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Journal Description

JMIR Public Health & Surveillance (JPHS, Editor-in-chief: Travis Sanchez, Emory University/Rollins School of Public Health) is an ESCI, Clarivate (SCIE, SSCI etc), ScopusPMC/PubMed- and MEDLINE-indexed, peer-reviewed international multidisciplinary journal with a unique focus on the intersection of innovation and technology in public health, and includes topics like public health informatics, surveillance (surveillance systems and rapid reports), participatory epidemiology, infodemiology and infoveillance, digital disease detection, digital epidemiology, electronic public health interventions, mass media/social media campaigns, health communication, and emerging population health analysis systems and tools. The 2020 Journal Impact Factor is expected to be released in 2021 by Clarivate and is expected to be >3.5.

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

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

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

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

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

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


Recent Articles:

  • Standardized response rates and the incidence of SARS-CoV-2 infection per 100,000 inhabitants by Italian region as of April 23, 2020. Source: Image created by the authors; Copyright: The Authors; License: Licensed by JMIR.

    Self-Reported Symptoms of SARS-CoV-2 Infection in a Nonhospitalized Population in Italy: Cross-Sectional Study of the EPICOVID19 Web-Based Survey


    Background: Understanding the occurrence of symptoms resembling those of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a large nonhospitalized population at the peak of the epidemic in Italy is of paramount importance; however, data are currently scarce. Objective: The aims of this study were to evaluate the association of self-reported symptoms with SARS-CoV-2 nasopharyngeal swab (NPS) test results in nonhospitalized individuals and to estimate the occurrence of symptoms associated with coronavirus disease (COVID-19) in a larger nontested population. Methods: EPICOVID19 is a self-administered cross-sectional voluntary web-based survey of adults throughout Italy who completed an anonymous questionnaire in the period of April 13 to 21, 2020. The associations between symptoms potentially related to SARS-CoV-2 infection and NPS results were calculated as adjusted odds ratios (aORs) with 95% CIs by multiple logistic regression analysis controlling for age, sex, education, smoking habits, and number of comorbidities. Thereafter, for each symptom and for combinations of the symptoms, we calculated the sensitivity, specificity, accuracy, and areas under the curve (AUCs) in a receiver operating characteristic (ROC) analysis to estimate the occurrence of COVID-19–like infection in the nontested population. Results: A total of 171,310 people responded to the survey, of whom 102,543 (59.9%) were women; mean age 47.4 years. Out of the 4785 respondents with known NPS test results, 4392 were not hospitalized. Among the 4392 nonhospitalized respondents, those with positive NPS tests (856, 19.5%) most frequently reported myalgia (527, 61.6%), olfactory and taste disorders (507, 59.2%), cough (466, 54.4%), and fever (444, 51.9%), whereas 7.7% were asymptomatic. Multiple regression analysis showed that olfactory and taste disorders (aOR 10.3, 95% CI 8.4-12.7), fever (aOR 2.5, 95% CI 2.0-3.1), myalgia (aOR 1.5, 95% CI 1.2-1.8), and cough (aOR 1.3, 95% CI 1.0-1.6) were associated with NPS positivity. Having two to four of these symptoms increased the aOR from 7.4 (95% CI 5.6-9.7) to 35.5 (95% CI 24.6-52.2). The combination of the four symptoms showed an AUC of 0.810 (95% CI 0.795-0.825) in classifying positive NPS test results and then was applied to the nonhospitalized and nontested sample (n=165,782). We found that 7739 to 20,103 of these 165,782 respondents (4.4% to 12.1%) had experienced symptoms suggestive of COVID-19 infection. Conclusions: Our results suggest that self-reported symptoms are reliable indicators of SARS-CoV-2 infection in a pandemic context. A nonnegligible number of symptomatic respondents (up to 12.1%) were undiagnosed and potentially contributed to the spread of the infection. Trial Registration: NCT04471701;

  • Source: Image created by the authors; Copyright: De Britto; URL:; License: Creative Commons Attribution (CC-BY).

    Prediction of the Transition From Subexponential to the Exponential Transmission of SARS-CoV-2 in Chennai, India: Epidemic Nowcasting


    Background: Several countries adopted lockdown to slowdown the exponential transmission of the coronavirus disease (COVID-19) epidemic. Disease transmission models and the epidemic forecasts at the national level steer the policy to implement appropriate intervention strategies and budgeting. However, it is critical to design a data-driven reliable model for nowcasting for smaller populations, in particular metro cities. Objective: The aim of this study is to analyze the transition of the epidemic from subexponential to exponential transmission in the Chennai metro zone and to analyze the probability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) secondary infections while availing the public transport systems in the city. Methods: A single geographical zone “Chennai-Metro-Merge” was constructed by combining Chennai District with three bordering districts. Subexponential and exponential models were developed to analyze and predict the progression of the COVID-19 epidemic. Probabilistic models were applied to assess the probability of secondary infections while availing public transport after the release of the lockdown. Results: The model predicted that transition from subexponential to exponential transmission occurs around the eighth week after the reporting of a cluster of cases. The probability of secondary infections with a single index case in an enclosure of the city bus, the suburban train general coach, and the ladies coach was found to be 0.192, 0.074, and 0.114, respectively. Conclusions: Nowcasting at the early stage of the epidemic predicts the probable time point of the exponential transmission and alerts the public health system. After the lockdown release, public transportation will be the major source of SARS-CoV-2 transmission in metro cities, and appropriate strategies based on nowcasting are needed.

  • Source: Pixabay; Copyright: Engin_Akyurt; URL:; License: Licensed by JMIR.

    Global Changes and Factors of Increase in Caloric/Salty Food Intake, Screen Use, and Substance Use During the Early COVID-19 Containment Phase in the General...


    Background: The international outbreak of coronavirus disease (COVID-19) has led many countries to enforce drastic containment measures. It has been suggested that this abrupt lockdown of populations will foster addiction-related habits such as caloric/salty food intake, screen use, and substance use. Objective: Our aim was to assess the global changes and factors of increase in addiction-related habits during the early COVID-19 containment phase in France. Methods: A web-based survey was provided from day 8 to day 13 of the containment and was completed by 11,391 participants. The questions explored sociodemographic features, psychiatric/addiction history, material conditions of lockdown, general stress, mental well-being, and reported changes in several addiction-related behaviors. Global changes were described and factors of increase were explored using population-weighted and adjusted logistic regression models, providing adjusted odds ratios (aORs) and their 95% confidence intervals. Results: Overall, the respondents reported more increases in addiction-related habits than decreases, specifically 28.4% (caloric/salty food intake), 64.6% (screen use), 35.6% (tobacco use), 24.8% (alcohol use), and 31.2% (cannabis use). Reduced well-being scores and increased stress scores were general factors of increase in addiction-related habits (P<.001 for all habits). Factors of increase in caloric/salty food intake (n=10,771) were female gender (aOR 1.62, 95% CI 1.48-1.77), age less than 29 years (P<.001), having a partner (aOR 1.19, 95% CI 1.06-1.35), being locked down in a more confined space (per 1 square meter/person decrease: aOR 1.02, 95% CI 1.01-1.03), being locked down alone (aOR 1.29, 95% CI 1.11-1.49), and reporting current (aOR 1.94, 95% CI 1.62-2.31) or past (aOR 1.27, 95% CI 1.09-1.47) psychiatric treatment. Factors of increase in screen use (n=11,267) were female gender (aOR 1.31, 95% CI 1.21-1.43), age less than 29 years (P<.001), having no partner (aOR 1.18, 95% CI 1.06-1.32), being employed (P<.001), intermediate/high education level (P<.001), being locked down with no access to an outdoor space (aOR 1.16, 95% CI 1.05-1.29), being locked down alone (aOR 1.15, 95% CI 1.01-1.32), living in an urban environment (P<.01), and not working (P<.001). Factors of increase in tobacco use (n=2787) were female gender (aOR 1.31, 95% CI 1.11-1.55), having no partner (aOR 1.30, 95% CI 1.06-1.59), intermediate/low education level (P<.01), and still working in the workplace (aOR 1.47, 95% CI 1.17-1.86). Factors of increase in alcohol use (n=7108) were age 30-49 years (P<.05), a high level of education (P<.001), and current psychiatric treatment (aOR 1.44, 95% CI 1.10-1.88). The only significant factor of increase in cannabis use (n=620) was intermediate/low level of education (P<.001). Conclusions: The early phase of COVID-19 containment in France led to widespread increases in addiction-related habits in the general population. Reduced well-being and increased stress were universal factors of increase. More specific factors were associated with increases in each of the explored habits.

  • Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Flexible, Freely Available Stochastic Individual Contact Model for Exploring COVID-19 Intervention and Control Strategies: Development and Simulation


    Background: Throughout March 2020, leaders in countries across the world were making crucial decisions about how and when to implement public health interventions to combat the coronavirus disease (COVID-19). They urgently needed tools to help them to explore what will work best in their specific circumstances of epidemic size and spread, and feasible intervention scenarios. Objective: We sought to rapidly develop a flexible, freely available simulation model for use by modelers and researchers to allow investigation of how various public health interventions implemented at various time points might change the shape of the COVID-19 epidemic curve. Methods: “COVOID” (COVID-19 Open-Source Infection Dynamics) is a stochastic individual contact model (ICM), which extends the ICMs provided by the open-source EpiModel package for the R statistical computing environment. To demonstrate its use and inform urgent decisions on March 30, 2020, we modeled similar intervention scenarios to those reported by other investigators using various model types, as well as novel scenarios. The scenarios involved isolation of cases, moderate social distancing, and stricter population “lockdowns” enacted over varying time periods in a hypothetical population of 100,000 people. On April 30, 2020, we simulated the epidemic curve for the three contiguous local areas (population 287,344) in eastern Sydney, Australia that recorded 5.3% of Australian cases of COVID-19 through to April 30, 2020, under five different intervention scenarios and compared the modeled predictions with the observed epidemic curve for these areas. Results: COVOID allocates each member of a population to one of seven compartments. The number of times individuals in the various compartments interact with each other and their probability of transmitting infection at each interaction can be varied to simulate the effects of interventions. Using COVOID on March 30, 2020, we were able to replicate the epidemic response patterns to specific social distancing intervention scenarios reported by others. The simulated curve for three local areas of Sydney from March 1 to April 30, 2020, was similar to the observed epidemic curve in terms of peak numbers of cases, total numbers of cases, and duration under a scenario representing the public health measures that were actually enacted, including case isolation and ramp-up of testing and social distancing measures. Conclusions: COVOID allows rapid modeling of many potential intervention scenarios, can be tailored to diverse settings, and requires only standard computing infrastructure. It replicates the epidemic curves produced by other models that require highly detailed population-level data, and its predicted epidemic curve, using parameters simulating the public health measures that were enacted, was similar in form to that actually observed in Sydney, Australia. Our team and collaborators are currently developing an extended open-source COVOID package comprising of a suite of tools to explore intervention scenarios using several categories of models.

  • Source: Unsplash; Copyright: Patrick Assalé; URL:; License: Licensed by JMIR.

    Using Open-Source Intelligence to Detect Early Signals of COVID-19 in China: Descriptive Study


    Background: The coronavirus disease (COVID-19) outbreak in China was first reported to the World Health Organization (WHO) on December 31, 2019, and the first cases were officially identified around December 8, 2019. Although the origin of COVID-19 has not been confirmed, approximately half of the early cases were linked to a seafood market in Wuhan. However, the first two documented patients did not visit the seafood market. News reports, social media, and informal sources may provide information about outbreaks prior to formal notification. Objective: The aim of this study was to identify early signals of pneumonia or severe acute respiratory illness (SARI) in China prior to official recognition of the COVID-19 outbreak in December 2019 using open-source data. Methods: To capture early reports, we searched an open source epidemic observatory, EpiWatch, for SARI or pneumonia-related illnesses in China from October 1, 2019. The searches were conducted using Google and the Chinese search engine Baidu. Results: There was an increase in reports following the official notification of COVID-19 to the WHO on December 31, 2019, and a report that appeared on December 26, 2019 was retracted. A report of severe pneumonia on November 22, 2019, in Xiangyang was identified, and a potential index patient was retrospectively identified on November 17. Conclusions: The lack of reports of SARI outbreaks prior to December 31, 2019, with a retracted report on December 26, suggests media censorship, given that formal reports indicate that cases began appearing on December 8. However, the findings also support a relatively recent origin of COVID-19 in November 2019. The case reported on November 22 was transferred to Wuhan approximately one incubation period before the first identified cases on December 8; this case should be further investigated, as only half of the early cases were exposed to the seafood market in Wuhan. Another case of COVID-19 has since been retrospectively identified in Hubei on November 17, 2019, suggesting that the infection was present prior to December.

  • Source: Freepik; Copyright: Freepic.diller; URL:; License: Licensed by JMIR.

    Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal...


    Background: Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. Objective: The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. Methods: We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. Results: Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. Conclusions: Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.

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

    Association Between Public Knowledge About COVID-19, Trust in Information Sources, and Adherence to Social Distancing: Cross-Sectional Survey


    Background: The success of behavioral interventions and policies designed to reduce the impact of the COVID-19 pandemic depends on how well individuals are informed about both the consequences of infection and the steps that should be taken to reduce the impact of the disease. Objective: The aim of this study was to investigate associations between public knowledge about COVID-19, adherence to social distancing, and public trust in government information sources (eg, the US Centers for Disease Control and Prevention), private sources (eg, FOX and CNN), and social networks (eg, Facebook and Twitter) to inform future policies related to critical information distribution. Methods: We conducted a cross-sectional survey (N=1243) between April 10 and 14, 2020. Data collection was stratified by US region and other demographics to ensure representativeness of the sample. Results: Government information sources were the most trusted among the public. However, we observed trends in the data that suggested variations in trust by age and gender. White and older populations generally expressed higher trust in government sources, while non-White and younger populations expressed higher trust in private sources (eg, CNN) and social networks (eg, Twitter). Trust in government sources was positively associated with accurate knowledge about COVID-19 and adherence to social distancing. However, trust in private sources (eg, FOX and CNN) was negatively associated with knowledge about COVID-19. Similarly, trust in social networks (eg, Facebook and Twitter) was negatively associated with both knowledge and adherence to social distancing. Conclusions: During pandemics such as the COVID-19 outbreak, policy makers should carefully consider the quality of information disseminated through private sources and social networks. Furthermore, when disseminating urgent health information, a variety of information sources should be used to ensure that diverse populations have timely access to critical knowledge.

  • Source: Pexels; Copyright: Edward Jenner; URL:; License: Licensed by JMIR.

    Consumer Reported Care Deferrals Due to the COVID-19 Pandemic, and the Role and Potential of Telemedicine: Cross-Sectional Analysis


    Background: The COVID-19 pandemic forced many health systems to proactively reduce care delivery to prepare for an expected surge in hospitalizations. There have been concerns that care deferral may have negative health effects, but it is hoped that telemedicine can provide a viable alternative. Objective: This study aimed to understand what type of health care services were being deferred during the COVID-19 pandemic lockdown, the role played by telemedicine to fill in care gaps, and changes in attitudes toward telemedicine. Methods: We conducted a cross-sectional analysis of survey responses from 1694 primary care patients in a mid-sized northeastern city. Our main outcomes were use of telemedicine and reports of care deferral during the shutdown. Results: Deferred care was widespread—48% (n=812) of respondents deferred care—but it was largely for preventive services, particularly dental and primary care, and did not cause concerns about negative health effects. In total, 30.2% (n=242) of those who delayed care were concerned about health effects, with needs centered around orthopedics and surgery. Telemedicine was viewed more positively than prior to the pandemic; it was seen as a viable option to deliver deferred care, particularly by respondents who were over 65 years of age, female, and college educated. Mental health services stood out for having high levels of deferred care. Conclusions: Temporary health system shutdowns will give rise to deferred care. However, much of the deferrals will be for preventive services. The effect of this on patient health can be moderated by prioritizing surgical and orthopedic services and delivering other services through telemedicine. Having telemedicine as an option is particularly crucial for mental health services.

  • County-wide COVID-19 prediction. Source: image created by authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Early Stage Machine Learning–Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach


    Background: The rapid spread of COVID-19 means that government and health services providers have little time to plan and design effective response policies. It is therefore important to quickly provide accurate predictions of how vulnerable geographic regions such as counties are to the spread of this virus. Objective: The aim of this study is to develop county-level prediction around near future disease movement for COVID-19 occurrences using publicly available data. Methods: We estimated county-level COVID-19 occurrences for the period March 14 to 31, 2020, based on data fused from multiple publicly available sources inclusive of health statistics, demographics, and geographical features. We developed a three-stage model using XGBoost, a machine learning algorithm, to quantify the probability of COVID-19 occurrence and estimate the number of potential occurrences for unaffected counties. Finally, these results were combined to predict the county-level risk. This risk was then used as an estimated after-five-day-vulnerability of the county. Results: The model predictions showed a sensitivity over 71% and specificity over 94% for models built using data from March 14 to 31, 2020. We found that population, population density, percentage of people aged >70 years, and prevalence of comorbidities play an important role in predicting COVID-19 occurrences. We observed a positive association at the county level between urbanicity and vulnerability to COVID-19. Conclusions: The developed model can be used for identification of vulnerable counties and potential data discrepancies. Limited testing facilities and delayed results introduce significant variation in reported cases, which produces a bias in the model.

  • Hands-on illustration of a weekly online questionnaire for study participants. Source: The authors; Copyright: Sébastien Martin; URL:; License: Creative Commons Attribution (CC-BY).

    An Online Influenza Surveillance System for Primary Care Workers in Switzerland: Observational Prospective Pilot Study


    Background: A better understanding of the influenza epidemiology among primary care workers could guide future recommendations to prevent transmission in primary care practices. Therefore, we designed a pilot study to assess the feasibility of using a work-based online influenza surveillance system among primary care workers. Such an approach is of particular relevance in the context of the coronavirus disease (COVID-19) pandemic, as its findings could apply to other infectious diseases with similar mechanisms of transmission. Objective: This study aims to determine the feasibility of using a work-based online influenza surveillance system for primary care workers in Switzerland. Methods: Physicians and staff of one walk-in clinic and two selected primary care practices were enrolled in this observational prospective pilot study during the 2017-2018 influenza season. They were invited to record symptoms of influenza-like illness in a weekly online survey sent by email and to self-collect a nasopharyngeal swab in case any symptoms were recorded. Samples were tested by real-time polymerase chain reaction for influenza A, influenza B, and a panel of respiratory pathogens. Results: Among 67 eligible staff members, 58% (n=39) consented to the study and 53% (n=36) provided data. From the time all participants were included, the weekly survey response rate stayed close to 100% until the end of the study. Of 79 symptomatic episodes (mean 2.2 episodes per participant), 10 episodes in 7 participants fitted the definition of an influenza-like illness case (attack rate: 7/36, 19%). One swab tested positive for influenza A H1N1 (attack rate: 3%, 95% CI 0%-18%). Swabbing was considered relatively easy. Conclusions: A work-based online influenza surveillance system is feasible for use among primary care workers. This promising methodology could be broadly used in future studies to improve the understanding of influenza epidemiology and other diseases such as COVID-19. This could prove to be highly useful in primary care settings and guide future recommendations to prevent transmission. A larger study will also help to assess asymptomatic infections.

  • Inside Rocinha favela, Rio de Janeiro, Brazil, 2010. Source: Wikimedia commons; Copyright: chensiyuan; URL:; License: Licensed by JMIR.

    COVID-19 and Slums: A Pandemic Highlights Gaps in Knowledge About Urban Poverty


    According to the United Nations, about 1 billion persons live in so-called slums. Numerous studies have shown that this population is particularly vulnerable to infectious diseases. The current COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, emphatically underlines this problem. The often high-density living quarters coupled with a large number of persons per dwelling and the lack of adequate sanitation are reasons why measures to contain the pandemic only work to a limited extent in slums. Furthermore, assignment to risk groups for severe courses of COVID-19 caused by noncommunicable diseases (eg, cardiovascular diseases) is not possible due to inadequate data availability. Information on people living in slums and their health status is either unavailable or only exists for specific regions (eg, Nairobi). We argue that one of the greatest problems with regard to the COVID-19 pandemic in the context of slums in the Global South is the lack of data on the number of people, their living conditions, and their health status.

  • Source:; Copyright: Scott Graham; URL:; License: Licensed by JMIR.

    Practical and Ethical Concerns in Implementing Enhanced Surveillance Methods to Improve Continuity of HIV Care: Qualitative Expert Stakeholder Study


    Background: Retention in HIV care is critical to maintaining viral suppression and preventing further transmission, yet less than 50% of people living with HIV in the United States are engaged in care. All US states have a funding mandate to implement Data-to-Care (D2C) programs, which use surveillance data (eg, laboratory, Medicaid billing) to identify out-of-care HIV-positive persons and relink them to treatment. Objective: The purpose of this qualitative study was to identify and describe practical and ethical considerations that arise in planning for and implementing D2C. Methods: Via purposive sampling, we recruited 43 expert stakeholders—including ethicists, privacy experts, researchers, public health personnel, HIV medical providers, legal experts, and community advocates—to participate in audio-recorded semistructured interviews to share their perspectives on D2C. Interview transcripts were analyzed across a priori and inductively derived thematic categories. Results: Stakeholders reported practical and ethical concerns in seven key domains: permission and consent, government assistance versus overreach, privacy and confidentiality, stigma, HIV exceptionalism, criminalization, and data integrity and sharing. Conclusions: Participants expressed a great deal of support for D2C, yet also stressed the role of public trust and transparency in addressing the practical and ethical concerns they identified. Trial Registration:

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