JMIR Publications

JMIR Public Health and Surveillance

A multidisciplinary journal that focuses on 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 (JPH, Editor-in-chief: Patrick Sullivan, Emory University/Rollins School of Public Health) is a new sister journal of the Journal of Medical Internet Research (JMIR), the top cited journal in health informatics (Impact Factor 2015: 4.532). JPH is a multidisciplinary journal that focuses on 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.

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:

  • Image Source: Author: KROMKRATHOG. License: Standard Licence with attribution.

    Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis


    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.

  • Flyers for recruitment in the NutriNet-Santé study. Image sourced and copyright owned by authors.

    Lessons Learned From Methodological Validation Research in E-Epidemiology


    Background: Traditional epidemiological research methods exhibit limitations leading to high logistics, human, and financial burden. The continued development of innovative digital tools has the potential to overcome many of the existing methodological issues. Nonetheless, Web-based studies remain relatively uncommon, partly due to persistent concerns about validity and generalizability. Objective: The objective of this viewpoint is to summarize findings from methodological studies carried out in the NutriNet-Santé study, a French Web-based cohort study. Methods: On the basis of the previous findings from the NutriNet-Santé e-cohort (>150,000 participants are currently included), we synthesized e-epidemiological knowledge on sample representativeness, advantageous recruitment strategies, and data quality. Results: Overall, the reported findings support the usefulness of Web-based studies in overcoming common methodological deficiencies in epidemiological research, in particular with regard to data quality (eg, the concordance for body mass index [BMI] classification was 93%), reduced social desirability bias, and access to a wide range of participant profiles, including the hard-to-reach subgroups such as young (12.30% [15,118/122,912], <25 years) and old people (6.60% [8112/122,912], ≥65 years), unemployed or homemaker (12.60% [15,487/122,912]), and low educated (38.50% [47,312/122,912]) people. However, some selection bias remained (78.00% (95,871/122,912) of the participants were women, and 61.50% (75,590/122,912) had postsecondary education), which is an inherent aspect of cohort study inclusion; other specific types of bias may also have occurred. Conclusions: Given the rapidly growing access to the Internet across social strata, the recruitment of participants with diverse socioeconomic profiles and health risk exposures was highly feasible. Continued efforts concerning the identification of specific biases in e-cohorts and the collection of comprehensive and valid data are still needed. This summary of methodological findings from the NutriNet-Santé cohort may help researchers in the development of the next generation of high-quality Web-based epidemiological studies.

  • Food and physical activity dictionaries. Image sourced and copyright owned by authors Quynh et al.

    Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity


    Background: Studies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research. Objective: The aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors. Methods: We utilized Twitter’s streaming application programming interface to continuously collect a random 1% subset of publicly available geolocated tweets for 1 year (April 2015 to March 2016). We collected 80 million geotagged tweets from 603,363 unique Twitter users across the contiguous United States. We validated our machine learning algorithms for constructing indicators of happiness, food, and physical activity by comparing predicted values to those generated by human labelers. Geotagged tweets were spatially mapped to the 2010 census tract and zip code areas they fall within, which enabled further assessment of the associations between Twitter-derived neighborhood variables and neighborhood demographic, economic, business, and health characteristics. Results: Machine labeled and manually labeled tweets had a high level of accuracy: 78% for happiness, 83% for food, and 85% for physical activity for dichotomized labels with the F scores 0.54, 0.86, and 0.90, respectively. About 20% of tweets were classified as happy. Relatively few terms (less than 25) were necessary to characterize the majority of tweets on food and physical activity. Data from over 70,000 census tracts from the United States suggest that census tract factors like percentage African American and economic disadvantage were associated with lower census tract happiness. Urbanicity was related to higher frequency of fast food tweets. Greater numbers of fast food restaurants predicted higher frequency of fast food mentions. Surprisingly, fitness centers and nature parks were only modestly associated with higher frequency of physical activity tweets. Greater state-level happiness, positivity toward physical activity, and positivity toward healthy foods, assessed via tweets, were associated with lower all-cause mortality and prevalence of chronic conditions such as obesity and diabetes and lower physical inactivity and smoking, controlling for state median income, median age, and percentage white non-Hispanic. Conclusions: Machine learning algorithms can be built with relatively high accuracy to characterize sentiment, food, and physical activity mentions on social media. Such data can be utilized to construct neighborhood indicators consistently and cost effectively. Access to neighborhood data, in turn, can be leveraged to better understand neighborhood effects and address social determinants of health. We found that neighborhoods with social and economic disadvantage, high urbanicity, and more fast food restaurants may exhibit lower happiness and fewer healthy behaviors.

  • A study participant in rural western India undergoing screening for Atrial Fibrillation using a mobile device. Image sourced and copyright owned by authors.

    High Burden of Unrecognized Atrial Fibrillation in Rural India: An Innovative Community-Based Cross-Sectional Screening Program


    Background: Atrial fibrillation, the world’s most common arrhythmia, is a leading risk factor for stroke, a disease striking nearly 1.6 million Indians annually. Early detection and management of atrial fibrillation is a promising opportunity to prevent stroke but widespread screening programs in limited resource settings using conventional methods is difficult and costly. Objective: The objective of this study is to screen people for atrial fibrillation in rural western India using a US Food and Drug Administration-approved single-lead electrocardiography device, Alivecor. Methods: Residents from 6 villages in Anand District, Gujarat, India, comprised the base population. After obtaining informed consent, a team of trained research coordinators and community health workers enrolled a total of 354 participants aged 50 years and older and screened them at their residences using Alivecor for 2 minutes on 5 consecutive days over a period of 6 weeks beginning June, 2015. Results: Almost two-thirds of study participants were 55 years or older, nearly half were female, one-third did not receive any formal education, and more than one-half were from households earning less than US $2 per day. Twelve participants screened positive for atrial fibrillation yielding a sample prevalence of 5.1% (95% CI 2.7-8.7). Only one participant had persistent atrial fibrillation throughout all of the screenings, and 9 screened positive only once. Conclusions: Our study suggests a prevalence of atrial fibrillation in this Indian region (5.1%) that is markedly higher than has been previously reported in India and similar to the prevalence estimates reported in studies of persons from North America and Europe. Historically low reported burden of atrial fibrillation among individuals from low and middle-income countries may be due to a lack of routine screening. Mobile technologies may help overcome resource limitations for atrial fibrillation screening in underserved and low-resource settings.

  • Online search. Image Source: Author: ChristianHoppe. License: CC0 Public Domain.

    Disease Monitoring and Health Campaign Evaluation Using Google Search Activities for HIV and AIDS, Stroke, Colorectal Cancer, and Marijuana Use in Canada: A...


    Background: Infodemiology can offer practical and feasible health research applications through the practice of studying information available on the Web. Google Trends provides publicly accessible information regarding search behaviors in a population, which may be studied and used for health campaign evaluation and disease monitoring. Additional studies examining the use and effectiveness of Google Trends for these purposes remain warranted. Objective: The objective of our study was to explore the use of infodemiology in the context of health campaign evaluation and chronic disease monitoring. It was hypothesized that following a launch of a campaign, there would be an increase in information seeking behavior on the Web. Second, increasing and decreasing disease patterns in a population would be associated with search activity patterns. This study examined 4 different diseases: human immunodeficiency virus (HIV) infection, stroke, colorectal cancer, and marijuana use. Methods: Using Google Trends, relative search volume data were collected throughout the period of February 2004 to January 2015. Campaign information and disease statistics were obtained from governmental publications. Search activity trends were graphed and assessed with disease trends and the campaign interval. Pearson product correlation statistics and joinpoint methodology analyses were used to determine significance. Results: Disease patterns and online activity across all 4 diseases were significantly correlated: HIV infection (r=.36, P<.001), stroke (r=.40, P<.001), colorectal cancer (r= −.41, P<.001), and substance use (r=.64, P<.001). Visual inspection and the joinpoint analysis showed significant correlations for the campaigns on colorectal cancer and marijuana use in stimulating search activity. No significant correlations were observed for the campaigns on stroke and HIV regarding search activity. Conclusions: The use of infoveillance shows promise as an alternative and inexpensive solution to disease surveillance and health campaign evaluation. Further research is needed to understand Google Trends as a valid and reliable tool for health research.

  • Automating data analytics. Image created and copyright owned by authors.

    IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics


    Background: We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. Objective: To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. Methods: The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Results: Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. Conclusions: IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise.

  • Image Source: Tablet social media, copyright, ijclark., Licensed under Creative Commons Attribution cc-by 2.0

    Investigating Sociodemographic Factors and HIV Risk Behaviors Associated With Social Networking Among Adolescents in Soweto, South Africa: A Cross-Sectional...


    Background: Internet access via mobile phones and computers facilitates interaction and potential health communication among individuals through social networking. Many South African adolescents own mobile phones and can access social networks via apps. Objective: We investigated sociodemographic factors and HIV risk behaviors of adolescent social networking users in Soweto, South Africa. Methods: We conducted an interviewer-administered, cross-sectional survey of adolescents aged 14-19 years. Independent covariates of social networking were assessed by multivariate logistic regression analysis. Results: Of 830 adolescents, 57% (475/830) were females and the median age was found to be 18 years (interquartile range 17-18). Social networking was used by 60% of adolescents (494/830); more than half, that is, 87% (396/494) accessed social networks through mobile phones and 56% (275/494) spent more than 4 hours per day using their mobile phones. Social networking was independently associated with mobile usage 2-4 hours (adjusted odds ratio [AOR]: 3.06, CI: 1.69-5.51) and more than 4 hours per day (AOR: 6.16, CI: 3.46-10.9) and one (AOR: 3.35, CI: 1.79-6.27) or more sexual partner(s) (AOR: 2.58, CI: 1.05-6.36). Conclusions: Mobile phone–based social networking is prevalent among sexually active adolescents living in Soweto and may be used as an entry point for health promotion and initiation of low-cost adolescent health interventions.

  • Example of Facebook advertisements before and after modifications. Image on the left was used before negative feedback was received. Image on the right was used after modifications were made to the images and the targeting criteria.

    How Best to Obtain Valid, Verifiable Data Online From Male Couples? Lessons Learned From an eHealth HIV Prevention Intervention for HIV-Negative Male Couples


    Background: As interest increases in the development of eHealth human immunodeficiency virus (HIV)-preventive interventions for gay male couples, Web-based methods must also be developed to help increase the likelihood that couples enrolled and data collected from them represent true unique dyads. Methods to recruit and collect reliable and valid data from both members of a couple are lacking, yet are crucial for uptake of novel sexual health and HIV-prevention eHealth interventions. Methods to describe best practices to recruit male couples using targeted advertisements on Facebook are also lacking in the literature, yet could also help in this uptake. Objective: The objective of our study was to describe challenges and lessons learned from experiences from two phases (developmental phase and online randomized controlled trial [RCT]) of an eHealth HIV-prevention intervention for concordant HIV-negative male couples in terms of (1) recruiting male couples using targeted advertisements on Facebook, (2) validating that data came from two partners of the couple, and (3) verifying that the two partners of the couple are in a relationship with each other. Methods: The developmental phase refined the intervention via in-person focus groups, whereas the pilot-testing phase included an online RCT. For both phases, couples were recruited via targeted Facebook advertisements. Advertisements directed men to a study webpage and screener; once eligible, participants provided consent electronically. A partner referral system was embedded in the consenting process to recruit the relationship partner of the participant. Both men of the couple had to meet all eligibility criteria—individually and as a couple—before they could enroll in the study. Verification of couples’ relationships was assessed via the concurrence of predetermined screener items from both partners, done manually in the developmental phase and electronically in the pilot-testing phase. A system of decision rules was developed to assess the validity that data came from two unique partners of a couple. Results: Several important lessons were learned from these experiences, resulting in recommendations for future eHealth studies involving male couples. Use of certain “interests” and types of images (eg, shirtless) in targeted Facebook advertisements should be avoided or used sparingly because these interests and types of images may generate adverse reactions from a broader audience. Development of a systematic approach with predetermined criteria and parameters to verify male couples’ relationships is strongly recommended. Further, researchers are encouraged to develop a system of decision rules to detect and handle suspicious data (eg, suspicious email addresses/names, multiple entries, same IP address used in multiple entries) to help validate the legitimacy of male couples’ relationships online. Conclusions: These lessons learned combined with recommendations for future studies aim to help enhance recruitment efforts and the validity and reliability of collecting dyadic data from male couples for novel eHealth HIV-preventive interventions.

  • Chiropractic joint correction. Image URL: License: This file is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported.

    Chiropractic and Spinal Manipulation Therapy on Twitter: Case Study Examining the Presence of Critiques and Debates


    Background: Spinal manipulation therapy (SMT) is a popular though controversial practice. The debates surrounding efficacy and risk of SMT are only partially evident in popular discourse. Objective: This study aims to investigate the presence of critiques and debates surrounding efficacy and risk of SMT on the social media platform Twitter. The study examines whether there is presence of debate and whether critical information is being widely disseminated. Methods: An initial corpus of 31,339 tweets was compiled through Twitter’s Search Application Programming Interface using the query terms “chiropractic,” “chiropractor,” and “spinal manipulation therapy.” Tweets were collected for the month of December 2015. Post removal of tweets made by bots and spam, the corpus totaled 20,695 tweets, of which a sample (n=1267) was analyzed for skeptical or critical tweets. Additional criteria were also assessed. Results: There were 34 tweets explicitly containing skepticism or critique of SMT, representing 2.68% of the sample (n=1267). As such, there is a presence of 2.68% of tweets in the total corpus, 95% CI 0-6.58% displaying explicitly skeptical or critical perspectives of SMT. In addition, there are numerous tweets highlighting the health benefits of SMT for health issues such as attention deficit hyperactivity disorder (ADHD), immune system, and blood pressure that receive scant critical attention. The presence of tweets in the corpus highlighting the risks of “stroke” and “vertebral artery dissection” is also minute (0.1%). Conclusions: In the abundance of tweets substantiating and promoting chiropractic and SMT as sound health practices and valuable business endeavors, the debates surrounding the efficacy and risks of SMT on Twitter are almost completely absent. Although there are some critical voices of SMT proving to be influential, issues persist regarding how widely this information is being disseminated.

  • A laptop keyboard with glasses. Image Source: License: Public Domain.

    Testing the Feasibility of a Passive and Active Case Ascertainment System for Multiple Rare Conditions Simultaneously: The Experience in Three US States


    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.

  • Source:; By Tungilik (Own work) [CC0] via Wikimedia Common.

    Determining Survey Satisficing of Online Longitudinal Survey Data in the Multicenter AIDS Cohort Study: A Group-Based Trajectory Analysis


    Background: Survey satisficing occurs when participants respond to survey questions rapidly without carefully reading or comprehending them. Studies have demonstrated the occurrence of survey satisficing, which can degrade survey quality, particularly in longitudinal studies. Objective: The aim of this study is to use a group-based trajectory analysis method to identify satisficers when similar survey questions were asked periodically in a long-standing cohort, and to examine factors associated with satisficing in the surveys having sensitive human immunodeficiency virus (HIV)-related behavioral questions. Methods: Behavioral data were collected semiannually online at all four sites of the Multicenter AIDS Cohort Study (MACS) from October 2008 through March 2013. Based on the start and end times, and the word counts per variable, response speed (word counts per second) for each participant visit was calculated. Two-step group-based trajectory analyses of the response speed across 9 study visits were performed to identify potential survey satisficing. Generalized linear models with repeated measures were used to investigate the factors associated with satisficing on HIV-related behavioral surveys. Results: Among the total 2138 male participants, the median baseline age was 51 years (interquartile range, 45-58); most of the participants were non-Hispanic white (62.72%, 1341/2138) and college graduates (46.59%, 996/2138), and half were HIV seropositive (50.00%, 1069/2138). A total of 543 men (25.40%, 543/2138) were considered potential satisficers with respect to their increased trajectory tendency of response speed. In the multivariate analysis, being 10 years older at the baseline visit increased the odds of satisficing by 44% (OR 1.44, 95% CI 1.27-1.62, P<.001). Compared with the non-Hispanic white participants, non-Hispanic black participants were 122% more likely to satisfice the HIV-related behavioral survey (OR 2.22, 95% CI 1.69-2.91, P<.001), and 99% more likely to do so for the other race/ethnicity group (OR 1.99, 95% CI 1.39-2.83, P<.001). Participants with a high school degree or less were 67% more likely to satisfice the survey (OR 1.67, 95% CI 1.26-2.21, P<.001) compared with those with a college degree. Having more than one sex partner and using more than one recreational drug reduced the odds of satisficing by 24% (OR 0.76, 95% CI 0.61-0.94, P=.013) and 28% (OR 0.72, 95% CI 0.55-0.93, P=.013), respectively. No statistically significant association of HIV serostatus with satisficing was observed. Conclusions: Using a group-based trajectory analysis method, we could identify consistent satisficing on HIV-related behavioral surveys among participants in the MACS, which was associated with being older, being non-white, and having a lower education level; however, there was no significant difference by HIV serostatus. Methods to minimize satisficing using longitudinal survey data are warranted.

  • Image Source: laboratory, copyright Nicolas Nova,,
Licensed under Creative Commons Attribution cc-by 2.0

    Acceptability of a Community-Based Outreach HIV-Testing Intervention Using Oral Fluid Collection Devices and Web-Based HIV Test Result Collection Among...


    Background: Late human immunodeficiency virus (HIV) diagnosis is common among sub-Saharan African migrants. To address their barriers to HIV testing uptake and improve timely HIV diagnoses and linkage to care, the outreach HIV testing intervention, “swab2know,” was developed. It combined a community-based approach with innovative testing methods: oral fluid self-sampling and the choice between Web-based HIV test result collections using a secured website or post-test counseling at a sexual health clinic. The sessions included an informational speech delivered by a physician of sub-Saharan African origin and testimonies by community members living with HIV. Objectives: The objectives of this study were to evaluate the intervention’s acceptability among sub-Saharan African migrants and its potential to reach subgroups at higher risk for HIV infection and to identify facilitators and barriers for HIV testing uptake. Methods: This mixed-method study combined qualitative (participant observations and informal interviews with testers and nontesters) and quantitative data (paper–pencil survey, laboratory data, and result collection files). Data were analyzed using a content analytical approach for qualitative and univariate analysis for quantitative data. Results: A total of 10 testing sessions were organized in sub-Saharan African migrant community venues in the city of Antwerp, Belgium, between December 2012 and June 2013. Overall, 18.2% of all people present (N=780) underwent HIV testing; 29.8% of them tested for HIV for the first time, 22.3% did not have a general practitioner, and 21.5% reported 2 or more sexual partners (last 3 months). Overall, 56.3% of participants chose to collect their HIV test results via the protected website. In total, 78.9% collected their results. The qualitative analysis of 137 participant observation field notes showed that personal needs and Internet literacy determined the choice of result collection method. Generally, the oral fluid collection devices were well accepted mainly because sub-Saharan African migrants dislike blood taking. For some participants, the method raised concerns about HIV transmission via saliva. The combination of information sessions, testimonies, and oral fluid collection devices was perceived as effectively reducing thresholds to participation. Acceptability of the intervention differed between individual participants and settings. Acceptance was higher among women, in churches and settings where community leaders were engaged in HIV awareness raising. Higher preventive outcomes were observed in settings with lower acceptance. The presence of the intervention team visualized the magnitude of the HIV epidemic to the public and promoted HIV testing uptake at large, for example, those who declined indicated they would take up testing later. Conclusions: When accompanied by tailored provision of information, outreach HIV testing interventions adopting a community-based approach and innovative methods such as Web-based result collection and oral fluid collection devices are acceptable and reduce thresholds for HIV testing uptake. The swab2know intervention was able to reach sub-Saharan African migrants at risk of HIV infection, and with limited access to regular HIV testing. Among nontesters, the intervention contributed to awareness raising and therefore has a place in a multipronged HIV test promotion strategy.

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