industrial Engineering and Technology Management, Holon Institute of Technology, Golomb St. 52, Holon, 5810201, Israel, 972 3 5026892, arrielb@hit.ac.il %K influenza %K vaccines %K vaccination %K social media %K online social networking %K health literacy %K eHealth %K information dissemination %K access to information %K COVID-19 %D 2021 %7 16.3.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Vaccines are one of the most important achievements of modern medicine. However, their acceptance is only partial, with vaccine hesitancy and refusal representing a major health threat. Influenza vaccines have low compliance since repeated, annual vaccination is required. Influenza vaccines stimulate discussions both in the real world and online. Social media is currently a significant source of health and medical information. Elucidating the association between social media engagement and influenza vaccination is important and may be applicable to other vaccines, including ones against COVID-19. Objective: The goal of this study is to characterize profiles of social media engagement regarding the influenza vaccine and their association with knowledge and compliance in order to support improvement of future web-associated vaccination campaigns. Methods: A weblink to an online survey in Hebrew was disseminated over social media and messaging platforms. The survey answers were collected during April 2020. Anonymous and volunteer participants aged 21 years and over answered 30 questions related to sociodemographics; social media usage; influenza- and vaccine-related knowledge and behavior; health-related information searching, its reliability, and its influence; and COVID-19-related information searching. A univariate descriptive data analysis was performed, followed by multivariate analysis via building a decision tree to define the most important attributes associated with vaccination compliance. Results: A total of 213 subjects responded to the survey, of whom 207 were included in the analysis; the majority of the respondents were female, were aged 21 to 40 years, had 1 to 2 children, lived in central Israel, were secular Israeli natives, had higher education, and had a salary close to the national average. Most respondents (128/207, 61.8%) were not vaccinated against influenza in 2019 and used social media. Participants that used social media were younger, secular, and living in high-density agglomerations and had lower influenza vaccination rates. The perceived influence and reliability of the information on social media about COVID-19 were generally similar to those perceptions about influenza. Conclusions: Using social media is negatively linked to compliance with seasonal influenza vaccination in this study. A high proportion of noncompliant individuals can lead to increased consumption of health care services and can, therefore, overload these health services. This is particularly crucial with a concomitant outbreak, such as COVID-19. Health care professionals should use improved and targeted health communication campaigns with the aid of experts in social media. Targeted communication, based on sociodemographic factors and personalized social media usage, might increase influenza vaccination rates and compliance with other vaccines as well. %M 33651709 %R 10.2196/25977 %U https://www.jmir.org/2021/3/e25977 %U https://doi.org/10.2196/25977 %U http://www.ncbi.nlm.nih.gov/pubmed/33651709 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 3 %P e24696 %T The Relationship Between the Global Burden of Influenza From 2017 to 2019 and COVID-19: Descriptive Epidemiological Assessment %A Baral,Stefan David %A Rucinski,Katherine Blair %A Twahirwa Rwema,Jean Olivier %A Rao,Amrita %A Prata Menezes,Neia %A Diouf,Daouda %A Kamarulzaman,Adeeba %A Phaswana-Mafuya,Nancy %A Mishra,Sharmistha %+ Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD, 21205, United States, 1 410 502 8975, sbaral@jhu.edu %K SARS-CoV-2 %K COVID-19 %K influenza %K descriptive epidemiology %K epidemiology %K assessment %K relationship %K flu %K virus %K burden %K global health %K public health %K transmission %K pattern %D 2021 %7 2.3.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: SARS-CoV-2 and influenza are lipid-enveloped viruses with differential morbidity and mortality but shared modes of transmission. Objective: With a descriptive epidemiological framing, we assessed whether recent historical patterns of regional influenza burden are reflected in the observed heterogeneity in COVID-19 cases across regions of the world. Methods: Weekly surveillance data reported by the World Health Organization from January 2017 to December 2019 for influenza and from January 1, 2020 through October 31, 2020, for COVID-19 were used to assess seasonal and temporal trends for influenza and COVID-19 cases across the seven World Bank regions. Results: In regions with more pronounced influenza seasonality, COVID-19 epidemics have largely followed trends similar to those seen for influenza from 2017 to 2019. COVID-19 epidemics in countries across Europe, Central Asia, and North America have been marked by a first peak during the spring, followed by significant reductions in COVID-19 cases in the summer months and a second wave in the fall. In Latin America and the Caribbean, COVID-19 epidemics in several countries peaked in the summer, corresponding to months with the highest influenza activity in the region. Countries from regions with less pronounced influenza activity, including South Asia and sub-Saharan Africa, showed more heterogeneity in COVID-19 epidemics seen to date. However, similarities in COVID-19 and influenza trends were evident within select countries irrespective of region. Conclusions: Ecological consistency in COVID-19 trends seen to date with influenza trends suggests the potential for shared individual, structural, and environmental determinants of transmission. Using a descriptive epidemiological framework to assess shared regional trends for rapidly emerging respiratory pathogens with better studied respiratory infections may provide further insights into the differential impacts of nonpharmacologic interventions and intersections with environmental conditions. Ultimately, forecasting trends and informing interventions for novel respiratory pathogens like COVID-19 should leverage epidemiologic patterns in the relative burden of past respiratory pathogens as prior information. %M 33522974 %R 10.2196/24696 %U https://publichealth.jmir.org/2021/3/e24696 %U https://doi.org/10.2196/24696 %U http://www.ncbi.nlm.nih.gov/pubmed/33522974 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 12 %P e19712 %T Effectiveness of a Mobile-Based Influenza-Like Illness Surveillance System (FluMob) Among Health Care Workers: Longitudinal Study %A Lwin,May Oo %A Lu,Jiahui %A Sheldenkar,Anita %A Panchapakesan,Chitra %A Tan,Yi-Roe %A Yap,Peiling %A Chen,Mark I %A Chow,Vincent TK %A Thoon,Koh Cheng %A Yung,Chee Fu %A Ang,Li Wei %A Ang,Brenda SP %+ School of New Media and Communication, Tianjin University, No. 92 Weijin Road, Tianjin, 300072, China, 86 18222418810, lujiahui@tju.edu.cn %K participatory surveillance %K syndromic surveillance %K mobile phone %K influenza-like illness %K health care workers %D 2020 %7 7.12.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Existing studies have suggested that internet-based participatory surveillance systems are a valid sentinel for influenza-like illness (ILI) surveillance. However, there is limited scientific knowledge on the effectiveness of mobile-based ILI surveillance systems. Previous studies also adopted a passive surveillance approach and have not fully investigated the effectiveness of the systems and their determinants. Objective: The aim of this study was to assess the efficiency of a mobile-based surveillance system of ILI, termed FluMob, among health care workers using a targeted surveillance approach. Specifically, this study evaluated the effectiveness of the system for ILI surveillance pertaining to its participation engagement and surveillance power. In addition, we aimed to identify the factors that can moderate the effectiveness of the system. Methods: The FluMob system was launched in two large hospitals in Singapore from April 2016 to March 2018. A total of 690 clinical and nonclinical hospital staff participated in the study for 18 months and were prompted via app notifications to submit a survey listing 18 acute respiratory symptoms (eg, fever, cough, sore throat) on a weekly basis. There was a period of study disruption due to maintenance of the system and the end of the participation incentive between May and July of 2017. Results: On average, the individual submission rate was 41.4% (SD 24.3%), with a rate of 51.8% (SD 26.4%) before the study disruption and of 21.5% (SD 30.6%) after the disruption. Multivariable regression analysis showed that the adjusted individual submission rates were higher for participants who were older (<30 years, 31.4% vs 31-40 years, 40.2% [P<.001]; 41-50 years, 46.0% [P<.001]; >50 years, 39.9% [P=.01]), ethnic Chinese (Chinese, 44.4% vs non-Chinese, 34.7%; P<.001), and vaccinated against flu in the past year (vaccinated, 44.6% vs nonvaccinated, 34.4%; P<.001). In addition, the weekly ILI incidence was 1.07% on average. The Pearson correlation coefficient between ILI incidence estimated by FluMob and that reported by Singapore Ministry of Health was 0.04 (P=.75) with all data and was 0.38 (P=.006) including only data collected before the study disruption. Health care workers with higher risks of ILI and influenza such as women, non-Chinese, allied health staff, those who had children in their households, not vaccinated against influenza, and reported allergy demonstrated higher surveillance correlations. Conclusions: Mobile-based ILI surveillance systems among health care workers can be effective. However, proper operation of the mobile system without major disruptions is vital for the engagement of participants and the persistence of surveillance power. Moreover, the effectiveness of the mobile surveillance system can be moderated by participants’ characteristics, which highlights the importance of targeted disease surveillance that can reduce the cost of recruitment and engagement. %M 33284126 %R 10.2196/19712 %U https://mhealth.jmir.org/2020/12/e19712 %U https://doi.org/10.2196/19712 %U http://www.ncbi.nlm.nih.gov/pubmed/33284126 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e21369 %T Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study %A Choo,Hyunwoo %A Kim,Myeongchan %A Choi,Jiyun %A Shin,Jaewon %A Shin,Soo-Yong %+ Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, 115, Irwon-ro, Gangnam-gu, Seoul , Republic of Korea, 82 2 3410 1449, sy.shin@skku.edu %K influenza %K screening tool %K patient-generated health data %K mobile health %K mHealth %K deep learning %D 2020 %7 29.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Screening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests. Objective: The aim of this study was to develop a machine learning–based screening tool using patient-generated health data (PGHD) obtained from a mobile health (mHealth) app. Methods: We trained a deep learning model based on a gated recurrent unit to screen influenza using PGHD, including each patient’s fever pattern and drug administration records. We used meteorological data and app-based surveillance of the weekly number of patients with influenza. We defined a single episode as the set of consecutive days, including the day the user was diagnosed with influenza or another disease. Any record a user entered 24 hours after his or her last record was considered to be the start of a new episode. Each episode contained data on the user’s age, gender, weight, and at least one body temperature record. The total number of episodes was 6657. Of these, there were 3326 episodes within which influenza was diagnosed. We divided these episodes into 80% training sets (2664/3330) and 20% test sets (666/3330). A 5-fold cross-validation was used on the training set. Results: We achieved reliable performance with an accuracy of 82%, a sensitivity of 84%, and a specificity of 80% in the test set. After the effect of each input variable was evaluated, app-based surveillance was observed to be the most influential variable. The correlation between the duration of input data and performance was not statistically significant (P=.09). Conclusions: These findings suggest that PGHD from an mHealth app could be a complementary tool for influenza screening. In addition, PGHD, along with traditional clinical data, could be used to improve health conditions. %M 33118941 %R 10.2196/21369 %U http://www.jmir.org/2020/10/e21369/ %U https://doi.org/10.2196/21369 %U http://www.ncbi.nlm.nih.gov/pubmed/33118941 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e16373 %T Electronic Health Record Portal Messages and Interactive Voice Response Calls to Improve Rates of Early Season Influenza Vaccination: Randomized Controlled Trial %A Wijesundara,Jessica G %A Ito Fukunaga,Mayuko %A Ogarek,Jessica %A Barton,Bruce %A Fisher,Lloyd %A Preusse,Peggy %A Sundaresan,Devi %A Garber,Lawrence %A Mazor,Kathleen M %A Cutrona,Sarah L %+ Health Services Research & Development, Center of Innovation, Edith Nourse Rogers Memorial Hospital, Veterans Health Administration, 200 Springs St, Building 70, Bedford, MA, 01730, United States, 1 508 856 4046, Sarah.Cutrona@umassmed.edu %K electronic health records %K influenza vaccination %K patient care %K patient engagement %D 2020 %7 25.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Patient reminders for influenza vaccination, delivered via an electronic health record patient portal and interactive voice response calls, offer an innovative approach to engaging patients and improving patient care. Objective: The goal of this study was to test the effectiveness of portal and interactive voice response outreach in improving rates of influenza vaccination by targeting patients in early September, shortly after vaccinations became available. Methods: Using electronic health record portal messages and interactive voice response calls promoting influenza vaccination, outreach was conducted in September 2015. Participants included adult patients within a large multispecialty group practice in central Massachusetts. Our main outcome was electronic health record–documented early influenza vaccination during the 2015-2016 influenza season, measured in November 2015. We randomly assigned all active portal users to 1 of 2 groups: (1) receiving a portal message promoting influenza vaccinations, listing upcoming clinics, and offering online scheduling of vaccination appointments (n=19,506) or (2) receiving usual care (n=19,505). We randomly assigned all portal nonusers to 1 of 2 groups: (1) receiving interactive voice response call (n=15,000) or (2) receiving usual care (n=43,596). The intervention also solicited patient self-reports on influenza vaccinations completed outside the clinic. Self-reported influenza vaccination data were uploaded into the electronic health records to increase the accuracy of existing provider-directed electronic health record clinical decision support (vaccination alerts) but were excluded from main analyses. Results: Among portal users, 28.4% (5549/19,506) of those randomized to receive messages and 27.1% (5294/19,505) of the usual care group had influenza vaccinations documented by November 2015 (P=.004). In multivariate analysis of portal users, message recipients were slightly more likely to have documented vaccinations when compared to the usual care group (OR 1.07, 95% CI 1.02-1.12). Among portal nonusers, 8.4% (1262/15,000) of those randomized to receive calls and 8.2% (3586/43,596) of usual care had documented vaccinations (P=.47), and multivariate analysis showed nonsignificant differences. Over half of portal messages sent were opened (10,112/19,479; 51.9%), and over half of interactive voice response calls placed (7599/14,984; 50.7%) reached their intended target, thus we attained similar levels of exposure to the messaging for both interventions. Among portal message recipients, 25.4% of message openers (2570/10,112) responded to a subsequent question on receipt of influenza vaccination; among interactive voice response recipients, 72.5% of those reached (5513/7599) responded to a similar question. Conclusions: Portal message outreach to a general primary care population achieved a small but statistically significant improvement in rates of influenza vaccination (OR 1.07, 95% CI 1.02-1.12). Interactive voice response calls did not significantly improve vaccination rates among portal nonusers (OR 1.03, 95% CI 0.96-1.10). Rates of patient engagement with both modalities were favorable. Trial Registration: ClinicalTrials.gov NCT02266277; https://clinicaltrials.gov/ct2/show/NCT02266277 %M 32975529 %R 10.2196/16373 %U http://www.jmir.org/2020/9/e16373/ %U https://doi.org/10.2196/16373 %U http://www.ncbi.nlm.nih.gov/pubmed/32975529 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e17242 %T An Online Influenza Surveillance System for Primary Care Workers in Switzerland: Observational Prospective Pilot Study %A Martin,Sébastien %A Maeder,Muriel Nirina %A Gonçalves,Ana Rita %A Pedrazzini,Baptiste %A Perdrix,Jean %A Rochat,Carine %A Senn,Nicolas %A Mueller,Yolanda %+ Center for Primary Care and Public Health (Unisanté), Lausanne, Department of Family Medicine, University of Lausanne, Rue du Bugnon 44, Lausanne, 1011, Switzerland, 41 21 314 60 63, sebastien.martin@unisante.ch %K influenza %K surveillance system %K primary care %K online %K nosocomial %K transmission %D 2020 %7 10.9.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: A better understanding of the influenza epidemiology among primary care workers could guide future recommendations to prevent transmission in primary care practices. Therefore, we designed a pilot study to assess the feasibility of using a work-based online influenza surveillance system among primary care workers. Such an approach is of particular relevance in the context of the coronavirus disease (COVID-19) pandemic, as its findings could apply to other infectious diseases with similar mechanisms of transmission. Objective: This study aims to determine the feasibility of using a work-based online influenza surveillance system for primary care workers in Switzerland. Methods: Physicians and staff of one walk-in clinic and two selected primary care practices were enrolled in this observational prospective pilot study during the 2017-2018 influenza season. They were invited to record symptoms of influenza-like illness in a weekly online survey sent by email and to self-collect a nasopharyngeal swab in case any symptoms were recorded. Samples were tested by real-time polymerase chain reaction for influenza A, influenza B, and a panel of respiratory pathogens. Results: Among 67 eligible staff members, 58% (n=39) consented to the study and 53% (n=36) provided data. From the time all participants were included, the weekly survey response rate stayed close to 100% until the end of the study. Of 79 symptomatic episodes (mean 2.2 episodes per participant), 10 episodes in 7 participants fitted the definition of an influenza-like illness case (attack rate: 7/36, 19%). One swab tested positive for influenza A H1N1 (attack rate: 3%, 95% CI 0%-18%). Swabbing was considered relatively easy. Conclusions: A work-based online influenza surveillance system is feasible for use among primary care workers. This promising methodology could be broadly used in future studies to improve the understanding of influenza epidemiology and other diseases such as COVID-19. This could prove to be highly useful in primary care settings and guide future recommendations to prevent transmission. A larger study will also help to assess asymptomatic infections. %M 32909955 %R 10.2196/17242 %U http://publichealth.jmir.org/2020/3/e17242/ %U https://doi.org/10.2196/17242 %U http://www.ncbi.nlm.nih.gov/pubmed/32909955 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e12842 %T An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study %A Sambaturu,Prathyush %A Bhattacharya,Parantapa %A Chen,Jiangzhuo %A Lewis,Bryan %A Marathe,Madhav %A Venkatramanan,Srinivasan %A Vullikanti,Anil %+ University of Virginia, Biocomplexity Institute and Initiative, 995 Research Park Boulevard, Charlottesville, VA, 22911, United States, 1 540 577 3102, vsakumar@virginia.edu %K epidemic data analysis %K summarization %K spatio-temporal patterns %K transactional data mining %D 2020 %7 4.9.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. Objective: Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. Methods: We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). Results: We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. Conclusions: Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives. %M 32701458 %R 10.2196/12842 %U http://publichealth.jmir.org/2020/3/e12842/ %U https://doi.org/10.2196/12842 %U http://www.ncbi.nlm.nih.gov/pubmed/32701458 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e14337 %T Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset %A Caldwell,Wendy K %A Fairchild,Geoffrey %A Del Valle,Sara Y %+ X Computational Physics Division, Los Alamos National Laboratory, P.O. Box 1663, Mail Stop T086, Los Alamos, NM, 87545, United States, 1 5056678593, wkcaldwell@lanl.gov %K influenza %K surveillance %K infoveillance %K infodemiology %K projections and predictions %K internet %K data sources %D 2020 %7 3.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Influenza epidemics result in a public health and economic burden worldwide. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1 to 2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics. Objective: This study aimed to present the first implementation of a novel dataset by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions. Methods: We used internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. We tested the traffic generated by 10 influenza-related pages in 8 states and 9 census divisions within the United States and compared it against clinical surveillance data. Results: Our results yielded an r2 value of 0.955 in the most successful case, promising results for some cases, and unsuccessful results for other cases. In the interest of scientific transparency to further the understanding of when internet data streams are an appropriate supplemental data source, we also included negative results (ie, unsuccessful models). Models that focused on a single influenza season were more successful than those that attempted to model multiple influenza seasons. Geographic resolution appeared to play a key role, with national and regional models being more successful, overall, than models at the state level. Conclusions: These results demonstrate that internet data may be able to complement traditional influenza surveillance in some cases but not in others. Specifically, our results show that the CDC website traffic may inform national- and division-level models but not models for each individual state. In addition, our results show better agreement when the data were broken up by seasons instead of aggregated over several years. We anticipate that this work will lead to more complex nowcasting and forecasting models using this data stream. %M 32437327 %R 10.2196/14337 %U https://www.jmir.org/2020/7/e14337 %U https://doi.org/10.2196/14337 %U http://www.ncbi.nlm.nih.gov/pubmed/32437327 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 12 %N 1 %P e10576 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2020 %7 ..2020 %9 %J Online J Public Health Inform %G English %X BackgroundDuring the 2009 H1N1 influenza pandemic (pH1N1), the proportion of outpatient visits to emergency departments, clinics and hospitals became elevated especially during the early months of the pandemic due to surges in sick, ‘worried well’ or returning patients seeking care. We determined the prevalence of return visits to a multispecialty clinic during the 2009 H1N1 influenza pandemic and identify subgroups at risk for return visits using model-based recursive partitioning.MethodsThis was a retrospective analysis of ILI-related medical care visits to multispecialty clinic in Houston, Texas obtained as part of the Houston Health Department Influenza Sentinel Surveillance Project (ISSP) during the 2009 H1N1 pandemic influenza (April 2009 – April 2010). The data comprised of 2680 individuals who made a total of 2960 clinic visits. Return visit was defined as any visit following the index visit after the wash-out phase prior to the study period. We applied nominal logistic regression and recursive partition models to determine the independent predictors and the response probabilities of return visits. The sensitivity and specificity of the outcomes probabilities was determined using receiver operating characteristic (ROC) curve.ResultsOverall, 4.56% (Prob. 0.0%-17.5%) of the cohort had return visits with significant variations observed attributed to age group (76.0%) and type of vaccine received by patients (18.4%) and Influenza A (pH1N1) test result (5.6%). Patients in age group 0-4 years were 9 times (aOR: 8.77, 95%CI: 3.39-29.95, p<0.0001) more likely than those who were 50+ years to have return visits. Similarly, patients who received either seasonal flu (aOR: 1.59, 95% CI 1.01-2.50, p=0.047) or pH1N1 (aOR: 1.74, 95%CI: 1.09-2.75, p=0.022) vaccines were about twice more likely to have return visits compared to those with no vaccination history. Model-based recursive partitioning yielded 19 splits with patients in subgroup I (patients of age group 0-4 years, who tested positive for pH1N1, and received both seasonal flu and pH1N1 vaccines) having the highest risk of return visits (Prob.=17.5%). The area under the curve (AUC) for both return and non-return visits was 72.9%, indicating a fairly accurate classification of the two groups.ConclusionsReturn visits in our cohort was more prevalent among children and young adults and those that received either seasonal flu or pH1N1 or both vaccines. Understanding the dynamics in care-seeking behavior during pandemic would assist policymakers with appropriate resource allocation, and in the design of initiatives aimed at mitigating surges and recurrent utilization of the healthcare system.Keywords: Model-based recursive partitioning, subgroup analysis, Influenza-like-illness, H1N1, influenza pandemic, care-seeking behavior, return visit %M 32577153 %R 10.5210/ojphi.v12i1.10576 %U %U https://doi.org/10.5210/ojphi.v12i1.10576 %U http://www.ncbi.nlm.nih.gov/pubmed/32577153 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 1 %P e14627 %T Media Reports as a Source for Monitoring Impact of Influenza on Hospital Care: Qualitative Content Analysis %A Reukers,Daphne F M %A Marbus,Sierk D %A Smit,Hella %A Schneeberger,Peter %A Donker,Gé %A van der Hoek,Wim %A van Gageldonk-Lafeber,Arianne B %+ Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Postbus 1, 3720 BA, Bilthoven, , Netherlands, 31 302743419, daphne.reukers@rivm.nl %K influenza %K severe acute respiratory infections %K SARI %K surveillance %K media reports %K news articles %K hospital care %D 2020 %7 4.3.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Netherlands, like most European countries, has a robust influenza surveillance system in primary care. However, there is a lack of real-time nationally representative data on hospital admissions for complications of influenza. Anecdotal information about hospital capacity problems during influenza epidemics can, therefore, not be substantiated. Objective: The aim of this study was to assess whether media reports could provide relevant information for estimating the impact of influenza on hospital capacity, in the absence of hospital surveillance data. Methods: Dutch news articles on influenza in hospitals during the influenza season (week 40 of 2017 until week 20 of 2018) were searched in a Web-based media monitoring program (Coosto). Trends in the number of weekly articles were compared with trends in 5 different influenza surveillance systems. A content analysis was performed on a selection of news articles, and information on the hospital, department, problem, and preventive or response measures was collected. Results: The trend in weekly news articles correlated significantly with the trends in all 5 surveillance systems, including severe acute respiratory infections (SARI) surveillance. However, the peak in all 5 surveillance systems preceded the peak in news articles. Content analysis showed hospitals (N=69) had major capacity problems (46/69, 67%), resulting in admission stops (9/46, 20%), postponement of nonurgent surgical procedures (29/46, 63%), or both (8/46, 17%). Only few hospitals reported the use of point-of-care testing (5/69, 7%) or a separate influenza ward (3/69, 4%) to accelerate clinical management, but most resorted to ad hoc crisis management (34/69, 49%). Conclusions: Media reports showed that the 2017/2018 influenza epidemic caused serious problems in hospitals throughout the country. However, because of the time lag in media reporting, it is not a suitable alternative for near real-time SARI surveillance. A robust SARI surveillance program is important to inform decision making. %M 32130197 %R 10.2196/14627 %U http://publichealth.jmir.org/2020/1/e14627/ %U https://doi.org/10.2196/14627 %U http://www.ncbi.nlm.nih.gov/pubmed/32130197 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 2 %P e16427 %T Effectiveness and Parental Acceptability of Social Networking Interventions for Promoting Seasonal Influenza Vaccination Among Young Children: Randomized Controlled Trial %A Liao,Qiuyan %A Fielding,Richard %A Cheung,Yee Tak Derek %A Lian,Jinxiao %A Yuan,Jiehu %A Lam,Wendy Wing Tak %+ University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong, China (Hong Kong), 852 3917 9289, qyliao11@hku.hk %K influenza vaccination %K social media %K intervention %K children %D 2020 %7 28.2.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Seasonal influenza vaccination (SIV) coverage among young children remains low worldwide. Mobile social networking apps such as WhatsApp Messenger are promising tools for health interventions. Objective: This was a preliminary study to test the effectiveness and parental acceptability of a social networking intervention that sends weekly vaccination reminders and encourages exchange of SIV-related views and experiences among mothers via WhatsApp discussion groups for promoting childhood SIV. The second objective was to examine the effect of introducing time pressure on mothers’ decision making for childhood SIV for vaccination decision making. This was done using countdowns of the recommended vaccination timing. Methods: Mothers of child(ren) aged 6 to 72 months were randomly allocated to control or to one of two social networking intervention groups receiving vaccination reminders with (SNI+TP) or without (SNI–TP) a time pressure component via WhatsApp discussion groups at a ratio of 5:2:2. All participants first completed a baseline assessment. Both the SNI–TP and SNI+TP groups subsequently received weekly vaccination reminders from October to December 2017 and participated in WhatsApp discussions about SIV moderated by a health professional. All participants completed a follow-up assessment from April to May 2018. Results: A total of 84.9% (174/205), 71% (57/80), and 75% (60/80) who were allocated to the control, SNI–TP, and SNI+TP groups, respectively, completed the outcome assessment. The social networking intervention significantly promoted mothers’ self-efficacy for taking children for SIV (SNI–TP: odds ratio [OR] 2.69 [1.07-6.79]; SNI+TP: OR 2.50 [1.13-5.55]), but did not result in significantly improved children’s SIV uptake. Moreover, after adjusting for mothers’ working status, introducing additional time pressure reduced the overall SIV uptake in children of working mothers (OR 0.27 [0.10-0.77]) but significantly increased the SIV uptake among children of mothers without a full-time job (OR 6.53 [1.87-22.82]). Most participants’ WhatsApp posts were about sharing experience or views (226/434, 52.1%) of which 44.7% (101/226) were categorized as negative, such as their concerns over vaccine safety, side effects and effectiveness. Although participants shared predominantly negative experience or views about SIV at the beginning of the discussion, the moderator was able to encourage the discussion of more positive experience or views and more knowledge and information. Most intervention group participants indicated willingness to receive the same interventions (110/117, 94.0%) and recommend the interventions to other mothers (102/117, 87.2%) in future Conclusions: Online information support can effectively promote mothers’ self-efficacy for taking children for SIV but alone it may not sufficient to address maternal concerns over SIV to achieve a positive vaccination decision. However, the active involvement of health professionals in online discussions can shape positive discussions about vaccination. Time pressure on decision making interacts with maternal work status, facilitating vaccination uptake among mothers who may have more free time, but having the opposite effect among busier working mothers. Trial Registration: Hong Kong University Clinical Trials Registry HKUCTR-2250; https://tinyurl.com/vejv276 %M 32130136 %R 10.2196/16427 %U http://www.jmir.org/2020/2/e16427/ %U https://doi.org/10.2196/16427 %U http://www.ncbi.nlm.nih.gov/pubmed/32130136 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 4 %P e12016 %T Enhanced Safety Surveillance of Influenza Vaccines in General Practice, Winter 2015-16: Feasibility Study %A de Lusignan,Simon %A Correa,Ana %A Dos Santos,Gaël %A Meyer,Nadia %A Haguinet,François %A Webb,Rebecca %A McGee,Christopher %A Byford,Rachel %A Yonova,Ivelina %A Pathirannehelage,Sameera %A Ferreira,Filipa Matos %A Jones,Simon %+ University of Surrey, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, United Kingdom, 44 01483 68 ext 3089, s.lusignan@surrey.ac.uk %K vaccines %K safety management %K medical records systems, computerized %K drug-related side effects and adverse reactions %K influenza, human %K influenza vaccines %K general practice %K England %D 2019 %7 14.11.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The European Medicines Agency (EMA) requires vaccine manufacturers to conduct enhanced real-time surveillance of seasonal influenza vaccination. The EMA has specified a list of adverse events of interest to be monitored. The EMA sets out 3 different ways to conduct such surveillance: (1) active surveillance, (2) enhanced passive surveillance, or (3) electronic health record data mining (EHR-DM). English general practice (GP) is a suitable setting to implement enhanced passive surveillance and EHR-DM. Objective: This study aimed to test the feasibility of conducting enhanced passive surveillance in GP using the yellow card scheme (adverse events of interest reporting cards) to determine if it has any advantages over EHR-DM alone. Methods: A total of 9 GPs in England participated, of which 3 tested the feasibility of enhanced passive surveillance and the other 6 EHR-DM alone. The 3 that tested EPS provided patients with yellow (adverse events) cards for patients to report any adverse events. Data were extracted from all 9 GPs’ EHRs between weeks 35 and 49 (08/24/2015 to 12/06/2015), the main period of influenza vaccination. We conducted weekly analysis and end-of-study analyses. Results: Our GPs were largely distributed across England with a registered population of 81,040. In the week 49 report, 15,863/81,040 people (19.57% of the registered practice population) were vaccinated. In the EPS practices, staff managed to hand out the cards to 61.25% (4150/6776) of the vaccinees, and of these cards, 1.98% (82/4150) were returned to the GP offices. Adverse events of interests were reported by 113 /7223 people (1.56%) in the enhanced passive surveillance practices, compared with 322/8640 people (3.73%) in the EHR-DM practices. Conclusions: Overall, we demonstrated that GPs EHR-DM was an appropriate method of enhanced surveillance. However, the use of yellow cards, in enhanced passive surveillance practices, did not enhance the collection of adverse events of interests as demonstrated in this study. Their return rate was poor, data entry from them was not straightforward, and there were issues with data reconciliation. We concluded that customized cards prespecifying the EMA’s adverse events of interests, combined with EHR-DM, were needed to maximize data collection. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2016-015469 %M 31724955 %R 10.2196/12016 %U http://publichealth.jmir.org/2019/4/e12016/ %U https://doi.org/10.2196/12016 %U http://www.ncbi.nlm.nih.gov/pubmed/31724955 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 11 %P e14186 %T Feasibility of Point-of-Care Testing for Influenza Within a National Primary Care Sentinel Surveillance Network in England: Protocol for a Mixed Methods Study %A de Lusignan,Simon %A Hoang,Uy %A Liyanage,Harshana %A Yonova,Ivelina %A Ferreira,Filipa %A Diez-Domingo,Javier %A Clark,Tristan %+ Department of Clinical and Experimental Medicine, University of Surrey, Leggett Building, Daphne Jackson Road, Guildford, GU2 7WG, United Kingdom, 44 1483 684802, simon.delusignan@phc.ox.ac.uk %K diagnosis %K influenza, human %K point-of-care systems %K general practice %D 2019 %7 11.11.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: Point-of-care testing (POCT) for influenza promises to provide real-time information to influence clinical decision making and improve patient outcomes. Public Health England has published a toolkit to assist implementation of these tests in the UK National Health Service. Objective: A feasibility study will be undertaken to assess the implementation of influenza POCT in primary care as part of a sentinel surveillance network. Methods: We will conduct a mixed methods study to compare the sampling rates in practices using POCT and current virology swabbing practices not using POCT, and to understand the issues and barriers to implementation of influenza POCT in primary care workflows. The study will take place between March and May 2019. It will be nested in general practices that are part of the English national sentinel surveillance network run by the Royal College of General Practitioners Research and Surveillance Centre. The primary outcome is the number of valid influenza swabs taken and tested by the practices involved in the study using the new POCT. Results: A total of 6 practices were recruited, and data collection commenced on March 11, 2019. Moreover, 312 swab samples had been collected at the time of submission of the protocol, which was 32.5% (312/960) of the expected sample size. In addition, 68 samples were positive for influenza, which was 20.1% (68/338) of the expected sample size. Conclusions: To the best of our knowledge, this is the first time an evaluation study has been undertaken on POCT for influenza in general practice in the United Kingdom. This proposed study promises to shed light on the feasibility of implementation of POCT in primary care and on the views of practitioners about the use of influenza POCT in primary care, including its impact on primary care workflows. International Registered Report Identifier (IRRID): DERR1-10.2196/14186 %M 31710303 %R 10.2196/14186 %U http://www.researchprotocols.org/2019/11/e14186/ %U https://doi.org/10.2196/14186 %U http://www.ncbi.nlm.nih.gov/pubmed/31710303 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e14276 %T The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study %A Kim,Myeongchan %A Yune,Sehyo %A Chang,Seyun %A Jung,Yuseob %A Sa,Soon Ok %A Han,Hyun Wook %+ Department of Biomedical Informatics, Graduate School of Medicine, CHA University, Pangyo-ro 335, Bundang-gu, Seongnam-si, Gyeonggi-do, 13488, Republic of Korea, 82 31 881 7109, stepano7@gmail.com %K data collection %K detecting epidemics %K mobile app %K health care app %K influenza epidemics %K influenza in children %D 2019 %7 17.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Effective surveillance of influenza requires a broad network of health care providers actively reporting cases of influenza-like illnesses and positive laboratory results. Not only is this traditional surveillance system costly to establish and maintain but there is also a time lag between a change in influenza activity and its detection. A new surveillance system that is both reliable and timely will help public health officials to effectively control an epidemic and mitigate the burden of the disease. Objective: This study aimed to evaluate the use of parent-reported data of febrile illnesses in children submitted through the Fever Coach app in real-time surveillance of influenza activities. Methods: Fever Coach is a mobile app designed to help parents and caregivers manage fever in young children, currently mainly serviced in South Korea. The app analyzes data entered by a caregiver and provides tailored information for care of the child based on the child’s age, sex, body weight, body temperature, and accompanying symptoms. Using the data submitted to the app during the 2016-2017 influenza season, we built a regression model that monitors influenza incidence for the 2017-2018 season and validated the model by comparing the predictions with the public influenza surveillance data from the Korea Centers for Disease Control and Prevention (KCDC). Results: During the 2-year study period, 70,203 diagnosis data, including 7702 influenza reports, were submitted. There was a significant correlation between the influenza activity predicted by Fever Coach and that reported by KCDC (Spearman ρ=0.878; P<.001). Using this model, the influenza epidemic in the 2017-2018 season was detected 10 days before the epidemic alert announced by KCDC. Conclusions: The Fever Coach app successfully collected data from 7.73% (207,699/2,686,580) of the target population by providing care instruction for febrile children. These data were used to develop a model that accurately estimated influenza activity measured by the central government agency using reports from sentinel facilities in the national surveillance network. %M 31625946 %R 10.2196/14276 %U https://mhealth.jmir.org/2019/10/e14276 %U https://doi.org/10.2196/14276 %U http://www.ncbi.nlm.nih.gov/pubmed/31625946 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 4 %P e13403 %T Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation %A Baltrusaitis,Kristin %A Vespignani,Alessandro %A Rosenfeld,Roni %A Gray,Josh %A Raymond,Dorrie %A Santillana,Mauricio %+ Computational Health Informatics Program, Boston Children’s Hospital, 1 Autumn St, Boston, MA, 02215, United States, 1 617 599 5460, msantill@fas.harvard.edu %K digital disease surveillance %K influenza %K surveillance %K participatory syndromic surveillance %K disease modeling %D 2019 %7 14.9.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition, coverage, and consistency of health care reports vary from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear. Objective: The objective of our study was to qualitatively and quantitatively compare national and region-specific ILI activity in the United States across 4 surveillance data sources—CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org—to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet’s data. We also compared the yearly percentage of FNY participants who sought health care for ILI symptoms across geographical areas. Methods: We compared the national and regional 2018-2019 ILI activity baselines, calculated using noninfluenza weeks from previous years, for each surveillance data source. We also compared measures of ILI activity across geographical areas during 3 influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source were also assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health care–seeking percentages were calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season. Pearson correlations were used to assess the association between the health care–seeking percentages and baselines for each surveillance data source. Results: We observed consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, whereas differences in ILI activity for HealthTweets.org were associated with the total number of tweets within a geographical area. The percentage of FNY participants who sought health care for ILI symptoms differed slightly across geographical areas, and these percentages were positively correlated with CDC ILINet and athenahealth baselines. Conclusions: Our findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models. %M 31579019 %R 10.2196/13403 %U https://publichealth.jmir.org/2019/4/e13403 %U https://doi.org/10.2196/13403 %U http://www.ncbi.nlm.nih.gov/pubmed/31579019 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 2 %P e9952 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X The prediction and characterization of outbreaks of infectious diseases such as influenza remains an open and important problem. This paper describes a framework for detecting and characterizing outbreaks of influenza and the results of testing it on data from ten outbreaks collected from two locations over five years. We model outbreaks with compartment models and expliccitly model non-influenza influenza-like illnesses. %M 31632600 %R 10.5210/ojphi.v11i2.9952 %U %U https://doi.org/10.5210/ojphi.v11i2.9952 %U http://www.ncbi.nlm.nih.gov/pubmed/31632600 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 3 %P e11780 %T Flucast: A Real-Time Tool to Predict Severity of an Influenza Season %A Moa,Aye %A Muscatello,David %A Chughtai,Abrar %A Chen,Xin %A MacIntyre,C Raina %+ Biosecurity Program, The Kirby Institute, University of New South Wales, Gate 9, High Street, Sydney, NSW 2052, Australia, 61 02 93850938, a.moa@unsw.edu.au %K prediction tool %K influenza %K risk assessment %D 2019 %7 23.07.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Influenza causes serious illness requiring annual health system surge capacity, yet annual seasonal variation makes it difficult to forecast and plan for the severity of an upcoming season. Research shows that hospital and health system stakeholders indicate a preference for forecasting tools that are easy to use and understand to assist with surge capacity planning for influenza. Objective: This study aimed to develop a simple risk prediction tool, Flucast, to predict the severity of an emerging influenza season. Methods: Study data were obtained from the National Notifiable Diseases Surveillance System and Australian Influenza Surveillance Reports from the Department of Health, Australia. We tested Flucast using retrospective seasonal data for 11 Australian influenza seasons. We compared five different models using parameters known early in the season that may be associated with the severity of the season. To calibrate the tool, the resulting estimates of seasonal severity were validated against independent reports of influenza-attributable morbidity and mortality. The model with the highest predictive accuracy against retrospective seasonal activity was chosen as a best-fit model to develop the Flucast tool. The tool was prospectively tested against the 2018 and the emerging 2019 influenza season. Results: The Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. With the use of real-time data, the tool provided a reasonable early prediction of a low to moderate season for the 2018 and severe seasonal activity for the upcoming 2019 season. The tool meets stakeholder preferences for simplicity and ease of use to assist with surge capacity planning. Conclusions: The Flucast tool may be useful to inform future health system influenza preparedness planning, surge capacity, and intervention programs in real time, and can be adapted for different settings and geographic locations. %M 31339102 %R 10.2196/11780 %U http://publichealth.jmir.org/2019/3/e11780/ %U https://doi.org/10.2196/11780 %U http://www.ncbi.nlm.nih.gov/pubmed/31339102 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9670 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveThe objectives of this project were to rapidly build and deploy a web-based reporting platform in response to a canine influenza H3N2 outbreak in New York City (NYC) and provide aggregate data back to the veterinary community as an interactive dashboard.IntroductionData-driven decision-making is a cornerstone of public health emergency response; therefore, a highly-configurable and rapidly deployable data capture system with built-in quality assurance (QA; e.g., completeness, standardization) is critical.1 Additionally, to keep key stakeholders informed of developments during an emergency, data need to be shared in a timely and effective manner. Dynamic data visualization is a particularly useful means of sharing data with healthcare providers and the public.2During Spring 2018, detection of canine influenza H3N2 among dogs in NYC caused concern in the veterinary community. Canine influenza is a highly contagious respiratory infection caused by an influenza A virus.3 However, no central database existed in NYC to monitor the outbreak and no single agency was responsible for data capture. Our team at the NYC Department of Health and Mental Hygiene (DOHMH) partnered with the NYC Veterinary Medical Association (VMA) to monitor the canine influenza H3N2 outbreak by building a web-based reporting platform and interactive dashboard.MethodsThe NYC DOHMH built and deployed a web-based reporting platform to aid veterinarians in reporting cases of canine influenza. We leveraged REDCap Cloud, a cloud-based graphical user interface data capture and management software. REDCap Cloud collected information regarding the provider, owner, dog, residence of dog, illness history, and influenza testing. We leveraged REDCap QA functionality in the form of mandatory questions to ensure data completeness. Several different field types — including dropdown menus, mutually exclusive radio buttons, and multi-select check boxes — were used to ensure data standardization. Skip logic was incorporated to guide users through unique sequences of questions based on the answers they entered. Reporting was voluntary.ResultsAfter requirements were gathered, the REDCap web-based reporting platform was rapidly deployed in approximately two business days. Over the course of one week, multiple versions of the dashboard were produced and the final iteration was completed. The entire system was built on server-side software that is available as free or open-source for individual licenses. The dashboard can be found at the following link: http://www.vmanyc.org/canine_influenza_dashboard.html.A total of 28 cases were reported by 6 providers during June–August 2018. All of the 28 cases were reported from 2 of the 5 NYC counties (boroughs); 17/28 (60.7%) were reported from Brooklyn and 11/28 (39.3%) were reported from Manhattan. We were able to collect mostly complete data by leveraging REDCap QA functionality. The reporting facility was listed in all cases, and an owner was listed in all but two cases. All reported cases used a PCR test for the detection of canine influenza H3N2. One reported case indicated polymerase chain reaction (PCR) test results as “not detected” which suggests that one negative case was reported through the system.ConclusionsUsing REDCap Cloud and R, we were able to rapidly build and deploy a web-based reporting platform and dynamic data visualization during an emergency response to an outbreak of canine influenza H3N2. Our system was used by veterinarians to report 28 cases of canine influenza. Future emergency responses for human disease outbreaks will likely benefit from the experience our team gained during our partnership with the NYC VMA.References1. Centers for Disease Control and Prevention. Public Health Emergency Response Guide for State, Local, and Tribal Public Health Directors. https://emergency.cdc.gov/planning/pdf/cdcresponseguide.pdf.2. Meyer M. The Rise of Healthcare Data Visualization. http://journal.ahima.org/2017/12/21/the-rise-of-healthcare-data-visualization/.3. American Veterinary Medical Association. Canine Influenza FAQ. https://www.avma.org/KB/Resources/FAQs/Pages/Control-of-Canine-Influenza-in-Dogs.aspx.4. Wickham H. R packages. http://r-pkgs.had.co.nz/. %R 10.5210/ojphi.v11i1.9670 %U %U https://doi.org/10.5210/ojphi.v11i1.9670 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9724 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo investigate whether Twitter data can be used as a proxy for the surveillance of the seasonal influenza epidemic in France and at the regional level.IntroductionSocial media as Twitter are used today by people to disseminate health information but also to share or exchange on their health. Based on this observation, recent studies showed that Twitter data can be used to monitor trends of infectious diseases such as influenza. These studies were mainly carried out in United States where Twitter is very popular1-4. In our knowledge, no research has been implemented in France to know whether Twitter data can be a complementary data source to monitor seasonal influenza epidemic.MethodsFor this exploratory study, an R program allowing to the collection, pre-processing (geolocation and classification) and analysis of Tweets related to influenza-like illness was developed.CollectionStream API was used to collect Tweets in French language that contained terms “grippe”,”grippal”, “grippaux” without to specify geolocation coordinates.Pre-processIn order to identify Tweets localized in France, a combination of automated filters has been implemented. At the end, were retained:● Tweets with geolocation coordinates in France (GPS coordinates, country code, country, place name)● Tweets whose place indicated in user’s profile matched with a city, department or region of France● Tweets included FR-related time zone but excluding all Tweets reporting a FR time zone but a non-FR place-code.In the second time, a support vector machine (SVM) classifier was used to filter out noise from the database. To train the classifier, 1500 Tweets were randomly sampled. Each of these 1500 training Tweets was manually inspected and tagged as valid or invalid according to the likelihood that they indicated influenza-like illness. This hand-tagged training set was converted to vector representation using their term-frequency-inverse document frequency (TF-IDF) scores. These TF-IDF vectors were then input to the SVM for training. To evaluate performances of the classifier: accurency, recall and F- measure were calculated from a 1000 randomly sampled Tweets manually tagged.AnalysisData collected over the period from August 8, 2016 to March 26, 2017 were compared to those of the French syndromic surveillance system SurSaUD® (OSCOUR® and SOS Médecins network)5 by Spearman''s rank correlation coefficient.EthicalIn accordance to the National Commission on Informatics and Liberty, information about user account were removed in database except location variables. Usernames contained in the text of the tweet have also been deleted.ResultsOver the study period, the system collected 238,244 influenza-related Tweets of which 130,559 were located in France. After a cleaning step, 22,939 Tweets were classified by the algorithm as an influenza-like illness (ILI). The performances of the classifier were 0.739 for accuracy, 0.725 for recall and 0.732 for F-measure. Figure 1 shows that the weekly number of ILI Tweets follows the same trend as the weekly number of ED visits and physicians consultations for ILI. Regardless of data source, Spearman''s correlation coefficients were positive and statistically significant at the national level and for each region of France (Table 1).ConclusionsThis exploratory study allowed to show that Twitter data can be used to monitor the epidemic of seasonal influenza in France and at regional level, in complementarity with existing systems. The system needs to be improved to confirm the trends observed during the next influenza epidemic.References1.Broniatowski DA, Paul MJ, Dredze M. National and local influenza surveillance through Twitter: An analysis of the 2012-2013 influenza epidemic. PLoS One. 2013;8(12):e83672.2.Gesualdo F, Stilo G, Agricola E, Gonfiantini MV, Pandolfi E, Velardi P, et al. Influenza-like illness surveillance on Twitter through automated learning of naïve language. PLoS One. 2013;8(12):e82489.3. Paul MJ, Dredze M, Broniatowski D. Twitter improves influenza forecasting. PLoS Curr. 2014;6.4. Allen C, Tsou MH, Aslam A, Nagel A, Gawron JM. Applying GIS and machine learning methods to Twitter data for multiscale surveillance of influenza. PLoS One. 2016;11(7):e0157734.5. Ruello M, Pelat C, Caserio-Schönemann C, et al. A regional approach for the influenza surveillance in France. OJPHI. 2017;9(1):e089. %R 10.5210/ojphi.v11i1.9724 %U %U https://doi.org/10.5210/ojphi.v11i1.9724 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9739 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveIn the presented study, we examined the impact of school holidays (Autumn, Winter, Summer, and Spring Breaks) and social events (Super Bowl, NBA Finals, World Series, and Black Friday) for five age groups (<4, 5-24, 25-44, 45-64, >65 years) on four health outcomes of influenza (total tested, all influenza positives, positives for influenza A, and B) in Milwaukee, WI, in 2004-2009 using routine surveillance.IntroductionInfluenza viral infection is contentious, has a short incubation period, yet preventable if multiple barriers are employed. At some extend school holidays and travel restrictions serve as a socially accepted control measure1,2. A study of a spatiotemporal spread of influenza among school-aged children in Belgium illustrated that changes in mixing patterns are responsible for altering disease seasonality3. Stochastic numerical simulations suggested that weekends and holidays can delay disease seasonal peaks, mitigate the spread of infection, and slow down the epidemic by periodically dampening transmission. While Christmas holidays had the largest impact on transmission, other school breaks may also help in reducing an epidemic size. Contrary to events reducing social mixing, sporting events and mass gatherings facilitate the spread of infections4. A study on county-level vital statistics of the US from 1974-2009 showed that Super Bowl social mixing affects influenza dissemination by decreasing mortality rates in older adults in Bowl-participating counties. The effect is most pronounced for highly virulent influenza strains and when the Super Bowl occurs closer to the influenza seasonal peak. Simulation studies exploring how social mixing affects influenza spread5 demonstrated that impact of the public gathering on prevalence of influenza depends on time proximity to epidemic peak. While the effects of holidays and social events on seasonal influenza have been explored in surveillance time series and agent-based modeling studies, the understanding of the differential effects across age groups is incomplete.MethodsThe City of Milwaukee Health Department Laboratory (MHDL), Wisconsin routinely collect tests from residents of metropolitan areas and vicinities of the Marquette University (MU). We obtained weekly counts of total tested, all influenza positives, positives for influenza A and B, from MHDL between 5/16/04-3/7/09 (before the surge of tests associated with “swine flu”). Cases for <1 and 1-4 age groups were combined. Meteorological data are routinely collected by a monitoring station at the General Mitchell International airport located 7.5 miles from Milwaukee. Daily dewpoint values representing the perceived ambient temperature corrected for the air moisture content were downloaded from the open source website6 and aggregated to weekly averages with Sunday designating the beginning of each week. School holidays were obtained from academic calendars on the MU website with holiday weeks defined as having one or more school holiday observed.7 Selected social events were retrieved from a public website.8 As part of exploratory analysis, average cases per week (c/w) for each outcome for school holiday and non-holiday weeks were compared using a non-parametric the Mann–Whitney U-test. We analyzed the association between weekly cases and holiday effects using negative binomial regression with sets of indicator variables for non-overlapping school holidays and social events and with adjustments for weather fluctuations with harmonic terms (Model 1). Results are presented as Relative Risk (RR) estimates along with their confidence intervals (95%CI). Further analyses examined seasonal signatures (lead-lag structures) using a segmented regression approach for weekly counts and rates 5 academic weeks (aw) before, 2-6 weeks during, and 5 weeks after select holidays (Model 2).ResultsOver 251 study weeks, 2282 tests were submitted, out of which 1098 cases were from 5-24 y.o. age group. 477 (21%) tests we positive, with 399 (84%) cases of influenza A (73 tests were not subtyped) and 78 (16%) cases of influenza B. Figure 1 shows the time series of weekly counts of influenza tests and percent positives with superimposed information on school holiday occurrences. Overall, during 135 weeks of the school period the average number of tests was two times higher as compared to those during 116 holiday weeks (11.9±10.3 vs 5.8±6.5 c/w, p<0.001). Similarly, the average weekly number of positive tests was higher in non-holiday than during holiday periods (2.9±5.7 vs 0.7±2.6 c/w, p<0.001). The reduction in tests during holidays was confirmed by the regression model (RR=0.71; 95%CI=[0.60-0.86]). The reduction in weekly tests was most pronounced during the Winter Break (15-19 aw) for all age groups (4.8±3.0 c/w, p<0.001; RR=0.3; 95%CI=[0.23-0.41]) and especially for school-aged children, young adults and adults (RR=0.14; 95%CI=[0.09-0.22] and RR=0.32; 95%CI=[0.16-0.62] for 5-24 and 25-44 age groups, respectively). In contrast, during the Spring Break (27-30 aw) the number of tests has almost doubled (20.4±10.4 c/w; p<0.001) as compared to the school period, with the most noticeable increase in 5-24 and 25-44 age groups. Spring Break differential effects were primarily due to later peaks in influenza B shown by segmented regression results in Figure 2. The seasonal increase in weekly rates is the steepest after the winter holidays. The effects of the selected sporting and social events were inconclusive.ConclusionsThe differential effects of calendar events on seasonal influenza can be detected by routine surveillance and further explored with respect to lead-lag structures. We recommend incorporating location-specific calendar effects in influenza near-term forecasting models tailored to susceptible age groups to better predict and assess targeted intervention measures.References1. Jackson C, et al. (2016). The relationship between school holidays and transmission of influenza in England and wales. Am Journal of Epidemiology. 184(9), 644-51.2. Chu Y, et al. (2017). Effects of school breaks on influenza-like illness incidence in a temperate Chinese region: an ecological study from 2008 to 2015. BMJ. 7(3), e013159.3. Luca G, et al. (2018) The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium. BMC Infect Dis. 18(1): 29.4. Stoecker C, et al. (2016) Success Is Something to Sneeze At: Influenza Mortality in Cities that Participate in the Super Bowl. Am Journal of Health Econ. 2(1):125-43.5. Shi P, et al. (2010) The impact of mass gatherings and holiday traveling on the course of an influenza pandemic: a computational model. BMC Public Health. 10: 778.6. www.wunderground.com.7. www.marquette.edu/mucentral/registrar/ArchivedAcademicCalendars.shtml.8. www.timeanddate.com. %R 10.5210/ojphi.v11i1.9739 %U %U https://doi.org/10.5210/ojphi.v11i1.9739 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9748 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo identify additional data elements in existing syndromic surveillance message feeds that can provide additional insight into public health concerns such as the influenza season.IntroductionSyndromic surveillance achieves timeliness by collecting prediagnostic data, such as emergency department chief complaints, from the start of healthcare interactions. The tradeoff is less precision than from diagnosis data, which takes longer to generate. As the use and sophistication of electronic health information systems increases, additional data that provide an intermediate balance of timeliness and precision are becoming available.Information about the procedures and treatments ordered for a patient can indicate what diagnoses are being considered. Procedure records can also be used to track the use of preventive measures such as vaccines that are also relevant to public health surveillance but not readily captured by typical syndromic data elements. Some procedures such as laboratory tests also provide results which can provide additional specificity about which diagnoses will be considered. If procedure and treatment orders and test results are included in existing syndromic surveillance feeds, additional specificity can be achieved with timeliness comparable to prediagnostic assessments.MethodsHL7 messages were collected for syndromic surveillance using EpiCenter software. They were retroactively scanned for PR1 procedure segments; procedure codes and descriptions were extracted when available. Influenza-related procedures were identified and classified as either a test for the virus or an administration of a vaccine. Classification was based on the procedure code when a standard code set was used and could be identified, otherwise it was based on the text description of the procedure.Messages were also scanned for the presence of ‘influenza’ in text fields. Influenza test results were identified first by selecting messages with ‘influenza’ in an OBX segment and then further refining based on the test code and description.ResultsA total of 443,074,748 messages from 2,577 healthcare facilities received between July 1, 2017 and August 31, 2018 were scanned for procedure information. Procedure codes were present in 39,142,670 messages from 287 facilities. The most common procedures included blood glucose measurements and other diabetes maintenance activities, incentive spirometry, blood count and metabolic panels, safety observation, and vital signs.Of those, 995,754 messages from 142 facilities contained influenza-related procedure codes for 106,610 visits. 14,672 visits from 62 facilities had one of 48 vaccine procedure codes, and 91, 948 visits from 127 facilities had one of 66 test codes. Time series of both types of procedures showed a seasonal trend consistent with the influenza season. Figure 1 shows the daily counts of influenza test orders and vaccine administrations. Figure 2 breaks out the test orders by test type (antibody assay, antigen assay, PCR, or unspecified).Seven facilities sent a total of 58,182 messages containing influenza test results. These included both positive and negative results. These results distinguished between influenza A and influenza B. Figure 3 shows the daily counts of both positive and negative results by virus type; this also follows the expected seasonal pattern.ConclusionsSince procedure information was not specifically requested from healthcare facilities, the overall representation of procedure data elements was low. These initial results indicate that such data would be useful both as a supplement to syndromic surveillance activities and as a new data source for other surveillance activities such as vaccine uptake tracking. Given the frequency of procedures and treatments for chronic diseases such as diabetes and heart disease, these data may be relevant for understanding the prevalence of those conditions as well. Tests and treatments relevant to other public health concerns like opioid use disorder were also present, suggesting a wide range of potential applications.It is also possible to obtain and extract influenza test results from these syndromic surveillance messages. Both positive and negative results were present, providing information not just on the number of positive cases but also the rate of testing and rate of positive results. The pattern of testing and results also indicates that at least some facilities test for influenza throughout the season, contrary to some conventional wisdom about testing patterns. %R 10.5210/ojphi.v11i1.9748 %U %U https://doi.org/10.5210/ojphi.v11i1.9748 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9752 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveUsing the information that we have available, our primary objective is to explore if there was any cross-correlation between pneumonia admissions and hospital influenza positivity. We then aim to develop a data driven approach to forecast pneumonia admissions using data from our hospital’s weekly surveillance. We also attempted using external sources of information such as national infectious diseases notifications and climate data to see if they were useful for our model.IntroductionInfluenza peaks around June and December in Singapore every year. Facing an ageing population, hospitals in Singapore have been constantly reaching maximum bed occupancy. The ability to be able to make early decisions during peak periods is important. Tan Tock Seng Hospital is the second largest adult acute care general hospital in Singapore. Pneumonia-related emergency department (ED) admissions are a huge burden to the hospital''s resources. The number of cases vary year on year as it depends on seasonal vaccine effectiveness and the population’s immunity to the circulating strain. While many pneumonia cases are of unknown origin, they tend to mirror the influenza seasons very closely.MethodsWe used data from epidemiological week (e-week) 1 of 2013 to e-week 34 of August 2017 to train our model, with the next 52 weeks (e-week of 35 of 2017 to e-week 34 of 2018 ) being used as validation cohort. Pneumonia and influenza data were obtained from our hospital’s weekly surveillance. National level acute upper respiratory illness (AURI) was obtained from Ministry of Health’s (MOH) weekly infectious diseases bulletin. Climate data were obtained from the National Environment Agency’s website. Daily rainfall, temperature and wind data from the S20 satellite station were used. Automatic autoregressive (A-ARIMA), non-seasonal and seasonal vector autoregressive models (VAR) were used to either analyse the univariate pneumonia trends or simultaneously model pneumonia, influenza, AURI notification and climatic data. Granger-causality tests were performed to check if these variables were causal of pneumonia admissions. As most of the seasonal variation are seen in older patients, stratified analysis were performed on those that were below and above 65 years old. Forecasts were calculated up to 3 weeks in advance. Mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) were used to validate the model performance. These performance metrics were applied on 3-week ahead forecasts comparing A-ARIMA, VAR, and seasonal-adjusted VAR.ResultsFigure 1 shows that both influenza and pneumonia admissions follow similar trends. We see that the number of influenza cases have reduced as compared to the previous years. The number hospital influenza cases and the number of AURI cases nationwide are strongly cross-correlated with pneumonia admissions. Granger-causality tests confirmed the directionality of the relationships (p <0.01). Climate factors do not strongly affect the number of pneumonia admissions. (Fig 2) Unsurprisingly, the A-ARIMA model showed that the 1-day forecasts were most accurate (MAE: 7.0; MAPE: 12.7; RMSE: 8.7 for elderly subgroup). However, the 3-day ahead forecasts were only slightly less precise (MAE: 7.2 ; MAPE: 13.2; RMSE: 9 for elderly subgroup). Testing for significant lags using the various information criteria suggested that a lag3 model should be used. The non-seasonal and seasonal VAR models showed that historical pneumonia admissions and influenza positivity was the best model. The MAPE for all 3 models hovered between 12-13%, with the A-ARIMA model performing slightly better. This is not surprising as the A-ARIMA takes the latest information at hand to derive the best model. Accounting for seasonality allowed better precision as compared to the non-seasonal VAR but was not better as compared to the A-ARIMA model.ConclusionsHospital surveillance data are the most useful for developing forecast models for hospital pneumonia admissions. Climate data were likely not to be useful as Singapore does not experience much variaton in weather throughout the year. Pneumonia peaks do not follow necessarily fall on the same week every season. Therefore, both the autoregressive and seasonal-adjusted vector autoregressive models can be useful complements to each other for forecasting pneumonia admissions. %R 10.5210/ojphi.v11i1.9752 %U %U https://doi.org/10.5210/ojphi.v11i1.9752 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9757 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo describe influenza laboratory testing and results in the Military Health System and how influenza laboratory results may be used in DoD Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE)IntroductionTimely influenza data can help public health decision-makers identify influenza outbreaks and respond with preventative measures. DoD ESSENCE has the unique advantage of ingesting multiple data sources from the Military Health System (MHS), including outpatient, inpatient, and emergency department (ED) medical encounter diagnosis codes and laboratory-confirmed influenza data, to aid in influenza outbreak monitoring. The Influenza-like Illness (ILI) syndrome definition includes ICD-9 or ICD-10 codes that may increase the number of false positive alerts. Laboratory-confirmed influenza data provides an increased positive predictive value (PPV). The gold standard for influenza testing is molecular assays or viral culture. However, the tests may take 3-10 days to result. Rapid influenza diagnostic tests (RIDTs) have a lower sensitivity, but the timeliness of receiving a result improves to within <15 minutes. We evaluate the utility of RIDTs for routine ILI surveillance.MethodsAdministrative medical encounters for ILI and influenza laboratory-confirmed data were analyzed from the MHS from June 2013 – September 2017 (Figure 1). The medical encounters and laboratory data include outpatient, inpatient, and ED data. The ILI syndrome case definition is a medical encounter during the study period with an ICD-9 or ICD-10 codes in any diagnostic position (ICD-9 codes = 79.99, 382.9, 460, 461.9, 465.8, 465.9, 466.0, 486, 487.0, 487.1, 487.8, 488, 490, 780.6, or 786.2; ICD-10 codes = B97.89, H66.9, J00, J01.9, J06.9, J09, J09.X, J10, J10.0, J10.1, J10.2, J10.8, J11, J11.0, J11.1, J11.2, J11.8, J12.89, J12.9, J18, J20.9, J40, R05, R50.9). The ILI dataset was limited to care provided in the MHS as laboratory data is only available for direct care. We describe influenza laboratory testing practices in the MHS. We aggregated the ILI encounters and RIDT positive results into daily counts and generated a weekly Pearson’s correlation.ResultsInfluenza tests are ordered throughout the year; the mean weekly percentage of ILI encounters in which an influenza laboratory test is ordered is 5.62%, with a range from 0.68% in the off season to 19.2% during peak influenza activity. The mean weekly percentage of positive influenza laboratory results among all ILI encounters is 0.82%, with a range from 0.01% to 5.73% (Figure 2). The percent of ILI encounters in which a test is ordered increases as the influenza season progresses. Influenza laboratory tests conducted in the MHS include RIDTs, PCR, culture, and DFA. Among all influenza tests ordered in the MHS, 66.0% were RIDTs, 22.7% were PCR, and 11.3% were viral culture. Often, a confirmatory test is ordered following a RIDT; 20% of RIDTs have follow-up tests. The mean timeliness of influenza test result data in the MHS was 11.26 days for viral culture, 2.94 days for PCR, and 0.11 days for RIDTs. The RIDT results were moderately correlated with ILI encounters for the entire year (mean weekly Pearson correlation coefficient rho=0.60, 95% CI: 0.55, 0.66, Figure 3). During the influenza season, the mean weekly Pearson correlation coefficient increases to rho=0.75, 95% CI: 0.70, 0.79.ConclusionsThe DoD has the unique advantage of access to the electronic health record and laboratory tests and results of all MHS beneficiaries. This analysis provides evidence for increased utilization of positive RIDTs in ESSENCE. The moderate correlation between the ILI syndrome and positive RIDTs may be associated with ICD-10 codes included in the ILI syndrome definition that contribute to false positive influenza cases. Ongoing research is focused on improving this ILI syndrome definition using ICD-10 codes. Rapid influenza diagnostic tests provide more timely results than other influenza test types. In conjunction with ILI medical encounter data, positive RIDT data provides a more complete and timely picture of the true burden of influenza on the MHS population for early warning of influenza outbreaks. %R 10.5210/ojphi.v11i1.9757 %U %U https://doi.org/10.5210/ojphi.v11i1.9757 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9758 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo describe population-level response to influenza-like illness (ILI) as measured by wearable mobile health (mHealth) devices across multiple dimensions including steps, heart rate, and sleep duration and to assess the potential for using large networks of mHealth devices for influenza surveillance.IntroductionInfluenza surveillance has been a major focus of Data Science efforts to use novel data sources in population and public health [1]. This interest reflects the public health utility of timely identification of flu outbreaks and characterization of their severity and dynamics. Such information can inform mitigation efforts including the targeting of interventions and public health messaging. The key requirement for influenza surveillance systems based on novel data streams is establishing their relationship with underlying influenza patterns [2]. We assess the potential utility of wearable mHealth devices by establishing the aggregate responses to ILI along three dimensions: steps, sleep, and heart rate. Surveillance based on mHealth devices may have several desirable characteristics including 1) high resolution individual-level responses that can be prospectively analyzed in near real-time, 2) indications of physiological responses to flu that should be resistant to feedback loops, changes in health seeking behavior, and changes in technology use, 3) a growing user-base often organized into networks by providers or payers with increasing data quality and completeness, 4) the ability to query individual users underlying aggregate signals, and 5) demographic and geographic information enabling detailed characterization. These features suggest the potential of mHealth data to deliver “faster, more locally relevant” surveillance systems [3].MethodsDuring the 2017/2018 influenza season, surveys were conducted within the Achievement platform, a health app that integrates with a variety of wearable trackers and consumer health applications [4]. The Achievement population has given consent agreeing to participation in studies like the one presented here and permitting access to their data. Surveys queried users as to whether they had experienced flu-like (ILI) symptoms in the preceding 14 days. Respondents who had experienced symptoms were then asked to identify symptom days. Those who had not experienced symptoms were queried again two weeks later. Positive responses were re-indexed to align by date of symptom onset. Individual respondent’s measures were standardized on a per-individual level in the 6 week period centered on the index date. Population-level mean signals were directly computed across several dimensions including steps, sleep, and heart rate. Uncertainty was quantified using resampling.ResultsBeginning February 17th, 2018, surveys were distributed to Achievement users. Within the first week 31,934 users had responded to the survey. Over a 12-week period, 124,892 individuals completed the survey with 25,512 reporting flu-like symptoms in a two week period prior to the survey. Of these, 9,495 had wearable device data in the 90-day window surrounding their symptom dates and 3,362 respondents had “dense” data defined as no more than 4 consecutive missing days in the 6-week period surrounding the index date.Population-level signals to ILI were clearly evident for five measures across the three dimensions. Step count [fig. 1] and time spent active [fig. 2] decreased 1 day prior to reported symptom onset date (index date), with a minimum at day 2 of -.24 std. dev. for step count and -.25 std. dev for time spent active, and a return to baseline at day 8. Sleeplessness [fig.3] and time spent in bed [fig. 4] increased one day prior to index, peaking 4 days after index at a mean increase of .16 std. dev. for sleeplessness and .13 std. dev. for time spent in bed, and returning to baseline at 7 days. Heart rate was elevated from 1 day before index to day 6 with a peak increase of .18 std. dev. on days 2 and 3 after index.ConclusionsThe potential of mHealth devices to register illness has been recognized [5]. This study is the first to present population-level influenza signals in a large network of mHealth users. Mobile health device data linked to ILI-specific survey responses taken during the 2017/18 flu season demonstrate clear aggregate patterns across several dimensions including sleep, steps, and heart rate. These signals suggest the potential for systems to rapidly process individual-level responses to classify ILI and to use such classifiers for ILI surveillance. The data described here, high resolution individual-level behavioral and physiological data linked to timely survey responses, suggests the potential to further enhance outbreak detection and improve characterization of ILI patterns. The setting of our study, a very large network of mobile health device users who have consented to the prospective use of their data and to being queried about their health status, could provide a framework for automated prospective influenza surveillance using “real world evidence” [6]. Employed over a population-representative sample, this approach could provide adjunct to standard clinically-based sentinel systems.References[1] Althouse, Benjamin M., et al. \"Enhancing disease surveillance with novel data streams: challenges and opportunities.\" EPJ Data Science 4.1 (2015): 17.[2] Henning KJ. What is syndromic surveillance?. Morbidity and Mortality Weekly Report. 2004 Sep 24:7-11[3] Simonsen L, Gog JR, Olson D, Viboud C. Infectious disease surveillance in the big data era: towards faster and locally relevant systems. The Journal of infectious diseases. 2016 Nov 14;214(suppl_4):S380-5.[4] https://www.myachievement.com/[5] Li, Xiao, et al. \"Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information.\" PLoS biology 15.1 (2017): e2001402.[6]https://www.fda.gov/scienceresearch/specialtopics/realworldevidence/default.htm %R 10.5210/ojphi.v11i1.9758 %U %U https://doi.org/10.5210/ojphi.v11i1.9758 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9785 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveThe purpose of the study was to characterize the spatial distribution and temporal patterns of laboratory confirmed H5N1 outbreaks from January 2007 to December 2017 in Ghana.IntroductionHighly pathogenic avian influenza (HPAI) subtype H5N1 virus causes a highly contagious disease in poultry with up to 100% mortality and occasionally causes sporadic human infection. The first outbreak of HPAI H5N1 in Africa was reported in Nigeria in 2006 and has since been reported in seven other African countries with confirmed human cases and outbreaks in poultry. Since the emergence of Highly Pathogenic Avian Influenza (HPAI), virus subtype H5N1 in Ghana in 2007, outbreaks in poultry have led to dire economic consequences for the poultry sector, resulting from mass destruction of affected flocks. An economy heavily dependent on agriculture, the persistence of outbreaks threaten the livelihood of farmers who depend on poultry production for survival.Despite significant efforts made in HPAI-H5N1 control and prevention in Ghana, outbreaks persist and continue to spread to new areas. It is uncertain to what extent different pathways contribute to the introduction and the dissemination of the virus in Ghana. There is a need to understand the complex nature of the interactions between local and migratory fowl, the risk of transmission due to human endeavor and trade mechanisms that increase the likelihood of HPAI-H5N1 outbreaks in Ghana.MethodsData for the study was sourced from national outbreak records at the Veterinary Services Directorate.The study analyzed outbreak data for the years 2007-2017. Data retrieved from outbreak reports included the date of onset of outbreak, location and geographic coordinates, type and number of poultry species affected, natural deaths of birds and type of farming system on outbreak farms. We calculated frequency distributions for the types of poultry species affected, the type of farming system and mortality rates on affected premises.We described the distribution of HPAI-H5N1 outbreaks using coordinate maps in ArcGIS and displayed relevant sites of waterfowl and wild bird habitation. To describe the temporal pattern of HPAI-H5N1 outbreaks in Ghana for the period, we created an epidemic curve by plotting the monthly number of outbreaks for the period January 2007 to December 2017 in Excel. We used space-time scan statistics to determine significant local clusters.ResultsA total of sixty-six (66) outbreaks of HPAI-H5N1 occurred in Ghana from January 2007 to December 2017. The outbreak sites were distributed in seven (7) out of ten (10) regions in Ghana. The affected regions are located in the southern and middle belt of Ghana. Most of the outbreaks (74.2%) occurred in densely populated areas of the Greater Accra region. Overall, layer flocks were mostly affected with 56% of affected premises constituting layer farms. Commercial farms and backyard farms made up the majority of affected farms (50% and 42.4%). Free ranging birds were the least affected farm type (7.6%). Two epidemic waves were identified for H5N1 in Ghana; the first wave with 6 outbreaks, lasted a period of four (4) months from April to July 2007, and the second with 60 outbreaks, spanned a period of 2 years from April 2015 to November 2016. Temporal distribution of the outbreaks showed that the outbreak peaked in May 2007 for the first wave and in July 2017 for the second wave with minor peaks observed in April and July 2016. The decrease in the number of the outbreaks after July in both waves is attributed to the onset of slaughter and trade restrictions for poultry in affected areas. Space-time scan statistics identified significant primary clusters of H5N1 outbreaks in the coastal belt of the Greater Accra region, characterized by major commercial activities and the presence of wetlands of relevance to wild birds and migratory waterfowl.ConclusionsTwo (2) major waves of H5N1 outbreaks occurred in Ghana between 2007 and 2017. The distribution of outbreaks and poultry species in both waves, show that the epidemiology of H5N1 virus in Ghana is changing. The findings highlight the importance of reviewing existing control and preventive measures as well as strengthening avian influenza surveillance in proposed high- risk areas.ReferencesForeign Animal Diseases. Revised 2008 Seventh Edition. Committee on foreign and emerging diseases of the United States Animal Health Association.Avian Influenza, OIE terrestrial manual 2015To K.K.W.et al. Avian influenza A H5N1 virus: a continuous threat to humans. Emerging Microbes Infections (2012) 1, e25.Watanabe Y. et al. The changing nature of avian influenza A virus (H5N1). Trends Microbiol. 2012 Jan; 20 (1):11-20. doi: 10.1016/j.tim.2011.10.003. Epub 2011 Dec 5 %R 10.5210/ojphi.v11i1.9785 %U %U https://doi.org/10.5210/ojphi.v11i1.9785 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9834 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveThis study aimed to explore the effects of El Niño and La Niña events on the timing of influenza A peak activity in European countries.IntroductionInfluenza causes a significant burden to the world every year. In the temperate zone, influenza usually prevalent in the winter season, however, it is hardly predictable when the influenza epidemic will begin and when the peak activity will come. Influenza has a peak in early winter sometimes and a peak in late winter in another year. However, it is not well known what determines these epidemics timing, and the global climate change is expected to influence the timing of influenza epidemics.MethodsThe weekly influenza surveillance data of 5 European countries (UK, Norway, Germany, Greece, and Italy) from January 2005 to July 2018 were retrieved from WHO FluNET database. UK and Norway are considered the northern part of Europe, otherwise Germany, Greece, and Italy are considered western southern part. The El Niño southern oscillation (ENSO) were retrieved from Korean Meteorological Administration. We used the definition of El Niño as the positive sea surface temperature anomalies (≥0.5 degree in Celcius), while La Niña events are negative anomalies (≤-0.5 degree) of 3 months moving average. The weeks with the highest activities of influenza A and B in each season were identified and coded as 1, 2, 3 if the peak appeared the 1st 2nd and 3rd week from the beginning of the year respectively. The influenza data of 2008/2009 and 2009/2010 were excluded from the analysis to eliminate the bias due to a pandemic influenza outbreak. We compared the means of these peak weeks according to the presence of the anomalies using the general linear model with Scheffe multiple comparison and Wilcoxon signed rank sum test.ResultsFrom January 2005 to July 2018, there were 3 El Niño and 5 La Niña events by the ENSO excluding 2009 El Niño. The influenza A peak activity was observed at 9th week (mean±SD, 8.7±4.8) from the beginning of the year in no anomaly event, but the peak appearance timing was significantly shortened to 6th week (6.2±2.7) and 5th week (5.1±3.9) when El Niño and La Niña events occurred, respectively (both p<0.05). Influenza A made the peak at usually 10th week (9.9±5.0) in northern 2 countries in no anomalies, but at 6th (6.4±3.9) week in any events of an anomaly in the surface sea temperature (p=0.072). In the southern 3 countries, influenza peaks were observed at 8th (7.9±4.8 ) week in usual without anomalies, but at 5th (5.0±3.3) week in El Niño or La Niña events (p=0.049).ConclusionsBoth El Niño and La Niña affect the timing of influenza A peak activity; the ENSO associated the early emergency of peak influenza activities in European countries.ReferencesFisman DN, et al. Impact of El Niño Southern Oscillation on infectious disease hospitalization risk in the United States. Proc Natl Acad Sci U S A. 2016; 113(51):14589-14594.Oluwole OSA. Seasonal Influenza Epidemics and El Niños. Front. Public Health 3:250.Zaraket H, et al. Association of early annual peak influenza activity with El Niño southern oscillation in Japan. Influenza andOther Respiratory Viruses 2008; 2(4): 127–130. %R 10.5210/ojphi.v11i1.9834 %U %U https://doi.org/10.5210/ojphi.v11i1.9834 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9844 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveUsing the epidemic of influenza type A in 2016 in Australia, we demonstrated a simple but statistically sound adaptive method of automatically representing the spatial intensity and evolution of an influenza epidemic that could be applied to a laboratory surveillance count data stream that does not have a denominator.IntroductionSurveillance of influenza epidemics is a priority for risk assessment and pandemic preparedness. Mapping epidemics can be challenging because influenza infections are incompletely ascertained, ascertainment can vary spatially, and often a denominator is not available. Rapid, more refined geographic or spatial intelligence could facilitate better preparedness and response.MethodsWeekly counts of persons with laboratory confirmed influenza type A infections in Australia in 2016 were analysed by 86 sub-state geographical areas. Weekly standardised epidemic intensity was represented by a z-score calculated using the standard deviation of below-median counts in the previous 52 weeks. A geographic information system was used to present the epidemic progression.ResultsThere were 79,628 notifications of influenza A infections included. Of these, 79,218 (99.5%) were allocated to a geographical area. The maps indicated areas of elevated epidemic intensity across Australia by week and area, that were consistent with the observed start, peak and decline of the epidemic when compared with weekly counts aggregated at the state and territory level. An example is shown in Figure 1.ConclusionsThe methods could be automated to rapidly generate spatially varying epidemic intensity maps using a surveillance data stream. This could improve local level epidemic intelligence in a variety of settings and for other diseases. It may also increase our understanding of geographic epidemic dynamics. %R 10.5210/ojphi.v11i1.9844 %U %U https://doi.org/10.5210/ojphi.v11i1.9844 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9873 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveIntensive Care Unit (ICU) data are registered for quality monitoring in the Netherlands with near 100% coverage. They are a ‘big data’ type source that may be useful for infectious disease surveillance. We explored their potential to enhance the surveillance of influenza which is currently based on the milder end of the disease spectrum. We ultimately aim to set up a real-surveillance system of severe acute respiratory infections.IntroductionWhile influenza-like-illness (ILI) surveillance is well-organized at primary care level in Europe, little data is available on more severe cases. With retrospective data from ICU’s we aim to fill this current knowledge gap and to explore its worth for prospective surveillance. Using multiple parameters proposed by the World Health Organization we estimated the burden of severe acute respiratory infections (SARI) to ICU and how this varies between influenza epidemics.MethodsWe analyzed weekly ICU admissions of adults in the Netherlands (2007-2016) from the national intensive care evaluation (NICE) quality registry (100% coverage of adult ICU in 2016; population size 14 million adults. A SARI syndrome was defined as admission diagnosis being any of 6 pneumonia or pulmonary sepsis codes in the Acute Physiology and Chronic Health Evaluation IV (APACHE IV) prognostic model. Influenza epidemic periods were retrieved from primary care sentinel influenza surveillance data. In recent years NICE has explored and promoted increased timeliness and automation of data transfer.ResultsAnnually, 11-14% of medical admissions to adult ICUs were for a SARI (5-25% weekly). Admissions for bacterial pneumonia (59%) and pulmonary sepsis (25%) contributed most to ICU-SARI. Between influenza epidemics, severity indicators varied: ICU-SARI incidence (between 558-2,400 cumulated admissions nation-wide, rate: 0.40-1.71/10,000 inhabitants), average APACHE score (between 71-78), ICU-SARI mortality (between 13-20%), ICU-SARI/ILI ratio (between 8-17 SARI ICU cases per 1,000 expected medically attended influenza-like-illness in primary care), peak incidence (between 101-188 ICU-SARI admissions nationally in the highest week, rate: between 0.07-0.13/10,000 population).ICUs use different types of electronic health records (EHRs). Data submitted to the NICE registry is mainly based on routinely collected data extracted from these EHRs. The timeliness of data submission varies between a few weeks and three months. Together with ICUs, the NICE registry has recently undertaken actions to increase timeliness of ICU data submission.ConclusionsIn ICU data, great variation can be seen between the yearly influenza epidemic periods in terms of different influenza severity parameters. The parameters also complement each other by reflecting different aspects of severity. Prospective syndromic ICU-SARI surveillance, as proposed by the World Health Organization would provide insight into severity of ongoing influenza epidemics which differ from season to season.Currently a subset of hospitals provide data with a 6-week delay. This can be a worthwhile addition to current influenza surveillance, which, while timelier, is based on milder cases seen by general practitioners (primary care). Future increases in data timeliness will remain an aim. %R 10.5210/ojphi.v11i1.9873 %U %U https://doi.org/10.5210/ojphi.v11i1.9873 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9877 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveDuring this session, participants will be able to understand how Harris County Public Health utilized data to make informed decisions on how to combat the influenza season.IntroductionThe 2017 – 2018 influenza season was classified by the Centers for Disease Control and Prevention (CDC) as ‘high severity’ across all age groups. Furthermore, CDC noted that this was the first year to be categorized as such, with the highest peak percentage of influenza-like-illnesses (ILI), since 2009. In Harris County alone, there were 2,665 positive flu tests reported in comparison to the previous season at 1,395 positive tests. In response to the severity of this year’s flu season, Harris County Public Health (HCPH) collaborated across the department to deploy five pop up influenza vaccination events utilizing our Mobile Fleets open to the general public.HCPH epidemiologists are able to collect influenza data from multiple systems and compile it into useful reports/tools. These data include latitudinal and longitudinal data, allowing us to create highly localized maps of where influenza has had impacted communities the hardest. This granular data allowed HCPH to target 5 areas with our Mobile Fleet that had a) high levels of influenza and b) generally limited healthcare/public health infrastructure.Our Mobile Fleet is made up of 8 different Recreational Vehicles that have been retrofitted to offer various public health services including: immunizations, medical visits, dental visits, pet adoptions, mosquito and vector control education, and a fresh food market. The Fleet allows HCPH to offer a full menu of public health services anywhere within the County. While our efforts for this abstract were focused on controlling the influenza outbreak, we leveraged the opportunity to engage with the public on multiple issues such as environmental, veterinary, mosquito control, dental health, and accessible healthy food options.MethodsAs positive flu reports mounted, our epidemiology program provided surveillance data of influenza and ILI in Harris County. Data was obtained through multiple sources including: National Electronic Disease Surveillance System (NEDSS), which includes electronic laboratory reporting; National Respiratory Enteric Virus Surveillance System (NREVSS), which includes all flu tests done in laboratories in Houston; and last, the Flu Portal, which school nurses in Harris County upload school absenteeism rates due to ILI. Once collected and compiled, our Geographic Information System (GIS) team used the data to generate spatial maps of Harris County illustrating the disproportionally high rates. Specifically, our GIS team was able to utilize ArcGIS, and cross layer them with the flu data provided from the epidemiologists. Utilizing these maps, HCPH leadership mobilized the preparedness team to lead a data driven response in five different zip codes throughout the county to hold the influenza vaccination events.ResultsThe Mobile Fleet was operational on five separate dates in five separate zip codes during February and March of 2018. Overall, 477 individuals were provided the influenza vaccine. Of those 477, 304 were 18 years or older, with 173 being under 18 years of age.ConclusionsHaving timely and actionable data is an essential first step to understand and stop an outbreak of any size. However, surveillance data alone won''t prevent an outbreak from spreading. That data must be married to effective public health action. Our Mobile Fleet is able to deliver precision public health services by targeting communities most affected and vulnerable to the spread of disease. As surveillance geospatial data becomes more granular so too must our public health service delivery modes become more precise and targeted. %R 10.5210/ojphi.v11i1.9877 %U %U https://doi.org/10.5210/ojphi.v11i1.9877 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 2 %P e10842 %T Text-Based Illness Monitoring for Detection of Novel Influenza A Virus Infections During an Influenza A (H3N2)v Virus Outbreak in Michigan, 2016: Surveillance and Survey %A Stewart,Rebekah J %A Rossow,John %A Eckel,Seth %A Bidol,Sally %A Ballew,Grant %A Signs,Kimberly %A Conover,Julie Thelen %A Burns,Erin %A Bresee,Joseph S %A Fry,Alicia M %A Olsen,Sonja J %A Biggerstaff,Matthew %+ Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, 1600 Clifton Road NE, MS E-10, Atlanta, GA, 30333, United States, 1 404 718 4580, yxp5@cdc.gov %K influenza %K surveillance %K novel %K agricultural %K fairs %K texting %D 2019 %7 26.04.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Rapid reporting of human infections with novel influenza A viruses accelerates detection of viruses with pandemic potential and implementation of an effective public health response. After detection of human infections with influenza A (H3N2) variant (H3N2v) viruses associated with agricultural fairs during August 2016, the Michigan Department of Health and Human Services worked with the US Centers for Disease Control and Prevention (CDC) to identify infections with variant influenza viruses using a text-based illness monitoring system. Objective: To enhance detection of influenza infections using text-based monitoring and evaluate the feasibility and acceptability of the system for use in future outbreaks of novel influenza viruses. Methods: During an outbreak of H3N2v virus infections among agricultural fair attendees, we deployed a text-illness monitoring (TIM) system to conduct active illness surveillance among households of youth who exhibited swine at fairs. We selected all fairs with suspected H3N2v virus infections. For fairs without suspected infections, we selected only those fairs that met predefined criteria. Eligible respondents were identified and recruited through email outreach and/or on-site meetings at fairs. During the fairs and for 10 days after selected fairs, enrolled households received daily, automated text-messages inquiring about illness; reports of illness were investigated by local health departments. To understand the feasibility and acceptability of the system, we monitored enrollment and trends in participation and distributed a Web-based survey to households of exhibitors from five fairs. Results: Among an estimated 500 households with a member who exhibited swine at one of nine selected fairs, representatives of 87 (17.4%) households were enrolled, representing 392 household members. Among fairs that were ongoing when the TIM system was deployed, the number of respondents peaked at 54 on the third day of the fair and then steadily declined throughout the rest of the monitoring period; 19 out of 87 household representatives (22%) responded through the end of the 10-day monitoring period. We detected 2 H3N2v virus infections using the TIM system, which represents 17% (2/12) of all H3N2v virus infections detected during this outbreak in Michigan. Of the 70 survey respondents, 16 (23%) had participated in the TIM system. A total of 73% (11/15) participated because it was recommended by fair coordinators and 80% (12/15) said they would participate again. Conclusions: Using a text-message system, we monitored for illness among a large number of individuals and households and detected H3N2v virus infections through active surveillance. Text-based illness monitoring systems are useful for detecting novel influenza virus infections when active monitoring is necessary. Participant retention and testing of persons reporting illness are critical elements for system improvement. %M 31025948 %R 10.2196/10842 %U http://publichealth.jmir.org/2019/2/e10842/ %U https://doi.org/10.2196/10842 %U http://www.ncbi.nlm.nih.gov/pubmed/31025948 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 2 %P e12214 %T Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries %A Clemente,Leonardo %A Lu,Fred %A Santillana,Mauricio %+ Computational Health Informatics Program, Boston Children's Hospital, 1 Autumn St, Boston, MA, 02215, United States, 1 617 919 1795, msantill@g.harvard.edu %K google flu trends %K influenza monitoring %K real-time disease surveillance %K digital epidemiology %K influenza, human %K developing countries %K machine learning %D 2019 %7 04.04.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Novel influenza surveillance systems that leverage Internet-based real-time data sources including Internet search frequencies, social-network information, and crowd-sourced flu surveillance tools have shown improved accuracy over the past few years in data-rich countries like the United States. These systems not only track flu activity accurately, but they also report flu estimates a week or more ahead of the publication of reports produced by healthcare-based systems, such as those implemented and managed by the Centers for Disease Control and Prevention. Previous work has shown that the predictive capabilities of novel flu surveillance systems, like Google Flu Trends (GFT), in developing countries in Latin America have not yet delivered acceptable flu estimates. Objective: The aim of this study was to show that recent methodological improvements on the use of Internet search engine information to track diseases can lead to improved retrospective flu estimates in multiple countries in Latin America. Methods: A machine learning-based methodology that uses flu-related Internet search activity and historical information to monitor flu activity, named ARGO (AutoRegression with Google search), was extended to generate flu predictions for 8 Latin American countries (Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay) for the time period: January 2012 to December of 2016. These retrospective (out-of-sample) Influenza activity predictions were compared with historically observed flu suspected cases in each country, as reported by Flunet, an influenza surveillance database maintained by the World Health Organization. For a baseline comparison, retrospective (out-of-sample) flu estimates were produced for the same time period using autoregressive models that only leverage historical flu activity information. Results: Our results show that ARGO-like models’ predictive power outperform autoregressive models in 6 out of 8 countries in the 2012-2016 time period. Moreover, ARGO significantly improves on historical flu estimates produced by the now discontinued GFT for the time period of 2012-2015, where GFT information is publicly available. Conclusions: We demonstrate here that a self-correcting machine learning method, leveraging Internet-based disease-related search activity and historical flu trends, has the potential to produce reliable and timely flu estimates in multiple Latin American countries. This methodology may prove helpful to local public health officials who design and implement interventions aimed at mitigating the effects of influenza outbreaks. Our methodology generally outperforms both the now-discontinued tool GFT, and autoregressive methodologies that exploit only historical flu activity to produce future disease estimates. %M 30946017 %R 10.2196/12214 %U https://publichealth.jmir.org/2019/2/e12214/ %U https://doi.org/10.2196/12214 %U http://www.ncbi.nlm.nih.gov/pubmed/30946017 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 3 %P e13699 %T Author Contribution Correction: An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation %A Yang,Cheng-Yi %A Chen,Ray-Jade %A Chou,Wan-Lin %A Lee,Yuarn-Jang %A Lo,Yu-Sheng %+ Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan, 886 939588576, Loyusen@tmu.edu.tw %D 2019 %7 12.03.2019 %9 Corrigenda and Addenda %J J Med Internet Res %G English %X %M 30860974 %R 10.2196/13699 %U http://www.jmir.org/2019/3/e13699/ %U https://doi.org/10.2196/13699 %U http://www.ncbi.nlm.nih.gov/pubmed/30860974 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 2 %P e12341 %T An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation %A Yang,Cheng-Yi %A Chen,Ray-Jade %A Chou,Wan-Lin %A Lee,Yuarn-Jang %A Lo,Yu-Sheng %+ Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan, 886 939588576, Loyusen@tmu.edu.tw %K influenza %K epidemics %K influenza surveillance %K electronic disease surveillance %K electronic medical records %K electronic health records %K public health %D 2019 %7 01.02.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Influenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement. Objective: We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems. Methods: First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics. Results: Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables—TMUHcS-RITP and TMUHcS-IMU—showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports. Conclusions: Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations. %M 30707099 %R 10.2196/12341 %U http://www.jmir.org/2019/2/e12341/ %U https://doi.org/10.2196/12341 %U http://www.ncbi.nlm.nih.gov/pubmed/30707099 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 1 %P e11333 %T Estimating Vaccine Effectiveness Against Hospitalized Influenza During Pregnancy: Multicountry Protocol for a Retrospective Cohort Study %A Naleway,Allison L %A Ball,Sarah %A Kwong,Jeffrey C %A Wyant,Brandy E %A Katz,Mark A %A Regan,Annette K %A Russell,Margaret L %A Klein,Nicola P %A Chung,Hannah %A Simmonds,Kimberley A %A Azziz-Baumgartner,Eduardo %A Feldman,Becca S %A Levy,Avram %A Fell,Deshayne B %A Drews,Steven J %A Garg,Shikha %A Effler,Paul %A Barda,Noam %A Irving,Stephanie A %A Shifflett,Patricia %A Jackson,Michael L %A Thompson,Mark G %+ Kaiser Permanente Northwest, Center for Health Research, 3800 North Interstate Avenue, Portland, OR, 97005, United States, 1 503 335 6352, allison.naleway@kpchr.org %K influenza %K pregnancy %K hospitalization %K epidemiology %K vaccines %D 2019 %7 21.01.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: Although pregnant women are believed to have elevated risks of severe influenza infection and are targeted for influenza vaccination, no study to date has examined influenza vaccine effectiveness (IVE) against laboratory-confirmed influenza-associated hospitalizations during pregnancy, primarily because this outcome poses many methodological challenges. Objective: The Pregnancy Influenza Vaccine Effectiveness Network (PREVENT) was formed in 2016 as an international collaboration with the Centers for Disease Control and Prevention; Abt Associates; and study sites in Australia, Canada, Israel, and the United States. The primary goal of this collaboration is to estimate IVE in preventing acute respiratory or febrile illness (ARFI) hospitalizations associated with laboratory-confirmed influenza virus infection during pregnancy. Secondary aims include (1) describing the incidence, clinical course, and severity of influenza-associated ARFI hospitalization during pregnancy; (2) comparing the characteristics of ARFI-hospitalized pregnant women who were tested for influenza with those who were not tested; (3) describing influenza vaccination coverage in pregnant women; and (4) comparing birth outcomes among women with laboratory-confirmed influenza-associated hospitalization versus other noninfluenza ARFI hospitalizations. Methods: For an initial assessment of IVE, sites identified a retrospective cohort of pregnant women aged from 18 to 50 years whose pregnancies overlapped with local influenza seasons from 2010 to 2016. Pregnancies were defined as those that ended in a live birth or stillbirth of at least 20 weeks gestation. The analytic sample for the primary IVE analysis was restricted to pregnant women who were hospitalized for ARFI during site-specific influenza seasons and clinically tested for influenza virus infection using real-time reverse transcription polymerase chain reaction. Results: We identified approximately 2 million women whose pregnancies overlapped with influenza seasons; 550,344 had at least one hospitalization during this time. After restricting to women who were hospitalized for ARFI and tested for influenza, the IVE analytic sample included 1005 women. Conclusions: In addition to addressing the primary question about the effectiveness of influenza vaccination, PREVENT data will address other important knowledge gaps including understanding the incidence, clinical course, and severity of influenza-related hospitalizations during pregnancy. The data infrastructure and international partnerships created for these analyses may be useful and informative for future influenza studies. International Registered Report Identifier (IRRID): DERR1-10.2196/11333 %M 30664495 %R 10.2196/11333 %U http://www.researchprotocols.org/2019/1/e11333/ %U https://doi.org/10.2196/11333 %U http://www.ncbi.nlm.nih.gov/pubmed/30664495 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 4 %P e11361 %T Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study %A Poirier,Canelle %A Lavenu,Audrey %A Bertaud,Valérie %A Campillo-Gimenez,Boris %A Chazard,Emmanuel %A Cuggia,Marc %A Bouzillé,Guillaume %+ Laboratoire Traitement du Signal et de l'Image, Université de Rennes 1, 2 rue Henri Le Guilloux, Rennes, 35033, France, 33 667857225, canelle.poirier@outlook.fr %K electronic health records %K big data %K infodemiology %K infoveillance %K influenza %K machine learning %K Sentinelles network %D 2018 %7 21.12.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with 1- to 3-week delay. Accurate real-time monitoring systems for influenza outbreaks could be useful for making public health decisions. Several studies have investigated the possibility of using internet users’ activity data and different statistical models to predict influenza epidemics in near real time. However, very few studies have investigated hospital big data. Objective: Here, we compared internet and electronic health records (EHRs) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods: We used Google data for internet data and the clinical data warehouse eHOP, which included all EHRs from Rennes University Hospital (France), for hospital data. We compared 3 statistical models—random forest, elastic net, and support vector machine (SVM). Results: For national ILI incidence rate, the best correlation was 0.98 and the mean squared error (MSE) was 866 obtained with hospital data and the SVM model. For the Brittany region, the best correlation was 0.923 and MSE was 2364 obtained with hospital data and the SVM model. Conclusions: We found that EHR data together with historical epidemiological information (French Sentinelles network) allowed for accurately predicting ILI incidence rates for the entire France as well as for the Brittany region and outperformed the internet data whatever was the statistical model used. Moreover, the performance of the two statistical models, elastic net and SVM, was comparable. %M 30578212 %R 10.2196/11361 %U http://publichealth.jmir.org/2018/4/e11361/ %U https://doi.org/10.2196/11361 %U http://www.ncbi.nlm.nih.gov/pubmed/30578212 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 3 %P e65 %T Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study %A Wakamiya,Shoko %A Kawai,Yukiko %A Aramaki,Eiji %+ Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, 630 0192, Japan, 81 743 72 6053, socialcomputing-office@is.naist.jp %K influenza surveillance %K location mention %K Twitter %K social network %K spatial analysis %K internet %K microblog %K infodemiology %K infoveillance %D 2018 %7 25.9.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor–based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results. Objective: This study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts. Methods: We presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information. Results: The estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points). Conclusions: The proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification. %M 30274968 %R 10.2196/publichealth.8627 %U http://publichealth.jmir.org/2018/3/e65/ %U https://doi.org/10.2196/publichealth.8627 %U http://www.ncbi.nlm.nih.gov/pubmed/30274968 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 6 %P e136 %T A New Influenza-Tracking Smartphone App (Flu-Report) Based on a Self-Administered Questionnaire: Cross-Sectional Study %A Fujibayashi,Kazutoshi %A Takahashi,Hiromizu %A Tanei,Mika %A Uehara,Yuki %A Yokokawa,Hirohide %A Naito,Toshio %+ Department of General Medicine, School of Medicine, Juntendo University, 3-1-3, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan, 81 3 5802 1190, kfujiba@juntendo.ac.jp %K influenza %K epidemiology %K pandemics %K internet %K participatory surveillance %K participatory epidemiology %D 2018 %7 06.06.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Influenza infections can spread rapidly, and influenza outbreaks are a major public health concern worldwide. Early detection of signs of an influenza pandemic is important to prevent global outbreaks. Development of information and communications technologies for influenza surveillance, including participatory surveillance systems involving lay users, has recently increased. Many of these systems can estimate influenza activity faster than the conventional influenza surveillance systems. Unfortunately, few of these influenza-tracking systems are available in Japan. Objective: This study aimed to evaluate the flu-tracking ability of Flu-Report, a new influenza-tracking mobile phone app that uses a self-administered questionnaire for the early detection of influenza activity. Methods: Flu-Report was used to collect influenza-related information (ie, dates on which influenza infections were diagnosed) from November 2016 to March 2017. Participants were adult volunteers from throughout Japan, who also provided information about their cohabiting family members. The utility of Flu-Report was evaluated by comparison with the conventional influenza surveillance information and basic information from an existing large-scale influenza-tracking system (an automatic surveillance system based on electronic records of prescription drug purchases). Results: Information was obtained through Flu-Report for approximately 10,094 volunteers. In total, 2134 participants were aged <20 years, 6958 were aged 20-59 years, and 1002 were aged ≥60 years. Between November 2016 and March 2017, 347 participants reported they had influenza or an influenza-like illness in the 2016 season. Flu-Report-derived influenza infection time series data displayed a good correlation with basic information obtained from the existing influenza surveillance system (rho, ρ=.65, P=.001). However, the influenza morbidity ratio for our participants was approximately 25% of the mean influenza morbidity ratio for the Japanese population. The Flu-Report influenza morbidity ratio was 5.06% (108/2134) among those aged <20 years, 3.16% (220/6958) among those aged 20-59 years, and 0.59% (6/1002) among those aged ≥60 years. In contrast, influenza morbidity ratios for Japanese individuals aged <20 years, 20-59 years, and ≥60 years were recently estimated at 31.97% to 37.90%, 8.16% to 9.07%, and 2.71% to 4.39%, respectively. Conclusions: Flu-Report supports easy access to near real-time information about influenza activity via the accumulation of self-administered questionnaires. However, Flu-Report users may be influenced by selection bias, which is a common issue associated with surveillance using information and communications technologies. Despite this, Flu-Report has the potential to provide basic data that could help detect influenza outbreaks. %R 10.2196/mhealth.9834 %U http://mhealth.jmir.org/2018/6/e136/ %U https://doi.org/10.2196/mhealth.9834 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8757 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveThe Oregon Child Absenteeism due to Respiratory Disease Study (ORCHARDS) was implemented to assess the relationships between cause-specific absenteeism within a school district and medically attended influenza visits within the same community.IntroductionTransmission and amplification of influenza within schools has been purported as a driving mechanism for subsequent outbreaks in surrounding communities. However, the number of studies assessing the utility of monitoring school absenteeism as an indicator of influenza in the community is limited. ORCHARDS was initiated to evaluate the relationships between all-cause (a-Tot), illness-related (a-I), and influenza-like illness (ILI)-related absenteeism (a-ILI) within a school district and medically attended influenza A or B visits within the same community.MethodsORCHARDS was based at the Oregon School District (OSD), which enrolls 3,640 students at six schools in south-central Wisconsin. Parents reported influenza-like symptoms on an existing phone-based absenteeism reporting system. Attendance staff identified ILI using a simple case definition. Absenteeism was logged into the OSD’s existing electronic information system (Infinite Campus), and an automated process extracted counts of a-Tot, a-I, and a-ILI each school day from 9/02/14 through 6/08/17.Parents of students with acute respiratory infections (ARI) were invited to contact study staff who assessed the students’ eligibility for the study based on presence of ILI symptoms. From 1/05/15 through 6/08/17, data and nasal swabs were collected from eligible OSD students whose parents volunteered to have a study home visit within 7 days of ILI onset. Specimens were tested for influenza A and B at the Wisconsin State Laboratory of Hygiene using the CDC Human Influenza Virus Real-time RT-PCR Diagnostic Panel.For community influenza, we used data from the Wisconsin Influenza Incidence Surveillance Project (WIISP) that monitors medically attended influenza using RT-PCR at five primary care clinics surrounding the OSD.Data analysis: Over-dispersed Poisson generalized additive log-linear regression models were fit to the daily number of medically attended influenza cases and daily absenteeism counts from three sources (a-Tot, a-I, and a-ILI) with year and season (calendar day within year) as smooth functions (thin plate regression splines). Two subgroups of a-ILI representing kindergarten through 4th grade (K-4) and 5th-12th grade (5-12) were also evaluated.ResultsDuring the study period, 168,859 total absentee days (8.57% of student days), 36,104 illness days (1.83%), and 4,232 ILI days (0.21%) were recorded. Home visits were completed on 700 children [mean age = 10.0 ± 3.5 (sd) years]. Influenza RT-PCR results were available for 695 (99.3%) children: influenza A was identified in 54 (13.3%) and influenza B in 51 (12.6%) specimens. There were one large and early outbreak of influenza A (H3N2) followed by B in 2014/15, an extremely late combined outbreak of influenza A (H1N1) and B in 2015/16, and a combined outbreak of influenza A/(H3N2) and B in 2016/17. PCR detection of influenza A or B, as compared to no influenza, was strongly associated with a child with a-ILI-positive status (OR=4.74; 95% CI: 2.78-8.18; P<0.001).Nearly 2,400 medically attended ARI visits were reported during the study period. Of these, 514 patients were positive for influenza (21.5%): 371 (15.5%) influenza A and 143 (6.0%) influenza B. The temporal patterns of medically attended influenza were very similar to influenza cases in OSD students.Comparisons of the regression models demonstrated the highest correlation between absenteeism and medically attended influenza for 5th-12th grade students absent with ILI with a -1 day time lag and for all students with a-ILI with a -1 day lag (Table); a-I also had moderate correlation with a -15 day lag period.ConclusionsCause-specific absenteeism measures (a-I and a-ILI) are moderately correlated with medically attended influenza in the community and are better predictors than all-cause absenteeism. In addition, a-I preceded community influenza cases by 15 days. The monitoring system was easily implemented: a-I surveillance was fully automated and a-ILI required only minor review by attendance staff. The resulting correlations were likely lowered by the presence of other viruses that resulted in a-ILI (e.g., adenovirus) and by breaks in the school year during which absenteeism data did not accrue.Automated systems that report cause-specific absenteeism data may provide a reliable method for the early identification of influenza outbreaks in communities. From a preparedness perspective, 15-day advance warning is significant. The addition of a laboratory component could increase usefulness of the cause-specific student absenteeism monitoring as an early-warning system during influenza pandemics. %R 10.5210/ojphi.v10i1.8757 %U %U https://doi.org/10.5210/ojphi.v10i1.8757 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8773 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo determine if social media data can be used as a surveillance tool for influenza at the local level.IntroductionThe use of social media as a syndromic sentinel for diseases is an emerging field of growing relevance as the public begins to share more online, particularly in the area of influenza. Several applications have been developed to predict or monitor influenza activity using publicly posted or self-reported online data; however, few have prioritized accuracy at the local level. In 2016, the Cook County Department of Public Health (CCDPH) collected localized Twitter information to evaluate its utility as a potential influenza sentinel data source. Tweets from MMWR week 40 through MMWR week 20 indicating influenza-like illness (ILI) in our jurisdiction were collected and analyzed for correlation with traditional surveillance indicators. Social media has the potential to join other sentinels, such as emergency room and outpatient provider data, to create a more accurate and complete picture of influenza in Cook County.MethodsWe developed a JAVA program which included a customized geofence around suburban Cook County to collect tweets from Twitter’s STREAM application programming interface (API) (available at https://github.com/FoodSafeCookCo/TwitterStream-Program). The JAVA program looked for tweets within the geofence or for tweets with a profile location naming a suburban Cook County municipality and copied them to a text file if the tweet contained “flu” or “influenza”. Captured data included the user’s Twitter handle, Tweet text, Tweet time and date, x and y coordinates (if available), and profile location. Tweets were then manually reviewed to determine if they met the following criteria: 1) language indicated the user was recently ill with influenza; 2) user appeared to reside in CCDPH jurisdiction. Tweets meeting these criteria were included in the analysis. Tweets were aggregated by MMWR week and analyzed for correlation, using Pearson methods (data were normal), with two traditional surveillance sources: 1) the percent of visits to all suburban Cook County emergency departments for ILI as reported to the Cook County Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE), and 2) the percent of laboratory specimens testing positive for influenza at seven local sentinel laboratories. Analysis was performed in Excel 2013 and SAS 9.4.ResultsFrom MMWR week 40-20, 113 tweets indicating influenza-like illness were collected within Cook County’s jurisdiction. Due to technical issues with the program, data were not collected for weeks 52, 2, and 17-19. Correlations were compared for the percent of laboratory specimens testing positive for influenza (LSL) and percent of visits to emergency departments for ILI (EDILI) to the total number of tweets per MMWR week. A strong correlation exists between LSL and EDILI r=0.92 (p-value<0.0001) indicating the traditional sentinels have a strong positive relationship. The correlation between number of tweets and LSL was 0.46 (p-value =0.0138), indicating a moderate positive relationship. Correlation between number of tweets and EDILI was similarly moderate, r=0.52 (p-value=0.0049). Correlations to EDILI stratified by age (0-4, 5-17, 18-64, 65+) also showed a moderate positive relationship (range 0.50 to 0.55, all p-values < 0.01). Twitter use peaked one week before the recorded peak of other surveillance indicators. When Twitter counts were shifted one week to align the peak in tweets with the peak of the influenza season, the correlations were 0.54 for LSL and 0.61 for EDILI (p-value=0.0034 and 0.0007, respectively).ConclusionsOverall, the tweets collected had a moderately positive relationship with the severity of influenza activity in Cook County. When the data were transitioned to match peaks, there was an increase in the correlations’ strength for both LSL and EDILI. This data indicates that publicly shared social media data may be an underutilized source of syndromic data at the local level, potentially capable of predicting seasonal influenza peaks before traditional data sources. Other jurisdictions may consider using the open source program created by CCDPH to determine the relationship of influenza related social media to their own local influenza surveillance data. For the 2017-2018 influenza season, we established a redundant system for tweet collection in case one of the systems goes down. Exploring machine learning (in place of manual review) to detect tweets indicating illness is also a promising avenue to simplify data collection and cleaning. Data will be collected using the same system for the 2017-2018 influenza season and correlations re-evaluated with more complete data. %R 10.5210/ojphi.v10i1.8773 %U %U https://doi.org/10.5210/ojphi.v10i1.8773 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8921 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X Objective: We describe the Bangkok Dusit Medical Services Surveillance System (BDMS-SS) and use of surveillance efforts for influenza as an example of surveillance capability in near real-time among a network of 20 hospitals in the Bangkok Dusit Medical Services group (BDMS).Introduction: Influenza is one of the significant causes of morbidity and mortality globally. Previous studies have demonstrated the benefit of laboratory surveillance and its capability to accurately detect influenza outbreaks earlier than syndromic surveillance.1-3 Current laboratory surveillance has an approximately 4-week lag due to laboratory test turn-around time, data collection and data analysis. As part of strengthening influenza virus surveillance in response to the 2009 influenza A (H1N1) pandemic, the real-time laboratory-based influenza surveillance system, the Bangkok Dusit Medical Services Surveillance System (BDMS-SS), was developed in 2010 by the Bangkok Health Research Center (BHRC). The primary objective of the BDMS-SS is to alert relevant stakeholders on the incidence trends of the influenza virus. Type-specific results along with patient demographic and geographic information were available to physicians and uploaded for public health awareness within 24 hours after patient nasopharyngeal swab was collected. This system advances early warning and supports better decision making during infectious disease events.2 The BDMS-SS operates all year round collecting results of all routinely tested respiratory clinical samples from participating hospitals from the largest group of private hospitals in Thailand.Methods: The BDMS has a comprehensive network of laboratory, epidemiologic, and early warning surveillance systems which represents the largest body of information from private hospitals across Thailand. Hospitals and clinical laboratories have deployed automatic reporting mechanisms since 2010 and have effectively improved timeliness of laboratory data reporting. In April 2017, the capacity of near real-time influenza surveillance in BDMS was found to have a demonstrated and sustainable capability.Results: From October 2010 to April 2017, a total of 482,789 subjects were tested and 86,110 (17.8%) cases of influenza were identified. Of those who tested positive for influenza they were aged <2 years old (4.6%), 2-4 year old (10.9%), 5-14 years old (29.8%), 15-49 years old (41.9%), 50-64 years old (8.3%) and >65 years old (3.7%). Approximately 50% of subjects were male and female. Of these, 40,552 (47.0%) were influenza type B, 31,412 (36.4%) were influenza A unspecified subtype, 6,181 (7.2%) were influenza A H1N1, 4,001 (4.6%) were influenza A H3N2, 3,835 (4.4%) were influenza A seasonal and 196 (0.4%) were respiratory syncytial virus (RSV).The number of influenza-positive specimens reported by the real-time influenza surveillance system were from week 40, 2015 to week 39, 2016. A total of 117,867 subjects were tested and 17,572 (14.91%) cases tested positive for the influenza virus (Figure 1). Based on the long-term monitoring of collected information, this system can delineate the epidemiologic pattern of circulating viruses in near real-time manner, which clearly shows annual peaks in winter dominated by influenza subtype B in 2015-1016 season. This surveillance system helps to provide near real-time reporting, enabling rapid implementation of control measures for influenza outbreaks.Conclusions: This surveillance system was the first real-time, daily reporting surveillance system to report on the largest data base of private hospitals in Thailand and provides timely reports and feedback to all stakeholders. It provides an important supplement to the routine influenza surveillance system in Thailand. This illustrates a high level of awareness and willingness among the BDMS hospital network to report emerging infectious diseases, and highlights the robust and sensitive nature of BDMS’s surveillance system. This system demonstrates the flexibility of the surveillance systems in BDMS to evaluate to emerging infectious disease and major communicable diseases. Through participation in the Thailand influenza surveillance network, BDMS can more actively collaborate with national counterparts and use its expertise to strengthen global and regional surveillance capacity in Southeast Asia, in order to secure advances for a world safe and secure from infectious disease. Furthermore, this system can be quickly adapted and used to monitor future influenzas pandemics and other major outbreaks of respiratory infectious disease, including novel pathogens. %R 10.5210/ojphi.v10i1.8921 %U %U https://doi.org/10.5210/ojphi.v10i1.8921 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8925 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveThe goal of this study was to identify gaps in the severe acute respiratory infection sentinel surveillance system at Surb Astvatsamayr Medical Center.IntroductionInfluenza is a priority in Armenia. There are two influenza surveillance systems in Armenia: population and sentinel. The medical center (MC) has been included in sentinel surveillance since 2012. In 2015 a study was undertaken to identify gaps in severe acute respiratory infection (SARI) sentinel surveillance system in Surb Astvatsamayr MC.MethodsMedical records and reporting forms of SARI cases were generated for individuals meeting the case definition and analyzed for age groups, risk factors, sentinel surveillance detection methods, laboratory conformation, number of days hospitalized and reporting.ResultsIn 2014, 3016 patients were admitted in the hospital with ARI, of whom 2982 were younger than 18 years. During the 2014-2015 influenza season (week 40, 2014-week 20, 2015), 77 swabs have been taken in total, of which five were influenza positive (4 B and 1 AH1N1). Also in the 2013-2014 influenza season, five samples tested positive (all influenza A). Sixty-one (48%) patients with respiratory disease met the WHO SARI case definition (2011), 84 (66%) of all reviewed patients would have met the SARI case definition. The numbers for the ICU (25 records reviewed) do not reflect the actual percentage of patients admitted with respiratory symptoms. The 33 additional cases taken from the sampling logbook were mainly hospitalized in the ICU. Influenza tests were performed on 34 patients (mainly ICU), five were positive for influenza (four B--all adults—and one AH1N1), and four tested positives for other respiratory pathogens (two RSV, one RV, one BV). All influenza positives had fever or a history of fever and 80% met the WHO SARI case definition (2011). Non-sampled cases generally have fewer reported symptoms, but still 44% of cases fits the WHO SARI case definition (2011).ConclusionsThe percentages of patients meeting the WHO SARI 2011 case definition and the WHO SARI 2014 definition shows that mainly caused by the absence of shortness of breath in the SARI 2014 definition 52% (2011) vs 66% (2014) in Surb Asvatsamayr. A large number of children from Neonatal and Children’s departments fulfil the SARI case definition and could potentially be swabbed in addition to ICU patients. There are gaps in WHO SARI case definitions. The sentinel surveillance system should be improved. %R 10.5210/ojphi.v10i1.8925 %U %U https://doi.org/10.5210/ojphi.v10i1.8925 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8939 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo establish morbidity patterns of influenza A/H1N1 in Swaziland from 10th July to 15th August 2017.IntroductionInfluenza infection is caused by the influenza virus, a single-stranded RNA virus belonging to the Orthomyxoviridae family. Influenza viruses are classified as types A, B and C. Influenza A and B viruses can cause epidemic disease in humans and type C viruses usually cause a mild, cold-like illness. The influenza virus spreads rapidly around the world in seasonal epidemics, resulting in significant morbidity and mortality. On the 10th of July 2017, a case of confirmed Influenza A/H1N1 was reported through the immediate disease notification system from a private hospital in the Hhohho region. A 49 year old female was diagnosed of Influenza A/H1N1 after presenting with flu-like symptoms. Contacts of the index case were followed and further positive cases were identified.MethodsUpon identification of the index case, the rapid response teams conducted further investigations. Two nasal swaps from each sample were taken and sent to a private laboratory in South Africa for the detection of the virus RNA using RT-PCR to assess for the presence Influenza A, B and Influenza A/H1N1. Further laboratory results were sourced from a private laboratory to monitor trends of influenza. Data was captured and analyzed in STATA version 12 from STATA cooperation. Descriptive statistics were carried out using means and standard deviations. The Pearson Chi square test and student t test were used to test for any possible association between influenza A/H1N1 and the explanatory variables (age and sex).ResultsSurveillance data captured between 10th July 2017 and 15th August 2017 indicated that a total of 87 patients had their samples taken for laboratory confirmation. There were 45 females and 42 males and the mean age was 27 years (SD= 17). At least 25 of the 87 patients tested positive for influenza A while only 1 tested positive for influenza B. The prevalence of influenza A/H1N1 was 16%. The prevalence of influenza A/H1N1 among males was 19% compared to 13% in females; however the difference was not statistically significant (p=0.469). There was no association noted between age and influenza A/H1N1 (p=427). Upon further sub-typing results indicated that the circulating strain was influenza A/H1N1 pdm 09 strain which is a seasonal influenza. The epidemic task forces held weekly and ad-hoc meetings to provide feedback to principals and health messaging to the general population to allay anxiety.ConclusionsThough WHO has classified the influenza A/H1N1 strain pdm 0029 as a seasonal influenza, surveillance remains important for early detection and management. There is therefore an urgent need to set up sentinel sites to monitor and understand the circulating influenza strains. Health promotion remains crucial to dispel anxiety as the general public still link any influenza to the 2009 pandemic influenza. Finally the Ministry of Health should consider introducing influenza vaccines into the routine immunization schedule especially for children.References1. Global Epidemiological Surveillance Standards for Influenza. 2014 [cited 2015 15 April]; Available from: http://www.who.int/influenza/resources/documents/influenza_surveillance_manual/en/.2. Human cases of influenza at the human-animal interface, 2013. Wkly Epidemiol Rec, 2014.89(28): p. 309-20.3. WHO Global Influenza Surveillance Network. Manual for the laboratory diagnosis and virological surveillance of influenza. 2011 [cited 2015 April27]; Available from: http://www.who.int/influenza/gisrs_laboratory/manual_diagnosis_surveillance_influenza/en/. %R 10.5210/ojphi.v10i1.8939 %U %U https://doi.org/10.5210/ojphi.v10i1.8939 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8948 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveDemonstrate performance of the Virena Global Wireless Surveillance System, an automated platform utilized in conjunction with the Sofia FIA Analyzer, for near real-time transmission of infectious disease test results to public health and other healthcare organizations.IntroductionPublic health agencies worldwide all enjoy the same mission—providing healthcare warnings, guidance, and support to the public and healthcare professionals they represent. A critical element in achieving this mission is accessing timely and comprehensive surveillance information about disease in their regions of responsibility. Advances in diagnostic technologies for infectious disease and in the wireless conveyance of information hold great promise for advancing the quality of surveillance information and in facilitating the delivery of timely, accurate, and impactful public health information. Quidel Corporation has developed a cloud–based, wireless communications system that is fully integrated with its Sofia fluorescence immunoassay (FIA) platform for rapid, point-of-care diagnosis of infectious disease. The system, called the Virena Global Wireless Surveillance System (hereinafter, Virena) provides test results to public health organizations and other appropriate entities in near-real time. Currently, more than 4,000 Sofia instruments are transmitting results automatically by Virena. This presentation describes the use of Virena in surveilling influenza in the U.S. in the 2016-2017 influenza season, when over 700,000 influenza-like-illness (ILI) patient results were transmitted. The methods employed, results, and the promise of this innovative system will be discussed.MethodsThe Sofia Fluorescent Immunoassay Analyzer (FIA) is a small FDA-cleared, CLIA-waived bench top device that uses immunofluorescence-based, lateral-flow technology for rapid analyte detection within 15 minutes for influenza. With Sofia2, a recent upgrade, positive influenza test results can be obtained in as few as 3 minutes, depending on virus levels. The results are encrypted, and automatically transmitted by Virena--often within 5 seconds--to a dual cloud system for further encryption and formatting. The test results (also including location, date, and patient age) are subsequently pushed to participating public health and healthcare organizations for daily collection and analysis. Healthcare providers utilizing the Virena system may also access their own data and facility-de-identified regional and national data, using a password-enabled internet application called MyVirena.com.ResultsBetween August 1, 2016 and October 6, 2017, 706,654 ILI patient results were transmitted by Virena from over 3,000 clinical sites in the United States. The influenza positivity rate (influenza A and B combined) peaked on February 9th at 33% and maintained this level for two weeks (Figure 1). During this period, as many as 7,048 results were transmitted by Virena per day. Influenza A activity peaked on the same day at 26%, and influenza B peaked at 18% nearly 6.5 weeks later. In the six months from December 15th to June 15th, the influenza positivity rate for patients with ILI was 10% or greater in the United States. Data analysis for individual states revealed significant differences in time of onset of influenza and in the peak positivity rates. For example, the state of Arizona experienced peak positivity rates for influenza activity (42%) as late as mid-May, driven largely by influenza B. In California, influenza A peaked at 43% on January 16th and maintained a positivity rate greater than 15% for nearly three months, while influenza B averaged below 4% for the entire period. Age-specific analysis showed that children in the 2 to 18 year old group had the highest positivity rate (44%, n=251,756) and the longest incidence period. Virena data demonstrated similar influenza activity trends on national and regional levels as that depicted by the clinical laboratory and NREVSS data collected by the CDC; however, the Virena data were collected and reported sooner (Figure 2).ConclusionsThe Virena system represents a major stride for disease surveillance, providing clinical testing data in near real-time time, with local, national, and global scope. This first substantial evaluation of the Virena system, with over 4,000 transmitting Sofia Analyzers, demonstrates capabilities for near real-time assessment of disease onset, regionally varying positivity rates, durations of outbreaks, differential assessment of influenza A and B prevalence, and dynamic mapping throughout the year. With expanding regional and metropolitan coverage, the Virena system holds promise as both a powerful surveillance tool, and as a valuable resource for healthcare quality initiatives, epidemiological research, and the development of new mathematical models for influenza forecasting . %R 10.5210/ojphi.v10i1.8948 %U %U https://doi.org/10.5210/ojphi.v10i1.8948 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8990 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X Introduction: Respiratory pathogens continue to present an ever increasing threat to public health (1,2). Influenza, Respiratory syncytial virus, human metapneumovirus and other respiratory viruses are major etiological agents for influenza like illnesses (ILI) (3-5). Establishment of viral causes of ILI is critical for prevention and mitigation strategies to disease threats. Makerere University Walter Reed Project (MUWRP) together with the Ugandan Ministry of Health and partners undertook surveillance to determine viral causes of influenza-like illness in Uganda.Methods: From 2008, MUWRP established hospital-based sentinel sites for surveillance activities. A total of five hospital-based sites were established, where patients aged 6 months or older presenting with ILI were enrolled. Consents were obtained as required, and a throat and/ or nasopharyngeal swab collected. Samples were screened by PCR for viral causes.Results: From October 2008 to March 2017 a total of 9,472 participants were enrolled in the study from five hospital-based surveillance sentinel sites. Majority of participants were children under 5 years n= 8,169 (86.2%). 615 (6.5%) samples tested positive for influenza A, while 385 (4.1%) tested positive for influenza B viruses and 10 (0.1%) were co-infections between influenza A and B. Of the 2,062 influenza negative samples, results indicated positivity for the following organisms; adenoviruses (9.4%), respiratory syncytial B (7.3%), parainfluenza-3 (4.5%), parainfluenza-1 (4.3%), respiratory syncytial A (3.5%), human bocavirus (1.7%), human metapneumovirus (1.7%), human coronavirus (1.5%), parainfluenza-4 (1.4%) and parainfluenza-2 (0.9%) by PCR.Conclusions: Influenza viruses account for about 11% of the causes of influenza like illness, with influenza A being the dominant type. Among the other viral causes of ILI, adenoviruses were the most dominant. Detection of other viral causes of ILI is an indication of the public health threats posed by respiratory pathogens. %R 10.5210/ojphi.v10i1.8990 %U %U https://doi.org/10.5210/ojphi.v10i1.8990 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e40 %T Estimating the Risk of Influenza-Like Illness Transmission Through Social Contacts: Web-Based Participatory Cohort Study %A Chan,Ta-Chien %A Hu,Tsuey-Hwa %A Hwang,Jing-Shiang %+ Institute of Statistical Science, Academia Sinica, 128 Academia Road, Section 2, Taipei City, 115, Taiwan, 886 2 2783 5611, hwang@sinica.edu.tw %K flu transmission %K social networks %K contact diary %K diet %K exercise %K sleep quality %D 2018 %7 09.04.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Epidemiological studies on influenza have focused mostly on enhancing vaccination coverage or promoting personal hygiene behavior. Few studies have investigated potential effects of personal health behaviors and social contacts on the risk of getting influenza-like illness (ILI). Objective: Taking advantage of an online participatory cohort, this study aimed to estimate the increased risk of getting ILI after contact with infected persons and examine how personal health behaviors, weather, and air pollution affect the probability of getting ILI. Methods: A Web-based platform was designed for participants to record daily health behaviors and social contacts during the influenza season of October 1, 2015 to March 31, 2016, in Taiwan. Data on sleep, diet, physical activity, self-reported ILI, and contact with infected persons were retrieved from the diaries. Measurements of weather and air pollutants were used for calculating environmental exposure levels for the participants. We fitted a mixed-effects logistic regression model to the daily measurements of the diary keepers to estimate the effects of these variables on the risk of getting ILI. Results: During the influenza season, 160 participants provided 14,317 health diaries and recorded 124,222 face-to-face contacts. The model estimated odds ratio of getting ILI was 1.87 (95% CI 1.40-2.50) when a person had contact with others having ILI in the previous 3 days. Longer duration of physical exercise and eating more fruits, beans, and dairy products were associated with lower risk of getting ILI. However, staying up late was linked to an elevated risk of getting ILI. Higher variation of ambient temperature and worse air quality were associated with increased risk of developing ILI. Conclusions: Developing a healthier lifestyle, avoiding contact with persons having ILI symptoms, and staying alert with respect to temperature changes and air quality can reduce the risk of getting ILI. %M 29631987 %R 10.2196/publichealth.8874 %U http://publichealth.jmir.org/2018/2/e40/ %U https://doi.org/10.2196/publichealth.8874 %U http://www.ncbi.nlm.nih.gov/pubmed/29631987 %0 Journal Article %@ 2369-6893 %I JMIR Publications %V 4 %N 1 %P e10640 %T Surveillance and Molecular Epidemiology of Avian Influenza H9N2 Viruses Circulating in Pakistan %A Farooq Tahir,Muhammad %D 2018 %7 29.03.2018 %9 Abstract %J iproc %G English %X Background: Avian influenza H9N2 is highly endemic in commercial and backyard poultry in Pakistan. Its widespread circulation and high mutation rates provide a possibility of novel reassorted viruses hence posing a serious public health threat. Objective: This study was aimed to isolate and evaluate the AI H9N2 viruses circulating in poultry as well as aquatic birds in Pakistan between 2014 and 2017. Methods: Specimens were collected from morbid or dead birds suspected for AI H9N2 on the basis of clinical signs or post-mortem lesions brought to five poultry diagnostic laboratories in Punjab. The samples were subjected for virus isolation. The isolates then were confirmed for H and N type using PCR. Six isolates were subjected to phylogenetic analysis of Haemagglutinin gene. The results were compared with isolate reported previously from Pakistan and other regional countries for homology. Results: 129,622 samples from 7481 poultry flocks were processed, 5.3% (399/7481) were positive for AIV H9N2. Sequence analysis showed that it had homology of 84-93% with different regional strains. Changes were seen at 24 different sites and at cleavage site at K148R and I151R in comparison to previous Pakistani isolates. Six possible glycosylation sites were observed. Neighbor joining phylogenetic tree confirmed its 93.4% homology with the isolate of Iran. The isolates were the same clade as other regional isolates and have common ancestors. Conclusions: The prevailing H9N2 viruses in Pakistan have certain markers and elements in the HA gene that may improve its avian to human transmission. Continuous surveillance of influenza A viruses is necessary to monitor their antigenic determinants. Protocols for the AI surveillance have officially been notified by Department of Livestock & Dairy Development Department, Punjab as a result of these findings. %R 10.2196/10640 %U http://www.iproc.org/2018/1/e10640/ %U https://doi.org/10.2196/10640 %0 Journal Article %@ 2369-6893 %I JMIR Publications %V 4 %N 1 %P e10619 %T Evaluation of Lab-based Influenza Surveillance System in Pakistan, 2017 %A Noreen,Nadia %D 2018 %7 29.03.2018 %9 Abstract %J iproc %G English %X Background: Globally 5-10% of adults and 20-30% of the children are affected by influenza annually. Annual epidemics results in 3-5 million cases and 500,000 deaths. Influenza is a common illness in Pakistan however absence of a robust surveillance system makes assessment of burden of disease an issue. Objective: The study was conducted to identify key strengths and weaknesses of the system and to make recommendations based on findings. Methods: An evaluative descriptive study was conducted from April to July 2017. The Lab-based Influenza Surveillance System was conducted at the national level. Assessment of qualitative and quantitative system attributes was done utilizing the CDC’s Updated Guidelines for Evaluating Public Health Surveillance Systems, 2001. Desk review of literature, departmental documents and reports were also conducted. The stakeholders were identified and interviewed using a semi-structured questionnaire. Results: The system was found to be simple and easy to operate but less flexible to integrate with other diseases. Data quality was good as 80% of observed forms were completely filled. Timeliness was good as the data takes 24-48 hours from sample collection to report submission to the central level. Acceptability is good as private and public-sector hospitals and labs are involved. Sensitivity calculated was 62% and Predictive Value Positive (PVP) was 37.2%. The representativeness of Lab based influenza surveillance system is poor as it is a sentinel surveillance with specific reporting sites strategically placed. Data from all sentinel sites is analyzed at national reference lab where it is summarized to use for planning and management purposes. Conclusions: The system is meeting its objectives. Sustainability and stability of the system needs to be improved by allocation of public funds. Extension of the coverage of the system will result in improved representativeness. Regular capacity building of the staff at reporting site will ensure continued quality of reporting. %R 10.2196/10619 %U http://www.iproc.org/2018/1/e10619/ %U https://doi.org/10.2196/10619 %0 Journal Article %@ 2369-6893 %I JMIR Publications %V 4 %N 1 %P e10599 %T Severe Acute Respiratory Infections with Influenza and Non-Influenza Respiratory Viruses: Yemen, 2011-2016 %A Al Amad,Mohammed %D 2018 %7 29.03.2018 %9 Abstract %J iproc %G English %X Background: Sentinel surveillance for severe acute respiratory infections (SARI) is an important tool to monitor influenza circulation and burden of other respiratory pathogens. In Yemen, two sites established at Sana'a and Aden city. Pharyngeal samples are tested for influenza and non- influenza by the Real-Time-PCR assay in NAMRU 3. Objective: Describe severity of SARI as indicated by admission to intensive care unit (ICU) and fatality as well as associated influenza and non-influenza viruses among patients in the two sites to provide recommendations for improving SARI surveillance. Methods: Data from 2012-2016 of SARI patients who admitted in the two sites based on WHO case definition was obtained from Ministry of Health, It analyzed by Epi info 7 and P<0.05 was the cut point for significance. Results: 2,211 patients were admitted in the two sites, 32% in 2013, 62% from Aden, 63% < two years, 20% had chronic diseases and 35% admitted to ICU. Overall SARI fatality was 8% which was significantly higher in Aden than Sana'a (10% vs. 5%, P<0.001), among patients with chronic disease (14% vs. 6.5% P<0.001) and admitted to ICU (10% vs. 7%, P=0.04). Samples of 82% (1,811) patients were tested where influenza viruses (75% Type A) were detected in 5% (89) more in Sana'a than Aden (6% vs. 4%, P=0.04) compared to 36% (655) of non-influenza viruses that included 43% (279) Respiratory Syncytial Virus and 17% (109) Adenovirus. The fatality of confirmed influenza was 9 % compared to 8% for non-influenza viruses. Conclusions: Our findings showed that children < 2 years are more affected by SARI. Both influenza and non-influenza viruses lead to mortality and necessitate prompt diagnosis and treatment. Expanding SARI surveillance to involve more hospitals at different governorate is recommended to give more comprehensive picture regarding SARI. %R 10.2196/10599 %U http://www.iproc.org/2018/1/e10599/ %U https://doi.org/10.2196/10599 %0 Journal Article %@ 2369-6893 %I JMIR Publications %V 4 %N 1 %P e10600 %T Defining Influenza Baseline and Threshold Values Using Surveillance Data - Egypt, Season 2016-17 %A AbdElGawad,Basma %A Refaey,S %A Abu El Sood,H %A El Shourbagy,S %A Mohsen,A %A Fahim,M %D 2018 %7 29.03.2018 %9 Abstract %J iproc %G English %X Background: Influenza infection represents a substantial public health problem resulting in global burden of mortality and morbidity. Influenza thresholds indicate level of disease activity that would signal the start or end of a season and provide an alert to an unusually severe or atypical season so, adjust preventive and control measures. Objective: To establish baseline and threshold values for 2016/17 season. Methods: Using Acute Respiratory Illness (ARI) surveillance data from 2013 to 2017, two parameters were assessed to monitor influenza activity: percentage of ARI samples positive for influenza and composite parameter (percentage of samples tested positive *ARI rate). Three threshold levels (baseline, alert and epidemic) were established by calculation of average of each week in all preceding seasons, 40% Upper Confidence Limit (UCL) and 90% UCL of each week respectively, then a four-week running average used to smooth the curve. Each parameter was compared against corresponding threshold and transmission intensity was categorized as low, moderate and high. Results: For season 2016/2017, both parameters showed two waves of activity crossing baseline threshold. First started at week 35 to 45 with dominance of Flu A/H3 activity (293(89) % of 329 positive samples, remain was Flu B) that exceeds epidemic threshold. The other, started week 12 to 14 with dominance of Flu B activity (136(99) % of 138 positive samples, remain was Flu A/H3). Percentage positive parameter signaled other weeks away from the defined season. Conclusions: Public health actions were taken in response to the observed increase flu A/H3 activity, to trim the impact and serious consequences of the disease. Continuous calculation of baseline and threshold levels can assess not only seasonal influenza but also potential pandemic influenza, contributing to the country’s pandemic preparedness and have important implications especially for resource-limited countries. %R 10.2196/10600 %U http://www.iproc.org/2018/1/e10600/ %U https://doi.org/10.2196/10600 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e32 %T Does Eating Chicken Feet With Pickled Peppers Cause Avian Influenza? Observational Case Study on Chinese Social Media During the Avian Influenza A (H7N9) Outbreak %A Chen,Bin %A Shao,Jian %A Liu,Kui %A Cai,Gaofeng %A Jiang,Zhenggang %A Huang,Yuru %A Gu,Hua %A Jiang,Jianmin %+ Zhejiang Provincial Center for Disease Control and Prevention, No. 3399, Binsheng Rd, Binjiang District, Hangzhou, 310051, China, 86 87115183, jmjiang@cdc.zj.cn %K social media %K misinformation %K infodemiology %K avian influenza A %K disease outbreak %D 2018 %7 29.03.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: A hot topic on the relationship between a popular avian-origin food and avian influenza occurred on social media during the outbreak of the emerging avian influenza A (H7N9). The misinformation generated from this topic had caused great confusion and public concern. Objective: Our goals were to analyze the trend and contents of the relevant posts during the outbreak. We also aimed to understand the characteristics of the misinformation and to provide suggestions to reduce public misconception on social media during the emerging disease outbreak. Methods: The original microblog posts were collected from China’s Sina Weibo and Tencent Weibo using a combination of keywords between April 1, 2013 and June 2, 2013. We analyzed the weekly and daily trend of the relevant posts. Content analyses were applied to categorize the posts into 4 types with unified sorting criteria. The posts’ characteristics and geographic locations were also analyzed in each category. We conducted further analysis on the top 5 most popular misleading posts. Results: A total of 1680 original microblog posts on the topic were retrieved and 341 (20.30%) of these posts were categorized as misleading messages. The number of relevant posts had not increased much during the first 2 weeks but rose to a high level in the next 2 weeks after the sudden increase in number of reported cases at the beginning of week 3. The posts under “misleading messages” occurred and increased from the beginning of week 3, but their daily posting number decreased when the daily number of posts under “refuting messages” outnumbered them. The microbloggers of the misleading posts had the lowest mean rank of followers and previous posts, but their posts had a highest mean rank of posts. The proportion of “misleading messages” in places with no reported cases was significantly higher than that in the epidemic areas (23.6% vs 13.8%). The popular misleading posts appeared to be short and consisted of personal narratives, which were easily disseminated on social media. Conclusions: Our findings suggested the importance of responding to common questions and misconceptions on social media platforms from the beginning of disease outbreaks. Authorities need to release clear and reliable information related to the popular topics early on. The microbloggers posting correct information should be empowered and their posts could be promoted to clarify false information. Equal importance should be attached to clarify misinformation in both the outbreak and nonoutbreak areas. %M 29599109 %R 10.2196/publichealth.8198 %U http://publichealth.jmir.org/2018/1/e32/ %U https://doi.org/10.2196/publichealth.8198 %U http://www.ncbi.nlm.nih.gov/pubmed/29599109 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 3 %P e71 %T Self-Swabbing for Virological Confirmation of Influenza-Like Illness Among an Internet-Based Cohort in the UK During the 2014-2015 Flu Season: Pilot Study %A Wenham,Clare %A Gray,Eleanor R %A Keane,Candice E %A Donati,Matthew %A Paolotti,Daniela %A Pebody,Richard %A Fragaszy,Ellen %A McKendry,Rachel A %A Edmunds,W John %+ Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom, 44 207 955 ext 6592, c.wenham@lse.ac.uk %K influenza %K influenza-like illness %K surveillance %K online %K cohort study %K virological confirmation %D 2018 %7 01.03.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Routine influenza surveillance, based on laboratory confirmation of viral infection, often fails to estimate the true burden of influenza-like illness (ILI) in the community because those with ILI often manage their own symptoms without visiting a health professional. Internet-based surveillance can complement this traditional surveillance by measuring symptoms and health behavior of a population with minimal time delay. Flusurvey, the UK’s largest crowd-sourced platform for surveillance of influenza, collects routine data on more than 6000 voluntary participants and offers real-time estimates of ILI circulation. However, one criticism of this method of surveillance is that it is only able to assess ILI, rather than virologically confirmed influenza. Objective: We designed a pilot study to see if it was feasible to ask individuals from the Flusurvey platform to perform a self-swabbing task and to assess whether they were able to collect samples with a suitable viral content to detect an influenza virus in the laboratory. Methods: Virological swabbing kits were sent to pilot study participants, who then monitored their ILI symptoms over the influenza season (2014-2015) through the Flusurvey platform. If they reported ILI, they were asked to undertake self-swabbing and return the swabs to a Public Health England laboratory for multiplex respiratory virus polymerase chain reaction testing. Results: A total of 700 swab kits were distributed at the start of the study; from these, 66 participants met the definition for ILI and were asked to return samples. In all, 51 samples were received in the laboratory, 18 of which tested positive for a viral cause of ILI (35%). Conclusions: This demonstrated proof of concept that it is possible to apply self-swabbing for virological laboratory testing to an online cohort study. This pilot does not have significant numbers to validate whether Flusurvey surveillance accurately reflects influenza infection in the community, but highlights that the methodology is feasible. Self-swabbing could be expanded to larger online surveillance activities, such as during the initial stages of a pandemic, to understand community transmission or to better assess interseasonal activity. %M 29496658 %R 10.2196/jmir.9084 %U http://www.jmir.org/2018/3/e71/ %U https://doi.org/10.2196/jmir.9084 %U http://www.ncbi.nlm.nih.gov/pubmed/29496658 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e4 %T Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis %A Lu,Fred Sun %A Hou,Suqin %A Baltrusaitis,Kristin %A Shah,Manan %A Leskovec,Jure %A Sosic,Rok %A Hawkins,Jared %A Brownstein,John %A Conidi,Giuseppe %A Gunn,Julia %A Gray,Josh %A Zink,Anna %A Santillana,Mauricio %+ Computational Health Informatics Program, Boston Children’s Hospital, 1 Autumn St, Boston, MA, 02215, United States, 1 617 919 1795, msantill@fas.harvard.edu %K epidemiology %K public health %K machine learning %K regression analysis %K influenza, human %K communicable diseases %K statistics %K patient generated data %D 2018 %7 09.01.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Influenza outbreaks pose major challenges to public health around the world, leading to thousands of deaths a year in the United States alone. Accurate systems that track influenza activity at the city level are necessary to provide actionable information that can be used for clinical, hospital, and community outbreak preparation. Objective: Although Internet-based real-time data sources such as Google searches and tweets have been successfully used to produce influenza activity estimates ahead of traditional health care–based systems at national and state levels, influenza tracking and forecasting at finer spatial resolutions, such as the city level, remain an open question. Our study aimed to present a precise, near real-time methodology capable of producing influenza estimates ahead of those collected and published by the Boston Public Health Commission (BPHC) for the Boston metropolitan area. This approach has great potential to be extended to other cities with access to similar data sources. Methods: We first tested the ability of Google searches, Twitter posts, electronic health records, and a crowd-sourced influenza reporting system to detect influenza activity in the Boston metropolis separately. We then adapted a multivariate dynamic regression method named ARGO (autoregression with general online information), designed for tracking influenza at the national level, and showed that it effectively uses the above data sources to monitor and forecast influenza at the city level 1 week ahead of the current date. Finally, we presented an ensemble-based approach capable of combining information from models based on multiple data sources to more robustly nowcast as well as forecast influenza activity in the Boston metropolitan area. The performances of our models were evaluated in an out-of-sample fashion over 4 influenza seasons within 2012-2016, as well as a holdout validation period from 2016 to 2017. Results: Our ensemble-based methods incorporating information from diverse models based on multiple data sources, including ARGO, produced the most robust and accurate results. The observed Pearson correlations between our out-of-sample flu activity estimates and those historically reported by the BPHC were 0.98 in nowcasting influenza and 0.94 in forecasting influenza 1 week ahead of the current date. Conclusions: We show that information from Internet-based data sources, when combined using an informed, robust methodology, can be effectively used as early indicators of influenza activity at fine geographic resolutions. %M 29317382 %R 10.2196/publichealth.8950 %U http://publichealth.jmir.org/2018/1/e4/ %U https://doi.org/10.2196/publichealth.8950 %U http://www.ncbi.nlm.nih.gov/pubmed/29317382 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 12 %P e416 %T Estimating the Population Impact of a New Pediatric Influenza Vaccination Program in England Using Social Media Content %A Wagner,Moritz %A Lampos,Vasileios %A Yom-Tov,Elad %A Pebody,Richard %A Cox,Ingemar J %+ Public Health England, 61 Colindale Ave, London, NW9 5EQ, United Kingdom, 44 7539078912, moritz.wagner.16@ucl.ac.uk %K health intervention %K influenza %K vaccination %K social media %K Twitter %D 2017 %7 21.12.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: The rollout of a new childhood live attenuated influenza vaccine program was launched in England in 2013, which consisted of a national campaign for all 2 and 3 year olds and several pilot locations offering the vaccine to primary school-age children (4-11 years of age) during the influenza season. The 2014/2015 influenza season saw the national program extended to include additional pilot regions, some of which offered the vaccine to secondary school children (11-13 years of age) as well. Objective: We utilized social media content to obtain a complementary assessment of the population impact of the programs that were launched in England during the 2013/2014 and 2014/2015 flu seasons. The overall community-wide impact on transmission in pilot areas was estimated for the different age groups that were targeted for vaccination. Methods: A previously developed statistical framework was applied, which consisted of a nonlinear regression model that was trained to infer influenza-like illness (ILI) rates from Twitter posts originating in pilot (school-age vaccinated) and control (unvaccinated) areas. The control areas were then used to estimate ILI rates in pilot areas, had the intervention not taken place. These predictions were compared with their corresponding Twitter-based ILI estimates. Results: Results suggest a reduction in ILI rates of 14% (1-25%) and 17% (2-30%) across all ages in only the primary school-age vaccine pilot areas during the 2013/2014 and 2014/2015 influenza seasons, respectively. No significant impact was observed in areas where two age cohorts of secondary school children were vaccinated. Conclusions: These findings corroborate independent assessments from traditional surveillance data, thereby supporting the ongoing rollout of the program to primary school-age children and providing evidence of the value of social media content as an additional syndromic surveillance tool. %M 29269339 %R 10.2196/jmir.8184 %U http://www.jmir.org/2017/12/e416/ %U https://doi.org/10.2196/jmir.8184 %U http://www.ncbi.nlm.nih.gov/pubmed/29269339 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e90 %T Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends %A Samaras,Loukas %A García-Barriocanal,Elena %A Sicilia,Miguel-Angel %+ Computer Science Department, University of Alcalá, Polytechnic Building, Ctra. Barcelona Km. 33.6, Alcalá de Henares (Madrid), 28871, Spain, 34 6974706531, lsamaras@ath.forthnet.gr %K Google Trends %K influenza %K Web, syndromic surveillance %K statistical correlation %K forecast %K ARIMA %D 2017 %7 20.11.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: An extended discussion and research has been performed in recent years using data collected through search queries submitted via the Internet. It has been shown that the overall activity on the Internet is related to the number of cases of an infectious disease outbreak. Objective: The aim of the study was to define a similar correlation between data from Google Trends and data collected by the official authorities of Greece and Europe by examining the development and the spread of seasonal influenza in Greece and Italy. Methods: We used multiple regressions of the terms submitted in the Google search engine related to influenza for the period from 2011 to 2012 in Greece and Italy (sample data for 104 weeks for each country). We then used the autoregressive integrated moving average statistical model to determine the correlation between the Google search data and the real influenza cases confirmed by the aforementioned authorities. Two methods were used: (1) a flu score was created for the case of Greece and (2) comparison of data from a neighboring country of Greece, which is Italy. Results: The results showed that there is a significant correlation that can help the prediction of the spread and the peak of the seasonal influenza using data from Google searches. The correlation for Greece for 2011 and 2012 was .909 and .831, respectively, and correlation for Italy for 2011 and 2012 was .979 and .933, respectively. The prediction of the peak was quite precise, providing a forecast before it arrives to population. Conclusions: We can create an Internet surveillance system based on Google searches to track influenza in Greece and Italy. %M 29158208 %R 10.2196/publichealth.8015 %U http://publichealth.jmir.org/2017/4/e90/ %U https://doi.org/10.2196/publichealth.8015 %U http://www.ncbi.nlm.nih.gov/pubmed/29158208 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e87 %T Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala %A Prieto,José Tomás %A Jara,Jorge H %A Alvis,Juan Pablo %A Furlan,Luis R %A Murray,Christian Travis %A Garcia,Judith %A Benghozi,Pierre-Jean %A Kaydos-Daniels,Susan Cornelia %+ Center for Health Studies, Universidad del Valle de Guatemala, 18 Av. 11-95, Zona 15, Vista Hermosa III, Guatemala City, 01015, Guatemala, +1 4044216455, josetomasprieto@gmail.com %K crowdsourcing %K human flu %K influenza %K grippe %K mHealth %K texting %K mobile apps %K short message service %K text message %K developing countries %D 2017 %7 14.11.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In many Latin American countries, official influenza reports are neither timely nor complete, and surveillance of influenza-like illness (ILI) remains thin in consistency and precision. Public participation with mobile technology may offer new ways of identifying nonmedically attended cases and reduce reporting delays, but no published studies to date have assessed the viability of ILI surveillance with mobile tools in Latin America. We implemented and assessed an ILI-tailored mobile health (mHealth) participatory reporting system. Objective: The objectives of this study were to evaluate the quality and characteristics of electronically collected data, the user acceptability of the symptom reporting platform, and the costs of running the system and of identifying ILI cases, and to use the collected data to characterize cases of reported ILI. Methods: We recruited the heads of 189 households comprising 584 persons during randomly selected home visits in Guatemala. From August 2016 to March 2017, participants used text messages or an app to report symptoms of ILI at home, the ages of the ILI cases, if medical attention was sought, and if medicines were bought in pharmacies. We sent weekly reminders to participants and compensated those who sent reports with phone credit. We assessed the simplicity, flexibility, acceptability, stability, timeliness, and data quality of the system. Results: Nearly half of the participants (47.1%, 89/189) sent one or more reports. We received 468 reports, 83.5% (391/468) via text message and 16.4% (77/468) via app. Nine-tenths of the reports (93.6%, 438/468) were received within 48 hours of the transmission of reminders. Over a quarter of the reports (26.5%, 124/468) indicated that at least someone at home had ILI symptoms. We identified 202 ILI cases and collected age information from almost three-fifths (58.4%, 118/202): 20 were aged between 0 and 5 years, 95 were aged between 6 and 64 years, and three were aged 65 years or older. Medications were purchased from pharmacies, without medical consultation, in 33.1% (41/124) of reported cases. Medical attention was sought in 27.4% (34/124) of reported cases. The cost of identifying an ILI case was US $6.00. We found a positive correlation (Pearson correlation coefficient=.8) between reported ILI and official surveillance data for noninfluenza viruses from weeks 41 (2016) to 13 (2017). Conclusions: Our system has the potential to serve as a practical complement to respiratory virus surveillance in Guatemala. Its strongest attributes are simplicity, flexibility, and timeliness. The biggest challenge was low enrollment caused by people’s fear of victimization and lack of phone credit. Authorities in Central America could test similar methods to improve the timeliness, and extend the breadth, of disease surveillance. It may allow them to rapidly detect localized or unusual circulation of acute respiratory illness and trigger appropriate public health actions. %M 29138128 %R 10.2196/publichealth.8610 %U http://publichealth.jmir.org/2017/4/e87/ %U https://doi.org/10.2196/publichealth.8610 %U http://www.ncbi.nlm.nih.gov/pubmed/29138128 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 11 %P e370 %T Subregional Nowcasts of Seasonal Influenza Using Search Trends %A Kandula,Sasikiran %A Hsu,Daniel %A Shaman,Jeffrey %+ Department of Environmental Health Sciences, Columbia University, ARB Building, 11th Floor, 722 West 168th Street, New York, NY, 10032, United States, 1 2123053590, sk3542@cumc.columbia.edu %K human influenza %K classification and regression trees %K nowcasts %K infodemiology %K infoveillance %K surveillance %D 2017 %7 06.11.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Limiting the adverse effects of seasonal influenza outbreaks at state or city level requires close monitoring of localized outbreaks and reliable forecasts of their progression. Whereas forecasting models for influenza or influenza-like illness (ILI) are becoming increasingly available, their applicability to localized outbreaks is limited by the nonavailability of real-time observations of the current outbreak state at local scales. Surveillance data collected by various health departments are widely accepted as the reference standard for estimating the state of outbreaks, and in the absence of surveillance data, nowcast proxies built using Web-based activities such as search engine queries, tweets, and access of health-related webpages can be useful. Nowcast estimates of state and municipal ILI were previously published by Google Flu Trends (GFT); however, validations of these estimates were seldom reported. Objective: The aim of this study was to develop and validate models to nowcast ILI at subregional geographic scales. Methods: We built nowcast models based on autoregressive (autoregressive integrated moving average; ARIMA) and supervised regression methods (Random forests) at the US state level using regional weighted ILI and Web-based search activity derived from Google's Extended Trends application programming interface. We validated the performance of these methods using actual surveillance data for the 50 states across six seasons. We also built state-level nowcast models using state-level estimates of ILI and compared the accuracy of these estimates with the estimates of the regional models extrapolated to the state level and with the nowcast estimates published by GFT. Results: Models built using regional ILI extrapolated to state level had a median correlation of 0.84 (interquartile range: 0.74-0.91) and a median root mean square error (RMSE) of 1.01 (IQR: 0.74-1.50), with noticeable variability across seasons and by state population size. Model forms that hypothesize the availability of timely state-level surveillance data show significantly lower errors of 0.83 (0.55-0.23). Compared with GFT, the latter model forms have lower errors but also lower correlation. Conclusions: These results suggest that the proposed methods may be an alternative to the discontinued GFT and that further improvements in the quality of subregional nowcasts may require increased access to more finely resolved surveillance data. %M 29109069 %R 10.2196/jmir.7486 %U http://www.jmir.org/2017/11/e370/ %U https://doi.org/10.2196/jmir.7486 %U http://www.ncbi.nlm.nih.gov/pubmed/29109069 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e83 %T Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches %A Brownstein,John S %A Chu,Shuyu %A Marathe,Achla %A Marathe,Madhav V %A Nguyen,Andre T %A Paolotti,Daniela %A Perra,Nicola %A Perrotta,Daniela %A Santillana,Mauricio %A Swarup,Samarth %A Tizzoni,Michele %A Vespignani,Alessandro %A Vullikanti,Anil Kumar S %A Wilson,Mandy L %A Zhang,Qian %+ Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute, Virginia Tech, 1015 Life Science Circle, Blacksburg, VA,, United States, 1 540 231 9210, amarathe@vt.edu %K forecasting %K disease surveillance %K crowdsourcing %K nonresponse bias %D 2017 %7 01.11.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. Objective: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. Methods: We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You). Results: WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information. Conclusions: While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world. %M 29092812 %R 10.2196/publichealth.7344 %U http://publichealth.jmir.org/2017/4/e83/ %U https://doi.org/10.2196/publichealth.7344 %U http://www.ncbi.nlm.nih.gov/pubmed/29092812 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e67 %T A Smart Card-Based Electronic School Absenteeism System for Influenza-Like Illness Surveillance in Hong Kong: Design, Implementation, and Feasibility Assessment %A Ip,Dennis KM %A Lau,Eric HY %A So,Hau Chi %A Xiao,Jingyi %A Lam,Chi Kin %A Fang,Vicky J %A Tam,Yat Hung %A Leung,Gabriel M %A Cowling,Benjamin J %+ WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 1/F, Patrick Manson Building (North Wing), 7 Sassoon Road, Hong Kong,, China (Hong Kong), 852 39176712, dkmip@hku.hk %K influenza %K public health surveillance %K school health %K absenteeism %K smart cards %D 2017 %7 06.10.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: School-aged children have the highest incidence of respiratory virus infections each year, and transmission of respiratory viruses such as influenza virus can be a major concern in school settings. School absenteeism data have been employed as a component of influenza surveillance systems in some locations. Data timeliness and system acceptance remain as key determinants affecting the usefulness of a prospective surveillance system. Objective: The aim of this study was to assess the feasibility of implementing an electronic school absenteeism surveillance system using smart card–based technology for influenza-like illness (ILI) surveillance among a representative network of local primary and secondary schools in Hong Kong. Methods: We designed and implemented a surveillance system according to the Protocol for a Standardized information infrastructure for Pandemic and Emerging infectious disease Response (PROSPER). We employed an existing smart card–based education and school administration platform for data capture, customized the user interface, and used additional back end systems built for other downstream surveillance steps. We invited local schools to participate and collected absenteeism data by the implemented system. We compared temporal trend of the absenteeism data with data from existing community sentinel and laboratory surveillance data. Results: We designed and implemented an ILI surveillance system utilizing smart card–based attendance tracking approach for data capture. We implemented the surveillance system in a total of 107 schools (including 66 primary schools and 41 secondary schools), covering a total of 75,052 children. The system successfully captured information on absences for 2 consecutive academic years (2012-2013 and 2013-2014). The absenteeism data we collected from the system reflected ILI activity in the community, with an upsurge in disease activity detected up to 1 to 2 weeks preceding other existing surveillance systems. Conclusions: We designed and implemented a novel smart card technology–based school absenteeism surveillance system. Our study demonstrated the feasibility of building a large-scale surveillance system riding on a routinely adopted data collection approach and the use of simple system enhancement to minimize workload implication and enhance system acceptability. Data from this system have potential value in supplementing existing sentinel influenza surveillance for situational awareness of influenza activity in the community. %M 28986338 %R 10.2196/publichealth.6810 %U http://publichealth.jmir.org/2017/4/e67/ %U https://doi.org/10.2196/publichealth.6810 %U http://www.ncbi.nlm.nih.gov/pubmed/28986338 %0 Journal Article %@ 2369-6893 %I JMIR Publications %V 3 %N 1 %P e56 %T Detecting Influenza Epidemics Using Self-reported Data Through Mobile App (FeverCoach) %A Kim,Myeongchan %A Yune,Sehyo %A Han,Hyun Wook %+ Department of Biomedical Informatics, School of Medicine, Ajou University, 206, World cup-ro, Suwon,, Republic Of Korea, 82 1062920812, james.hw.han@gmail.com %K children %K epidemics %K health care %K human influenza %K Mobile health (mHealth) %D 2017 %7 22.9.2017 %9 Abstract %J iproc %G English %X Background: Timely forecast of influenza activity is critical for a public health system to prepare for an influenza epidemic and mitigate its burden. Currently, influenza surveillance relies on traditional data sources such as reports from health care providers, which lag behind real-time by several days to weeks. In an effort to reduce the time lag, internet search information, voluntary web-based records, and electronic health records have been suggested as the alternative data sources for influenza surveillance. However, low specificity, low rate of report, or privacy concerns limits the use of such data. Objective: FeverCoach mobile application provides tailored information to help caregivers manage a febrile child. Using the self-reported diagnosis data submitted to the app, we developed a new algorithm that accurately predicted the influenza trend in South Korea. Methods: Users of FeverCoach agreed to the use of de-identified data for research purposes. The app shows information about use of antipyretics and adjuvant way to relieve fever when users enter the child’s age, sex, body temperature, and the duration of fever. Users can choose from the list of 21 candidate diseases including Influenza after a physician office visit. Additional information about the disease was provided following submission of the diagnosis. Public influenza-like illness (ILI) data was obtained from the Korea Centers for Disease Control and Prevention (KCDC) website. The data was collected from September 2016 to March 2017. Ordinary least squares linear regression was used to build a model using the data from the app to predict the influenza trend. To perform linear regression, we calculate logit(Pcdc) and logit(Papp) where logit(p) is natural log of p/(1-p), Pcdc is (ILI visit counts)/(total patient visit counts) and Papp is (Influenza report on FeverCoach)/(total diagnosis report on FeverCoach). Results: We collected 13,014 self-reported diagnoses. Of all users, 81% of the children were under 5 years of age. The animated visualization of spatiotemporal diagnosis report is available online at https://www.youtube.com/watch?v=-8kDXz43gO8. Ordinary least square regression showed significant association between logit(Pcdc) and logit(Papp) (R2=0.860, P<.001). Using this regression model, we could detect an influenza epidemic 5 days before the 2016-2017 season’s influenza epidemic alert by KCDC. Conclusions: We found that it is possible to predict influenza epidemics earlier than KCDC with a relatively small amoount data. Collection of specific and accurate data was made possible by targeting a well-defined population. %R 10.2196/iproc.8686 %U http://www.iproc.org/2017/1/e56/ %U https://doi.org/10.2196/iproc.8686 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 3 %P e66 %T Influenzanet: Citizens Among 10 Countries Collaborating to Monitor Influenza in Europe %A Koppeschaar,Carl E %A Colizza,Vittoria %A Guerrisi,Caroline %A Turbelin,Clément %A Duggan,Jim %A Edmunds,W John %A Kjelsø,Charlotte %A Mexia,Ricardo %A Moreno,Yamir %A Meloni,Sandro %A Paolotti,Daniela %A Perrotta,Daniela %A van Straten,Edward %A Franco,Ana O %+ De Grote Griepmeting, Science in Action BV, Postbus 1786, Amsterdam, 1000 BT, Netherlands, 31 620621593, carlkop@xs4all.nl %K influenza %K surveillance %K Internet %K vaccination %K Europe %D 2017 %7 19.09.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The wide availability of the Internet and the growth of digital communication technologies have become an important tool for epidemiological studies and health surveillance. Influenzanet is a participatory surveillance system monitoring the incidence of influenza-like illness (ILI) in Europe since 2003. It is based on data provided by volunteers who self-report their symptoms via the Internet throughout the influenza season and currently involves 10 countries. Objective: In this paper, we describe the Influenzanet system and provide an overview of results from several analyses that have been performed with the collected data, which include participant representativeness analyses, data validation (comparing ILI incidence rates between Influenzanet and sentinel medical practice networks), identification of ILI risk factors, and influenza vaccine effectiveness (VE) studies previously published. Additionally, we present new VE analyses for the Netherlands, stratified by age and chronic illness and offer suggestions for further work and considerations on the continuity and sustainability of the participatory system. Methods: Influenzanet comprises country-specific websites where residents can register to become volunteers to support influenza surveillance and have access to influenza-related information. Participants are recruited through different communication channels. Following registration, volunteers submit an intake questionnaire with their postal code and sociodemographic and medical characteristics, after which they are invited to report their symptoms via a weekly electronic newsletter reminder. Several thousands of participants have been engaged yearly in Influenzanet, with over 36,000 volunteers in the 2015-16 season alone. Results: In summary, for some traits and in some countries (eg, influenza vaccination rates in the Netherlands), Influenzanet participants were representative of the general population. However, for other traits, they were not (eg, participants underrepresent the youngest and oldest age groups in 7 countries). The incidence of ILI in Influenzanet was found to be closely correlated although quantitatively higher than that obtained by the sentinel medical practice networks. Various risk factors for acquiring an ILI infection were identified. The VE studies performed with Influenzanet data suggest that this surveillance system could develop into a complementary tool to measure the effectiveness of the influenza vaccine, eventually in real time. Conclusions: Results from these analyses illustrate that Influenzanet has developed into a fast and flexible monitoring system that can complement the traditional influenza surveillance performed by sentinel medical practices. The uniformity of Influenzanet allows for direct comparison of ILI rates between countries. It also has the important advantage of yielding individual data, which can be used to identify risk factors. The way in which the Influenzanet system is constructed allows the collection of data that could be extended beyond those of ILI cases to monitor pandemic influenza and other common or emerging diseases. %M 28928112 %R 10.2196/publichealth.7429 %U http://publichealth.jmir.org/2017/3/e66/ %U https://doi.org/10.2196/publichealth.7429 %U http://www.ncbi.nlm.nih.gov/pubmed/28928112 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 9 %P e315 %T Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data %A Kagashe,Ireneus %A Yan,Zhijun %A Suheryani,Imran %+ School of Management and Economics, Beijing Institute of Technology, Main Building, No. 5 South Zhongguancun Street, Haidian, Beijing, 100081, China, 86 10 68912845, yanzhijun@bit.edu.cn %K machine learning %K Twitter messaging %K social media %K disease outbreaks %K influenza %K public health surveillance %K natural language processing %K influenza vaccines %D 2017 %7 12.09.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery. Hence, there is a need to use alternative efficient means such as Twitter data and machine learning (ML) techniques. Objective: Using Twitter data, the aim of the study was to (1) provide a methodological extension for efficiently extracting widely consumed drugs during seasonal influenza and (2) extract topics from the tweets of these drugs and to infer how the insights provided by these topics can enhance seasonal influenza surveillance. Methods: From tweets collected during the 2012-13 flu season, we first identified tweets with mentions of drugs and then constructed an ML classifier using dependency words as features. The classifier was used to extract tweets that evidenced consumption of drugs, out of which we identified the mostly consumed drugs. Finally, we extracted trending topics from each of these widely used drugs’ tweets using latent Dirichlet allocation (LDA). Results: Our proposed classifier obtained an F1 score of 0.82, which significantly outperformed the two benchmark classifiers (ie, P<.001 with the lexicon-based and P=.048 with the 1-gram term frequency [TF]). The classifier extracted 40,428 tweets that evidenced consumption of drugs out of 50,828 tweets with mentions of drugs. The most widely consumed drugs were influenza virus vaccines that had around 76.95% (31,111/40,428) share of the total; other notable drugs were Theraflu, DayQuil, NyQuil, vitamins, acetaminophen, and oseltamivir. The topics of each of these drugs exhibited common themes or experiences from people who have consumed these drugs. Among these were the enabling and deterrent factors to influenza drugs uptake, which are keys to mitigating the severity of seasonal influenza outbreaks. Conclusions: The study results showed the feasibility of using tweets of widely consumed drugs to enhance seasonal influenza surveillance in lieu of the traditional or conventional surveillance approaches. Public health officials and other stakeholders can benefit from the findings of this study, especially in enhancing strategies for mitigating the severity of seasonal influenza outbreaks. The proposed methods can be extended to the outbreaks of other diseases. %M 28899847 %R 10.2196/jmir.7393 %U http://www.jmir.org/2017/9/e315/ %U https://doi.org/10.2196/jmir.7393 %U http://www.ncbi.nlm.nih.gov/pubmed/28899847 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 2 %P e8004 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveThe objective was to forecast and validate prediction estimates of influenza activity in Houston, TX using four years of historical influenza-like illness (ILI) from three surveillance data capture mechanisms.BackgroundUsing novel surveillance methods and historical data to estimate future trends of influenza-like illness can lead to earlier detection of influenza activity increases and decreases. Anticipating surges gives public health professionals more time to prepare and increase prevention efforts.MethodsData was obtained from three surveillance systems, Flu Near You, ILINet, and hospital emergency center (EC) visits, with diverse data capture mechanisms. Autoregressive integrated moving average (ARIMA) models were fitted to data from each source for week 27 of 2012 through week 26 of 2016 and used to forecast influenza-like activity for the subsequent 10 weeks. Estimates were then compared to actual ILI percentages for the same period.ResultsForecasted estimates had wide confidence intervals that crossed zero. The forecasted trend direction differed by data source, resulting in lack of consensus about future influenza activity. ILINet forecasted estimates and actual percentages had the least differences. ILINet performed best when forecasting influenza activity in Houston, TX.ConclusionThough the three forecasted estimates did not agree on the trend directions, and thus, were considered imprecise predictors of long-term ILI activity based on existing data, pooling predictions and careful interpretations may be helpful for short term intervention efforts. Further work is needed to improve forecast accuracy considering the promise forecasting holds for seasonal influenza prevention and control, and pandemic preparedness. %M 29026453 %R 10.5210/ojphi.v9i2.8004 %U %U https://doi.org/10.5210/ojphi.v9i2.8004 %U http://www.ncbi.nlm.nih.gov/pubmed/29026453 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 3 %P e57 %T Evaluation of Sampling Recommendations From the Influenza Virologic Surveillance Right Size Roadmap for Idaho %A Rosenthal,Mariana %A Anderson,Katey %A Tengelsen,Leslie %A Carter,Kris %A Hahn,Christine %A Ball,Christopher %+ Centers for Disease Control and Prevention, Idaho Department of Health and Welfare, 450 W. State Street, 4th Floor, Boise, ID, 83720, United States, 1 619 808 3992, mariana.rosenthal@doh.wa.gov %K influenza %K sample size %K public health surveillance %D 2017 %7 24.08.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Right Size Roadmap was developed by the Association of Public Health Laboratories and the Centers for Disease Control and Prevention to improve influenza virologic surveillance efficiency. Guidelines were provided to state health departments regarding representativeness and statistical estimates of specimen numbers needed for seasonal influenza situational awareness, rare or novel influenza virus detection, and rare or novel influenza virus investigation. Objective: The aim of this study was to compare Roadmap sampling recommendations with Idaho’s influenza virologic surveillance to determine implementation feasibility. Methods: We calculated the proportion of medically attended influenza-like illness (MA-ILI) from Idaho’s influenza-like illness surveillance among outpatients during October 2008 to May 2014, applied data to Roadmap-provided sample size calculators, and compared calculations with actual numbers of specimens tested for influenza by the Idaho Bureau of Laboratories (IBL). We assessed representativeness among patients’ tested specimens to census estimates by age, sex, and health district residence. Results: Among outpatients surveilled, Idaho’s mean annual proportion of MA-ILI was 2.30% (20,834/905,818) during a 5-year period. Thus, according to Roadmap recommendations, Idaho needs to collect 128 specimens from MA-ILI patients/week for situational awareness, 1496 influenza-positive specimens/week for detection of a rare or novel influenza virus at 0.2% prevalence, and after detection, 478 specimens/week to confirm true prevalence is ≤2% of influenza-positive samples. The mean number of respiratory specimens Idaho tested for influenza/week, excluding the 2009-2010 influenza season, ranged from 6 to 24. Various influenza virus types and subtypes were collected and specimen submission sources were representative in terms of geographic distribution, patient age range and sex, and disease severity. Conclusions: Insufficient numbers of respiratory specimens are submitted to IBL for influenza laboratory testing. Increased specimen submission would facilitate meeting Roadmap sample size recommendations. %M 28838883 %R 10.2196/publichealth.6648 %U http://publichealth.jmir.org/2017/3/e57/ %U https://doi.org/10.2196/publichealth.6648 %U http://www.ncbi.nlm.nih.gov/pubmed/28838883 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 6 %P e211 %T Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design %A Spreco,Armin %A Eriksson,Olle %A Dahlström,Örjan %A Cowling,Benjamin John %A Timpka,Toomas %+ Faculty of Health Sciences, Department of Medical and Health Sciences, Linköping University, IMH:s kansli, Sandbäcksgatan 7, Linköping, 581 83, Sweden, 46 737543032, armin.spreco@liu.se %K human influenza %K algorithms %K epidemiological surveillance %K public health surveillance %K evaluation research %K epidemiological methods %D 2017 %7 15.06.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic “big data” from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics. Objective: The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county. Methods: An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source. Results: The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used based on the assumption that the beginning of a winter influenza season has an exponential growth of infected individuals. For prediction modeling, linear regression was applied on 7-day periods at the time in order to find the peak timing, whereas a derivate of a normal distribution density function was used to find the peak intensity. We found that the integrated detection and prediction method detected the 2008-09 winter influenza season on its starting day (optimal timeliness 0 days), whereas the predicted peak was estimated to occur 7 days ahead of the factual peak and the predicted peak intensity was estimated to be 26% lower than the factual intensity (6.3 compared with 8.5 influenza-diagnosis cases/100,000). Conclusions: Our detection and prediction method is one of the first integrated methods specifically designed for local application on influenza data electronically available for surveillance. The performance of the method in a retrospective study indicates that further prospective evaluations of the methods are justified. %M 28619700 %R 10.2196/jmir.7101 %U http://www.jmir.org/2017/6/e211/ %U https://doi.org/10.2196/jmir.7101 %U http://www.ncbi.nlm.nih.gov/pubmed/28619700 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7592 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ntroductionInfluenza is a contagious disease that causes epidemics in manyparts of the world. The World Health Organization estimates thatinfluenza causes three to five million severe illnesses each year and250,000-500,000 deaths [1]. Predicting and characterizing outbreaksof influenza is an important public health problem and significantprogress has been made in predicting single outbreaks. However,multiple temporally overlapping outbreaks are also common.These may be caused by different subtypes or outbreaks in multipledemographic groups. We describe ourMultiple Outbreak DetectionSystem(MODS) and its performance on two actual outbreaks.This work extends previous work by our group [2,3,4] by using model-averaging and a new method to estimate non-influenza influenza-likeillness (NI-ILI). We also apply MODS to a real dataset with a doubleoutbreak.MethodsMODS is part of a framework for disease surveillance developedby our group. In this framework, a natural language processing systemextracts symptoms from emergency department patient-care reports.These features are combined with laboratory results and passed to acase detection system that infers a probability distribution over thediseases each patient may have. These diseases include influenza,NI-ILI, and other (appendicitis, trauma, etc.). This distribution isexpressed in terms of the likelihoods of the patients’ data. These aregiven to MODS which searches a space of multiple outbreak models,computes the likelihood of each model, and calculates the expectednumber of influenza cases day-by-day. This work differs from pastwork in three important ways. First, we address the problem ofdetecting and characterizing multiple, overlapping outbreaks. Second,we do not rely on simple counts, but use likelihoods given evidencein the free-text portion of patient-care reports as well as laboratoryfindings. Third, we explicitly account for non-influenza influenza-like illnesses. This is important because some forms of influenza-likeillness (such as respiratory syncytial virus) are contagious and exhibitoutbreak activity. This research was approved by the University ofPittsburgh and Intermountain Healthcare IRBs.ResultsWe conducted a set of experiments with simulated outbreaks.MODS is able to detect a single outbreak six to eight weeks beforethe peak. It is also able to recognize a second outbreak approximatelyhalfway between peaks for simulated double outbreaks. Weconducted experiments using real outbreaks and compared ourresults to thermometer sales [5]. Using data from Allegheny CountyPennsylvania for the 2009-2010 influenza season, on September1 MODS predicted an outbreak with a peak on October 5. Thethermometer peak was October 21. The figure “Prediction on October1 for Allegheny County” compares MODS’ prediction on October 1to thermometer sales. Using data from Salt Lake City Utah for the2010-2011 influenza season, on November 1 MODS predicted anoutbreak with peak on December 7. The first thermometer peak wasDecember 29. On January 20 MODS predicted a second outbreakwith peak on February 9. The second thermometer peak was March5. The figure “Prediction on January 20 for Salt Lake City” comparesMODS’ prediction on January 20 to thermometer sales.ConclusionsWe have built aMultiple Outbreak Detection Systemthat candetect and characterize overlapping outbreaks of influenza. Althoughthe system currently predicts outbreaks of influenza, it is built on ageneral Bayesian framework that can be extended to other diseases.Future work includes incorporating multiple forms of evidence,modeling other known contagious diseases, and detecting outbreaksof new previously unknown diseases.Prediction on October 1 for Allegheny County 2009-2010Prediction on January 20 for Salt Lake City 2010-2011 %R 10.5210/ojphi.v9i1.7592 %U %U https://doi.org/10.5210/ojphi.v9i1.7592 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7603 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveWe aimed to describe the theoretical basis and the potentialapplications of the test-negative design for estimating influenzavaccination effectiveness in sentinel influenza surveillance.IntroductionThe test-negative design is a variation of the case-control study,in which patients are enrolled in outpatient clinics (and/or hospitals)based on a clinical case definition such as influenza-like illness (ILI).Patients are then tested for influenza virus, and VE is estimated fromthe odds ratio comparing the odds of vaccination among patientstesting positive for influenza versus those testing negative, adjustingfor potential confounding factors. The design leverages existingdisease surveillance networks and as a result, studies using it areincreasingly being reported.MethodsWe sought to examine the theoretical basis for this design usingcausal analysis including directed acyclic graphs. We reviewedstudies that used this design and examined the study populations andsettings, the methodologic choices including analytic approaches, andthe estimates of influenza VE provided. We conducted simulationstudies to examine specific potential biases.ResultsWe show how studies using this design can avoid or minimizebias, and where bias may be introduced with particular study designvariations. A purported advantage of the test-negative designis to minimise selection bias by health-care seeking behaviourand we demonstrate why residual bias may occur. Anotherpurported advantage of the test-negative design is minimization ofmisclassification of the exposure; however we show how this sourceof bias may persist and how exposure misclassification may bea greater cause for concern not dealt with by the study design. Inour review, we found great variation in estimates, but consistencybetween interim and final VE estimates from the same locations,and consistency between VE estimates from inpatient and outpatientstudies in the same locations, age groups and years. One outstandingissue is the potential bias due to non-collapsibility.ConclusionsOur work provides a starting point for further consideration of thevalidity of the test-negative design, which is an efficient approachfor routine monitoring of influenza VE that can be implemented inexisting surveillance systems without substantial additional resources.Harmonization of analytic approaches may improve the potential forpooling VE estimates. %R 10.5210/ojphi.v9i1.7603 %U %U https://doi.org/10.5210/ojphi.v9i1.7603 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7629 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveTo explore how outpatient and urgent care syndromic surveillancefor influenza-like illness (ILI) compare with emergency departmentsyndromic ILI and other seasonal ILI surveillance indicators.IntroductionThe North Dakota Department of Health (NDDoH) collectsoutpatient ILI data through North Dakota Influenza-like IllnessNetwork (ND ILINet), providing situational awareness regardingthe percent of visits for ILI at sentinel sites across the state. Becauseof increased clinic staff time devoted to electronic health initiativesand an expanding population, we have found sentinel sites have beenharder to maintain in recent years, and the number of participatingsentinel sites has decreased. Outpatient sentinel surveillance forinfluenza is an important component of influenza surveillance becausehospital and death surveillance does not capture the full spectrum ofinfluenza illness.Syndromic surveillance (SyS) is another possible source ofinformation for outpatient ILI that can be used for situationalawareness during the influenza season; one benefit of SyS is that itcan provide more timely information than traditional outpatient ILIsurveillance [1,2]. The NDDoH collects SyS data from hospitals(emergency department and inpatient visits) and outpatient clinics,including urgent and primary care locations. Visits include chiefcomplaint and/or diagnosis code data. This data is sent to theBioSense 2.0 SyS platform. We compared our outpatient SyS ILI withour ND ILINet and reported influenza cases, and included hospitaland combined SyS ILI for comparison.MethodsWeekly rates from ND ILINet, SyS ILI, and counts of reportedcases from the influenza season (annual weeks 40 through 20) forthe 2014-2015 and 2015-2016 seasons were compiled. Syndromiccategories for outpatient, hospital (emergency department andinpatient), and combined hospital and outpatient data were created,and the BioSense 2.0 definition for ILI was used. These includeddata from 127,050 outpatient and 323,318 hospital visits for2014-15, and 124,597 outpatient and 424,097 hospital visits for2015-16. Because influenza is a reportable condition in North Dakota,case data is routinely used to represent the seasonal influenza trend,and is useful when other respiratory viruses are circulating. A PearsonCorrelation Coefficient was calculated on all variables using SAS 9.4.Alpha was set to 0.05. There was no overlap between the outpatientclinics providing syndromic surveillance data and clinics participatingin ND ILINet.ResultsAll outpatient, hospital, and combined outpatient and hospitalILI rates from SyS data were positively and significantly correlatedwith both ND ILINet rates and influenza case counts (Table 1). Thecorrelation between outpatient SyS ILI rates and traditional influenzaindicators was lower than for hospital SyS ILI rates for both years,with correlation coefficients ranging from 0.38-0.48 and 0.56-0.92,respectively. Generally SyS data was more highly correlated withcase counts than ND ILINET rates. For the 2014-15 season, hospitalSyS data was the most strongly correlated with traditional influenzaindicators. For 2015-16, combined SyS data was the most stronglycorrelated. Visual inspection of the chief complaint data for ILI visitsfound a significant number of gastrointestinal visits that included thephrase “flu-like illness” in both outpatient and hospital SyS data.ConclusionsAlthough correlation coefficients were lower for outpatient SySILI rates, they are significant enough to be included in our ongoinginfluenza surveillance. One possible confounding factor for therelationship between ED surveillance and reported cases is that peoplewith more severe illness may be more likely to be tested for influenza,and may be more likely to seek medical attention at a hospital setting.This may explain why hospital SyS data provided the strongestcorrelation during the 2014-15 season, a season with higher rates ofmore severe illness than 2015-16. The combination of outpatient dataand hospital data provided the strongest correlation for the 2015-16influenza season, indicating the addition of outpatient data, which mayincrease representativeness of ILI data, may be beneficial to SyS ILIsurveillance. We used an existing ILI syndrome from the BioSense2.0 tool, and revising this syndrome may improve correlationsbetween SyS ILI and ND ILINet and case count data. Negation termsto remove visits for GI illness incorrectly referred to as “flu-like”would be one useful change. The nature of visits for influenza atoutpatient clinic versus hospitals is different, and it is possible thismay account for the difference in the strength of correlations betweenthe two data sources. Use of a different ILI syndrome definition foroutpatient SyS data should be investigated.Table 1. Pearson correlation coefficient values for influenza-like illness in threesyndromic surveillance categories compared with ND ILINET and influenzacase counts. %R 10.5210/ojphi.v9i1.7629 %U %U https://doi.org/10.5210/ojphi.v9i1.7629 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7636 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveThis session will provide an overview of the current systemsfor influenza surveillance; review the role of schools in influenzatransmission; discuss relationships between school closures, schoolabsenteeism, and influenza transmission; and explore the usefulnessof school absenteeism and unplanned school closure monitoring forearly detection of influenza in schools and broader communities.IntroductionInfluenza surveillance is conducted through a complex networkof laboratory and epidemiologic systems essential for estimatingpopulation burden of disease, selecting influenza vaccine viruses,and detecting novel influenza viruses with pandemic potential (1).Influenza surveillance faces numerous challenges, such as constantlychanging influenza viruses, substantial variability in the number ofaffected people and the severity of disease, nonspecific symptoms,and need for laboratory testing to confirm diagnosis. Exploringadditional components that provide morbidity information mayenhance current influenza surveillance.School-aged children have the highest influenza incidence ratesamong all age groups. Due to the close interaction of children inschools and subsequent introduction of influenza into households,it is recognized that schools can serve as amplification points ofinfluenza transmission in communities. For this reason, pandemicpreparedness recommendations include possible pre-emptive schoolclosures, before transmission is widespread within a school system orbroader community, to slow influenza transmission until appropriatevaccines become available. During seasonal influenza epidemics,school closures are usually reactive, implemented in response tohigh absenteeism of students and staff after the disease is alreadywidespread in the community. Reactive closures are often too late toreduce influenza transmission and are ineffective.To enhance timely influenza detection, a variety of nontraditionaldata sources have been explored. School absenteeism was suggestedby several research groups to improve school-based influenzasurveillance. A study conducted in Japan demonstrated that influenza-associated absenteeism can predict influenza outbreaks with highsensitivity and specificity (2). Another study found the use of all-causes absenteeism to be too nonspecific for utility in influenzasurveillance (3). Creation of school-based early warning systemsfor pandemic influenza remains an interest, and further studies areneeded. The panel will discuss how school-based surveillance cancomplement existing influenza surveillance systems. %R 10.5210/ojphi.v9i1.7636 %U %U https://doi.org/10.5210/ojphi.v9i1.7636 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7656 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveTo characterize and describe influenza-associated pediatric deathsin the United States over five influenza seasons, 2010–11 through2014–15.IntroductionCommunity influenza infection rates are highest among children.In children, influenza can cause severe illness and complicationsincluding, respiratory failure and death. Annual influenza vaccinationis recommended for all persons aged≥6 months. In 2004, influenza-associated deaths in children became a notifiable condition.MethodsDeaths that occurred in children aged <18 years with laboratory-confirmed influenza virus infection were reported from states andterritories to the Centers for Disease Control and Prevention on astandard case report form. We used population estimates from theU.S. Census Bureau, 2011 to 2015, to calculate age group-adjustedincidence. We used Wilcoxon-rank-sum test to compare medians andchi-square and Mantel-Haenszel chi-square to compare differencesbetween proportions of two groups.ResultsFrom October 2010 through September 2015, 590 influenza-associated pediatric deaths were reported. The median age at timeof death was 6 years (interquartile range, 1–12 years). Half of thechildren (285/572) had at least one underlying medical condition.Neurologic conditions (26%) and development delay (21%) weremost commonly reported. The average annual incidence rate was0.16 per 100,000 children (95% confidence interval [CI]: 0.15–0.17)and was highest among children aged <6 months (0.75, 95% CI,0.60–0.94 per 100,000 children), followed by children aged6–23 months (0.34, 95% CI, 0.28–0.41 per 100,000 children). Only21% (87/409) of pediatric deaths in children≥6 months had evidenceof full influenza vaccination. Vaccination coverage was lower inchildren aged 6–23 months (15%) and 5–8 years (17%) than withthose aged 2–4 years and 9–17 years (25%, p<0.01). The majorityof children aged <2 years who died had no underlying medicalconditions (63%, 105/167); this proportion was significantly higherthan that in children aged≥2 years (45%, 182/405, p<0.01).Overall 65% (383) of pediatric deaths had influenza A virusdetected, and 33% had influenza B virus detected. Children infectedwith influenza B virus had a higher frequency of sepsis/shock(41%, 72/174), acute respiratory distress syndrome (ARDS, 33%,58/174), and hemorrhagic pneumonia/pneumonitis (8%, 14/174) thanchildren infected with either influenza A(H1N1) pdm09 or influenzaA(H3N2) virus (p=0.01, 0.03, 0.03, respectively).Overall 81% (421/521) of children had an influenza-associatedcomplication; the most commonly reported were pneumonia (40%),sepsis/shock (31%) and ARDS (29%). Among those with testingreported, invasive bacteria coinfections were identified in 43%(139/322);β-hemolyticStreptococcus(20%) andStaphylococcusaureus(17%) were reported most frequently.Most children (39%, 212/548) died within 3 days of symptomonset, 28% died 4–7 days after onset, and 34% died≥8 days afteronset. The median days from illness onset to death for children withan underlying condition was significantly longer than the time forpreviously healthy children (7 versus 4 days, p<0.01).ConclusionsEach year, a substantial number of influenza-associated deathsoccur among U.S. children, with rates highest among those aged<2 years. While half of the deaths were among children withunderlying conditions, the majority of children <2 years who diedwere previously healthy. Vaccination coverage was very low.Influenza vaccination among pregnant women, young children andchildren with high-risk underlying conditions should be encouragedand could reduce influenza-associated mortality among children. %R 10.5210/ojphi.v9i1.7656 %U %U https://doi.org/10.5210/ojphi.v9i1.7656 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7671 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveTo describe the results of the new organization of influenzasurveillance in France, based on a regional approach.IntroductionIn France, until winter 2014-2015, management and preventiveactions for the control of the flu epidemic were implemented whenthe national incidence of influenza-like illness (ILI) consultationsin general practice was over an epidemic threshold. The 2014-2015influenza epidemic had a major public health impact, particularly inthe elderly, and caused a severe overloading of the health care system,in particular emergency departments (ED) [1]. The epidemic alertemitted by the French National Public Health Agency at the nationallevel was too late for the hospitals to prepare themselves in manyregions.After a national feedback organized in April 2015 with allpartners involved in influenza surveillance and management, it wasrecommended to improve influenza surveillance in France following3 axes: 1) regionalize surveillance so that healthcare structures canadapt to the particular situation of their region; 2) use a pre-epidemicalert level for better anticipating the outbreak; 3) use multiple datasources and multiple outbreak detection methods to strengthen thedetermination of influenza alert level.MethodsA user-friendly web application was developed to provide commondata visualizations and statistical results of outbreak detectionmethods to all the epidemiologists involved in influenza surveillanceat the national level or in the 15 regional units of our agency [2].It relies on 3 data sources, aggregated on a weekly time step: 1) theproportion of ILI among all coded attendances in the ED participatingto the OSCOUR Network [3] ; 2) the proportion of ILI among allcoded visits made by emergency general practitioners (GPs) workingin the SOS Médecins associations [3]; 3) the incidence rate of ILIestimated from a sample of sentinel GPs [4].For each region each week, 3 statistical outbreak detection methodswere applied to the 3 data sources, generating 9 results that werecombined to obtain a weekly regional influenza alarm level. Basedon this alarm level and on other information (e.g.virological data),the epidemiologists then determined the epidemiological status ofeach region as either 1) epidemic-free, 2) in pre/post epidemic or 3)epidemic.The R software was used for programming algorithms and buildingthe web interface (package shiny).ResultsThe epidemiological status of influenza at the regional level wascommunicated through maps published in the weekly influenzareports of the Agency throughout the surveillance season [5].In week 2016-W03, Brittany was the first French region to declarethe influenza epidemic, with nine other regions in pre-epidemic alert.The epidemic then spread over the whole mainland territory. The peakof the epidemic was declared in week 11, the end in week 16.ConclusionsThis regional multi-source approach has been made possible bythe sharing of data visualizations and statistical results through a webapplication. This application helped detecting early the epidemicstart and allowed a reactive communication with the regionalhealth authorities in charge of the organization of health care, themanagement and the setting up of the appropriate preventivemeasures. %R 10.5210/ojphi.v9i1.7671 %U %U https://doi.org/10.5210/ojphi.v9i1.7671 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7683 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveTo determine avian influenza A(H5N6) virus infection in humanand environment using extensive surveillances. To evaluate theprevalence of H5N6 infection among high risk population.IntroductionSince the emergence of avian influenza A(H7N9) virus in 2013,extensive surveillances have been established to monitor the humaninfection and environmental contamination with avian influenza virusin southern China. At the end of 2015, human infection with influenzaA(H5N6) virus was identified in Shenzhen for the first time throughthese surveillances. These surveillances include severe pneumoniascreening, influenza like illness (ILI) surveillance, follow-up onclose contact of the confirmed case, serological survey among poultryworkers, environment surveillance in poultry market.MethodsSevere pneumonia screening was carried out in all hospitals ofShenzhen. When a patient with severe pneumonia is suspected forinfection with avian influenza virus, after consultation with at leasttwo senior respiratory physicians from the designated expert paneland gaining their approval, the patient will be reported to local CDC,nasal and pharyngeal swabs will be collected and sent for detectionof H5N6 virus by RT-PCR.ILI surveillance was conducted in 11 sentinel hospitals, 5-20 ILIcases were sampled for detection of seasonal influenza virus by RT-PCR test every week for one sentinel. If swab sample is tested positivefor influenza type A and negative for subtypes of seasonal A(H3N2)and A(H1N1), it will be detected further for influenza A(H5N6) virus.Follow-up on close contacts was immediately carried out whenhuman case of infection with H5N6 was identified. All of closecontacts were requested to report any signs and symptoms of acuterespiratory illness for 10 days, nasal and pharyngeal swabs werecollected and tested for influenza A(H5N6) virus by RT-PCR test.In the meantime, environmental samples were collected in the marketwhich was epidemiologically associated with patient and tested forH5N6 virus by RT-PCR test.Serological survey among poultry workers was conducted in tendistricts of Shenzhen. Poultry workers were recruited in poultrymarkets and screened for any signs and symptoms of acute respiratoryillness, blood samples were collected to detect haemagglutination-inhibition (HI) antibody for influenza A(H5N6) virus.Environment surveillance was conducted twice a month in tendistricts of Shenzhen. For each district, 10 swab samples werecollected at a time. All environmental samples were tested forinfluenza A(H5N6) virus by RT-PCR test.ResultsFrom Nov 1, 2015 to May 31, 2016, 50 patients with severepneumonia were reported and detected for H5N6 virus, three patientswere confirmed to be infected with H5N6 virus. Case 1 was a 26 yearsold woman and identified on Dec 29, 2015. She purchased a duck ata live poultry stall of nearby market, cooked and ate the duck 4 daysbefore symptom onset. After admission to hospital on Dec 27, hercondition deteriorated rapidly, on Dec 30 she died. The case 2 was a25 years old man and confirmed on Jan 7, 2016. He visited a marketeveryday and had no close contact with poultry, except for passingby live poultry stalls. He recovered and was discharged from hospitalon Jan 22. The case 3 was is a 31 years old woman and reported onJan 16, 2016, she had no contact with live poultry and died on Feb 8.For 60 close contacts of three cases, none of them reported signsor symptoms of acute respiratory illness, all of nasal and pharyngealswabs were tested negative for influenza A(H5N6) virus by RT-PCRtest. Of 146 environmental swabs collected in the case’s living placesand relevant poultry markets, 38 were tested positive for influenzaA(H5N6) virus by RT-PCR test.From Nov 1, 2015 to May 31, 2016, 2812 ILI cases were sampledand tested for influenza type A and subtypes of seasonal influenza.Those samples tested positive for influenza type A could be furthersubtyped to seasonal A(H3N2) or A(H1N1), therefore no sample fromILI case was tested for influenza A(H5N6) virus.Serological surveys among poultry workers were conductedtwice, for the first survey 186 poultry workers were recruited in Oct2015, for the second survey 195 poultry workers were recruited inJan 2016. Blood sample were collected and tested for HI antibodyof influenza A(H5N6) virus. 2 individuals had H5N6 HI antibodytiter of 1:40, 5 individuals had H5N6 HI antibody titer of 1:20, rest ofthem had H5N6 HI antibody titer of <1:20. According to the WHOguideline, HI antibody titer of≥1:160 against avian influenza viruswere considered positive.From Nov 1, 2015 to May 31, 2016, of 1234 environmental swabscollected in poultry markets, 339 (27.5%)were tested positive forinfluenza A(H5N6) virus by RT-PCR test. Each of the ten districtshad poultry markets which was contaminated by influenza A(H5N6)virus.ConclusionsIn 2015-2016 winter, three cases of infection with influenzaA(H5N6) virus were identified in Shenzhen, all of them were youngindividuals with average age of 27.3 years and developed severepneumonia soon after illness onset, two cases died. For acute andsevere disease, early detection and treatment is the key measure forpatient’s prognosis.H5N6 virus was identified in poultry market and other placeswhere patient appeared, implying poultry market probably was thesource of infection. Despite the high contamination rate of H5N6virus in poultry market, we found that the infection with H5N6 virusamong poultry workers was not prevalent, with infection rate being0/381. Human infection with H5N6 virus seemed to be a sporadicoccurrence, poultry-human transmission of H5N6 virus might not bevery effective. %R 10.5210/ojphi.v9i1.7683 %U %U https://doi.org/10.5210/ojphi.v9i1.7683 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7689 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveThis study assessed the transmission of low pathogenic avianinfluenza in live poultry market setting, using paired fecal anddrinking water samples from a longitudinal surveillance program.The relative contribution of transmission via direct fecal-oral routeversus drinking water will be determined.IntroductionLive poultry markets (LPMs) continue to operate in many Asiancountries. Low pathogenic avian influenza (LPAI) viruses areoften endemic in the poultry, and LPM presents the opportunity forhuman-poultry interactions and potential human infections with avianinfluenza viruses.As a series of interventions to control avian influenza transmissionin Hong Kong LPMs, local health authority implemented marketrest days once every month since mid-2001, and an additional restday every month since 2003, during which all unsold poultry wereslaughtered and the stalls cleaned and disinfected. Rest days werefound to effectively reduce avian influenza A(H9N2) isolation rateto baseline level for a few days following the rest days. However,H9N2 isolation rate was still observed to be increasing between therest days, indicating the existence of efficient transmission in spite ofrapid turnover of poultry.In LPMs, poultry are usually stored in cages where drinkingwater is shared among poultry. This is analogous to environmentalcontamination in the wild, but transmissibility may even be higherdue to the dense environment. The use of drinking water for avianinfluenza surveillance in LPM setting was suggested to be moresensitive than fecal samples (1). However, the relative contributionof direct fecal-oral versus water transmission routes in the LPMsetting was not yet understood. This study aimed to determine theirrole, which will have implications in the control of avian influenzatransmission.MethodsWe analyzed 7,321 paired fecal and drinking water samplesfrom a longitudinal surveillance programme during the period with2 monthly rest days in the LPMs. Samples were collected fromchicken cages and subsequently cultured. Positive isolates weresubtyped by hemagglutination-inhibition tests and neuraminidaseinhibition test. Data were aggregated by sampling occasion and daysafter the rest days.A compartmental transmission model which incorporated turnoverand overnight stay of poultry, virus contamination and decay indrinking water was fitted to the data (Figure 1). A 12-hour tradingday was assumed. Based on the parameterized model, we simulatedthe scenario that water transmission was prohibited to assess the roleof transmission via drinking water.ResultsH9N2 isolation rates ranged from 0-25% for fecal samples and0-56% for drinking water samples. A clear increasing trend can beseen over days after the rest days (Figure 2). The estimated parameterfor water transmission is higher than the parameter for direct fecal-oraltransmission. Simulation results show that transmission via drinkingwater plays a major role in the amplification of LPAI in the LPMsetting (Figure 2).ConclusionsOur study showed that drinking water has a major role in thetransmission and amplification of LPAI H9N2 in LPMs, comparingto direct fecal-oral transmission route. Given the relatively lowprevalence of H9N2 in chicken, direct transmission is governed bychance events, while chickens are consistently exposed to viruses indrinking water if contaminated. Drinking water could be targeted forintervention to control LPAI transmission in LPM. The use of drinkingfountain or frequent disinfection of drinking water may be considered.Avian influenza viruses (e.g. H5N1) may differ in their pattern ofvirus shedding via oral versus fecal routes and thus extrapolation ofthese results to other viruses needs to be done with caution. However,H7N9 viruses are similar to H9N2 viruses by being shed primarilyvia the respiratory / oral route (2) and it is reasonable to assume thatthese conclusions would apply to H7N9 virus which is of major publichealth concern. However, our model could not differentiate the effectof indirect fecal-oral transmission through contamination of drinkingwater by droppings versus contamination through drinking. %R 10.5210/ojphi.v9i1.7689 %U %U https://doi.org/10.5210/ojphi.v9i1.7689 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7701 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveTo determine if all-cause and cause-specific school absencesimprove predictions of virologically confirmed influenza in thecommunity.IntroductionSchool-based influenza surveillance has been considered forreal-time monitoring of influenza, as children 5-17 years old play animportant role in community-level transmission.MethodsThe Allegheny County Department of Health provided virologicallyconfirmed influenza data collected from all emergency departmentsand outpatient providers in the county for 2007 and 2011-2016.All-cause school absence rates were collected from nine schooldistricts within Allegheny County for 2010-2015. For a subset ofthese schools, in addition to all-cause absences, influenza-like illness(ILI)-specific absences were collected using a standard protocol:10 K-5 schools in one school district (2007-2008), nine K-12 schoolsin two school districts (2012-2013), and nine K-12 schools from threeschool districts (2015-2016). We used negative binomial regressionto predict weekly county-level influenza cases in Allegheny County,Pennsylvania, during the 2010-2015 influenza seasons. We includedthe following covariates in candidate models: all-cause school absencerates with different lags (weekly, 1-3 week lags, assessed in separatemodels using all other covariates) and administrative levels (county,school type, and grade), week and month of the year (assessed inseparate models), average weekly temperature, and average weeklyrelative humidity. Separately, for the three districts for whichILI-specific and all-cause absences were available, we predictedweekly county-level influenza cases using all-cause and ILI-specificabsences with all previously stated covariates. We used several cross-validation approaches to assess models, including leave 20% of weeksout, leave 20% of schools out, and leave 52-weeks out.ResultsOverall, 2,395,020 all-cause absences were observed in nineschool districts. From the subset of schools that collected ILI-specificabsences, 14,078 all-cause and 2,617 ILI-related absences werereported. A total of 11,946 virologically confirmed influenza caseswere reported in Allegheny County (Figure 1). Inclusion of 1-weeklagged absence rates in multivariate models improved model fits andpredictions of influenza cases over models using week of year andweekly average temperature (change in AIC=-4). Using grade-specificall-cause absences, absences from lower grades explained data best.For example, kindergarten absences explained 22.1% of modeldeviance compared to 0.43% using 12thgrade absences in validation.Multivariate models of week-lagged kindergarten absences, week ofyear, and weekly average temperature had the best fits over othergrade-specific multivariate models (change in AIC=-6 comparingK to 12thgrade). The utility of ILI-specific absences compared to totalabsences is mixed, performing marginally better, adjusting for othercovariates, in 2 years, but markedly worse in 1 year. However, theseresults were based on a small number of observations.ConclusionsOur findings suggest models including younger student absencesimprove predictions of virologically confirmed influenza. We foundILI-specific absences performed similarly to all-cause absences;however, more observations are needed to assess the relativeperformances of these two datasets. %R 10.5210/ojphi.v9i1.7701 %U %U https://doi.org/10.5210/ojphi.v9i1.7701 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7703 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveTo examine the baseline influenza-like illness (ILI) rates in theemergency departments (ED) of a large academic medical center(AMC), community hospital (CH), and neighboring adult andpediatric primary care clinics.IntroductionThe primary goal of syndromic surveillance is early recognitionof disease trends, in order to identify and control infectious diseaseoutbreaks, such as influenza. For surveillance of influenza-like illness(ILI), public health departments receive data from multiple sourceswith varying degrees of patient acuity, including outpatient clinicsand emergency departments. However, the lack of standardization ofthese data sources may lead to varying baseline levels of ILI activitywithin a local area.MethodsGeographic Utilization of Artificial Intelligence in Real-Timefor Disease Identification and Alert Notification (GUARDIAN) – asyndromic surveillance program – was used to automate ILI detectionusing free text chief complaint/reason for visit fields and vital signsfor a large AMC - ED, CH - ED, and neighboring outpatient clinicsduring the summer (June 15, 2016 to August 18, 2016) in order tocreate a baseline. The GUARDIAN system defined ILI as fever(temperature≥100°F) and cough and/or sore throat. Descriptiveanalysis of the observed ILI rates along with bivariate ANOVA withpost hoc Bonferroni and t-test were utilized to examine the differencewithin the settings.ResultsThe average ILI rate for EDs is higher than the clinics by at least0.39%. The CH- ED had 4.23% baseline ILI rate as compared to1.35% for AMC-ED. While the AMC – Clinics have 0.96% baselineILI rate as compared to 0.25% for CH – Clinics. The CH- ED andAMC – Clinics represented higher variations. Based on bivariate test,CH – ED was significantly different than AMC – ED, AMC - Clinics,and CH – Clinics (F= 10.58, df = 1238, p<0.05). For the AMC –Clinics, the average ILI rate for clinics providing services to adultpatients was 0.66% (SD: 4.5%) as compared to 2.03% (SD: 10.81%)for pediatric clinics, which was not statistically significant.ConclusionsThe CH - ED has higher baseline ILI rates compared to othersettings, as well as the CDC Region 5’s baseline (1.9% for 2015-2016). Based on previous studies1, this is likely due to providers’use of chief complaint free text fields. Thus, the CH – ED will havehigher thresholds for widespread ILI activity. In addition, differencesin baseline ILI rates between AMC - ED, AMC - Clinics, and CH -Clinics may result in different thresholds for widespread ILI activity(i.e., Average + 3 Standard Deviations). The CH – ED and AMC –Clinics had higher baseline standard deviations, indicting variationsin underlying patient populations. In addition, pediatric clinics havehigher baseline ILI activity but also higher variations, indicating theunique characteristics of pediatric patients. Thus, due to the abovefindings, there is a need to closely monitor the ILI rates at varioushealthcare sites for both timing of onset, as well as the intensity ofILI activity. %R 10.5210/ojphi.v9i1.7703 %U %U https://doi.org/10.5210/ojphi.v9i1.7703 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7704 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveTo estimate mortality attributable to influenza adjusted for othercommon respiratory pathogens, baseline seasonal trends and extremetemperatures.IntroductionAssigning causes of deaths to seasonal infectious diseases is difficultin part due to laboratory testing prior to death being uncommon. Sinceinfluenza (and other common respiratory pathogens) are thereforenotoriously underreported as a (contributing) cause of death in death-cause statistics modeling studies are commonly used to estimate theimpact of influenza on mortality.MethodsUsing primary cause of death (Statistics Netherlands) we modeledweekly timeseries of1) respiratory deaths (ICD10 codes J00-J99) and2) circulatory deaths (ICD10 codes I00-I99).We used regression models with an identity link and Poissonerror to relate mortality to counts of influenza A & B diagnoses.We adjusted for other common respiratory pathogens (all pathogendata was at population level from the national laboratory surveillance),temperature (from the Dutch Royal Meteorological Institute), andbaseline linear and cyclical (i.e. seasonal) trends. To account forthe yearly variation in the severity of the main circulating influenzaA strain we used time dependent variables for influenza A (fixedat lag 0 – assuming a direct effect of influenza. For influenza Band the confoundig pathogens we considered a 0 tot -4 time lag(thus allowing infection to precede death for up to 4 weeks).We performed the analyses separately per death cause group and by3 different age groups (0-64, 65-74,75+ years) over a 14-year time-period (mid 1999-mid 2013, thus 14 complete winter seasons).ResultsIn the Netherlands on average 2,636 all cause deaths occurper week varying by season (lower in summer min: 2,219 and higherin winter max: 3,564) with yearly incidence ranging from 20/10,000in 0-64 year olds to 885/10,000 in 75-plus year olds.Circulatory mortality (31% of total deaths) was higher thanrespiratory mortality (10% of total deaths) and both showed clearseasonality in all age-groups. Overall, 0.14% of all deaths wereactually coded as influenza deaths.Preliminary model estimates showed that the proportion ofrespiratory deaths attributable to influenza A were quite similar for 0-64and 65-74 year olds but higher in 75+ (5.1%, 5.7%, 7.0% respectively)while this proportion was stable across age-groups for circulatorydeaths (approximately 1.5% in all agegroups for influenza A).Influenza B was significantly associated with respiratory deathsand circulatory deaths in the oldest age group of 75+ years(with proportions of 0.7% and 0.2% respectively) while in the65-74 year olds it was associated only with circulatory deaths (0.2%).Influenza B was not significantly associated with either respiratory orcirculatory mortality in the 0-64 year age group.On average, yearly in the 75+ age group 70/10,000 respiratorydeaths and 39/10,000 circulatory deaths were attributable to influenzaA. For influenza B the incidences were 7 to 10 fold lower (7/10,000and 6/10,000 respectively).ConclusionsInfluenza A was significantly associated with respiratory andcirculatory mortality in all age groups while influenza B wassignificantly associated with respiratory and circulatory mortality inthe elderly only. %R 10.5210/ojphi.v9i1.7704 %U %U https://doi.org/10.5210/ojphi.v9i1.7704 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7738 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveA preliminary serological survey was carried out to assess thelikelihood of Influenza A (IA) infection in wild boars and begin tocharacterize the role of wild boars in the epidemiology of the IA virus.IntroductionDomestic swine have been viewed as important for the adaptationand spillover of IA from birds into human populations as they aresensitive to both avian and mammalian (including human) influenzaviruses [1]. However, in much of Eurasia and North America wildswine are geographically widespread, abundant and often come inclose contact with humans in rural and agricultural settings. Untilrecently, little attention has been paid to this as an alternate routefor IA transmission to human and domestic populations and itssignificance is not clear.Therefore, the monitoring of the exposure of wild mammals toIA was viewed as essential as potential vectors impacting domesticanimals and public health.MethodsFrom September to December 2014, wild boar sera were collectedby professional hunters in 4 Oblasts of Ukraine: Volyn, Rivne,Zhytomyr, and Chernihiv. Blood was collected from jugular veins.Sera were collected in Eppendorf type tubes, separated from wholeblood without centrifugation and stored at -20C until serologicallytested. To detect antibodies to IA, a blocking ELISA was used.Serum samples were tested using commercial test kits “InfluenzaA Ab Test” (IDEXX, USA). Specific antibodies in wild boarserum samples were detected based on manufacturer’s instructions.Briefly, sera were diluted 1:10, and incubated in test wells for60 minutes at room temperature, followed by three washes. Anti-IAHorseradish Peroxidase HRP conjugate was then added and incubatedfor 30 minutes at room temperature. Following three washes,3'',5,5’-tetramethylbenzidine (TMB), as a substrate, was addedand incubated for 15 minutes. Absorbencies were measured at 650A using a iMark Microplate Absorbance Reader and data wereanalyzed using Microsoft Excel. Based on the manufacturer’sinstructions, a serum sample was considered positive if the sample/negative control ratio (S/N) did not exceed a threshold of 0.60.Statistical analyses were performed with the program “StatisticsCalculator”.ResultsSera from 120 wild boars that were shot in 2014 were tested. Thirtyboars from each of 4 Oblasts were collected in the north central andnorthwestern regions of Ukraine. Antibodies against IAV weredetected using ELISA in 27 samples (22.5 %), (Table 1). Antibodiesto IA virus were detected in at least some of the wild boars from all ofthe 4 Oblasts. The highest percentages of seropositive samples weredetected in wild boar from Volyn and Zhytomyr Oblasts (Fig. 1).The prevalence differences were statistically significant only betweensamples from Volyn and Chernihiv Oblasts (P<0.05). The averageS/N value of all positive serum samples was 0.36±0.03.ConclusionsThis preliminary survey of IA antibodies in wild boar populationsof northern Ukraine indicates a substantial presence of exposure toIAV throughout the region.Infection of wild boar populations provides an alternative oradditional route for spillover from wild populations to domesticanimals and humans. This potential has received relatively littleattention until recently, likely in part because feral swine populationshave not been viewed as a serious challenge in most regions of theworld where the natural history of IA has received serious study.Table 1Seroprevalence of IA virus in wild boars in UkraineFigure 1Serological surveillance of wild boars for IA virus innorthern Ukraine %R 10.5210/ojphi.v9i1.7738 %U %U https://doi.org/10.5210/ojphi.v9i1.7738 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7739 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveThe performance of comparative analysis of sensitivity and resultsof detection of avian influenza virus by real time polymerase chainreaction (PCR-RT) and loop-mediated isothermal amplification of thenucleic acids (LAMP) was the main goal of the study.IntroductionAs part of this surveillance study for Avian Influenza both activeand passive surveillance samples were tested using PCR and alsoutilized to validate the LAMP method. Active surveillance samplesinclude pathological material and tracheal and cloacal swabs fromill poultry, which were subsequently assessed for avian influenzaduring diagnosis, and birds collected by hunters. Passive surveillanceincluded environmental samples such as sand and bird faeces.Active surveillance samples were taken mostly from poultry farmsacross Ukraine, where infected birds are required to be diagnosedby State Scientific Research Institute of Laboratory Diagnosticsand Veterinary Sanitary Expertise (SSRILDVSE) by Ukraine Law.Passive surveillance samples were taken primarily during the annualbird migration season. Development of simple, sensitive, and cheapmethods for diagnostics of avian influenza is a very important taskfor practical veterinary medicine. LAMP is one of such methods.The technique is based on isothermal amplification of nucleic acids.It does not require special conditions and equipment (PCR cyclers),therefore it is cheaper in comparison with PCR. Accurate diagnosisis necessary for determining the risk associated with avian influenzain Ukraine and along the Dnipro River during the migratory season.MethodsFor the research, we used PCR-RT commercial kit Bird-Flu-PCR(Ukrzoovetprompostach, Ukraine), LAMP (the protocol has beenoptimized and patented by SSRILDVSE), QIAamp®Viral RNA MiniKit. For the study, we used pathological and biological materials frombirds, which were sent to the SSRILDVSE from all regions of Ukraineaccording to the 2013–2014 State monitoring plan.Set up of the real time PCR reactions and parameters ofamplifications are indicated in the instruction to the kit.The following protocol was used to set up the RT- LAMP: 2.5μL10 X Thermopol buffer, 1 mmol/L betaine, 5 mmol/L MgSO4,1.4 mmol/L - BNTP, 12.5μmol/L SYBR GREEN, 0.5 mmol/LMnCL2, up to 25μL Nuclease-free water, 8 U Bsm DNA polymerase,0.1μM/1 of F3, 0.1μM/1 of B3, 0.8μM/1 of FIP, 0.8μM/1 of BIP,0.4μM/1 of LF, 0.4 of LB, 2μL cDNA.During our work, we used the following optimal temperature andtime for the amplification – 59°C and 60 minutes.The sensitivity of diagnostic kit Bird-Flu-PCR and RT- LAMP wasdetermined by testing cDNA of the reference strain of AIV H5N1,which was provided to us by NSC Institute for Experimental andClinical Veterinary Medicine (Kharkiv, Ukraine). For the standard,we employed concentration in the range of 10.0-0.01 ng/sample.ResultsTable 1.This table shows the reproducibility results obtained by bothmethods. However, taken into account absence of highly pathogenicavian influenza virus circulating in Ukraine during the studied period,it was not possible to confirm these results with protocols of positivesamples.Table 2.It has been established that the sensitivity of PCR-RT kit Bird-Flu-PCR is 0.01 ng/sample for gene M and 0.1 ng/sample for subtypeH5N1.Fig. 1. Visual detection of LAMP products with differentconcentrations of cDNA of avian influenza virus (ng per sample):1 – 10; 2 – 5; 3 – 1.0; 4 – 0.1; 5–7 – 0.01; 8–9 – 0.1; 10 – negative.We have examined the LAMP results using electrophoresis forthe confirmation of visual detection and correct interpretation of theresults (Fig. 2).Fig.2. Electrophoresis results for LAMP products. M –molecular weight marker; 1 – 10.0; 2 – 5.0; 3 – 1.0; 4 – 0.1; 5–7– 0.01; 8 - negative control.It has been established that the sensitivity of LAMP is0.1 ng/sample. Slightly lower sensitivity of LAMP in comparisonto PCR-RT can be explained by visual detection of the products ofthe LAMP reaction.Conclusions1. Sensitivity of both methods is high.2. LAMP is a perspective screening method for the diagnosis ofviral infectious diseases supported by confirmation of positive resultsby PCR-RT. %R 10.5210/ojphi.v9i1.7739 %U %U https://doi.org/10.5210/ojphi.v9i1.7739 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7749 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveThe purpose of this study was to identify zoonotic influenzaviruses in swine and poultry populations in Georgia and to definetheir pandemic potential.IntroductionAquatic birds are the main reservoirs of influenza viruses,however pigs represent an essential host in virus ecology as they aresusceptible to both avian and human influenza viruses. Circulatingzoonotic influenza (A/H7N9, A/H5N1, and A/H3N2v) viruses couldmutate into forms easily transmissible from human-to-human andbecome a public health concern. Georgia is located along routes usedby migrating birds where different species of aquatic birds are found.In 2006, highly pathogenic influenza virus A/H5N1 was detected intwo wild swans in Adjara (western Georgia). Moreover, in the frameof wild bird surveillance, various subtypes of influenza A viruseswere detected in mallard and gulls in Georgia (Lewis, 2013). Thusdomestic animals in Georgia have a potential chance to contractinfluenza viruses from wild birds.MethodsThe Kakheti region, the leading region in cattle breeding andpoultry production in Georgia, was selected for study. Villages wereselected for door-to-door visits to search for ill backyard animalsshowing influenza-like symptoms. In case of identification of a sickanimal, samples were obtained for laboratory investigations; samplecollection forms were filled out to generate epidemiological data.Cloacal and tracheal swabs were taken from poultry; and pharyngealand nasal swabs were collected from pigs. Each specimen wasscreened for influenza A matrix gene by real-time RT-PCR using aprotocol from the Centers for Disease Control Prevention.ResultsEighty four villages in the Kakheti region were surveyed fordomestic animals with influenza-like illness symptoms. In total,164 specimens were collected from 112 backyard animals in55 households (107 samples were from 55 poultry and 57 sampleswere from 57 pigs). All samples tested negative for Influenza A virusby real time RT-PCR. The questionnaire data revealed that the agerange of both pigs and poultry varied from one month to two years;median and mode were both 1 year. Chickens and ducks primarilyfreely ranged in backyards (67%), while half the number of pigs werekept in closed premises. Equally, 61% of pigs and poultry had contactwith other pigs or poultry within the premises.ConclusionsIn spite of the negative findings, we cannot exclude the circulationof influenza viruses in domestic animals in Georgia. Especially,considering the fact that a domestic duck with influenza A/H10virus was identified during veterinarian training in 2010 in Grigoleti(Black sea cost of Georgia) manifesting no clinical symptoms.Therefore, larger scale studies, including swabbing more backyardanimals without any clinical symptoms are necessary to identify inter-species virus transmission in the country. %R 10.5210/ojphi.v9i1.7749 %U %U https://doi.org/10.5210/ojphi.v9i1.7749 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7755 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X IntroductionUkraine’s ability to respond to the spread of viruses that causepandemics and reduce economic losses from influenza, can bestrengthened only in the presence of a developed surveillance networkincluding the monitoring of virus circulation in humans. Specialistsof Dnipropetrovsk Oblast have great experience in virologicalsurveillance on the circulation of influenza virus A/California/H1N1and timely determination of the etiology of outbreaks caused by thevirus.MethodsLaboratory diagnostics of influenza was performed usingserological methods, PCR, and virological studies in the cell culture.During the last seven epidemic seasons, including the flu pandemicof 2009-2010, most of samples came from four health-care facilitiesof Dnipropetrovsk, which were determined as basic hospitals forthe sentinel center. Patients with severe acute respiratory infections(SARI) were examined. Nasopharyngeal washouts and swabs werecollected into cryo-tubes with a transport medium. The samples werestored at hospitals in Dewar flasks.The delivery of the samples to the laboratory was performedaccording to cold-chain rules. After sample preparation stage, thesamples were tested for the presence of influenza A/B virus RNA byPCR using Bio-Rad CFX-96 cycler and the following commercialtest-kits AmpliSens® Influenza virus A/B-FL, AmpliSens® Influenzavirus A/H1-swine, and AmpliSens® Influenza virus A-type-FL.All positive samples with detected RNA of influenza virusA/H1-swine were tested using MDSK cell cultures (Canine KidneyEpithelial Cells). Flu viruses caused cytopathic changes in the cellcultures in the form of poppy-sand-like degeneracy not earlier thanin 72 hours after the infection of the cells followed by cell monolayerfragmentation.Fig. 1 MDSK cell cultureFig. 2 MDSK cell culture 72 hours after infection with influenzavirus A (H1N1)Express immunochromatic tests «Cito test influenza A+B» oragglutination test (AT) using erythrocyte suspension of human 0 (I)group blood were used for the determination of haemagglutinatingagents.ResultsDuring the seven epidemic seasons, 5,467 people were examinedfor flu and acute respiratory viral infections. During the swine flupandemic in 2009-2010, 1,217 severely ill patients were tested.Positive results were found in 50% of cases (607 persons). Fromthose, pandemic influenza virus (RNA of influenza A/H1-swinevirus) was detected in 100% of positive cases.Fig.3 Data on the determined pandemic flu virus strains(RNA of influenza A/H1-swine virus) using PCR duringepidemiological seasons from 2009 to 2016 in DnipropetrovskOblast, UkraineFrequency of pandemic influenza virus detection declined to zeroin the following epidemic seasons (2010-2011 and 2011-2012).However, incidence of the virus variant (influenza A/H1-swine)began to grow slowly during the last four epidemic flu seasonsfrom separate cases (6 in 2012-2013, 1 in 2012-2013) to 26 cases in2014-2015. During the last epidemic season (2015-2016), the numberof pandemic influenza cases increased dramatically to 166, accounting29% of all examined persons.Fig. 4 Results of isolation of pandemic strains of influenzaviruses in cell culture MDSK flu epidemic seasons from 2009-2010to 2015-2016 in Dnipropetrovsk Oblast, UkraineMost of the virus isolates were sent for confirmation and furtheridentification to the Ukrainian Center for Influenza and to theworld influenza centers (Atlanta, USA and London, UK) in order tosupport Ukraine’s participation in the worldwide pandemic influenzasurveillance. The world flu centers confirmed the isolates to beinfluenza virus strain A/California/(H1N1)/07/2009.Conclusions1. Circulation of the pandemic type of influenza virus A/California/(H1N1)/ 07/2009 among the population of Dnipropetrovsk oblast isof sporadic character.2. The return of the virus A/California/(H1N1)/07/2009 after the2009-2010 pandemic occurred during the last 2015-2016 epidemicseason.3. Application of PCR can significantly shorten the examination ofpatients with severe course of influenza, but cannot help with virusisolation.4. The use of express immunoassay tests accelerates theidentification of viruses isolates.5. The employment MDSK cell culture for influenza virusisolation allows obtaining of a spectrum of influenza strainscirculating during an epidemic period including the strainA/California/(H1N1)/07/2009. %R 10.5210/ojphi.v9i1.7755 %U %U https://doi.org/10.5210/ojphi.v9i1.7755 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7761 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveTo assess the use of medical claims records for surveillance andepidemiological inference through a case study that examines howecological and social determinants and measurement error contributeto spatial heterogeneity in reports of influenza-like illness across theUnited States.IntroductionTraditional infectious disease epidemiology is built on thefoundation of high quality and high accuracy data on disease andbehavior. Digital infectious disease epidemiology, on the other hand,uses existing digital traces, re-purposing them to identify patterns inhealth-related processes. Medical claims are an emerging digital datasource in surveillance; they capture patient-level data across an entirepopulation of healthcare seekers, and have the benefits of medicalaccuracy through physician diagnoses, and fine spatial and temporalresolution in near real-time.Our work harnesses the large volume and high specificity ofdiagnosis codes in medical claims to improve our understanding ofthe mechanisms driving spatial variation in reported influenza activityeach year. The mechanisms hypothesized to drive these patterns areas varied as: environmental factors affecting transmission or virussurvival, travel flows between different populations, population agestructure, and socioeconomic factors linked to healthcare access andquality of life. Beyond process mechanisms, the nature of surveillancedata collection may affect our interpretation of spatial epidemiologicalpatterns [1], particularly since influenza is a non-reportable diseasewith non-specific symptoms ranging from asymptomatic to severe.Considering the ways in which medical claims are generated, biasesmay arise from healthcare-seeking behavior, insurance coverage, andmedical claims database coverage in study populations.MethodsUsing aggregated U.S. medical claims for influenza-like illness(ILI) from the 2001-2002 through 2008-2009 flu seasons [2],we developed a Bayesian hierarchical modeling framework toestimate the importance of both ecological and social determinantsand measurement-related factors on observed county-level variationof influenza disease burden across the United States. Integrated NestedLaplace Approximation (INLA) techniques for Bayesian inferencewere used to render our questions computationally tractable due tothe high spatial resolution of our data (Figure 1) and the multiplicityof models in our analysis [3]. Linking data from a variety of publiclyavailable sources, we determined the strength, directionality, andconsistency of these factors over multiple flu seasons.ResultsWe found that measurement-related factors – healthcare-seekingbehavior, insurance coverage, and medical claims database coverage– were strong predictors of greater ILI intensity across seasons.Secondarily, poverty and specific humidity were negatively associatedwith ILI intensity for several seasons. Finally, by incorporatingmechanistic and measurement factors into our model, our modelpredictions present an improved map of influenza-like illness in theUnited States for the flu seasons in our study period.ConclusionsWe present a flexible modeling approach that applies to differentmedical claims diagnosis codes and disease surveillance data anddemonstrates the utility of Bayesian hierarchical models for large-scale ecological analyses. Our results increase our knowledge of thespatial distribution of influenza and the underlying processes thatdrive these patterns, promote finer spatial targeting for differenttypes of interventions, and enable the interpolation of burden in areasdifficult to surveil through traditional public health. Moreover, theyhighlight the relative contributions of surveillance data collectionand ecological processes to spatial variation in disease, and highlightthe importance of considering measurement biases when usingsurveillance data for epidemiological inference. %R 10.5210/ojphi.v9i1.7761 %U %U https://doi.org/10.5210/ojphi.v9i1.7761 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7768 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveThe study aimed at: i) analyses the regional characteristics and riskfactors of severe influenza, taking into account dominant circulatingvirus(es) ii) estimate the regional completeness of the surveillancesystem.IntroductionEvery year, circulating influenza viruses generate a significantnumber of deaths. During the 2009 pandemic influenza A(H1N1),a national non mandatory surveillance system of severe influenzacases admitted to intensive care units(ICU) was set up in France.This surveillance is regionally driven by the regional offices (CIRE)of Santé publique France, the French Public Health Agency. Thisreport provides epidemiologic analysis of the recorded data sincethe implementation of surveillance in the Centre-Val de Loire regionover seasons 2009-10 to 2015-16 in regard of influenza epidemicsdynamics.MethodsSurveillance was carried out each year from October to April.Descriptive and analytic analyses were conducted to comparepopulation characteristics, pre-existing risk factors and the clinicaldata according to influenza season and dominant circulatinginfluenza virus(es). Logistic regressions were performed to identifyfactors associated with an increased risk of acute respiratory distresssyndrome (ARDS) or death. Two capture-recapture analyses wereperformed to establish the completeness of the surveillance systemin the region. The first one was realized on all cases, using two datasources (hospital records/surveillance data) and the second one, onlyon deaths, using three data sources(additional source: medical deathcertificates).ResultsFrom 2009-10 to 2015-16, the outbreak of influenza epidemicswas started more and more late. The number of severe influenzacases reported in the Loire Valley varied from 19 in 2010-11 to 75 in2014-15. Overall, the most affected population was adults, from 41%in 2011-12 to 83% in 2009-10. However seniors (more than 65 yearsold) represented an important part of patients during three epidemics:50% in 2011-12 and around 45% during the two last seasons;during these epidemics, men, (60%-68%), were more affected thanwomen. Patients’ pre-existing risk factors were mainly: being olderthan 65 years old and suffering of cardiac or pulmonary diseases.The comparison by dominant viruses over the seasons revealed thatwhen A(H1N1) virus prevailed, severe influenza occurred mainlyin adults patients with any type of pre-existing risk factors whereaswhen A(H3N2) virus prevailed, seniors with pre-existing pulmonarydisease were the most affected. More than a third of patientsdeclared an ARDS. The overall observed lethality was close to 16%.ARDS occurred more frequently in patients who were middle-aged(45-64 years), immunocompromised or infected with A(H1N1).Pre-existing pulmonary disease was a protective factor. Risk factorsassociated with death were being older than 65 years, male and havingdeclared an ARDS. The completeness of this surveillance system wasestimated by capture-recapture methods at 59% for severe influenzacases and 40% for death cases.ConclusionsThe epidemiology of severe influenza and epidemics dynamics inthe Centre-Val de Loire follow the national trends. Every season ischaracterized by the same dominant virus at national and regionallevels in intensive care units. Influenza epidemics 2009-10 and2014-15 were particularly long and severe, the first dominatedby the A(H1N1)pdm09 virus and the second by the A(H3N2).Our study has demonstrated that the populations at risk of severeinfluenza differ according to the circulating virus(es). Accordingto the obtained estimations, the completeness of the surveillancesystem, based on voluntary report by physicians, can be consideredas satisfactory. Regarding influenza deaths relatively low percentageof completeness may be explained by the fact that two sources arehospital based whereas the third one, medical death certificates,includes all influenzadeaths with no information on the death place.Many patients were not vaccinated or their status was unknown. Mostcases admitted to ICU presented pre-existing risk factors includedin eligibility criteria in influenza vaccination policies. This studyoutlines the importance of vaccination as the first prevention measure. %R 10.5210/ojphi.v9i1.7768 %U %U https://doi.org/10.5210/ojphi.v9i1.7768 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 3 %P e7011 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X Background: Influenza (flu) surveillance using Twitter data can potentially save lives and increase efficiency by providing governments and healthcare organizations with greater situational awareness. However, research is needed to determine the impact of Twitter users’ misdiagnoses on surveillance accuracy.Objective: This study establishes the importance of Twitter users’ misdiagnoses by showing that Twitter flu surveillance in the United States failed during the 2011-2012 flu season, estimates the extent of misdiagnoses, and tests several methods for reducing the adverse effects of misdiagnoses.Methods: Metrics representing flu prevalence, seasonal misdiagnosis patterns, diagnosis uncertainty, flu symptoms, and noise were produced using Twitter data in conjunction with OpenSextant for geo-inferencing, and a maximum entropy classifier for identifying tweets related to illness. These metrics were tested for correlations with World Health Organization (WHO) positive specimen counts of flu from 2011 to 2014.Results: Twitter flu surveillance erroneously indicated a typical flu season during 2011-2012, even though the flu season peaked three months late, and erroneously indicated plateaus of flu tweets before the 2012-2013 and 2013-2014 flu seasons. Enhancements based on estimates of misdiagnoses removed the erroneous plateaus and increased the Pearson correlation coefficients by .04 and .23, but failed to correct the 2011-2012 flu season estimate. A rough estimate indicates that approximately 40% of flu tweets reflected misdiagnoses.Conclusions: Further research into factors affecting Twitter users’ misdiagnoses, in conjunction with data from additional atypical flu seasons, is needed to enable Twitter flu surveillance systems to produce reliable estimates during atypical flu seasons. %M 28210419 %R 10.5210/ojphi.v8i3.7011 %U %U https://doi.org/10.5210/ojphi.v8i3.7011 %U http://www.ncbi.nlm.nih.gov/pubmed/28210419 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 2 %P e161 %T Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis %A Sharpe,J Danielle %A Hopkins,Richard S %A Cook,Robert L %A Striley,Catherine W %+ Rollins School of Public Health, Department of Epidemiology, Emory University, 1518 Clifton Road NE, Atlanta, GA, 30322, United States, 1 912 399 2811, danielle.sharpe@emory.edu %K Internet %K social media %K Bayes theorem %K public health surveillance %K influenza, human %D 2016 %7 20.10.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Traditional influenza surveillance relies on influenza-like illness (ILI) syndrome that is reported by health care providers. It primarily captures individuals who seek medical care and misses those who do not. Recently, Web-based data sources have been studied for application to public health surveillance, as there is a growing number of people who search, post, and tweet about their illnesses before seeking medical care. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia to complement traditional surveillance for ILI. However, past studies have evaluated these Web-based sources individually or dually without comparing all 3 of them, and it would be beneficial to know which of the Web-based sources performs best in order to be considered to complement traditional methods. Objective: The objective of this study is to comparatively analyze Google, Twitter, and Wikipedia by examining which best corresponds with Centers for Disease Control and Prevention (CDC) ILI data. It was hypothesized that Wikipedia will best correspond with CDC ILI data as previous research found it to be least influenced by high media coverage in comparison with Google and Twitter. Methods: Publicly available, deidentified data were collected from the CDC, Google Flu Trends, HealthTweets, and Wikipedia for the 2012-2015 influenza seasons. Bayesian change point analysis was used to detect seasonal changes, or change points, in each of the data sources. Change points in Google, Twitter, and Wikipedia that occurred during the exact week, 1 preceding week, or 1 week after the CDC’s change points were compared with the CDC data as the gold standard. All analyses were conducted using the R package “bcp” version 4.0.0 in RStudio version 0.99.484 (RStudio Inc). In addition, sensitivity and positive predictive values (PPV) were calculated for Google, Twitter, and Wikipedia. Results: During the 2012-2015 influenza seasons, a high sensitivity of 92% was found for Google, whereas the PPV for Google was 85%. A low sensitivity of 50% was calculated for Twitter; a low PPV of 43% was found for Twitter also. Wikipedia had the lowest sensitivity of 33% and lowest PPV of 40%. Conclusions: Of the 3 Web-based sources, Google had the best combination of sensitivity and PPV in detecting Bayesian change points in influenza-related data streams. Findings demonstrated that change points in Google, Twitter, and Wikipedia data occasionally aligned well with change points captured in CDC ILI data, yet these sources did not detect all changes in CDC data and should be further studied and developed. %M 27765731 %R 10.2196/publichealth.5901 %U http://publichealth.jmir.org/2016/2/e161/ %U https://doi.org/10.2196/publichealth.5901 %U http://www.ncbi.nlm.nih.gov/pubmed/27765731 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 7 %P e177 %T Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea %A Woo,Hyekyung %A Cho,Youngtae %A Shim,Eunyoung %A Lee,Jong-Koo %A Lee,Chang-Gun %A Kim,Seong Hwan %+ Department of Health Science and Service, School of Public Health, Seoul National University, 1 Kwanakro, Kwanakgu, Seoul, 151-172, Republic Of Korea, 82 10 7135 4610, youngtae@snu.ac.kr %K influenza %K surveillance %K population surveillance %K infodemiology %K infoveillance %K Internet search %K query %K social media %K big data %K forecasting %K epidemiology %K early response %D 2016 %7 04.07.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: As suggested as early as in 2006, logs of queries submitted to search engines seeking information could be a source for detection of emerging influenza epidemics if changes in the volume of search queries are monitored (infodemiology). However, selecting queries that are most likely to be associated with influenza epidemics is a particular challenge when it comes to generating better predictions. Objective: In this study, we describe a methodological extension for detecting influenza outbreaks using search query data; we provide a new approach for query selection through the exploration of contextual information gleaned from social media data. Additionally, we evaluate whether it is possible to use these queries for monitoring and predicting influenza epidemics in South Korea. Methods: Our study was based on freely available weekly influenza incidence data and query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014. To select queries related to influenza epidemics, several approaches were applied: (1) exploring influenza-related words in social media data, (2) identifying the chief concerns related to influenza, and (3) using Web query recommendations. Optimal feature selection by least absolute shrinkage and selection operator (Lasso) and support vector machine for regression (SVR) were used to construct a model predicting influenza epidemics. Results: In total, 146 queries related to influenza were generated through our initial query selection approach. A considerable proportion of optimal features for final models were derived from queries with reference to the social media data. The SVR model performed well: the prediction values were highly correlated with the recent observed influenza-like illness (r=.956; P<.001) and virological incidence rate (r=.963; P<.001). Conclusions: These results demonstrate the feasibility of using search queries to enhance influenza surveillance in South Korea. In addition, an approach for query selection using social media data seems ideal for supporting influenza surveillance based on search query data. %M 27377323 %R 10.2196/jmir.4955 %U http://www.jmir.org/2016/7/e177/ %U https://doi.org/10.2196/jmir.4955 %U http://www.ncbi.nlm.nih.gov/pubmed/27377323 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 6 %P e175 %T Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits %A Klembczyk,Joseph Jeffrey %A Jalalpour,Mehdi %A Levin,Scott %A Washington,Raynard E %A Pines,Jesse M %A Rothman,Richard E %A Dugas,Andrea Freyer %+ Johns Hopkins University, School of Medicine, 128 S Belvedere Dr, Hampstead, NC, 28443, United States, 1 518 573 2045, jjklem@gmail.com %K influenza %K surveillance %K emergency department %K google flu trends %K infoveillance %D 2016 %7 28.06.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations. Objective: The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness. Methods: Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression. Results: Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P<.10) with improved GFT surveillance include higher proportion of female population, higher proportion with Medicare coverage, higher ED visits per capita, and lower socioeconomic status. Conclusions: GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness. %M 27354313 %R 10.2196/jmir.5585 %U http://www.jmir.org/2016/6/e175/ %U https://doi.org/10.2196/jmir.5585 %U http://www.ncbi.nlm.nih.gov/pubmed/27354313 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 5 %N 2 %P e56 %T Improving Rates of Influenza Vaccination Through Electronic Health Record Portal Messages, Interactive Voice Recognition Calls and Patient-Enabled Electronic Health Record Updates: Protocol for a Randomized Controlled Trial %A Cutrona,Sarah L %A Sreedhara,Meera %A Goff,Sarah L %A Fisher,Lloyd D %A Preusse,Peggy %A Jackson,Madeline %A Sundaresan,Devi %A Garber,Lawrence D %A Mazor,Kathleen M %+ University of Massachusetts School of Medicine, Division of General Medicine/Primary Care, 55 Lake Street, Worcester, MA, 01605, United States, 1 5088563085, Sarah.Cutrona@umassmemorial.org %K electronic health records %K influenza vaccines %K clinical decision support %K Internet %K Telephone %K Electronic Mail %K Health Records, Personal %K Medical Informatics Applications %D 2016 %7 06.05.2016 %9 Protocol %J JMIR Res Protoc %G English %X Background: Clinical decision support (CDS), including computerized reminders for providers and patients, can improve health outcomes. CDS promoting influenza vaccination, delivered directly to patients via an electronic health record (EHR) patient portal and interactive voice recognition (IVR) calls, offers an innovative approach to improving patient care. Objective: To test the effectiveness of an EHR patient portal and IVR outreach to improve rates of influenza vaccination in a large multispecialty group practice in central Massachusetts. Methods: We describe a nonblinded, randomized controlled trial of EHR patient portal messages and IVR calls designed to promote influenza vaccination. In our preparatory phase, we conducted qualitative interviews with patients, providers, and staff to inform development of EHR portal messages with embedded questionnaires and IVR call scripts. We also provided practice-wide education on influenza vaccines to all physicians and staff members, including information on existing vaccine-specific EHR CDS. Outreach will target adult patients who remain unvaccinated for more than 2 months after the start of the influenza season. Using computer-generated randomization and a factorial design, we will assign 20,000 patients who are active users of electronic patient portals to one of the 4 study arms: (1) receipt of a portal message promoting influenza vaccines and offering online appointment scheduling; (2) receipt of an IVR call with similar content but without appointment facilitation; (3) both (1) and (2); or (4) neither (1) nor (2) (usual care). We will randomize patients without electronic portals (10,000 patients) to (1) receipt of IVR call or (2) usual care. Both portal messages and IVR calls promote influenza vaccine completion. Our primary outcome is percentage of eligible patients with influenza vaccines administered at our group practice during the 2014-15 influenza season. Both outreach methods also solicit patient self-report on influenza vaccinations completed outside the clinic or on barriers to influenza vaccination. Self-reported data from both outreach modes will be uploaded into the EHR to increase accuracy of existing provider-directed EHR CDS (vaccine alerts). Results: With our proposed sample size and using a factorial design, power calculations using baseline vaccination rate estimates indicated that 4286 participants per arm would give 80% power to detect a 3% improvement in influenza vaccination rates between groups (α=.05; 2-sided). Intention-to-treat unadjusted chi-square analyses will be performed to assess the impact of portal messages, either alone or in combination with the IVR call, on influenza vaccination rates. The project was funded in January 2014. Patient enrollment for the project described here completed in December 2014. Data analysis is currently under way and first results are expected to be submitted for publication in 2016. Conclusions: If successful, this study’s intervention may be adapted by other large health care organizations to increase vaccination rates among their eligible patients. ClinicalTrial: ClinicalTrials.gov NCT02266277; https://clinicaltrials.gov/ct2/show/NCT02266277 (Archived by WebCite at http://www.webcitation.org/6fbLviHLH). %M 27153752 %R 10.2196/resprot.5478 %U http://www.researchprotocols.org/2016/2/e56/ %U https://doi.org/10.2196/resprot.5478 %U http://www.ncbi.nlm.nih.gov/pubmed/27153752 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6412 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X We aimed to estimate the influenza epidemic burden on ED attendances and hospitalizations among patients over 65y, during 2010-2015 period. Weekly numbers of visits and hospitalizations for influenza proxy-variables were modeled separately using a negative binomial regression model, including laboratory confirmed influenza identifications. Attendances and hospitalizations for acute bronchitis, pneumonia,COPD, dyspnea, asthma, acute cardiac failure, and dehydration were significantly associated with influenza positivity rate. We showed that the burden of influenza is underestimated among the elderly and should be better estimated using a dedicated diagnostic codes grouping. %R 10.5210/ojphi.v8i1.6412 %U %U https://doi.org/10.5210/ojphi.v8i1.6412 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6434 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X There are constant exposure of influenza A(H7N9) virus from live poultry market for poultry workers and the general population, but rapid reduction of viable virus in the market setting can be achieved by market closure and disinfection. Our findings highlight the value in intensive surveillance in a natural live poultry market setting, to assess human infection risk at the human-animal interface and effect of control measures on virus activity. %R 10.5210/ojphi.v8i1.6434 %U %U https://doi.org/10.5210/ojphi.v8i1.6434 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6437 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X This session will provide an overview of the pilot project regarding a laboratory-linked telephone health helpline based surveillance system. The surveillance system uses syndromic surveillance tools for early detection of illness and links it to a specimen available for laboratory testing. Through the health helpline, people with influenza-like illness are recruited and sent a nasal swab to obtain a specimen via self-swabbing that can be used to test for influenza viruses. The surveillance system is available to all residents of Ontario and was in operation for one year. Some of the results, analyses, and limitations of this project will be discussed during this session. %R 10.5210/ojphi.v8i1.6437 %U %U https://doi.org/10.5210/ojphi.v8i1.6437 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6438 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X We present a model for forecasting influenza severity which uses historic and current data from both ILINet and Google Flu Trends. The model takes advantage of the accuracy of ILINet data and the real-time updating of Google Flu Trends data, while also accounting for potential bias in Google Flu Trends data. Using both data sources allows the model to more accurately forecast important characteristics of influenza outbreaks than using ILINet data alone. %R 10.5210/ojphi.v8i1.6438 %U %U https://doi.org/10.5210/ojphi.v8i1.6438 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6449 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X Emergency department (ED) data are key components for syndromic surveillance systems. However, the lack of standardization for the content in chief complaint (CC) free-text fields may make it challenging to use these elements in syndromic surveillance systems. Furthermore, little is known regarding how ED data sources should be structured or combined to increase sensitivity without elevating false positives. In this study, we constructed two different models of ED data sources and evaluated the resulting ILI rates obtained in two different institutions. %R 10.5210/ojphi.v8i1.6449 %U %U https://doi.org/10.5210/ojphi.v8i1.6449 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6450 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X This was a retrospective cross-sectional study of 100 emergency department positive influenza-like illness (ILI) patients at an academic medical center to investigate which section(s) of a patient''s electronic medical record (EMR) contains the most relevant information for timely detection of ILI. The history of present illness and review of systems, followed by the nursing notes sections of the EMR were information rich and the most relevant sections for ILI surveillance for the study site. %R 10.5210/ojphi.v8i1.6450 %U %U https://doi.org/10.5210/ojphi.v8i1.6450 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6463 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X Throughout the largest outbreak of Highly Pathogenic Avian Influenza in the U.S., NBIC worked closely with DOI and USDA liaisons and continuously monitored open source media to provide situation summaries and integrated maps not initially available elsewhere to personnel across all levels of government. %R 10.5210/ojphi.v8i1.6463 %U %U https://doi.org/10.5210/ojphi.v8i1.6463 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6468 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X U.S. military influenza surveillance utilizes electronic reporting of clinical diagnoses to monitor health of military personnel and detect naturally occurring and bioterrorism-related epidemics. While accurate, these systems lack in timeliness. More recently, researchers have used novel data sources to detect influenza in real-time and capture non-traditional populations. With data-mining techniques, military social media users are identified and influenza-related discourse is integrated along with medical data into a comprehensive disease model. By leveraging heterogeneous data streams and developing dashboard biosurveillance analytics, the researchers hope to increase the speed at which outbreaks are detected and provide accurate disease forecasting among military personnel. %R 10.5210/ojphi.v8i1.6468 %U %U https://doi.org/10.5210/ojphi.v8i1.6468 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6484 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X Kansas'' primary method of Influenza-like Illness (ILI) surveillance is the U.S. Outpatient ILI Surveillance Network (ILINet), which experiences data submission delays. Kansas'' method of syndromic surveillance is the National Syndromic Surveillance Program (NSSP), which provides real-time data. Data from the 2014-2015 influenza season were compared. The weekly proportions of ILI patients reported to ILINet and NSSP were highly correlated, both when comparing all data at the season''s end and when comparing only data submitted before the weekly ILINet deadline. NSSP data may provide situational awareness for states whose ILINet providers do not meet the weekly submission deadline. %R 10.5210/ojphi.v8i1.6484 %U %U https://doi.org/10.5210/ojphi.v8i1.6484 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6488 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X Syndromic surveillance can be used not only to monitor overall influenza trends, but is also effective for timely surveillance and estimation of influenza activity in three target populations: a) adults > 65, b) pregnant women, and c) children < 5 in Florida. %R 10.5210/ojphi.v8i1.6488 %U %U https://doi.org/10.5210/ojphi.v8i1.6488 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6493 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X ILINet data is a central element of influenza surveillance, but data collection is resource-intensive. Increasingly, ambulatory practices are submitting data automatically to syndromic surveillance systems. These syndromic surveillance feeds could potentially provide data to ILINet for a larger number of practices due to the reduced burden on the practices. This work demonstrates that syndromic surveillance data can demonstrate comparable trends to existing ILINet data. However, some allowances in ILI definition need to be made to account for symptom summarization by registrars. %R 10.5210/ojphi.v8i1.6493 %U %U https://doi.org/10.5210/ojphi.v8i1.6493 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6496 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X We compared tempOral patterns of respiratory illness-related unplanned school closures (USC) with influenza-like illness (ILI) data from outpatient provider visits to determine usability of these data for additional insight regarding ILI activity. We found significant correlation between USC and ILINet data (R= 0.54 with p-value <0.0001). The occurrence pattern of respiratory illness-related USCs similarly corresponded with that of ILI activity regardless of the severity of influenza season. This suggests that monitoring USCs can be a useful addition to existing influenza surveillance systems, particularly during severe influenza seasons when respiratory illness-related USCs may occur more frequently. %R 10.5210/ojphi.v8i1.6496 %U %U https://doi.org/10.5210/ojphi.v8i1.6496 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6518 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X Because of the potential threats flu viruses pose, the United States, like many developed countries, has a very well established flu surveillance system consisting of 10 components collecting laboratory data, mortality data, hospitalization data and sentinel outpatient care data. Currently, this surveillance system is estimated to lag behind the actual seasonal outbreak by one to two weeks. As new data streams come online, it is important to understand what added benefit they bring to the flu surveillance system complex. For data streams to be effective, they should provide data in a more timely fashion or provide additional data that current surveillance systems cannot provide. Two multiplexed diagnostic tools designed to test syndromically relevant pathogens and wirelessly upload data for rapid integration and interpretation were evaluated to see how they fit into the influenza surveillance scheme in California. %R 10.5210/ojphi.v8i1.6518 %U %U https://doi.org/10.5210/ojphi.v8i1.6518 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6557 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X Many countries prospectively monitor influenza-attributable mortality using a variation of the Serfling seasonal time series model. Our aim is to demonstrate use of routine laboratory-confirmed influenza surveillance data to forecast predicted influenza-attributable deaths during the current influenza season. The two models provided a reasonable forecast for 2012. The model forecasts of weekly deaths during 2012 were compared against observed deaths using root mean squared error (RMSE). The results shown that the model including influenza type A and B provided a better fit. Here, we demonstrated a time series model for influenza-attributable mortality surveillance based on laboratory surveillance information. %R 10.5210/ojphi.v8i1.6557 %U %U https://doi.org/10.5210/ojphi.v8i1.6557 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6567 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X National Influenza Sentinel Surveillance (NISS) was established in Nigeria in 2006 to monitor influenza occurrence in humans in Nigeria and provide a foundation for detecting outbreaks of novel strains of influenza. The evaluation was conducted to assess the performance of the surveillance system from January to December 2014 and identify factors affecting the performance. The system was determined to be useful, flexible, acceptable, and simple. However, timeliness and stability need to be strengthened. %R 10.5210/ojphi.v8i1.6567 %U %U https://doi.org/10.5210/ojphi.v8i1.6567 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6575 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X Processing free-text clinical information in an electronic medical record may enhance surveillance systems for early identification of influenza-like illness outbreaks. However, processing clinical text using natural language processing (NLP) poses a challenge in preserving the semantics of the original information recorded. In this study, we discuss several NLP and technical issues as well as potential solutions for implementation in syndromic surveillance systems. %R 10.5210/ojphi.v8i1.6575 %U %U https://doi.org/10.5210/ojphi.v8i1.6575 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6581 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X Public health agencies strive to develop and maintain cost-effective disease surveillance systems to better understand the burden of disease within their jurisdiction. The emergence of novel avian influenza and other respiratory viruses such as MERS-CoV along with other emerging diseases including Ebola virus disease offer new challenges to public health practitioners. The authors conducted a series of surveys of influenza surveillance coordinators to identify and define these challenges. The results emphasize the importance of maintaining sufficient infrastructure and the trained personnel needed to operate these surveillance systems for optimal disease detection and public health preparedness and response readiness. %R 10.5210/ojphi.v8i1.6581 %U %U https://doi.org/10.5210/ojphi.v8i1.6581 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 4 %N 2 %P e74 %T Household Transmission of Zoonotic Influenza Viruses in a Cohort of Egyptian Poultry Growers %A El Rifay,Amira S %A Elabd,Mona A %A Abu Zeid,Dina %A Gomaa,Mokhtar R %A Tang,Li %A McKenzie,Pamela P %A Webby,Richard J %A Ali,Mohamed A %A Kayali,Ghazi %+ St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, United States, 1 9015953400, ghazi.kayali@stjude.org %K influenza %K avian %K epidemiology %K cohort %D 2015 %7 22.06.2015 %9 Original Paper %J JMIR Res Protoc %G English %X Background: The highly pathogenic avian influenza H5N1 viruses and the low pathogenic H9N2 viruses are enzootic in Egyptian poultry. Several cases of human infection with H5N1 were reported in Egypt. We previously determined that the seroprevalence of H5N1 antibodies in Egyptians exposed to poultry is 2.1% (15/708), suggesting that mild or subclinical infections with this virus occur. We aim to measure the incidence of avian influenza infection in Egyptians exposed to poultry, study risk factors of infection, study the resulting immune response, study household transmission rates, and characterize the viruses causing infections. Objective: The objective of the study is to design a 7-year, prospective, household-based cohort investigation to determine incidence and household transmission of avian influenza viruses in humans exposed to poultry. Methods: At baseline, we will collect sera to measure antibodies against influenza A. Field nurses will visit enrolled subjects at least weekly to check for influenza-like illness symptoms and verify influenza infection by a point of care rapid test. From subjects with influenza infection and their household contacts, we will collect nasal swabs, throat swabs, and nasal washes to characterize the antigenic and genetic makeup of influenza viruses infecting humans. The nurse will also obtain 2x 3-ml blood samples, one for serology, and another for isolating peripheral blood mononuclear cells. Results: Results from this cohort will enhance our understanding of the transmission of avian influenza viruses to humans in a country where such viruses are enzootic. Conclusions: This may enhance public health efforts aimed at reducing this burden. %M 26099368 %R 10.2196/resprot.4331 %U http://www.researchprotocols.org/2015/2/e74/ %U https://doi.org/10.2196/resprot.4331 %U http://www.ncbi.nlm.nih.gov/pubmed/26099368 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 1 %P e5 %T Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study %A Broniatowski,David Andre %A Dredze,Mark %A Paul,Michael J %A Dugas,Andrea %+ Department of Engineering Management and Systems Engineering, The George Washington University, Science and Engineering Hall, 800 22nd Street NW, #2700, Washington, DC, 20052, United States, 1 2029943751, broniatowski@gwu.edu %K Web mining %K social computing %K time series analysis %D 2015 %7 29.05.2015 %9 Short Paper %J JMIR Public Health Surveill %G English %X Background: Public health officials and policy makers in the United States expend significant resources at the national, state, county, and city levels to measure the rate of influenza infection. These individuals rely on influenza infection rate information to make important decisions during the course of an influenza season driving vaccination campaigns, clinical guidelines, and medical staffing. Web and social media data sources have emerged as attractive alternatives to supplement existing practices. While traditional surveillance methods take 1-2 weeks, and significant labor, to produce an infection estimate in each locale, web and social media data are available in near real-time for a broad range of locations. Objective: The objective of this study was to analyze the efficacy of flu surveillance from combining data from the websites Google Flu Trends and HealthTweets at the local level. We considered both emergency department influenza-like illness cases and laboratory-confirmed influenza cases for a single hospital in the City of Baltimore. Methods: This was a retrospective observational study comparing estimates of influenza activity of Google Flu Trends and Twitter to actual counts of individuals with laboratory-confirmed influenza, and counts of individuals presenting to the emergency department with influenza-like illness cases. Data were collected from November 20, 2011 through March 16, 2014. Each parameter was evaluated on the municipal, regional, and national scale. We examined the utility of social media data for tracking actual influenza infection at the municipal, state, and national levels. Specifically, we compared the efficacy of Twitter and Google Flu Trends data. Results: We found that municipal-level Twitter data was more effective than regional and national data when tracking actual influenza infection rates in a Baltimore inner-city hospital. When combined, national-level Twitter and Google Flu Trends data outperformed each data source individually. In addition, influenza-like illness data at all levels of geographic granularity were best predicted by national Google Flu Trends data. Conclusions: In order to overcome sensitivity to transient events, such as the news cycle, the best-fitting Google Flu Trends model relies on a 4-week moving average, suggesting that it may also be sacrificing sensitivity to transient fluctuations in influenza infection to achieve predictive power. Implications for influenza forecasting are discussed in this report. %M 27014744 %R 10.2196/publichealth.4472 %U http://publichealth.jmir.org/2015/1/e5/ %U https://doi.org/10.2196/publichealth.4472 %U http://www.ncbi.nlm.nih.gov/pubmed/27014744 %0 Journal Article %I %V %N %P %T %D %7 .. %9 %J %G English %X %U %0 Journal Article %I %V %N %P %T %D %7 .. %9 %J %G English %X %U %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5694 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X During the 2009 influenza pandemic, due to the 2009 pandemic influenza A (pH1N1) virus, there were an estimated 44 infections for every excess emergency department visit for influenza-like illness in Florida. %R 10.5210/ojphi.v7i1.5694 %U %U https://doi.org/10.5210/ojphi.v7i1.5694 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5705 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X Wikipedia usage data has been harnessed to estimate the prevalence of influenza-like illness (ILI) in the US population. By observing the number of times certain key Wikipedia articles are viewed each day, a model was developed that accurately estimated ILI, within 0.27% of official Centers for Disease Control and Prevention data. Additionally, this method was able to accurately determine the week in which ILI peaked 17% more often than Google Flu Trends. This work demonstrates the power of open, freely available data to aid in disease surveillance. %R 10.5210/ojphi.v7i1.5705 %U %U https://doi.org/10.5210/ojphi.v7i1.5705 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5707 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X (Introduction): In the tropics, influenza age-risk groups and the temporal distribution are not as thoroughly studied. (Objective): Here we determine these aspects in Abidjan. Materials and methods: We conducted a review from INHP influenza surveillance database and climatological data from the National Weather Service from 2007 to 2012. (Results): The largest number of positive specimens was from young children aged 0-4 years. The highest monthly and seasonally proportions of influenza viruses were observed in the long rainy season. ARIMAX (2,0,0)RF perform best only with rainfall. (Conclusion): Public health measures must be strengthened at the approach of rainy seasons. %R 10.5210/ojphi.v7i1.5707 %U %U https://doi.org/10.5210/ojphi.v7i1.5707 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5719 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X We developed early warning algorithms for influenza using data from the Alberta Real-Time Syndromic Surveillance Net (ARTSSN). In addition to looking for signatures of potential pandemics, the model was operationalized by using the algorithms to provide regular weekly forecasts on the influenza trends in Alberta during 2012-2014. We describe the development of the early warning model and the predicted influenza peak time and attack rate results. We report on the usefulness of this model using real-time ARTSSN data, discuss how it was used by decision makers and suggest future enhancements for this promising tool in influenza planning and preparedness. %R 10.5210/ojphi.v7i1.5719 %U %U https://doi.org/10.5210/ojphi.v7i1.5719 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5737 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X We provided emergency department providers with a real-time laboratory-based influenza surveillance tool, and evaluated the utility and acceptability of the surveillance information using provider surveys. The majority of emergency department providers found the surveillance data useful and indicated the additional information impacted their clinical decision making regarding influenza testing and treatment. %R 10.5210/ojphi.v7i1.5737 %U %U https://doi.org/10.5210/ojphi.v7i1.5737 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5753 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X Google Flu Trends (GFT) is an internet search query-based application that has been proven to add value to influenza surveillance and forecasting tools. Previous validation studies have focused on national or regional predictions. While these results have been promising, GFT has yet to be extensively validated at the city level. The AHRQ has provided weekly data for influenza-related emergency room visits across 19 cities. Correlation coefficients with city-level GFT range from .67 to .93 with a median of .84. Characterizing the effectiveness of GFT at the local level is crucial to its integration into new surveillance and prediction tools. %R 10.5210/ojphi.v7i1.5753 %U %U https://doi.org/10.5210/ojphi.v7i1.5753 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5757 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X Description of a statistical model to account for weather variation in influenza-like illness surveillance. %R 10.5210/ojphi.v7i1.5757 %U %U https://doi.org/10.5210/ojphi.v7i1.5757 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5758 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X This study estimated the early warning timeliness of a chief complaint-based syndromic surveillance system towards seasonal influenza epidemics. Findings showed that the timliness of ILI data sources changed across two influenza epidemic seasons. ILI reported from different levels of health facilities and patient groups showed distinct timeliness towards influenza epidemics indicated by virus positive rate (VPR) from National Influenza Surveillance Network. The changes of dominant strains, clinical manifestations, population groups affected in different influenza seasons might account for this inconsistency. %R 10.5210/ojphi.v7i1.5758 %U %U https://doi.org/10.5210/ojphi.v7i1.5758 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5761 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X To track outbreaks of influenza (flu), we computed twitter rates from 31 US cities and compare rates to influenza-like-illness (ILI) surveillance rates. Over 2 flu seasons, 2012-14, significant correlations and similar graphic patterns were observed. We demonstrate an interactive dashboard \"SMART\" that allows practitioners to monitor and visualize daily changes of flu tweets and related news. Compared to regional or national approaches such a GoogleFluTrends, this system allows rapid public opinion analysis and flu outbreak detection at the local level. The SMART dashboard can provide timely and actionable information for local for agencies and practitioners. %R 10.5210/ojphi.v7i1.5761 %U %U https://doi.org/10.5210/ojphi.v7i1.5761 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5787 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X ILINet is used by sentinel healthcare providers for reporting influenza surveillance data. The Florida Department of Health receives urgent care center data through the ESSENCE syndromic surveillance system from participating facilities, and which can include discharge diagnoses. Seminole County is unique in that its sentinel providers located in four separate urgent care centers report into both systems, and their discharge diagnoses are recorded in ESSENCE. Data from the two systems were therefore compared both among and between the individual sentinel providers in order to identify differences in reporting influenza in ILINet from actual discharge diagnoses for influenza identified through ESSENCE. %R 10.5210/ojphi.v7i1.5787 %U %U https://doi.org/10.5210/ojphi.v7i1.5787 %0 Journal Article %I %V %N %P %T %D %7 .. %9 %J %G English %X %U %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5821 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X Description of the development of an auto-generated color coded map showing an academic medical centers current seven day moving average influenza-like illness rate by zip code. %R 10.5210/ojphi.v7i1.5821 %U %U https://doi.org/10.5210/ojphi.v7i1.5821 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 7 %N 1 %P e5829 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2015 %7 ..2015 %9 %J Online J Public Health Inform %G English %X The abstract is devoted to monitoring studies of circulation of the AIV subtypes H5 and H7 in wild waterfowl and shorebirds around the Azov-Black Sea in Ukraine %R 10.5210/ojphi.v7i1.5829 %U %U https://doi.org/10.5210/ojphi.v7i1.5829 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 1 %P e15 %T Knowledge, Attitudes, and Practices Regarding Avian Influenza A (H7N9) Among Mobile Phone Users: A Survey in Zhejiang Province, China %A Gu,Hua %A Jiang,Zhenggang %A Chen,Bin %A Zhang,Jueman (Mandy) %A Wang,Zhengting %A Wang,Xinyi %A Cai,Jian %A Chen,Yongdi %A Zheng,Dawei %A Jiang,Jianmin %+ Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou, 310051, China, 86 57187115009, jmjiang@cdc.zj.cn %K influenza A virus, subtype H7N9 %K knowledge %K attitude %K surveillance %D 2015 %7 04.02.2015 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Understanding people’s knowledge, attitudes, and practices (KAP) regarding a new infectious disease is crucial to the prevention and control of it. Human infection with avian influenza A (H7N9) was first identified on March 31, 2013 in China. Out of the total number of 134 cases confirmed from March to September 2013 in China, Zhejiang Province saw the greatest number (46 cases). Objective: This study employed a mobile Internet survey to assess KAP regarding H7N9 among mobile phone users in Zhejiang Province. This study intended to examine KAP by region and the association between sociodemographic variables and KAP. Methods: An anonymous questionnaire was designed by Zhejiang Provincial Center for Disease Control and Prevention (CDC). A cross-sectional survey was executed through a mobile Internet application platform of China Unicom in 5 regions in Zhejiang Province. Stratified and clustered sampling methods were applied and mobile phone users were invited to participate in the study voluntarily. Results: A total of 9582 eligible mobile phone users participated in the survey with a response rate of 1.92% (9582/5,000,000). A total of 9105 valid responses (95.02%) were included for statistical analysis. Generally, more than three-quarters of the participants had some basic knowledge of H7N9 and held the attitude recommended by the Zhejiang CDC toward eating cooked poultry (77.55%, 7061/9105) and visiting a hospital at the occurrence of symptoms (78.51%, 7148/9105). Approximately half of the participants worried about contracting H7N9, and took preventive practices recommended by the Zhejiang CDC. But only 14.29% (1301/9105) of participants kept eating cooked poultry as usual. Although worry about H7N9 infection did not differ by region, Hangzhou saw the largest proportion of participants with knowledge of H7N9, which was probably because Hangzhou had the greatest number of H7N9 cases. KAP varied by some sociodemographic variables. Female participants were more likely to know about symptoms of H7N9 (OR 1.32, 95% CI 1.08-1.61), to worry about contracting it (OR 1.15, 95% CI 1.04-1.27), and to report their lives being influenced by it (OR 1.27, 95% CI 1.15-1.41). They were also more likely to take the recommended precautions. Male participants and younger participants were less likely to comply with advocated protective practices. Conclusions: The results suggest that health education should be customized depending on sociodemographic variables to achieve more effective behavioral outcomes. %M 25653213 %R 10.2196/mhealth.3394 %U http://mhealth.jmir.org/2015/1/e15/ %U https://doi.org/10.2196/mhealth.3394 %U http://www.ncbi.nlm.nih.gov/pubmed/25653213 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 16 %N 12 %P e289 %T Cumulative Query Method for Influenza Surveillance Using Search Engine Data %A Seo,Dong-Woo %A Jo,Min-Woo %A Sohn,Chang Hwan %A Shin,Soo-Yong %A Lee,JaeHo %A Yu,Maengsoo %A Kim,Won Young %A Lim,Kyoung Soo %A Lee,Sang-Il %+ Asan Medical Center, Department of Preventive Medicine, University of Ulsan, College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-gu, Seoul, 138-769, Republic Of Korea, 82 2 3010 3350, leiseo@hanmail.net %K syndromic surveillance system %K influenza %K influenza-like illness %K Google Flu Trends %K Internet search %K query %D 2014 %7 16.12.2014 %9 Original Paper %J J Med Internet Res %G English %X Background: Internet search queries have become an important data source in syndromic surveillance system. However, there is currently no syndromic surveillance system using Internet search query data in South Korea. Objectives: The objective of this study was to examine correlations between our cumulative query method and national influenza surveillance data. Methods: Our study was based on the local search engine, Daum (approximately 25% market share), and influenza-like illness (ILI) data from the Korea Centers for Disease Control and Prevention. A quota sampling survey was conducted with 200 participants to obtain popular queries. We divided the study period into two sets: Set 1 (the 2009/10 epidemiological year for development set 1 and 2010/11 for validation set 1) and Set 2 (2010/11 for development Set 2 and 2011/12 for validation Set 2). Pearson’s correlation coefficients were calculated between the Daum data and the ILI data for the development set. We selected the combined queries for which the correlation coefficients were .7 or higher and listed them in descending order. Then, we created a cumulative query method n representing the number of cumulative combined queries in descending order of the correlation coefficient. Results: In validation set 1, 13 cumulative query methods were applied, and 8 had higher correlation coefficients (min=.916, max=.943) than that of the highest single combined query. Further, 11 of 13 cumulative query methods had an r value of ≥.7, but 4 of 13 combined queries had an r value of ≥.7. In validation set 2, 8 of 15 cumulative query methods showed higher correlation coefficients (min=.975, max=.987) than that of the highest single combined query. All 15 cumulative query methods had an r value of ≥.7, but 6 of 15 combined queries had an r value of ≥.7. Conclusions: Cumulative query method showed relatively higher correlation with national influenza surveillance data than combined queries in the development and validation set. %M 25517353 %R 10.2196/jmir.3680 %U http://www.jmir.org/2014/12/e289/ %U https://doi.org/10.2196/jmir.3680 %U http://www.ncbi.nlm.nih.gov/pubmed/25517353 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 16 %N 11 %P e250 %T The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance %A Aslam,Anoshé A %A Tsou,Ming-Hsiang %A Spitzberg,Brian H %A An,Li %A Gawron,J Mark %A Gupta,Dipak K %A Peddecord,K Michael %A Nagel,Anna C %A Allen,Christopher %A Yang,Jiue-An %A Lindsay,Suzanne %+ Department of Geography, San Diego State University, Storm Hall 313C, 5500 Campanile Drive, San Diego, CA, 92115, United States, 1 619 594 0205, mtsou@mail.sdsu.edu %K Twitter %K tweets %K infoveillance %K infodemiology %K syndromic surveillance %K influenza %K Internet %D 2014 %7 14.11.2014 %9 Original Paper %J J Med Internet Res %G English %X Background: Existing influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza. Objective: There were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego. Methods: Tweets containing the keyword “flu” were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was “valid”, or from a user who was likely ill with the flu. Results: Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier. Conclusions: Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data. %M 25406040 %R 10.2196/jmir.3532 %U http://www.jmir.org/2014/11/e250/ %U https://doi.org/10.2196/jmir.3532 %U http://www.ncbi.nlm.nih.gov/pubmed/25406040 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 16 %N 10 %P e236 %T A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives %A Nagar,Ruchit %A Yuan,Qingyu %A Freifeld,Clark C %A Santillana,Mauricio %A Nojima,Aaron %A Chunara,Rumi %A Brownstein,John S %+ Children's Hospital Informatics Program, Boston Children's Hospital, 1 Autumn Street, Boston, MA, , United States, 1 2817258062, ruchit.nagar@yale.edu %K influenza %K Twitter %K New York City %K spatiotemporal %K Google Flu Trends %K infodemiology %K mHealth %K social media, natural language processing %K medical informatics %D 2014 %7 20.10.2014 %9 Original Paper %J J Med Internet Res %G English %X Background: Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter’s relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches. Objective: The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases. Methods: From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords “flu”, “influenza”, “gripe”, and “high fever”. The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis. Results: Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay’s Center and the Atlantic Avenue Terminal. Conclusions: While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter’s strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable. %M 25331122 %R 10.2196/jmir.3416 %U http://www.jmir.org/2014/10/e236/ %U https://doi.org/10.2196/jmir.3416 %U http://www.ncbi.nlm.nih.gov/pubmed/25331122 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 16 %N 9 %P e221 %T An Internet-Based Epidemiological Investigation of the Outbreak of H7N9 Avian Influenza A in China Since Early 2013 %A Mao,Chen %A Wu,Xin-Yin %A Fu,Xiao-Hong %A Di,Meng-Yang %A Yu,Yuan-Yuan %A Yuan,Jin-Qiu %A Yang,Zu-Yao %A Tang,Jin-Ling %+ School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, 4/F, School of Public Health and Primary Care, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, , China (Hong Kong), 852 22528779, jltang@cuhk.edu.hk %K influenza A virus, H7N9 subtype %K Internet %K big data %K disease outbreaks %K epidemiology %D 2014 %7 25.09.2014 %9 Original Paper %J J Med Internet Res %G English %X Background: In early 2013, a new type of avian influenza, H7N9, emerged in China. It quickly became an issue of great public concern and a widely discussed topic on the Internet. A considerable volume of relevant information was made publicly available on the Internet through various sources. Objective: This study aimed to describe the outbreak of H7N9 in China based on data openly available on the Internet and to validate our investigation by comparing our findings with a well-conducted conventional field epidemiologic study. Methods: We searched publicly accessible Internet data on the H7N9 outbreak primarily from government and major mass media websites in China up to February 10, 2014. Two researchers independently extracted, compared, and confirmed the information of each confirmed H7N9 case using a self-designed data extraction form. We summarized the epidemiological and clinical characteristics of confirmed H7N9 cases and compared them with those from the field study. Results: According to our data updated until February 10, 2014, 334 confirmed H7N9 cases were identified. The median age was 58 years and 67.0% (219/327) were males. Cases were reported in 15 regions in China. Five family clusters were found. Of the 16.8% (56/334) of the cases with relevant data, 69.6% (39/56) reported a history of exposure to animals. Of the 1751 persons with a close contact with a confirmed case, 0.6% (11/1751) of them developed respiratory symptoms during the 7-day surveillance period. In the 97.9% (327/334) of the cases with relevant data, 21.7% (71/327) died, 20.8% (68/327) were discharged from a hospital, and 57.5% (188/327) were of uncertain status. We compared our findings before February 10, 2014 and those before December 1, 2013 with those from the conventional field study, which had the latter cutoff date of ours in data collection. Our study showed most epidemiological and clinical characteristics were similar to those in the field study, except for case fatality (71/327, 21.7% for our data before February 10; 45/138, 32.6% for our data before December 1; 47/139, 33.8% for the field study), time from illness onset to first medical care (4 days, 3 days, and 1 day), and time from illness onset to death (16.5 days, 17 days, and 21 days). Conclusions: Findings from our Internet-based investigation were similar to those from the conventional field study in most epidemiological and clinical aspects of the outbreak. Importantly, publicly available Internet data are open to any interested researchers and can thus greatly facilitate the investigation and control of such outbreaks. With improved efforts for Internet data provision, Internet-based investigation has a great potential to become a quick, economical, novel approach to investigating sudden issues of great public concern that involve a relatively small number of cases like this H7N9 outbreak. %M 25257217 %R 10.2196/jmir.3763 %U http://www.jmir.org/2014/9/e221/ %U https://doi.org/10.2196/jmir.3763 %U http://www.ncbi.nlm.nih.gov/pubmed/25257217 %0 Journal Article %@ 14388871 %I JMIR Publications Inc. %V 16 %N 4 %P e116 %T Performance of eHealth Data Sources in Local Influenza Surveillance: A 5-Year Open Cohort Study %A Timpka,Toomas %A Spreco,Armin %A Dahlström,Örjan %A Eriksson,Olle %A Gursky,Elin %A Ekberg,Joakim %A Blomqvist,Eva %A Strömgren,Magnus %A Karlsson,David %A Eriksson,Henrik %A Nyce,James %A Hinkula,Jorma %A Holm,Einar %+ Department of Medical and Health Sciences, Linköping University, Linköping University Hospital Campus, Linköping, SE58183, Sweden, 46 101030000, toomas.timpka@liu.se %K influenza %K infectious disease surveillance %K Internet %K eHealth %K Google Flu Trends %K telenursing call centers %K website usage %K open cohort design %K public health %D 2014 %7 28.04.2014 %9 Original Paper %J J Med Internet Res %G English %X Background: There is abundant global interest in using syndromic data from population-wide health information systems—referred to as eHealth resources—to improve infectious disease surveillance. Recently, the necessity for these systems to achieve two potentially conflicting requirements has been emphasized. First, they must be evidence-based; second, they must be adjusted for the diversity of populations, lifestyles, and environments. Objective: The primary objective was to examine correlations between data from Google Flu Trends (GFT), computer-supported telenursing centers, health service websites, and influenza case rates during seasonal and pandemic influenza outbreaks. The secondary objective was to investigate associations between eHealth data, media coverage, and the interaction between circulating influenza strain(s) and the age-related population immunity. Methods: An open cohort design was used for a five-year study in a Swedish county (population 427,000). Syndromic eHealth data were collected from GFT, telenursing call centers, and local health service website visits at page level. Data on mass media coverage of influenza was collected from the major regional newspaper. The performance of eHealth data in surveillance was measured by correlation effect size and time lag to clinically diagnosed influenza cases. Results: Local media coverage data and influenza case rates showed correlations with large effect sizes only for the influenza A (A) pH1N1 outbreak in 2009 (r=.74, 95% CI .42-.90; P<.001) and the severe seasonal A H3N2 outbreak in 2011-2012 (r=.79, 95% CI .42-.93; P=.001), with media coverage preceding case rates with one week. Correlations between GFT and influenza case data showed large effect sizes for all outbreaks, the largest being the seasonal A H3N2 outbreak in 2008-2009 (r=.96, 95% CI .88-.99; P<.001). The preceding time lag decreased from two weeks during the first outbreaks to one week from the 2009 A pH1N1 pandemic. Telenursing data and influenza case data showed correlations with large effect sizes for all outbreaks after the seasonal B and A H1 outbreak in 2007-2008, with a time lag decreasing from two weeks for the seasonal A H3N2 outbreak in 2008-2009 (r=.95, 95% CI .82-.98; P<.001) to none for the A p H1N1 outbreak in 2009 (r=.84, 95% CI .62-.94; P<.001). Large effect sizes were also observed between website visits and influenza case data. Conclusions: Correlations between the eHealth data and influenza case rates in a Swedish county showed large effect sizes throughout a five-year period, while the time lag between signals in eHealth data and influenza rates changed. Further research is needed on analytic methods for adjusting eHealth surveillance systems to shifts in media coverage and to variations in age-group related immunity between virus strains. The results can be used to inform the development of alert-generating eHealth surveillance systems that can be subject for prospective evaluations in routine public health practice. %M 24776527 %R 10.2196/jmir.3099 %U http://www.jmir.org/2014/4/e116/ %U https://doi.org/10.2196/jmir.3099 %U http://www.ncbi.nlm.nih.gov/pubmed/24776527 %0 Journal Article %@ 14388871 %I JMIR Publications Inc. %V 16 %N 3 %P e78 %T Determinants of Follow-Up Participation in the Internet-Based European Influenza Surveillance Platform Influenzanet %A Bajardi,Paolo %A Vespignani,Alessandro %A Funk,Sebastian %A Eames,Ken TD %A Edmunds,W John %A Turbelin,Clément %A Debin,Marion %A Colizza,Vittoria %A Smallenburg,Ronald %A Koppeschaar,Carl E %A Franco,Ana O %A Faustino,Vitor %A Carnahan,Annasara %A Rehn,Moa %A Paolotti,Daniela %+ Institute for Scientific Interchange Foundation, via Alassio 11/c, Torino, , Italy, 39 011 6603090, daniela.paolotti@isi.it %K participatory surveillance %K Internet %K influenza %D 2014 %7 10.03.2014 %9 Original Paper %J J Med Internet Res %G English %X Background: “Influenzanet” is a network of Internet-based platforms aimed at collecting real-time data for influenza surveillance in several European countries. More than 30,000 European volunteers participate every year in the study, representing one of the largest existing Internet-based multicenter cohorts. Each week during the influenza season, participants are asked to report their symptoms (if any) along with a set of additional questions. Objective: Focusing on the first influenza season of 2011-12, when the Influenzanet system was completely harmonized within a common framework in Sweden, the United Kingdom, the Netherlands, Belgium, France, Italy, and Portugal, we investigated the propensity of users to regularly come back to the platform to provide information about their health status. Our purpose was to investigate demographic and behavioral factors associated with participation in follow-up. Methods: By means of a multilevel analysis, we evaluated the association between regular participation during the season and sociodemographic and behavioral characteristics as measured by a background questionnaire completed by participants on registration. Results: We found that lower participation in follow-up was associated with lower educational status (odds ratio [OR] 0.80, 95% CI 0.75-0.85), smoking (OR 0.64, 95% CI 0.59-0.70), younger age (OR ranging from 0.30, 95% CI 0.26-0.33 to 0.70, 95% CI 0.64-0.77), not being vaccinated against seasonal influenza (OR 0.77, 95% CI 0.72-0.84), and living in a household with children (OR 0.69, 95% CI 0.65-0.74). Most of these results hold when single countries are analyzed separately. Conclusions: Given the opportunistic enrollment of self-selected volunteers in the Influenzanet study, we have investigated how sociodemographic and behavioral characteristics may be associated with follow-up participation in the Influenzanet cohort. The study described in this paper shows that, overall, the most important determinants of participation are related to education and lifestyle: smoking, lower education level, younger age, people living with children, and people who have not been vaccinated against seasonal influenza tend to have a lower participation in follow-up. Despite the cross-country variation, the main findings are similar in the different national cohorts, and indeed the results are found to be valid also when performing a single-country analysis. Differences between countries do not seem to play a crucial role in determining the factors associated with participation in follow-up. %M 24613818 %R 10.2196/jmir.3010 %U http://www.jmir.org/2014/3/e78/ %U https://doi.org/10.2196/jmir.3010 %U http://www.ncbi.nlm.nih.gov/pubmed/24613818 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5046 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X The Houston Department of Health Department of Health and Human Services (HDHHS) monitors emergency departments (ED) chief complaints across the Houston metropolitan area, Harris County, and the surrounding jurisdictions by Real-time Outbreak Disease Surveillance (RODS). The influenza-like illnesses (ILI) data is collected by sentinel surveillance provider network of 12 physicians and RODS, an electronic syndromic surveillance database consisting of about 30 EDs in metropolitan Houston. Previous research indicates that there is a relationship between new HIV diagnoses and neighborhood poverty. However, there is limited research on health disparity to investigate the association between influenza-like illnesses (ILI) and social determinants of health (SDH), such as poverty. This cross-sectional study investigates the relationship between ILI and SDH. %R 10.5210/ojphi.v6i1.5046 %U %U https://doi.org/10.5210/ojphi.v6i1.5046 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5078 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X High rates of absences from influenza-like-illness (ILI) resulted in closure of a Kentucky school district for 4-days during the 2012-2013 influenza season. We calculated average daily rates of household ILI as recalled in paper surveys by parents for the weeks before, during, and after the closure. Average daily rates of ILI in the district that closed were not significantly reduced when compared with rates in 2 surrounding school districts that did not close. %R 10.5210/ojphi.v6i1.5078 %U %U https://doi.org/10.5210/ojphi.v6i1.5078 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5102 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X We evaluated the Singapore Ministry of Health''s sentinel surveillance system for influenza virus, which included the monitoring of virological samples from patients with influenza-like illness seen at government primary care clinics and private general practitoner clinics in 2011-12. Using a systematic approach, we analysed weekly data collected for the full two year period from 2011-12. Criteria applied for evaluation were based on the US Centers for Disease Control''s Guidelines for Evaluating Public Health Surveillance Systems, and included quality of the data, acceptability and geographic representativeness. The current surveillance system is satisfactory but could be enhanced by focusing on strategies to improve its acceptability and representativeness. We recommend enhancing quality of the data submitted through further engagement and information sharing with stakeholders involved. %R 10.5210/ojphi.v6i1.5102 %U %U https://doi.org/10.5210/ojphi.v6i1.5102 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5105 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X During the spring of 2013 there were human disease outbreaks caused by two emerging novel viruses: avian Influenza A (H7N9) virus in China and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in the Middle East and Europe. During these two events NBIC leveraged its expertise in enhancing collaboration and shared situational awareness among federal agencies. Facilitating collaboration allowed shared situational awareness and enhanced decision for senior leadership of federal agencies. NBIC coordinated with its interagency partners to provide federal government leadership with an overview of the situation as it has evolved for avian influenza A virus (H7N9) and MERS-CoV. %R 10.5210/ojphi.v6i1.5105 %U %U https://doi.org/10.5210/ojphi.v6i1.5105 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5106 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X Infectious disease surveillance is a process, the product of which reflects both real illness and public awareness of the disease. To develop a statistical framework to characterize influenza surveillance systems, Bayesian hierarchical model was applied to estimate the statistical relationships between influenza surveillance data and information environment (e.g. HealthMap, Google search volume,etc.) The model identified characteristics of surveillance systems that are more resistant to the information environment (percentage data, broad case definition and the senior population). General practitioner (%ILI-visit) and Laboratory (%positive) seem to capture the true infection at a constant proportion, and are less influenced by information environment. %R 10.5210/ojphi.v6i1.5106 %U %U https://doi.org/10.5210/ojphi.v6i1.5106 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5120 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X The LASUTH Site of the National Influenza Surveillance site (NISS) commenced operation in 2009 It was set up to characterize the epidemiology of seasonal human influenza. We did a secondary analysis of the data from the site. Types A (60.4%) and B (3.96%) flu were identified. The sub-types of A viruses were A/H1 (3.3%), A/H3 (57.4%), Novel AH1/ N1 (37.7%), A/ un-subtyped. The ISS site is achieving the aim for which it was set up. %R 10.5210/ojphi.v6i1.5120 %U %U https://doi.org/10.5210/ojphi.v6i1.5120 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5121 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X Since 2009, VA Office of Public Health has monitored influenza and influenza-like-illness (ILI) activity using the VA''s Healthcare Associated Infection and Influenza Surveillance System (HAIISS). Analysis of data from the 2012-2013 Influenza Season showed increases in outpatient visits, hospitalizations, telephone triage calls, total testing and positive influenza tests, indicating that the 2012-2013 season required more healthcare resource utilization. Additionally, more Veterans ≥ 65 years of age sought care compared to the last 2 seasons. Strain characterization demonstrated HA epitope differences compared to vaccine strains. Vaccine procedure data showed that influenza immunization among VA patients could be improved upon. %R 10.5210/ojphi.v6i1.5121 %U %U https://doi.org/10.5210/ojphi.v6i1.5121 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5149 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X Advanced techniques in fuzzy association rule data mining and integrating evidence from multiple sources are used to predict levels of influenza incidence several weeks in advance and display results on a map in order to help public health professionals prepare mitigation measures. This approach exploits the complicated relationships between disease incidence and measurable environmental, biological, and sociological variables that were found to have associations with the disease in other studies. Predictions were compared with data not used in model development in order to avoid exaggerated values of performance. The positive and negative predictive values were 0.941 and 0.935, respectively. %R 10.5210/ojphi.v6i1.5149 %U %U https://doi.org/10.5210/ojphi.v6i1.5149 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5179 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X The WHO/NREVSS Influenza laboratory surveillance system has been in use for ~40 years. Through multiple reporting methods, partner labs can share their influenza laboratory testing data to the Influenza Divsion at CDC. Over time, this system has evolved in complexity, and the most recent enhancement has been the addition of HL7 laboratory messaging through the Public Health Laboratory Interoperability Project. This reporting has been challenging to implement, but has added great value to the system, including an increased potential for new data analyses, increased functionality, and a braoder use of the resulting data. %R 10.5210/ojphi.v6i1.5179 %U %U https://doi.org/10.5210/ojphi.v6i1.5179 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5180 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X To early detection of influenza outbreak in the rural China, we collected the 1-year data of ILI through the web-based syndromic surveillance system in rural China (ISSC). Linear growth curve model (LGM) can be used to predict growth trajectory of ILI over 7 days (one week) in each healthcare unit by the introduction of random effects. LGM is applicable in modeling the growth and variation of daily outpatient visits of ILI in rural healthcare units. The growth rate curves of ILI surveillance data might be useful for the early detection of influenza epidemic in rural China. %R 10.5210/ojphi.v6i1.5180 %U %U https://doi.org/10.5210/ojphi.v6i1.5180 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5187 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X This study demonstrated the innovative two-stage approach for detecting ILI aberrations from daily claim data. The timeliness and comprehensiveness of the ILI surveillance could be improved by this approach. %R 10.5210/ojphi.v6i1.5187 %U %U https://doi.org/10.5210/ojphi.v6i1.5187 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5188 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X The National Center for Health Statistics (NCHS) and the Influenza Division are collaborating to increase accuracy and decrease resources needed for pneumonia and influenza mortality surveillance in the United States Electronic death registration systems as well as funding to states have made reporting of mortality data to NCHS near real-time. We assessed the timeliness of the NCHS data and compared the data to the 122 Cities Reporting System (CMRS). Because of increased timeliness of the NCHS data and correlation to the 122 CMRS we will continue to monitor data from NCHS as a potential replacement for the 122 CMRS. %R 10.5210/ojphi.v6i1.5188 %U %U https://doi.org/10.5210/ojphi.v6i1.5188 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5189 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X Administrative and vital statistics databases are frequently used for public health surveillance of influenza incidence and outcomes. We used population based, probabilistic record linkage of laboratory diagnosed influenza, emergency department, hospital admission and death registration databases to determine how frequently laboratory diagnosed influenza is coded as influenza or recorded as a cause of death. Influenza was substantially under-recorded as a cause of emergency presentation, hospitalization and death. Influenza type A infection was more likely than type B to lead to coding of influenza. Despite under-coding, time series of coded influenza from databases do reflect trends in influenza incidence. %R 10.5210/ojphi.v6i1.5189 %U %U https://doi.org/10.5210/ojphi.v6i1.5189 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 6 %N 1 %P e5016 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2014 %7 ..2014 %9 %J Online J Public Health Inform %G English %X We assessed human influenza forecasting studies to spur translation of these novel methods to practice. Searching 3 databases for papers in English, year 2000-, that validated against independent data, we included 36. They were population-based, hospital-based, and forecast pandemic spread (N=28, 4, 4, respectively); and used curve-prediction and diffusion models (N=19, 17, respectively). Four and 5 used internet search and meteorological data, respectively, besides clinical data. Eight reported sensitivity analyses; 1 compared agent-based and compartmental models. Several showed favorable 4-week-ahead skill, but lack of sensitivity analysis and model comparisons, and implementation challenges for complex models, may hinder translation to practice. %R 10.5210/ojphi.v6i1.5016 %U %U https://doi.org/10.5210/ojphi.v6i1.5016 %0 Journal Article %@ 14388871 %I JMIR Publications Inc. %V 16 %N 1 %P e20 %T Importance of Internet Surveillance in Public Health Emergency Control and Prevention: Evidence From a Digital Epidemiologic Study During Avian Influenza A H7N9 Outbreaks %A Gu,Hua %A Chen,Bin %A Zhu,Honghong %A Jiang,Tao %A Wang,Xinyi %A Chen,Lei %A Jiang,Zhenggang %A Zheng,Dawei %A Jiang,Jianmin %+ Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Rd, Binjiang District, Hangzhou, 310051, China, 86 571 87115009, jmjiang@cdc.zj.cn %K influenza A virus, H7N9 subtype %K Internet %K surveillance %K disease outbreak %D 2014 %7 17.01.2014 %9 Original Paper %J J Med Internet Res %G English %X Background: Outbreaks of human infection with a new avian influenza A H7N9 virus occurred in China in the spring of 2013. Control and prevention of a new human infectious disease outbreak can be strongly affected by public reaction and social impact through the Internet and social media. Objective: This study aimed to investigate the potential roles of Internet surveillance in control and prevention of the human H7N9 outbreaks. Methods: Official data for the human H7N9 outbreaks were collected via the China National Health and Family Planning Committee website from March 31 to April 24, 2013. We obtained daily posted and forwarded number of blogs for the keyword “H7N9” from Sina microblog website and a daily Baidu Attention Index (BAI) from Baidu website, which reflected public attention to the outbreak. Rumors identified and confirmed by the authorities were collected from Baidu search engine. Results: Both daily posted and forwarded number and BAI for keyword H7N9 increased quickly during the first 3 days of the outbreaks and remained at a high level for 5 days. The total daily posted and forwarded number for H7N9 on Sina microblog peaked at 850,000 on April 3, from zero blogs before March 31, increasing to 97,726 on April 1 and to 370,607 on April 2, and remaining above 500,000 from April 5-8 before declining to 208,524 on April 12. The total daily BAI showed a similar pattern of change to the total daily posted and forwarded number over time from March 31 to April 12. When the outbreak locations spread, especially into other areas of the same province/city and the capital, Beijing, daily posted and forwarded number and BAI increased again to a peak at 368,500 and 116,911, respectively. The median daily BAI during the studied 25 days was significantly higher among the 7 provinces/cities with reported human H7N9 cases than the 2 provinces without any cases (P<.001). So were the median daily posted and forwarded number and daily BAI in each province/city except Anhui province. We retrieved a total of 32 confirmed rumors spread across 19 provinces/cities in China. In all, 84% (27/32) of rumors were disseminated and transmitted by social media. Conclusions: The first 3 days of an epidemic is a critical period for the authorities to take appropriate action through Internet surveillance to prevent and control the epidemic, including preparation of personnel, technology, and other resources; information release; collection of public opinion and reaction; and clarification, prevention, and control of rumors. Internet surveillance can be used as an efficient and economical tool to prevent and control public health emergencies, such as H7N9 outbreaks. %M 24440770 %R 10.2196/jmir.2911 %U http://www.jmir.org/2014/1/e20/ %U https://doi.org/10.2196/jmir.2911 %U http://www.ncbi.nlm.nih.gov/pubmed/24440770 %0 Journal Article %@ 14388871 %I JMIR Publications Inc. %V 15 %N 10 %P e237 %T The Complex Relationship of Realspace Events and Messages in Cyberspace: Case Study of Influenza and Pertussis Using Tweets %A Nagel,Anna C %A Tsou,Ming-Hsiang %A Spitzberg,Brian H %A An,Li %A Gawron,J Mark %A Gupta,Dipak K %A Yang,Jiue-An %A Han,Su %A Peddecord,K Michael %A Lindsay,Suzanne %A Sawyer,Mark H %+ Department of Geography, San Diego State University, Storm Hall #326, 5500 Campanile Dr, San Diego, CA, 92182, United States, 1 619 594 0205, mtsou@mail.sdsu.edu %K Twitter %K infoveillance %K infodemiology %K cyberspace %K syndromic surveillance %K influenza %K pertussis %K whooping cough %D 2013 %7 26.10.2013 %9 Original Paper %J J Med Internet Res %G English %X Background: Surveillance plays a vital role in disease detection, but traditional methods of collecting patient data, reporting to health officials, and compiling reports are costly and time consuming. In recent years, syndromic surveillance tools have expanded and researchers are able to exploit the vast amount of data available in real time on the Internet at minimal cost. Many data sources for infoveillance exist, but this study focuses on status updates (tweets) from the Twitter microblogging website. Objective: The aim of this study was to explore the interaction between cyberspace message activity, measured by keyword-specific tweets, and real world occurrences of influenza and pertussis. Tweets were aggregated by week and compared to weekly influenza-like illness (ILI) and weekly pertussis incidence. The potential effect of tweet type was analyzed by categorizing tweets into 4 categories: nonretweets, retweets, tweets with a URL Web address, and tweets without a URL Web address. Methods: Tweets were collected within a 17-mile radius of 11 US cities chosen on the basis of population size and the availability of disease data. Influenza analysis involved all 11 cities. Pertussis analysis was based on the 2 cities nearest to the Washington State pertussis outbreak (Seattle, WA and Portland, OR). Tweet collection resulted in 161,821 flu, 6174 influenza, 160 pertussis, and 1167 whooping cough tweets. The correlation coefficients between tweets or subgroups of tweets and disease occurrence were calculated and trends were presented graphically. Results: Correlations between weekly aggregated tweets and disease occurrence varied greatly, but were relatively strong in some areas. In general, correlation coefficients were stronger in the flu analysis compared to the pertussis analysis. Within each analysis, flu tweets were more strongly correlated with ILI rates than influenza tweets, and whooping cough tweets correlated more strongly with pertussis incidence than pertussis tweets. Nonretweets correlated more with disease occurrence than retweets, and tweets without a URL Web address correlated better with actual incidence than those with a URL Web address primarily for the flu tweets. Conclusions: This study demonstrates that not only does keyword choice play an important role in how well tweets correlate with disease occurrence, but that the subgroup of tweets used for analysis is also important. This exploratory work shows potential in the use of tweets for infoveillance, but continued efforts are needed to further refine research methods in this field. %M 24158773 %R 10.2196/jmir.2705 %U http://www.jmir.org/2013/10/e237/ %U https://doi.org/10.2196/jmir.2705 %U http://www.ncbi.nlm.nih.gov/pubmed/24158773 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4446 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Information available in ED reports has the potential to improve detection of syndromic diseases. Our goal is to provide a machine-learning model characterized by improved predictive accuracy of influenza syndrome. Seven machine-learning algorithms (K2-BN, NB, EBMC, SVM, LR, ANN, RF) for the construction of models were used. Our dataset correspond to 40853 ED cases (67% training, 33% testing). The measurements used were AUROC, calibration and statistical significance testing. The results show high AUROCs with no significant difference between the algorithms and the expert model. EBMC is the most general algorithms. %R 10.5210/ojphi.v5i1.4446 %U %U https://doi.org/10.5210/ojphi.v5i1.4446 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4567 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X To develop a statistical tool for characterizing multiple influenza surveillance data for situational awareness, we used Bayesian statistical model incorporating factors such as disease transmission, behavior patterns in healthcare seeking and provision, biases and errors embedded in the reporting process, with the observed data from Hong Kong. The patterns in the ratios of paired data streams help to characterize influenza surveillance systems. To better interpret influenza surveillance data, behavior data related to healthcare resources utilization need to be collected in real-time. %R 10.5210/ojphi.v5i1.4567 %U %U https://doi.org/10.5210/ojphi.v5i1.4567 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4594 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X In response to the 2009 H1N1 pandemic, the Early Warning Infectious Disease Surveillance Program (EWIDS) from the Office of Binational Border Health California Department of Public Health, sought to strengthen outpatient ILI surveillance along the California/Baja California border region by creating a binational outpatient provider influenza surveillance network. Since the 2009-2010 influenza season the network monitors both syndromic and virologic influenza activity. The network serves as an example of a successful binational coordinated effort to establish an early warning system for enhancing situational awareness of influenza activity in a cross-border setting. %R 10.5210/ojphi.v5i1.4594 %U %U https://doi.org/10.5210/ojphi.v5i1.4594 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4605 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Avian influenza (\"bird flu\") is an infectious disease of birds caused by type A strains of the influenza virus. The infection is known to cross species barrier to infect humans. Between March 2006 and September 2007 Avian influenza (AI) outbreaks occurred in 99 poultry farms in Lagos State. The only human case of AI in Nigeria was detected at a health facility in Lagos in Jan 2007. The outbreak was curbed in the State by the end of year 2007. The collaboration between Veterinary, Health, and Information departments following the AI outbreaks aided the early control of the disease. %R 10.5210/ojphi.v5i1.4605 %U %U https://doi.org/10.5210/ojphi.v5i1.4605 %0 Journal Article %I %V %N %P %T %D %7 .. %9 %J %G English %X %U %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4403 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X In Nigeria, anecdotal reports of poultry disease with high mortality began in December 2005. H5N1 was first confirmed on February 8, 2006 among poultry from a commercial farm in Kaduna state in northern Nigeria. This was the first confirmation of the presence of H5N1 virus in Africa. Shortly after H5N1 infection in birds was confirmed in Nigeria, national and local authorities initiated culling of birds at farms with laboratory confirmed and probable out breaks of H5N. On January 17, 2007, Nigeria recorded the first human fatal case of avian influenza in a 22year old lady. %R 10.5210/ojphi.v5i1.4403 %U %U https://doi.org/10.5210/ojphi.v5i1.4403 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4406 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X As a result of antigenic drift of the influenza viruses, the composition of the influenza vaccine is updated yearly to match circulating strains. Consequently, there is need to assess the effectiveness of the influenza vaccine (VE). We aimed to measure VE among US military dependents and US-Mexico Border populations during the 2011-12 influenza season. A total of 155 influenza positive cases and 429 influenza negative controls were included in the analysis. Overall adjusted VE against laboratory-confirmed influenza was 46% (95% CI, 19‚Ä∞√õ√í64%); unadjusted was 39% (95% CI, 11‚Ä∞√õ√í58%). Seasonal vaccination was moderately protective against influenza in this population. %R 10.5210/ojphi.v5i1.4406 %U %U https://doi.org/10.5210/ojphi.v5i1.4406 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4415 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Estimates of influenza based on influenza like illness (ILI) may not capture the full spectrum of illness or result in early warning. We tested a syndromic surveillance method using hospital staff influenza like absence (ILA) to potentially enhance ILI. Rates of ILA were compared to regional surveillance data on ILI and confirmed positive influenza A test results (PITR) in hospitalised patients. ILA demonstrated accurate seasonal trends in influenza as defined by ILI, but provided more realistic estimates of the relative burden of pH1N1, and potentially earlier warning than ILI and PITR, which is likely to improve accuracy of influenza monitoring. %R 10.5210/ojphi.v5i1.4415 %U %U https://doi.org/10.5210/ojphi.v5i1.4415 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4456 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X In order to simultaneously learn about influenza activity and epidemiology across the nation, we harnessed the Internet and volunteers from around the nation to develop a participatory system for monitoring influenza-like-illness, called Flu Near You. Building on the work of participatory systems in other countries, we created a platform for weekly collection of the prevalence of 10 symptoms from volunteers. A freely available website provides an illustration of the distribution of users and their symptoms, by week. After a year of operation and with user feedback, we are able to evaluate design of the platform. Subsequent years will focus on expanding the system and detailed analysis of the data. %R 10.5210/ojphi.v5i1.4456 %U %U https://doi.org/10.5210/ojphi.v5i1.4456 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4461 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X This study compared weekly percent of influenza-like illness (ILI) in Early Notification of Community-based Epidemics (ESSENCE) to weekly counts of laboratory confirmed influenza cases and evaluated the early warning potential of the ESSENCE weekly ILI percent for five consecutive influenza seasons (2006-11) in Missouri. ESSENCE weekly ILI percent was significantly correlated with weekly counts of laboratory-confirmed influenza cases. Use of the ESSENCE percent ILI baseline provided two weeks of advanced warning for seasonal influenza activity. These findings justify the use of ESSENCE for influenza surveillance. %R 10.5210/ojphi.v5i1.4461 %U %U https://doi.org/10.5210/ojphi.v5i1.4461 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4470 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X We sought to develop a practical influenza forecast model, based on real-time, geographically focused, and easy to access data, to provide individual medical centers with advanced warning of the number of influenza cases, thus allowing sufficient time to implement an intervention. Secondly, we evaluated how the addition of a real-time influenza surveillance system, Google Flu Trends, would impact the forecasting capabilities of this model. The final model selection demonstrated that autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. %R 10.5210/ojphi.v5i1.4470 %U %U https://doi.org/10.5210/ojphi.v5i1.4470 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4484 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Interventions introduced to increase ILI screening documentation exhibited a correlation with an improved documentation rate. Aggregated data across sites demonstrate that the greatest impact is associated with email reminders for recording ILI screening results, meetings on how to improve adherence and media broadcasts associated with the circulating pandemic influenza. When one site reliably reported a period of one-to-one nurse reminders to record the ILI screening result was analyzed, one of the strongest correlations to increased adherence was demonstrated. While the results suggest more direct interventions have a significant impact, further research to isolate which interventions had the greatest impact is warranted. %R 10.5210/ojphi.v5i1.4484 %U %U https://doi.org/10.5210/ojphi.v5i1.4484 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4492 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X We constructed the School Absenteeism Surveillance System (SASSy) and the Nursery School Absenteeism Surveillance System (NSASSy), and proved that thses are quite useful for monitoring of influenza outbreak in schools and it will be gold standard of surveillance for school children in Japan. This study also showed incidence rate of influenza in children at schools, kindergartens, and nursery schools, and proved the highest incidence was in the first grade of the elementary school. This is the first finding using such the huge number of subjects, which is more than 2 million. %R 10.5210/ojphi.v5i1.4492 %U %U https://doi.org/10.5210/ojphi.v5i1.4492 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4515 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Whether antiviral or antibacterial prescriptions correlate with influenza coded encounters is unknown. Oseltamivir, zanamivir and azithromycin outpatient prescriptions from VA Corporate Data Warehouse and respiratory syndrome, influenza-like-illness (ILI) and influenza-specific ICD-9-CM coded visits from outpatient ESSENCE were analyzed for the 2010-2012 influenza seasons in all VA medical centers and outpatient clinics. Significantly more ILI and respiratory syndrome encounters occurred compared to antiviral prescriptions dispensed with marginal temporal correlation between visits and antiviral prescriptions. Azithromycin prescriptions tracked closely with the onset and peaks of the influenza season. Surprisingly, antiviral prescription data provided minimal additional information for influenza trend monitoring in VA. %R 10.5210/ojphi.v5i1.4515 %U %U https://doi.org/10.5210/ojphi.v5i1.4515 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4533 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Washington State Department of Health and Public Health Seattle King County sought to evaluate the utility of electronic ambulatory data for monitoring influenza-like illness (ILI). A definition of ILI that was previously validated using emergency department data was applied to ambulatory care records. During August 2007 through August 2012, the proportion of ILI visits strongly correlated with the number and percentage of positive influenza tests reported by the network laboratory. The results will aid in formulating guidance for ambulatory care providers who wish to utilize electronic medical record systems for weekly ILINet reporting. %R 10.5210/ojphi.v5i1.4533 %U %U https://doi.org/10.5210/ojphi.v5i1.4533 %0 Journal Article %@ 1929-073X %I JMIR Publications Inc. %V 1 %N 2 %P e20 %T Issues Regarding the Implementation of eHealth: Preparing for Future Influenza Pandemics %A Li,Junhua %A Seale,Holly %A Ray,Pradeep %A Rawlinson,William %A Lewis,Lundy %A MacIntyre,C. Raina %+ Asia-Pacific Ubiquitous Healthcare Research Centre, The University of New South Wales, APuHC, Room 1039, 1st floor, west wing, Quadrangle Building, kensington Campus, University of New South Wales, Sydney, , Australia, 61 (2) 9931 9308, junhua.li.syd@gmail.com %K eHealth %K influenza pandemic %K preparedness assessment %K case study %D 2012 %7 06.12.2012 %9 Original Paper %J Interact J Med Res %G English %X Background: eHealth is a tool that may be used to facilitate responses to influenza pandemics. Prior to implementation of eHealth in the hospital setting, assessment of the organizational preparedness is an important step in the planning process. Including this step may increase the chance of implementation success. Objective: To identify the preparedness issues in relation to implementation of eHealth for future influenza pandemics. Methods: One hospital was selected in Australia for this study. We conducted 12 individual interviews to gather a rich data set in relation to eHealth preparedness in the context of the 2009 influenza A (H1N1) pandemic at this major teaching hospital. These participants’ views were analyzed according to five main themes: (1) challenges in present practices or circumstances for pandemic responses, which indicates a need for change, (2) healthcare providers’ exposure to eHealth, (3) organizational technological capacity to support an IT innovation for medical practices, (4) resource preparedness, and (5) socio-cultural issues in association with eHealth implementation in response to a pandemic. Results: This article reports a subset of the issues identified during the case study. These issues include, for example, poor sharing of patient health records, poor protection of patient privacy, clinicians’ concerns about IT reliability and dissatisfaction with the software in use, clinicians’ concerns about IT’s impact on professional autonomy versus having inefficient IT support, and inefficient communication across departments in the form of consultation. Conclusions: Based on discussions with the participants and interpretation of their responses, we assessed the hospital’s preparedness status and also identified areas of deficiency. Accordingly, we suggest possible solutions for the areas in need of improvement to facilitate eHealth implementation’s success. The study results will also provide policymakers at national, state and local levels with insights to refine relevant public health policies for the planning and management of pandemics from the eHealth perspective. %M 23611788 %R 10.2196/ijmr.2357 %U http://www.i-jmr.org/2012/2/e20/ %U https://doi.org/10.2196/ijmr.2357 %U http://www.ncbi.nlm.nih.gov/pubmed/23611788 %0 Journal Article %@ 1438-8871 %I Gunther Eysenbach %V 14 %N 1 %P e14 %T Real-time Prescription Surveillance and its Application to Monitoring Seasonal Influenza Activity in Japan %A Sugawara,Tamie %A Ohkusa,Yasushi %A Ibuka,Yoko %A Kawanohara,Hirokazu %A Taniguchi,Kiyosu %A Okabe,Nobuhiko %+ National Institute of Infectious Diseases, Infectious Disease Surveillance Center, 1-23-1Toyama, Shinjuku, Tokyo, 162-8640, Japan, 81 3 5285 1111, tammy@nih.go.jp %K Surveillance %K influenza %K real-time surveillance %K prescriptions %K pharmacy %K anti-influenza virus %K automatic surveillance %K early response %D 2012 %7 16.01.2012 %9 Original Paper %J J Med Internet Res %G English %X Background: Real-time surveillance is fundamental for effective control of disease outbreaks, but the official sentinel surveillance in Japan collects information related to disease activity only weekly and updates it with a 1-week time lag. Objective: To report on a prescription surveillance system using electronic records related to prescription drugs that was started in 2008 in Japan, and to evaluate the surveillance system for monitoring influenza activity during the 2009–2010 and 2010–2011 influenza seasons. Methods: We developed an automatic surveillance system using electronic records of prescription drug purchases collected from 5275 pharmacies through the application service provider’s medical claims service. We then applied the system to monitoring influenza activity during the 2009–2010 and 2010–2011 influenza seasons. The surveillance system collected information related to drugs and patients directly and automatically from the electronic prescription record system, and estimated the number of influenza cases based on the number of prescriptions of anti-influenza virus medication. Then it shared the information related to influenza activity through the Internet with the public on a daily basis. Results: During the 2009–2010 influenza season, the number of influenza patients estimated by the prescription surveillance system between the 28th week of 2009 and the 12th week of 2010 was 9,234,289. In the 2010–2011 influenza season, the number of influenza patients between the 36th week of 2010 and the 12th week of 2011 was 7,153,437. The estimated number of influenza cases was highly correlated with that predicted by the official sentinel surveillance (r = .992, P < .001 for 2009–2010; r = .972, P < .001 for 2010–2011), indicating that the prescription surveillance system produced a good approximation of activity patterns. Conclusions: Our prescription surveillance system presents great potential for monitoring influenza activity and for providing early detection of infectious disease outbreaks. %M 22249906 %R 10.2196/jmir.1881 %U http://www.jmir.org/2012/1/e14/ %U https://doi.org/10.2196/jmir.1881 %U http://www.ncbi.nlm.nih.gov/pubmed/22249906 %0 Journal Article %@ 1438-8871 %I Gunther Eysenbach %V 13 %N 4 %P e85 %T Digital Dashboard Design Using Multiple Data Streams for Disease Surveillance With Influenza Surveillance as an Example %A Cheng,Calvin KY %A Ip,Dennis KM %A Cowling,Benjamin J %A Ho,Lai Ming %A Leung,Gabriel M %A Lau,Eric HY %+ School of Public Health, The University of Hong Kong, 5/F William MW Mong Block, Faculty of Medicine Building, 21 Sassoon Road,, Hong Kong, China, 852 3906 2019, ehylau@hku.hk %K Dashboard %K dissemination %K surveillance %K influenza %D 2011 %7 14.10.2011 %9 Original Paper %J J Med Internet Res %G English %X Background: Great strides have been made exploring and exploiting new and different sources of disease surveillance data and developing robust statistical methods for analyzing the collected data. However, there has been less research in the area of dissemination. Proper dissemination of surveillance data can facilitate the end user's taking of appropriate actions, thus maximizing the utility of effort taken from upstream of the surveillance-to-action loop. Objective: The aims of the study were to develop a generic framework for a digital dashboard incorporating features of efficient dashboard design and to demonstrate this framework by specific application to influenza surveillance in Hong Kong. Methods: Based on the merits of the national websites and principles of efficient dashboard design, we designed an automated influenza surveillance digital dashboard as a demonstration of efficient dissemination of surveillance data. We developed the system to synthesize and display multiple sources of influenza surveillance data streams in the dashboard. Different algorithms can be implemented in the dashboard for incorporating all surveillance data streams to describe the overall influenza activity. Results: We designed and implemented an influenza surveillance dashboard that utilized self-explanatory figures to display multiple surveillance data streams in panels. Indicators for individual data streams as well as for overall influenza activity were summarized in the main page, which can be read at a glance. Data retrieval function was also incorporated to allow data sharing in standard format. Conclusions: The influenza surveillance dashboard serves as a template to illustrate the efficient synthesization and dissemination of multiple-source surveillance data, which may also be applied to other diseases. Surveillance data from multiple sources can be disseminated efficiently using a dashboard design that facilitates the translation of surveillance information to public health actions. %M 22001082 %R 10.2196/jmir.1658 %U http://www.jmir.org/2011/4/e85/ %U https://doi.org/10.2196/jmir.1658 %U http://www.ncbi.nlm.nih.gov/pubmed/22001082 %0 Journal Article %@ 1438-8871 %I Gunther Eysenbach %V 13 %N 2 %P e36 %T Natural Supplements for H1N1 Influenza: Retrospective Observational Infodemiology Study of Information and Search Activity on the Internet %A Hill,Shawndra %A Mao,Jun %A Ungar,Lyle %A Hennessy,Sean %A Leonard,Charles E %A Holmes,John %+ Operations and Information Management, The Wharton School, University of Pennsylvania, 3730 Walnut Street, Suite 500, Philadelphia,PA, 19104, United States, 1 2155735677, shawndra@wharton.upenn.edu %K Internet search %K pandemic %K herbal supplements %K H1N1 influenza %D 2011 %7 10.05.2011 %9 Original Paper %J J Med Internet Res %G English %X Background: As the incidence of H1N1 increases, the lay public may turn to the Internet for information about natural supplements for prevention and treatment. Objective: Our objective was to identify and characterize websites that provide information about herbal and natural supplements with information about H1N1 and to examine trends in the public’s behavior in searching for information about supplement use in preventing or treating H1N1. Methods: This was a retrospective observational infodemiology study of indexed websites and Internet search activity over the period January 1, 2009, through November 15, 2009. The setting is the Internet as indexed by Google with aggregated Internet user data. The main outcome measures were the frequency of “hits” or webpages containing terms relating to natural supplements co-occurring with H1N1/swine flu, terms relating to natural supplements co-occurring with H1N1/swine flu proportional to all terms relating to natural supplements, webpage rank, webpage entropy, and temporal trend in search activity. Results: A large number of websites support information about supplements and H1N1. The supplement with the highest proportion of H1N1/swine flu information was a homeopathic remedy known as Oscillococcinum that has no known side effects; supplements with the next highest proportions have known side effects and interactions. Webpages with both supplement and H1N1/swine flu information were less likely to be medically curated or authoritative. Search activity for supplements was temporally related to H1N1/swine flu-related news reports and events. Conclusions: The prevalence of nonauthoritative webpages with information about supplements in the context of H1N1/swine flu and the increasing number of searches for these pages suggest that the public is interested in alternatives to traditional prevention and treatment of H1N1. The quality of this information is often questionable and clinicians should be cognizant that patients may be at risk of adverse events associated with the use of supplements for H1N1. %M 21558062 %R 10.2196/jmir.1722 %U http://www.jmir.org/2011/2/e36/ %U https://doi.org/10.2196/jmir.1722 %U http://www.ncbi.nlm.nih.gov/pubmed/21558062