BACKGROUND Vitamin D metabolites support innate immune responses to Mycobacterium tuberculosis. Data from phase 3, randomized, controlled trials of vitamin D supplementation to prevent tuberculosis infection are lacking. METHODS We randomly assigned children who had negative results for M. tuberculosis infection according to the QuantiFERON-TB Gold In-Tube assay (QFT) to receive a weekly oral dose of either 14,000 IU of vitamin D 3 or placebo for 3 years. The primary outcome was a positive QFT result at the 3-year follow-up, expressed as a proportion of children. Secondary outcomes included the serum 25-hydroxyvitamin D (25[OH]D) level at the end of the trial and the incidence of tuberculosis disease, acute respiratory infection, and adverse events. RESULTS A total of 8851 children underwent randomization: 4418 were assigned to the vitamin D group, and 4433 to the placebo group; 95.6% of children had a baseline serum 25(OH)D level of less than 20 ng per milliliter. Among children with a valid QFT result at the end of the trial, the percentage with a positive result was 3.6% (147 of 4074 children) in the vitamin D group and 3.3% (134 of 4043) in the placebo group (adjusted risk ratio, 1.10; 95% confidence interval [CI], 0.87 to 1.38; P = 0.42). The mean 25(OH)D level at the end of the trial was 31.0 ng per milliliter in the vitamin D group and 10.7 ng per milliliter in the placebo group (mean between-group difference, 20.3 ng per milliliter; 95% CI, 19.9 to 20.6). Tuberculosis disease was diagnosed in 21 children in the vitamin D group and in 25 children in the placebo group (adjusted risk ratio, 0.87; 95% CI, 0.49 to 1.55). A total of 29 children in the vitamin D group and 34 in the placebo group were hospitalized for treatment of acute respiratory infection (adjusted risk ratio, 0.86; 95% CI, 0.52 to 1.40). The incidence of adverse events did not differ significantly between the two groups. CONCLUSIONS Vitamin D supplementation did not result in a lower risk of tuberculosis infection, tuberculosis disease, or acute respiratory infection than placebo among vitamin Ddeficient schoolchildren in Mongolia. (Funded by the National Institutes of Health; ClinicalTrials.gov number, NCT02276755.

Background: Contact tracing is a core element of the public health response to emerging infectious diseases including COVID-19. Better understanding the implementation context of contact tracing for pandemics, including individual- and systems-level predictors of success, is critical to preparing for future epidemics.Methods: We carried out a prospective implementation study of an emergency volunteer contact tracing program established in New Haven, Connecticut between April 4 and May 19, 2020. We assessed the yield and timeliness of case and contact outreach in reference to CDC benchmarks, and identified individual and programmatic predictors of successful implementation using multivariable regression models. We synthesized our findings using the RE-AIM implementation framework.Results: Case investigators interviewed only 826 (48%) of 1,705 cases and were unable to reach 545 (32%) because of incomplete information and 334 (20%) who missed or declined repeated outreach calls. Contact notifiers reached just 687 (28%) of 2,437 reported contacts, and were unable to reach 1,597 (66%) with incomplete information and 153 (6%) who missed or declined repeated outreach calls. The median time-to-case-interview was 5 days and time-to-contact-notification 8 days. However, among notified contacts with complete time data, 457 (71%) were reached within 6 days of exposure. The least likely groups to be interviewed were elderly (adjusted relative risk, aRR 0.74, 95% CI 0.61–0.89, p = 0.012, vs. young adult) and Black/African-American cases (aRR 0.88, 95% CI 0.80–0.97, pairwise p = 0.01, vs. Hispanic/Latinx). However, ties between cases and their contacts strongly influenced contact notification success (Intraclass Correlation Coefficient (ICC) 0.60). Surging caseloads and high volunteer turnover (case investigator n = 144, median time from sign-up to retirement from program was 4 weeks) required the program to supplement the volunteer workforce with paid public health nurses.Conclusions: An emergency volunteer-run contact tracing program fell short of CDC benchmarks for time and yield, largely due to difficulty collecting the information required for outreach to cases and contacts. To improve uptake, contact tracing programs must professionalize the workforce; better integrate testing and tracing services; capitalize on positive social influences between cases and contacts; and address racial and age-related disparities through enhanced community engagement.

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Background Mobile technologies are being increasingly developed to support the practice of medicine, nursing, and public health, including HIV testing and prevention. Chatbots using artificial intelligence (AI) are novel mobile health strategies that can promote HIV testing and prevention among men who have sex with men (MSM) in Malaysia, a hard-to-reach population at elevated risk of HIV, yet little is known about the features that are important to this key population. Objective The aim of this study was to identify the barriers to and facilitators of Malaysian MSM’s acceptance of an AI chatbot designed to assist in HIV testing and prevention in relation to its perceived benefits, limitations, and preferred features among potential users. Methods We conducted 5 structured web-based focus group interviews with 31 MSM in Malaysia between July 2021 and September 2021. The interviews were first recorded, transcribed, coded, and thematically analyzed using NVivo (version 9; QSR International). Subsequently, the unified theory of acceptance and use of technology was used to guide data analysis to map emerging themes related to the barriers to and facilitators of chatbot acceptance onto its 4 domains: performance expectancy, effort expectancy, facilitating conditions, and social influence. Results Multiple barriers and facilitators influencing MSM’s acceptance of an AI chatbot were identified for each domain. Performance expectancy (ie, the perceived usefulness of the AI chatbot) was influenced by MSM’s concerns about the AI chatbot’s ability to deliver accurate information, its effectiveness in information dissemination and problem-solving, and its ability to provide emotional support and raise health awareness. Convenience, cost, and technical errors influenced the AI chatbot’s effort expectancy (ie, the perceived ease of use). Efficient linkage to health care professionals and HIV self-testing was reported as a facilitating condition of MSM’s receptiveness to using an AI chatbot to access HIV testing. Participants stated that social influence (ie, sociopolitical climate) factors influencing the acceptance of mobile technology that addressed HIV in Malaysia included privacy concerns, pervasive stigma against homosexuality, and the criminalization of same-sex sexual behaviors. Key design strategies that could enhance MSM’s acceptance of an HIV prevention AI chatbot included an anonymous user setting; embedding the chatbot in MSM-friendly web-based platforms; and providing user-guiding questions and options related to HIV testing, prevention, and treatment. Conclusions This study provides important insights into key features and potential implementation strategies central to designing an AI chatbot as a culturally sensitive digital health tool to prevent stigmatized health conditions in vulnerable and systematically marginalized populations. Such features not only are crucial to designing effective user-centered and culturally situated mobile health interventions for MSM in Malaysia but also illuminate the importance of incorporating social stigma considerations into health technology implementation strategies.

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