JMIR Publications

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:

  • A train station in Yaoundé, Cameroon. Source: Flickr; Copyright: Elin B; URL:; License: Creative Commons Attribution (CC-BY).

    Sampling Key Populations for HIV Surveillance: Results From Eight Cross-Sectional Studies Using Respondent-Driven Sampling and Venue-Based Snowball Sampling


    Background: In using regularly collected or existing surveillance data to characterize engagement in human immunodeficiency virus (HIV) services among marginalized populations, differences in sampling methods may produce different pictures of the target population and may therefore result in different priorities for response. Objective: The objective of this study was to use existing data to evaluate the sample distribution of eight studies of female sex workers (FSW) and men who have sex with men (MSM), who were recruited using different sampling approaches in two locations within Sub-Saharan Africa: Manzini, Swaziland and Yaoundé, Cameroon. Methods: MSM and FSW participants were recruited using either respondent-driven sampling (RDS) or venue-based snowball sampling. Recruitment took place between 2011 and 2016. Participants at each study site were administered a face-to-face survey to assess sociodemographics, along with the prevalence of self-reported HIV status, frequency of HIV testing, stigma, and other HIV-related characteristics. Crude and RDS-adjusted prevalence estimates were calculated. Crude prevalence estimates from the venue-based snowball samples were compared with the overlap of the RDS-adjusted prevalence estimates, between both FSW and MSM in Cameroon and Swaziland. Results: RDS samples tended to be younger (MSM aged 18-21 years in Swaziland: 47.6% [139/310] in RDS vs 24.3% [42/173] in Snowball, in Cameroon: 47.9% [99/306] in RDS vs 20.1% [52/259] in Snowball; FSW aged 18-21 years in Swaziland 42.5% [82/325] in RDS vs 8.0% [20/249] in Snowball; in Cameroon 15.6% [75/576] in RDS vs 8.1% [25/306] in Snowball). They were less educated (MSM: primary school completed or less in Swaziland 42.6% [109/310] in RDS vs 4.0% [7/173] in Snowball, in Cameroon 46.2% [138/306] in RDS vs 14.3% [37/259] in Snowball; FSW: primary school completed or less in Swaziland 86.6% [281/325] in RDS vs 23.9% [59/247] in Snowball, in Cameroon 87.4% [520/576] in RDS vs 77.5% [238/307] in Snowball) than the snowball samples. In addition, RDS samples indicated lower exposure to HIV prevention information, less knowledge about HIV prevention, limited access to HIV prevention tools such as condoms, and less-reported frequency of sexually transmitted infections (STI) and HIV testing as compared with the venue-based samples. Findings pertaining to the level of disclosure of sexual practices and sexual practice–related stigma were mixed. Conclusions: Samples generated by RDS and venue-based snowball sampling produced significantly different prevalence estimates of several important characteristics. These findings are tempered by limitations to the application of both approaches in practice. Ultimately, these findings provide further context for understanding existing surveillance data and how differences in methods of sampling can influence both the type of individuals captured and whether or not these individuals are representative of the larger target population. These data highlight the need to consider how program coverage estimates of marginalized populations are determined when characterizing the level of unmet need.

  • Source: iStock by Getty Images; Copyright: GCShutter; URL:; License: Licensed by the authors.

    Identifying Sentiment of Hookah-Related Posts on Twitter


    Background: The increasing popularity of hookah (or waterpipe) use in the United States and elsewhere has consequences for public health because it has similar health risks to that of combustible cigarettes. While hookah use rapidly increases in popularity, social media data (Twitter, Instagram) can be used to capture and describe the social and environmental contexts in which individuals use, perceive, discuss, and are marketed this tobacco product. These data may allow people to organically report on their sentiment toward tobacco products like hookah unprimed by a researcher, without instrument bias, and at low costs. Objective: This study describes the sentiment of hookah-related posts on Twitter and describes the importance of debiasing Twitter data when attempting to understand attitudes. Methods: Hookah-related posts on Twitter (N=986,320) were collected from March 24, 2015, to December 2, 2016. Machine learning models were used to describe sentiment on 20 different emotions and to debias the data so that Twitter posts reflected sentiment of legitimate human users and not of social bots or marketing-oriented accounts that would possibly provide overly positive or overly negative sentiment of hookah. Results: From the analytical sample, 352,116 tweets (59.50%) were classified as positive while 177,537 (30.00%) were classified as negative, and 62,139 (10.50%) neutral. Among all positive tweets, 218,312 (62.00%) were classified as highly positive emotions (eg, active, alert, excited, elated, happy, and pleasant), while 133,804 (38.00%) positive tweets were classified as passive positive emotions (eg, contented, serene, calm, relaxed, and subdued). Among all negative tweets, 95,870 (54.00%) were classified as subdued negative emotions (eg, sad, unhappy, depressed, and bored) while the remaining 81,667 (46.00%) negative tweets were classified as highly negative emotions (eg, tense, nervous, stressed, upset, and unpleasant). Sentiment changed drastically when comparing a corpus of tweets with social bots to one without. For example, the probability of any one tweet reflecting joy was 61.30% from the debiased (or bot free) corpus of tweets. In contrast, the probability of any one tweet reflecting joy was 16.40% from the biased corpus. Conclusions: Social media data provide researchers the ability to understand public sentiment and attitudes by listening to what people are saying in their own words. Tobacco control programmers in charge of risk communication may consider targeting individuals posting positive messages about hookah on Twitter or designing messages that amplify the negative sentiments. Posts on Twitter communicating positive sentiment toward hookah could add to the normalization of hookah use and is an area of future research. Findings from this study demonstrated the importance of debiasing data when attempting to understand attitudes from Twitter data.

  • Source: Pixabay; Copyright: Free-Photos; URL:; License: Public Domain (CC0).

    Implications of Attrition in a Longitudinal Web-Based Survey: An Examination of College Students Participating in a Tobacco Use Study


    Background: Web-based survey research has several benefits, including low cost and burden, as well as high use of the Internet, particularly among young adults. In the context of longitudinal studies, attrition raises concerns regarding the validity of data, given the potential associations with individual and institutional characteristics, or the focal area of study (eg, cigarette use). Objectives: The objective of this study was to compare baseline characteristics of nonresponders versus responders in a sample of young adult college students in a Web-based longitudinal study regarding tobacco use. Methods: We conducted a secondary data analysis of 3189 college students from seven Georgia colleges and universities in a 2-year longitudinal study. We examined baseline tobacco use, as well as individual- and institutional-level factors, as predictors of attrition between wave 1 (October and November 2014) and wave 2 (February and March 2015) using multilevel modeling. Results: A total 13.14% (419/3189) participants were lost to follow-up at wave 2. Predictors of nonresponse were similar in the models examining individual-level factors and institutional-level factors only and included being black versus white (odds ratio [OR] 1.74, CI 1.23-2.46); being male versus female (OR 1.41, CI 1.10-1.79); seeking a bachelor’s degree versus advanced degree (OR 1.41, CI 1.09-1.83); not residing on campus (OR 0.62, CI 0.46-0.84); past 30-day tobacco use (OR 1.41, CI 1.10-1.78); attending a nonprivate college (OR 0.48, CI 0.33-0.71); and attending a college with ≤10,000 students (OR 0.56, CI 0.43-0.73). Conclusions: Future longitudinal studies should assess predictors of attrition to examine how survey topic and other individual and institutional factors might influence the response to allow for correction of selection bias.

  • Source: iStock by Getty Images; Copyright: Rawpixel; URL:; License: Licensed by the authors.

    College Freshmen Students’ Perspectives on Weight Gain Prevention in the Digital Age: Web-Based Survey


    Background: College freshmen are highly vulnerable to experiencing weight gain, and this phenomenon is associated with an increased risk of chronic diseases and mortality in older adulthood. Technology offers an attractive and scalable way to deliver behavioral weight gain prevention interventions for this population. Weight gain prevention programs that harness the appeal and widespread reach of Web-based technologies (electronic health or eHealth) are increasingly being evaluated in college students. Yet, few of these interventions are informed by college students’ perspectives on weight gain prevention and related lifestyle behaviors. Objective: The objective of this study was to assess college freshmen students’ concern about weight gain and associated topics, as well as their interest in and delivery medium preferences for eHealth programs focused on these topics. Methods: Web-based surveys that addressed college freshmen students’ (convenience sample of N=50) perspectives on weight gain prevention were administered at the beginning and end of the fall 2015 semester as part of a longitudinal investigation of health-related issues and experiences in first semester college freshmen. Data on weight gain prevention-related concerns and corresponding interest in eHealth programs targeting topics of potential concern, as well as preferred program delivery medium and current technology use were gathered and analyzed using descriptive statistics. Results: A considerable proportion of the freshmen sample expressed concern about weight gain (74%, 37/50) and both traditional (healthy diet: 86%, 43/50; physical activity: 64%, 32/50) and less frequently addressed (stress: 82%, 41/50; sleep: 74%, 37/50; anxiety and depression: 60%, 30/50) associated topics within the context of behavioral weight gain prevention. The proportion of students who reported interest in eHealth promotion programs targeting these topics was also generally high (ranging from 52% [26/50] for stress management to 70% [35/50] for eating a healthy diet and staying physically active). Email was the most frequently used electronic platform, with 96% (48/50) of students reporting current use of it. Email was also the most frequently cited preferred eHealth delivery platform, with 86% (43/50) of students selecting it. Facebook was preferred by the second greatest proportion of students (40%, 20/50). Conclusions: Most college freshmen have concerns about an array of weight gain prevention topics and are generally open to the possibility of receiving eHealth interventions designed to address their concerns, preferably via email compared with popular social media platforms. These preliminary findings offer a foundation to build upon when it comes to future descriptive investigations focused on behavioral weight gain prevention among college freshmen in the digital age.

  • A PODD team member completes a health survey in Chiang Mai, Thailand. Source: Chiang Mai University, Participatory One Health Disease Detection Project; Copyright: The Authors; URL:; License: Licensed by the authors.

    Participatory Disease Surveillance: Engaging Communities Directly in Reporting, Monitoring, and Responding to Health Threats


    Background: Since 2012, the International Workshop on Participatory Surveillance (IWOPS) has served as an informal network to share best practices, consult on analytic methods, and catalyze innovation to advance the burgeoning method of direct engagement of populations in voluntary monitoring of disease. Objective: This landscape provides an overview of participatory disease surveillance systems in the IWOPS network and orients readers to this growing field of practice. Methods: Authors reviewed participatory approaches that include human and animal health surveillance, both syndromic (self- reported symptoms) and event-based, and how these tools have been leveraged for disease modeling and forecasting. The authors also discuss benefits, challenges, and future directions for participatory disease surveillance. Results: There are at least 23 distinct participatory surveillance tools or programs represented in the IWOPS network across 18 countries. Organizations supporting these tools are diverse in nature. Conclusions: Participatory disease surveillance is a promising method to complement both traditional, facility-based surveillance and newer digital epidemiology systems.

  • Smart-card based Electronic School Absenteeism System for Influenza-like Illness Surveillance in Hong Kong. Source: Image created by Study Collaborator; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    A Smart Card-Based Electronic School Absenteeism System for Influenza-Like Illness Surveillance in Hong Kong: Design, Implementation, and Feasibility Assessment


    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.

  • Ingredients available in Norway to make Pakistani food. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    The Association Between Commonly Investigated User Factors and Various Types of eHealth Use for Self-Care of Type 2 Diabetes: Case of First-Generation...


    Background: Sociodemographic and health-related factors are often investigated for their association with the active use of electronic health (eHealth). The importance of such factors has been found to vary, depending on the purpose or means of eHealth and the target user groups. Pakistanis are one of the biggest immigrant groups in the Oslo area, Norway. Due to an especially high risk of developing type 2 diabetes (T2D) among this population, knowledge about their use of eHealth for T2D self-management and prevention (self-care) will be valuable for both understanding this vulnerable group and for developing effective eHealth services. Objective: The aim of this study was to examine how commonly were the nine types of eHealth for T2D self-care being used among our target group, the first-generation Pakistani immigrants living in the Oslo area. The nine types of eHealth use are divided into three broad categories based on their purpose: information seeking, communication, and active self-care. We also aimed to investigate how sociodemographic factors, as well as self-assessment of health status and digital skills are associated with the use of eHealth in this group. Methods: A survey was carried out in the form of individual structured interviews from September 2015 to January 2016 (N=176). For this study, dichotomous data about whether or not an informant had used each of the nine types of eHealth in the last 12 months and the total number of positive answers were used as dependent variables in a regression analysis. The independent variables were age, gender, total years of education, digital skills (represented by frequency of asking for help when using information and communication technology [ICT]), and self-assessment of health status. Principal component analyses were applied to make categories of independent variables to avoid multicollinearity. Results: Principal component analysis yielded three components: knowledge, comprising total years of education and digital skills; health, comprising age and self-assessment of health status; and gender, as being a female. With the exception of closed conversation with a few specific acquaintances about self-care of T2D (negatively associated, P=.02) and the use of ICT for relevant information-seeking by using search engines (not associated, P=.18), the knowledge component was positively associated with all the other dependent variables. The health component was negatively associated with the use of ICT for closed conversation with a few specific acquaintances about self-care of T2D (P=.01) but not associated with the other dependent variables. Gender component showed no association with any of the dependent variables. Conclusions: In our sample, knowledge, as a composite measure of education and digital skills, was found to be the main factor associated with eHealth use regarding T2D self-care. Enhancing digital skills would encourage and support more active use of eHealth for T2D self-care.

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

    Twitter and Public Health (Part 2): Qualitative Analysis of How Individual Health Professionals Outside Organizations Use Microblogging to Promote and...


    Background: Twitter is the most popular form of microblogging that is being utilized in public health to engage audiences and to communicate health-related information. Although there is some research showing the various forms of Twitter use in public health, little is known about how individual public health professionals are using their personal Twitter accounts to disseminate health information. Objective: The purpose of this research was to categorize public health professionals’ tweets to evaluate how individual public health professionals are furthering the mission of public health. Methods: Twitter accounts held by public health professionals were identified, and researchers proceeded to record 6 months’ worth of each individual’s Twitter feed. During the 6-month period, a total of 15,236 tweets were collected and analyzed using the constant comparison method. Results: A total of 23 tweet categories among the 15,236 tweets were initially identified. Some of the most common topics among the 23 categories included the following: health nutrition (n=2008), conferences (n=815), Ebola (n=789), Affordable Care Act (ACA)/health care (n=627), and social justice (n=626). Each of these categories were then stratified into one of four themes: (1) informing and educating, (2) monitoring health statuses and trends, (3) social justice, and (4) professional development. Conclusions: Using Twitter, public health professionals are helping dispel misinformation through education and by translating technical research into lay terms, advocating for health inequalities, and using it as a means to promote professional development.

  • Smog in China. Source: The Authors; Copyright: Baidu; URL:; License: Public Domain (CC0).

    Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform


    Background: In October 2013, the International Agency for Research on Cancer classified the particulate matter from outdoor air pollution as a group 1 carcinogen and declared that particulate matter can cause lung cancer. Fine particular matter (PM2.5) pollution is becoming a serious public health concern in urban areas of China. It is essential to emphasize the importance of the public’s awareness and knowledge of modifiable risk factors of lung cancer for prevention. Objective: The objective of our study was to explore the public’s awareness of the association of PM2.5 with lung cancer risk in China by analyzing the relationship between the daily PM2.5 concentration and searches for the term “lung cancer” on an Internet big data platform, Baidu. Methods: We collected daily PM2.5 concentration data and daily Baidu Index data in 31 Chinese capital cities from January 1, 2014 to December 31, 2016. We used Spearman correlation analysis to explore correlations between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration. Granger causality test was used to analyze the causal relationship between the 2 time-series variables. Results: In 23 of the 31 cities, the pairwise correlation coefficients (Spearman rho) between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration were positive and statistically significant (P<.05). However, the correlation between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration was poor (all r2s<.1). Results of Granger causality testing illustrated that there was no unidirectional causality from the daily PM2.5 concentration to the daily Baidu Index for lung cancer searches, which was statistically significant at the 5% level for each city. Conclusions: The daily average PM2.5 concentration had a weak positive impact on the daily search interest for lung cancer on the Baidu search engine. Well-designed awareness campaigns are needed to enhance the general public’s awareness of the association of PM2.5 with lung cancer risk, to lead the public to seek more information about PM2.5 and its hazards, and to cope with their environment and its risks appropriately.

  • Mo-Buzz Dengue hotspot mapping (montage). Source: Cosmic Centre NTU / JMIR Publication; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Lessons From the Implementation of Mo-Buzz, a Mobile Pandemic Surveillance System for Dengue


    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.

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

    Classification of Twitter Users Who Tweet About E-Cigarettes


    Background: Despite concerns about their health risks, e‑cigarettes have gained popularity in recent years. Concurrent with the recent increase in e‑cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e‑cigarettes. Monitoring the trends in e‑cigarette–related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e‑cigarette–related content on Twitter. Objective: The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e‑cigarette–related topics into distinct categories. Methods: We collected approximately 11.5 million e‑cigarette–related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e‑cigarettes. Trained human coders examined the handles’ profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users’ metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types. Results: Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e‑cigarettes (average F1 score=83.3%). Accuracy varied by user type, with F1 scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1%, 84.4%, 81.2%, 79.5%, and 47.1%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance—an overall F1 score gain of 10.6%—beyond metadata features alone. Conclusions: This study provides a method for classifying five different types of users who tweet about e‑cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e‑cigarettes online to help inform public health surveillance, education, and regulatory efforts.

  • Source: Pixabay; Copyright: Free-Photos; URL:; License: Public Domain (CC0).

    Twitter and Public Health (Part 1): How Individual Public Health Professionals Use Twitter for Professional Development


    Background: The use of social networking sites is increasingly being adopted in public health, in part, because of the barriers to funding and reduced resources. Public health professionals are using social media platforms, specifically Twitter, as a way to facilitate professional development. Objective: The objective of this study was to identify public health professionals using Twitter and to analyze how they use this platform to enhance their formal and informal professional development within the context of public health. Methods: Keyword searches were conducted to identify and invite potential participants to complete a survey related to their use of Twitter for public health and professional experiences. Data regarding demographic attributes, Twitter usage, and qualitative information were obtained through an anonymous Web-based survey. Open-response survey questions were analyzed using the constant comparison method. Results: “Using Twitter makes it easier to expand my networking opportunities” and “I find Twitter useful for professional development” scored highest, with a mean score of 4.57 (standard deviation [SD] 0.74) and 4.43 (SD 0.76) on a 5-point Likert scale. Analysis of the qualitative data shows the emergence of the following themes for why public health professionals mostly use Twitter: (1) geography, (2) continuing education, (3) professional gain, and (4) communication. Conclusions: For public health professionals in this study, Twitter is a platform best used for their networking and professional development. Furthermore, the use of Twitter allows public health professionals to overcome a series of barriers and enhances opportunities for growth.

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  • The acceptability and feasibility of implementing a bio-behavioural enhanced surveillance tool for sexually transmitted infections in England: a mixed methods study

    Date Submitted: Sep 21, 2017

    Open Peer Review Period: Oct 17, 2017 - Oct 31, 2017

    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: We as...

    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: We assessed the acceptability and feasibility of implementing a bio-behavioural enhanced surveillance tool (BBEST), comprising of a self-administered online survey among sexual health clinic (SHC) attendees, and linking this to their electronic patient records (EPR) held in England’s national STI surveillance system. Methods: Staff from 16 purposively selected SHCs across England, and men who have sex with men (MSM) and black Caribbeans, due to high STI burden among these groups, were interviewed to assess the acceptability of the proposed BBEST model. Subsequently, SHC staff invited all attendees to complete an online survey on drivers of STI risk using a study tablet or participants’ own digital device. They recorded the numbers of attendees invited and participants’ clinic numbers, which were used to link survey data to the EPR. Participants’ online consent was obtained, separately for survey participation and linkage. In post-implementation phase, SHC staff was re-interviewed to assess the feasibility of implementing BBEST. Acceptability and feasibility of implementing BBEST were assessed by analysing these qualitative and quantitative data. Results: Pre-BBEST implementation, SHC staff and attendees emphasised the importance of free internet/wifi access, confidentiality and anonymity for increasing the acceptability of BBEST among attendees. Implementation of BBEST across SHCs varied considerably and was influenced by SHCs’ culture of prioritisation of research and innovation, and available resources. Of 7367 attendees invited, 85.3% agreed to participate. Of these 6283, 73.0% logged into the survey; 70.6% (n=4437) were eligible and completed it. Of these, 91.2% (n=4,046) consented to EPR linkage, which did not differ by age or gender but was higher among MSM than heterosexual men (95.5% vs. 88.4%; P<0.01), and lower among Black Caribbean than white participants (87.1% vs 93.8%; P <0.01). Linkage was achieved for 88.9% of consenting participants. Conclusions: Implementing the BBEST in SHCs was feasible and acceptable to staff and groups at STI risk; however ensuring participants’ confidentiality and anonymity, and availability of resources is vital. BBEST could enable timely collection of detailed behavioural data for effective commissioning of sexual health services.

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    Date Submitted: Oct 13, 2017

    Open Peer Review Period: Oct 13, 2017 - Oct 21, 2017

    Background: West-Nile virus (WNV) is an arbovirus responsible of an infection, which tends to peak during the late summer and early fall period. Recently, tools monitoring web searches are emerging as...

    Background: West-Nile virus (WNV) is an arbovirus responsible of an infection, which tends to peak during the late summer and early fall period. Recently, tools monitoring web searches are emerging as powerful sources of data concerning especially infectious diseases. Objective: This study aimed at exploring the potentially predictive power of WNV-related web searches. Methods: Google Trends (GT) was used to extract search trends. Data regarding WNV were obtained from Centers for Disease Control and prevention (CDC). Data were analyzed using times series analysis. Results: We found that there was a good correlation between web searches and real-world epidemiological figures (Spearman's coefficient 0.73, p<0.0001). The best seasonal autoregressive integrated moving average (SARIMA) model was (0,1,1)X(0,1,1)4. Using data from 2004 to 2015 we were able to predict data for 2016. Conclusions: Our study demonstrated correlation between epidemiological data and search pattern related to WNV. Based on this correlation, further studies are needed to examine the practicality of these findings, making our model more accurate and reliable.

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    Date Submitted: Sep 22, 2017

    Open Peer Review Period: Oct 6, 2017 - Oct 20, 2017

    Background: New Nursing Homes (NH) data warehouses fed from residents’ medical records open the way for monitoring health elderly population on a daily basis. Elsewhere syndromic surveillance has al...

    Background: New Nursing Homes (NH) data warehouses fed from residents’ medical records open the way for monitoring health 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 through a long term follow-up of the same cohort. Objective: The goal of this study was to build and assess a national ecological NH PH surveillance system (SS). Methods: Using a national network of 126 NH, an elderly cohort was built. Medical and personal data were extracted from residents’ electronic health records (EHR) and transmitted through internet to a national server in almost real time. Socio-demographic, autonomy and syndromic information were recorded. A set of twenty-six syndromes was defined using pattern matching with the standard query language LIKE operator and a Delphi-like method, through [November 2010 - June 2016]. Early aberration reporting system EARS and Bayes surveillance algorithms of the R surveillance® package were then used to assess this syndromic data against the Sentinelles network data, French epidemics gold-standard, following Centers for Disease and Control (CDC) surveillance system assessment guidelines. Results: By extracting all socio-demographic residents’ data, a cohort of 41 061 elderly was built. EARS_C3 algorithm on NH influenza and acute gastro enteritis AGE syndromic data gave respectively sensitivities of 0.482 and 0.539 and specificities of 0.844 and 0.952 over a 6 year-period, allowing to forecast the last influenza outbreak by more than 2 weeks. Also, assessing influenza and AGE syndromic data quality, precisions through last season epidemic weeks’ peaks (weeks 03-2017 and 01-2017) were of 0.98 and 0.96, whereas through last summer epidemic weeks’ low of 0.95 and 0.92 (week 33-2016). Conclusions: This study confirmed that using the NH medical care transmissions gives a good opportunity to develop a genuine French national PH SS dedicated to the elderly. Access to seniors’ free-text validated health data responds to a PH issue for the surveillance of this fragile population. This database will also make possible new ecological research that will improve prevention, care and rapid response when facing health threats. Clinical Trial: Not applicable