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. Author manuscript; available in PMC: 2026 Apr 23.
Published before final editing as: Drug Alcohol Depend. 2025 Sep 12;276:112879. doi: 10.1016/j.drugalcdep.2025.112879

Alcohol interventions for persons with HIV: Meta-analysis of randomized controlled trials using phosphatidylethanol and self-report

Judith A Hahn 1,2,±,*, Jeremy C Kane 3,±, Robin Fatch 1, Cristina Espinosa da Silva 1, Nneka I Emenyonu 1, Aaron Scheffler 2, Priya Chirayil 3, Kaku So-Armah 4, Christopher W Kahler 5, Amy A Conroy 6, E Jennifer Edelman 7, Sarah Woolf-King 8, Charles DH Parry 9,10, Susan M Kiene 11,12, Gabriel Chamie 1, Winnie R Muyindike 13, Julian Adong 13, Vivian F Go 14, Robert L Cook 15, Neo K Morojele 16, David A Fiellin 7,17, Glenn-Milo Santos 18, Peggy Tahir 19, Jeffrey Samet 4, Evgeny Krupitsky 20,21, Isabel Elaine Allen 2
PMCID: PMC13100752  NIHMSID: NIHMS2163337  PMID: 40992009

Abstract

Background:

Interventions are needed to reduce alcohol use for people living with HIV (PWH), but prior randomized controlled trials (RCT) evaluated efficicy by self-reported alcohol use, potentially hampering validity. We aimed to determine alcohol intervention efficacy using the alcohol biomarker, phosphatidylethanol (PEth), combined with self-report, and compare results to each analysed alone.

Methods:

We conducted a systematic review of alcohol intervention RCTs to April 2023, followed by an individual participant data meta-analysis (IPD-MA) using two-step random effects modeling. Our primary outcome was unhealthy alcohol use defined as PEth/self-report (binary), i.e., PEth≥50 ng/mL or Alcohol Use Disorders Identification Test – Consumption (AUDIT-C) ≥3 (women) and ≥4 (men). We also evaluated PEth and self-report alone.

Results:

We screened 280 studies, found 20 eligible, and obtained IPD for 16 (N=3,559, median age=41, 72.8% male). Participants receiving an alcohol intervention had significantly lower odds of follow-up unhealthy alcohol use by PEth/self-report (OR=0.69, 95% CI: 0.55-0.86; Cohen’s d=0.21; I2=29.4%, 95% CI: 0.0%-62.8%). Risk-of-bias assessment indicated low or moderate risk. The certainty of evidence was moderate. Findings for PEth alone (OR=0.81, 95% CI: 0.69-0.97; Cohen’s d=0.12; I2=5.7%, 95% CI: 0.0%-49.5%) and self-report alone (OR=0.67, 95% CI: 0.50-0.89; Cohen’s d=0.22; I2=68.7%, 95% CI: 6.6%-84.5%) outcomes were similar, but heterogeneity was greater for self-report alone. Findings were robust to higher PEth/self-report cutoffs and continuous measures.

Conclusions:

Results confirm prior findings of significant efficacy of alcohol interventions for PWH. Effect sizes were small across measurements, while heterogeneity was high when using self-report alone. Combined PEth/self-report is a useful primary outcome for alcohol intervention studies.

Introduction

Unhealthy alcohol use, which refers to drinking behavior above recommended cutoffs that is likely to or already has negatively impacted one’s health (Patel and Balasanova, 2021), is common among people with HIV (PWH), with prevalence estimates of 25% in low-and-middle-income countries and 42% in high-income countries (Duko et al., 2019; Necho et al., 2020). The elevated prevalence among PWH compared to the general population is of concern given that unhealthy alcohol use has been linked to poor HIV outcomes, including viral non-suppression, low adherence to antiretroviral therapy (ART), and increased risk of transmission (Greene et al., 2017; Justice et al., 2016; Kane et al., 2018; Ragan et al., 2020; Williams et al., 2007). The burden of unhealthy alcohol use in terms of morbidity and mortality is also higher among PWH than people without HIV (Justice et al., 2016).

Previous reviews of the literature have indicated that behavioral interventions can be effective at reducing unhealthy alcohol consumption among PWH, although the findings have not been strong. A meta-analysis of 21 behavioral alcohol intervention studies among PWH found small but significant effects for the interventions in reducing consumption and improving ART adherence and viral suppression (Scott-Sheldon et al., 2017). A meta-analysis of 13 randomized controlled trials testing brief interventions for alcohol reduction among PWH found significant moderate effects for reducing drinking frequency and quantity and a reduction in the odds of mortality compared to control conditions; however, no effects for brief interventions were found for ART adherence or viral suppression (Ghosh et al., 2023). A meta-analysis of 19 trials of behavioral interventions for alcohol reduction in sub-Saharan Africa (including both people with and without HIV) found significant effects of interventions for alcohol abstinence but no other alcohol outcome (e.g., percentage of drinking days, drinks per drinking day, Alcohol Use Disorders Identification Test [AUDIT] score)(Sileo et al., 2021). A 21-study systematic review of psychological interventions (primarily motivational interviewing or cognitive-behavioral therapy-based) for unhealthy alcohol use found a lack of strong evidence for significant, sustained treatment effects (Madhombiro et al., 2019). In all four of these prior reviews, between-study heterogeneity, which was likely impacted by self-report biases and varying outcome assessments, was noted as a substantial concern.

A limitation of the intervention studies included in prior reviews is that they exclusively measured alcohol consumption outcomes via self-report, most commonly via the AUDIT, AUDIT-C (Babor et al., 2001), or alcohol Timeline Followback (TLFB)(Sobell and Sobell, 1992). Self-report methods of evaluating alcohol consumption are subject to measurement error among certain populations due to social desirability and recall biases (Asiimwe et al., 2015; Bajunirwe et al., 2014; Cherpitel et al., 2018; Ekholm, 2004; Glass et al., 2013; Latkin et al., 2016; Michalak and Trocki, 2009; Schensul et al., 2017). For example, PWH living in sub-Saharan Africa have been found to underreport how much they drink (Adong et al., 2019; Bajunirwe et al., 2014; Muyindike et al., 2017). Information bias can also be caused by a lack of clarity on standard drink sizes and concentration in alcohol questionnaires, as well as heterogeneity in drink sizes and containers across countries (Bond et al., 2014; Devos-Comby and Lange, 2008; LEMMENS, 1994). These biases may collectively lead to incorrect inference on intervention efficacy, particularly if measurement error is differential by treatment group (Rothman et al., 2008). Differential treatment error may occur, for example, if participants who received an alcohol reduction intervention that includes alcohol counseling may be more likely to underreport their alcohol use at a follow-up assessment compared to control participants who did not receive any intervention.

Alcohol biomarkers, and in particular phosphatidylethanol (PEth), are increasingly being used in research studies as objective alcohol measurements. PEth is an abnormal phospholipid that is only produced in the presence of alcohol and has been found to have high sensitivity and specificity for unhealthy alcohol use, including PWH (Andresen-Streichert et al., 2018; Hahn et al., 2021; Shayani et al., 2019). Several controlled laboratory administration studies have shown that PEth is formed after one to five successive days of alcohol consumption, and that PEth levels decline over follow up (up to fourteen days) when alcohol intake ceases (Aboutara et al., 2023; Gnann et al., 2012; Javors et al., 2016; Schröck et al., 2017; Spleis et al., 2025). In naturalistic settings, the correlation of PEth with the self-reported volume of alcohol consumed over the past 2-4 weeks ranges from 0.53 to 0.80. There is not a direct conversion to number of drinks consumed, due to inter-person variability by biologic factors that affect alcohol metabolism, such as body mass index (Hahn et al., 2021), and other factors affecting PEth formation and elimination (Perilli et al., 2023). PEth is detectable in blood samples with a 4-10 day half-life (Hahn et al., 2016a; Helander et al., 2019); it can be treated either continuously or as a categorical variable in analyses (Helander and Hansson, 2013; Ulwelling and Smith, 2018). A combined PEth/self-report outcome may increase sensitivity for detecting unhealthy alcohol use compared to either self-report or PEth alone given that self-report tends to be highly specific (Livingston and Callinan, 2015). PEth has two major advantages as an outcome in alcohol intervention studies: it is an objective measure and therefore not subject to reporting bias, and it is comparable across study settings and contexts.

In this meta-analysis, we sought to estimate the efficacy of interventions to reduce alcohol use as measured by a combined PEth/self-report variable among PWH. A secondary aim was to compare the pooled treatment effect estimates for the PEth/self-report combined variable with estimates generated by self-report and PEth variables alone. This meta-analysis breaks new ground from previous reviews by using a combined PEth/self-report outcome measure and assessing individual participant data in meta-analysis alcohol interventions for PWH.

Methods

Search strategy and selection criteria

This was a systematic review and an individual participant data meta-analysis (IPD-MA). Inclusion criteria were: 1) randomized controlled trials that evaluated an alcohol reduction intervention (behavioral or pharmacological) among PWH 15 years of age or older; 2) alcohol consumption was a primary or secondary outcome and was measured by both PEth and self-report; 3) there was at least one post-baseline assessment of the alcohol outcome; 4) published in English; and 5) data collection was completed by August 31, 2023. There were no restrictions on study setting or timing of assessments. We excluded crossover trials and quasi-experimental designs, prior systematic reviews, and meta-analyses.

The review was conducted through searches in academic databases: Pubmed, Web of Science, Embase, and CINAHL. A search for ongoing studies with data collection scheduled to be completed by August 31, 2023 was also conducted using a keyword search in Clinicaltrials.gov. Searches in the academic databases and Clinicaltrials.gov were conducted between April 8 and April 18, 2023.

The search strategy was executed by a health services librarian with results uploaded to Covidence. Duplicate entries were removed. Two reviewers independently reviewed all titles/abstracts in the list. Discrepancies were resolved through discussion.Studies were determined to be “yes” if the title and abstract described an alcohol intervention RCT that explicitly included PWH and both self-report and PEth were measured. Studies were categorized as “maybe” if the abstract described an alcohol intervention RCT among persons with HIV but it was not clear if self-report and/or PEth were measured. Studies were marked “no” if they were not an alcohol intervention RCT among PWH. Articles categorized as “yes” or “maybe” were retained for full-text review. Two reviewers again independently screened the full text to evaluate eligibility criteria, with discrepancies resolved through discussion.

Once the list of included full texts was finalized, two reviewers searched the reference lists of included studies to identify any additional eligible articles. The list was then shared with the investigator team and additional articles were included based on investigator knowledge of known papers. We contacted the principal investigators of all included studies to inquire about participating and their willingness to contribute data for the IPD meta-analysis; data use agreements were setup for those studies interested and willing to share data. Studies for which access to IPD was not obtained or for which data collection was not completed by August 31, 2023 were excluded.

Data extraction and analysis

Data from eligible studies were harmonized into a central database. The primary outcome was a PEth/self-report binary variable of unhealthy alcohol use (Hahn et al., 2018, 2016b; So-Armah et al., 2019). This variable coded as positive for unhealthy alcohol use if PEth (homolog 16:0/18:1) was ≥50 ng/mL or if Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) was positive (≥3 among females; ≥4 among males)(Bradley et al., 2007). This PEth cutoff is consistent with recently proposed cutoffs of 40-60 ng/mL for regularly consuming 1-2 drinks per day and was used in a recent alcohol intervention trial (Puryear et al., 2023a). For studies that did not use AUDIT-C for self-reported alcohol use but used the TLFB instead, the three AUDIT-C questions were estimated using data from the TLFB. For example, we calculated the total number of drinking days in the past 30 days from the TLFB, and categorized those results to match the categories of AUDIT-C question 1 (i.e. 3 drinking days on the TLFB was determined to be equivalent to the AUDIT-C category of “2-4 times per month”). To approximate the number of drinks on a typical drinking day (AUDIT-C question 2), we calculated the within person median number of drinks reported on drinking days using the TLFB, and then converted to match the categories of AUDIT-C question 2. Finally, we used the number of days where the participant reported 6 or more drinks on the TLFB to approximate the categories of AUDIT-C question 3 on binge drinking. This “approximated” AUDIT-C score is thus used throughout for the studies that used the TLFB for self-report.

We used trial baseline and follow-up data. We aimed to use six-month follow-up after baseline as the primary endpoint, which was available from the majority of studies. For studies that did not have a six-month assessment, we used the time point closest to six months. For studies with more than one intervention arm, all intervention particpants were grouped together.

Secondary outcomes included: unhealthy alcohol use binary variables as measured by PEth alone and AUDIT-C alone (using the same cut-offs as in the combined variable); PEth and AUDIT-C as continuous variables; binary variables (combined, PEth alone, and self-report alone) of alcohol use using high-risk/excessive thresholds of PEth≥200 ng/mL (Luginbühl et al., 2022) and AUDIT-C≥6 (Rubinsky et al., 2013).

Analyses were conducted using Stata (StataCorp, 2017). We used the Stata ipdmetan package(Fisher, 2015) to conduct a two-step meta-analysis, in which treatment effects were calculated within each study using generalized linear models, and then combined in a random effects model using maximum likelihood. If the random effects model using maximum likelihood produced an error message, then the DerSimonian-Laird estimator was used. For the primary outcome and secondary binary outcomes, we estimated logistic models for which the measure of effect was the odds ratio with a 95% confidence interval. For secondary continuous outcome variables, we estimated linear models. We calculated Cohen’s d effect size statistics (for both continuous variables and binary variables using a standard conversion approach(Sánchez-Meca et al., 2003)). Effect sizes represented the difference in unhealthy alcohol use between treatment and control participants at 6-mpnth follow-up.

We created summary forest plots, and calculated I2 to estimate heterogeneity. Heterogeneity statistics (specifically I-squared and Cochran’s Q statistics) are interpreted the same way in IPD meta-analyses as in standard systematic reviews. They measure the variability between the studies included in the IPD meta-analysis and are interpreted as the percentage of heterogeneity between studies and the significance of the variability between studies, respectively. It is calculated the same way as in non-IPD meta-analyses for both one-stage and two-stage IDP meta-analyses as discussed in Chen et al (2017) and in Riley et al (2021).

We constructed funnel plots to examine the risk of publication bias (Lin and Chu, 2018). We conducted multiple imputation using chained equations (within each study) to address missing data(Schafer and Olsen, 2010) and also conducted a sensitivity analysis with complete case data.

We conducted a series of a priori planned stratified analyses with our primary outcome model. These included stratifying the main outcome model by: gender (men/women), baseline very high risk self-reported hazardous alcohol use (AUDIT-C ≥8 or <8), study location/setting (low-, middle-, high-income country), intervention type (pharmacological or behavioral), and self-report recall period (AUDIT-C, for the prior 3 months, vs. TLFB, for the prior 30 days).

We conducted several sensitivity analyses with the primary outcome model. First, we re-estimated the model excluding studies that had an active control. Second, we re-estimated the model excluding participants who had baseline PEth <8 ng/ml (the frequent lower limit of detection), an indication of possible overreporting of alcohol use at baseline. Finally, there was one eligible study for which inclusion in the original primary outcome model posed an analytic problem because unhealthy alcohol use was very low at follow-up , which caused perfect prediction in the model. In a sensitivity analysis, we modified one observation from that study to allow these data to be included with other IPD in an exploratory model.

We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to rate the quality of the primary outcome evidence (Guyatt et al., 2011). GRADE incorporates risk of bias (as recommended by the PRISMA-IPD statement)(Stewart et al., 2015), inconsistency, indirectness, imprecision, and publication bias in determining the quality rating (high, moderate, low, very low) for the outcome. In determining the risk of bias rating as part of this process, two reviewers independently reviewed each included study using the Cochrane Collaboration’s tool for assessing risk of bias in randomized trials (Higgins et al., 2011) and then met to compare results and come to a consensus on risk of bias rating.

Results of the review follow guidelines established through the PRISMA-IPD statement, which was developed specifically for IPD meta-analyses (Stewart et al., 2015). The protocol was registered with the International Registration of Systematic Reviews (PROSPERO) on 11/30/2022 with registration number CRD42022373640, and the protocol was previously peer-reviewed and published (Kane et al., 2023).

Role of the funding source

The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Results

The search resulted in N=280 unique records, of which N=15 were found to be eligible (figure 1). Five additional studies were added to the list following investigator review. We were in contact with the investigators of all 20 studies; 16 were able to provide IPD, one was not included because IPD were not available by the analysis lock date, and three were excluded because IPD access was not possible. Among the three studies excluded for lack of IPD access, two found intervention efficacy for self-reported alcohol use outcomes compared to control conditions (Edelman et al., 2025; Papas et al., 2020) and one did not (Magidson et al., 2021). One of the 16 studies was included only in a post-hoc sensitivity analysis due to small cell size and perfect prediction in the models as described previously. This study was excluded from our primary analysis.

Figure 1. Study selection.

Figure 1.

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The 16 studies comprised 3559 participants who met eligibility criteria (persons without HIV were excluded), 72·8% of whom (N=2591) were male and with a median age of 41 [IQR: 35-49]. Eight studies were conducted in high-income countries, three in middle-income, and five in low-income. 3397 (95·6%) participants met criteria for unhealthy alcohol use at baseline using the combined binary PEth/self-report primary outcome variable. Characteristics for the individual studies and overall are summarized in table 1.

Table 1.

Characteristics of included studies

Authors
(Publication Year)
Sample Size*		 Country Intervention type, details Age
median
[IQR]		 Gender
male n (%)		 Alcohol
self-report
measure
Chamie et al(2023)(Chamie et al., 2023) N= 680 Uganda Behavioral

Experimental arms: Monthly over 6 months) incentives for recent alcohol abstinence (based on urine ethyl glucuronide); incentives for recent isoniazid adherence (based on urine Isoscreen); incentives for recent alcohol abstinence (based on urine ethyl glucuronide) and/or isoniazid adherence (based on urine Isoscreen)
Comparator arm: No incentives, brief advice plus referrals		 39 [32-37] 470 (69.1) AUDIT-C
Conroy et al (2024)(Conroy et al., 2024) N= 78 Malawi Behavioral

Experimental arm: Financial literacy plus relationship-strengthening sessions (10 sessions); incentivized savings account
Comparator arm: brief alcohol advice plus usual care		 45 [41-50] 75 (96.2) AUDIT-C
Cook et al (2019)(Cook et al., 2019) N= 194 USA (FL) Pharmacological

Experimental arm: Naltrexone 50mg orally for 4 months
Comparator arm: Placebo orally for 4 months		 50 [44-54] 0 (0.0) TLFB
Edelman et al (2019a)(Edelman et al., 2019a) N= 95 USA (DC GA, NY, TX) Behavioral/Pharmacological

Experimental arms: Integrated stepped alcohol treatment during which patients were stepped up to the next intervention if they met a priori criteria at designated times: Step 1: brief negotiated interview with telephone booster, Step 2: motivational enhancement therapy, Step 3: addiction physician management, with consideration of provision of medication for alcohol use disorder
Comparator arm: Treatment as usual (receipt of health handout plus routine care)		 61 [57-65] 95 (100) TLFB
Edelman et al (2019b)(Edelman et al., 2019b) N= 128 USA (DC, GA, NY, TX) Behavioral/Pharmacological

Experimental arm: Integrated stepped alcohol treatment during which patients were stepped up to the next intervention if they met a priori criteria at designated times: Step 1: addiction physician management encouraging use of medication for alcohol use disorder, Step 2: additional physician management plus motivational enhancement therapy, Step 3: referral for specialty addiction treatment services including intensive outpatient or residential treatment
Comparator arm: Treatment as usual (referral to specialty addiction treatment at the discretion of HIV clinician)		 57 [51-61] 125 (97.7) TLFB
Edelman et al (2020)(Edelman et al., 2020) N= 93 USA (DC, GA, NY, TX) Behavioral/Pharmacological

Experimental arm: Integrated stepped alcohol treatment during which patients were stepped up to the next intervention if they met a priori criteria at designated times: Step 1: brief negotiated interview with telephone booster, Step 2: motivational enhancement therapy, Step 3: addiction physician management encouraging use of medication for alcohol use disorder
Comparator arm: Treatment as usual (receipt of a health handout plus routine care)		 59 [53-64] 90 (96.8) TLFB
Go et al (2020)(Go et al., 2020) N= 440 Vietnam Behavioral

Experimental arms: Combined motivational enhancement therapy and cognitive behavioral therapy (6 in-person sessions and 3 optional group sessions); brief intervention with similar components as the combined intervention (2 in-person sessions and 2 telephone sessions)
Comparator arm: Treatment as usual		 39 [36-43] 426 (96.8) TLFB
Hahn et al (2023)(Hahn et al., 2023) N= 269 Uganda Behavioral

Experimental arms: Brief counselling (2 in-person sessions over 3 months) with interim boosters delivered by live calls (monthly) or technology (twice weekly, by short messaging service [SMS] or interactive voice response [IVR])
Comparator arm: Treatment as usual, plus brief advice and referrals		 40 [33-46] 176 (65.4) AUDIT-C
Kahler et al (2018)(Kahler et al., 2018) N= 92 USA (MA) Behavioral

Experimental arm: Motivational interviewing (1 in-person session followed by 2 brief phone calls and in-person booster sessions at 3 and 6 months)
Comparator arm: Treatment as usual		 45 [36-52] 92 (100) TLFB
Kahler et al (2024)(Kahler et al., 2024) N= 1411 USA (MA) Behavioral

Experimental arms: Motivational interviewing; extended duration intervention; interactive text messaging
Comparator arms: Brief advice		 52 [45-57] 141 (100) TLFB
Kiene et al (unpublished) N= 160 Uganda Behavioral

Experimental arm: 4 sessions (2 individual, 2 group) over 6 weeks, behavioral economic and motivational interviewing-based intervention plus saving money/work payments via mobile phone banking and 2x weekly text message reminders of goals.
Comparator arm: Single session brief alcohol screening feedback, referrals for alcohol counseling, and HIV treatment counseling.		 38 [34-43] 160 (100) AUDIT-C
Parry et al (2023)(Parry et al., 2023) N= 309 South Africa Behavioral

Experimental arm: 4 modules (over 2 sessions) of Motivational interviewing (Ml) and problem-solving therapy (PST)
Comparator arm: Treatment as usual		 41 [35-46] 133 (43.0) AUDIT-C
Puryear et al (2023)(Puryear et al., 2023b) N= 400 Uganda, Kenya Behavioral

Experimental arm: Alcohol counseling consisting of 2 in-person counselling sessions with brief phone-based booster sessions performed every 3 weeks in the interim

Comparator arm: Brief advice		 37 [31-43] 269 (67.3) AUDIT-C
Santos et al (2022)(Santos et al., 2022) N= 31 (subset of study participants living with HIV) USA (CA) Pharmacological

Experimental arm: Oral naltrexone (50mg) and behavioral counseling for 12 weeks
Comparator arm: Placebo with behavioral counseling for 12 weeks		 38 [33-48] 31 (100) AUDIT-C
Tindle et al (2022)(Tindle et al., 2022) N= 400 Russia Pharmacological

Experimental arms: Active varenicline plus placebo nicotine replacement therapy; active cytisine plus placebo nicotine replacement therapy
Comparator arms: Placebo varenicline and active nicotine replacement therapy; placebo cytisine and active nicotine replacement therapy		 38 [35-42] 263 (65.8) TLFB
Woolf-King et al (unpublished) N= 49 USA Behavioral

Experimental arm: 6 weekly telephone-delivered sessions of Brief Acceptance and Commitment Therapy (ACT) delivered over 6-8 weeks
Comparator arm: Two telephone delivered? standard brief alcohol intervention sessions, plus two booster calls delivered over 6-8 weeks		 55 [49-60] 45 (93.8) TLFB
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AUDIT-C = Alcohol Use Disorders Identification Test Consumption. CA = California, DC = Washington DC. FL = Florida. GA = Georgia. MA = Massachusetts. NY = New York. TLFB = Alcohol Timeline Followback.

1

Includes only participants from one site where PEth data were available at baseline and follow-up.

Participants were only included in analysis if they were expected to have PEth data

In the primary outcome analysis (figure 2), which focused on the combined PEth/self-report variable, participants receiving an intervention had significantly lower odds of unhealthy alcohol use at follow-up compared to control (OR=0·69, 95% CI: 0·55 to 0·86, d=0·21). Heterogeneity was low (I2=29·4%, 95% CI: 0·0% to 62·8%; Cochrane’s Q p-value=0·14). The odds ratios were similar when the outcome was the binary PEth alone variable (figure 3; OR=0·81, 95% CI: 0·69 to 0.97, d=0·12), and the binary AUDIT-C self-report alone variable (figure 4; OR=0·67, 95% CI: 0·50 to 0·89, d=0·22); however, heterogeneity was not significant in the PEth alone model (I2=5·7%, 95% CI: 0·0% to 49·5%; Cochrane’s Q p-value=0·39) but was significant in the self-report model (I2=68·7%, 95% CI: 6·6% to 84·5%; Cochrane’s Q p-value<0·01).

Figure 2. Treatment effects of intervention vs. control with PEth/self-report combined binary outcome.

Figure 2.

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Shaded box around point estimate reflects % weight in the meta-analysis based on sample size.

Figure 3. Treatment effects of intervention vs. control with PEth binary outcome.

Figure 3.

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Shaded box around point estimate reflects % weight in the meta-analysis based on sample size.

Figure 4. Treatment effects of intervention vs. control with self-report binary outcome.

Figure 4.

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Shaded box around point estimate reflects % weight in the meta-analysis based on sample size.

We conducted a sensitivity analysis to explore if there were any meaningful differences between our estimates using multiply imputed data compared to a complete case analysis. The complete case analysis showed similar effect sizes as the primary analysis for all three outcomes: PEth/self-report (OR=0.70; 95% CI: 0.54 to 0.92), PEth alone (OR=0.81, 95% CI: 0.67 to 0.97) and self-report alone (OR=0.68; 95% CI: 0.48 to 0.96). Heterogeneity increased across all three outcomes relative to the primary analysis: PEth alone (I2=21.5%, p=.21), self-report alone (I2=75.9%, p<.01), and PEth/self-report (I2=42.1%, p=.04). We also estimated the proportion of unhealthy drinkers at 6-month follow-up stratified by treatment and control groups, in the complete cases. For the combined PEth/self-report outcome, 78.8% of control participants had unhealthy alcohol use compared to 71.8% of intervention participants. For the PEth alone and self-report alone outcomes, the proportions were 57.0% (intervention) vs. 61.7% (control), and 48.2% (intervention) vs. 60.7% (control), respectively.

Results from the additional secondary outcome analyses, stratified analyses, and sensitivity analyses are presented in a Supplemental File. In summary, secondary and sensitivity analyses did not yield meaningfully different inferences in intervention effectiveness compared to the primary outcome model. For example, the pooled treatment effect for males using the combined PEth/self-report outcome was OR=0.73 (95% CI: 0.55 to 0.98); for women, the treatment effect was OR=0.58 (95 CI: 0.43 to 0.77). The pooled treatment effect for participants with no, low, or moderate alcohol risk at baseline was OR=0.59 (95% CI: 0.47 to 0.75); the pooled effect for participants with high risk alcohol at baseline was OR=0.76 (95% CI: 0.56 to 1.02).

The funnel plot for the primary outcome had no significant aymmetry and suggested low risk of publication bias (see Supplemental File). The risk of bias analysis found that seven studies had some concern of bias and nine studies had low risk (see Supplemental File). The GRADE rating for our primary outcome effect estimate started with a rating of 4 because all studies included were RCTs. One point was reduced due to some risk of bias in the effect estimate based on the risk of bias assessment. The effect estimate did not have a large confidence interval; heterogeneity was low; there were no concerns of indirectness; and risk of publication bias was low. Therefore, the final GRADE rating for the primary outcome was 3: moderate confidence in the effect estimate.

Discussion

We conducted an IPD-MA to evaluate the effectiveness of alcohol interventions among PWH, which examined intervention effects on a combined PEth and self-report variable to bolster the validity of the findings. We found that alcohol interventions for PWH had a small, statistically significant effect on reducing unhealthy alcohol use. The pooled effect size was similar for combined PEth/self-report, PEth alone, and self-report alone outcomes. However, the self-report alone outcome demonstrated significant between-study heterogeneity. Heterogeneity is generally considered to be higher in self-reported data compared to objective measures because self-reporting can be influenced by reporting biases such as social desirability, recall bias, and challenges to understanding the questions asked (Kessler et al., 2017). Our primary finding of small, significant treatment effects is consistent with prior reviews (Ghosh et al., 2023; Madhombiro et al., 2019; Scott-Sheldon et al., 2017; Sileo et al., 2021). The results are encouraging for the future of alcohol interventions for PWH because they show that, overall, these interventions can reduce objectively measured alcohol consumption in PWH, which is a meaningful public health achievement given the extensive literature showing the detrimental impacts of alcohol on HIV outcomes (Greene et al., 2017; Justice et al., 2016; Kane et al., 2018; Ragan et al., 2020; Williams et al., 2007). Forthcoming work with these data will explore whether these interventions had an impact on viral suppression. The moderate level GRADE rating further increases confidence in the strength of the available evidence for alcohol interventions; none of the studies in the review were identified as having a high risk of bias, and the majority were determined to be low risk.

Our IPD-MA expands on prior reviews by exploring how the measurement method for alcohol consumption may impact the inference of intervention efficacy. We found that effect sizes were similar regardless of whether self-report was used alone, PEth was used alone, or a composite PEth/self-report outcome was used. However, the use of self-report alone resulted in substantial between-study heterogeneity, which has been cited as a major concern in prior alcohol intervention reviews (Ghosh et al., 2023; Madhombiro et al., 2019; Scott-Sheldon et al., 2017; Sileo et al., 2021). Thus, our review is also consistent with prior ones in that the pooled effect from the self-report outcome should be interpreted with caution. The objective PEth alone outcome variable had the lowest level of heterogeneity in our review. Because the heterogeneity was much smaller using a PEth/self-report or PEth alone measure, we suggest that future intervention studies use PEth combined with self-report to reduce the risk of heterogeneity inherent in self-report that may be beyond investigator control. While the heterogeneity was lowest for PEth alone, we suggest using PEth/AUDIT-C rather than PEth alone, because PEth is not completely sensitive for unhealthy alcohol use and the heterogeneity was low with this measure (Hahn et al., 2021).

Secondary outcome findings were similar to the main results: when using higher cut-off levels for self-report alone, PEth alone, and the combined variable, and also when PEth and self-report were measured as continuous variables, these analyses similarly showed small, significant effects across the outcome types. We also found higher heterogeneity when measured by these alternative self-report alone measured compared to when PEth was incorporated. The robustness of our findings to higher PEth/self-report cutoffs is notable in that it suggests that while intensive treatment for alcohol use disorder may be warranted for some, the interventions included in this analysis, many of which were conducted in low-resource settings, may be helpful to reduce alcohol use across a broad range of unhealthy alcohol use. All sensitivity analyses showed similar results, which adds to the robustness of the main findings.

Stratified analyses mostly suggested similar effect sizes across subgroups such as gender and intervention type, with some exceptions. We note that the odds ratio for an intervention effect in the stratum of studies conducted in high-income settings was close to one. There may be a lesser effect in these settings due to saturation of interventions; participants in low and middle-income settings may be more receptive to interventions in settings where health care is considered scarce. More work is needed to understand this.

To our knowledge, in addition to being the first meta-analysis using PEth combined with self-report as the primary outcome variable, this is the first IPD-MA of alcohol interventions for PWH. Additional strengths included the high participation of eligible studies, and the robustness of the findings across several secondary outcomes and sensitivity analyses. However, this review is not without limitations. Although the inclusion of PEth is a strength, we also acknowledge that due to the focus of our review and choice of a composite PEth/self-report primary outcome, only studies that had the intention and ability to measure the PEth biomarker were eligible, which may reduce generalizability. We were not able to consider liver disease, which is a possible limitation given previous research has found that liver fibrosis impacts PEth sensitivity (Hahn et al., 2021). We also reviewed only English language abstracts; we believe it is unlikely that there are eligible studies featuring PEth that were not included. However, we acknowledge this possibility. Because we converted TLFB data to AUDIT-C scores using established methods for studies lacking the AUDIT-C, there is a possibility that some participants were misclassified.. Finally, we did not have measures of intervention fidelity of intervention exposure/dose to explore in stratified analyses.

In conclusion, we found a consistent small effect of alcohol interventions on unhealthy drinking in PWH. The effect was consistent across measurement strategies, but with high heterogeneity when self-report alone was used. We recommend further work to increase effect sizes for alcohol interventions, including combinations of approaches, that include emerging pharmacologic agents and/or contingency management-based interventions that have shown strong effects in some populations. Future trials of alcohol interventions should include PEth to reduce study measurement heterogeneity. Future directions should also include determining the effect of providing PEth test results to research participants and whether PEth testing might be a useful tool for clinical care, and barriers and facilitators to this approach, including cost.

Supplementary Material

Supplemental file Hahn

Funding statement:

This study is supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA; R01AA029962). JCK’s and JAH’s contributions were also supported by NIAAA K01AA026523 and K24AA022586. GMS was supported by K24AA029958. KSA was supported by P01AA029541 and P30AI042853. Funding for studies included in this analysis included NIAAA (U01AA020797) and the Medical Research Council (Parry/Morojele).

Footnotes

Competing interests: Dr. Judith Hahn received consulting fees from Pear Therapeutics in 2022. Authors have no other competing interests to disclose.

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