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Journal Description

JMIR Public Health & Surveillance (JPHS, Editor-in-chief: Travis Sanchez, Emory University/Rollins School of Public Health) is a PubMed-indexed, peer-reviewed international multidisciplinary journal with a unique focus on the intersection of innovation and technology in public health, and includes topics like public health informatics, surveillance (surveillance systems and rapid reports), participatory epidemiology, infodemiology and infoveillance, digital disease detection, digital epidemiology, electronic public health interventions, mass media/social media campaigns, health communication, and emerging population health analysis systems and tools. 

JPHS has an international author- and readership and welcomes submissions from around the world.

We publish regular articles, reviews, protocols/system descriptions and viewpoint papers on all aspects of public health, with a focus on innovation and technology in public health. The main themes/topics covered by this journal can be found here.

Apart from publishing traditional public health research and viewpoint papers as well as reports from traditional surveillance systems, JPH was one of the first (if not the only) peer-reviewed journals to publish papers with surveillance or pharmacovigilance data from non-traditional, unstructured big data and text sources such as social media and the Internet (infoveillance, digital disease detection), or reports on novel participatory epidemiology projects, where observations are solicited from the public.  

Among other innovations, JPHS is also dedicated to support rapid open data sharing and rapid open access to surveillance and outbreak data. As one of the novel features we plan to publish rapid or even real-time surveillance reports and open data. The methods and description of the surveillance system may be peer-reviewed and published only once in detail, in a  "baseline report" (in a JMIR Res Protoc or a JMIR Public Health & Surveill paper), and authors then have the possibility to publish data and reports in frequent intervals rapidly and with only minimal additional peer-review (we call this article type "Rapid Surveillance Reports"). JMIR Publications may even work with authors/researchers and developers of selected surveillance systems on APIs for semi-automated reports (e.g. weekly reports to be automatically published in JPHS and indexed in PubMed, based on data-feeds from surveillance systems and minimal narratives and abstracts).

Furthermore, during epidemics and public health emergencies, submissions with critical data will be processed with expedited peer-review to enable publication within days or even in real-time.

We also publish descriptions of open data resources and open source software. Where possible, we can and want to publish or even host the actual software or dataset on the journal website.

 

Recent Articles:

  • Untitled. Copyright: The authors; License: Licensed by the authors.

    The Eastern Mediterranean Public Health Network: A Resource for Improving Public Health in the Eastern Mediterranean Region

    Authors List:

    Abstract:

  • Source: Freepik; Copyright: ijeab; URL: https://www.freepik.com/free-photo/business-people-meeting_1236189.htm; License: Licensed by the authors.

    Communicating Science in the Digital and Social Media Ecosystem: Scoping Review and Typology of Strategies Used by Health Scientists

    Abstract:

    Background: The public’s understanding of science can be influential in a wide range of areas related to public health, including policy making and self-care. Through the digital and social media ecosystem, health scientists play a growing role in public science communication (SC). Objective: This review aimed to (1) synthesize the literature on SC initiated by health scientists targeting the public in the digital and social media ecosystem and (2) describe the SC strategies and communication channels used. Methods: This scoping review was based on the Joanna Briggs Institute Methodological Framework. A systematic search was performed in 6 databases (January 2000 to April 2018). Title and abstract screening, full-text review, data charting, and critical appraisal were performed independently by two review authors. Data regarding included studies and communication channels were synthesized descriptively. A typology of SC strategies was developed using a qualitative and inductive method of data synthesis. Results: Among 960 unique publications identified, 18 met inclusion criteria. A third of publications scored good quality (6/18, 33%), half scored moderate quality (9/18, 50%), and less than a fifth scored low quality (3/18, 16%). Overall, 75 SC strategies used by health scientists were identified. These were grouped into 9 types: content, credibility, engagement, intention, linguistics, planification, presentation, social exchange, and statistics. A total of 5 types of communication channels were identified: social networking platforms (eg, Twitter), content-sharing platforms (eg, YouTube), digital research communities (eg, ResearchGate), personal blogs and websites (eg, WordPress), and social news aggregation and discussion platforms (eg, Reddit). Conclusions: Evidence suggests that multiple types of SC strategies and communication channels are used by health scientists concurrently. Few empirical studies have been conducted on SC by health scientists in the digital and social media ecosystem. Future studies should examine the appropriateness and effectiveness of SC strategies for improving public health–related outcomes and identify the barriers, facilitators, and ethical considerations inherent to the involvement of health scientists in the digital and social media ecosystem.

  • Source: freepik; Copyright: katemangostar; URL: https://www.freepik.com/free-photo/content-pretty-student-girl-using-smartphone_4530136.htm#page=6&query=using+phone&position=13; License: Licensed by JMIR.

    Associations Between Immigration-Related User Factors and eHealth Activities for Self-Care: Case of First-Generation Immigrants From Pakistan in the Oslo...

    Abstract:

    Background: Immigrant populations are often disproportionally affected by chronic diseases, such as type 2 diabetes mellitus (T2DM). Use of information and communication technology (ICT) is one promising approach for better self-care of T2DM to mitigate the social health inequalities, if designed for a wider population. However, knowledge is scarce about immigrant populations’ diverse electronic health (eHealth) activities for self-care, especially in European countries. Objective: With a target group of first-generation immigrants from Pakistan in the Oslo area, Norway, we aimed to understand their diverse eHealth activities for T2DM self-care in relation to immigration-related user factors specific to this target group: proficiency in relevant languages (Urdu, Norwegian, English), length of residence in Norway, and diagnosis of T2DM compared with general user factors (age, gender, education and digital skills, and self-rated health status). Methods: Data were from a survey among the target population (N=176) conducted in 2015-2016. Using logistic regression, we analyzed associations between user factors and experiences of each of the following eHealth activities for T2DM self-care in the last 12 months: first, information seeking by (1) search engines and (2) Web portals or email subscriptions; second, communication and consultation (1) by closed conversation with a few acquaintances using ICT and (2) on social network services; and third, active decision making by using apps for (1) tracking health information and (2) self-assessment of health status. Using Poisson regression, we also assessed the relationship between user factors and variety of eHealth activities experienced. The Bonferroni correction was used to address the multiple testing problem. Results: Regression analyses yielded the following significantly positive associations: between Urdu literacy and (1) information seeking by Web portals or email subscriptions (odds ratio [OR] 2.155, 95% CI 1.388-3.344), (2) communication and consultation on social network services (OR 5.697, 95% CI 2.487-13.053), and (3) variety (estimate=0.350, 95% CI 0.148-0.552); between length of residence in Norway and (1) communication and consultation by closed conversation with a few acquaintances using ICT (OR 1.728, 95% CI 1.193-2.503), (2) communication and consultation on social network services (OR 2.098, 95% CI 1.265-3.480), and (3) variety (estimate=0.270, 95% CI 0.117-0.424); between Norwegian language proficiency and active decision making by using apps for self-assessment of health status (OR 2.285, 95% CI 1.294-4.036); between education and digital skills and active decision making by using apps for tracking health information (OR 3.930, 95% CI 1.627-9.492); and between being a female and communication and consultation by closed conversation with a few acquaintances using ICT (OR 2.883, 95% CI 1.335-6.227). Conclusions: This study implies immigration-related factors may confound associations between general user factors and eHealth activities. Further studies are needed to explore the influence of immigration-related user factors for eHealth activities in other immigrant groups and countries. International Registered Report: RR2-DOI 10.2196/resprot.5468

  • Call center worker. Source: Image created by the Authors; Copyright: Kate Reiss; URL: http://mhealth.jmir.org/2019/3/12233; License: Creative Commons Attribution (CC-BY).

    Using a Call Center to Reduce Harm From Self-Administration of Reproductive Health Medicines in Bangladesh: Interrupted Time-Series

    Abstract:

    Background: Annually, there are approximately 25 million unsafe abortions, and this remains a leading cause of maternal morbidity and mortality. In settings where abortion is restricted, women are increasingly able to self-manage abortions by purchasing abortion medications such as misoprostol and mifepristone (RU-486) from pharmacies or other drug sellers. Better availability of these drugs has been shown to be associated with reductions in complications from unsafe abortions. In Bangladesh, abortion is restricted; however, menstrual regulation (MR) was introduced in the 1970s as an interim method of preventing pregnancy. Pharmacy provision of medications for MR is widespread, but customers purchasing these drugs from pharmacies often do not have access to quality information on dosage and potential complications. Objective: This study aimed to describe a call center intervention in Bangladesh, and assess call center use over time and how this changed when a new MR product (combined mifepristone-misoprostol) was introduced into the market. Methods: In 2010, Marie Stopes Bangladesh established a care provider–assisted call center to reduce potential harm from self-administration of MR medications. The call center number was advertised widely in pharmacies and on MR product packaging. We conducted a secondary analysis of routine data collected by call center workers between July 2012 and August 2016. We investigated the reported types of callers, the reason for call, and reported usage of MR products before and after November 2014. We used an interrupted time series (ITS) analysis to formally assess levels of change in caller characteristics and reasons for calling. Results: Over the 4-year period, 287,095 calls about MR were received and the number of users steadily increased over time. The most common callers (of 287,042 callers) were MR users (67,438, 23.49%), their husbands (65,999, 22.99%), pharmacy workers (65,828, 22.93%), and village doctors (56,036, 19.52%). Most MR calls were about misoprostol, but after November 2014, a growing proportion of calls were about the mifepristone-misoprostol regimen. The most common reasons (of 287,042 reasons) for calling were to obtain information about the regimen (208,605, 72.66%), to obtain information about side effects (208,267, 72.54%), or to report side effects (49,930, 17.39%). The ITS analyses showed that after November 2014, an increasing number of calls were from MR users who had taken the complete regimen (P=.02 and who were calling to discuss reported side effects (P=.01) and pain medication (P=.01), and there were fewer calls asking about dosages (P<.001). Conclusions: The high call volume suggests that this call center intervention addressed an unmet demand for information about MR medications from both MR users and health care providers. Call center interventions may improve the quality of information available by providing information directly to MR users and drug sellers, and thus reducing the potential harm from self-management of MR medications.

  • People walking outdoors. Source: Pexels; Copyright: Blue Ox Studio; URL: https://www.pexels.com/photo/photo-of-people-walking-outdoors-2014342/; License: Licensed by JMIR.

    Estimating the Size of Key Populations in Kampala, Uganda: 3-Source Capture-Recapture Study

    Abstract:

    Background: Key populations, including people who inject drugs (PWID), men who have sex with men (MSM), and female sex workers (FSW), are disproportionately affected by the HIV epidemic. Understanding the magnitude of, and informing the public health response to, the HIV epidemic among these populations requires accurate size estimates. However, low social visibility poses challenges to these efforts. Objective: The objective of this study was to derive population size estimates of PWID, MSM, and FSW in Kampala using capture-recapture. Methods: Between June and October 2017, unique objects were distributed to the PWID, MSM, and FSW populations in Kampala. PWID, MSM, and FSW were each sampled during 3 independent captures; unique objects were offered in captures 1 and 2. PWID, MSM, and FSW sampled during captures 2 and 3 were asked if they had received either or both of the distributed objects. All captures were completed 1 week apart. The numbers of PWID, MSM, and FSW receiving one or both objects were determined. Population size estimates were derived using the Lincoln-Petersen method for 2-source capture-recapture (PWID) and Bayesian nonparametric latent-class model for 3-source capture-recapture (MSM and FSW). Results: We sampled 467 PWID in capture 1 and 450 in capture 2; a total of 54 PWID were captured in both. We sampled 542, 574, and 598 MSM in captures 1, 2, and 3, respectively. There were 70 recaptures between captures 1 and 2, 103 recaptures between captures 2 and 3, and 155 recaptures between captures 1 and 3. There were 57 MSM captured in all 3 captures. We sampled 962, 965, and 1417 FSW in captures 1, 2, and 3, respectively. There were 316 recaptures between captures 1 and 2, 214 recaptures between captures 2 and 3, and 235 recaptures between captures 1 and 3. There were 109 FSW captured in all 3 rounds. The estimated number of PWID was 3892 (3090-5126), the estimated number of MSM was 14,019 (95% credible interval (CI) 4995-40,949), and the estimated number of FSW was 8848 (95% CI 6337-17,470). Conclusions: Our population size estimates for PWID, MSM, and FSW in Kampala provide critical population denominator data to inform HIV prevention and treatment programs. The 3-source capture-recapture is a feasible method to advance key population size estimation.

  • Source: freepik; Copyright: katemangostar; URL: https://www.freepik.com/free-photo/sick-woman-working-laptop-computer-sofa-home_4332860.htm; License: Licensed by JMIR.

    Preliminary Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention...

    Abstract:

    Background: Social networking sites (SNSs) such as Twitter are widely used by diverse demographic populations. The amount of data within SNSs has created an efficient resource for real-time analysis. Thus, data from SNSs can be used effectively to track disease outbreaks and provide necessary warnings. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. Objective: The objective of this study was to propose an efficient and accurate framework that uses data from SNSs to track disease outbreaks and provide early warnings, even for newest outbreaks, accurately. Methods: We presented a framework of outbreak prediction that included 3 main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module used the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText (FT) and 6 conventional machine learning algorithms, were evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers were trained and tested using a prelabeled dataset of flu-related and unrelated Twitter postings. The selected text classifier was then used to classify over 8,400,000 tweet documents. The flu-related documents were then mapped on a weekly basis using a mapping module. Finally, the mapped results were passed together with historical Centers for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. Results: The evaluation of flu tweet classification showed that FT, together with the extracted features, achieved accurate results with an F-measure value of 89.9% in addition to its efficiency. Therefore, FT was chosen to be the classification module to work together with the other modules in the proposed framework, including a regression-based estimator, for flu trend predictions. The estimator was evaluated using several regression models. Regression results show that the linear regression–based estimator achieved the highest accuracy results using the measure of Pearson correlation. Thus, the linear regression model was used for the module of weekly flu rate estimation. The prediction results were compared with the available recent data from CDC as the ground truth and showed a strong correlation of 96.29% . Conclusions: The results demonstrated the efficiency and the accuracy of the proposed framework that can be used even for new outbreaks with new signs and symptoms. The classification results demonstrated that the FT-based framework improves the accuracy and the efficiency of flu disease surveillance systems that use unstructured data such as data from SNSs.

  • The HIV Care Continuum Dashboard (montage). Source: The Authors / Placeit; Copyright: The Authors; URL: https://publichealth.jmir.org/2019/2/e13086; License: Licensed by JMIR.

    New York City HIV Care Continuum Dashboards: Using Surveillance Data to Improve HIV Care Among People Living With HIV in New York City

    Abstract:

    Background: HIV surveillance data can be used to improve patient outcomes. Objective: This study aimed to describe and present findings from the HIV care continuum dashboards (CCDs) initiative, which uses surveillance data to quantify and track outcomes for HIV patients at major clinical institutions in New York City. Methods: HIV surveillance data collected since 2011 were used to provide high-volume New York City clinical facilities with their performance on two key outcomes: linkage to care (LTC), among patients newly diagnosed with HIV and viral load suppression (VLS), among patients in HIV care. Results: The initiative included 21 facilities covering 33.78% (1135/3360) of new HIV diagnoses and 46.34% (28,405/61,298) of patients in HIV care in New York City in 2011 and was extended to a total of 47 sites covering 44.23% (1008/2279) of new diagnoses and 69.59% (43,897/63,083) of New York City patients in care in 2016. Since feedback of outcomes to providers began, aggregate LTC has improved by 1 percentage point and VLS by 16 percentage points. Conclusions: Disseminating information on key facility–level HIV outcomes promotes collaboration between public health and the clinical community to end the HIV epidemic. Similar initiatives can be adopted by other jurisdictions with mature surveillance systems and supportive laws and policies.

  • Children awaiting urine sample collection in Torrock, south of Chad. Source: Image created by the Authors; Copyright: The Authors; URL: https://publichealth.jmir.org/2019/2/e13359; License: Creative Commons Attribution (CC-BY).

    Prevalence of Schistosoma Haematobium Measured by a Mobile Health System in an Unexplored Endemic Region in the Subprefecture of Torrock, Chad

    Abstract:

    Background: Schistosoma haematobium is a parasitic digenetic trematode responsible for schistosomiasis (also known as bilharzia). The disease is caused by penetration of the skin by the parasite, spread by intermediate host molluscs in stagnant waters, and can be treated by administration of praziquantel. Schistosomiasis is considered to be an important but neglected tropical disease. Objective: The aim of this pilot study was to investigate the prevalence of schistosomiasis in the subprefecture of Torrock, an endemic area in Chad where no earlier investigation had been conducted and no distribution system for pharmacotherapy has ever existed. Methods: This study examined 1875 children aged 1 to 14 years over a period of 1 year. After centrifugation, urine examination was performed by a direct microscopic investigation for eggs. The investigation was conducted with a mobile health (mHealth) approach, using short message service (SMS) for communication among parents, local health workers, a pharmacist, and a medical doctor. An initial awareness campaign requested parents to have their children examined for schistosomiasis. Urine was then collected at home by the parents following the SMS request. Urine results that proved positive were sent to a medical doctor by SMS, who in turn ordered a pharmacist by SMS to distribute praziquantel to the infected children. Results: Direct microscopic examination of urine found 467 positive cases (24.9% of the total sample). Of all male and female samples, 341 (34%) and 127 (14.4%) samples were positive, respectively. The infection rate was equally distributed over age groups. The newly developed mHealth system had a limited level of participation (8%) from an estimated total of 25,000 children in the target group. Conclusions: The prevalence of schistosomiasis in children in the subprefecture of Torrock is moderately high. Efforts will be required to enhance the awareness of parents and to reach a larger percentage of the population. Systematic governmental measures should be put in place as soon as possible to increase awareness in the area and to diagnose and treat cases of schistosomiasis.

  • Source: Flickr; Copyright: alobos life; URL: https://www.flickr.com/photos/armandolobos/9226094041/; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Factors Associated With Willingness to Use Pre-Exposure Prophylaxis in Brazil, Mexico, and Peru: Web-Based Survey Among Men Who Have Sex With Men

    Abstract:

    Background: HIV disproportionally affects key populations including men who have sex with men (MSM). HIV prevalence among MSM varies from 17% in Brazil and Mexico to 13% in Peru, whereas it is below 0.5% for the general population in each country. Pre-exposure prophylaxis (PrEP) with emtricitabine/tenofovir is being implemented in the context of combination HIV prevention. Reports on willingness to use PrEP among MSM have started to emerge over the last few years. Previously reported factors associated with willingness to use PrEP include awareness, higher sexual risk behavior, and previous sexually transmitted infection. Objective: This study aimed to evaluate the factors associated with willingness to use daily oral PrEP among MSM in 3 Latin American, middle-income countries (Brazil, Mexico, and Peru). Methods: This Web-based, cross-sectional survey was advertised in 2 gay social network apps (Grindr and Hornet) used by MSM to find sexual partners and on Facebook during 2 months in 2018. Inclusion criteria were being 18 years or older, cisgender men, and HIV-negative by self-report. Eligible individuals answered questions on demographics, behavior, and PrEP (awareness, willingness to use, barriers, and facilitators). Multivariable logistic regression modeling was performed to assess the factors associated with willingness to use daily oral PrEP in each country. Results: From a total sample of 43,687 individuals, 44.54% of MSM (19,457/43,687) were eligible and completed the Web-based survey—Brazil: 58.42% (11,367/19,457), Mexico: 30.50% (5934/19,457), and Peru: 11.08% (2156/19,457); median age was 28 years (interquartile range: 24-34), and almost half lived in large urban cities. Most participants were recruited on Grindr (69%, 13,349/19,457). Almost 20% (3862/19,352) had never tested for HIV, and condomless receptive anal sex was reported by 40% (7755/19,326) in the previous 6 months. Whereas 67.51% (13,110/19,376) would be eligible for PrEP, only 9.80% (1858/18,959) of participants had high HIV risk perception. PrEP awareness was reported by 64.92% (12,592/19,396); this was lower in Peru (46.60%, 1002/2156). Overall, willingness to use PrEP was reported by 64.23% (12,498/19,457); it was highest in Mexico (70%, 4158/5934) and lowest in Peru (58%, 1241/2156). In multivariate regression models adjusted for age, schooling, and income in each country, willingness to use PrEP was positively associated with PrEP awareness and PrEP facilitators (eg, free PrEP and HIV testing) and negatively associated with behavioral (eg, concerned by daily pill regimen) and belief barriers (eg, sexual partners may expect condomless sex). Conclusions: In this first cross-country, Web-based survey in Latin America, willingness to use PrEP was found to be high and directly related to PrEP awareness. Interventions to increase awareness and PrEP knowledge about safety and efficacy are crucial to increase PrEP demand. This study provides important information to support the implementation of PrEP in Brazil, Mexico, and Peru.

  • The PrEP4Love website (montage). Source: The Authors / Placeit; Copyright: JMIR Publications; URL: https://publichealth.jmir.org/2019/2/e12822; License: Creative Commons Attribution (CC-BY).

    #PrEP4Love: An Evaluation of a Sex-Positive HIV Prevention Campaign

    Abstract:

    Background: Pre-exposure prophylaxis (PrEP) is an effective but underutilized method for preventing HIV transmission in communities vulnerable to HIV. Public health campaigns aimed at increasing PrEP awareness and access have less evaluation data. Objective: The aim of this study was to evaluate Chicago’s PrEP campaign, PrEP4Love (P4L), a campaign that uses health equity and sex-positivity approaches for information dissemination. Methods: P4L launched in February 2016 and remains an active campaign to date. The analysis period for this paper was from the launch date in February 2016 through May 15, 2016. Our analysis reviews the Web-based reach of the campaign through views on social media platforms (Facebook and Instagram), smart ads, or ads served to individuals across a variety of Web platforms based on their demographics and browsing history, and P4L website clicks. Results: In total, 40,913,560 unique views were generated across various social media platforms. A total of 24,548 users clicked on P4L ads and 32,223,987 views were received from smart ads. The 3 most clicked on ads were STD Signs & Symptoms—More Information on STD Symptoms, HIV & AIDS Prevention, and HIV Prevention Medication. An additional 6,970,127 views were gained through Facebook and another 1,719,446 views through Instagram. There was an average of 182 clicks per day on the P4L website. Conclusions: This is the first study investigating public responses to a health equity and sex-positive social marketing campaign for PrEP. Overall, the campaign reached millions of individuals. More studies of PrEP social marketing are needed to evaluate the relationship of targeted public health campaigns on stigma and to guide future PrEP promotion strategies.

  • Source: Flickr; Copyright: Guille Locke; URL: https://www.flickr.com/photos/133791503@N04/39917136755; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Detection of Spatiotemporal Prescription Opioid Hot Spots With Network Scan Statistics: Multistate Analysis

    Abstract:

    Background: Overuse and misuse of prescription opioids have become significant public health burdens in the United States. About 11.5 million people are estimated to have misused prescription opioids for nonmedical purposes in 2016. This has led to a significant number of drug overdose deaths in the United States. Previous studies have examined spatiotemporal clusters of opioid misuse, but they have been restricted to circular shaped regions. Objective: The goal of this study was to identify spatiotemporal hot spots of opioid users and opioid prescription claims using Medicare data. Methods: We examined spatiotemporal clusters with significantly higher number of beneficiaries and rate of prescriptions for opioids using Medicare payment data from the Centers for Medicare & Medicaid Services. We used network scan statistics to detect significant clusters with arbitrary shapes, the Kulldorff scan statistic to examine the significant clusters for each year (2013, 2014, and 2015) and an expectation-based version to examine the significant clusters relative to past years. Regression analysis was used to characterize the demographics of the counties that are a part of any significant cluster, and data mining techniques were used to discover the specialties of the anomalous providers. Results: We examined anomalous spatial clusters with respect to opioid prescription claims and beneficiary counts and found some common patterns across states: the counties in the most anomalous clusters were fairly stable in 2014 and 2015, but they have shrunk from 2013. In Virginia, a higher percentage of African Americans in a county lower the odds of the county being anomalous in terms of opioid beneficiary counts to about 0.96 in 2015. For opioid prescription claim counts, the odds were 0.92. This pattern was consistent across the 3 states and across the 3 years. A higher number of people in the county with access to Medicaid increased the odds of the county being in the anomalous cluster to 1.16 in both types of counts in Virginia. A higher number of people with access to direct purchase of insurance plans decreased the odds of a county being in an anomalous cluster to 0.85. The expectation-based scan statistic, which captures change over time, revealed different clusters than the Kulldorff statistic. Providers with an unusually high number of opioid beneficiaries and opioid claims include specialties such as physician’s assistant, nurse practitioner, and family practice. Conclusions: Our analysis of the Medicare claims data provides characteristics of the counties and provider specialties that have higher odds of being anomalous. The empirical analysis identifies highly refined spatial hot spots that are likely to encounter prescription opioid misuse and overdose. The methodology is generic and can be applied to monitor providers and their prescription behaviors in regions that are at a high risk of abuse.

  • Source: Freepik; Copyright: rawpixel.com; URL: https://www.freepik.com/free-photo/business-people-working-data-project_3218163.htm; License: Licensed by JMIR.

    Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study

    Abstract:

    Background: Although dynamic models are increasingly used by decision makers as a source of insight to guide interventions in order to control communicable disease outbreaks, such models have long suffered from a risk of rapid obsolescence due to failure to keep updated with emerging epidemiological evidence. The application of statistical filtering algorithms to high-velocity data streams has recently demonstrated effectiveness in allowing such models to be automatically regrounded by each new set of incoming observations. The attractiveness of such techniques has been enhanced by the emergence of a new generation of geospatially specific, high-velocity data sources, including daily counts of relevant searches and social media posts. The information available in such electronic data sources complements that of traditional epidemiological data sources. Objective: This study aims to evaluate the degree to which the predictive accuracy of pandemic projection models regrounded via machine learning in daily clinical data can be enhanced by extending such methods to leverage daily search counts. Methods: We combined a previously published influenza A (H1N1) pandemic projection model with the sequential Monte Carlo technique of particle filtering, to reground the model bu using confirmed incident case counts and search volumes. The effectiveness of particle filtering was evaluated using a norm discrepancy metric via predictive and dataset-specific cross-validation. Results: Our results suggested that despite the data quality limitations of daily search volume data, the predictive accuracy of dynamic models can be strongly elevated by inclusion of such data in filtering methods. Conclusions: The predictive accuracy of dynamic models can be notably enhanced by tapping a readily accessible, publicly available, high-velocity data source. This work highlights a low-cost, low-burden avenue for strengthening model-based outbreak intervention response planning using low-cost public electronic datasets.

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