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BackgroundUnderstanding patient-reported reasons for lapses of retention in human immunodeficiency virus (HIV) treatment can drive improvements in the care cascade. A systematic assessment of outcomes among a random sample of patients lost to follow-up (LTFU) from 32 clinics in Zambia to understand the reasons for silent transfers and disengagement from care was undertaken.MethodsWe traced a simple random sample of LTFU patients (>90 days from last scheduled visit) as determined from clinic-based electronic medical records from a probability sample of facilities. Among patients found in person, we solicited reasons for either stopping or switching care and predictors for re-engagement. We coded reasons into structural, psychosocial, and clinic-based barriers.ResultsAmong 1751 LTFU patients traced and found alive, 31% of patients starting antiretroviral therapy (ART) between 1 July 2013 and 31 July 2015 silently transferred or were disengaged (40% male; median age, 35 years; median CD4 level, 239 cells/μL); median time on ART at LTFU was 480 days (interquartile range, 110-1295). Among the 544 patients not in care, median prevalences for patient-reported structural, psychosocial, and clinic-level barriers were 27.3%, 13.9%, and 13.4%, respectively, and were highly variable across facilities. Structural reasons, including, "relocated to a new place" were mostly cited among 289 patients who silently transferred (35.5%). We found that men were less likely to re-engage in care than women (odds ratio, .39; 95% confidence interval, .22-.67; P = .001).ConclusionsEfforts to improve retention of patients on ART may need to be tailored at the facility level to address patient-reported barriers.

BackgroundRetention in human immunodeficiency virus (HIV) care is dynamic, with patients frequently transitioning in and out of care. Analytical approaches (eg, survival analyses) commonly used to assess HIV care cascade outcomes fail to capture such transitions and therefore incompletely represent care outcomes over time.MethodsWe analyzed antiretroviral therapy (ART)-eligible adults newly linking to care at 64 clinics in Zambia between 1 April 2014 and 31 July 2015. We used electronic medical record data and supplemented these with updated care outcomes ascertained by tracing a multistage random sample of patients lost to follow-up (LTFU, >90 days late for last appointment). We performed multistate analyses, incorporating weights from sampling, to estimate the prevalence of 9 care states over time since linkage with respect to ART initiation, retention in care, transfers, and mortality.ResultsIn sum, 23 227 patients (58% female; median age 34 years [interquartile range 28-41]) were ART-eligible at enrollment. At 1 year, 75.2% had initiated ART and were in care: 61.8% were continuously retained, 6.1% had reengaged after LTFU, and 7.3% had transferred. Also, 10.1% were LTFU within 7 days of enrollment, and 15.2% were LTFU at 1 year (6.7% prior to ART). One year after LTFU, 51.6% of those LTFU prior to ART remained out of care compared to 30.2% of those LTFU after initiating ART. Overall, 6.9% of patients had died by 1 year with 3.0% dying prior to ART.ConclusionMultistate analyses provide more complete assessments of longitudinal HIV cascade outcomes and reveal treatment gaps at distinct timepoints in care that will still need to be addressed even with universal treatment.

BackgroundMen in sub-Saharan Africa have lower engagement and retention in HIV services compared to women, which may result in differential survival. However, the true magnitude of difference in HIV-related mortality between men and women receiving antiretroviral therapy (ART) is incompletely characterized.Methods and findingsWe evaluated HIV-positive adults ≥18 years old newly initiating ART in 4 Zambian provinces (Eastern, Lusaka, Southern, and Western). In addition to mortality data obtained from routine electronic medical records, we intensively traced a random sample of patients lost to follow-up (LTFU) and incorporated tracing outcomes through inverse probability weights. Sex-specific mortality rates and rate differences were determined using Poisson regression. Parametric g-computation was used to estimate adjusted mortality rates by sex and age. The study included 49,129 adults newly initiated on ART between August 2013 and July 2015; overall, the median age among patients was 35 years, the median baseline CD4 count was 262 cells/μl, and 37.2% were men. Men comprised a smaller proportion of individuals starting ART (37.2% versus 62.8%), tended to be older (median age 37 versus 33 years), and tended to have lower CD4 counts (median 220 versus 289 cells/μl) at the time of ART initiation compared to women. The overall rate of mortality among men was 10.3 (95% CI 8.2-12.4) deaths/100 person-years (PYs), compared to 5.5 (95% CI 4.3-6.8) deaths/100 PYs among women (difference +4.7 [95% CI 2.3-7.2] deaths/100 PYs; p < 0.001). Compared to women in the same age groups, men's mortality rates were particularly elevated among those

[This corrects the article DOI: 10.1371/journal.pmed.1002811.].

BackgroundAlthough the success of HIV treatment programs depends on retention and viral suppression, routine program monitoring of these outcomes may be incomplete. We used data from the national electronic medical record (EMR) system in Zambia to enumerate a large and regionally representative cohort of patients on treatment. We traced a random sample with unknown outcomes (lost to follow-up) to document true care status and HIV RNA levels.Methods and findingsOn 31 July 2015, we selected facilities from 4 provinces in 12 joint strata defined by facility type and province with probability proportional to size. In each facility, we enumerated adults with at least 1 clinical encounter after treatment initiation in the previous 24 months. From this cohort, we identified lost-to-follow-up patients (defined as 90 or more days late for their last appointment), selected a random sample, and intensively reviewed their records and traced them via phone calls and in-person visits in the community. In 1 of 4 provinces, we also collected dried blood spots (DBSs) for plasma HIV RNA testing. We used inverse probability weights to incorporate sampling outcomes into Aalen-Johansen and Cox proportional hazards regression to estimate retention and viremia. We used a bias analysis approach to correct for the known inaccuracy of plasma HIV RNA levels obtained from DBSs. From a total of 64 facilities with 165,464 adults on ART, we selected 32 facilities with 104,966 patients, of whom 17,602 (17%) were lost to follow-up: Those lost to follow-up had median age 36 years, 60% were female (N = 11,241), they had median enrollment CD4 count of 220 cells/μl, and 38% had WHO stage 1 clinical disease (N = 10,690). We traced 2,892 (16%) and found updated outcomes for 2,163 (75%): 412 (19%) had died, 836 (39%) were alive and in care at their original clinic, 457 (21%) had transferred to a new clinic, 255 (12%) were alive and out of care, and 203 (9%) were alive but we were unable to determine care status. Estimates using data from the EMR only suggested that 42.7% (95% CI 38.0%-47.1%) of new ART starters and 72.3% (95% CI 71.8%-73.0%) of all ART users were retained at 2 years. After incorporating updated data through tracing, we found that 77.3% (95% CI 70.5%-84.0%) of new initiates and 91.2% (95% CI 90.5%-91.8%) of all ART users were retained (at original clinic or transferred), indicating that routine program data underestimated retention in care markedly. In Lusaka Province, HIV RNA levels greater than or equal to 1,000 copies/ml were present in 18.1% (95% CI 14.0%-22.3%) of patients in care, 71.3% (95% CI 58.2%-84.4%) of lost patients, and 24.7% (95% CI 21.0%-29.3%). The main study limitations were imperfect response rates and the use of self-reported care status.ConclusionsIn this region of Zambia, routine program data underestimated retention, and the point prevalence of unsuppressed HIV RNA was high when lost patients were accounted for. Viremia was prevalent among patients who unofficially transferred: Sustained engagement remains a challenge among HIV patients in Zambia, and targeted sampling is an effective strategy to identify such gaps in the care cascade and monitor programmatic progress.

: Introduction: Poor client−provider communication is a critical barrier to long‐term retention in care among people living with HIV. However, standardized assessments of this key metric are limited in Africa. We used the Roter Interaction Analysis System (RIAS) to quantitatively characterize patterns of person‐centred communication (PCC) behaviours in Zambia. Methods: We enrolled pairs of people living with HIV making routine HIV follow‐up visit and their providers at 24 Ministry of Health‐facilities supported by the Centre for Infectious Disease Research in Zambia in Lusaka province between August 2019 and November 2021. Client−provider encounters were audio‐recorded and coded using RIAS by trained research staff. We performed latent class analysis to identify interactions with distinctive patterns of provider PCC behaviours (i.e. rapport building, person‐centred counselling, PCC micropractices [e.g. brief empathy statements], assessing barriers to care, shared decision‐making and leveraging discretionary power) and compared their distribution across client, provider, interaction and facility characteristics. Results: We enrolled 478 people living with HIV and 139 providers (14% nurses, 73.6% clinical officers, 12.3% were medical officers). We identified four distinct profiles: (1) “Medically Oriented Interaction, Minimal PCC Behaviours” (47.6% of interactions) was characterized by medical discussion, minimal psychosocial/non‐medical talk and low use of PCC behaviours; (2) “Balanced Medical/Non‐medical Interaction, Low PCC Behaviours” (21.0%) was characterized by medical and non‐medical discussion but limited use of other PCC behaviours; (3) “Medically Oriented Interaction, Good PCC Behaviours” (23.9%) was characterized by medically oriented discussion, more information‐giving and increased use of PCC behaviours; and (4) “Highly person‐centred Interaction” (7.5%) was characterized by both balanced medical/non‐medical focus and the highest use of PCC behaviours. Nurse interactions were more likely to be characterized by more PCC behaviours (i.e. Class 3 or 4) (44.8%), followed by medical officers (33.9%) and clinical officers (27.3%) (p = 0.031). Longer interactions were also more likely to integrate more PCC behaviours (p < 0.001). Conclusions: PCC behaviours are relatively uncommon in HIV care in Zambia, and often limited to brief rapport‐building statements and PCC micropractices. Strengthening PCC, such as shared decision‐making and leveraging discretionary power to better accommodate client needs and preferences, may be an important strategy for improving the quality in HIV treatment programmes., INTRODUCTION The encounter with healthcare providers is a pivotal moment in the client experience, but negative experiences with providers have remained one of the critical drivers of loss to follow‐up [...]

Introduction: Tracing patients lost to follow-up (LTFU) from HIV care is widely practiced, yet we have little knowledge of its causal effect on care engagement. In a prospective, Zambian cohort, we examined the effect of tracing on return to care within 2 years of LTFU. Methods: We traced a stratified, random sample of LTFU patients who had received HIV care between August 2013 and July 2015. LTFU was defined as a gap of >90 days from last scheduled appointment in the routine electronic medical record. Extracting 2 years of follow-up visit data through 2017, we identified patients who returned. Using random selection for tracing as an instrumental variable (IV), we used conditional two-stage least squares regression to estimate the local average treatment effect of tracer contact on return. We examined the observational association between tracer contact and return among patient sub-groups self-confirmed as disengaged from care. Results: Of the 24,164 LTFU patients enumerated, 4380 were randomly selected for tracing and 1158 were contacted by a tracer within a median of 14.8 months post-loss. IV analysis found that patients contacted by a tracer because they were randomized to tracing were no more likely to return than those not contacted (adjusted risk difference [aRD]: 3%, 95% CI: -2%, 8%, p = 0.23). Observational data showed that among contacted, disengaged patients, the rate of return was higher in the week following tracer contact (IR 5.74, 95% CI: 3.78-8.71) than in the 2 weeks to 1-month post-contact (IR 2.28, 95% CI: 1.40-3.72). There was a greater effect of tracing among patients lost for >6 months compared to those contacted within 3 months of loss. Conclusions: Overall, tracer contact did not causally increase LTFU patient return to HIV care, demonstrating the limited impact of tracing in this program, where contact occurred months after patients were LTFU. However, observational data suggest that tracing may speed return among some LTFU patients genuinely out-of-care. Further studies may improve tracing effectiveness by examining the mechanisms underlying the impact of tracing on return to care, the effect of tracing at different times-since-loss and using more accurate identification of patients who are truly disengaged to target tracing. Keywords: causality; HIV; instrumental variable; lost to follow-up; retention in care; tracing, 1 I INTRODUCTION Consistent engagement in HIV care is critical for patient health [1-5] and achieving global epidemic control targets [6]. Retention is challenging, however, with approximately one-third of patients [...]

BackgroundSurvival represents the single most important indicator of successful HIV treatment. Routine monitoring fails to capture most deaths. As a result, both regional assessments of the impact of HIV services and identification of hotspots for improvement efforts are limited. We sought to assess true mortality on treatment, characterize the extent under-reporting of mortality in routine health information systems in Zambia, and identify drivers of mortality across sites and over time using a multistage, regionally representative sampling approach.Methods and findingsWe enumerated all HIV infected adults on antiretroviral therapy (ART) who visited any one of 64 facilities across 4 provinces in Zambia during the 24-month period from 1 August 2013 to 31 July 2015. We identified a probability sample of patients who were lost to follow-up through selecting facilities probability proportional to size and then a simple random sample of lost patients. Outcomes among patients lost to follow-up were incorporated into survival analysis and multivariate regression through probability weights. Of 165,464 individuals (64% female, median age 39 years (IQR 33-46), median CD4 201 cells/mm3 (IQR 111-312), the 2-year cumulative incidence of mortality increased from 1.9% (95% CI 1.7%-2.0%) to a corrected rate of 7.0% (95% CI 5.7%-8.4%) (all ART users) and from 2.1% (95% CI 1.8%-2.4%) to 8.3% (95% CI 6.1%-10.7%) (new ART users). Revised provincial mortality rates ranged from 3-9 times higher than naïve rates for new ART users and were lowest in Lusaka Province (4.6 per 100 person-years) and highest in Western Province (8.7 per 100 person-years) after correction. Corrected mortality rates varied markedly by clinic, with an IQR of 3.5 to 7.5 deaths per 100 person-years and a high of 13.4 deaths per 100 person-years among new ART users, even after adjustment for clinical (e.g., pretherapy CD4) and contextual (e.g., province and clinic size) factors. Mortality rates (all ART users) were highest year 1 after treatment at 4.6/100 person-years (95% CI 3.9-5.5), 2.9/100 person-years (95% CI 2.1-3.9) in year 2, and approximately 1.6% per year through 8 years on treatment. In multivariate analysis, patient-level factors including male sex and pretherapy CD4 levels and WHO stage were associated with higher mortality among new ART users, while male sex and HIV disclosure were associated with mortality among all ART users. In both cases, being late (>14 days late for appointment) or lost (>90 days late for an appointment) was associated with deaths. We were unable to ascertain the vital status of about one-quarter of those lost and selected for tracing and did not adjudicate causes of death.ConclusionsHIV treatment in Zambia is not optimally effective. The high and sustained mortality rates and marked under-reporting of mortality at the provincial-level and unexplained heterogeneity between regions and sites suggest opportunities for the use of corrected mortality rates for quality improvement. A regionally representative sampling-based approach can bring gaps and opportunities for programs into clear epidemiological focus for local and global decision makers.

Background Retention in HIV treatment must be improved to advance the HIV response, but research to characterize gaps in retention has focused on estimates from single time points and population-level averages. These approaches do not assess the engagement patterns of individual patients over time and fail to account for both their dynamic nature and the heterogeneity between patients. We apply group-based trajectory analysis-a special application of latent class analysis to longitudinal data-among new antiretroviral therapy (ART) starters in Zambia to identify groups defined by engagement patterns over time and to assess their association with mortality. Methods and findings We analyzed a cohort of HIV-infected adults who newly started ART between August 1, 2013, and February 1, 2015, across 64 clinics in Zambia. We performed group-based multi-trajectory analysis to identify subgroups with distinct trajectories in medication possession ratio (MPR, a validated adherence metric based on pharmacy refill data) over the past 3 months and loss to follow-up (LTFU, >90 days late for last visit) among patients with at least 180 days of observation time. We used multinomial logistic regression to identify baseline factors associated with belonging to particular trajectory groups. We obtained Kaplan-Meier estimates with bootstrapped confidence intervals of the cumulative incidence of mortality stratified by trajectory group and performed adjusted Poisson regression to estimate adjusted incidence rate ratios (aIRRs) for mortality by trajectory group. Inverse probability weights were applied to all analyses to account for updated outcomes ascertained from tracing a random subset of patients lost to follow-up as of July 31, 2015. Overall, 38,879 patients (63.3% female, median age 35 years [IQR 29-41], median enrollment CD4 count 280 cells/[mu]l [IQR 146-431]) were included in our cohort. Analyses revealed 6 trajectory groups among the new ART starters: (1) 28.5% of patients demonstrated consistently high adherence and retention; (2) 22.2% showed early nonadherence but consistent retention; (3) 21.6% showed gradually decreasing adherence and retention; (4) 8.6% showed early LTFU with later reengagement; (5) 8.7% had early LTFU without reengagement; and (6) 10.4% had late LTFU without reengagement. Identified groups exhibited large differences in survival: after adjustment, the 'early LTFU with reengagement' group (aIRR 3.4 [95% CI 1.2-9.7], p = 0.019), the 'early LTFU' group (aIRR 6.4 [95% CI 2.5-16.3], p < 0.001), and the 'late LTFU' group (aIRR 4.7 [95% CI 2.0-11.3], p = 0.001) had higher rates of mortality as compared to the group with consistently high adherence/retention. Limitations of this study include using data observed after baseline to identify trajectory groups and to classify patients into these groups, excluding patients who died or transferred within the first 180 days, and the uncertain generalizability of the data to current care standards. Conclusions Among new ART starters in Zambia, we observed 6 patient subgroups that demonstrated distinctive engagement trajectories over time and that were associated with marked differences in the subsequent risk of mortality. Further efforts to develop tailored intervention strategies for different types of engagement behaviors, monitor early engagement to identify higher-risk patients, and better understand the determinants of these heterogeneous behaviors can help improve care delivery and survival in this population., Author(s): Aaloke Mody 1,*, Ingrid Eshun-Wilson 1, Kombatende Sikombe 2, Sheree R. Schwartz 3, Laura K. Beres 3, Sandra Simbeza 2, Njekwa Mukamba 2, Paul Somwe 2, Carolyn Bolton-Moore 2,4, [...]

An abstract of the article "Facing the Realities of HIV Universal Test and Treat: Early Lessons Learned from the Delivery of the HPTN071/PopART Study Intervention in Zambia" by Kwame Shanaube, Sam Griffith and colleagues is presented.

Background: This pilot study evaluates the effectiveness of mobile talk-time incentives in maintaining participation in a longitudinal mobile health (mHealth) data collection program among people living with HIV in Lusaka, Zambia. While mHealth tools, such as mobile phone surveys, provide vital health feedback, optimal incentive strategies to ensure long-term engagement remain limited. This study explores how different incentive levels affect response rates in multiple survey rounds, providing insights into effective methods for encouraging ongoing participation, especially in the context of Zambia's prepaid mobile system and multi-SIM usage, a common practice in sub-Saharan Africa., Objective: This study aimed to assess the response rate success across multiple invitations to participate in a care experience survey using a mobile phone short codes and unstructured supplementary service data (USSD) model among individuals in an HIV care setting in the Lusaka, Zambia., Methods: Participants were recruited from 2 study clinics-1 in a periurban setting and 1 in an urban setting. A total of 2 rounds of survey invitations were sent to study participants on a 3-month interval between November 1, 2018, and September 23, 2019. Overall, 3 incentive levels were randomly assigned by participant and survey round: (1) no incentive, (2) 2 Zambian Kwacha (ZMW; US $0.16), and (3) 5 ZMW (US $0.42). Survey response rates were analyzed using mixed-effects Poisson regression, adjusting for individual- and facility-level factors. Probability plots for survey completion were generated based on language, incentive level, and survey round. We projected the cost per additional response for different incentive levels., Results: A total of 1006 participants were enrolled, with 72.3% (727/1006) from the urban HIV care facility and 62.4% (628/1006) requesting the survey in English. We sent a total of 1992 survey invitations for both rounds. Overall, survey completion across both surveys was 32.1% (637/1992), with significantly different survey completion between the first (40.5%, 95% CI 37.4-43.6%) and second (23.7%, 95% CI 21.1-26.4) invitations. Implementing a 5 ZMW (US $0.42) incentive significantly increased the adjusted prevalence ratio (aPR) for survey completion compared with those that received no incentive (aPR 1.35, 95% CI 1.11-1.63). The cost per additional response was highest at 5 ZMW, equivalent to US $0.42 (72.8 ZMW [US $5.82] per 1% increase in response)., Conclusions: We observed a sharp decline of almost 50% in survey completion success from the initial invitation to follow-up survey administered 3 months later. This substantial decrease suggests that longitudinal data collection potential for a care experience survey may be limited without additional sensitization and, potentially, added survey reminders. Implementing a moderate incentive increased response rates to our health care experience survey. Tailoring survey strategies to accommodate language preferences and providing moderate incentives can optimize response rates in Zambia., Trial Registration: Pan African Clinical Trial Registry PACTR202101847907585; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=14613., (©Jacob Mutale, Kombatende Sikombe, Boroma Mwale, Mwansa Lumpa, Sandra Simbeza, Chama Bukankala, Njekwa Mukamba, Aaloke Mody, Laura K Beres, Charles B Holmes, Carolyn Bolton Moore, Elvin H Geng, Izukanji Sikazwe, Jake M Pry. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.09.2024.)

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: Evidence-based HIV interventions often fail to reach anticipated impact due to insufficient utilization in real-world health systems. Human-centered design (HCD) represents a novel approach in tailoring innovations to fit end-users, narrowing the gap between efficacious interventions and impact at scale., Methods: We combined a narrative literature review of HCD in HIV programs with our experience using HCD to redesign an intervention promoting patient-centered care (PCC) practices among health care workers (HCW) in Zambia. We summarize the use and results of HCD in the global HIV response and share case study insights to advance conceptualization of HCD applications., Results: The literature review identified 13 articles (representing 7 studies) on the use of HCD in HIV. All studies featured HCD hallmarks including empathy development, user-driven inquiry, ideation, and iterative refinement. HCD was applied to mHealth design, a management intervention and pre-exposure prophylaxis delivery. Our HCD application addressed a behavioral service delivery target: changing HCW patient-centered beliefs, attitudes, and practices. Through in-depth developer-user interaction, our HCD approach revealed specific HCW support for and resistance to PCC, suggesting intervention revisions to improve feasibility and acceptability and PCC considerations that could inform implementation in transferable settings., Conclusions: As both a research and implementation tool, HCD has potential to improve effective implementation of the HIV response, particularly for product development; new intervention introduction; and complex system interventions. Further research on HCD application strengths and limitations is needed. Those promoting PCC may improve implementation success by seeking out resonance and anticipating the challenges our HCD process identified.