%0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 2 %P e12383 %T Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study %A Alessa,Ali %A Faezipour,Miad %+ Department of Computer Science and Engineering, University of Bridgeport, 221 University Avenue, Bridgeport, CT, 06604, United States, 1 203 576 4702, mfaezipo@bridgeport.edu %K infodemiology %K influenza %K machine learning %K social networking site %K text classification %D 2019 %7 25.06.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X 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 present 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. %R 10.2196/12383 %U http://publichealth.jmir.org/2019/2/e12383/ %U https://doi.org/10.2196/12383 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 2 %P e13086 %T New York City HIV Care Continuum Dashboards: Using Surveillance Data to Improve HIV Care Among People Living With HIV in New York City %A Braunstein,Sarah L %A Coeytaux,Karen %A Sabharwal,Charulata J %A Xia,Qiang %A Robbins,Rebekkah S %A Obeng,Beverly %A Daskalakis,Demetre C %+ HIV Epidemiology and Field Services Program, Bureau of HIV Prevention and Control, New York City Department of Health and Mental Hygiene, 42-09 28th Street, Long Island City, NY, 11101, United States, 1 347 396 7760, sbraunstein@health.nyc.gov %K HIV %K surveillance %K quality of care %K best practices %D 2019 %7 19.06.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X 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. %M 31219053 %R 10.2196/13086 %U http://publichealth.jmir.org/2019/2/e13086/ %U https://doi.org/10.2196/13086 %U http://www.ncbi.nlm.nih.gov/pubmed/31219053 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 1 %P e11465 %T A Syndrome-Based Surveillance System for Infectious Diseases Among Asylum Seekers in Austrian Reception Centers, 2015-2018: Analysis of Reported Data %A El-Khatib,Ziad %A Taus,Karin %A Richter,Lukas %A Allerberger,Franz %A Schmid,Daniela %+ Department of Surveillance and Infectious Disease Epidemiology, Institute of Medical Microbiology and Hygiene, Austrian Agency for Health and Food Safety, Währingerstraße 25a, Vienna, 1096, Austria, 43 50 555 37304, daniela.schmid@ages.at %K Austria %K refugee health %K asylum seekers %K syndrome surveillance system %K mass health monitoring %K refugees %K population surveillance %K public health surveillance %K epidemiological monitoring %D 2019 %7 27.02.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Austria has been among the main European countries hosting incoming asylum seekers since 2015. Consequently, there was an urgent need to predict any public health threats associated with the arriving asylum seekers. The Department of Surveillance and Infectious Disease Epidemiology at the Austrian Agency for Health and Food Safety (AGES) was mandated to implement a national syndrome-based surveillance system in the 7 reception centers by the Austrian Ministry of Interior and Ministry of Health. Objective: We aimed to analyze the occurrence and spread of infectious diseases among asylum seekers using data reported by reception centers through the syndrome-based surveillance system from September 2015 through February 2018. Methods: We deployed a daily data collection system for 13 syndromes: rash with fever; rash without fever; acute upper respiratory tract infection; acute lower respiratory tract infection; meningitis or encephalitis; fever and bleeding; nonbloody gastroenteritis or watery diarrhea; bloody diarrhea; acute jaundice; skin, soft tissue, or bone abnormalities; acute flaccid paralysis; high fever with no other signs; and unexplained death. General practitioners, the first professionals to consult for health problems at reception centers in Austria, sent the tally sheets on identified syndromes daily to the AGES. Results: We identified a total of 2914 cases, presenting 8 of the 13 syndromes. A total of 405 signals were triggered, and 6.4% (26/405) of them generated alerts. Suspected acute upper respiratory tract infection (1470/2914, 50.45% of cases), rash without fever (1174/2914, 40.29% of cases), suspected acute lower respiratory tract infection (159/2914, 5.46% of cases), watery diarrhea (73/2914, 2.51% of cases), and skin, soft tissue, or bone abnormalities (32/2914, 1.10% of cases) were the top 5 syndromes. Conclusions: The cooperation of the AGES with reception center health care staff, supported by the 2 involved ministries, was shown to be useful for syndromic surveillance of infectious diseases among asylum seekers. None of the identified alerts escalated to an outbreak. %M 30810535 %R 10.2196/11465 %U http://publichealth.jmir.org/2019/1/e11465/ %U https://doi.org/10.2196/11465 %U http://www.ncbi.nlm.nih.gov/pubmed/30810535 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 1 %P e12032 %T Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks %A Velappan,Nileena %A Daughton,Ashlynn Rae %A Fairchild,Geoffrey %A Rosenberger,William Earl %A Generous,Nicholas %A Chitanvis,Maneesha Elizabeth %A Altherr,Forest Michael %A Castro,Lauren A %A Priedhorsky,Reid %A Abeyta,Esteban Luis %A Naranjo,Leslie A %A Hollander,Attelia Dawn %A Vuyisich,Grace %A Lillo,Antonietta Maria %A Cloyd,Emily Kathryn %A Vaidya,Ashvini Rajendra %A Deshpande,Alina %+ Los Alamos National Laboratory, TA-43, bldg1, MS - M888, Los Alamos, NM, 87545, United States, 1 505 667 9938, deshpande_a@lanl.gov %K epidemiology %K infectious diseases %K algorithm %K public health informatics %K web browser %D 2019 %7 25.02.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak. %M 30801254 %R 10.2196/12032 %U http://publichealth.jmir.org/2019/1/e12032/ %U https://doi.org/10.2196/12032 %U http://www.ncbi.nlm.nih.gov/pubmed/30801254 %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 %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e10978 %T Design, Development, and Evaluation of an Injury Surveillance App for Cricket: Protocol and Qualitative Study %A Soomro,Najeebullah %A Chhaya,Meraj %A Soomro,Mariam %A Asif,Naukhez %A Saurman,Emily %A Lyle,David %A Sanders,Ross %+ Broken Hill University Department of Rural Health, University of Sydney, Corrindah Court, Broken Hill, 2880, Australia, 61 880801282, naj.soomro@sydney.edu.au %K cricket %K injury surveillance %K mobile app %K mobile phone %K TeamDoc %K mHealth %D 2019 %7 22.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Injury surveillance and workload monitoring are important aspects of professional sports, including cricket. However, at the community level, there is a dearth of accessible and intelligent surveillance tools. Mobile apps are an accessible tool for monitoring cricket-related injuries at all levels. Objective: The objective of this paper is to share the novel methods associated with the development of the free TeamDoc app and provide evidence from an evaluation of the user experience and perception of the app regarding its functionality, utility, and design. Methods: TeamDoc mobile app for Android and Apple smartphones was developed using 3 languages: C++, Qt Modeling Language, and JavaScript. For the server-side connectivity, Hypertext Preprocessor (PHP) was used as it is a commonly used cross-platform language. PHP includes components that interact with popular database management systems, allowing for secure interaction with databases on a server level. The app was evaluated by administrating a modified user version of the Mobile App Rating Scale (uMARS; maximum score: 5). Results: TeamDoc is the first complementary, standalone mobile app that records cricket injuries through a smartphone. It can also record cricketing workloads, which is a known risk factor for injury. The app can be used without the need for supplementary computer devices for synchronization. The uMARS scores showed user satisfaction (overall mean score 3.6 [SD 0.5]), which demonstrates its acceptability by cricketers. Conclusions: Electronic injury surveillance systems have been shown to improve data collection during competitive sports. Therefore, TeamDoc may assist in improving injury reporting and may also act as a monitoring system for coaching staff to adjust individual training workloads. The methods described in this paper provide a template for researchers to develop similar apps for other sports. %M 30668516 %R 10.2196/10978 %U http://mhealth.jmir.org/2019/1/e10978/ %U https://doi.org/10.2196/10978 %U http://www.ncbi.nlm.nih.gov/pubmed/30668516 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 5 %N 1 %P e10447 %T Self-Care Behaviors of Ovarian Cancer Patients Before Their Diagnosis: Proof-of-Concept Study %A Flanagan,James M %A Skrobanski,Hanna %A Shi,Xin %A Hirst,Yasemin %+ Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, United Kingdom, 44 20 75942127, j.flanagan@imperial.ac.uk %K cancer %K early diagnosis %K proof of concept %K focus group %K acceptability %K data linkage %K cancer surveillance %D 2019 %7 17.01.2019 %9 Original Paper %J JMIR Cancer %G English %X Background: Longer patient intervals can lead to more late-stage cancer diagnoses and higher mortality rates. Individuals may delay presenting to primary care with red flag symptoms and instead turn to the internet to seek information, purchase over-the-counter medication, and change their diet or exercise habits. With advancements in machine learning, there is the potential to explore this complex relationship between a patient’s symptom appraisal and their first consultation at primary care through linkage of existing datasets (eg, health, commercial, and online). Objective: Here, we aimed to explore feasibility and acceptability of symptom appraisal using commercial- and health-data linkages for cancer symptom surveillance. Methods: A proof-of-concept study was developed to assess the general public’s acceptability of commercial- and health-data linkages for cancer symptom surveillance using a qualitative focus group study. We also investigated self-care behaviors of ovarian cancer patients using high-street retailer data, pre- and postdiagnosis. Results: Using a high-street retailer’s data, 1118 purchases—from April 2013 to July 2017—by 11 ovarian cancer patients and one healthy individual were analyzed. There was a unique presence of purchases for pain and indigestion medication prior to cancer diagnosis, which could signal disease in a larger sample. Qualitative findings suggest that the public are willing to consent to commercial- and health-data linkages as long as their data are safeguarded and users of this data are transparent about their purposes. Conclusions: Cancer symptom surveillance using commercial data is feasible and was found to be acceptable. To test efficacy of cancer surveillance using commercial data, larger studies are needed with links to individual electronic health records. %M 30664464 %R 10.2196/10447 %U https://cancer.jmir.org/2019/1/e10447/ %U https://doi.org/10.2196/10447 %U http://www.ncbi.nlm.nih.gov/pubmed/30664464 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 1 %P e11357 %T Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment %A Talaei-Khoei,Amir %A Wilson,James M %A Kazemi,Seyed-Farzan %+ Department of Information Systems, University of Nevada Reno, Ansari Business Building, 1664 N Virginia Street, Room 314F, Reno, NV, 89557, United States, 1 7754407005, atalaeikhoei@unr.edu %K autocorrelation %K disease counts %K prediction %K public health surveillance %K time-series analysis %D 2019 %7 15.01.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The literature in statistics presents methods by which autocorrelation can identify the best period of measurement to improve the performance of a time-series prediction. The period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a limitation to the length of the measurement period that can offer meaningful and valuable predictions. Objective: This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts. Methods: The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. The disease counts for chlamydia, salmonella, respiratory syncytial virus, gonorrhea, viral meningitis, and influenza A were predicted. Results: Our results showed that autocorrelation could not guarantee the best performance for prediction of disease counts. However, the proposed method with the change-point analysis suggests a period of measurement that is operationally acceptable and performance that is not significantly different from the best prediction. Conclusions: The use of change-point analysis with autocorrelation provides the best and most practical period of measurement. %M 30664479 %R 10.2196/11357 %U http://publichealth.jmir.org/2019/1/e11357/ %U https://doi.org/10.2196/11357 %U http://www.ncbi.nlm.nih.gov/pubmed/30664479 %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 4 %P e69 %T Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study %A Delespierre,Tiba %A Josseran,Loic %+ Equipe de recherche (HANDIReSP), UFR des Sciences de la Santé Simone Veil, Université de Versailles Saint-Quentin-en-Yvelines et Université Paris-Saclay, 2 Avenue de la Source de la Bièvre, Montigny-le-Bretonneux, 78180, France, 33 658376503, tiba.baroukh@gmail.com %K Centers for Disease Control and Prevention %K nursing homes %K syndromic surveillance %K pattern recognition %K Delphi technique %K sentinel surveillance %D 2018 %7 13.12.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: New nursing homes (NH) data warehouses fed from residents’ medical records allow monitoring the health of elderly population on a daily basis. Elsewhere, syndromic surveillance has already shown that professional data can be used for public health (PH) surveillance but not during a long-term follow-up of the same cohort. Objective: This study aimed to build and assess a national ecological NH PH surveillance system (SS). Methods: Using a national network of 126 NH, we built a residents’ cohort, extracted medical and personal data from their electronic health records, and transmitted them through the internet to a national server almost in real time. After recording sociodemographic, autonomic and syndromic information, a set of 26 syndromes was defined using pattern matching with the standard query language-LIKE operator and a Delphi-like technique, between November 2010 and June 2016. We used early aberration reporting system (EARS) and Bayes surveillance algorithms of the R surveillance package (Höhle) to assess our influenza and acute gastroenteritis (AGE) syndromic data against the Sentinelles network data, French epidemics gold standard, following Centers for Disease Control and Prevention surveillance system assessment guidelines. Results: By extracting all sociodemographic residents’ data, a cohort of 41,061 senior citizens was built. EARS_C3 algorithm on NH influenza and AGE syndromic data gave sensitivities of 0.482 and 0.539 and specificities of 0.844 and 0.952, respectively, over a 6-year period, forecasting the last influenza outbreak by catching early flu signals. In addition, assessment of influenza and AGE syndromic data quality showed precisions of 0.98 and 0.96 during last season epidemic weeks’ peaks (weeks 03-2017 and 01-2017) and precisions of 0.95 and 0.92 during last summer epidemic weeks’ low (week 33-2016). Conclusions: This study confirmed that using syndromic information gives a good opportunity to develop a genuine French national PH SS dedicated to senior citizens. Access to senior citizens’ free-text validated health data on influenza and AGE responds to a PH issue for the surveillance of this fragile population. This database will also make possible new ecological research on other subjects that will improve prevention, care, and rapid response when facing health threats. %M 30545816 %R 10.2196/publichealth.9022 %U http://publichealth.jmir.org/2018/4/e69/ %U https://doi.org/10.2196/publichealth.9022 %U http://www.ncbi.nlm.nih.gov/pubmed/30545816 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 12 %P e11072 %T Acceptance and Resistance of New Digital Technologies in Medicine: Qualitative Study %A Safi,Sabur %A Thiessen,Thomas %A Schmailzl,Kurt JG %+ Medical School Brandenburg, digilog, Center for Connected Health Care UG, Fehrbelliner Straße 38, Neuruppin, 16816, Germany, 49 40 3612264 9828, sabur.safi@gmx.de %K innovation diffusion %K information and technology communication %K telematics infrastructure %K health care innovation %D 2018 %7 04.12.2018 %9 Original Paper %J JMIR Res Protoc %G English %X Background: This study discusses the acceptance of new medical technologies in health care settings and resistance to these technologies from hospitals, doctors’ surgical centers, electronic health (eHealth) centers, and related institutions. We suggest a novel method of identifying factors that influence the acceptance of, and resistance to, new technologies by medical staff and patients. Objective: The objective of this study was to determine and evaluate the factors that influence acceptance and resistance to achieve a successful implementation of new technologies. Methods: The target group was patients residing in Brandenburg and major stakeholders in the local health care structure, for instance, medical institutions and medical professionals. The process relies on 3 models: the technology acceptance model, the unified technology acceptance and use of technology model, and the theory of technical innovation diffusion. Qualitative methodology was employed in this study, and an exploratory design was adopted to gain new insights into a poorly understood phenomenon in the German context. This enabled the researcher to take a flexible approach toward exploring a wide range of secondary data and to choose a different approach when unexpected information emerged. Content analysis was used to identify and interpret the data, and the researcher assured that the meaning associated with the information has concurred with that of the original source. Results: This study confirmed that adoption of new technologies in health care depended on individual opinions of the factors relating to them. Some medical professionals believed that technology would interfere with their ability to make independent diagnoses and their relationships with patients. Doctors also feared that technology was a means of management control. In contrast, other medical staff welcomed technology because it provided them with more opportunities to interact with patients and their carers. Generally, patients were more enthusiastic about technology than medical professionals and health care managers because it allowed them to have greater autonomy in selecting health care options. The need for all groups to be involved in the development of the new health care approach was an important outcome, otherwise resistance to it was likely to be greater. In other words, the strategy for change management was the indicator of success or failure. Therefore, following our analysis, a number of practical precepts emerged that could facilitate user acceptance of digital solutions and innovative medical technologies. Conclusions: The acceptance of digital solutions and innovative medical technology by patients and professionals relies on understanding their anxieties and feelings of insecurity. The process will take time because individuals accept change at different rates. Hence, the development of an extensive user community to fully and successfully implement eHealth is less likely in the short term; however, this should not prevent the push for changes in health care technology. %M 30514693 %R 10.2196/11072 %U http://www.researchprotocols.org/2018/12/e11072/ %U https://doi.org/10.2196/11072 %U http://www.ncbi.nlm.nih.gov/pubmed/30514693 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 4 %P e11354 %T Conurbation, Urban, and Rural Living as Determinants of Allergies and Infectious Diseases: Royal College of General Practitioners Research and Surveillance Centre Annual Report 2016-2017 %A de Lusignan,Simon %A McGee,Christopher %A Webb,Rebecca %A Joy,Mark %A Byford,Rachel %A Yonova,Ivelina %A Hriskova,Mariya %A Matos Ferreira,Filipa %A Elliot,Alex J %A Smith,Gillian %A Rafi,Imran %+ Department of Clinical and Experimental Medicine, University of Surrey, Leggett Building, Daphne Jackson Road, Guildford,, United Kingdom, 44 01483 6848002, s.lusignan@surrey.ac.uk %K population surveillance %K respiratory tract infections %K conjunctivitis, allergic %K asthma %K urinary tract infections %K gastroenteritis %K healthcare disparities %K socioeconomic factors %K social determinants of health %K medical records systems, computerized %K data collection %K records as topic %K primary health care %K general practice %K infectious diseases %D 2018 %7 26.11.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Living in a conurbation, urban, or rural environment is an important determinant of health. For example, conurbation and rural living is associated with increased respiratory and allergic conditions, whereas a farm or rural upbringing has been shown to be a protective factor against this. Objective: The objective of the study was to assess differences in general practice presentations of allergic and infectious disease in those exposed to conurbation or urban living compared with rural environments. Methods: The population was a nationally representative sample of 175 English general practices covering a population of over 1.6 million patients registered with sentinel network general practices. General practice presentation rates per 100,000 population were reported for allergic rhinitis, asthma, and infectious conditions grouped into upper and lower respiratory tract infections, urinary tract infection, and acute gastroenteritis by the UK Office for National Statistics urban-rural category. We used multivariate logistic regression adjusting for age, sex, ethnicity, deprivation, comorbidities, and smoking status, reporting odds ratios (ORs) with 95% CIs. Results: For allergic rhinitis, the OR was 1.13 (95% CI 1.04-1.23; P=.003) for urban and 1.29 (95% CI 1.19-1.41; P<.001) for conurbation compared with rural dwellers. Conurbation living was associated with a lower OR for both asthma (OR 0.70, 95% CI 0.67-0.73; P<.001) and lower respiratory tract infections (OR 0.94, 95% CI 0.90-0.98; P=.005). Compared with rural dwellers, the OR for upper respiratory tract infection was greater in urban (OR 1.06, 95% CI 1.03-1.08; P<.001) but no different in conurbation dwellers (OR 1.00, 95% CI 0.97-1.03; P=.93). Acute gastroenteritis followed the same pattern: the OR was 1.13 (95% CI 1.01-1.25; P=.03) for urban dwellers and 1.04 (95% CI 0.93-1.17; P=.46) for conurbation dwellers. The OR for urinary tract infection was lower for urban dwellers (OR 0.94, 95% CI 0.89-0.99; P=.02) but higher in conurbation dwellers (OR 1.06, 95% CI 1.00-1.13; P=.04). Conclusions: Those living in conurbations or urban areas were more likely to consult a general practice for allergic rhinitis and upper respiratory tract infection. Both conurbation and rural living were associated with an increased risk of urinary tract infection. Living in rural areas was associated with an increased risk of asthma and lower respiratory tract infections. The data suggest that living environment may affect rates of consultations for certain conditions. Longitudinal analyses of these data would be useful in providing insights into important determinants. %M 30478022 %R 10.2196/11354 %U http://publichealth.jmir.org/2018/4/e11354/ %U https://doi.org/10.2196/11354 %U http://www.ncbi.nlm.nih.gov/pubmed/30478022 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 11 %P e10497 %T Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application %A Leroy,Gondy %A Gu,Yang %A Pettygrove,Sydney %A Galindo,Maureen K %A Arora,Ananyaa %A Kurzius-Spencer,Margaret %+ University of Arizona, 1130 E Helen Street, McClelland Hall, Tucson, AZ, 85721, United States, 1 5206214106, gondyleroy@email.arizona.edu %K parser %K natural language processing %K complex entity extraction %K Autism Spectrum Disorder %K DSM %K electronic health records %K decision tree %K machine learning %D 2018 %7 07.11.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the Centers for Disease Control and Prevention to track cases of autism spectrum disorder (ASD). This process currently comprises manual collection and review of EHRs of 4- and 8-year old children in 11 US states for the presence of ASD criteria. The work is time-consuming and expensive. Objective: Our objective was to automatically extract from EHRs the description of behaviors noted by the clinicians in evidence of the diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Previously, we reported on the classification of entire EHRs as ASD or not. In this work, we focus on the extraction of individual expressions of the different ASD criteria in the text. We intend to facilitate large-scale surveillance efforts for ASD and support analysis of changes over time as well as enable integration with other relevant data. Methods: We developed a natural language processing (NLP) parser to extract expressions of 12 DSM criteria using 104 patterns and 92 lexicons (1787 terms). The parser is rule-based to enable precise extraction of the entities from the text. The entities themselves are encompassed in the EHRs as very diverse expressions of the diagnostic criteria written by different people at different times (clinicians, speech pathologists, among others). Due to the sparsity of the data, a rule-based approach is best suited until larger datasets can be generated for machine learning algorithms. Results: We evaluated our rule-based parser and compared it with a machine learning baseline (decision tree). Using a test set of 6636 sentences (50 EHRs), we found that our parser achieved 76% precision, 43% recall (ie, sensitivity), and >99% specificity for criterion extraction. The performance was better for the rule-based approach than for the machine learning baseline (60% precision and 30% recall). For some individual criteria, precision was as high as 97% and recall 57%. Since precision was very high, we were assured that criteria were rarely assigned incorrectly, and our numbers presented a lower bound of their presence in EHRs. We then conducted a case study and parsed 4480 new EHRs covering 10 years of surveillance records from the Arizona Developmental Disabilities Surveillance Program. The social criteria (A1 criteria) showed the biggest change over the years. The communication criteria (A2 criteria) did not distinguish the ASD from the non-ASD records. Among behaviors and interests criteria (A3 criteria), 1 (A3b) was present with much greater frequency in the ASD than in the non-ASD EHRs. Conclusions: Our results demonstrate that NLP can support large-scale analysis useful for ASD surveillance and research. In the future, we intend to facilitate detailed analysis and integration of national datasets. %M 30404767 %R 10.2196/10497 %U https://www.jmir.org/2018/11/e10497/ %U https://doi.org/10.2196/10497 %U http://www.ncbi.nlm.nih.gov/pubmed/30404767 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 4 %P e68 %T Assessing the Concepts and Designs of 58 Mobile Apps for the Management of the 2014-2015 West Africa Ebola Outbreak: Systematic Review %A Tom-Aba,Daniel %A Nguku,Patrick Mboya %A Arinze,Chinedu Chukwujekwu %A Krause,Gerard %+ Department of Epidemiology, Helmholtz Centre for Infection Research (HZI), Inhoffen Strasse 7, Braunschweig, 38124, Germany, 49 01739136081, daniel.tom-aba@helmholtz-hzi.de %K case management %K contact tracing %K Ebola virus disease %K eHealth %K mHealth %K systematic review %K West Africa %D 2018 %7 29.10.2018 %9 Review %J JMIR Public Health Surveill %G English %X Background: The use of mobile phone information technology (IT) in the health sector has received much attention especially during the 2014-2015 Ebola virus disease (EVD) outbreak. mHealth can be attributed to a major improvement in EVD control, but there lacks an overview of what kinds of tools were available and used based on the functionalities they offer. Objective: We aimed to conduct a systematic review of mHealth tools in the context of the recent EVD outbreak to identify the most promising approaches and guide further mHealth developments for infectious disease control. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched for all reports on mHealth tools developed in the context of the 2014-2015 EVD outbreak published between January 1, 2014 and December 31, 2015 on Google Scholar, MEDLINE, CAB Abstracts (Global Health), POPLINE, and Web of Science in any language using the search strategy: (“outbreak” OR “epidemic”) AND (“mobile phone” OR “smartphone” OR “smart phone” OR “mobile phone” OR “tablet” OR “mHealth”) AND (“Ebola” OR ”EVD” OR “VHF” OR “Ebola virus disease” OR “viral hemorrhagic fever”) AND (“2014” OR “2015”). The relevant publications were selected by 2 independent reviewers who applied a standardized data extraction form on the tools’ functionalities. Results: We identified 1220 publications through the search strategy, of which 6.31% (77/1220) were original publications reporting on 58 specific mHealth tools in the context of the EVD outbreak. Of these, 62% (34/55) offered functionalities for surveillance, 22% (10/45) for case management, 18% (7/38) for contact tracing, and 6% (3/51) for laboratory data management. Only 3 tools, namely Community Care, Sense Ebola Followup, and Surveillance and Outbreak Response Management and Analysis System supported all four of these functionalities. Conclusions: Among the 58 identified tools related to EVD management in 2014 and 2015, only 3 appeared to contain all 4 key functionalities relevant for the response to EVD outbreaks and may be most promising for further development. %M 30373727 %R 10.2196/publichealth.9015 %U http://publichealth.jmir.org/2018/4/e68/ %U https://doi.org/10.2196/publichealth.9015 %U http://www.ncbi.nlm.nih.gov/pubmed/30373727 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 4 %P e66 %T Establishing a Demographic, Development and Environmental Geospatial Surveillance Platform in India: Planning and Implementation %A Dixit,Shikha %A Arora,Narendra K %A Rahman,Atiqur %A Howard,Natasha J %A Singh,Rakesh K %A Vaswani,Mayur %A Das,Manoja K %A Ahmed,Faruqueuddin %A Mathur,Prashant %A Tandon,Nikhil %A Dasgupta,Rajib %A Chaturvedi,Sanjay %A Jethwaney,Jaishri %A Dalpath,Suresh %A Prashad,Rajendra %A Kumar,Rakesh %A Gupta,Rakesh %A Dube,Laurette %A Daniel,Mark %+ Research, Epidemiology, The INCLEN Trust International, F-1/5, Okhla Industrial Area, Phase-1, New Delhi, 110025, India, 91 11 47730000 ext 109, nkarora@inclentrust.org %K geospatial surveillance %K health and nonhealth data harmonization %K spatial epidemiology %K participatory GIS %K caste, socioeconomic transition %K ground truthing %K built environment %D 2018 %7 05.10.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Inadequate administrative health data, suboptimal public health infrastructure, rapid and unplanned urbanization, environmental degradation, and poor penetration of information technology make the tracking of health and well-being of populations and their social determinants in the developing countries challenging. Technology-integrated comprehensive surveillance platforms have the potential to overcome these gaps. Objective: This paper provides methodological insights into establishing a geographic information system (GIS)-integrated, comprehensive surveillance platform in rural North India, a resource-constrained setting. Methods: The International Clinical Epidemiology Network Trust International established a comprehensive SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in rural Palwal, a district in Haryana, North India. The surveillance platform evolved by adopting four major steps: (1) site preparation, (2) data construction, (3) data quality assurance, and (4) data update and maintenance system. Arc GIS 10.3 and QGIS 2.14 software were employed for geospatial data construction. Surveillance data architecture was built upon the geospatial land parcel datasets. Dedicated software (SOMAARTH-1) was developed for handling high volume of longitudinal datasets. The built infrastructure data pertaining to land use, water bodies, roads, railways, community trails, landmarks, water, sanitation and food environment, weather and air quality, and demographic characteristics were constructed in a relational manner. Results: The comprehensive surveillance platform encompassed a population of 0.2 million individuals residing in 51 villages over a land mass of 251.7 sq km having 32,662 households and 19,260 nonresidential features (cattle shed, shops, health, education, banking, religious institutions, etc). All land parcels were assigned georeferenced location identification numbers to enable space and time monitoring. Subdivision of villages into sectors helped identify socially homogenous community clusters (418/676, 61.8%, sectors). Water and hygiene parameters of the whole area were mapped on the GIS platform and quantified. Risk of physical exposure to harmful environment (poor water and sanitation indicators) was significantly associated with the caste of individual household (P=.001), and the path was mediated through the socioeconomic status and density of waste spots (liquid and solid) of the sector in which these households were located. Ground-truthing for ascertaining the land parcel level accuracies, community involvement in mapping exercise, and identification of small habitations not recorded in the administrative data were key learnings. Conclusions: The SOMAARTH DDESS experience allowed us to document and explore dynamic relationships, associations, and pathways across multiple levels of the system (ie, individual, household, neighborhood, and village) through a geospatial interface. This could be used for characterization and monitoring of a wide range of proximal and distal determinants of health. %M 30291101 %R 10.2196/publichealth.9749 %U https://publichealth.jmir.org/2018/4/e66/ %U https://doi.org/10.2196/publichealth.9749 %U http://www.ncbi.nlm.nih.gov/pubmed/30291101 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 3 %P e62 %T Cross-Jurisdictional Data Exchange Impact on the Estimation of the HIV Population Living in the District of Columbia: Evaluation Study %A Hamp,Auntre D %A Doshi,Rupali K %A Lum,Garret R %A Allston,Adam %+ HIV/AIDS, Hepatitis, STD, Tuberculosis Administration, District of Columbia Department of Health, 899 N Capital Street, NE, Fourth Floor, Washington, DC, 20002, United States, 1 202 487 3013, auntre.hamp@georgetown.edu %K HIV %K surveillance %K data sharing %K public health %K cross-jurisdictional %D 2018 %7 13.08.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Accurate HIV surveillance data are essential to monitor trends to help end the HIV epidemic. Owing to strict policies around data security and confidentiality, HIV surveillance data have not been routinely shared across jurisdictions except a biannual case-by-case review process to identify and remove duplicate cases (Routine Interstate Duplicate Review, RIDR). HIV surveillance estimates for the District of Columbia (DC) are complicated by migration and care seeking throughout the metropolitan area, which includes Maryland and Virginia. To address gaps in HIV surveillance data, health departments of DC, Maryland, and Virginia have established HIV surveillance data sharing agreements. Although the Black Box (a privacy data integration tool external to the health departments) facilitates the secure exchange of data between DC, Maryland, and Virginia, its previous iterations were limited by the frequency and scope of information exchanged. The health departments of DC, Maryland, and Virginia engaged in data sharing to further improve HIV surveillance estimates. Objective: This study assessed the impact of cross-jurisdictional data sharing on the estimation of people living with HIV in DC and reduction of cases in the RIDR process. Methods: Data sharing agreements established in 2014 allowed for the exchange of HIV case information (eg, current residential address) and laboratory information (eg, test types, result dates, and results) from the enhanced HIV/AIDS Reporting System (eHARS). Regular data exchanges began in 2017. The participating jurisdictions transferred data (via secure file transfer protocol) for individuals having a residential address in a partnering jurisdiction at the time of HIV diagnosis or evidence of receiving HIV-related services at a facility located in a partnering jurisdiction. The DC Department of Health compared the data received to DC eHARS and imported updated data that matched existing cases. Evaluation of changes in current residential address and HIV prevalence was conducted by comparing data before and after HIV surveillance data exchanges. Results: After the HIV surveillance data exchange, an average of 396 fewer cases were estimated to be living in DC each year from 2012 to 2016. Among cases with a residential status change, 66.4% (1316/1982) had relocated to Maryland and 19.8% (392/1982) to Virginia; majority of these had relocated to counties bordering DC. Relocation in and out of DC differed by mode of transmission, race and ethnicity, age group, and gender. After data exchange, the volume of HIV cases needing RIDR decreased by 74% for DC-Maryland and 81% for DC-Virginia. Conclusions: HIV surveillance data exchange between the public health departments of DC, Maryland, and Virginia reduced the number of cases misclassified as DC residents and reduced the number of cases needing RIDR. Continued data exchanges will enhance the ability of DC Department of Health to monitor the local HIV epidemic. %M 30104182 %R 10.2196/publichealth.9800 %U http://publichealth.jmir.org/2018/3/e62/ %U https://doi.org/10.2196/publichealth.9800 %U http://www.ncbi.nlm.nih.gov/pubmed/30104182 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 8 %P e10886 %T Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System %A Wang,Mei-Hua %A Chen,Han-Kun %A Hsu,Min-Huei %A Wang,Hui-Chi %A Yeh,Yu-Ting %+ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wuxing Street, Taipei,, Taiwan, 886 22490088 ext 8901, yuting@tmu.edu.tw %K laboratory autoreporting system %K HALR %K electronic medical records %D 2018 %7 08.08.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Outbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross-system surveillance and control system for infectious diseases. Objective: The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness. Methods: We collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web-based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity. Results: The HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1%, respectively. After the reported data were integrated with patients’ diagnostic results in their electronic medical records (EMRs), the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1%, respectively. Conclusions: The HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases’ clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases. %M 30089608 %R 10.2196/10886 %U http://www.jmir.org/2018/8/e10886/ %U https://doi.org/10.2196/10886 %U http://www.ncbi.nlm.nih.gov/pubmed/30089608 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 3 %P e11203 %T Bringing Real-Time Geospatial Precision to HIV Surveillance Through Smartphones: Feasibility Study %A Nsabimana,Alain Placide %A Uzabakiriho,Bernard %A Kagabo,Daniel M %A Nduwayo,Jerome %A Fu,Qinyouen %A Eng,Allison %A Hughes,Joshua %A Sia,Samuel K %+ Junco Labs, 423 W 127th Street, Ground Floor, New York, NY,, United States, 1 518 880 9667, samuelsia@juncolabs.com %K HIV surveillance %K smartphones %K mobile phones %K geospatial data %D 2018 %7 07.08.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Precise measurements of HIV incidences at community level can help mount a more effective public health response, but the most reliable methods currently require labor-intensive population surveys. Novel mobile phone technologies are being tested for adherence to medical appointments and antiretroviral therapy, but using them to track HIV test results with automatically generated geospatial coordinates has not been widely tested. Objective: We customized a portable reader for interpreting the results of HIV lateral flow tests and developed a mobile phone app to track HIV test results in urban and rural locations in Rwanda. The objective was to assess the feasibility of this technology to collect front line HIV test results in real time and with geospatial context to help measure HIV incidences and improve epidemiological surveillance. Methods: Twenty health care workers used the technology to track the test results of 2190 patients across 3 hospital sites (2 urban sites in Kigali and a rural site in the Western Province of Rwanda). Mobile phones for less than US $70 each were used. The mobile phone app to record HIV test results could take place without internet connectivity with uploading of results to the cloud taking place later with internet. Results: A total of 91.51% (2004/2190) of HIV test results could be tracked in real time on an online dashboard with geographical resolution down to street level. Out of the 20 health care workers, 14 (70%) would recommend the lateral flow reader, and 100% would recommend the mobile phone app. Conclusions: Smartphones have the potential to simplify the input of HIV test results with geospatial context and in real time to improve public health surveillance of HIV. %M 30087088 %R 10.2196/11203 %U http://publichealth.jmir.org/2018/3/e11203/ %U https://doi.org/10.2196/11203 %U http://www.ncbi.nlm.nih.gov/pubmed/30087088 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 3 %P e59 %T Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology %A Meyers,Lindsay %A Ginocchio,Christine C %A Faucett,Aimie N %A Nolte,Frederick S %A Gesteland,Per H %A Leber,Amy %A Janowiak,Diane %A Donovan,Virginia %A Dien Bard,Jennifer %A Spitzer,Silvia %A Stellrecht,Kathleen A %A Salimnia,Hossein %A Selvarangan,Rangaraj %A Juretschko,Stefan %A Daly,Judy A %A Wallentine,Jeremy C %A Lindsey,Kristy %A Moore,Franklin %A Reed,Sharon L %A Aguero-Rosenfeld,Maria %A Fey,Paul D %A Storch,Gregory A %A Melnick,Steve J %A Robinson,Christine C %A Meredith,Jennifer F %A Cook,Camille V %A Nelson,Robert K %A Jones,Jay D %A Scarpino,Samuel V %A Althouse,Benjamin M %A Ririe,Kirk M %A Malin,Bradley A %A Poritz,Mark A %+ BioFire Diagnostics, 515 Colorow Drive, Salt Lake City, UT, 84108, United States, 1 8017366354 ext 365, lindsay.meyers@biofiredx.com %K epidemiology %K patients %K privacy %K communicable disease %K internet %K pathology, molecular %D 2018 %7 06.07.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Health care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy. Objective: The aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems. Methods: We describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States. Results: The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present. Conclusions: Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks. %M 29980501 %R 10.2196/publichealth.9876 %U http://publichealth.jmir.org/2018/3/e59/ %U https://doi.org/10.2196/publichealth.9876 %U http://www.ncbi.nlm.nih.gov/pubmed/29980501 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e10218 %T A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability %A Al Manir,Mohammad Sadnan %A Brenas,Jon Haël %A Baker,Christopher JO %A Shaban-Nejad,Arash %+ Oak Ridge National Laboratory Center for for Biomedical Informatics, Department of Pediatrics, The University of Tennessee Health Science Center, 50 N Dunlap Street, R492, Memphis, TN, 38103, United States, 1 901 287 5836, ashabann@uthsc.edu %K malaria surveillance %K global health %K interoperability %K change management %K Web services %K population health intelligence %D 2018 %7 15.06.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: According to the World Health Organization, malaria surveillance is weakest in countries and regions with the highest malaria burden. A core obstacle is that the data required to perform malaria surveillance are fragmented in multiple data silos distributed across geographic regions. Furthermore, consistent integrated malaria data sources are few, and a low degree of interoperability exists between them. As a result, it is difficult to identify disease trends and to plan for effective interventions. Objective: We propose the Semantics, Interoperability, and Evolution for Malaria Analytics (SIEMA) platform for use in malaria surveillance based on semantic data federation. Using this approach, it is possible to access distributed data, extend and preserve interoperability between multiple dynamic distributed malaria sources, and facilitate detection of system changes that can interrupt mission-critical global surveillance activities. Methods: We used Semantic Automated Discovery and Integration (SADI) Semantic Web Services to enable data access and improve interoperability, and the graphical user interface-enabled semantic query engine HYDRA to implement the target queries typical of malaria programs. We implemented a custom algorithm to detect changes to community-developed terminologies, data sources, and services that are core to SIEMA. This algorithm reports to a dashboard. Valet SADI is used to mitigate the impact of changes by rebuilding affected services. Results: We developed a prototype surveillance and change management platform from a combination of third-party tools, community-developed terminologies, and custom algorithms. We illustrated a methodology and core infrastructure to facilitate interoperable access to distributed data sources using SADI Semantic Web services. This degree of access makes it possible to implement complex queries needed by our user community with minimal technical skill. We implemented a dashboard that reports on terminology changes that can render the services inactive, jeopardizing system interoperability. Using this information, end users can control and reactively rebuild services to preserve interoperability and minimize service downtime. Conclusions: We introduce a framework suitable for use in malaria surveillance that supports the creation of flexible surveillance queries across distributed data resources. The platform provides interoperable access to target data sources, is domain agnostic, and with updates to core terminological resources is readily transferable to other surveillance activities. A dashboard enables users to review changes to the infrastructure and invoke system updates. The platform significantly extends the range of functionalities offered by malaria information systems, beyond the state-of-the-art. %M 29907554 %R 10.2196/10218 %U http://publichealth.jmir.org/2018/2/e10218/ %U https://doi.org/10.2196/10218 %U http://www.ncbi.nlm.nih.gov/pubmed/29907554 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 6 %N 2 %P e36 %T Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore %A Hardjojo,Antony %A Gunachandran,Arunan %A Pang,Long %A Abdullah,Mohammed Ridzwan Bin %A Wah,Win %A Chong,Joash Wen Chen %A Goh,Ee Hui %A Teo,Sok Huang %A Lim,Gilbert %A Lee,Mong Li %A Hsu,Wynne %A Lee,Vernon %A Chen,Mark I-Cheng %A Wong,Franco %A Phang,Jonathan Siung King %+ Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, MD1, #10-01, 12 Science Drive 2, Singapore, 117549, Singapore, 65 65165781, mark_chen@nuhs.edu.sg %K natural language processing %K communicable diseases %K epidemiology %K surveillance %K syndromic surveillance %K electronic health records %D 2018 %7 11.6.2018 %9 Original Paper %J JMIR Med Inform %G English %X Background: Free-text clinical records provide a source of information that complements traditional disease surveillance. To electronically harness these records, they need to be transformed into codified fields by natural language processing algorithms. Objective: The aim of this study was to develop, train, and validate Clinical History Extractor for Syndromic Surveillance (CHESS), an natural language processing algorithm to extract clinical information from free-text primary care records. Methods: CHESS is a keyword-based natural language processing algorithm to extract 48 signs and symptoms suggesting respiratory infections, gastrointestinal infections, constitutional, as well as other signs and symptoms potentially associated with infectious diseases. The algorithm also captured the assertion status (affirmed, negated, or suspected) and symptom duration. Electronic medical records from the National Healthcare Group Polyclinics, a major public sector primary care provider in Singapore, were randomly extracted and manually reviewed by 2 human reviewers, with a third reviewer as the adjudicator. The algorithm was evaluated based on 1680 notes against the human-coded result as the reference standard, with half of the data used for training and the other half for validation. Results: The symptoms most commonly present within the 1680 clinical records at the episode level were those typically present in respiratory infections such as cough (744/7703, 9.66%), sore throat (591/7703, 7.67%), rhinorrhea (552/7703, 7.17%), and fever (928/7703, 12.04%). At the episode level, CHESS had an overall performance of 96.7% precision and 97.6% recall on the training dataset and 96.0% precision and 93.1% recall on the validation dataset. Symptoms suggesting respiratory and gastrointestinal infections were all detected with more than 90% precision and recall. CHESS correctly assigned the assertion status in 97.3%, 97.9%, and 89.8% of affirmed, negated, and suspected signs and symptoms, respectively (97.6% overall accuracy). Symptom episode duration was correctly identified in 81.2% of records with known duration status. Conclusions: We have developed an natural language processing algorithm dubbed CHESS that achieves good performance in extracting signs and symptoms from primary care free-text clinical records. In addition to the presence of symptoms, our algorithm can also accurately distinguish affirmed, negated, and suspected assertion statuses and extract symptom durations. %M 29907560 %R 10.2196/medinform.8204 %U http://medinform.jmir.org/2018/2/e36/ %U https://doi.org/10.2196/medinform.8204 %U http://www.ncbi.nlm.nih.gov/pubmed/29907560 %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 %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e51 %T Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach %A Bollegala,Danushka %A Maskell,Simon %A Sloane,Richard %A Hajne,Joanna %A Pirmohamed,Munir %+ Department of Computer Science, University of Liverpool, Ashton Street,, Liverpool, L693BX, United Kingdom, 44 1517954283, danushka.bollegala@liverpool.ac.uk %K machine learning %K ADR detection %K causality %K lexical patterns %K causality detection %K support vector machines %D 2018 %7 09.05.2018 %9 Proposal %J JMIR Public Health Surveill %G English %X Background: Detecting adverse drug reactions (ADRs) is an important task that has direct implications for the use of that drug. If we can detect previously unknown ADRs as quickly as possible, then this information can be provided to the regulators, pharmaceutical companies, and health care organizations, thereby potentially reducing drug-related morbidity and saving lives of many patients. A promising approach for detecting ADRs is to use social media platforms such as Twitter and Facebook. A high level of correlation between a drug name and an event may be an indication of a potential adverse reaction associated with that drug. Although numerous association measures have been proposed by the signal detection community for identifying ADRs, these measures are limited in that they detect correlations but often ignore causality. Objective: This study aimed to propose a causality measure that can detect an adverse reaction that is caused by a drug rather than merely being a correlated signal. Methods: To the best of our knowledge, this was the first causality-sensitive approach for detecting ADRs from social media. Specifically, the relationship between a drug and an event was represented using a set of automatically extracted lexical patterns. We then learned the weights for the extracted lexical patterns that indicate their reliability for expressing an adverse reaction of a given drug. Results: Our proposed method obtains an ADR detection accuracy of 74% on a large-scale manually annotated dataset of tweets, covering a standard set of drugs and adverse reactions. Conclusions: By using lexical patterns, we can accurately detect the causality between drugs and adverse reaction–related events. %M 29743155 %R 10.2196/publichealth.8214 %U http://publichealth.jmir.org/2018/2/e51/ %U https://doi.org/10.2196/publichealth.8214 %U http://www.ncbi.nlm.nih.gov/pubmed/29743155 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e52 %T The Acceptability and Feasibility of Implementing a Bio-Behavioral Enhanced Surveillance Tool for Sexually Transmitted Infections in England: Mixed-Methods Study %A Wayal,Sonali %A Reid,David %A Blomquist,Paula B %A Weatherburn,Peter %A Mercer,Catherine H %A Hughes,Gwenda %+ Centre for Population Research in Sexual Health and HIV, Institute for Global Health, University College London, 3rd Floor, Mortimer Market Centre, London, WC1E6JB, United Kingdom, 44 02031082064, s.wayal@ucl.ac.uk %K public health surveillance %K sexually transmitted diseases %K feasibility studies %K electronic health records %K Web-based survey %D 2018 %7 04.05.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Sexually transmitted infection (STI) surveillance is vital for tracking the scale and pattern of epidemics; however, it often lacks data on the underlying drivers of STIs. Objective: This study aimed to assess the acceptability and feasibility of implementing a bio-behavioral enhanced surveillance tool, comprising a self-administered Web-based survey among sexual health clinic attendees, as well as linking this to their electronic health records (EHR) held in England’s national STI surveillance system. Methods: Staff from 19 purposively selected sexual health clinics across England and men who have sex with men and black Caribbeans, because of high STI burden among these groups, were interviewed to assess the acceptability of the proposed bio-behavioral enhanced surveillance tool. Subsequently, sexual health clinic staff invited all attendees to complete a Web-based survey on drivers of STI risk using a study tablet or participants’ own digital device. They recorded the number of attendees invited and participants’ clinic numbers, which were used to link survey data to the EHR. Participants’ online consent was obtained, separately for survey participation and linkage. In postimplementation phase, sexual health clinic staff were reinterviewed to assess the feasibility of implementing the bio-behavioral enhanced surveillance tool. Acceptability and feasibility of implementing the bio-behavioral enhanced surveillance tool were assessed by analyzing these qualitative and quantitative data. Results: Prior to implementation of the bio-behavioral enhanced surveillance tool, sexual health clinic staff and attendees emphasized the importance of free internet/Wi-Fi access, confidentiality, and anonymity for increasing the acceptability of the bio-behavioral enhanced surveillance tool among attendees. Implementation of the bio-behavioral enhanced surveillance tool across sexual health clinics varied considerably and was influenced by sexual health clinics’ culture of prioritization of research and innovation and availability of resources for implementing the surveys. Of the 7367 attendees invited, 85.28% (6283) agreed to participate. Of these, 72.97% (4585/6283) consented to participate in the survey, and 70.62% (4437/6283) were eligible and completed it. Of these, 91.19% (4046/4437) consented to EHR linkage, which did not differ by age or gender but was higher among gay/bisexual men than heterosexual men (95.50%, 722/756 vs 88.31%, 1073/1215; P<.003) and lower among black Caribbeans than white participants (87.25%, 568/651 vs 93.89%, 2181/2323; P<.002). Linkage was achieved for 88.88% (3596/4046) of consenting participants. Conclusions: Implementing a bio-behavioral enhanced surveillance tool in sexual health clinics was feasible and acceptable to staff and groups at STI risk; however, ensuring participants’ confidentiality and anonymity and availability of resources is vital. Bio-behavioral enhanced surveillance tools could enable timely collection of detailed behavioral data for effective commissioning of sexual health services. %M 29728348 %R 10.2196/publichealth.9010 %U http://publichealth.jmir.org/2018/2/e52/ %U https://doi.org/10.2196/publichealth.9010 %U http://www.ncbi.nlm.nih.gov/pubmed/29728348 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e29 %T Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning %A Munkhdalai,Tsendsuren %A Liu,Feifan %A Yu,Hong %+ Department of Computer Science, University of Massachusetts Lowell, 1 University Ave, Lowell, MA, 01854, United States, 1 9789343620, hong_yu@uml.edu %K medical informatics applications %K drug-related side effects and adverse reactions %K neural networks %K natural language processing %K electronic health records %D 2018 %7 25.04.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Medication and adverse drug event (ADE) information extracted from electronic health record (EHR) notes can be a rich resource for drug safety surveillance. Existing observational studies have mainly relied on structured EHR data to obtain ADE information; however, ADEs are often buried in the EHR narratives and not recorded in structured data. Objective: To unlock ADE-related information from EHR narratives, there is a need to extract relevant entities and identify relations among them. In this study, we focus on relation identification. This study aimed to evaluate natural language processing and machine learning approaches using the expert-annotated medical entities and relations in the context of drug safety surveillance, and investigate how different learning approaches perform under different configurations. Methods: We have manually annotated 791 EHR notes with 9 named entities (eg, medication, indication, severity, and ADEs) and 7 different types of relations (eg, medication-dosage, medication-ADE, and severity-ADE). Then, we explored 3 supervised machine learning systems for relation identification: (1) a support vector machines (SVM) system, (2) an end-to-end deep neural network system, and (3) a supervised descriptive rule induction baseline system. For the neural network system, we exploited the state-of-the-art recurrent neural network (RNN) and attention models. We report the performance by macro-averaged precision, recall, and F1-score across the relation types. Results: Our results show that the SVM model achieved the best average F1-score of 89.1% on test data, outperforming the long short-term memory (LSTM) model with attention (F1-score of 65.72%) as well as the rule induction baseline system (F1-score of 7.47%) by a large margin. The bidirectional LSTM model with attention achieved the best performance among different RNN models. With the inclusion of additional features in the LSTM model, its performance can be boosted to an average F1-score of 77.35%. Conclusions: It shows that classical learning models (SVM) remains advantageous over deep learning models (RNN variants) for clinical relation identification, especially for long-distance intersentential relations. However, RNNs demonstrate a great potential of significant improvement if more training data become available. Our work is an important step toward mining EHRs to improve the efficacy of drug safety surveillance. Most importantly, the annotated data used in this study will be made publicly available, which will further promote drug safety research in the community. %M 29695376 %R 10.2196/publichealth.9361 %U http://publichealth.jmir.org/2018/2/e29/ %U https://doi.org/10.2196/publichealth.9361 %U http://www.ncbi.nlm.nih.gov/pubmed/29695376 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e3 %T Sustainable Monitoring and Surveillance Systems to Improve HIV Programs: Review %A Low-Beer,Daniel %A Mahy,Mary %A Renaud,Francoise %A Calleja,Txema %+ World Health Organization, 20 avenue Appia, Geneva, 1211, Switzerland, 41 795090693, daniellowbeer@gmail.com %K surveillance %K HIV %K development monitoring %K evaluation %D 2018 %7 24.04.2018 %9 Viewpoint %J JMIR Public Health Surveill %G English %X HIV programs have provided a major impetus for investments in surveillance data, with 5-10% of HIV program budgets recommended to support data. However there are questions concerning the sustainability of these investments. The Sustainable Development Goals have consolidated health into one goal and communicable diseases into one target (Target 3.3). Sustainable Development Goals now introduce targets focused specifically on data (Targets 17.18 and 17.19). Data are seen as one of the three systemic issues (in Goal 17) for implementing Sustainable Development Goals, alongside policies and partnerships. This paper reviews the surveillance priorities in the context of the Sustainable Development Goals and highlights the shift from periodic measurement towards sustainable disaggregated, real-time, case, and patient data, which are used routinely to improve programs. Finally, the key directions in developing person-centered monitoring systems are assessed with country examples. The directions contribute to the Sustainable Development Goal focus on people-centered development applied to data. %M 29691202 %R 10.2196/publichealth.8173 %U http://publichealth.jmir.org/2018/2/e3/ %U https://doi.org/10.2196/publichealth.8173 %U http://www.ncbi.nlm.nih.gov/pubmed/29691202 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 4 %P e142 %T Identifying Unmet Treatment Needs for Patients With Osteoporotic Fracture: Feasibility Study for an Electronic Clinical Surveillance System %A Lin,Fong-Ci %A Wang,Chen-Yu %A Shang,Rung Ji %A Hsiao,Fei-Yuan %A Lin,Mei-Shu %A Hung,Kuan-Yu %A Wang,Jui %A Lin,Zhen-Fang %A Lai,Feipei %A Shen,Li-Jiuan %A Huang,Chih-Fen %+ Department of Pharmacy, National Taiwan University Hospital, 33 Linsen S. Rd, Taipei, 10050, Taiwan, 886 223562162, cfhuang1023@ntu.edu.tw %K information systems %K public health surveillance %K osteoporotic fractures %K pharmacovigilance %K guideline adherence %D 2018 %7 24.04.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Traditional clinical surveillance relied on the results from clinical trials and observational studies of administrative databases. However, these studies not only required many valuable resources but also faced a very long time lag. Objective: This study aimed to illustrate a practical application of the National Taiwan University Hospital Clinical Surveillance System (NCSS) in the identification of patients with an osteoporotic fracture and to provide a high reusability infrastructure for longitudinal clinical data. Methods: The NCSS integrates electronic medical records in the National Taiwan University Hospital (NTUH) with a data warehouse and is equipped with a user-friendly interface. The NCSS was developed using professional insight from multidisciplinary experts, including clinical practitioners, epidemiologists, and biomedical engineers. The practical example identifying the unmet treatment needs for patients encountering major osteoporotic fractures described herein was mainly achieved by adopting the computerized workflow in the NCSS. Results: We developed the infrastructure of the NCSS, including an integrated data warehouse and an automatic surveillance workflow. By applying the NCSS, we efficiently identified 2193 patients who were newly diagnosed with a hip or vertebral fracture between 2010 and 2014 at NTUH. By adopting the filter function, we identified 1808 (1808/2193, 82.44%) patients who continued their follow-up at NTUH, and 464 (464/2193, 21.16%) patients who were prescribed anti-osteoporosis medications, within 3 and 12 months post the index date of their fracture, respectively. Conclusions: The NCSS systems can integrate the workflow of cohort identification to accelerate the survey process of clinically relevant problems and provide decision support in the daily practice of clinical physicians, thereby making the benefit of evidence-based medicine a reality. %M 29691201 %R 10.2196/jmir.9477 %U http://www.jmir.org/2018/4/e142/ %U https://doi.org/10.2196/jmir.9477 %U http://www.ncbi.nlm.nih.gov/pubmed/29691201 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e36 %T Strengthening Routine Data Systems to Track the HIV Epidemic and Guide the Response in Sub-Saharan Africa %A Rice,Brian %A Boulle,Andrew %A Baral,Stefan %A Egger,Matthias %A Mee,Paul %A Fearon,Elizabeth %A Reniers,Georges %A Todd,Jim %A Schwarcz,Sandra %A Weir,Sharon %A Rutherford,George %A Hargreaves,James %+ Social and Environmental Health Research, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, United Kingdom, 44 (0)2079272567, brian.rice@lshtm.ac.uk %K HIV %K data %K systems %K surveillance %K sub-Saharan Africa %K testing %K treatment %K prevention %K cascade %K monitoring %K quality assessment %K clinical %K program %D 2018 %7 03.04.2018 %9 Viewpoint %J JMIR Public Health Surveill %G English %X The global HIV response has entered a new phase with the recommendation of treating all persons living with HIV with antiretroviral therapy, and with the goals of reducing new infections and AIDS-related deaths to fewer than 500,000 by 2020. This new phase has intensive data requirements that will need to utilize routine data collected through service delivery platforms to monitor progress toward these goals. With a focus on sub-Saharan African, we present the following priorities to improve the demand, supply, and use of routine HIV data: (1) strengthening patient-level HIV data systems that support continuity of clinical care and document sentinel events; (2) leveraging data from HIV testing programs; (3) using targeting data collection in communities and among clients; and (4) building capacity and promoting a culture of HIV data quality assessment and use. When fully leveraged, routine data can efficiently provide timely information at a local level to inform action, as well as provide information at scale with wide geographic coverage to strengthen estimation efforts. %M 29615387 %R 10.2196/publichealth.9344 %U http://publichealth.jmir.org/2018/2/e36/ %U https://doi.org/10.2196/publichealth.9344 %U http://www.ncbi.nlm.nih.gov/pubmed/29615387 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e25 %T A Participatory System for Preventing Pandemics of Animal Origins: Pilot Study of the Participatory One Health Disease Detection (PODD) System %A Yano,Terdsak %A Phornwisetsirikun,Somphorn %A Susumpow,Patipat %A Visrutaratna,Surasing %A Chanachai,Karoon %A Phetra,Polawat %A Chaisowwong,Warangkhana %A Trakarnsirinont,Pairat %A Hemwan,Phonpat %A Kaewpinta,Boontuan %A Singhapreecha,Charuk %A Kreausukon,Khwanchai %A Charoenpanyanet,Arisara  %A Robert,Chongchit Sripun %A Robert,Lamar %A Rodtian,Pranee %A Mahasing,Suteerat %A Laiya,Ekkachai %A Pattamakaew,Sakulrat %A Tankitiyanon,Taweesart %A Sansamur,Chalutwan %A Srikitjakarn,Lertrak %+ Faculty of Veterinary Medicine, Chiang Mai University, Chonlapratan Road, Maehia, Muang, Chiang Mai, 50100, Thailand, 66 871850280, lertrak.s@gmail.com %K community-owned disease surveillance system %K PODD %K mobile app %K one health %K participatory approach %K backyard chicken %K pandemic prevention %D 2018 %7 21.03.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Aiming for early disease detection and prompt outbreak control, digital technology with a participatory One Health approach was used to create a novel disease surveillance system called Participatory One Health Disease Detection (PODD). PODD is a community-owned surveillance system that collects data from volunteer reporters; identifies disease outbreak automatically; and notifies the local governments (LGs), surrounding villages, and relevant authorities. This system provides a direct and immediate benefit to the communities by empowering them to protect themselves. Objective: The objective of this study was to determine the effectiveness of the PODD system for the rapid detection and control of disease outbreaks. Methods: The system was piloted in 74 LGs in Chiang Mai, Thailand, with the participation of 296 volunteer reporters. The volunteers and LGs were key participants in the piloting of the PODD system. Volunteers monitored animal and human diseases, as well as environmental problems, in their communities and reported these events via the PODD mobile phone app. LGs were responsible for outbreak control and provided support to the volunteers. Outcome mapping was used to evaluate the performance of the LGs and volunteers. Results: LGs were categorized into one of the 3 groups based on performance: A (good), B (fair), and C (poor), with the majority (46%,34/74) categorized into group B. Volunteers were similarly categorized into 4 performance groups (A-D), again with group A showing the best performance, with the majority categorized into groups B and C. After 16 months of implementation, 1029 abnormal events had been reported and confirmed to be true reports. The majority of abnormal reports were sick or dead animals (404/1029, 39.26%), followed by zoonoses and other human diseases (129/1029, 12.54%). Many potentially devastating animal disease outbreaks were detected and successfully controlled, including 26 chicken high mortality outbreaks, 4 cattle disease outbreaks, 3 pig disease outbreaks, and 3 fish disease outbreaks. In all cases, the communities and animal authorities cooperated to apply community contingency plans to control these outbreaks, and community volunteers continued to monitor the abnormal events for 3 weeks after each outbreak was controlled. Conclusions: By design, PODD initially targeted only animal diseases that potentially could emerge into human pandemics (eg, avian influenza) and then, in response to community needs, expanded to cover human health and environmental health issues. %M 29563079 %R 10.2196/publichealth.7375 %U http://publichealth.jmir.org/2018/1/e25/ %U https://doi.org/10.2196/publichealth.7375 %U http://www.ncbi.nlm.nih.gov/pubmed/29563079 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e23 %T Defining Care Patterns and Outcomes Among Persons Living with HIV in Washington, DC: Linkage of Clinical Cohort and Surveillance Data %A Castel,Amanda D %A Terzian,Arpi %A Opoku,Jenevieve %A Happ,Lindsey Powers %A Younes,Naji %A Kharfen,Michael %A Greenberg,Alan %A , %+ Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, The George Washington University, 950 New Hampshire Ave NW, 5th Floor, Washington, DC, 20052, United States, 1 202 994 8325, acastel@gwu.edu %K HIV/AIDS %K health information technology %K surveillance %K retention %K viral suppression %K antiretroviral therapy %D 2018 %7 16.03.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Triangulation of data from multiple sources such as clinical cohort and surveillance data can help improve our ability to describe care patterns, service utilization, comorbidities, and ultimately measure and monitor clinical outcomes among persons living with HIV infection. Objectives: The objective of this study was to determine whether linkage of clinical cohort data and routinely collected HIV surveillance data would enhance the completeness and accuracy of each database and improve the understanding of care patterns and clinical outcomes. Methods: We linked data from the District of Columbia (DC) Cohort, a large HIV observational clinical cohort, with Washington, DC, Department of Health (DOH) surveillance data between January 2011 and June 2015. We determined percent concordance between select variables in the pre- and postlinked databases using kappa test statistics. We compared retention in care (RIC), viral suppression (VS), sexually transmitted diseases (STDs), and non-HIV comorbid conditions (eg, hypertension) and compared HIV clinic visit patterns determined using the prelinked database (DC Cohort) versus the postlinked database (DC Cohort + DOH) using chi-square testing. Additionally, we compared sociodemographic characteristics, RIC, and VS among participants receiving HIV care at ≥3 sites versus <3 sites using chi-square testing. Results: Of the 6054 DC Cohort participants, 5521 (91.19%) were included in the postlinked database and enrolled at a single DC Cohort site. The majority of the participants was male, black, and had men who have sex with men (MSM) as their HIV risk factor. In the postlinked database, 619 STD diagnoses previously unknown to the DC Cohort were identified. Additionally, the proportion of participants with RIC was higher compared with the prelinked database (59.83%, 2678/4476 vs 64.95%, 2907/4476; P<.001) and the proportion with VS was lower (87.85%, 2277/2592 vs 85.15%, 2391/2808; P<.001). Almost a quarter of participants (23.06%, 1279/5521) were identified as receiving HIV care at ≥2 sites (postlinked database). The participants using ≥3 care sites were more likely to achieve RIC (80.7%, 234/290 vs 62.61%, 2197/3509) but less likely to achieve VS (72.3%, 154/213 vs 89.51%, 1869/2088). The participants using ≥3 care sites were more likely to have unstable housing (15.1%, 64/424 vs 8.96%, 380/4242), public insurance (86.1%, 365/424 vs 57.57%, 2442/4242), comorbid conditions (eg, hypertension) (37.7%, 160/424 vs 22.98%, 975/4242), and have acquired immunodeficiency syndrome (77.8%, 330/424 vs 61.20%, 2596/4242) (all P<.001). Conclusions: Linking surveillance and clinical data resulted in the improved completeness of each database and a larger volume of available data to evaluate HIV outcomes, allowing for refinement of HIV care continuum estimates. The postlinked database also highlighted important differences between participants who sought HIV care at multiple clinical sites. Our findings suggest that combined datasets can enhance evaluation of HIV-related outcomes across an entire metropolitan area. Future research will evaluate how to best utilize this information to improve outcomes in addition to monitoring them. %M 29549065 %R 10.2196/publichealth.9221 %U http://publichealth.jmir.org/2018/1/e23/ %U https://doi.org/10.2196/publichealth.9221 %U http://www.ncbi.nlm.nih.gov/pubmed/29549065 %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 %@ 2291-9694 %I JMIR Publications %V 6 %N 1 %P e12 %T Potential Application of Digitally Linked Tuberculosis Diagnostics for Real-Time Surveillance of Drug-Resistant Tuberculosis Transmission: Validation and Analysis of Test Results %A Ng,Kamela Charmaine %A Meehan,Conor Joseph %A Torrea,Gabriela %A Goeminne,Léonie %A Diels,Maren %A Rigouts,Leen %A de Jong,Bouke Catherine %A André,Emmanuel %+ Mycobacteriology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, Nationalestraat 155, Antwerp, 2000, Belgium, 32 (0) 33455345, kng@itg.be %K tuberculosis %K drug resistance %K rifampicin-resistant tuberculosis %K rapid diagnostic tests %K Xpert MTB/RIF %K Genotype MTBDRplus v2.0 %K Genoscholar NTM + MDRTB II %K RDT probe reactions %K rpoB mutations %K validation and analysis %K real-time detection %D 2018 %7 27.02.2018 %9 Original Paper %J JMIR Med Inform %G English %X Background: Tuberculosis (TB) is the highest-mortality infectious disease in the world and the main cause of death related to antimicrobial resistance, yet its surveillance is still paper-based. Rifampicin-resistant TB (RR-TB) is an urgent public health crisis. The World Health Organization has, since 2010, endorsed a series of rapid diagnostic tests (RDTs) that enable rapid detection of drug-resistant strains and produce large volumes of data. In parallel, most high-burden countries have adopted connectivity solutions that allow linking of diagnostics, real-time capture, and shared repository of these test results. However, these connected diagnostics and readily available test results are not used to their full capacity, as we have yet to capitalize on fully understanding the relationship between test results and specific rpoB mutations to elucidate its potential application to real-time surveillance. Objective: We aimed to validate and analyze RDT data in detail, and propose the potential use of connected diagnostics and associated test results for real-time evaluation of RR-TB transmission. Methods: We selected 107 RR-TB strains harboring 34 unique rpoB mutations, including 30 within the rifampicin resistance–determining region (RRDR), from the Belgian Coordinated Collections of Microorganisms, Antwerp, Belgium. We subjected these strains to Xpert MTB/RIF, GenoType MTBDRplus v2.0, and Genoscholar NTM + MDRTB II, the results of which were validated against the strains’ available rpoB gene sequences. We determined the reproducibility of the results, analyzed and visualized the probe reactions, and proposed these for potential use in evaluating transmission. Results: The RDT probe reactions detected most RRDR mutations tested, although we found a few critical discrepancies between observed results and manufacturers’ claims. Based on published frequencies of probe reactions and RRDR mutations, we found specific probe reactions with high potential use in transmission studies: Xpert MTB/RIF probes A, Bdelayed, C, and Edelayed; Genotype MTBDRplus v2.0 WT2, WT5, and WT6; and Genoscholar NTM + MDRTB II S1 and S3. Inspection of probe reactions of disputed mutations may potentially resolve discordance between genotypic and phenotypic test results. Conclusions: We propose a novel approach for potential real-time detection of RR-TB transmission through fully using digitally linked TB diagnostics and shared repository of test results. To our knowledge, this is the first pragmatic and scalable work in response to the consensus of world-renowned TB experts in 2016 on the potential of diagnostic connectivity to accelerate efforts to eliminate TB. This is evidenced by the ability of our proposed approach to facilitate comparison of probe reactions between different RDTs used in the same setting. Integrating this proposed approach as a plug-in module to a connectivity platform will increase usefulness of connected TB diagnostics for RR-TB outbreak detection through real-time investigation of suspected RR-TB transmission cases based on epidemiologic linking. %M 29487047 %R 10.2196/medinform.9309 %U http://medinform.jmir.org/2018/1/e12/ %U https://doi.org/10.2196/medinform.9309 %U http://www.ncbi.nlm.nih.gov/pubmed/29487047 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e17 %T Near-Real-Time Surveillance of Illnesses Related to Shellfish Consumption in British Columbia: Analysis of Poison Center Data %A Wan,Victoria %A McIntyre,Lorraine %A Kent,Debra %A Leong,Dennis %A Henderson,Sarah B %+ Environmental Health Services, British Columbia Centre for Disease Control, LL0073, 655 12th Avenue West, Vancouver, BC, V5Z4R4, Canada, 1 6047072400 ext 273721, victoria.wan@bccdc.ca %K poison control centers %K public health surveillance %K shellfish poisoning %K norovirus %K Vibrio parahaemolyticus %D 2018 %7 23.02.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Data from poison centers have the potential to be valuable for public health surveillance of long-term trends, short-term aberrations from those trends, and poisonings occurring in near-real-time. This information can enable long-term prevention via programs and policies and short-term control via immediate public health response. Over the past decade, there has been an increasing use of poison control data for surveillance in the United States, Europe, and New Zealand, but this resource still remains widely underused. Objective: The British Columbia (BC) Drug and Poison Information Centre (DPIC) is one of five such services in Canada, and it is the only one nested within a public health agency. This study aimed to demonstrate how DPIC data are used for routine public health surveillance in near-real-time using the case study of its alerting system for illness related to consumption of shellfish (ASIRCS). Methods: Every hour, a connection is opened between the WBM software Visual Dotlab Enterprise, which holds the DPIC database, and the R statistical computing environment. This platform is used to extract, clean, and merge all necessary raw data tables into a single data file. ASIRCS automatically and retrospectively scans a 24-hour window within the data file for new cases related to illnesses from shellfish consumption. Detected cases are queried using a list of attributes: the caller location, exposure type, reasons for the exposure, and a list of keywords searched in the clinical notes. The alert generates a report that is tailored to the needs of food safety specialists, who then assess and respond to detected cases. Results: The ASIRCS system alerted on 79 cases between January 2015 and December 2016, and retrospective analysis found 11 cases that were missed. All cases were reviewed by food safety specialists, and 58% (46/79) were referred to designated regional health authority contacts for follow-up. Of the 42% (33/79) cases that were not referred to health authorities, some were missing follow-up information, some were triggered by allergies to shellfish, and some were triggered by shellfish-related keywords appearing in the case notes for nonshellfish-related cases. Improvements were made between 2015 and 2016 to reduce the number of cases with missing follow-up information. Conclusions: The surveillance capacity is evident within poison control data as shown from the novel use of DPIC data for identifying illnesses related to shellfish consumption in BC. The further development of surveillance programs could improve and enhance response to public health emergencies related to acute illnesses, chronic diseases, and environmental exposures. %M 29475825 %R 10.2196/publichealth.8944 %U http://publichealth.jmir.org/2018/1/e17/ %U https://doi.org/10.2196/publichealth.8944 %U http://www.ncbi.nlm.nih.gov/pubmed/29475825 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e18 %T Know Your Epidemic, Strengthen Your Response: Developing a New HIV Surveillance Architecture to Guide HIV Resource Allocation and Target Decisions %A Rice,Brian %A Sanchez,Travis %A Baral,Stefan %A Mee,Paul %A Sabin,Keith %A Garcia-Calleja,Jesus M %A Hargreaves,James %+ Department of Social and Environmental Health Research, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London,, United Kingdom, 44 2079272567, brian.rice@lshtm.ac.uk %K HIV %K data %K systems %K surveillance %K testing %K treatment %K prevention %K monitoring %K key populations %D 2018 %7 14.02.2018 %9 Editorial %J JMIR Public Health Surveill %G English %X To guide HIV prevention and treatment activities up to 2020, we need to generate and make better use of high quality HIV surveillance data. To highlight our surveillance needs, a special collection of papers in JMIR Public Health and Surveillance has been released under the title “Improving Global and National Responses to the HIV Epidemic Through High Quality HIV Surveillance Data.” We provide a summary of these papers and highlight methods for developing a new HIV surveillance architecture. %M 29444766 %R 10.2196/publichealth.9386 %U http://publichealth.jmir.org/2018/1/e18/ %U https://doi.org/10.2196/publichealth.9386 %U http://www.ncbi.nlm.nih.gov/pubmed/29444766 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e94 %T A Smartphone App (AfyaData) for Innovative One Health Disease Surveillance from Community to National Levels in Africa: Intervention in Disease Surveillance %A Karimuribo,Esron Daniel %A Mutagahywa,Eric %A Sindato,Calvin %A Mboera,Leonard %A Mwabukusi,Mpoki %A Kariuki Njenga,M %A Teesdale,Scott %A Olsen,Jennifer %A Rweyemamu,Mark %+ Department of Veterinary Medicine and Public Health, College of Veterinary Medicine and Biomedical Sciences, Sokoine University of Agriculture, PO Box 3021 Chuo Kikuu, Morogoro, Tanzania, United Republic Of Tanzania, 255 695760, ekarimu@yahoo.co.uk %K public health %K surveillance systems %K epidemics %K outbreak %K technology %D 2017 %7 18.12.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: We describe the development and initial achievements of a participatory disease surveillance system that relies on mobile technology to promote Community Level One Health Security (CLOHS) in Africa. Objective: The objective of this system, Enhancing Community-Based Disease Outbreak Detection and Response in East and Southern Africa (DODRES), is to empower community-based human and animal health reporters with training and information and communication technology (ICT)–based solutions to contribute to disease detection and response, thereby complementing strategies to improve the efficiency of infectious disease surveillance at national, regional, and global levels. In this study, we refer to techno-health as the application of ICT-based solutions to enhance early detection, timely reporting, and prompt response to health events in human and animal populations. Methods: An EpiHack, involving human and animal health experts as well as ICT programmers, was held in Tanzania in 2014 to identify major challenges facing early detection, timely reporting, and prompt response to disease events. This was followed by a project inception workshop in 2015, which brought together key stakeholders, including policy makers and community representatives, to refine the objectives and implementation plan of the DODRES project. The digital ICT tools were developed and packaged together as the AfyaData app to support One Health disease surveillance. Community health reporters (CHRs) and officials from animal and human health sectors in Morogoro and Ngorongoro districts in Tanzania were trained to use the AfyaData app. The AfyaData supports near- to real-time data collection and submission at both community and health facility levels as well as the provision of feedback to reporters. The functionality of the One Health Knowledge Repository (OHKR) app has been integrated into the AfyaData app to provide health information on case definitions of diseases of humans and animals and to synthesize advice that can be transmitted to CHRs with next step response activities or interventions. Additionally, a WhatsApp social group was made to serve as a platform to sustain interactions between community members, local government officials, and DODRES team members. Results: Within the first 5 months (August-December 2016) of AfyaData tool deployment, a total of 1915 clinical cases in livestock (1816) and humans (99) were reported in Morogoro (83) and Ngorongoro (1832) districts. Conclusions: These initial results suggest that the DODRES community-level model creates an opportunity for One Health engagement of people in their own communities in the detection of infectious human and animal disease threats. Participatory approaches supported by digital and mobile technologies should be promoted for early disease detection, timely reporting, and prompt response at the community, national, regional, and global levels. %M 29254916 %R 10.2196/publichealth.7373 %U http://publichealth.jmir.org/2017/4/e94/ %U https://doi.org/10.2196/publichealth.7373 %U http://www.ncbi.nlm.nih.gov/pubmed/29254916 %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 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 %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e65 %T Lessons From the Implementation of Mo-Buzz, a Mobile Pandemic Surveillance System for Dengue %A Lwin,May Oo %A Jayasundar,Karthikayen %A Sheldenkar,Anita %A Wijayamuni,Ruwan %A Wimalaratne,Prasad %A Ernst,Kacey C %A Foo,Schubert %+ Wee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link, Singapore, 637718, Singapore, 65 67906669, tmaylwin@ntu.edu.sg %K pandemics %K dengue %K health communication %K telemedicine %K epidemiology %K participatory surveillance %K participatory epidemiology %D 2017 %7 02.10.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Approximately 128 countries and 3.9 billion people are at risk of dengue infection. Incidence of dengue has increased over the past decades, becoming a growing public health concern for countries with populations that are increasingly susceptible to this vector-borne disease, such as Sri Lanka. Almost 55,150 dengue cases were reported in Sri Lanka in 2016, with more than 30.40% of cases (n=16,767) originating from Colombo, which struggles with an outdated manual paper-based dengue outbreak management system. Community education and outreach about dengue are also executed using paper-based media channels such as pamphlets and brochures. Yet, Sri Lanka is one of the countries with the most affordable rates of mobile services in the world, with penetration rates higher than most developing countries. Objectives: To combat the issues of an exhausted dengue management system and to make use of new technology, in 2015, a mobile participatory system for dengue surveillance called Mo-Buzz was developed and launched in Colombo, Sri Lanka. This paper describes the system’s components and uptake, along with other similar disease surveillance systems. Methods: We developed Mo-Buzz and tested its feasibility for dengue. Two versions of the app were developed. The first was for use by public health inspectors (PHIs) to digitize form filling and recording of site visit information, and track dengue outbreaks on a real-time dengue hotspot map using the global positioning system technology. The system also provides updated dengue infographics and educational materials for the PHIs to educate the general public. The second version of Mo-Buzz was created for use by the general public. This system uses dynamic mapping to help educate and inform the general public about potential outbreak regions and allow them to report dengue symptoms and post pictures of potential dengue mosquito–breeding sites, which are automatically sent to the health authorities. Targeted alerts can be sent to users depending on their geographical location. Results: We assessed the usage and the usability of the app and its impact on overall dengue transmission in Colombo. Initial uptake of Mo-Buzz for PHIs was low; however, after more training and incentivizing of usage, the uptake of the app in PHIs increased from less than 10% (n=3) to 76% (n=38). The general public user evaluation feedback was fruitful in providing improvements to the app, and at present, a number of solutions are being reviewed as viable options to boost user uptake. Conclusions: From our Mo-Buzz study, we have learned that initial acceptance of such systems can be slow but eventually positive. Mobile and social media interventions, such as Mo-Buzz, are poised to play a greater role in shaping risk perceptions and managing seasonal and sporadic outbreaks of infectious diseases in Asia and around the world. %M 28970191 %R 10.2196/publichealth.7376 %U https://publichealth.jmir.org/2017/4/e65/ %U https://doi.org/10.2196/publichealth.7376 %U http://www.ncbi.nlm.nih.gov/pubmed/28970191 %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 %@ 1929-0748 %I JMIR Publications %V 6 %N 7 %P e138 %T Data Preparation for West Nile Virus Agent-Based Modelling: Protocol for Processing Bird Population Estimates and Incorporating ArcMap in AnyLogic %A Nasrinpour,Hamid Reza %A Reimer,Alexander A %A Friesen,Marcia R %A McLeod,Robert D %+ University of Manitoba, Electrical and Computer Engineering Department, E1-450 EITC, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada, 1 204 474 7360, Hamid.Nasrinpour@UManitoba.CA %K AnyLogic %K shapefiles %K ArcMap %K West Nile Virus %K land cover %K bird roosts %K bird home range %K Manitoba %D 2017 %7 17.07.2017 %9 Protocol %J JMIR Res Protoc %G English %X Background: West Nile Virus (WNV) was first isolated in 1937. Since the 1950s, many outbreaks have occurred in various countries. The first appearance of infected birds in Manitoba, Canada was in 2002. Objective: This paper describes the data preparation phase of setting up a geographic information system (GIS) simulation environment for WNV Agent-Based Modelling in Manitoba. Methods: The main technology used in this protocol is based on AnyLogic and ArcGIS software. A diverse variety of topics and techniques regarding the data collection phase are presented, as modelling WNV has many disparate attributes, including landscape and weather impacts on mosquito population dynamics and birds’ roosting locations, population count, and movement patterns. Results: Different maps were combined to create a grid land cover map of Manitoba, Canada in a shapefile format compatible with AnyLogic, in order to modulate mosquito parameters. A significant amount of data regarding 152 bird species, along with their population estimates and locations in Manitoba, were gathered and assembled. Municipality shapefile maps were converted to built-in AnyLogic GIS regions for better compatibility with census data and initial placement of human agents. Accessing shapefiles and their databases in AnyLogic are also discussed. Conclusions: AnyLogic simulation software in combination with Esri ArcGIS provides a powerful toolbox for developers and modellers to simulate almost any GIS-based environment or process. This research should be useful to others working on a variety of mosquito-borne diseases (eg, Zika, dengue, and chikungunya) by demonstrating the importance of data relating to Manitoba and/or introducing procedures to compile such data. %M 28716770 %R 10.2196/resprot.6213 %U http://www.researchprotocols.org/2017/7/e138/ %U https://doi.org/10.2196/resprot.6213 %U http://www.ncbi.nlm.nih.gov/pubmed/28716770 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 3 %P e44 %T Feasibility of Establishing HIV Case-Based Surveillance to Measure Progress Along the Health Sector Cascade: Situational Assessments in Tanzania, South Africa, and Kenya %A Harklerode,Richelle %A Schwarcz,Sandra %A Hargreaves,James %A Boulle,Andrew %A Todd,Jim %A Xueref,Serge %A Rice,Brian %+ University of California San Francisco, Global Health Sciences, 550 16th Street, Third Floor, San Francisco, CA, 94158, United States, 1 415 476 5766, richelle.harklerode@ucsf.edu %K HIV %K surveillance %K case reports %K continuum of care %K epidemiologic surveillance %D 2017 %7 10.07.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: To track the HIV epidemic and responses to it, the World Health Organization recommends 10 global indicators to collect information along the HIV care cascade. Patient diagnosis and medical record data, harnessed through case-based surveillance (CBS), can be used to measure 8 of these. While many high burden countries have well-established systems for monitoring patients on HIV treatment, few have formally adopted CBS. Objective: In response to the need for improved strategic HIV information and to facilitate the development of CBS in resource-limited countries, we aimed to conduct situational assessments of existing data collection systems in Tanzania, South Africa, and Kenya. Methods: We developed a standardized protocol and a modularized data collection tool to be adapted for the particular focus of the assessments within each country. The three countries were selected based on their stage of readiness for CBS. The assessment included three parts: a desk review of relevant materials on HIV surveillance and program monitoring, stakeholder meetings, and site visits. Results: In all three countries, routine HIV program monitoring is conducted, and information on new HIV diagnoses and persons accessing HIV care and treatment services is collected. Key findings from the assessments included substantial stakeholder support for the development of CBS, significant challenges in linking data within and between systems, data quality, the ability to obtain data from multiple sources, and information technology infrastructure. Viral load testing capacity varied by country, and vital registry data were not routinely linked to health systems to update medical records. Conclusions: Our findings support the development of CBS systems to systematically capture routinely collected health data to measure and monitor HIV epidemics and guide responses. Although there were wide variations in the systems examined, some of the current program and patient monitoring systems can be adapted to function effectively for CBS, especially if supported by an improved patient registration system with shared unique health identifiers. %M 28694240 %R 10.2196/publichealth.7610 %U http://publichealth.jmir.org/2017/3/e44/ %U https://doi.org/10.2196/publichealth.7610 %U http://www.ncbi.nlm.nih.gov/pubmed/28694240 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 3 %P e42 %T A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports %A Quade,Patrick %A Nsoesie,Elaine Okanyene %+ Iwaspoisoned.com, 322 W 52nd St #633, New York, NY, 10101, United States, 1 9179034815, patrick@dinesafe.org %K foodborne illness surveillance %K crowdsourced surveillance %K foodborne diseases %K infectious diseases %K outbreaks %K food poisoning %K Internet %K mobile %K participatory surveillance %K participatory epidemiology %D 2017 %7 05.07.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Underreporting of foodborne illness makes foodborne disease burden estimation, timely outbreak detection, and evaluation of policies toward improving food safety challenging. Objective: The objective of this study was to present and evaluate Iwaspoisoned.com, an openly accessible Internet-based crowdsourcing platform that was launched in 2009 for the surveillance of foodborne illness. The goal of this system is to collect data that can be used to augment traditional approaches to foodborne disease surveillance. Methods: Individuals affected by a foodborne illness can use this system to report their symptoms and the suspected location (eg, restaurant, hotel, hospital) of infection. We present descriptive statistics of users and businesses and highlight three instances where reports of foodborne illness were submitted before the outbreaks were officially confirmed by the local departments of health. Results: More than 49,000 reports of suspected foodborne illness have been submitted on Iwaspoisoned.com since its inception by individuals from 89 countries and every state in the United States. Approximately 95.51% (42,139/44,119) of complaints implicated restaurants as the source of illness. Furthermore, an estimated 67.55% (3118/4616) of users who responded to a demographic survey were between the ages of 18 and 34, and 60.14% (2776/4616) of the respondents were female. The platform is also currently used by health departments in 90% (45/50) of states in the US to supplement existing programs on foodborne illness reporting. Conclusions: Crowdsourced disease surveillance through systems such as Iwaspoisoned.com uses the influence and familiarity of social media to create an infrastructure for easy reporting and surveillance of suspected foodborne illness events. If combined with traditional surveillance approaches, these systems have the potential to lessen the problem of foodborne illness underreporting and aid in early detection and monitoring of foodborne disease outbreaks. %M 28679492 %R 10.2196/publichealth.7076 %U http://publichealth.jmir.org/2017/3/e42/ %U https://doi.org/10.2196/publichealth.7076 %U http://www.ncbi.nlm.nih.gov/pubmed/28679492 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 2 %P e41 %T HIV Strategic Information in Non-European Union Countries in the World Health Organization European Region: Capacity Development Needs %A Bozicevic,Ivana %A Handanagic,Senad %A Cakalo,Jurja Ivana %A Rinder Stengaard,Annemarie %A Rutherford,George %+ WHO Collaborating Centre for HIV Strategic Information, University of Zagreb School of Medicine, Rockefeller str. 4, Zagreb, 10000, Croatia, 385 992984138, Ivana.Bozicevic@lshtm.ac.uk %K HIV %K surveillance %K evaluation %K Europe %K aptitude %D 2017 %7 23.06.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Capacity building of the national HIV strategic information system is a core component of the response to the HIV epidemic as it enables understanding of the evolving nature of the epidemic, which is critical for program planning and identification of the gaps and deficiencies in HIV programs. Objective: The study aims to describe the results of the assessment of the needs for further development of capacities in HIV strategic information systems in the non-European Union (EU) countries in the World Health Organization European Region (EUR). Methods: Self-administered questionnaires were distributed to national AIDS programs. The first questionnaire was sent to all countries (N=18) to find out, among other issues, the priority level for strengthening a range of HIV surveillance areas and their key gaps and weaknesses. The second questionnaire was sent to 15 countries to more specifically determine capacities for the analysis of the HIV care cascade. Results: Responses to the first questionnaire were received from 10 countries, whereas 13 countries responded to the second questionnaire. Areas that were most frequently marked as being of high to moderate priority for strengthening were national electronic patient monitoring systems, evaluation of HIV interventions and impact analysis, implementation science, and data analysis. Key weaknesseses were lack of electronic reporting of HIV cases, problems with timeliness and completeness of reporting in HIV cases, under-estimates of the reported number of HIV-related deaths, and limited CD4 count testing at the time of HIV diagnosis. Migrant populations, internally displaced persons, and refugees were most commonly mentioned as groups not covered by surveillance, followed by clients of sex workers and men who have sex with men. The majority of countries reported that they were able to provide the number of people diagnosed with HIV who know their HIV status, which is important for the analysis of cross-sectional and longitudinal HIV care cascades. Ability to report on some of the key impact indicators of HIV programs—viral load suppression and mortality—should be considerably strengthened. Conclusions: The assessment found a substantial need to invest in surveillance capacities, which is a cornerstone in the development of an evidence-informed response to HIV epidemics. %M 28645888 %R 10.2196/publichealth.7357 %U https://publichealth.jmir.org/2017/2/e41/ %U https://doi.org/10.2196/publichealth.7357 %U http://www.ncbi.nlm.nih.gov/pubmed/28645888 %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 %@ 2369-2960 %I JMIR Publications %V 3 %N 2 %P e30 %T Experiences From Developing and Upgrading a Web-Based Surveillance System for Malaria Elimination in Cambodia %A Fu,Clementine %A Lopes,Sérgio %A Mellor,Steve %A Aryal,Siddhi %A Sovannaroth,Siv %A Roca-Feltrer,Arantxa %+ Malaria Consortium Cambodia, House # 91, St. 95, Boeung Trabek, Chamkar Morn, Phnom Penh, 12305, Cambodia, 855 017753032, s.lopes@malariaconsortium.org %K malaria elimination %K surveillance system %K Cambodia %D 2017 %7 14.06.2017 %9 Viewpoint %J JMIR Public Health Surveill %G English %X Strengthening the surveillance component is key toward achieving country-wide malaria elimination in Cambodia. A Web-based upgraded malaria information system (MIS) was deemed to essentially act as the central component for surveillance strengthening. New functionality (eg, data visualization) and operational (eg, data quality) attributes of the system received particular attention. However, building from the lessons learned in previous systems’ developments, other aspects unique to Cambodia were considered to be equally important; for instance, feasibility issues, particularly at the field level (eg, user acceptability at various health levels), and sustainability needs (eg, long-term system flexibility). The Cambodian process of identifying the essential changes and critical attributes for this new information system can provide a model for other countries at various stages of the disease control and elimination continuum. Sharing these experiences not only facilitates the establishment of “best practices” but also accelerates global and regional malaria elimination efforts. In this article, Cambodia’s experience in developing and upgrading its MIS to remain responsive to country-specific needs demonstrates the necessity for considering functionality, operationalization, feasibility, and sustainability of an information system in the context of malaria elimination. %M 28615155 %R 10.2196/publichealth.6942 %U http://publichealth.jmir.org/2017/2/e30/ %U https://doi.org/10.2196/publichealth.6942 %U http://www.ncbi.nlm.nih.gov/pubmed/28615155 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 2 %P e25 %T The RSVP Project: Factors Related to Disengagement From Human Immunodeficiency Virus Care Among Persons in San Francisco %A Scheer,Susan %A Chen,Miao-Jung %A Parisi,Maree Kay %A Yoshida-Cervantes,Maya %A Antunez,Erin %A Delgado,Viva %A Moss,Nicholas J %A Buchacz,Kate %+ HIV Epidemiology Section, San Francisco Department of Public Health, 25 Van Ness Avenue, Suite 500, San Francisco, CA, 94114, United States, 1 415 437 6259, susan.scheer@sfdph.org %K HIV care %K HIV care continuum %K engagement in HIV care %K HIV surveillance %K viral suppression %K linkage and navigation to care %D 2017 %7 04.05.2017 %9 Short Paper %J JMIR Public Health Surveill %G English %X Background: In the United States, an estimated two-thirds of persons with human immunodeficiency virus (HIV) infection do not achieve viral suppression, including those who have never engaged in HIV care and others who do not stay engaged in care. Persons with an unsuppressed HIV viral load might experience poor clinical outcomes and transmit HIV. Objective: The goal of the Re-engaging Surveillance-identified Viremic Persons (RSVP) project in San Francisco, CA, was to use routine HIV surveillance databases to identify, contact, interview, and reengage in HIV care persons who appeared to be out of care because their last HIV viral load was unsuppressed. We aimed to interview participants about their HIV care and barriers to reengagement. Methods: Using routinely collected HIV surveillance data, we identified persons with HIV who were out of care (no HIV viral load and CD4 laboratory reports during the previous 9-15 months) and with their last plasma HIV RNA viral load >200 copies/mL. We interviewed the located persons, at baseline and 3 months later, about whether and why they disengaged from HIV care and the barriers they faced to care reengagement. We offered them assistance with reengaging in HIV care from the San Francisco Department of Public Health linkage and navigation program (LINCS). Results: Of 282 persons selected, we interviewed 75 (26.6%). Of these, 67 (89%) reported current health insurance coverage, 59 (79%) had ever been prescribed and 45 (60%) were currently taking HIV medications, 59 (79%) had seen an HIV provider in the past year, and 34 (45%) had missed an HIV appointment in the past year. Reasons for not seeing a provider included feeling healthy, using alcohol or drugs, not having enough money or health insurance, and not wanting to take HIV medicines. Services needed to get to an HIV medical care appointment included transportation assistance, stable living situation or housing, sound mental health, and organizational help and reminders about appointments. A total of 52 (69%) accepted a referral to LINCS. Additionally, 64 (85%) of the persons interviewed completed a follow-up interview 3 months later and, of these, 62 (97%) had health insurance coverage and 47 (73%) reported having had an HIV-related care appointment since the baseline interview. Conclusions: Rather than being truly out of care, most participants reported intermittent HIV care, including recent HIV provider visits and health insurance coverage. Participants also frequently reported barriers to care and unmet needs. Health department assistance with HIV care reengagement was generally acceptable. Understanding why people previously in HIV care disengage from care and what might help them reengage is essential for optimizing HIV clinical and public health outcomes. %M 28473307 %R 10.2196/publichealth.7325 %U http://publichealth.jmir.org/2017/2/e25/ %U https://doi.org/10.2196/publichealth.7325 %U http://www.ncbi.nlm.nih.gov/pubmed/28473307 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 1 %P e2 %T Using Administrative Data to Ascertain True Cases of Muscular Dystrophy: Rare Disease Surveillance %A Smith,Michael G %A Royer,Julie %A Mann,Joshua R %A McDermott,Suzanne %+ Department of Epidemiology and Biostatistics, University of South Carolina, 915 Greene Street, Room 417, Columbia, SC,, United States, 1 803 777 7225, smcdermo@mailbox.sc.edu %K muscular dystrophy %K algorithm %K administrative records %D 2017 %7 12.01.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Administrative records from insurance and hospital discharge data sources are important public health tools to conduct passive surveillance of disease in populations. Identifying rare but catastrophic conditions is a challenge since approaches for maximizing valid case detection are not firmly established. Objective: The purpose of our study was to explore a number of algorithms in which International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and other administrative variables could be used to identify cases of muscular dystrophy (MD). Methods: We used active surveillance to identify possible cases of MD in medical practices in neurology, genetics, and orthopedics in 5 urban South Carolina counties and to identify the cases that had diagnostic support (ie, true cases). We then developed an algorithm to identify cases based on a combination of ICD-9-CM codes and administrative variables from a public (Medicaid) and private insurer claims-based system and a statewide hospital discharge dataset (passive surveillance). Cases of all types of MD and those with Duchenne or Becker MD (DBMD) that were common to both surveillance systems were examined to identify the most specific administrative variables for ascertainment of true cases. Results: Passive statewide surveillance identified 3235 possible cases with MD in the state, and active surveillance identified 2057 possible cases in 5 actively surveilled counties that included 2 large metropolitan areas where many people seek medical care. There were 537 common cases found in both the active and passive systems, and 260 (48.4%) were confirmed by active surveillance to be true cases. Of the 260 confirmed cases, 70 (26.9%) were recorded as DBMD. Conclusions: Accuracy of finding a true case in a passive surveillance system was improved substantially when specific diagnosis codes, number of times a code was used, age of the patient, and specialty provider variables were used. %M 28082256 %R 10.2196/publichealth.6720 %U http://publichealth.jmir.org/2017/1/e2/ %U https://doi.org/10.2196/publichealth.6720 %U http://www.ncbi.nlm.nih.gov/pubmed/28082256 %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 %@ 1929-0748 %I JMIR Publications %V 5 %N 4 %P e197 %T Existing Models of Maternal Death Surveillance Systems: Protocol for a Scoping Review %A Abouchadi,Saloua %A Shahabuddin,ASM %A Zhang,Wei Hong %A Firoz,Tabassum %A Englert,Yvon %A Nejjari,Chakib %A De Brouwere,Vincent %+ Ecole Nationale de Santé Publique, Madinat Al Irfane, Rue Lamfadel Cherkaoui, Rabat,, Morocco, 212 537683162, s.abouchadi@yahoo.fr %K maternal mortality %K surveillance systems %K completeness %K usefulness %K scoping review %K protocol %D 2016 %7 11.10.2016 %9 Protocol %J JMIR Res Protoc %G English %X Background: Maternal mortality measurement remains a critical challenge, particularly in low and middle income countries (LMICs) where little or no data are available and maternal mortality and morbidity are often the highest in the world. Despite the progress made in data collection, underreporting and translating the results into action are two major challenges that maternal death surveillance systems (MDSSs) face in LMICs. Objective: This paper presents a protocol for a scoping review aimed at synthesizing the existing models of MDSSs and factors that influence their completeness and usefulness. Methods: The methodology for scoping reviews from the Joanna Briggs Institute was used as a guide for developing this protocol. A comprehensive literature search will be conducted across relevant electronic databases. We will include all articles that describe MDSSs or assess their completeness or usefulness. At least two reviewers will independently screen all articles, and discrepancies will be resolved through discussion. The same process will be used to extract data from studies fulfilling the eligibility criteria. Data analysis will involve quantitative and qualitative methods. Results: Currently, the abstracts screening is under way and the first results are expected to be publicly available by mid-2017. The synthesis of the reviewed materials will be presented in tabular form completed by a narrative description. The results will be classified in main conceptual categories that will be obtained during the results extraction. Conclusions: We anticipate that the results will provide a broad overview of MDSSs and describe factors related to their completeness and usefulness. The results will allow us to identify research gaps concerning the barriers and facilitating factors facing MDSSs. Results will be disseminated through publication in a peer-reviewed journal and conferences as well as domestic and international agencies in charge of implementing MDSS. %M 27729305 %R 10.2196/resprot.5758 %U http://www.researchprotocols.org/2016/4/e197/ %U https://doi.org/10.2196/resprot.5758 %U http://www.ncbi.nlm.nih.gov/pubmed/27729305 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 2 %P e151 %T Testing the Feasibility of a Passive and Active Case Ascertainment System for Multiple Rare Conditions Simultaneously: The Experience in Three US States %A Reichard,Amanda %A McDermott,Suzanne %A Ruttenber,Margaret %A Mann,Joshua %A Smith,Michael G %A Royer,Julie %A Valdez,Rodolfo %+ Institute on Disability, University of New Hampshire, 10 West Edge Dr, Suite 101, Durham, NH, 03824, United States, 1 603 862 5266, amanda.reichard@unh.edu %K spina bifida %K muscular dystrophy %K fragile X syndrome %K surveillance %D 2016 %7 29.08.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Owing to their low prevalence, single rare conditions are difficult to monitor through current state passive and active case ascertainment systems. However, such monitoring is important because, as a group, rare conditions have great impact on the health of affected individuals and the well-being of their caregivers. A viable approach could be to conduct passive and active case ascertainment of several rare conditions simultaneously. This is a report about the feasibility of such an approach. Objective: To test the feasibility of a case ascertainment system with passive and active components aimed at monitoring 3 rare conditions simultaneously in 3 states of the United States (Colorado, Kansas, and South Carolina). The 3 conditions are spina bifida, muscular dystrophy, and fragile X syndrome. Methods: Teams from each state evaluated the possibility of using current or modified versions of their local passive and active case ascertainment systems and datasets to monitor the 3 conditions. Together, these teams established the case definitions and selected the variables and the abstraction tools for the active case ascertainment approach. After testing the ability of their local passive and active case ascertainment system to capture all 3 conditions, the next steps were to report the number of cases detected actively and passively for each condition, to list the local barriers against the combined passive and active case ascertainment system, and to describe the experiences in trying to overcome these barriers. Results: During the test period, the team from South Carolina was able to collect data on all 3 conditions simultaneously for all ages. The Colorado team was also able to collect data on all 3 conditions but, because of age restrictions in its passive and active case ascertainment system, it was able to report few cases of fragile X syndrome. The team from Kansas was able to collect data only on spina bifida. For all states, the implementation of an active component of the ascertainment system was problematic. The passive component appears viable with minor modifications. Conclusions: Despite evident barriers, the joint passive and active case ascertainment of rare disorders using modified existing surveillance systems and datasets seems feasible, especially for systems that rely on passive case ascertainment. %M 27574026 %R 10.2196/publichealth.5516 %U http://publichealth.jmir.org/2016/2/e151/ %U https://doi.org/10.2196/publichealth.5516 %U http://www.ncbi.nlm.nih.gov/pubmed/27574026 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 7 %P e149 %T A Social Media mHealth Solution to Address the Needs of Dengue Prevention and Management in Sri Lanka %A Lwin,May O %A Vijaykumar,Santosh %A Rathnayake,Vajira Sampath %A Lim,Gentatsu %A Panchapakesan,Chitra %A Foo,Schubert %A Wijayamuni,Ruwan %A Wimalaratne,Prasad %A Fernando,Owen Noel Newton %+ Nanyang Technological University, School of Computer Science and Engineering, 50 Nanyang Avenue, North Spine, N4-02c-75, Singapore, 639798, Singapore, 65 67905783, santoshv@ntu.edu.sg %K dengue %K public health inspector %K mhealth %K social media %K surveillance %K needs assessment %K prevention and management %D 2016 %7 01.07.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Sri Lanka has witnessed a series of dengue epidemics over the past five years, with the western province, home to the political capital of Colombo, bearing more than half of the dengue burden. Existing dengue monitoring prevention programs are exhausted as public health inspectors (PHIs) cope with increasing workloads and paper-based modes of surveillance and education, characterizing a reactive system unable to cope with the enormity of the problem. On the other hand, the unprecedented proliferation and affordability of mobile phones since 2009 and a supportive political climate have thus far remained unexploited for the use of mobile-based interventions for dengue management. Objective: To conduct a needs assessment of PHIs in Colombo with respect to their dengue-related tasks and develop a new mobile-based system to address these needs while strengthening existing systems. Methods: One-on-one in-depth interviews were conducted with 29 PHIs to a) gain a nuanced, in-depth understanding of the current state of surveillance practices, b) understand the logistical, technological and social challenges they confront, and c) identify opportunities for mobile-based interventions. Quantitative analysis included simple descriptive statistics while qualitative analysis comprised textual analysis of 209 pages of transcripts (or nearly 600 minutes of conversations) using grounded theory approaches. Results: Current paper-based data collection practices for dengue surveillance involved a circuitous, time consuming process that could take between 7-10 days to officially report and record a single case. PHIs confronted challenges in terms of unreliable, standalone GIS devices, delays in registering mosquito breeding sites and lack of engagement from communities while delivering dengue education. These findings, in concert with a high motivation to use mobile-based systems, informed the development of Mo-Buzz, a mobile-based system that integrates three components – digitized surveillance, dynamic disease mapping and digitized dengue education – on a common platform. The system was developed through an iterative, evolutionary, collaborative process, consistent with the Spiral model of software development and is currently being used by all 55 PHIs in the CMC system. Conclusions: Given the entrenched nature of existing paper-based systems in PHIs’ work habits, we expect a gradual adoption curve for Mo-Buzz in the future. Equally, we expect variable adoption of the system with respect to its specific components, and specific PHI sub-groups (younger versus older). The Mo-Buzz intervention is a response to multiple calls by the global mHealth community for collaborations in the area of mobile interventions for global health. Our experience revealed that the benefits of this paradigm lies in alleviating country-specific public health challenges through a commonly shared understanding of cultural mores, and sharing of knowledge and technologies. We call upon future researchers to further dissect the applicability of the Spiral Model of software development to mHealth interventions and contribute to the mHealth evidence debate from theoretical and applied perspectives. %M 27369296 %R 10.2196/jmir.4657 %U http://www.jmir.org/2016/7/e149/ %U https://doi.org/10.2196/jmir.4657 %U http://www.ncbi.nlm.nih.gov/pubmed/27369296 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 2 %P e122 %T A Registry for Evaluation of Efficiency and Safety of Surgical Treatment of Cartilage Defects: The German Cartilage Registry (KnorpelRegister DGOU) %A Maurer,Julia %A Grotejohann,Birgit %A Jenkner,Carolin %A Schneider,Carla %A Flury,Thomas %A Tassoni,Adrian %A Angele,Peter %A Fritz,Jürgen %A Albrecht,Dirk %A Niemeyer,Philipp %+ Clinical Trials Unit, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Elsaesser Str 2, Freiburg im Breisgau, 79110, Germany, 49 761 270 ext 77830, julia.maurer@uniklinik-freiburg.de %K ankle joint %K cartilage defect %K chondral defect %K hip joint %K knee joint %K patient registry %D 2016 %7 29.06.2016 %9 Original Paper %J JMIR Res Protoc %G English %X Background: The need for documentation in cartilage defects is as obvious as in other medical specialties. Cartilage defects can cause significant pain, and lead to reduced quality of life and loss of function of the affected joint. The risk of developing osteoarthritis is high. Therefore, the socioeconomic burden of cartilage defects should not be underestimated. Objective: The objective of our study was to implement and maintain a registry of all patients undergoing surgical treatment of cartilage defects. Methods: We designed this multicenter registry for adults whose cartilage defects of a knee, ankle, or hip joint are treated surgically. The registry consists of two parts: one for the physician and one for the patient. Data for both parts will be gathered at baseline and at 6-, 12-, 24-, 36-, 60-, and 120-month follow-ups. Results: To date, a wide range of German, Swiss, and Austrian trial sites are taking part in the German Cartilage Registry, soon to be followed by further sites. More than 2124 (as of January 31, 2016) cases are already documented and the first publications have been released. Conclusions: The German Cartilage Registry addresses fundamental issues regarding the current medical care situation of patients with cartilage defects of knee, ankle, and hip joints. In addition, the registry will help to identify various procedure-specific complications, along with putative advantages and disadvantages of different chondrocyte products. It provides an expanding large-scale, unselected, standardized database for cost and care research for further retrospective studies. Trial Registration: German Clinical Trials Register: DRKS00005617; https://drks-neu.uniklinik-freiburg.de/ drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00005617 (Archived by WebCite at http://www.webcitation.org/6hbFqSws0) %M 27357998 %R 10.2196/resprot.5895 %U http://www.researchprotocols.org/2016/2/e122/ %U https://doi.org/10.2196/resprot.5895 %U http://www.ncbi.nlm.nih.gov/pubmed/27357998 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 4 %N 2 %P e65 %T Perceptions of the Feasibility and Practicalities of Text Messaging-Based Infectious Disease Surveillance: A Questionnaire Survey %A Dang,Linh Thuy %A Vu,Nguyen Cong %A Vu,Thiem Dinh %A James,Spencer L %A Katona,Peter %A Katona,Lindsay %A Rosen,Joseph M %A Nguyen,Cuong Kieu %+ Institute of Population, Health and Development, 18 Lane 132, Hoa Bang Street, Hanoi, 122667, Vietnam, 84 437822388 ext 822, cuong.kieu.nguyen@phad.org %K SMS %K SMS-based %K infectious diseases %K text messaging %K surveillance %K Vietnam %D 2016 %7 25.05.2016 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: In Vietnam, infectious disease surveillance data are collected via a paper-based system through four government tiers leading to a large delay. Meanwhile, mobile phones are abundant and very popular in the country, and known to be a useful tool in health care worldwide. Therefore, there is a great potential for the development of a timely disease surveillance system through the use of mobile phone short message service (SMS) text messages. Objective: This study aims to explore insights about the feasibility and practicalities of the utilization of SMS text messaging-based interventions in disease-reporting systems by identifying potential challenges and barriers in the text messaging process and looking at lessons learned. Methods: An SMS text messaging-based disease tracking system was set up in Vietnam with patient reports texted by clinic staff. Two 6-month trials utilizing this disease tracking system were designed and implemented in two northern provinces of Vietnam to report two infectious diseases: diarrhea and influenza-like illness. A structured self-reported questionnaire was developed to measure the feasibility and practicalities of the system from the participants. On the completion of the second trial in 2013, participating health staff from 40 commune health centers in the two pilot provinces were asked to complete the survey (N=80). Results: Most participants were female (61%, 49/80) and nearly half (44%, 35/80) were heads of a commune health center. Approximately two-thirds (63%, 50/80) of participants retained the basic structure of the SMS text message report and there was a strong influence (OR 28.2, 95% CI 5.3-151.2) of those people on the time they spent texting the information. The majority (88%, 70/80) felt the information conveyed in the SMS text message report was not difficult to understand. Most (86%, 69/80) believed that they could report all 28 infectious diseases asked for by the Ministry of Health by using SMS text messaging. Conclusions: From a health center staff perspective, a disease-reporting system utilizing text messaging technology is easy to use and has great potential to be implemented and expanded nationwide. The survey showed positive perceptions and feedback from the participants and contributed to a promising practical solution to improve the surveillance system of infectious disease in Vietnam. %M 27226418 %R 10.2196/mhealth.4509 %U http://mhealth.jmir.org/2016/2/e65/ %U https://doi.org/10.2196/mhealth.4509 %U http://www.ncbi.nlm.nih.gov/pubmed/27226418 %0 Journal Article %@ 2369-2960 %I Gunther Eysenbach %V 2 %N 1 %P e25 %T Shining a Light on America’s HIV Epidemic among Men who Have Sex with Men %A Valdiserri,Ronald O %+ Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD, 21205, United States, rvaldis1@jhu.edu %K HIV surveillance %K HIV diagnosis %K HIV prevalence %K HIV incidence %K men who have sex with men %K gay and bisexual men %K demography %D 2016 %7 17.05.2016 %9 Guest Editorial %J JMIR Public Health Surveill %G English %X %M 27244772 %R 10.2196/publichealth.5864 %U http://publichealth.jmir.org/2016/1/e25/ %U https://doi.org/10.2196/publichealth.5864 %U http://www.ncbi.nlm.nih.gov/pubmed/27244772 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 1 %P e20 %T Effectiveness of Implementation of Electronic Malaria Information System as the National Malaria Surveillance System in Thailand %A Ma,Shaojin %A Lawpoolsri,Saranath %A Soonthornworasiri,Ngamphol %A Khamsiriwatchara,Amnat %A Jandee,Kasemsak %A Taweeseneepitch,Komchaluch %A Pawarana,Rungrawee %A Jaiklaew,Sukanya %A Kijsanayotin,Boonchai %A Kaewkungwal,Jaranit %+ Department of Tropical Hygiene (Biomedical and Health Informatics), Faculty of Tropical Medicine, Mahidol University, 420/6 Ratchawithi Road, Ratchathewi, Bangkok, 10400, Thailand, 66 23549181, jaranit.kae@mahidol.ac.th %K surveillance system %K epidemiology %K mixed-methods %K evaluation %K malaria %K eMIS %K data quality %K public health informatics %K Thailand %D 2016 %7 06.05.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In moving toward malaria elimination, one strategy is to implement an active surveillance system for effective case management. Thailand has developed and implemented the electronic Malaria Information System (eMIS) capturing individualized electronic records of suspected or confirmed malaria cases. Objective: The main purpose of this study was to determine how well the eMIS improves the quality of Thailand’s malaria surveillance system. In particular, the focus of the study was to evaluate the effectiveness of the eMIS in terms of the system users’ perception and the system outcomes (ie, quality of data) regarding the management of malaria patients. Methods: A mixed-methods technique was used with the framework based on system effectiveness attributes: data quality, timeliness, simplicity, acceptability, flexibility, stability, and usefulness. Three methods were utilized: data records review, survey of system users, and in-depth interviews with key stakeholders. From the two highest endemic provinces, paper forms matching electronic records of 4455 noninfected and 784 malaria-infected cases were reviewed. Web-based anonymous questionnaires were distributed to all 129 eMIS data entry staff throughout Thailand, and semistructured interviews were conducted with 12 management-level officers. Results: The eMIS is well accepted by system users at both management and operational levels. The data quality has enabled malaria personnel to perform more effective prevention and control activities. There is evidence of practices resulting in inconsistencies and logical errors in data reporting. Critical data elements were mostly completed, except for a few related to certain dates and area classifications. Timeliness in reporting a case to the system was acceptable with a delay of 3-4 days. The evaluation of quantitative and qualitative data confirmed that the eMIS has high levels of simplicity, acceptability, stability, and flexibility. Conclusions: Overall, the system implemented has achieved its objective. The results of the study suggested that the eMIS helps improve the quality of Thailand’s malaria surveillance system. As the national malaria surveillance system, the eMIS’s functionalities have provided the malaria staff working at the point of care with close-to-real-time case management data quality, covering case detection, case investigation, drug compliance, and follow-up visits. Such features has led to an improvement in the quality of the malaria control program; the government officials now have quicker access to both individual and aggregated data to promptly react to possible outbreak. The eMIS thus plays one of the key roles in moving toward the national goal of malaria elimination by the next decade. %M 27227156 %R 10.2196/publichealth.5347 %U http://publichealth.jmir.org/2016/1/e20/ %U https://doi.org/10.2196/publichealth.5347 %U http://www.ncbi.nlm.nih.gov/pubmed/27227156 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 1 %P e12 %T Use of Electronic Health Records and Geographic Information Systems in Public Health Surveillance of Type 2 Diabetes: A Feasibility Study %A Laranjo,Liliana %A Rodrigues,David %A Pereira,Ana Marta %A Ribeiro,Rogério T %A Boavida,José Manuel %+ Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera road, Sydney, 2109, Australia, 61 413461852, liliana.laranjo@gmail.com %K electronic health records %K diabetes mellitus %K geographic information systems %K primary health care %K health records %K personal %D 2016 %7 17.03.2016 %9 Short Paper %J JMIR Public Health Surveill %G English %X Background: Data routinely collected in electronic health records (EHRs) offer a unique opportunity to monitor chronic health conditions in real-time. Geographic information systems (GIS) may be an important complement in the analysis of those data. Objective: The aim of this study was to explore the feasibility of using primary care EHRs and GIS for population care management and public health surveillance of chronic conditions, in Portugal. Specifically, type 2 diabetes was chosen as a case study, and we aimed to map its prevalence and the presence of comorbidities, as well as to identify possible populations at risk for cardiovascular complications. Methods: Cross-sectional study using individual-level data from 514 primary care centers, collected from three different types of EHRs. Data were obtained on adult patients with type 2 diabetes (identified by the International Classification of Primary Care [ICPC-2] code, T90, in the problems list). GISs were used for mapping the prevalence of diabetes and comorbidities (hypertension, dyslipidemia, and obesity) by parish, in the region of Lisbon and Tagus Valley. Descriptive statistics and multivariate logistic regression were used for data analysis. Results: We identified 205,068 individuals with the diagnosis of type 2 diabetes, corresponding to a prevalence of 5.6% (205,068/3,659,868) in the study population. The mean age of these patients was 67.5 years, and hypertension was present in 71% (144,938/205,068) of all individuals. There was considerable variation in diagnosed comorbidities across parishes. Diabetes patients with concomitant hypertension or dyslipidemia showed higher odds of having been diagnosed with cardiovascular complications, when adjusting for age and gender (hypertension odds ratio [OR] 2.16, confidence interval [CI] 2.10-2.22; dyslipidemia OR 1.57, CI 1.54-1.60). Conclusions: Individual-level data from EHRs may play an important role in chronic disease surveillance, namely through the use of GIS. Promoting the quality and comprehensiveness of data, namely through patient involvement in their medical records, is crucial to enhance the feasibility and usefulness of this approach. %M 27227147 %R 10.2196/publichealth.4319 %U http://publichealth.jmir.org/2016/1/e12/ %U https://doi.org/10.2196/publichealth.4319 %U http://www.ncbi.nlm.nih.gov/pubmed/27227147 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 1 %P e3 %T Improving HIV Surveillance Data for Public Health Action in Washington, DC: A Novel Multiorganizational Data-Sharing Method %A Ocampo,Joanne Michelle F %A Smart,JC %A Allston,Adam %A Bhattacharjee,Reshma %A Boggavarapu,Sahithi %A Carter,Sharon %A Castel,Amanda D %A Collmann,Jeff %A Flynn,Colin %A Hamp,Auntré %A Jordan,Diana %A Kassaye,Seble %A Kharfen,Michael %A Lum,Garret %A Pemmaraju,Raghu %A Rhodes,Anne %A Stover,Jeff %A Young,Mary A %+ The Office of the Senior Vice President for Research, Georgetown University, 2115 Wisconsin Avenue NW, suite 603, Washington, DC, 20007, United States, 1 2026874092, jfo36@georgetown.edu %K HIV %K surveillance %K data sharing %K public health %K technology %D 2016 %7 15.01.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The National HIV/AIDS Strategy calls for active surveillance programs for human immunodeficiency virus (HIV) to more accurately measure access to and retention in care across the HIV care continuum for persons living with HIV within their jurisdictions and to identify persons who may need public health services. However, traditional public health surveillance methods face substantial technological and privacy-related barriers to data sharing. Objective: This study developed a novel data-sharing approach to improve the timeliness and quality of HIV surveillance data in three jurisdictions where persons may often travel across the borders of the District of Columbia, Maryland, and Virginia. Methods: A deterministic algorithm of approximately 1000 lines was developed, including a person-matching system with Enhanced HIV/AIDS Reporting System (eHARS) variables. Person matching was defined in categories (from strongest to weakest): exact, very high, high, medium high, medium, medium low, low, and very low. The algorithm was verified using conventional component testing methods, manual code inspection, and comprehensive output file examination. Results were validated by jurisdictions using internal review processes. Results: Of 161,343 uploaded eHARS records from District of Columbia (N=49,326), Maryland (N=66,200), and Virginia (N=45,817), a total of 21,472 persons were matched across jurisdictions over various strengths in a matching process totaling 21 minutes and 58 seconds in the privacy device, leaving 139,871 uniquely identified with only one jurisdiction. No records matched as medium low or low. Over 80% of the matches were identified as either exact or very high matches. Three separate validation methods were conducted for this study, and they all found ≥90% accuracy between records matched by this novel method and traditional matching methods. Conclusions: This study illustrated a novel data-sharing approach that may facilitate timelier and better quality HIV surveillance data for public health action by reducing the effort needed for traditional person-matching reviews without compromising matching accuracy. Future analyses will examine the generalizability of these findings to other applications. %M 27227157 %R 10.2196/publichealth.5317 %U http://publichealth.jmir.org/2016/1/e3/ %U https://doi.org/10.2196/publichealth.5317 %U http://www.ncbi.nlm.nih.gov/pubmed/27227157 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 2 %P e20 %T Implementation of a Multimodal Mobile System for Point-of-Sale Surveillance: Lessons Learned From Case Studies in Washington, DC, and New York City %A Cantrell,Jennifer %A Ganz,Ollie %A Ilakkuvan,Vinu %A Tacelosky,Michael %A Kreslake,Jennifer %A Moon-Howard,Joyce %A Aidala,Angela %A Vallone,Donna %A Anesetti-Rothermel,Andrew %A Kirchner,Thomas R %+ Truth Initiative, Evaluation Science and Research, 900 G Street, NW, Fourth Floor, Washington, DC, 20001, United States, 1 202 436 2118, jcantrell@truthinitiative.org %K mobile technology %K public health surveillance %K tobacco %K point-of-sale %K implementation %K tobacco industry advertising %K marketing %D 2015 %7 26.11.2015 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In tobacco control and other fields, point-of-sale surveillance of the retail environment is critical for understanding industry marketing of products and informing public health practice. Innovations in mobile technology can improve existing, paper-based surveillance methods, yet few studies describe in detail how to operationalize the use of technology in public health surveillance. Objective: The aims of this paper are to share implementation strategies and lessons learned from 2 tobacco, point-of-sale surveillance projects to inform and prepare public health researchers and practitioners to implement new mobile technologies in retail point-of-sale surveillance systems. Methods: From 2011 to 2013, 2 point-of-sale surveillance pilot projects were conducted in Washington, DC, and New York, New York, to capture information about the tobacco retail environment and test the feasibility of a multimodal mobile data collection system, which included capabilities for audio or video recording data, electronic photographs, electronic location data, and a centralized back-end server and dashboard. We established a preimplementation field testing process for both projects, which involved a series of rapid and iterative tests to inform decisions and establish protocols around key components of the project. Results: Important components of field testing included choosing a mobile phone that met project criteria, establishing an efficient workflow and accessible user interfaces for each component of the system, training and providing technical support to fieldworkers, and developing processes to integrate data from multiple sources into back-end systems that can be utilized in real-time. Conclusions: A well-planned implementation process is critical for successful use and performance of multimodal mobile surveillance systems. Guidelines for implementation include (1) the need to establish and allow time for an iterative testing framework for resolving technical and logistical challenges; (2) developing a streamlined workflow and user-friendly interfaces for data collection; (3) allowing for ongoing communication, feedback, and technology-related skill-building among all staff; and (4) supporting infrastructure for back-end data systems. Although mobile technologies are evolving rapidly, lessons learned from these case studies are essential for ensuring that the many benefits of new mobile systems for rapid point-of-sale surveillance are fully realized. %M 27227138 %R 10.2196/publichealth.4191 %U http://publichealth.jmir.org/2015/2/e20/ %U https://doi.org/10.2196/publichealth.4191 %U http://www.ncbi.nlm.nih.gov/pubmed/27227138 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 2 %P e12 %T Texting-Based Reporting of Adverse Drug Reactions to Ensure Patient Safety: A Feasibility Study %A Vergeire-Dalmacion,Godofreda %A Castillo-Carandang,Nina T %A Juban,Noel R %A Amarillo,Maria Lourdes %A Tagle,Maria Pamela %A Baja,Emmanuel S %+ College of Medicine, Department of Pharmacology and Toxicology, University of the Philippines-Manila, Department of Clinical Epidemiology Room 103, Paz Mendoza Building, Pedro Gil Street, Ermita, Manila, 1000, Philippines, 63 2 525 4098, jody.dalmacion@gmail.com %K adverse drug reactions %K pharmacovigilance %K postmarketing %K spontaneous reporting %K texting %D 2015 %7 19.11.2015 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Paper-based adverse drug reaction (ADR) reporting has been in practice for more than 6 decades. Health professionals remain the primary source of reports, while the value of patients’ reporting is yet unclear. With the increasing popularity of using electronic gadgets in health, it is expected that the electronic transmission of reports will become the norm within a few years. Objective: The aims of this study are to investigate whether short messaging service or texting can provide an alternative or supplemental method for ADR reporting given the increasing role of mobile phones in health care monitoring; to determine the usefulness of texting in addition to paper-based reporting of ADRs by resident physicians; and to describe the barriers to ADR reporting and estimate the cost for setting up and maintaining a texting-computer reporting system. Methods: This was a pre-post cross-sectional study that measured the number of ADRs texted by 51 resident physicians for 12 months from the Department of Obstetrics and Gynecology and the Department of Adult Medicine of a tertiary government hospital in Manila, Philippines, with 1350-bed capacity. Reports were captured by a texting-computer reporting system. Prior to its implementation, key informant interview and focus group discussion were conducted. Baseline information and practice on the existing paper-based reporting system were culled from the records of the hospital’s Pharmacy and Therapeutics Committee. A postintervention survey questionnaire was administered at the end of 12 months. Results: Only 3 ADRs were texted by 51 resident physicians in 12 months (reporting rate 3/51 or 6%). By contrast, 240 ADRs from the paper-based reporting system from 848 resident physicians of the study hospital were collected and tabulated (reporting rate 240/848 or 28.3%). Texting ADRs was not efficient because of power interruption, competition with the existing paper-based reporting system, and unforeseen expiration of prepaid text loads/credits. The 3 ADRs texted were a report of vivid dreams and nightmares, a report of disturbing dreams and memory lapses, both of which were due to montelukast use, and a report of hepatitis from an isoniazid/rifampicin fixed-dose combination. Nineteen of 51 resident physicians (37%) registered in the reporting system responded to the postintervention survey. The most common reasons for not reporting ADRs were no adverse reaction identified 11/19 (58%) and restrictive reporting syntax 4/19 (21%). All doctors preferred a free form of reporting. The direct cost of the texting-based reporting system was calculated to be US $5581.40 and the indirect cost was US $9989.40. The total cost for texting-based ADR reporting system for 12 months was US $15,570.79. Conclusions: Reporting of ADRs via texting could be lower compared with an existing ADR paper-based system. Problems of Internet connectivity, reporting syntax, and expiration and reliability of text loads/credits should be addressed while implementing a text-based ADR reporting system in a developing country. %M 27227130 %R 10.2196/publichealth.4605 %U http://publichealth.jmir.org/2015/2/e12/ %U https://doi.org/10.2196/publichealth.4605 %U http://www.ncbi.nlm.nih.gov/pubmed/27227130 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 2 %P e15 %T The US National Tuberculosis Surveillance System: A Descriptive Assessment of the Completeness and Consistency of Data Reported from 2008 to 2012 %A Yelk Woodruff,Rachel S %A Pratt,Robert H %A Armstrong,Lori R %+ Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Division of Tuberculosis Elimination, 1600 Clifton Road NE, Mailstop E10, Atlanta, GA, 30333, United States, 1 404 639 6018, zex5@cdc.gov %K public health surveillance %K disease notification %K information systems %K data cleaning %K quality assurance %D 2015 %7 15.10.2015 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In 2009, the Tuberculosis (TB) Information Management System transitioned into the National TB Surveillance System to allow use of 4 different types of electronic reporting schemes: state-built, commercial, and 2 schemes developed by the Centers for Disease Control and Prevention. Simultaneously, the reporting form was revised to include additional data fields. Objective: Describe data completeness for the years 2008-2012 and determine the impact of surveillance changes. Methods: Data were categorized into subgroups and assessed for completeness (eg, the percentage of patients dead at diagnosis who had a date of death reported) and consistency (eg, the percentage of patients alive at diagnosis who erroneously had a date of death reported). Reporting jurisdictions were grouped to examine differences by reporting scheme. Results: Each year less than 1% of reported cases had missing information for country of origin, race, or ethnicity. Patients reported as dead at diagnosis had death date (a new data field) missing for 3.6% in 2009 and 4.4% in 2012. From 2010 to 2012, 313 cases (1%) reported as alive at diagnosis had a death date and all of these were reported through state-built or commercial systems. The completeness of reporting for guardian country of birth for pediatric patients (a new data field) ranged from 84% in 2009 to 88.2% in 2011. Conclusions: Despite major changes, completeness has remained high for most data elements in TB surveillance. However, some data fields introduced in 2009 remain incomplete; continued training is needed to improve national TB surveillance data. %M 27227133 %R 10.2196/publichealth.4991 %U http://publichealth.jmir.org/2015/2/e15/ %U https://doi.org/10.2196/publichealth.4991 %U http://www.ncbi.nlm.nih.gov/pubmed/27227133 %0 Journal Article %@ 2291-9694 %I Gunther Eysenbach %V 3 %N 3 %P e31 %T A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation %A Tseng,Yi-Ju %A Wu,Jung-Hsuan %A Lin,Hui-Chi %A Chen,Ming-Yuan %A Ping,Xiao-Ou %A Sun,Chun-Chuan %A Shang,Rung-Ji %A Sheng,Wang-Huei %A Chen,Yee-Chun %A Lai,Feipei %A Chang,Shan-Chwen %+ Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, No. 7 Chung-Shan South Road, Taipei, 100, Taiwan, 886 223123456 ext 65054, yeechunchen@gmail.com %K health care-associated infection %K infection control %K information systems %K surveillance %K Web-based services %D 2015 %7 21.09.2015 %9 Original Paper %J JMIR Med Inform %G English %X Background: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. Objective: To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. Methods: We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. Results: In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20; r=.999 P<.001) and by time (n=14; r=.941; P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. Conclusions: This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system. %M 26392229 %R 10.2196/medinform.4171 %U http://medinform.jmir.org/2015/3/e31/ %U https://doi.org/10.2196/medinform.4171 %U http://www.ncbi.nlm.nih.gov/pubmed/26392229 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 2 %P e8 %T The Tobacco Pack Surveillance System: A Protocol for Assessing Health Warning Compliance, Design Features, and Appeals of Tobacco Packs Sold in Low- and Middle-Income Countries %A Smith,Katherine %A Washington,Carmen %A Brown,Jennifer %A Vadnais,Alison %A Kroart,Laura %A Ferguson,Jacqueline %A Cohen,Joanna %+ Institute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Rm 726, 624 N. Broadway, Baltimore, MD, 21215, United States, 1 4105020025, katecsmith@jhu.edu %K tobacco products %K cigarettes %K public health surveillance %K health communication %K national health policy %K marketing %K developing countries %D 2015 %7 12.08.2015 %9 Protocol %J JMIR Public Health Surveill %G English %X Background: Tobacco remains the world’s leading preventable cause of death, with the majority of tobacco-caused deaths occurring in low- and middle-income countries. The first global health treaty, the Framework Convention on Tobacco Control (FCTC), outlines a set of policy initiatives that have been demonstrated as effective in reducing tobacco use. Article 11 of the FCTC focuses on using the tobacco package to communicate tobacco-caused harms; it also seeks to restrict the delivery of misleading information about the product on the pack. Objective: The objective of this study was to establish a surveillance system for tobacco packs in the 14 low- and middle-income countries with the greatest number of smokers. The Tobacco Pack Surveillance System (TPackSS) monitors whether required health warnings on tobacco packages are being implemented as intended, and identifies pack designs and appeals that might violate or detract from the communication of harm-related information and undermine the impact of a country’s tobacco packaging laws. The protocol outlined is intended to be applicable or adaptable for surveillance efforts in other countries. Methods: Tobacco packs were collected in 14 countries during 2013. The intention was, to the extent possible, to construct a census of “unique” pack presentations available for purchase in each country. The TPackSS team partnered with in-country field staff to implement a standardized protocol for acquiring packs from 36 diverse neighborhoods across three cities in each country. At the time of purchase, data on price and place of acquisition of each pack was recorded. The field staff, according to a standardized protocol, then photographed packs before they were shipped to the United States for coding and archiving. Results: Each pack was coded for compliance with the country-specific health warning label laws, as well as for key design features of the pack and appeals of the branding elements. The coding protocols were developed based upon prior research, expert opinion, and communication theories. Each pack was coded by two independent coders, with consistency of personnel across the project. We routinely measured intercoder reliability, and only retained variables for which a good level of reliability was achieved. Variables where reliability was too low were not included in final analyses, and any inconsistencies in coding were resolved on a daily basis. Conclusions: Across the 14 countries, the TPackSS team collected 3307 tobacco packs. We have established a publicly accessible, Internet archive of these packs that is intended for use by the tobacco control policy advocacy and research community. %M 27227142 %R 10.2196/publichealth.4616 %U http://publichealth.jmir.org/2015/2/e8/ %U https://doi.org/10.2196/publichealth.4616 %U http://www.ncbi.nlm.nih.gov/pubmed/27227142 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 4 %N 2 %P e75 %T A National Surveillance Survey on Noncommunicable Disease Risk Factors: Suriname Health Study Protocol %A Krishnadath,Ingrid SK %A Smits,Christel CF %A Jaddoe,Vincent WV %A Hofman,Albert %A Toelsie,Jerry R %+ Department of Public Health, Faculty of Medical Sciences, Anton de Kom University of Suriname, Kernkampweg 3-5, Paramaribo, , Suriname, 597 441007 ext 280, Ingrid.Krishnadath@uvs.edu %K ethnicity %K multistage cluster sample %K noncommunicable disease risk factors %K STEPwise approach to surveillance %K Suriname %D 2015 %7 17.06.2015 %9 Protocol %J JMIR Res Protoc %G English %X Background: Noncommunicable diseases (NCDs) are the leading cause of death in low- and middle-income countries. Therefore, the surveillance of risk factors has become an issue of major importance for planning and implementation of preventive measures. Unfortunately, in these countries data on NCDs and their risk factors are limited. This also prevails in Suriname, a middle-income country of the Caribbean, with a multiethnic/multicultural population living in diverse residential areas. For these reasons, “The Suriname Health Study” was designed. Objective: The main objective of this study is to estimate the prevalence of NCD risk factors, including metabolic syndrome, hypertension, and diabetes in Suriname. Differences between specific age groups, sexes, ethnic groups, and geographical areas will be emphasized. In addition, risk groups will be identified and targeted actions will be designed and evaluated. Methods: In this study, several methodologies were combined. A stratified multistage cluster sample was used to select the participants of 6 ethnic groups (Hindustani, Creole, Javanese, Maroon, Chinese, Amerindians, and mixed) divided into 5 age groups (between 15 and 65 years) who live in urban/rural areas or the hinterland. A standardized World Health Organization STEPwise approach to surveillance questionnaire was adapted and used to obtain information about demographic characteristics, lifestyle, and risk factors. Physical examinations were performed to measure blood pressure, height, weight, and waist circumference. Biochemical analysis of collected blood samples evaluated the levels of glucose, high-density-lipoprotein cholesterol, total cholesterol, and triglycerides. Statistical analysis will be used to identify the burden of modifiable and unmodifiable risk factors in the aforementioned subgroups. Subsequently, tailor-made interventions will be prepared and their effects will be evaluated. Results: The data as collected allow for national inference and valid analysis of the age, sex, and ethnicity subgroups in the Surinamese population. A publication of the basic survey results is anticipated in mid-2015. Secondary results on the effect of targeted lifestyle interventions are anticipated in late 2017. Conclusions: Using the data collected in this study, the national prevalence of NCD risk factors will be approximated and described in a diverse population. This study is an entry point for formulating the structure of NCD prevention and surveillance. %M 26085372 %R 10.2196/resprot.4205 %U http://www.researchprotocols.org/2015/2/e75/ %U https://doi.org/10.2196/resprot.4205 %U http://www.ncbi.nlm.nih.gov/pubmed/26085372 %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 %@ 2369-2960 %I JMIR Publications %V 1 %N 1 %P e3 %T The Annual American Men's Internet Survey of Behaviors of Men Who Have Sex With Men in the United States: Protocol and Key Indicators Report 2013 %A Sanchez,Travis Howard %A Sineath,R Craig %A Kahle,Erin M %A Tregear,Stephen James %A Sullivan,Patrick Sean %+ Emory University, 1518 Clifton Road NE, Atlanta, GA, 30322, United States, 1 404 727 8403, Travis.Sanchez@emory.edu %K MSM %K gay %K homosexual %K bisexual %K HIV %K STD %K Internet %K survey %K surveillance %D 2015 %7 17.04.2015 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Men who have sex with men (MSM) are disproportionately affected by human immunodeficiency virus (HIV) and there is evidence that this population is participating in increasingly risky sexual behavior. These changes are occurring in the context of new modes of online social interaction—many MSM now report first meeting their sex partners on the Internet. Better monitoring of key behavioral indicators among MSM requires the use of surveillance strategies that capitalize on these new modes of interaction. Therefore, we developed an annual cross-sectional behavioral survey of MSM in the United States, the American Men's Internet Survey (AMIS). Objective: The purpose of this paper was to provide a description of AMIS methods. In addition we report on the first cycle of data collection (December 2013 through May 2014; AMIS-2013) on the same key indicators used for national HIV behavioral surveillance. Methods: AMIS-2013 recruited MSM from a variety of websites using banner advertisements or email blasts. Adult men currently residing in the United States were eligible to participate if they had ever had sex with a man. We examined demographic and recruitment characteristics using multivariable regression modeling (P<.05) stratified by the participants' self-reported HIV status. Results: In the AMIS-2013 round, 79,635 persons landed on the study page and 14,899 were eligible, resulting in 10,377 completed surveys from MSM representing every US state. Participants were mainly white, 40 years or older, living in the US South, living in urban areas, and recruited from a general social networking website. Self-reported HIV prevalence was 10.73% (n=1113). Compared to HIV-negative/unknown status participants, HIV-positive participants were more likely to have had anal sex without a condom with any male partner in the past 12 months (72.24% versus 61.24%, respectively; P<.001) and more likely to have had anal sex without a condom with their last male sex partner who was discordant/unknown HIV status (42.95% versus 13.62%, respectively; P<.001). Illicit substance use in the past 12 months was more likely to be reported by HIV-positive participants than HIV-negative/unknown status participants (39.17% versus 26.85%, respectively; P<.001). The vast majority of HIV-negative/unknown status participants (84.05%) had been previously HIV tested, but less than half (44.20%) had been tested in the past 12 months. Participants 18-24 years of age were more likely than those 40 years or older to have had anal sex without a condom with a discordant/unknown HIV status partner, were more likely to report substance use, and were less likely to have been HIV tested. Compared to general social networking, those from a geospatial social networking website were more likely to have reported all risk behaviors but were more likely to have been HIV tested. Conclusions: The first round of AMIS generated useful behavioral measures from more than 10,000 MSM Internet users. Preliminary findings identified some subgroups of MSM Internet users that are at potentially higher risk of HIV acquisition/transmission. AMIS will provide an ongoing data source for examining trends in sexual risk behavior of MSM. This will help to plan and monitor the impact of programs to improve this population's health. %M 27227126 %R 10.2196/publichealth.4314 %U http://publichealth.jmir.org/2015/1/e3/ %U https://doi.org/10.2196/publichealth.4314 %U http://www.ncbi.nlm.nih.gov/pubmed/27227126