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Background: Given available effective biomedical and behavioral prevention and treatment interventions, HIV-related implementation research (IR) is expanding. The rapid generation and dissemination of IR to inform guidelines and practice has the potential to optimize the impact of the Ending the Epidemic Initiative and the HIV pandemic response more broadly., Methods: We leveraged a prior mapping review of NIH-funded awards in HIV and IR from January 2013 to March 2018 and identified all publications linked to those grants in NIH RePORTER through January 1, 2021 (n = 1509). Deduplication and screening of nonoriginal research reduced the count to 1032 articles, of which 952 were eligible and included in this review. Publication volume and timing were summarized; Kaplan-Meier plots estimated time to publication., Results: Among the 215 NIH-funded IR-related awards, 127 of 215 (59%) published original research directly related to the grant, averaging 2.0 articles (SD: 3.3) per award, largely in the early IR phases. Many articles (521 of 952, 55%) attributed to grants did not report grant-related data. Time from article submission to publication averaged 205 days (SD: 107). The median time-to-first publication from funding start was 4 years. Data dissemination velocity varied by award type, trending toward faster publication in recent years. Delays in data velocity included (1) time from funding to enrollment, (2) enrollment length, and (3) time from data collection completion to publication., Conclusion: Research publication was high overall, and time-to-publication is accelerating; however, over 40% of grants have yet to publish findings from grant-related data. Addressing bottlenecks in the production and dissemination of HIV-related IR would reinforce its programmatic and policy relevance in the HIV response., Competing Interests: E.G. has received non–drug-related funding from ViiV healthcare. The remaining authors have no conflicts of interest to disclose., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Dynamic movement of patients in and out of HIV care is prevalent, but there is limited information on patterns of patient re-engagement or predictors of return to guide HIV programs to better support patient engagement., Methods: From a probability-based sample of lost to follow-up, adult patients traced by peer educators from 31 Zambian health facilities, we prospectively followed disengaged HIV patients for return clinic visits. We estimated the cumulative incidence of return and the time to return using Kaplan-Meier methods. We used univariate and multivariable Cox proportional hazards regression to conduct a risk factor analysis identifying predictors of incident return across a social ecological framework., Results: Of the 556 disengaged patients, 73.0% [95% confidence interval (CI): 61.0 to 83.8] returned to HIV care. The median follow-up time from disengagement was 32.3 months (interquartile range: 23.6-38.9). The rate of return decreased with time postdisengagement. Independent predictors of incident return included a previous gap in care [adjusted Hazard Ratio (aHR): 1.95, 95% CI: 1.23 to 3.09] and confronting a stigmatizer once in the past year (aHR: 2.14, 95% CI: 1.25 to 3.65). Compared with a rural facility, patients were less likely to return if they sought care from an urban facility (aHR: 0.68, 95% CI: 0.48 to 0.96) or hospital (aHR: 0.52, 95% CI: 0.33 to 0.82)., Conclusions: Interventions are needed to hasten re-engagement in HIV care. Early and differential interventions by time since disengagement may improve intervention effectiveness. Patients in urban and tertiary care settings may need additional support. Improving patient resilience, outreach after a care gap, and community stigma reduction may facilitate return. Future re-engagement research should include causal evaluation of identified factors., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Patients report varied barriers to HIV care across multiple domains, but specific barrier patterns may be driven by underlying, but unobserved, behavioral profiles., Methods: We traced a probability sample of patients lost to follow-up (>90 days late) as of July 31, 2015 from 64 clinics in Zambia. Among those found alive, we ascertained patient-reported reasons for care disruptions. We performed latent class analysis to identify patient subgroups with similar patterns of reasons reported and assessed the association between class membership and care status (ie, disengaged versus silently transferred to a new site)., Results: Among 547 patients, we identified 5 profiles of care disruptions: (1) "Livelihood and Mobility" (30.6% of the population) reported work/school obligations and mobility/travel as reasons for care disruptions; (2) "Clinic Accessibility" (28.9%) reported challenges with attending clinic; (3) "Mobility and Family" (21.9%) reported family obligations, mobility/travel, and transport-related reasons; (4) "Doubting Need for HIV care" (10.2%) reported uncertainty around HIV status or need for clinical care, and (5) "Multidimensional Barriers to Care" (8.3%) reported numerous (mean 5.6) reasons across multiple domains. Patient profiles were significantly associated with care status. The "Doubting Need for HIV Care" class were mostly disengaged (97.9%), followed by the "Multidimensional Barriers to Care" (62.8%), "Clinic Accessibility" (62.4%), "Livelihood and Mobility" (43.6%), and "Mobility and Family" (23.5%) classes., Conclusion: There are distinct HIV care disruption profiles that are strongly associated with patients' current engagement status. Interventions targeting these unique profiles may enable more effective and tailored strategies for improving HIV treatment outcomes.

Background: Homelessness and unstable housing (HUH) negatively impact care outcomes for people living with HIV (PLWH). To inform the design of a clinic program for PLWH experiencing HUH, we quantified patient preferences and trade-offs across multiple HIV-service domains using a discrete choice experiment (DCE)., Methods: We sequentially sampled PLWH experiencing HUH presenting at an urban HIV clinic with ≥1 missed primary care visit and viremia in the last year to conduct a DCE. Participants chose between 2 hypothetical clinics varying across 5 service attributes: care team "get to know me as a person" versus not; receiving $10, $15, or $20 gift cards for clinic visits; drop-in versus scheduled visits; direct phone communication to care team versus front-desk staff; and staying 2 versus 20 blocks from the clinic. We estimated attribute relative utility (ie, preference) using mixed-effects logistic regression and calculated the monetary trade-off of preferred options., Results: Among 65 individuals interviewed, 61% were >40 years old, 45% White, 77% men, 25% heterosexual, 56% lived outdoors/emergency housing, and 44% in temporary housing. Strongest preferences were for patient-centered care team [β = 3.80; 95% confidence interval (CI): 2.57 to 5.02] and drop-in clinic appointments (β = 1.33; 95% CI: 0.85 to 1.80), with a willingness to trade $32.79 (95% CI: 14.75 to 50.81) and $11.45 (95% CI: 2.95 to 19.95) in gift cards/visit, respectively., Conclusions: In this DCE, PLWH experiencing HUH were willing to trade significant financial gain to have a personal relationship with and drop-in access to their care team rather than more resource-intensive services. These findings informed Ward 86's "POP-UP" program for PLWH-HUH and can inform "ending the HIV epidemic" efforts.

Background: Although differentiated service delivery (DSD) models for stable patients on antiretroviral therapy (ART) offer a range of health systems innovations, their comparative desirability to patients remains unknown. We conducted a discrete choice experiment to quantify service attributes most desired by patients to inform model prioritization., Methods: Between July and December 2016, a sample of HIV-positive adults on ART at 12 clinics in Zambia were asked to choose between 2 hypothetical facilities that differed across 6 DSD attributes. We used mixed logit models to explore preferences, heterogeneity, and trade-offs., Results: Of 486 respondents, 59% were female and 85% resided in urban locations. Patients strongly preferred infrequent clinic visits [3- vs. 1-month visits: β (ie, relative utility) = 2.84; P < 0.001]. Milder preferences were observed for waiting time for ART pick-up (1 vs. 6 hours.; β = -0.67; P < 0.001) or provider (1 vs. 3 hours.; β = -0.41; P = 0.002); "buddy" ART collection (β = 0.84; P < 0.001); and ART pick-up location (clinic vs. community: β = 0.35; P = 0.028). Urban patients demonstrated a preference for collecting ART at a clinic (β = 1.32, P < 0.001), and although most rural patients preferred community ART pick-up (β = -0.74, P = 0.049), 40% of rural patients still preferred facility ART collection., Conclusions: Stable patients on ART primarily want to attend clinic infrequently, supporting a focus in Zambia on optimizing multimonth prescribing over other DSD features-particularly in urban areas. Substantial preference heterogeneity highlights the need for DSD models to be flexible, and accommodate both setting features and patient choice in their design.