The proposed research will conduct a fully-powered efficacy trial of this approach in areas with large populations of AA and H/L MSM and high HIV incidence: Jackson, MS, Los Angeles, CA, and Boston, MA. High-risk MSM who have not tested for HIV in the last year will be recruited from MSM-oriented "hook-up" mobile apps, and assigned to receive either (1) HBST with post-test phone counseling/referral ("eTEST" condition), (2) "standard" HBST without active follow-up, or (3) reminders to get tested for HIV at a local clinic ("control" condition) at three month intervals over the course of 12 months. The investigators will explore the impact of the eTEST system on key outcomes, including rates of HIV testing, receipt of additional HIV prevention services, and PrEP initiation, compared with standard HBST or clinic-based testing reminders alone. The investigators will also explore the cost effectiveness of the eTEST system under various scenarios compared with relying on traditional, clinic-based testing alone.
HIV disproportionately affects men who have sex with men (MSM) in the United States, and new infections continue to increase particularly among African American (AA) and Hispanic/Latino (H/L) MSM. Past studies estimate that up to 50% of these new infections originate from the approximately 20% of MSM who are unaware of their status. Expanded HIV testing can produce reductions in incidence when implemented on a broad scale by facilitating earlier diagnosis and treatment. Rates of HIV testing are particularly low among AA and H/L MSM, and innovative approaches to encourage testing may help address high incidence in these men. Home-based, self-testing (HBST) for HIV offers considerable promise for increasing the number of MSM who are aware of their status by overcoming key barriers to clinic-based testing, such as inconvenience and confidentiality concerns. HBST may also be particularly well-suited for AA and H/L MSM, given that stigma and mistrust of medical care contribute to low testing rates. Despite its promise, however, many are concerned that HBST does not sufficiently connect users with critical post-testing resources, such as confirmatory testing and care among those who test positive, and that these limitations may result in delayed linkage to care. Existing, FDA-approved HBST kits provide a free, 24-hour helpline that offers these services to those who seek it, but few users do, and this "passive" approach may miss critical opportunities to engage with MSM for further prevention services. To address these challenges, the investigators developed a mobile health platform ("eTEST") that uses internet-of-things (IoT) technologies to monitor when HBST users open their tests in real time, allowing the investigators to provide timely, "active" follow-up counseling and referral over the phone after they do so. In a pilot study, the investigators show that providing HBST by mail at regular intervals boosted rates of any/repeat HIV testing among high-risk MSM compared with clinic-based testing reminders. Moreover, those who received follow-up phone counseling after HBST were more likely to receive risk reduction counseling, to consult with a medical provider about PrEP, and to initiate PrEP. Given these promising results, the proposed research will conduct a fully-powered efficacy trial of this approach in areas with large populations of AA and H/L MSM and high HIV incidence: Jackson, MS, Los Angeles, CA, and Boston, MA. High-risk MSM who have not tested for HIV in the last year will be recruited from MSM-oriented "hook-up" mobile apps, and assigned to receive either (1) HBST with post-test phone counseling/referral ("eTEST" condition), (2) "standard" HBST without active follow-up, or (3) reminders to get tested for HIV at a local clinic ("control" condition) at three month intervals over the course of 12 months. The investigators will explore the impact of the eTEST system on key outcomes, including rates of HIV testing, receipt of additional HIV prevention services, and PrEP initiation, compared with standard HBST or clinic-based testing reminders alone. The investigators will also explore the cost effectiveness of the eTEST system under various scenarios compared with relying on traditional, clinic-based testing alone.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
TRIPLE
Enrollment
811
Home delivery of HIV self-test kits (OraSure OraQuick Rapid HIV test)
Post-Test HIV Risk ReductionCounseling
Brown University School of Public Health
Providence, Rhode Island, United States
Model Adjusted Probability of Any HIV Testing
We used logistic regression with dummy-coded condition assignment as a predictor to test differences in outcomes across experimental conditions. A dummy-coded covariate indicating whether participants reported testing fewer than three times in the 3 years prior to enrolling was included in all models of HIV testing. We fit longitudinal mixed effects models for two outcomes, HIV testing and high-risk CAS events within a given follow-up period, given that these outcomes varied within participants across the study period. We specified distributions appropriate for each outcome (logistic for HIV testing and negative binomial for high-risk CAS events) with suitable link functions, unstructured covariance structures and robust standard errors. Time was included as a continuous covariate. A covariate reflecting pre-enrolment HIV testing and baseline CAS events were included in these models. We used an intent-to-treat approach for all analyses. Missing data were considered missing at random.
Time frame: 12 month study period
Model Adjusted Probabilities of Repeat HIV Testing (>1)
We used logistic regression with dummy-coded condition assignment as a predictor to test differences in outcomes across experimental conditions. A dummy-coded covariate indicating whether participants reported testing fewer than three times in the 3 years prior to enrolling was included in all models of HIV testing. We fit longitudinal mixed effects models for two outcomes, HIV testing and high-risk CAS events within a given follow-up period, given that these outcomes varied within participants across the study period. We specified distributions appropriate for each outcome (logistic for HIV testing and negative binomial for high-risk CAS events) with suitable link functions, unstructured covariance structures and robust standard errors. Time was included as a continuous covariate. A covariate reflecting pre-enrolment HIV testing and baseline CAS events were included in these models. We used an intent-to-treat approach for all analyses. Missing data were considered missing at random.
Time frame: 12 months
HIV Diagnoses
count of participants who were ultimately diagnosed with HIV during the course of the study
Time frame: 12 months
Model Predicted Probability of Receipt of a Prescription for Pre-exposure Prophylaxis (PrEP)
We used logistic regression with dummy-coded condition assignment as a predictor to test differences in outcomes across experimental conditions. A binary variable reflecting whether participants had ever had a PrEP prescription in the past was included for the PrEP prescription model. We specified two-way interactions between these covariates and condition assignment in all models, but none were significant and were excluded.
Time frame: 12 month study period
Model Predicted Probability of Receipt of Testing for Other Sexually-transmitted Infections
We used logistic regression with dummy-coded condition assignment as a predictor to test differences in outcomes across experimental conditions. A dummy-coded covariate indicating whether participants reported testing fewer than three times in the 3 years prior to enrolling was included in all models of HIV testing. A similar covariate for STI testing was included in the STI testing model. We specified two-way interactions between these covariates and condition assignment in all models, but none were significant and were excluded.
Time frame: 12 months
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