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JMIR Public Health and Surveillance

A multidisciplinary journal that focuses on the intersection of public health and technology, public health informatics, mass media campaigns, surveillance, participatory epidemiology, and innovation in public health practice and research.


Journal Description

JMIR Public Health & Surveillance (JPHS, Editor-in-chief: Travis Sanchez, Emory University/Rollins School of Public Health) is a PubMed-indexed, peer-reviewed sister journal of the Journal of Medical Internet Research (JMIR), the top cited journal in health informatics (Impact Factor 2016: 5.175). JPH is a multidisciplinary journal with a unique focus on the intersection of innovation and technology in public health, and includes topics like health communication, public health informatics, surveillance, participatory epidemiology, infodemiology and infoveillance, digital disease detection, digital public health interventions, mass media/social media campaigns, and emerging population health analysis systems and tools. 

We publish regular articles, reviews, protocols/system descriptions and viewpoint papers on all aspects of public health, with a focus on innovation and technology in public health.

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

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

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

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


Recent Articles:

  • Weekly relative search volume from Google Trends for the term "morcellation" (montage). Source: The Authors / / Google; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Public Awareness of Uterine Power Morcellation Through US Food and Drug Administration Communications: Analysis of Google Trends Search Term Patterns


    Background: Uterine power morcellation, where the uterus is shred into smaller pieces, is a widely used technique for removal of uterine specimens in patients undergoing minimally invasive abdominal hysterectomy or myomectomy. Complications related to power morcellation of uterine specimens led to US Food and Drug Administration (FDA) communications in 2014 ultimately recommending against the use of power morcellation for women undergoing minimally invasive hysterectomy. Subsequently, practitioners drastically decreased the use of morcellation. Objective: We aimed to determine the effect of increased patient awareness on the decrease in use of the morcellator. Google Trends is a public tool that provides data on temporal patterns of search terms, and we correlated this data with the timing of the FDA communication. Methods: Weekly relative search volume (RSV) was obtained from Google Trends using the term “morcellation.” Higher RSV corresponds to increases in weekly search volume. Search volumes were divided into 3 groups: the 2 years prior to the FDA communication, a 1-year period following, and thereafter, with the distribution of the weekly RSV over the 3 periods tested using 1-way analysis of variance. Additionally, we analyzed the total number of websites containing the term “morcellation” over this time. Results: The mean RSV prior to the FDA communication was 12.0 (SD 15.8), with the RSV being 60.3 (SD 24.7) in the 1-year after and 19.3 (SD 5.2) thereafter (P<.001). The mean number of webpages containing the term “morcellation” in 2011 was 10,800, rising to 18,800 during 2014 and 36,200 in 2017. Conclusions: Google search activity about morcellation of uterine specimens increased significantly after the FDA communications. This trend indicates an increased public awareness regarding morcellation and its complications. More extensive preoperative counseling and alteration of surgical technique and clinician practice may be necessary.

  • Source: New York Blood Center; Copyright: New York Blood Center; URL:; License: Licensed by the authors.

    The Development of a Social Networking–Based Relatedness Intervention Among Young, First-Time Blood Donors: Pilot Study


    Background: Increasing repeat blood donation behavior is a critical public health goal. According to self-determination theory, the process of developing internal motivation to give blood and an associated self-identity as a blood donor may be promoted by feelings of “relatedness” or a connection to other donors, which may be enhanced through social relations and interactions. Objective: The purpose of this report it to describe the development and pilot testing of a social networking-based (Facebook) intervention condition designed to increase feelings of relatedness via virtual social interaction and support. Methods: To develop the intervention condition content, images, text, polls, and video content were assembled. Ohio University college students (N=127) rated the content (82 images/text) presented by computer in random order using a scale of one to five on various dimensions of relatedness. Mean ratings were calculated and analyses of variance were conducted to assess associations among the dimensions. Based on these results, the relatedness intervention was adapted and evaluated for feasibility, acceptability, and preliminary efficacy among 24 first-time donors, aged 18 to 24 years, in a 30-day pilot trial. Paired t-tests were conducted to examine change over time in relatedness and connectedness. Results: The intervention condition that was developed was acceptable and feasible. Results of the uncontrolled, preintervention, and postintervention evaluation revealed that feelings of individual-level relatedness increased significantly after the intervention. Conclusions: By promoting first-time blood donor relatedness, our goal is to enhance internal motivation for donating and the integration of the blood donor identity, thus increasing the likelihood of future repeat donation. Trial Registration: NCT02717338; (Archived by WebCite at

  • Source:; Copyright: bedneyimages; URL:; License: Licensed by JMIR.

    Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning


    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.

  • Source:; Copyright: James Pursey, Elizabeth Glaser Pediatric AIDS Foundation; URL:; License: Public Domain (CC0).

    Sustainable Monitoring and Surveillance Systems to Improve HIV Programs: Review


    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.

  • Source: Pxhere; Copyright: Pxhere; URL:; License: Public Domain (CC0).

    Developing Sustainable and Impactful Mobile Phone HIV Testing Interventions for Spanish-Speaking Men Who Have Sex With Men in the United States: Lessons...


    Background: Although many men who have sex with men (MSM) test for HIV at least once in their lifetime, opportunities to improve regular HIV testing, particularly among Hispanic or Latino MSM, is needed. Many mHealth interventions in development, including the ones on HIV testing, have primarily focused on English-speaking white, black, and MSM of other races. To date, no studies have assessed app use, attitudes, and motivations for downloading and sustaining use of mobile apps and preferences with respect to HIV prevention among Spanish-speaking, Hispanic MSM in the United States. Objective: The primary aims of this study were to determine what features and functions of smartphone apps do Hispanic, Spanish-speaking MSM believe are associated with downloading apps to their smartphones, (2) what features and functions of smartphone apps are most likely to influence men’s sustained use of apps over time, and (3) what features and functions do men prefer in a smartphone app aimed to promote regular testing for HIV. Methods: Interviews (N=15) were conducted with a racially diverse group of sexually active, HIV-negative, Spanish-speaking, Hispanic MSM in Miami, Florida. Interviews were digitally recorded, transcribed verbatim, translated back to English, and de-identified for analysis. A constant-comparison method (ie, grounded theory coding) was employed to examine themes that emerged from the interviews. Results: Personal interest was the primary reason associated with whether men downloaded an app. Keeping personal information secure, cost, influence by peers and posted reviews, ease of use, and functionality affected whether they downloaded and used the app over time. Men also reported that entertainment value and frequency of updates influenced whether they kept and continued to use an app over time. There were 4 reasons why participants chose to delete an app—dislike, lack of use, cost, and lack of memory or space. Participants also shared their preferences for an app to encourage regular HIV testing by providing feedback on test reminders, tailored testing interval recommendations, HIV test locator, and monitoring of personal sexual behaviors. Conclusions: The features and functions of mobile apps that Spanish-speaking MSM in this study believed were associated with downloading and/or sustained engagement of an app generally reflected the priorities mentioned in an earlier study with English-speaking MSM. Unlike the earlier study, Spanish-speaking MSM prioritized personal interest in a mobile app and de-emphasized the efficiency of an app to make their lives easier in their decision to download an app to their mobile device. Tailoring mobile apps to the language and needs of Spanish-speaking MSM is critical to help increase their willingness to download a mobile app. Despite the growing number of HIV-prevention apps in development, few are tailored to Spanish-speaking MSM, representing an important gap that should be addressed in future research.

  • Source: Pixabay; Copyright: rawpixel; URL:; License: Public Domain (CC0).

    Using Social Media to Generate and Collect Primary Data: The #ShowsWorkplaceCompassion Twitter Research Campaign


    Background: Compassion is a core value embedded in the concept of quality in healthcare. The need for compassion toward healthcare staff in the workplace, for their own health and well-being and also to enable staff to deliver compassionate care for patients, is increasingly understood. However, we do not currently know how healthcare staff understand and characterize compassion toward themselves as opposed to patients. Objective: The aim of this study was to use social media for the generation and collection of primary data to gain understanding of the concept of workplace compassion. Methods: Tweets that contained the hashtag #ShowsWorkplaceCompassion were collected from Twitter and analyzed. The study took place between April 21 and May 21, 2016. Participants were self-selecting users of the social media service Twitter. The study was promoted by a number of routes: the National Health Service (NHS) England website, the personal Twitter accounts of the research team, internal NHS England communications, and via social media sharing. Participants were asked to contribute their views about what activities, actions, policies, philosophies or approaches demonstrate workplace compassion in healthcare using the hashtag #ShowsWorkplaceCompassion. All tweets including the research hashtag #ShowsWorkplaceCompassion were extracted from Twitter and studied using content analysis. Data concerning the frequency, nature, origin, and location of Web-based engagement with the research campaign were collected using Bitly (Bitly, Inc, USA) and Symplur (Symplur LLC, USA) software. Results: A total of 260 tweets were analyzed. Of the 251 statements within the tweets that were coded, 37.8% (95/251) of the statements concerned Leadership and Management aspects of workplace compassion, 29.5% (74/251) were grouped under the theme related to Values and Culture, 17.5% (44/251) of the statements related to Personalized Policies and Procedures that support workplace compassion, and 15.2% (38/251) of the statements concerned Activities and Actions that show workplace compassion. Content analysis showed that small acts of kindness, an embedded organizational culture of caring for one another, and recognition of the emotional and physical impact of healthcare work were the most frequently mentioned characteristics of workplace compassion in healthcare. Conclusions: This study presents a new and innovative research approach using Twitter. Although previous research has analyzed the nature and pattern of tweets retrospectively, this study used Twitter to both recruit participants and collect primary data.

  • Source: Freepik; Copyright: Prakasit Khuansuwan; URL:; License: Licensed by JMIR.

    Sentiment Analysis of Health Care Tweets: Review of the Methods Used


    Background: Twitter is a microblogging service where users can send and read short 140-character messages called “tweets.” There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. Objective: The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. Methods: A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. Results: A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study’s final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. Conclusions: Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting–specific corpus of manually annotated tweets first.

  • Using the app for data collection. Source: Image created by the Authors; Copyright: Diane Santa Maria; URL:; License: Licensed by JMIR.

    Predicting Sexual Behaviors Among Homeless Young Adults: Ecological Momentary Assessment Study


    Background: Homeless youth continue to be disproportionately affected by HIV compared with their housed peers, with prevalence rates as high as 13%. Yet, HIV prevention in this high-risk population has been only marginally effective. Objective: The aim of this study was to use ecological momentary assessments to examine real-time factors to determine the predictors of sexual activity among homeless youth. Methods: Youth experiencing homelessness aged between 18 and 24 years were recruited from a drop-in center in Houston, Texas, between August 2015 and May 2016. All the participants received a study-issued mobile phone that prompted brief ecological momentary assessments (EMAs) 5 times a day for 21 days. EMA items assessed near real-time sexual behaviors, cognitions, stress, affect, environmental factors, and environmental circumstances. Results: Participants (N=66) were predominantly male (41/66, 64%) and black (43/66, 66%) with a median age of 20 years. The mean number of EMAs completed by each participant was 45 out of 105 possible observations. During the study, 70% (46/66) of participants were sexually active and reported condomless sex in 102 of the 137 cases of sexual intercourse (74.5%). In total, 82% (38/46) of the youth who reported having sex during the 3 weeks of data collection also reported engaging in high-risk sexual activities, including having condomless sex (24/46, 53%), having multiple sexual partners on the same day (12/46, 26%), trading sex (7/46, 16%), and sharing needles while injecting drugs (1/46, 3%). Of those, 71% (27/38) were engaged in multiple sexual risk behaviors. The predictive model was based on observations from 66 subjects who reported 137 cases of sexual intercourse over 811 days; sexual orientation, race, mental health, drug use, and sexual urge were included as predictors in the parsimonious generalized linear mixed model selected on the basis of the Akaike information criterion. The estimated odds ratios (ORs) were notable for same-day drug use (OR 2.17, 95% CI 4.48-17.31; P<.001) and sexual urge (OR 1.44, 95% CI 1.60-11.28; P=.004). The performance of the risk estimator was satisfactory, as indicated by the value of 0.834 for the area under the receiver operating characteristic curve. Conclusions: Real-time EMA data can be used to predict sexual intercourse among a sample of high-risk, predominately unsheltered homeless youth. Sexual urge and drug use accounts for increased odds of engaging in sexual activity on any given day. Interventions targeting sexual urge and drug use may help predict sexual activity among a population at high risk of HIV.

  • Businessman with flu after going to work. Source: Freepik; Copyright: yanalya; URL:; License: Licensed by JMIR.

    Estimating the Risk of Influenza-Like Illness Transmission Through Social Contacts: Web-Based Participatory Cohort Study


    Background: Epidemiological studies on influenza have focused mostly on enhancing vaccination coverage or promoting personal hygiene behavior. Few studies have investigated potential effects of personal health behaviors and social contacts on the risk of getting influenza-like illness (ILI). Objective: Taking advantage of an online participatory cohort, this study aimed to estimate the increased risk of getting ILI after contact with infected persons and examine how personal health behaviors, weather, and air pollution affect the probability of getting ILI. Methods: A Web-based platform was designed for participants to record daily health behaviors and social contacts during the influenza season of October 1, 2015 to March 31, 2016, in Taiwan. Data on sleep, diet, physical activity, self-reported ILI, and contact with infected persons were retrieved from the diaries. Measurements of weather and air pollutants were used for calculating environmental exposure levels for the participants. We fitted a mixed-effects logistic regression model to the daily measurements of the diary keepers to estimate the effects of these variables on the risk of getting ILI. Results: During the influenza season, 160 participants provided 14,317 health diaries and recorded 124,222 face-to-face contacts. The model estimated odds ratio of getting ILI was 1.87 (95% CI 1.40-2.50) when a person had contact with others having ILI in the previous 3 days. Longer duration of physical exercise and eating more fruits, beans, and dairy products were associated with lower risk of getting ILI. However, staying up late was linked to an elevated risk of getting ILI. Higher variation of ambient temperature and worse air quality were associated with increased risk of developing ILI. Conclusions: Developing a healthier lifestyle, avoiding contact with persons having ILI symptoms, and staying alert with respect to temperature changes and air quality can reduce the risk of getting ILI.

  • Researcher analyzing network diagram of Facebook pages linked to LGBTQ community-based organizations in Metro Vancouver (montage). Source: The Authors /; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Predictors of Facebook User Engagement With Health-Related Content for Gay, Bisexual, and Other Men Who Have Sex With Men: Content Analysis


    Background: Social media is used by community-based organizations (CBOs) to promote the well-being of gay and bisexual men (GBM). However, few studies have quantified which factors facilitate the diffusion of health content tailored for sexual minorities. Objective: The aim of this study was to identify post characteristics that can be leveraged to optimize the health promotion efforts of CBOs on Facebook. Methods: The Facebook application programming interface was used to collect 5 years’ of posts shared across 10 Facebook pages administered by Vancouver-based CBOs promoting GBM health. Network analysis assessed basic indicators of network structure. Content analyses were conducted using informatics-based approaches. Hierarchical negative binomial regression of post engagement data was used to identify meaningful covariates of engagement. Results: In total, 14,071 posts were shared and 21,537 users engaged with these posts. Most users (n=13,315) engaged only once. There was moderate correlation between the number of posts and the number of CBOs users engaged with (r=.53, P<.001). Higher user engagement was positively associated with positive sentiment, sharing multimedia, and posting about pre-exposure prophylaxis, stigma, and mental health. Engagement was negatively associated with asking questions, posting about dating, and sharing posts during or after work (versus before). Conclusions: Results highlight the existence of a core group of Facebook users who facilitate diffusion. Factors associated with greater user engagement present CBOs with a number of strategies for improving the diffusion of health content.

  • Analyzing consumer price index and unemployment trends, 2008-2016 (montage). Source: The Authors /; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Economic Recession and Obesity-Related Internet Search Behavior in Taiwan: Analysis of Google Trends Data


    Background: Obesity is highly correlated with the development of chronic diseases and has become a critical public health issue that must be countered by aggressive action. This study determined whether data from Google Trends could provide insight into trends in obesity-related search behaviors in Taiwan. Objective: Using Google Trends, we examined how changes in economic conditions—using business cycle indicators as a proxy—were associated with people’s internet search behaviors related to obesity awareness, health behaviors, and fast food restaurants. Methods: Monthly business cycle indicators were obtained from the Taiwan National Development Council. Weekly Taiwan Stock Exchange (TWSE) weighted index data were accessed and downloaded from Yahoo Finance. The weekly relative search volumes (RSV) of obesity-related terms were downloaded from Google Trends. RSVs of obesity-related terms and the TWSE from January 2007 to December 2011 (60 months) were analyzed using correlation analysis. Results: During an economic recession, the RSV of obesity awareness and health behaviors declined (r=.441, P<.001; r=.593, P<.001, respectively); however, the RSV for fast food restaurants increased (r=−.437, P<.001). Findings indicated that when the economy was faltering, people tended to be less likely to search for information related to health behaviors and obesity awareness; moreover, they were more likely to search for fast food restaurants. Conclusions: Macroeconomic conditions can have an impact on people’s health-related internet searches.

  • Source: Flickr; Copyright: Arne Hoel / World Bank; URL:; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Strengthening Routine Data Systems to Track the HIV Epidemic and Guide the Response in Sub-Saharan Africa


    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.

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