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. Author manuscript; available in PMC: 2025 Mar 1.
Published in final edited form as: AIDS. 2023 Oct 19;38(3):339–349. doi: 10.1097/QAD.0000000000003763

Dynamic Choice HIV Prevention Intervention at Outpatient Departments in Rural Kenya and Uganda: A Randomized Trial

Catherine A KOSS 1, James AYIEKO 2, Jane KABAMI 3, Laura B BALZER 4, Elijah KAKANDE 5, Helen SUNDAY 6, Marilyn NYABUTI 7, Erick WAFULA 8, Starley SHADE 9, Edith BIIRA 10, Fred OPEL 11, Hellen N ATUHAIRE 12, Hideaki OKOCHI 13, Sabina OGACHI 14, Monica GANDHI 15, Melanie C BACON 16, Elizabeth A BUKUSI 17, Gabriel CHAMIE 18, Maya L PETERSEN 19, Moses R KAMYA 20, Diane V HAVLIR 21; SEARCH study team
PMCID: PMC11251703  NIHMSID: NIHMS1938804  PMID: 37861683

Abstract

Objective:

HIV prevention service delivery models that offer product choices, and the option to change preferences over time, may increase prevention coverage. Outpatient departments in sub-Saharan Africa diagnose a high proportion of new HIV infections, but are an understudied entry point to biomedical prevention.

Design:

Individually randomized trial of dynamic choice HIV prevention (DCP) intervention vs. standard-of-care (SOC) among individuals with current/anticipated HIV exposure risk at outpatient departments in rural Kenya and Uganda (SEARCH; NCT04810650).

Methods:

Our DCP intervention included 1) product choice (oral pre-exposure prophylaxis [PrEP] or post-exposure prophylaxis [PEP]) with option to switch over time, 2) HIV provider- or self-testing, 3) service location choice (community vs. clinic-based), 4) provider training on patient-centered care. Primary outcome was proportion of follow-up covered by PrEP/PEP over 48 weeks assessed via self-report.

Results:

We enrolled 403 participants (61% women; median 27 years, IQR 22,37). In the DCP arm, 86% ever chose PrEP, 15% ever chose PEP over 48 weeks; selection of HIV self-testing increased from 26% to 51% and of out-of-facility visits from 8% to 52%. Among 376/403 (93%) with outcomes ascertained, time covered by PrEP/PEP was higher in DCP (47.5%) vs. SOC (18.3%); difference=29.2% (95%CI:22.7–35.7%; p<0.001). Effects were similar among women and men (28.2% and 31.0% higher coverage in DCP, respectively) and larger during periods of self-reported HIV risk (DCP 64.9% vs. SOC 26.3%; difference=38.6%; 95%CI:31.0–46.2%; p<0.001).

Conclusion:

A dynamic choice HIV prevention intervention resulted in two-fold greater time covered by biomedical prevention products compared to standard-of-care in general outpatient departments in eastern Africa.

Keywords: Pre-exposure prophylaxis, post-exposure prophylaxis, HIV prevention, dynamic choice, HIV self-testing

INTRODUCTION

Despite substantial progress in the HIV epidemic, 1.3 million new HIV infections occurred globally in 2022, highlighting a pressing need for strengthened approaches to HIV prevention.[1] Alongside HIV testing and antiretroviral therapy (ART), scale-up of biomedical prevention products, such as oral pre-exposure prophylaxis (PrEP), post-exposure prophylaxis (PEP), and new options, such as long-acting injectable cabotegravir (CAB-LA),[2, 3] could substantially reduce HIV incidence.[48] Although oral PrEP use has expanded substantially in southern and eastern Africa in recent years, PrEP use remains below UNAIDS 2025 targets.[1] PEP use is largely limited to occupational exposures and gender-based violence, rather than use as an additional primary prevention choice.[9] Innovative delivery strategies are needed to optimize uptake of existing and forthcoming biomedical prevention options.

We hypothesized that a dynamic choice HIV prevention (DCP) service delivery model[10] – that offers choices in prevention products and services, and the option to change preferences over time – may increase HIV prevention coverage. Dynamic choice models could serve as one example of differentiated and patient-centered service delivery for HIV prevention, which has been endorsed by the World Health Organization (WHO).[11] Prior studies, such as discrete choice experiments and qualitative studies have provided important insights into theoretical choices for HIV prevention. This research has highlighted substantial variability in potential users’ stated preferences for prevention choices, as well as strong interest in long-acting and on-demand prevention approaches.[1220] However, data are limited on actual product choices made by clients and the impact of choice-based delivery models on prevention coverage.

Optimizing coverage of biomedical HIV prevention will require offering choices in settings that are acceptable and convenient to potential clients. In eastern Africa, HIV prevention services are often offered at HIV clinics within public sector health facilities, with a focus on serodifferent couples, although other service delivery points are expanding. Receiving PrEP in HIV clinics has been associated with anticipated stigma, with both men and women concerned about being perceived as living with HIV.[21, 22] Expanding HIV prevention services to other settings could both reduce stigma and facilitate engagement of persons not otherwise reached at HIV care clinics. Outpatient departments (OPD) in health facilities provide primary and urgent care services, and are a common entry point to health care. Moreover, outpatient departments diagnose a substantial proportion of new HIV infections,[23, 24] but are an understudied entry point to biomedical HIV prevention. Accessing services at OPD may be more acceptable and could reach otherwise healthy men and women. Therefore, offering dynamic choices in HIV prevention at outpatient departments may be a promising approach to expand biomedical prevention coverage.

We developed a patient-centered dynamic choice HIV prevention intervention with structured choices in HIV prevention product (PrEP or PEP), HIV testing modality (provider- of self-administered), and service location (health facility or out-of-facility community setting). We conducted a randomized trial to evaluate whether this intervention increased HIV prevention coverage among persons seeking care in outpatient departments in rural Kenya and Uganda compared to standard-of-care (SOC) HIV prevention services.

METHODS

Study setting and procedures

The Sustainable East Africa Research in Community Health [SEARCH] Sapphire study (NCT04810650) is a two-phase study consisting of pilot trials of dynamic choice HIV prevention and treatment interventions (Phase A),[2527] followed by testing of optimized interventions in a community cluster randomized trial (Phase B). The study is being conducted in rural western Kenya (Homa Bay and Migori Counties) and southwestern Uganda (Ntungamo and Sheema Districts). This manuscript describes the Phase A pilot randomized trial of the DCP intervention vs. SOC among individuals with current or anticipated risk of HIV exposure at four public sector OPD clinics: two in Kenya, two in Uganda.

We conducted an individually randomized trial of the DCP intervention compared to SOC HIV prevention services among individuals presenting to OPD facilities. Eligibility criteria included: age ≥15 years, HIV-negative by country-standard rapid HIV testing algorithm, and risk of HIV exposure, assessed via 1) Ministry of Health PrEP screening tool for each country (e.g. partner living with HIV, transactional sex, recent sexually transmitted infection, or recurrent PEP use)[28, 29] or 2) self-assessed current or anticipated HIV risk in the next 3 months. Self-assessed HIV risk allowed for more inclusive eligibility criteria for persons who did not meet criteria for PrEP based on MoH tools but had other reasons for seeking HIV prevention. Exclusion criteria included: inability or unwillingness to provide consent per country guidelines or participation in another concurrent SEARCH trial.

Among consented participants, demographics, self-reported PrEP or PEP use, and partner status were assessed at enrollment. Participants were randomized to DCP intervention or SOC by selecting a sequentially numbered scratch card, revealing the trial arm only after scratched by the participant. The computer-generated randomization sequence was stratified on country and sex with random block sizes of 2 and 4. Participants were not blinded by arm, but the study statistician (LBB) was blinded until trial completion and analysis.

In the SOC arm, at the enrollment visit, participants received referrals to standard HIV prevention services available at the health facility. Typically, this included referral for screening for PrEP eligibility based on country guidelines at the adjacent HIV clinic.[28, 29] Under SOC, PrEP was provided for a 1-month supply at baseline and for a 1- to 3-month supply at follow-up visits, with standard rapid blood-based HIV testing, at the health facility. PEP was not routinely available except in cases of gender-based violence or occupational exposure. HIV self-testing, community-based visits, and provider training in patient-centered care were not provided as SOC.

In the DCP arm, the intervention, described below, was offered at each study visit (enrollment; weeks 4, 12, 24, 36, 48) by clinical officers and nurses.

At weeks 24 and 48, participants in both the DCP and SOC arms were seen by study staff for follow-up visits for outcomes ascertainment. A structured survey was administered to participants to assess PrEP and PEP use (any doses taken), receipt or possession of PrEP and PEP pills, and self-assessed risk of HIV exposure (including sexual partner living with HIV or of unknown HIV status) over each of the preceding 6 months, in one-month increments. Blood-based rapid HIV testing and HIV RNA testing (Gene Xpert, Cepheid) were performed at weeks 24 and 48 for participants in both arms. Study procedures were offered at no cost to participant; no incentives were provided.

Dynamic choice prevention intervention

As previously described,[10] we developed the DCP intervention using the PRECEDE framework for health promotion strategies to address “predisposing” factors (knowledge, attitudes, or beliefs that impact behavior), “enabling” factors to facilitate behavior, and “reinforcing” factors that include consequences of following a behavior.[30] Intervention components were selected based on qualitative and survey data and included structured choices in biomedical prevention product, HIV test modality, and service delivery location, together with patient-centered staffing, service provision, and client support.[10]

The DCP intervention (Figure, Supplemental Digital Content [SDC] 1) included: 1) product choice (daily oral PrEP [TDF/FTC or TDF/3TC] or post-exposure prophylaxis [PEP] with TDF/3TC/dolutegravir provided from MoH supplies) with option to switch products over time; 2) HIV testing choice at follow-up visits (standard blood-based rapid test or oral fluid HIV self-test (HIVST; OraQuick); and 3) service location choice (health facility – typically OPD – or out-of-facility community setting, such as home) for study visits offering product and testing. Patient-centered care included: 24/7 phone access to clinician, structured assessment of barriers to PrEP/PEP start and adherence, with personalized plans in response, and psychological support for trauma. Provider training on patient-centered care included education on dynamic prevention products and emphasis on respecting client agency in selecting their preferred prevention product, HIV testing, and visit location. Providers received training on the intervention and patient-centered care for both facility- and community-based visits.

Eligibility criteria for PrEP and PEP were based on country guidelines.[28, 29] Up to a 3-month supply of PrEP was provided at each visit. PEP was offered with a “pill-in-pocket” option of 3–5 pills to have on-hand for unanticipated HIV exposures, as well as an HIVST kit. Participants were instructed to take PEP as soon as possible after an exposure and to contact the study clinician for rapid HIV testing and the remaining supply of PEP. At each visit, participants were offered a choice of rapid blood-based HIV testing or oral fluid HIVST, with the option of clinician assistance for HIVST. Given the lower sensitivity of HIVST, particularly with oral fluid, for detection of acute HIV infection, blood-based rapid HIV testing was provided to participants who were pregnant or breastfeeding. As noted above, blood-based rapid HIV and HIV RNA testing were also performed at weeks 24 and 48 for all participants.

Outcomes

The primary outcome was the proportion of study follow-up time covered by PrEP or PEP assessed via self-report. Secondary outcomes included PrEP/PEP coverage during periods of retrospectively self-assessed risk of HIV exposure.

In the DCP arm, implementation outcomes included visit attendance among eligible participants (excluding withdrawals and seroconversions). We also assessed the proportion of participants selecting each DCP option (product, testing, and location) over 48 weeks of follow-up and at each scheduled visit.

In both arms, we assessed drug levels of tenofovir in small hair samples to validate self-reported PrEP/PEP pill-taking. Among participants who reported taking any PrEP or PEP doses in the past 30 days at week 24, we collected small samples of scalp hair (50–100 strands) for analysis of hair concentrations of tenofovir. Drug levels in the one centimeter of hair closest to the scalp (reflecting the most recent month of drug exposure) were analyzed via liquid chromatography-tandem mass spectrometry using validated methods in the Hair Analytical Laboratory at the University of California, San Francisco.[31, 32]

Statistical analysis

Based on a two-sample t-test, we estimated 400 participants (200/arm) would provide 80% power to detect a 10% absolute increase in the primary outcome of PrEP/PEP coverage, assuming standard deviation of 0.35.

Follow-up time covered by PrEP/PEP was compared between arms with targeted minimum loss-based estimation (TMLE), an approach that adaptively adjusts for baseline covariates to maximize precision, while preserving Type-I error control.[33, 34] Specifically, we used 10-fold cross-validation to select from the following prespecified candidate adjustment variables: country, sex, age, prior use of PrEP/PEP, or nothing (unadjusted). We calculated two-sided 95% confidence intervals (CIs) and tested the null hypothesis that the DCP intervention did not change prevention coverage at the 5% significance level.

Prespecified subgroups included country, sex, age (15–24 years vs. 25+ years), and alcohol use (any vs. no use in 3 months prior to enrolment). Using analogous methods, we also compared follow-up time covered by PrEP/PEP during periods of self-reported HIV risk. Among participants with hair samples analyzed, we compared the proportion with detectable hair tenofovir concentrations (>0.002 ng/mg hair) by arm using a two-sample test of proportions. Within the DCP intervention arm, we also summarized visit coverage and choice of the intervention components (PrEP/PEP, HIV testing modality, visit location). Analyses were conducted in R (version 4.2.1).

Ethical considerations

This study was approved by the institutional review boards of Makerere University (Kampala, Uganda), Kenya Medical Research Institute (Nairobi, Kenya), and University of California, San Francisco (San Francisco, CA, USA). All participants provided written informed consent in their preferred language.

RESULTS

Study participants

A total of 917 persons attending OPD facilities in the study communities were screened for eligibility from April 21, 2021-July 1, 2021 (Figure 1). Among the 403 participants enrolled in the trial (197 randomized to DCP, 206 to SOC), 61% were women, median age 27 years (IQR 22,37), 88% reported having a sex partner with HIV/unknown HIV status in the past 6 months 7% had previously used PrEP or PEP (Table 1). Among enrolled men, 65/158 (41%) were 15–24 years; among enrolled women, 84/245 (34%) were 15–24 years (Table, SDC 2).

Figure 1.

Figure 1.

CONSORT Diagram

Table 1:

Baseline characteristics by study arm

DCP Intervention
(N=197)
SOC
(N=206)
Total
(N=403)
Female 120/197 (61%) 125/206 (61%) 245/403 (61%)
Age, median [Q1,Q3] 27 [22,36] 29 [22,37] 27 [22,37]
Age 15–24 years 76/197 (39%) 73/206 (35%) 149/403 (37%)
Country
-Kenya 97/197 (49%) 104/206 (50%) 201/403 (50%)
-Uganda 100/197 (51%) 102/206 (50%) 202/403 (50%)
Marital status
-Unmarried 51/195 (26%) 57/206 (28%) 108/401 (27%)
-Married/living with partner 136/195 (70%) 140/206 (68%) 276/401 (69%)
-Divorced/separated/widowed 8/195 (4%) 9/206 (4%) 17/401 (4%)
Occupation
-Farmer 76/196 (39%) 76/206 (37%) 152/402 (38%)
-Housewife/homemaker 4/196 (2%) 6/206 (3%) 10/402 (2%)
-Shopkeeper/market vendor 19/196 (10%) 14/206 (7%) 33/402 (8%)
-Student 30/196 (15%) 26/206 (13%) 56/402 (14%)
-Manual labor/construction 7/196 (4%) 13/206 (6%) 20/402 (5%)
-Fishing/Fishmonger 1/196 (1%) 2/206 (1%) 3/402 (1%)
-Transportation 8/196 (4%) 13/206 (6%) 21/402 (5%)
-Bar worker/owner 4/196 (2%) 4/206 (2%) 8/402 (2%)
-Teacher 6/196 (3%) 4/206 (2%) 10/402 (2%)
-Other 18/196 (9%) 22/206 (11%) 40/402 (10%)
-No job 23/196 (12%) 26/206 (13%) 49/402 (12%)
HIV risk by sexual partners
-Partner HIV+/unknown in past 6mo 174/197 (88%) 179/206 (87%) 353/403 (88%)
-Primary partner living with HIV1 26/119 (22%) 35/123 (28%) 61/242 (25%)
-Primary partner with HIV is on ART2 26/26 (100%) 32/32 (100%) 58/58 (100%)
Alcohol use (any, prior 3mo) 24/197 (12%) 26/205 (13%) 50/402 (12%)
Mobile (at least 1 night away in past 3mo) 62/195 (32%) 56/204 (27%) 118/399 (30%)
Pregnant (female only) 3/115 (3%) 3/118 (3%) 6/233 (3%)
Circumcised (male only) 47/77 (61%) 52/80 (65%) 99/157 (63%)
Prior PrEP/PEP use (any, past 6 mo) 19/197 (10%) 11/206 (5%) 30/403 (7%)
1.

Among participants reporting a primary partner

2.

Among participants reporting primary partner is living with HIV

DCP intervention arm implementation and choices

In the DCP intervention arm (197 participants), at least 89% were seen and received the DCP intervention at each visit week. At baseline 165/197 (84%) selected PrEP and 17 (9%) selected PEP, with most participants selecting PrEP at each subsequent visit (Figure 2). Over 48 weeks, 170 (86%) ever chose PrEP and 29 (15%) ever chose PEP; the choice of HIVST increased from 26% to 51%, and choice of out-of-facility visits increased from 8% to 52%.

Figure 2.

Figure 2.

Choice of HIV prevention product, HIV testing modality, and visit location in DCP intervention arm through week 48

Primary outcome: Follow-up time covered by PrEP or PEP

Among 376 (93%) participants with outcomes ascertained (182 [92%] in intervention and 194 [94%] in SOC), the primary reason for missing outcomes was participants being out of the study region at the time of the week 24 or 48 follow-up visit. Mean follow-up time covered by PrEP/PEP was higher in the DCP intervention arm (47.5%) vs. SOC (18.3%); a difference of 29.2% (95% CI 22.7–35.7%; p<0.001; Figure 3). Intervention effect sizes were similar among women and men (28.2% and 31% higher coverage in the DCP arm, respectively). Effect sizes were also similar among subgroups, including youth ages 15–24 years, persons using alcohol, and by country (Table, SDC 3).

Figure 3.

Figure 3.

Primary outcome: Proportion of follow-up time covered by PrEP or PEP, by arm

We also assessed whether use of PrEP or PEP differed by arm during periods of self-reported risk of HIV exposure. When restricting follow-up time to periods of potential HIV exposure, the intervention effect on biomedical coverage was even larger. In the DCP arm, 64.9% of follow-up time during periods of HIV risk was covered by PrEP or PEP vs. 26.3% in SOC (difference 38.6%; 95%CI: 31.0–46.2%; p<0.001; Figure 4). Effects during periods of potential HIV exposure were similar by sex and among other subgroups (Table, SDC 4).

Figure 4.

Figure 4.

Heatmaps of self-reported PrEP/PEP use and HIV exposure risk over time in DCP intervention (a) and SOC (b) arms

Hair concentrations of tenofovir vs. self-reported adherence to PrEP and PEP

At week 24, 73/172 (40%) participants in the DCP arm and 28/179 (16%) in SOC reported taking at least 1 dose of PrEP or PEP in the last 30 days. Among 38 participants who reported PrEP or PEP use in the last 30 days and had drug levels in hair measured, 27 (71.1%) had detectable hair tenofovir concentrations, consistent with ingestion of PrEP or PEP in the preceding 30 days. Proportions with detectable tenofovir concentrations were similar by arm: 18/25 (72.0%) in DCP and 9/13 (69.2%) in SOC arm (p=1.0).

HIV infections

Two incident HIV infections occurred: one per study arm. A 27-year-old woman in the DCP arm reported STI symptoms at enrollment and that both she and her husband had several sexual partners. She selected PrEP and continued until week 24, but with 7–16 missed doses reported per month. At week 24, rapid HIV testing was reactive and HIV RNA was 1,640,000 copies/ml. One of her partners also received HIV testing and was newly diagnosed with HIV. A 42-year-old woman in the SOC arm reported at enrollment that her husband was living with HIV but she was uncertain if he was taking ART. After referral for HIV prevention services at enrollment, she sought PrEP services at another clinic that she preferred, but did not access care due to long wait times and ultimately did not start PrEP. She was diagnosed with HIV at week 24 based on the country-standard HIV rapid testing algorithm.

DISCUSSION

In this randomized trial, we tested a dynamic choice HIV prevention intervention with choice of PrEP or PEP, HIV testing modality, and visit location, plus patient-centered care. This intervention resulted in over two-fold greater time covered by PrEP or PEP compared to standard-of-care among both women and men at risk of HIV exposure seen in general outpatient department facilities in rural Kenya and Uganda. Moreover, the effect of the intervention was greater during periods of perceived risk of HIV exposure. This suggests that dynamic choice strategies can also be used to foster “prevention-effective” adherence,[35, 36] or the option to change prevention methods over time based on partnerships and life circumstances. Our findings suggest that dynamic choice service delivery models are a promising approach to expand coverage of biomedical HIV prevention, and respond to client choices and changes in preferences over time.

Our DCP intervention aimed to provide low-barrier, patient-centered care in a setting where individuals are already seeking health care: the outpatient departments of public sector health facilities. We found that when offered HIV prevention services at OPD, both men and women engaged with the intervention and with biomedical prevention products. Moreover, the DCP intervention was designed to expand choices for biomedical prevention products, as well as the option to switch between products over time. While there is tremendous, and well justified, enthusiasm for forthcoming long-acting products (e.g. CAB-LA and dapirivine vaginal ring[37, 38]), these products are not yet routinely available in facilities in Kenya and Uganda. There is, thus, an unrealized opportunity to expand prevention choices using existing products that are currently available. In prior work by our group in offering PrEP at a population level[6, 39], we found that while many participants were interested in a biomedical prevention product, daily oral PrEP was not preferred by some individuals.[21] For example, some participants reported infrequent potential HIV exposures (e.g. sporting events, school holidays) or found that predicting “seasons of risk” could be difficult (e.g. spouse returning from travel), thereby making daily oral PrEP less desirable or impractical. Moreover, although 2–1-1 dosing of oral PrEP[40] is highly effective and has been endorsed by the WHO,[11] the 2–1-1 strategy has not been studied for vaginal exposures, thus limiting options for “on-demand” PrEP use for many clients in generalized epidemic settings.

In response to client and community concerns, we previously piloted offering PEP in community settings in rural Kenya and Uganda,[9, 41] where PEP has traditionally been offered only in cases of occupational exposure or gender-based violence. Offering PEP as part of routine sexual health care incorporates a product that can be started after (vs. before) an HIV exposure and taken for a more limited time (28 days). Moreover, current integrase strand transfer inhibitor-based PEP regimens are well tolerated. In our prior work, we found that offering PEP was feasible and could be a gateway to other prevention options for persons with ongoing exposure or a bridge for persons who have needs for short‐term protection. In the current study, we also offered PEP with a pill-in-pocket option,[42] as well as an HIV self-test kit to have on hand in the event of an exposure, to lower barriers to PEP start. Participants were then instructed to contact the study provider as soon as possible after exposure for HIV testing and the remainder of the 28-day supply of PEP. We found that while most participants selected PrEP as their preferred product, a substantial minority (15%) selected PEP at least once during follow-up. Offering PEP alongside PrEP could overcome a potential missed opportunity for biomedical HIV prevention in this population. These findings are consistent with work in other settings, such as Toronto and London, where self-start of PEP has been offered.[43, 44] Moving forward, efforts to expand the offer of PEP beyond occupational exposures as part of routine sexual health care should be considered. Moreover, given the modest uptake of PEP in this group, further diversifying product choices, including CAB-LA, should be prioritized.

PrEP and PEP coverage, as measured via self-report, during study follow-up were two-fold higher in the DCP arm compared to standard of care. In early PrEP trials, self-reported adherence was limited by recall and social desirability biases[45, 46] and found to overestimate adherence.[47, 48] In this study, it is plausible that more frequent visits and interactions with study staff in the DCP arm could have influenced self-reported outcomes. Therefore, given the limitations of self-report, we sought to validate our primary outcome of self-reported product use by measuring tenofovir concentrations in small hair samples (an objective metric of pill ingestion). We found that tenofovir concentrations in hair were highly correlated with self-report in both the DCP and SOC arms.

To our knowledge, our study is among the first to offer a choice of HIV testing methods (rapid blood-based testing or HIV self-testing) for PrEP continuation. We found that half of participants in the DCP arm chose HIVST by week 48, suggesting that HIVST could be highly acceptable as part of PrEP continuation and HIV prevention services. Access to HIVST is expanding in many settings in sub-Saharan Africa to increase reach and identify new HIV diagnoses.[49] Incorporating HIVST into service delivery for HIV prevention,[5053] including for PrEP continuation,[54] has the potential to lower barriers and increase efficiency by reducing the number of visits to a health facility. Moreover, oral HIVST could increase choices for persons who may not wish to have blood-based testing with a provider. The Jipime-JiPrEP trial, conducted in a peri-urban setting in Kenya, recently demonstrated that among participants randomized to 6-month PrEP dispensing with quarterly oral fluid or blood-based HIVST, outcomes were non-inferior to standard-of-care 3-monthly facility visits with 3-month dispensing.[55, 56] The use of HIVST in PrEP services was also deployed early in the COVID-19 pandemic to facilitate testing while reducing visits to facilities.[57, 58] The findings of our study support recent 2022 WHO technical guidance that HIVST can be incorporated into oral PrEP continuation.[11]

Our study sought to engage sub-populations that may not be reached by current HIV prevention approaches, including men, who account for one-third of new HIV diagnoses in rural Kenya and Uganda. [59] In this study, one-third of enrolled participants were men, suggesting that this population can be reached for offer of biomedical prevention at OPD facilities. Moreover, prevention coverage was similar when stratified by sex, suggesting that both men and women were reached by the DCP intervention.

The DCP intervention serves as one potential model for offering choices as new HIV prevention products, such as CAB-LA, become available. It is notable that although biomedical prevention coverage was higher in the DCP study arm, a proportion of follow-up time during which participants reported HIV exposure risk was not covered by PrEP or PEP. In our preliminary qualitative work, consistent with other studies, many participants have reported interest in additional HIV prevention options that do not require daily pill-taking or are more discreet. We are currently studying the effect of the DCP intervention on prevention coverage in an extension of the current trial, offering a choice of oral PrEP, PEP, or CAB-LA. Adding CAB-LA and other long-acting products, such as the dapivirine vaginal ring, has the potential to further respond to client preferences and improve HIV prevention coverage.

Strengths of this study include that it is among first studies to offer choices in biomedical prevention products, and the option to switch products over time, rather than assessing theoretical choices (such as through discrete choice experiments). Moreover, it is among the first to offer a choice of HIVST for PrEP continuation and among the first to offer PEP with a pill-in-pocket option outside of urban settings in Canada and England. The products offered (PrEP and PEP) were obtained via routine Ministry of Health supplies.

Limitations of this study include the 48-week follow-up time, although the effect of the DCP intervention over time will be assessed in an extension phase of this study also offering CAB-LA. In addition, as noted above, product use was primarily measured via self-report, although tenofovir concentrations in hair were highly correlated with self-report in both arms. Questions also remain regarding which aspects of this multi-component intervention were most impactful and desirable, which we are exploring through qualitative investigations with both participants and health care providers. Two other pilot randomized trials of the DCP intervention have demonstrated effectiveness in antenatal and community settings.[26, 27] Further studies are also needed on the generalizability of this approach to other health care settings and in urban areas. Although this pilot trial was not powered to assess the impact of the DCP intervention on HIV incidence, we plan to test the effect of the DCP model, together with optimized HIV treatment interventions, delivered at scale in a large community cluster randomized trial starting in 2023.

In conclusion, we found that a dynamic choice HIV prevention service delivery model, incorporating client choices for HIV prevention product, HIV testing modality, and service location, as well as an emphasis on patient-centered care, resulted in two-fold greater time covered by PrEP or PEP compared to standard-of-care. As scale-up of PrEP and PEP accelerates and additional prevention options, such as CAB-LA, become available, the DCP model is a promising approach to expand HIV prevention coverage and, importantly, respond to client preferences. Continued innovation around flexible, patient-centered, differentiated service delivery for HIV prevention, holds promise to further reduce HIV incidence globally.

Supplementary Material

CONSORT checklist
SDC 1 - Figure
SDC 2 - Table
SDC 3 - Table
SDC 4 - Table

Acknowledgements

Research reported in this manuscript was supported by the U.S. National Institute of Allergy and Infectious Diseases (NIAID), the National Heart, Lung, and Blood Institute (NHLBI), and the National Institute of Mental Health (NIMH) and co-funded under award number U01AI150510. This research was also supported by the National Institutes of Health under award numbers R01AI098472 (for measurement of tenofovir hair concentrations), K23MH114760, and R01AI167753. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The SEARCH study gratefully acknowledges the Ministry of Health of Kenya, Ministry of Health of Uganda, our research teams and administrative teams in Kenya, and Uganda, and the United States, collaborators and advisory boards, and especially all communities and participants involved. We thank Hana Rivera Garza, Anindita Chattopadhyay, and Karen Kuncze of the Hair Analytical Laboratory at the University of California, San Francisco for their work on analyzing tenofovir concentrations in hair samples provided by participants. We thank Joshua Schwab at the University of California, Berkeley for assistance with statistical analysis of the tenofovir concentration data.

Footnotes

Conflicts of interest

CAK has received grant support paid to the University of California, San Francisco from Gilead Sciences. All other authors declare no competing interests.

Contributor Information

Catherine A. KOSS, University of California San Francisco, CA, United States

James AYIEKO, Kenya Medical Research Institute, Kisumu, Kenya.

Jane KABAMI, Infectious Diseases Research Collaboration, Kampala, Uganda.

Laura B. BALZER, University of California Berkeley, Berkeley, CA, United States

Elijah KAKANDE, Infectious Diseases Research Collaboration, Kampala, Uganda.

Helen SUNDAY, Infectious Diseases Research Collaboration, Kampala, Uganda.

Marilyn NYABUTI, Kenya Medical Research Institute, Kisumu, Kenya.

Erick WAFULA, Global Programs for Research and Training, Kisumu, Kenya.

Starley SHADE, University of California San Francisco, CA, United States.

Edith BIIRA, Infectious Diseases Research Collaboration, Kampala, Uganda.

Fred OPEL, Kenya Medical Research Institute, Kisumu, Kenya.

Hellen N. ATUHAIRE, Infectious Diseases Research Collaboration, Kampala, Uganda

Hideaki OKOCHI, University of California San Francisco, CA, United States.

Sabina OGACHI, Kenya Medical Research Institute, Kisumu, Kenya.

Monica GANDHI, University of California San Francisco, CA, United States.

Melanie C. BACON, National Institute of Allergy and Infectious Diseases, Bethesda, MD, United States

Elizabeth A. BUKUSI, Kenya Medical Research Institute, Kisumu, Kenya

Gabriel CHAMIE, University of California San Francisco, CA, United States.

Maya L. PETERSEN, University of California Berkeley, Berkeley, CA, United States

Moses R. KAMYA, Makerere University College of Health Sciences, Kampala, Uganda

Diane V. HAVLIR, University of California San Francisco, CA, United States

Data sharing statement

A complete de-identified patient dataset sufficient to reproduce the study findings will be made available approximately one year after completion of the ongoing trial (NCT04810650), following approval of a concept sheet summarizing the analyses to be performed. Further inquiries can be directed to the SEARCH Scientific Committee at douglas.black@ucsf.edu.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

CONSORT checklist
SDC 1 - Figure
SDC 2 - Table
SDC 3 - Table
SDC 4 - Table

Data Availability Statement

A complete de-identified patient dataset sufficient to reproduce the study findings will be made available approximately one year after completion of the ongoing trial (NCT04810650), following approval of a concept sheet summarizing the analyses to be performed. Further inquiries can be directed to the SEARCH Scientific Committee at douglas.black@ucsf.edu.

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