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Effects of integrated interventions on transmission risk and care continuum outcomes in persons living with HIV: meta-analysis, 1996–2014

Crepaz, Nicole; Baack, Brittney N.; Higa, Darrel H.; Mullins, Mary M.

doi: 10.1097/QAD.0000000000000879
EDITORIAL REVIEW
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Background: Reducing HIV infection and improving outcomes along the continuum of HIV care are high priorities of the US National HIV/AIDS strategy. Interventions that target multiple problem behaviors simultaneously in an integrated approach (referred to as integrated interventions) may improve prevention and care outcomes of persons living with HIV (PLWH). This systematic review and meta-analysis examines the effects of integrated interventions.

Methods: A systematic review, including both electronic and hand searches, was conducted to identify randomized controlled trials (RCTs) published between 1996 and 2014 that were designed to target at least two of the following behaviors among PLWH: HIV transmission risk behaviors, HIV care engagement, and medication adherence. Effect sizes were meta-analyzed using random-effects models.

Results: Fifteen RCTs met the inclusion criteria. Integrated interventions significantly reduced sex without condoms [odds ratio (OR) = 0.74, 95% confidence interval (CI) = 0.59, 0.94, P = 0.013, 13 effect sizes] and had marginally significant effects on improving medication adherence behaviors (OR = 1.35, 95% CI = 0.98, 1.85, P = 0.063, 12 effect sizes) and undetectable viral load (OR = 1.46, 95% CI = 0.93, 2.27, P = 0.098, seven effect sizes). Significant intervention effects on at least two outcomes were seen in RCTs tailored to individual needs, delivered one on one, or in settings wherein PLWH received services or care.

Conclusion: Integrated interventions produced some favorable prevention and care continuum outcomes in PLWH. How to incorporate integrated interventions with other combination HIV prevention strategies to reach the optimal impact requires further research.

Supplemental Digital Content is available in the text

Division of HIV/AIDS Prevention, US Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Correspondence to Nicole Crepaz, PhD, Division of HIV/AIDS Prevention, US Centers for Disease Control and Prevention, 1600 Clifton Rd., Mailstop E-37, Atlanta, Georgia, 30329, USA. Tel: +1 404 639 6149; fax: +1 404 639 1950; e-mail: ncrepaz@cdc.gov

Received 6 April, 2015

Revised 1 September, 2015

Accepted 1 September, 2015

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Website (http://www.AIDSonline.com).

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Introduction

The National HIV/AIDS strategy (NHAS) [1] outlines several goals for ending the domestic HIV epidemic, including use of evidence-based prevention strategies to reduce HIV transmission, increase access to care, and optimize health outcomes for persons living with HIV (PLWH). The most up-to-date estimates show that 1.2 million persons were living with HIV infection in the United States in 2012. Among these PLWH, 39% were engaged in HIV medical care, 36% were prescribed antiretroviral therapy (ART), and 30% achieved viral suppression [2]. These figures call for further improvements across the HIV care continuum in order to reach NHAS’ prevention and care goals.

Engaging in HIV medical care shortly after HIV diagnosis and sustaining routine care with high adherence to ART can improve health outcomes of PLWH and prevent HIV transmission [3]. Nonengagement in HIV care, nonadherence to ART, and nonadherence to safer sex can each have adverse health consequences for PLWH and their partners. Evidence also suggests that these behaviors are associated with each other. Sexual risk among PLWH was found to be associated with not being engaged in HIV care [4] or not adhering to ART [5]. Nonengagement in HIV care was found to be associated with poor medication adherence and detectable viral load [6]. These associations suggest the need for interventions that target multiple behaviors to reduce HIV transmission and improve health outcomes of PLWH.

Intervening on multiple behaviors at one time strengthens the connection between prevention and care and is consistent with combination HIV prevention [3,7][3,7]. Integrated interventions are defined here as interventions that target multiple behaviors of PLWH. By simultaneously addressing problem behaviors caused by similar influencing factors (e.g. motivation, knowledge, skills, stigma, mental health, and homelessness), integrated interventions may be more practical and economical than interventions that target one behavior at a time (single-target interventions). However, addressing multiple behavioral targets may potentially dilute the intervention effects on any single outcome.

Before considering integrated interventions as part of combination HIV prevention, it is important to examine whether integrated interventions are effective in improving prevention and care outcomes. Several systematic reviews and meta-analyses have examined the effects of interventions that reduce behavioral risk of transmitting HIV [8–12][8–12][8–12][8–12][8–12], promote HIV care engagement and utilization [13,14][13,14], and improve adherence to HIV medication and viral suppression [15–17][15–17][15–17] among PLWH. To our knowledge, there is no systematic review or meta-analysis that evaluates the effects of integrated interventions. In this meta-analysis, we systematically reviewed US-based randomized controlled trials (RCTs) that evaluated integrated interventions specifically designed for PLWH and addressed at least two of the following behaviors: transmission risk behaviors, HIV care engagement, and medication adherence. Our goals are to describe the characteristics of currently available integrated interventions, assess intervention effects on prevention and care continuum outcomes, and identify research gaps to inform prevention and treatment efforts.

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Methods

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [18] to report our systematic review and meta-analysis. Supplementary Material A, http://links.lww.com/QAD/A774, provides the PRISMA checklist. A study protocol is not available for this review.

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Search strategy

We used the Centers for Disease Control and Prevention's Prevention Research Synthesis (PRS) project's cumulative HIV/AIDS/sexually transmitted disease (STD) research database for identifying relevant reports. The PRS database is annually updated following a well established systematic search protocol, which consists of automated and manual searches [19]. Each year, four comprehensive searches are conducted to locate citations related to HIV risk reduction, medication adherence, linkage to and retention and reengagement in HIV care, and systematic reviews of HIV prevention. All four searches include the electronic databases (and platforms): EMBASE (OVID), MEDLINE (OVID), and PsycINFO (OVID). Additional electronic databases (e.g. Sociological Abstracts, CINAHL, and CAB Global Health) are included for some searches (see Supplementary Material B, http://links.lww.com/QAD/A774, for detailed information).

Each comprehensive, automated search combines keywords and index terms used to describe concepts within a domain. For example, the risk reduction search consists of three domains: HIV, AIDS, or STD index terms; prevention, intervention, or evaluation terms; and behavior or outcome terms. The Boolean operator ‘OR’ is used to consolidate each domain with an ‘AND’ operator used to cross-reference each domain. No language restriction was applied to the automated search. The full search strategy of the MEDLINE database for each of the four comprehensive searches is provided as Supplementary Material C, http://links.lww.com/QAD/A774. The searches of the other databases are available from the corresponding author.

The manual search included three components: first, quarterly searches of all reports published in the previous 3 months of 60 journals (see Supplementary Material D, http://links.lww.com/QAD/A774) to identify potentially relevant citations not yet indexed in electronic databases; second, review of the reference lists of pertinent articles; and third, searches of HIV/AIDS Internet listservs and other research databases (e.g. ISI Web of Knowledge, RePORTER, and Cochrane Library).

Citations identified through automated and manual searches were downloaded and deduplicated in the PRS database before conducting title/abstract screening and full-report coding. The last date we searched the PRS database was 2 January 2015.

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Inclusion criteria

Inclusion criteria were RCTs that evaluated interventions specifically designed for PLWH; were conducted in the United States; were published or in press between 1996 and 2014; tested interventions that addressed at least two of the behaviors: HIV transmission risk behaviors, HIV care engagement, or medication adherence; and reported at least two of the following relevant outcomes:

  1. Behaviors (i.e. sex without condoms, number of sex partners, needle sharing, injection drug use) or biological outcomes (i.e. STD) that increase HIV transmission risk,
  2. HIV care engagement (i.e. retention in HIV care measured by the number of missed or kept HIV care appointments or having two HIV medical visits within past 6 months), and
  3. HIV medication adherence (i.e. being on ART, behavioral measures of adherence by medication event monitoring system, electronic drug monitoring, pill count, pharmacy refill, or self-report; viral load level measured by self-report or medical records).
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Data abstraction

Pairs of trained coders independently coded each eligible intervention using standardized coding forms for the following: study characteristics (e.g. study date, location, study design, sample size, and data collection method), participant characteristics (e.g. target population, sex, race/ethnicity, and sexual orientation), intervention characteristics (e.g. components, delivery method, duration, and time span), outcomes, and risk of bias. Linked citations, defined as publications offering additional information on the same study, were included if they provided relevant intervention and evaluation information. The overall percentage agreement among trained coders is 96% with a kappa rate of 80%, indicating a high interrater reliability. We contacted the primary study investigator to obtain additional information as needed. The response rate was 90%.

Owing to the fact that studies differ in reporting outcomes and findings, we applied the following rules for guiding data abstraction for analyses. For studies that reported multiple outcomes of interest, separate analyses were conducted for sex without condoms, number of sex partners, STD, needle sharing, injection drug use, taking ART, HIV care engagement, medication adherence, and viral load suppression. This approach allowed us to examine intervention effects on different outcomes as the prevention literature showed some outcomes (e.g. number of sex partners and STD) were more difficult to change than other outcomes (e.g. sex without condoms) [9].

If sex behavior data for different types of partners were reported, the analysis focused on sex with at-risk partners (i.e. HIV-negative or status-unknown partners) rather than HIV-positive or all partners. For studies that reported medication adherence outcomes based on self-report or medication event monitoring system data, the latter was used in the analysis. For studies that reported multiple follow-up assessments, we selected the time point closest to 3 months post-completion of the intervention for interventions that are clearly discrete (i.e. all the sessions are thought to be necessary and sufficient for yielding the desired change) and the last assessment point for interventions that are designed to be ongoing (i.e. receiving the intervention at each clinic visit). To reduce the impact of group differences at baseline on the outcome, we calculated effect sizes for the follow-up outcome data by adjusting for baseline differences.

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Risk of bias assessment

Study quality was assessed using adapted Cochrane risk-of-bias variables [20]. Each intervention was evaluated for participant selection (sequence generation and allocation concealment), blinding (personnel and outcome assessors), and attrition bias [intent to treat (ITT), differences between those lost and retained, overall retention [≥80 vs. <80%], and differential attrition [≤10 vs. >10%]). Each item was scored as either high or unclear risk of bias (0) or low risk of bias (1). Overall, study quality was scored from 0 to 8, with a higher score indicating a lower risk of bias.

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Data analysis

Standard meta-analytical methods were used [21,22][21,22]. Effect sizes were estimated using odds ratios (OR) because the majority of the studies reported dichotomous outcomes. For studies reporting means and standard deviation values on continuous outcomes, standardized mean differences were calculated and converted into OR values [21,22][21,22]. Random-effects models with two-tailed tests were used to calculate aggregated effects for all outcomes of interest [23]. For HIV transmission risk outcomes, an OR less than 1 indicates a greater reduction in odds of reporting sex without condoms, multiple sex partners, STD, needle sharing, or injection drug use in the intervention group, relative to the comparison group. For HIV engagement and medication adherence outcomes, an OR more than 1 indicates a greater increase in odds of being retained in HIV care, being on ART, adhering to HIV medication, or having an undetectable viral load in the intervention group, relative to the comparison group.

The magnitude of heterogeneity of the effect sizes was tested using the Q statistic, for which a significant result indicates the existence of heterogeneity, and I2 statistic, which quantifies the percentage of variation across studies that was because of heterogeneity [24]. For outcomes that had a significant Q statistic or moderate-to-high levels of heterogeneity (I2 ≥ 50), we conducted stratified analyses to assess the impact of intervention as well as study design characteristics on the outcomes to further explore the heterogeneity when there were sufficient numbers of studies (>6). Specifically, we assessed between-group differences (QB) using the mixed-effects model [22] to determine whether intervention and study design characteristics were associated with effect sizes. There were a limited number of studies for specific subgroups of PLWH and thus stratified analyses were not conducted by participant characteristics. All the analyses were carried out using the Comprehensive Meta-Analysis software (version 2) [25]. Meta-regression was considered, but not used because of a small number of stratified variables with significant between-group differences.

Sensitivity analyses were conducted to test the robustness of the findings. We removed one study at a time from each set of aggregated analyses to determine if any one study affected the aggregated effect size. In addition, we redid the analyses with the longest follow-up time point available from each study to determine if the findings were stable at time points farther removed from the intervention. Publication bias was ascertained by inspection of a funnel plot of standard error estimates vs. effect-size estimates and by a linear regression test [22,26][22,26].

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Results

The study selection process is summarized in Fig. 1. Among 148 intervention studies that were specifically designed for PLWH in the United States, 15 RCTs, consisting of 4487 PLWH, met the inclusion criteria (see Supplementary Materials F, http://links.lww.com/QAD/A774, for excluded studies).

Fig. 1

Fig. 1

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Overall characteristics of integrated interventions for persons living with HIV in the United States

Table 1 provides brief descriptive characteristics of the 15 integrated interventions. Interventions targeted a variety of PLWH subgroups, including (not mutually exclusive) clinic patients [27–32][27–32][27–32][27–32][27–32][27–32], youth or young adults [31–33][31–33][31–33], persons who use/inject drugs [30,33,34][30,33,34][30,33,34], women [35,36][35,36], inmates reentering the community [37,38][37,38], women with histories of sexual abuse [36], persons who were homeless or at risk of homelessness [39], and other high-risk PLWH (e.g. persons who engaged in unprotected sex with HIV-negative/status unknown partners or had medication/visit adherence problems) [28,31,40][28,31,40][28,31,40].

Table 1

Table 1

Table 1

Table 1

Regarding the intervention characteristics, nine studies addressed risk reduction and medication adherence [29–32,35,36,38,40,41][29–32,35,36,38,40,41][29–32,35,36,38,40,41][29–32,35,36,38,40,41][29–32,35,36,38,40,41][29–32,35,36,38,40,41][29–32,35,36,38,40,41][29–32,35,36,38,40,41][29–32,35,36,38,40,41], four studies examined all three behaviors [33,34,37,39][33,34,37,39][33,34,37,39][33,34,37,39], and two studies focused on HIV care engagement and medication adherence [27,28][27,28]. Almost half of the interventions were tailored to an individual's needs by using less-structured sessions [27–29,31,32,35,38][27–29,31,32,35,38][27–29,31,32,35,38][27–29,31,32,35,38][27–29,31,32,35,38][27–29,31,32,35,38][27–29,31,32,35,38]. The majority of the interventions were delivered one on one [27–29,31–33,37,39,40][27–29,31–33,37,39,40][27–29,31–33,37,39,40][27–29,31–33,37,39,40][27–29,31–33,37,39,40][27–29,31–33,37,39,40][27–29,31–33,37,39,40][27–29,31–33,37,39,40][27–29,31–33,37,39,40] and in settings wherein PLWH receive services or care (e.g., HIV outpatient clinics, community AIDS service centers, and methadone treatment clinics) [27–32,35,40,41][27–32,35,40,41][27–32,35,40,41][27–32,35,40,41][27–32,35,40,41][27–32,35,40,41][27–32,35,40,41][27–32,35,40,41][27–32,35,40,41]. Interventions were delivered by trained facilitators [27,28,34,36,37,39–41][27,28,34,36,37,39–41][27,28,34,36,37,39–41][27,28,34,36,37,39–41][27,28,34,36,37,39–41][27,28,34,36,37,39–41][27,28,34,36,37,39–41][27,28,34,36,37,39–41] or by healthcare providers or counselors [30–33,35,38][30–33,35,38][30–33,35,38][30–33,35,38][30–33,35,38][30–33,35,38]. One was a computer-delivered intervention [29]. The number of intervention sessions ranged from 3 to 48 with a median of eight sessions. The median time per session was 90 min (range: 30–120 min/session) and the median total time of the interventions was 10.5 h (range: 2–96 h).

Regarding the study design and quality, the sample sizes ranged from 56 to 966 with a median of 175 participants. Five studies [29,34,39–41][29,34,39–41][29,34,39–41][29,34,39–41][29,34,39–41] reported power analyses for estimating the sample sizes needed for detecting moderate effect sizes. Although all studies were RCTs, the level of risk of bias varied (see Supplementary Material F, http://links.lww.com/QAD/A774). Out of eight risk of bias variables, seven RCTs scored 0–4 (higher risk of bias), five scored 5, and three scored 6–7 (lower risk of bias). The majority of studies retained more than 80% of participants (12 studies) and had differential retention of less than 10% (12 studies). The most common risk of bias was not clearly reporting blinding, ITT, or allocation concealment.

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Efficacy of integrated interventions

Figure 2 presents the aggregated effect sizes for the nine outcomes related to HIV transmission risk, HIV care engagement, and medication adherence. Overall, PLWH receiving integrated interventions were significantly less likely than comparison participants to report sex without condoms. The intervention effects on HIV medication adherence behavior and undetectable viral load approached statistical significance. No significant intervention effects were observed for number of sex partners, STD, needle sharing, injection drug use, retention in HIV care, and being on ART.

Fig. 2

Fig. 2

Fig. 2

Fig. 2

Fig. 2

Fig. 2

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Heterogeneity, sensitivity tests, and publication bias

As seen in Fig. 2, four out of nine outcomes (i.e. sex without condoms, number of sex partners, medication adherence, and undetectable viral load) had significant Q statistics or a moderate-to-high level of heterogeneity across studies (I2 > 50). Sensitivity tests did not reveal any single study that exerted influence on the overall effect size for the majority of outcomes, except for medication adherence behavior. When either one of the two studies [33,39][33,39] was excluded, the overall intervention effect on the medication adherence behavior became significant (OR = 1.48, 95% CI = 1.11, 1.97, P = 0.007 when removed [39]; OR = 1.44, 95% CI = 1.04, 1.98, P = 0.028 when removed [33]). However, neither study significantly reduced the overall heterogeneity. Additional sensitivity tests using the longest follow-ups when data were available did not significantly change the findings for any of the outcomes reported in Fig. 2.

Based on the inspection of funnel plots and the linear regression tests, there was no evidence that our effect-size estimates for sex without condoms, medication adherence behavior, and undetectable viral load were influenced by noninclusion of studies with nonsignificant findings.

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Stratified analysis

The results of stratified analyses for sex without condoms, medication adherence behavior, and undetectable viral load are presented in Table 2. When comparing intervention groups to comparison groups, significant intervention effects on at least two of three outcomes were seen in RCTs that were tailored to individual needs (for all three outcomes), delivered one on one (for sex without condoms and undetectable viral load), delivered in settings wherein PLWH receive services or care (for sex without condoms and medication adherence), had more than four sessions (for sex without condoms and medication adherence), had lower risk of bias (for sex without condoms and undetectable viral load), and used standard of care or wait list control (for sex without condoms and undetectable viral load). The QB statistics showed that several (but not all) intervention and study design characteristics remained statistically significant.

Table 2

Table 2

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Discussion

This meta-analysis is the first to focus on integrated interventions for PLWH. Our findings show that integrated interventions are effective in reducing sex without condoms and potentially improve medication adherence behavior and undetectable viral load. The overall intervention effects on sex without condoms (OR = 0.74), medication adherence (OR = 1.35), and undetectable viral load (OR = 1.46) observed in this meta-analysis were comparable to the magnitude of effect sizes observed in previously published meta-analyses of RCTs for PLWH (sex without condoms: OR = 0.57 [8]; sex without condoms with at-risk partners: OR = 0.79 [11]; medication adherence: OR = 1.50 [16]; undetectable viral load: OR = 1.25 [16]). Results indicate no evidence that integrated interventions have effects on changing the number of sex partners, STD, needle sharing, injection drug use, retention in HIV care, or being on ART. The lack of evidence on these outcomes might imply that some behaviors are more difficult to change [9,13,14][9,13,14][9,13,14]. Alternatively, addressing multiple behavioral targets simultaneously may dilute the intervention effect on some of these outcomes, especially when the problem behaviors do not share common influencing factors that the interventions were intended to address. Owing to the fact that few studies evaluating the outcomes show null results, the findings need to be reassessed when additional data become available.

Apart from overall intervention effects, stratified analyses indicated several patterns that deserve attention. The effect sizes tended to be significant in interventions that were tailored to individual needs, delivered one on one, or delivered in settings wherein PLWH receive services or care. These findings corroborate previous meta-analysis findings on sexual risk behavior [8] and the recently released recommendations for HIV prevention with adults and adolescents with HIV in the United States by Centers for Disease Control and Prevention, Health Resources and Services Administration, and National Institutes of Health [3]. In addition, studies using standard of care or wait list control were more likely than studies using demand or attention control to show stronger intervention effects on sex without condoms and undetectable viral load. For HIV-related comparison groups, using variations of the interventions as comparison groups may greatly reduce the ability to detect intervention effects [42]. Using a standardized comparison arm that the HIV prevention field could agree upon as a prevention standard can facilitate comparing intervention effects across studies.

Our findings must be viewed within the context of the limitations of the available evidence and point to further research needs. Although interventions were designed for PLWH and some specifically targeted subgroups of PLWH, there were a limited number of studies to further examine which intervention strategies work best for specific groups. Given that MSM and transgender women are disproportionately affected by HIV [1], it is important to further evaluate whether the strategies identified here work well within these groups and to determine what additional strategies may be effective in improving prevention and care outcomes for these most affected groups. Another limitation is that not all included studies clearly reported blinding, ITT, or allocation concealment. Improving reporting of RCTs by following the Consolidated Standards of Reporting Trials statement [43] and implementing strategies to reduce the risk of bias [44] would further facilitate evaluation of HIV prevention research. Similarly, improving reporting of serostatus of partners can provide better data for assessing seroadaptive strategies practiced by PLWH and determining the level of risk that sexual behaviors pose for HIV transmission. Self-reported outcomes, such as sex without condoms and medication adherence, may be open to socially desirable responding. This might contribute to the difference in effectiveness observed on different outcomes. Acknowledging the possibility of self-reported bias, many studies attempted to ensure confidentiality of data by using computer-assisted assessments. In addition, all studies had a comparison group and randomly assigned participants that may reduce the likelihood that impression management, the driver of socially desirable responding, influenced the intervention effect.

Our meta-analysis is intended to examine a fundamental question – are integrated interventions effective in improving prevention and care outcomes? Whether integrated interventions are more ‘optimal’ than single-target interventions is an important question, but it is beyond the scope of this systematic review. From an experimental research point of view, a single-target intervention can inform what works for changing one behavior at a time. However, using single-target interventions to address multiple problem behaviors may require more resources (i.e. more sessions) and time. Integrated interventions, on the other hand, can be more practical and closer to the reality of regular programmatic practices in the field. There are a few important implementation questions to consider for better informing best practices: Would the implementation of integrated interventions yield more favorable prevention and care outcomes than the use of bundled single-target interventions? What contributes to the synergistic effects of integrated interventions that are not available in single-target interventions? What are the optimal ways to combine integrated interventions with biomedical and structural interventions to reach NHAS prevention and care goals [1]?

In conclusion, we found evidence of benefits of integrated interventions on some HIV transmission risk behavior and medication adherence outcomes for PLWH. Insufficient evidence was found for STD, needle sharing, injection drug use, and HIV care engagement partially because of a limited number of studies. When selecting integrated interventions for PLWH, prevention providers may consider the effective intervention strategies identified in this meta-analysis. How to incorporate integrated interventions with other combination HIV prevention strategies, such as biomedical and structural interventions, to reach the optimal HIV prevention and care outcomes among PLWH requires further research.

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Acknowledgements

The authors would like to thank other members of the HIV/AIDS Prevention Research Synthesis (PRS) Project members for their contribution to the coding and maintenance of the PRS database that was used for this systematic review (listed alphabetically): Adebukola Adegbite, Terrika Barham, Julia B. DeLuca, Theresa A. Sipe, Maria Luisa Tungol, H. Waverly Vosburgh, and Christina White. They would also like to thank the following authors who provided additional analysis for our review: Maria Holstad, Ann Kurth, Mary Jane Rotheram-Borus/W. Scott Comulada/Steve Morin, and David Purcell/Yuko Mizuno.

Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention.

Financial support: This work was supported by the Division of HIV/AIDS Prevention at the US Centers for Disease Control and Prevention and was not funded by any other organization.

Author contributions: N.C. conceptualized the systematic review, analyzed and interpreted the data, and wrote the manuscript. M.M.M. undertook the comprehensive literature search. N.C., B.N.B, D.H.H., and M.M.M. did coding, provided technical and material support, and involved in manuscript review and editing. N.C. has full access to all the data and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Conflicts of interest

There are no conflicts of interest.

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Keywords:

HIV transmission risk; integrated HIV interventions; medication adherence; people living with HIV; retention in HIV care

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