Charles M. Cleland,Marya Gwadz,Linda M. Collins,Leo Wilton,Noelle R. Leonard,Amanda S. Ritchie,Belkis Y. Martinez,Elizabeth Silverman,Dawa Sherpa,Caroline Dorsen
Effects of Behavioral Intervention Components for African American/Black and Latino Persons Living with HIV with Non-suppressed Viral Load Levels: Results of an Optimization Trial
Multiphase optimization strategy (MOST),Optimization trial,Fractional factorial design,Racial/ethnic inequities,HIV viral suppression,HIV care continuum
There is an urgent need for efficient behavioral interventions to increase rates of HIV viral suppression for populations with serious barriers to engagement along the HIV care continuum. We carried out an optimization trial to test the effects of five behavioral intervention components designed to address barriers to HIV care continuum engagement for African American/Black and Latino persons living with HIV (PLWH) with non-suppressed HIV viral load levels: motivational interviewing sessions (MI), focused support groups (SG), peer mentorship (PM), pre-adherence skill building (SB), and navigation with two levels, short (NS) and long (NL). The primary outcome was HIV viral suppression (VS) and absolute viral load (VL) and health-related quality of life were secondary outcomes. Participants were 512 African American/Black and Latino PLWH poorly engaged in HIV care and with detectable HIV viral load levels in New York City, recruited mainly through peer referral. Overall, VS increased to 37%, or 45% in a sensitivity analysis. MI and SG seemed to have antagonistic effects on VS (z = − 1.90; p = 0.057); the probability of VS was highest when either MI or SG was assigned, but not both. MI (Mean Difference = 0.030; 95% CI 0.007–0.053; t(440) = 2.60; p = 0.010) and SB (Mean Difference = 0.030; 95% CI 0.007–0.053; t(439) = 2.54; p = 0.012) improved health-related quality of life. This is the first optimization trial in the field of HIV treatment. The study yields a number of insights into approaches to improve HIV viral suppression in PLWH with serious barriers to engagement along the HIV care continuum, including chronic poverty, and underscores challenges inherent in doing so. Supplementary Information The online version contains supplementary material available at 10.1007/s10461-023-04086-0.
Effects of Behavioral Intervention Components for African American/Black and Latino Persons Living with HIV with Non-suppressed Viral Load Levels: Results of an Optimization Trial

There is an urgent need for efficient behavioral interventions to increase rates of HIV viral suppression for populations with serious barriers to engagement along the HIV care continuum. We carried out an optimization trial to test the effects of five behavioral intervention components designed to address barriers to HIV care continuum engagement for African American/Black and Latino persons living with HIV (PLWH) with non-suppressed HIV viral load levels: motivational interviewing sessions (MI), focused support groups (SG), peer mentorship (PM), pre-adherence skill building (SB), and navigation with two levels, short (NS) and long (NL). The primary outcome was HIV viral suppression (VS) and absolute viral load (VL) and health-related quality of life were secondary outcomes. Participants were 512 African American/Black and Latino PLWH poorly engaged in HIV care and with detectable HIV viral load levels in New York City, recruited mainly through peer referral. Overall, VS increased to 37%, or 45% in a sensitivity analysis. MI and SG seemed to have antagonistic effects on VS (z = − 1.90; p = 0.057); the probability of VS was highest when either MI or SG was assigned, but not both. MI (Mean Difference = 0.030; 95% CI 0.007–0.053; t(440) = 2.60; p = 0.010) and SB (Mean Difference = 0.030; 95% CI 0.007–0.053; t(439) = 2.54; p = 0.012) improved health-related quality of life. This is the first optimization trial in the field of HIV treatment. The study yields a number of insights into approaches to improve HIV viral suppression in PLWH with serious barriers to engagement along the HIV care continuum, including chronic poverty, and underscores challenges inherent in doing so.

The online version contains supplementary material available at 10.1007/s10461-023-04086-0.

The present study addresses the need for efficient and effective behavioral interventions to increase rates of HIV viral suppression in populations with serious barriers to engagement along the HIV care continuum. Indeed, high rates of consistent involvement along the continuum are essential so that persons living with HIV (PLWH) maintain optimal immune system functioning, overall health, and a high quality of life [1, 2]. Yet, racial/ethnic inequities in engagement in HIV care, HIV medication uptake, medication adherence, and HIV viral suppression are serious and persistent in the United States [1]. Although rates are improving on average [3], a substantial proportion of PLWH, mainly those from African American/Black and Latino (AABL) racial/ethnic backgrounds and from the lower socioeconomic strata, are insufficiently or inconsistently engaged along the continuum [3]. For example, rates of sustained HIV viral suppression are lowest among African American/Black PLWH where an estimated 41% sustain viral suppression, compared to 50% among Latino and 56% among White PLWH [4]. AABL-PLWH commonly have periods of sustained HIV viral suppression, but also are vulnerable to disengagement from the HIV care continuum in times of adversity, in large part because chronic poverty leads to insufficient resources to buffer hardships [5, 6]. The United States public health system has set a goal of ending the HIV epidemic by 2030 [7, 8]. This will entail 95% of those living with HIV being diagnosed, 95% of those diagnosed receiving HIV medication, and 95% of people receiving medication reaching HIV viral suppression, called the “95–95–95 goals [9]. However, these 95–95–95 goals will not be achieved without reducing or eliminating racial/ethnic inequities in engagement along the HIV care continuum [10]. The seriousness and persistence of these inequities, along with growing awareness of the challenges health systems face implementing effective behavioral interventions, signal the need for economical and potent intervention approaches [11]. In the present study, we describe results from an optimization trial grounded in the multiphase optimization strategy (MOST) framework [12, 13]. The MOST framework has three phases: preparation (focused on identifying intervention components, developing a conceptual model, and creating the optimization objective to guide future decisions about whether or how to combine individual components), optimization (evaluating the effects of components in a fully powered trial and applying the optimization objective to create a new multi-component intervention), and evaluation (testing the new optimized intervention in a randomized controlled trial) [12, 13]. The present study describes a study in the optimization phase of the MOST framework. The MOST framework has some advantages over the classical approach to intervention development. Whereas a typical clinical trial is designed for direct comparison of the means of experimental conditions in order to examine the effects of an intervention as a whole, an optimization trial is an experiment designed to examine the effects of individual intervention components. Then, depending on results and the study’s optimization objective, a subset of the individual intervention components can be combined into a multi-component intervention, and poorly performing, inefficient, or counter-productive components can be set aside. The optimization trial described in the present study tested the effects of five behavioral intervention components, which were designed to be culturally and structurally salient by addressing the primary barriers to HIV care and HIV viral suppression for AABL-PLWH from the lower socioeconomic strata [12–14]. The intervention components are described in detail elsewhere [14, 15]. The aims of the present study were to examine the main effects and two-way interactions of the individual behavioral intervention components on the primary outcome, HIV viral suppression, and on two secondary outcomes: namely, HIV viral load levels and health-related quality of life. Clearly, HIV viral suppression is an important outcome for HIV care, treatment, and interventions. Further, reductions in HIV viral load levels can indicate a trajectory toward viral suppression [16] and PLWH with lower HIV viral load levels may experience fewer adverse health consequences and evidence reduced risk of forward HIV transmission to others than PLWH with higher levels [16]. Moreover, as the HIV care and treatment system evolves, and the longevity of PLWH increases, attention has turned to the importance of improving and supporting quality of life among PLWH, in both physical and mental health domains [17]. In fact, as progress is made toward the UNAIDS 95–95–95 targets in many contexts, health-related quality of life is now considered a fourth “95” goal [17]. The overarching goal of our optimization trial and the trial’s optimization objective is to identify the most cost-effective combination of intervention components, with the understanding that there may be more than one cost-effective combination. The present study seeks to understand the main and interaction effects of individual intervention components, to inform the future decision-making process to determine the next phase in this program of research, which may involve evaluating a cost-effective optimized intervention in a randomized controlled trial or, alternately, suggest the need for a return to a preparation or optimization phase of research. Thus, the present study is an important first step to understand the effects of the individual intervention components. This is the first application of the MOST framework to the field of HIV treatment.

The present study describes results from an optimization trial conducted in New York City, focused on AABL-PLWH from low socioeconomic status backgrounds who were poorly engaged along the HIV care continuum, specifically, those who did not engage in HIV care at recommended levels and who evidenced both poor adherence to HIV medication and detectable HIV viral load levels. The optimization trial used an efficient fractional factorial experimental design to test the effects of five intervention components and their two-way interactions. Participants also received a core health education session. HIV viral suppression was the trial’s primary outcome, assessed by laboratory report. Viral load levels and quality of life were secondary outcomes. The optimization trial was registered with ClinicalTrials.gov (NCT02801747). Participants gave signed informed consent for trial activities. The trial was approved by the Institutional Review Board at the New York University Grossman School of Medicine. The trial and specific intervention components are described in more detail elsewhere [13–15].

The inclusion criteria were: (1) age 18–65 years; (2) African American or Black race or Latino/a or Hispanic ethnicity; (3) HIV diagnosed for at least 6 months; (4) HIV antiretroviral therapy adherence less than 50% in the past 6 weeks (by self-report) and detectable HIV viral load based on a laboratory report (> 20 copies/mL); (5) sub-optimal engagement in HIV care assessed by self-report (operationalized as less than one visit in every 4 month period in the past year or > two missed visits without prior cancellation in the past year; or for those diagnosed more than 6 but less than 12 months ago, no appointments attended or > one missed visit without prior cancellation); (6) resides in the New York City metropolitan area; (7) able to conduct research activities in English or Spanish; (8) willing to provide a blood specimen at screening to assess HIV viral load; and (9) willing to receive a core intervention session (health education) and be randomly assigned to 1–5 intervention components. Participants were found eligible if they met all the inclusion criteria. Although we enrolled those with detectable HIV viral load defined as > 20 copies/mL, in analyses we defined the primary outcome as suppressed viral load (< 200 copies/mL) to better align with the larger literature [4]. A total of 18% of the sample had HIV viral load levels between 20 and 200 copies/mL at enrollment.

The behavioral intervention components were designed to address cultural or structural barriers to engagement along the HIV care continuum at individual/attitudinal, social, or structural levels of influence. They were grounded in a conceptual model that combines critical race theory, harm reduction, and self-determination theory, called the integrated conceptual model [14, 15]. Implementation of the components was guided by manuals and was flexible and individualized. Each component aligned with the motivational interviewing approach (e.g., supporting autonomy, rolling with resistance), and used other evidence-based behavior change techniques as appropriate. Each component was designed to be independent of others, in that no component depended on the presence of another to be effective. Components were also designed to harmonize with each other, in that no component was expected to have a counter-productive effect when another was provided. The components are described briefly in the sections that follow [13–15, 18]. All participants received a core intervention session at the beginning of their intervention period, comprised of a single meeting with participants individually (< 60 min) with two main goals. The first goal was to provide or reinforce basic health education on HIV management that comprises the standard of care (e.g., regarding the expected frequency of HIV care visits and HIV medication adherence patterns to achieve vital suppression). The second was to introduce the participant to the study ethos grounded in the integrated conceptual model to thereby begin to foster a constructive relationship between the study and the participant and to begin to build trust, to support engagement in other intervention components. Because all participants receive the core intervention, its effects are not assessed in the analyses. The intervention components included motivational interviewing sessions (MI; four sessions, 60 min each). This component used specific motivational interviewing techniques (e.g., developing discrepancy, analyzing readiness for increasing HIV medication adherence) to address salient individual/attitudinal-level barriers to engagement along the HIV care continuum in order to foster durable intrinsic motivation for behavior change. This included addressing culturally salient barriers to engagement, such as health beliefs and emotions (e.g., outcome expectancies, self-efficacy, medical distrust, concerns about and fears of HIV medication, and counter-narratives about the origins and treatment of HIV). This component’s primary mediators/mechanisms of action were health beliefs and emotions. Thus, while all components were consistent with a motivational interviewing approach in tone and spirit, as noted above, only this component, MI, used specific motivational interviewing techniques. Another component provided focused support groups (SG; six groups, 90 min. each), designed to provide social support and reduce stigma regarding care and HIV medication use, with a focus on culturally salient factors that impede engagement such as medical distrust and fear, and structurally salient factors (e.g., poverty, poor-quality housing, pharmacies illegally buying medications from PLWH). Its primary mediators/mechanisms of action were social support and stigma. A peer mentorship component (PM) was structured as an introductory meeting and frequent contact by text and phone over 4 months. PM was facilitated by a “successful” peer mentor (i.e., a PLWH demographically similar to study participants who had consistently engaged in care and was taking HIV medication with high levels of adherence) and sought to provide peer modeling of and shape peer norms regarding HIV management (primary goals), and provide social support and combat stigma (secondary goals). Its primary mediators/mechanisms of action were peer modeling and peer norms, and secondarily, social support and stigma. Pre-adherence skill building (SB) was comprised of an introductory session, weekly meetings, and a closing session over 4 months. The goals of SB were to help participants learn, practice, and build behavioral skills to manage HIV medication adherence such as habits, while attending to cultural and structural factors that can impede adherence (e.g., lack of private living space). Its primary mediators/mechanisms of action were behavioral skills. The navigation component was designed to identify and ameliorate structural barriers to HIV care and HIV medication, as well as to ancillary services for substance use, mental health, housing, and other needs. Its primary mediators/mechanisms of action were reducing structural barriers to health. Component acronyms are found in Table 1. In past research we found that the overall optimization trial and the specific intervention components had high levels of acceptability and feasibility [14, 15]. The optimization trial was grounded in a conceptual pathway model made up of intervention components, their specific mediators, and outcomes. This model is included as a supplementary figure and is described in more detail elsewhere [13].

The recruitment approach for the optimization trial comprised a hybrid sampling strategy with peer-to-peer recruitment; direct recruitment by study staff members in HIV service, HIV housing, and other community-based organizations; and advertisements in a local free newspaper. Peer recruitment was tracked with a coupon system that linked the recruiter to the recruit, and recruiters received modest compensation for referring peers ($15/recruit). Most enrolled participants were recruited by peers (75%); 9% were recruited through newspaper ads, and 16% through other means.

Participants were screened for eligibility after providing informed consent. Screening included assessment of HIV viral load levels via laboratory report obtained from a commercial laboratory (participants were typically escorted to the lab by study staff to facilitate timely reports). Those found eligible provided signed informed consent and completed a structured baseline assessment battery lasting 60–90 min. The baseline was conducted in the Research Electronic Data Capture (REDCap) platform, a secure cloud-based resource for data capture designed for clinical research [19, 20]. Participants were compensated $15 for the screening interview, $15 for providing the blood specimen for HIV viral load testing, and $25 for the baseline assessment, along with funds for local round-trip public transportation. In-person study activities took place in confidential offices at a project field site in lower Manhattan in New York City.

The performance of each component was tested in a 25–1 fractional factorial experiment, with each factor corresponding to a component. Each factor had two “levels.” Most factors (MI, SB, PM, SG) had levels off (participant did not receive) and on (participant did receive). The factor corresponding to navigation had two levels: short navigation (NS; 3 months) and long navigation (NL: 6 months). With five components, a full factorial design would have 32 experimental conditions, a design that would be challenging to implement with fidelity. The advantage of a fractional factorial design is the use of a subset (fraction) of the full set of possible conditions to estimate the effects [12]. This improves study feasibility. The number of experimental conditions in the present study was reduced by half; that is, to 16 conditions. These 16 conditions were selected using PROC FACTEX of SAS [13, 21] with the requirement that all main effects and two-way interactions could be estimated. The tradeoff associated with any fractional factorial design is aliasing (combining) of effects. In the design used in the present study, each main effect was aliased with a four-way interaction, and each two-way interaction was aliased with a three-way interaction. This means that to interpret the main effects and two-way interactions, it is necessary to assume that the three-way and higher-order interactions are negligible in size. According to our conceptual pathway model, which describes the putative meditating variables associated with each component, moderating variables, and the pathway to behavior change, this is a reasonable assumption, since we do not hypothesize higher-order interactions [13]. This fractional factorial design required exactly the same number of participants to maintain the desired level of power as a corresponding 25 design but was more efficient because it was comprised of 16 rather than 32 experimental conditions. After completing the baseline assessment, participants were randomly assigned to one of the 16 experimental conditions, using a randomization table created by the study’s biostatistician and integrated into REDCap. Each experimental condition comprised the core health education session plus a different combination of the five behavioral intervention components. Randomization was stratified by age with separate allocation tables for participants who were younger than 36 years of age at enrollment and participants who were 36 years old or older. Within each stratum, a table was generated with each of the sixteen conditions repeated one hundred times. Then, these 1600 allocations were randomly shuffled within each stratum. Thus, assignment was simple random assignment to the 16 conditions within each stratum. Supplementary Table 1 shows the 16 conditions included in the fractional factorial design and the number of participants assigned to each.

The period during which participants engaged in intervention activities ranged from 4 to 8 months, depending on random assignment. Components were administered by trained and supervised clinical interventionists, most with a master’s degree in social work, psychology, or public health. Regarding the timing of intervention activities, the core intervention was provided first followed by NS or NL, to begin to address structural barriers to engagement along the HIV care continuum (all participants received either 3 or 6 months of navigation). MI was scheduled next for those randomly assigned to an experimental condition that included it. PM and SG could be administered in the same period (although not on the same days), and SB was provided last for those assigned to receive it. Participants were compensated for intervention attendance ($25 per session/activity for most components as well as funds for local round-trip public transportation).

Participants received follow-up assessments at 4-, 8-, and 12-months post-baseline, comprised of a structured interview in REDCap and an HIV viral load test at a commercial laboratory (at 8-, and 12-months). Participants were compensated $15 for providing the blood specimen for HIV viral load testing and $25 for follow-up assessments, along with funds for local round-trip public transportation.

The first case of COVID-19 was diagnosed in New York City on March 1, 2020. On March 12, 2020, in-person activities with human subjects were suspended at our institution, although virtual activities could continue with IRB approval. At this point in the study, 241/512 (47%) had completed participation in the optimization trial, with the remainder still scheduled to attend intervention activities and/or follow-up assessments. At this point, we conducted intervention components and follow-up assessments in a virtual format with the exception of SG, which was not feasible given most participants’ lack of internet, smartphone, and/or computer access [22]. We could not escort participants to a commercial laboratory since travel to commercial settings was restricted, and instead requested that participants provide a recent (within the past 2 months) laboratory report from their existing HIV care clinic, if possible, for which they would be compensated $30. If not, participants could present independently to a commercial laboratory and the research study would compensate the lab for the viral load test. Yet, because early in the pandemic HIV services and travel were disrupted due to the public health order to remain at home, it was commonly challenging for participants to carry out laboratory tests or obtain reports. The COVID-19 pandemic thus resulted in some delays in engagement in study activities, and modestly reduced retention and the proportion that provided HIV viral load results.

We assessed age, sex assigned at birth, gender identity, sexual minority status (i.e., identifies as gay, lesbian, bisexual, queer, or other non-heterosexual identity), race/ethnicity, housing status, and indications of extreme poverty (how often unable to pay for necessities in the past year and food insecurity) with structured instruments developed for populations in high-risk contexts [23]. We used a version of the HIV Cost and Services Utilization Study (HCSUS) [24] instrument to assess years since first HIV diagnosis, whether ever took HIV medication, and if yes, whether took HIV medication at all in the 6 weeks before enrollment. HIV viral load was assessed with a laboratory report from a commercial laboratory (Quest, LabCorp) and suppressed viral load was coded as < 200 copies/mL (i.e., log10 viral load < 2.3). We used the SF-12 items [25] to create the SF-6D preference-based measure of health described by Brazier and Roberts (2004) [26]. SF-6D scores can range from 0.35 to 1.0 with higher values indicating better health-related quality of life. The recent median SF-6D score for the adult United States population is 0.8 [27].

A generalized linear model was used to analyze the primary outcome of viral suppression. Specifically, the model for viral suppression was a logistic regression analysis (i.e., binomial family generalized linear model with logit link function). In the primary outcome model, the only covariate was log10 viral load at enrollment (mean-centered). In the primary analysis, participants with missing viral load results at both the 8- and 12-month follow-ups (n = 82; 16%) were assumed to have unsuppressed viral load. Intervention component variables were effects coded, and both main effects and two-way interactions among the five components were included. This means coefficients for the main effects are estimating one half of the difference in logits (log odds) between the two levels of an intervention component and doubling the coefficient and exponentiating estimates how assignment to the more intensive component level changes the odds of follow-up viral suppression. In this and all other models, we took an intention to treat approach with all participants analyzed as assigned. The model for log10 viral load, a secondary outcome, was a linear regression analysis (i.e., gaussian family generalized linear model with identity link function). The model included covariates for log10 viral load at enrollment (mean-centered) and days between the enrollment and follow-up viral load result (mean-centered). If the final, 12-month viral load result was not missing, it was used in analysis. If the 12-month viral load result was missing but the 8-month viral load was not missing, the 8-month viral load was used in analysis. If both the 8- and 12-month viral load results were missing, follow-up log10 viral load was imputed 40 times using a fully conditional specification that matched the substantive analysis model [28]. Rubin’s rules were used to pool estimates from the 40 imputed datasets. As in the model for the primary outcome of suppression, intervention component variables were effects coded and both the main effects and all two-way interaction effects among the five components were included. This means coefficients for the main effects are estimating one half of the adjusted mean difference between the two levels of an intervention component and doubling a main effect coefficient estimates the adjusted mean difference between the less and more intensive levels of that component. A linear mixed-effects regression model was used to analyze the secondary outcome of health-related quality of life (SF-6D). Participants with at least one assessment of health-related quality of life (n = 468; 91%) at the 4-, 8-, or 12-month were included in this analysis. Baseline SF-6D (mean-centered) was the only covariate included. The only random effect was the intercept, which estimated between-person variability in health-related quality of life not accounted for by assignment to intervention components. As in the models for viral suppression and log10 viral load, intervention component variables were effects coded and both the main effects and all two-way interaction effects among the five components were included. This means coefficients for the main effects are estimating one half of the adjusted mean difference between the two levels of an intervention component and doubling a main effect coefficient estimates the adjusted mean difference between the less and more intensive levels of that component. When the p-value for a two-way interaction effect was low (p < 0.12), we estimated the conditional main effects of components involved in those interactions. For the primary and secondary viral load outcomes, sensitivity analysis for treatment of missing data included inverse probability of follow-up weighting [29]. Non-missing follow-up viral load was predicted by the main effects and two-way interactions of all five intervention components as well as log10 viral load at enrollment in a logistic regression analysis. The inverse of the predicted probability was used as a weight in analysis of participants with non-missing follow-up to bring the estimates into alignment with the full baseline sample. The R survey package [30] was used for these sensitivity analyses. All analysis was conducted in the R statistical computing environment [31]. Conditional main effects were estimated using the emmeans R package [32].

Figure 1 shows the flow from the screening of potential participants to follow-up of enrolled participants. The screening of the first potential participant took place on March 20, 2017. Enrollment of participants ended on November 12, 2019. The last 12-month interview took place on March 3, 2021. For the 430 participants with a follow-up viral load result (84.0%), on average, there were 406 days (SD = 94) between the viral load result at enrollment and the follow-up viral load result. The switch to virtual activities resulted in some delays so not all participants completed the study within the desired 12-month period.

Table 2 shows demographic and other characteristics of enrolled participants at baseline. Participants were 47.0 years old, on average (SD = 10.7 years). Most were African American or Black and Non-Hispanic (68.6%) and assigned male sex at birth (70.1%). More than half (59.2%) were sexual or gender minorities. Homelessness was common and only 10.5% had never been homeless in their lifetimes. Indications of extreme poverty at least monthly were prevalent (57.1%). On average, participants had been diagnosed with HIV 18.2 years prior (SD = 8.61 years). Almost all (94.7%) had attended at least one HIV care appointment in the past year (although were not satisfactorily engaged in HIV care based on eligibility criteria). The average HIV viral load level at enrollment was 3.77 log10 (SD = 1.20), and a small percentage (18.0%) had viral load levels > 20 copies/mL but < 200 copies/mL at enrollment as described in the Methods section. Nearly all had taken HIV medication previously (98.4%) and many had taken at least some HIV medication in the 6-week period before enrollment (44.4%). Health utility (SF-6D) was below typical average scores. Other baseline characteristics of the sample have been described previously [22, 33, 34].

Overall, rates of HIV viral suppression increased during the optimization trial from 18 to 37% (main analysis assuming all participants lost to follow-up are not suppressed) or as high as 45% (sensitivity analysis using weighting to address missing follow-up data; data not shown on tables). Table 3 shows the main and two-way interaction effects of intervention components on the viral suppression outcome. The intercept term is the expected probability of viral suppression at follow-up (in logits) averaging over all study conditions and viral load at enrollment. The logit value of -0.525 indicates a probability of 0.37 (95% CI 0.33–0.42). Higher viral load at enrollment was associated with decreased odds of follow-up viral suppression (z = − 6.20; OR 0.60; 95% CI 0.51–0.70; p < 0.001). We did not find evidence of intervention component main effects. However, there appeared to be an interaction effect between the MI and SG components (z = − 1.90; p = 0.057).

Because two-way interaction effect between the MI component and the SG component raised doubts about the null hypothesis of no interaction, we estimated the conditional main effects of each of those components when the other component was assigned vs. not assigned. When SG was not assigned, the MI component increased the odds of follow-up viral suppression (z = 1.75; OR 1.61; 95% CI 0.95–2.74; p = 0.080). When SG was assigned, the MI component had no effect on the odds of follow-up viral suppression (z = − 0.96; OR 0.76; 95% CI 0.44–1.33; p = 0.338). Figure 2 shows the estimated marginal means of viral load suppression by assignment to the MI and SG components. Each component had a stronger effect on viral suppression when the other component was not assigned. The probability of viral suppression was highest when either MI or SG was assigned, but not both.

Table 4 shows the main and two-way interaction effects of intervention components on the log10 viral load outcome. The intercept term is the expected log10 viral load at follow-up averaging over all study conditions, viral load at enrollment, and days between enrollment and follow-up viral load results. We did not find evidence of intervention component main effects. However, the SG*NL (t (1024.7) = 1.57; p = 0.116) and PM*SB (t (1493.1) = − 1.62; p = 0.105) interaction effects raised doubts about the null hypothesis of no interaction and led us to estimate conditional main effects. When NS was assigned, SG decreased follow-up log10 viral load (mean difference = − 0.42; 95% CI − 0.80 to − 0.03; t (901.1) = − 2.14; p = 0.033). When NL was assigned, SG had no effect on follow-up log10 viral load (Mean Difference = 0.02; 95% CI − 0.37 to 0.40; t (1119.5) = 0.08; p = 0.933). Figure 3 illustrates the interaction between the SG and NL components. The figure suggests both SG and NL may be effective in reducing log10 viral load, but neither yields additional benefit when the other component is already present. The SB component seemed to have a more favorable impact on follow-up viral load when PM was assigned (mean difference = − 0.25; 95% CI − 0.64 to 0.15, t (1433.3) = -1.24; p = 0.216) than when PM was not assigned (mean difference = 0.19; 95% CI − 0.16 to 0.54, t (1756.9) = 1.05; p = 0.293). Figure 4 illustrates this interaction between the PM and SB components. Consistent with the conditional main effects, each component required the other to be present for a positive impact on viral load. As the figure shows, the best viral load outcomes were produced by both components set to Yes, closely followed by both components set to No.

Table 5 shows the main and two-way interaction effects of intervention components on the SF-6D health utility outcome. The intercept term is the expected health utility during follow-up averaging over all study conditions, and health utility at baseline. MI, SB, and PM components increased health utility. Participants assigned to MI had higher health utility than those not assigned (Mean Difference = 0.030; 95% CI 0.007–0.053; t (440) = 2.60; p = 0.010). Participants assigned to SB also had higher health utility than those not assigned (Mean Difference = 0.030; 95% CI 0.007–0.053; t (439) = 2.54; p = 0.012). Participants assigned to PM reported higher health utility than those not assigned (Mean Difference = 0.022; 95% CI − 0.002 to 0.044; t (439) = 1.84; p = 0.066). The interaction between MI and SB (t (443) = -1.60; p = 0.111) suggests both yield less additional benefit when the other component is already present. The SB component had a more favorable impact on health-related quality of life when MI was not assigned (Mean Difference = 0.05; 95% CI 0.02–0.08, t (440) = 3.02; p = 0.003) than when MI was assigned (Mean Difference = 0.01; 95% CI − 0.02 to 0.04, t (440) = 0.64; p = 0.523). Likewise, the MI component had a more favorable effect on health-related quality of life when SB was not assigned (Mean Difference = 0.05; 95% CI 0.02–0.08, t (435) = 3.07; p = 0.002) than when SB was assigned (Mean Difference = 0.02; 95% CI − 0.02 to 0.05, t (445) = 0.68; p = 0.494).

Supplementary Table 2 shows estimates of intervention component effects on HIV viral load suppression using inverse probability of follow-up weights. Apart from the intercept, which was expected to yield a higher estimate of the prevalence of viral load suppression at follow-up, estimates were similar to the primary analysis approach where participants missing follow-up viral load were assumed to be unsuppressed. Supplementary Table 3 shows estimates of intervention component effects on follow-up log10 viral load using inverse probability of follow-up weights. Estimates were similar to the primary analysis approach, which used multiple imputation to address missing follow-up viral load. To explore potential effects of the COVID-19 pandemic on our results, we compared the viral suppression outcomes of participants with final follow-up due before (n = 220; 43%) vs. after (n = 292; 57%) March 12, 2020. Controlling for assigned intervention components and viral load at baseline, the difference in the odds of viral suppression was modest when adding the indicator of when final follow-up was due (before vs. during the COVID period). The odds ratio in both the main analysis model (z = − 0.72; OR 0.92; p = 0.47) and in the sensitivity analysis with weights (t (412) = − 0.20; OR 0.97; p = 0.84) were close to no effect (OR 1.0). We also explored potential interaction effects between intervention components and COVID period. In these versions of the main analysis model for viral suppression and the sensitivity analysis with weights, no interaction effect was statistically significant (all p > 0.10).

Interventions to engage AABL-PLWH in HIV care and initiate or re-initiate HIV medication to achieve HIV viral suppression are urgently needed to maximize health, quality of life, and longevity, and to end the HIV epidemic [35]. The present study seeks to advance behavioral intervention approaches for AABL-PLWH from low socioeconomic status backgrounds who are poorly engaged along the HIV care continuum. This sub-population of AABL-PLWH experiences serious barriers to such engagement and these impediments also create challenges for research studies and clinical settings that seek to locate them and involve them in services or research [36, 37]. Many AABL-PLWH are long-term HIV survivors, and past research suggests periods of stopping HIV medication are common in this group, including in response to adversity and disruptions in important relationships or housing, all of which are common in the context of chronic poverty [5]. Thus, intervention approaches are needed for AABL-PLWH with low rates of participation along the HIV care continuum to support HIV management decisions and behavior, which in turn will maximize periods of sustained HIV viral suppression. The present study presents results from an optimization trial for this serious public health problem. As noted above, overall, rates of HIV viral suppression increased during the optimization trial from 18 to 37% (main analysis assuming all participants lost to follow-up are not suppressed) or as high as 45% (sensitivity analysis using weighting to address missing follow-up data). We did not find unconditional improvements in viral suppression associated with the individual intervention components. Instead, our results suggest the possibility of interactions between components. First, we found a negative interaction between MI and SG. When SG was not assigned, MI increased the odds of HIV viral suppression to above 50%. Thus, SG and MI seem to have antagonistic effects. This was unexpected because, as noted above, each component was designed so that it could be implemented independently, and no component was designed to be dependent on the presence of another to be effective. The components were also designed to harmonize with each other, so no component was expected to have an antagonistic effect when another was provided. We interpret this antagonistic interaction between MI and SG in the section that follows. However, we wish to note that although estimation of conditional main effects is a helpful strategy for understanding interactions, all else being equal, there is not as much statistical power for detection of such effects as there is for detection of main effects and interactions because the sample size is effectively cut in half. The acceptability, feasibility, and utility of support group and group-based interventions are well established for a range of HIV outcomes [38, 39]. Groups can be a general intervention delivery modality, so it is necessary to identify the specific content and goals of a particular support group intervention. In the present study, the SG component was designed to complement groups provided in social service settings but not duplicate them and to focus on the most salient issues experienced by AABL-PLWH who are poorly engaged along the HIV care continuum found in past research, without assuming participants wished to or were able to reinitiate HIV medication or increase adherence patterns at that time. As described in more detail elsewhere [15], topics for discussion in SG were selected by participants, guided by a menu of options that included barriers to and decisions regarding HIV care/medication, reasons for not taking HIV medication, distrust and fear of and counter-narratives about HIV medication, race/ethnicity and social class and HIV medication, substance use and HIV medication, coping with HIV medication, managing feeling pressured to take HIV medication in HIV care, mental health, stigma, managing communication with health care providers, and adherence to HIV medication. The menu of topics was intended to signal to participants that SG allowed for free and open discussion of aspects of HIV management that commonly affect AABL-PLWH but that are typically challenging to discuss in most health care and social service settings. MI used motivational interviewing techniques to foster motivation and readiness for behavior change by addressing barriers to HIV care continuum engagement, mainly health beliefs relevant to HIV care continuum engagement and emotions (e.g., outcome expectancies, self-efficacy, medical distrust, concerns/fears of HIV medication, counter-narratives). MI focused on uncovering and discussing culturally specific barriers to HIV care continuum engagement, and incorporating participants’ own views on how to best manage their HIV care and medication [15]. In recent research we found that acceptability and feasibility were high for both SG and MI [15]. Theoretically, one main difference between SG and MI is that, consistent with the motivational interviewing approach, MI was designed to guide participants toward articulating HIV-related goals, formulating levels of readiness for HIV medication adherence, committing to goals, and making plans, as appropriate. Consistent with group intervention theory and practice, SG was intended to provide social support to participants and reduce stigma by fostering a productive group process through which group members could explore health-related challenges and solutions. SG was not designed to guide participants. One interpretation of results is that, since participants had detectable HIV viral load at enrollment and many remained non-suppressed during the study, the group process and group interactions did not challenge or change group norms about the value of HIV viral suppression, but perhaps reinforced group norms unsupportive of HIV-related behavior change. In contrast, when SG was absent, the guiding approach taken in MI may have triggered psychosocial processes leading to HIV viral suppression for some. Yet when SG was present, no effects of MI were found. We also examined the effects of components on log10 HIV viral load at follow-up. Reductions in HIV viral load can be an indication that PLWH are on a trajectory toward HIV viral suppression [16], and overall, although viral suppression is certainly the ideal outcome, those with lower HIV viral load levels may experience fewer adverse consequences of HIV on health and wellbeing, and reduced chances of forward HIV transmission compared to those with higher levels [16]. Results suggested a potential interaction between SG and NL. When SG was not assigned, NL (compared with NS) decreased follow-up log10 viral load. When the NS level was assigned, SG decreased follow-up log10 viral load. Thus, means are similar when: (1) both NL and SG are assigned; (2) NL is assigned, but SG is not; and (3) NS is assigned and SG is assigned. The combination of NS and no SG stands out as the condition with the least favorable outcome. When NL is provided, SG does not seem to give an additional benefit. When SG is assigned, NL does not seem to give an additional benefit relative to NS. As described in more detail elsewhere, the goals of the navigation component were to assist participants in identifying and overcoming structural barriers to effective HIV care and ancillary services [14, 15]. Individual, menu-based, highly focused sessions were held approximately weekly, if needed, beginning with an in-person 30–60-min meeting during which participants’ barriers to HIV care/medication (including substance use and mental health issues) were reviewed, and Change Plan/Action Plans were created. Weekly (at minimum) check-ins via telephone, text message, email, and/or in-person meetings facilitated work on, as needed, menu items that included screening and “Fast Track” referrals for housing, substance use, mental health, and other concerns; joint problem-solving barriers to appointments; and providing accompaniment to health care appointments as needed. We first speculated these results may have been due to greater attendance at SG when the NS was assigned compared to NL. However, the number of SG sessions attended was similar regardless of navigation duration (data not shown). It is possible that since navigation was individualized and provided as needed, the content of SG may have “crowded out” greater use of what NL had to offer. On the other hand, the NS level of navigation may have allowed for or encouraged participants to engage in other components including SG. These findings highlight challenges inherent in participants’ ability to engage in multiple intervention components with repeated contacts simultaneously. It also points to the importance of empirical studies of the ideal timing, sequence, and duration of intervention components [40]. We also found a potential interaction between PM and SB. Each component required the other to be present for a positive effect on HIV viral load. Thus, the best outcomes on viral load were produced when both components were provided, closely followed by when both components were not provided. The interaction may have arisen due to an unexpected dependency between the content of PM and SB. PM was facilitated by a “successful” peer mentor living with HIV and provided peer modeling of HIV management to thereby shape peer norms (primary goals), and also to provide social support and combat stigma (secondary goals). SB was designed to help participants build concrete behavioral skills to manage HIV medication adherence such as daily habits [14, 15]. Although the components are not designed to be dependent on one another, one possibility is that the PM component primed participants to benefit from the content of SB [41]. In future research, we will explore the effects of components and their interactions on the variables hypothesized to mediate effects, which may shed light on this interaction. As the population of PLWH grows, including those considered long-term survivors of HIV, the importance of quality of life has gained prominence and can even be considered a fourth “95” goal [17]. The MI, SB, and PM components all seemed to increase quality of life, also called health utility. The findings regarding the effectiveness of MI and PM are consistent with numerous past studies that have shown the positive effects of these types of interventions on a range of health outcomes [42–44]. There is a smaller literature on the positive effects of SB-type interventions on health utility [45, 46]. Interestingly, we found no evidence that navigation and SG improve health utility. As noted above, in future studies we will examine the effects of components on mediating variables, which may allow us to better understand each component’s specific mechanism of action. Findings have potential to inform future intervention development for AABL-PLWH and highlight some of the benefits of the MOST framework. First, findings suggest intervention components can interact with each other in a number of ways: they may have antagonistic effects, diminishing returns in the presence of other components, or be dependent on each other for effects. One advantage to the factorial design, including the fractional factorial design, is the ability to examine the effects of individual intervention components and their interactions. Results also suggest that some components, such as longer versus shorter components, can crowd out the effect of others, or interfere with the effectiveness of that component even when rates of attendance are not reduced. For all three outcomes, there were lessons learned about the SG component: it tended to reduce the effects of MI on HIV viral suppression, it harmonized with NS but had no added benefit along with NL, and it had no effect on health utility. Yet, as we found in past research, SG was highly acceptable among participants and rates of attendance were high [14, 15]. Taken together, findings also underscore the challenges inherent in improving HIV viral suppression and reducing HIV viral load levels in a population of AABL-PLWH who have lived with HIV for 18 years, on average, and who suffer a wide range of barriers to engagement along the HIV care continuum, including chronic poverty.

As noted above, in future research, we will examine the factors that mediate intervention effects, to better understand the components’ mechanisms of action. These results may provide support for the conceptual pathway model that underlies the study, or indicate that the model needs to be refined. The study’s optimization objective is to identify the most cost-effective combination of intervention components. Results from those modeling analyses will be combined with the present study, the analysis of mediators, and other analyses in a decision-making process to determine the next phase in this program of research. Results may be sufficiently strong to warrant testing a cost-effective combination of components in a randomized controlled trial. However, the present study highlights how challenging it is to develop interventions to increase HIV care continuum engagement among AABL-PLWH and the unanticipated ways that components may interact. Indeed, this is one benefit of the MOST framework in comparison to the classical approach to intervention development, which does not enable an examination of interactions between components. Thus, it is possible results will lead to a return to the preparation or optimization phase of the MOST framework, taking lessons learned from the present study to refine the approach to increasing HIV care continuum engagement for this large and growing population of PLWH.

The study has several limitations. First, it, and the study participants, were affected by the COVID-19 pandemic. Indeed, New York City was a COVID-19 epicenter with a large outbreak that began early in the course of the nation’s epidemic [47]. Although the optimization trial shifted to a virtual format as quickly as possible and overall retention rates were acceptable in the trial, clearly COVID-19 had a cascade of effects on the lives of AABL-PLWH and the clinical settings that serve them, some of which have been documented and others that are unknown at this time [22, 48], Further, as noted above, there is not as much statistical power for detection of conditional main effects as there is for detection of main effects and interactions because the sample size is effectively cut in half. Thus, conditional main effects must be interpreted with caution. The peer-to-peer recruitment method could have introduced bias by recruiting substantial numbers of participants in less-connected social networks that may not represent the larger population well. Although we did not systematically explore network homophily (the tendency for recruiters to recruit people who share a personal characteristic, such as age), we did find substantial heterogeneity among participants’ background factors and sociodemographic characteristics. For example, roughly half the sample were sexual minorities. The distributions of sex at birth and younger (< 36) vs. older (36 +) are consistent with New York City Department of Health Surveillance on people living with HIV reported for 2018 [49]. And substantial variability in physical and mental health, based on the SF-12, was observed. While the heavy reliance on peer referral produces what should be considered a convenience sample, we find no obvious divergence between the sample and what is known about AABL-PLWH in New York City, and believe it is reasonable to generalize results to that population.

AABL-PLWH from the lower socioeconomic strata experience a set of serious barriers to consistent engagement along the HIV care continuum and to an acceptable quality of life. This paper summarizes a set of results from the first optimization trial in the field of HIV treatment, focused on understanding the effects of individual intervention components on three important outcomes. Some components or combinations of components showed promise, particularly on quality-of-life outcomes.

Below is the link to the electronic supplementary material.Supplementary file1 (PDF 88 KB)Supplementary file2 (DOCX 29 KB)