Background
It is estimated that 30–50% of people living with HIV (PLWH) have alcohol use disorders (AUDs) and consequently tend to have unfavourable HIV treatment outcomes [
1,
2]. Viral load suppression and testing-and-treating are key targets of the UNAIDS 90-90-90 goals, aimed at eliminating HIV by 2030 [
2]. Alcohol use is associated with risky sexual behaviour, sexually transmitted infections and condomless sex, which are all associated with increased transmission of HIV [
3‐
6]. Alcohol use is also associated with reduced uptake of pre-exposure prophylaxis (PrEP) and post-exposure prophylaxis (PEP) [
3‐
6], delayed HIV testing, treatment initiation, reduced adherence to antiretroviral therapy (ART), more treatment interruptions and lack of viral suppression [
2]. In addition, alcohol use is associated with traffic accidents, intimate partner violence, liver disease and cancers, which are all associated with premature deaths [
7‐
11].
Evaluation of the extent alcohol use in most settings is usually done through screening with self-report questionnaires and clinical examination. Rarely are laboratory investigations, such as alcohol biomarkers, employed. Alcohol self-report assessments tools include the Cut-Annoyed-Guilty-Eye-opener (CAGE), which is a 4-question screener particularly suited for the presence of dependency and the Alcohol Use Disorders Identification Test (AUDIT), which is a 10-question instrument developed by the World Health Organization (WHO) with scores ranging from 0 to 40 [
11‐
13]. A short form of the AUDIT, the AUDIT-C, is also increasingly used to reduce administration time [
14]. Biological measures of alcohol use include blood alcohol concentration (BAC) or surrogates such as liver transaminases, such as gamma glutamyl transaminase (GGT) and mean corpuscular volume (MCV), which are non-specific as they may be changed by liver disease and haematological disease. BAC is, however, able to assess current use but is often unavailable in many settings. Newer biomarkers such as phosphotidyl ethanol (PEth) and ethyl glucuronide (EtG) are promising although the costs may be prohibitive, especially in low-resource settings [
15].
Recommended treatments for AUDs include evidence-based therapies such as motivational interviewing, cognitive behavioural therapy, risk reduction, problem-solving techniques, case management and adjunct pharmacological interventions, especially where there is evidence for dependence. Psychological interventions can be delivered in diverse formats, such as individual or group or both. Treatment settings include hospital-based, community, primary care or emergency services [
16]. Lately, there has been an increase in the use of smartphones and other mobile devices to deliver these interventions [
17], as they increase access in hard-to-reach populations. These technologies are also cost-effective [
18,
19].
Given that adherence to ART is the single most important determinant of HIV treatment success, alcohol-focused psychological interventions may significantly improve HIV treatment outcomes [
20]. Reviews of interventions that target adherence only, without control of alcohol use, have been inconclusive, leading to calls for interventions that target both adherence and problematic alcohol use [
7,
21]. Psychological interventions need to be tailored to address comorbid conditions, such as depression and anxiety, and other psychosocial sequelae (e.g. stigma) that are implicated in poor ART adherence [
21,
22]. Psychological interventions may work by addressing the stigma, including self-stigma that PLWH often face. They may also work by assisting PLWH to acquire new problem-solving skills that may be useful in dealing with other life problems. However, the effectiveness of these interventions may be limited by other unresolved psychosocial challenges and the presence of cognitive impairments (e.g. memory impairment) that can lead to unintentional skipping of medication.
Currently, there are insufficient data on the effectiveness of psychological interventions for AUDs in PLWH, specifically with regard to the active ingredients of each intervention, the dosing required, and the circumstances under which they work [
16]. It is thus essential that these aspects be teased out in order for firm up treatment recommendations. Brown et al. [
16] called for efficacious interventions to be developed and implemented [
16]. This systematic review synthesises current evidence on the effectiveness of psychological and behavioural interventions for AUDs in PLWH.
Methods
The protocol of this review was registered with PROSPERO (CRD42017063856). The review is reported using PRISMA guidelines [
23].
Criteria for considering studies for this review
Types of studies
Studies included in the review were randomised controlled trials, including where the control was a waiting list, and designs that used a quasi-random allocation mechanism, such as alternating assignment or next available treatment slot controls.
Types of participants
Participants were PLWH aged 16 years and above who had AUDs with or without other substance use and were on ART at hospitals, clinics or in the community.
Types of intervention
The interventions included motivational interviewing, motivational enhancement therapy, cognitive behavioural therapy, community contingency therapy, group therapy or any combination of the above that target AUDs, with or without other substance use. Control conditions included adherence counselling, pharmacological detoxification with benzodiazepines, anti-craving medication and referral to psychiatric units or usual care.
Types of outcome measures
Search methods for identification of studies
Data collection and analysis
Two reviewers (MM and JJ) independently screened the titles, abstracts, and then full texts to select studies that met our inclusion criteria. The review authors reconciled any differences through discussions and consensus at each stage. MM and JJ who searched the databases and selected the studies achieved a concordance of 0.63, and MM and AM who extracted data on risk of bias assessment achieved a concordance of 0.71.
Two reviewers (MM and AM) extracted data independently using a pre-piloted data extraction form developed and piloted for this review. Whenever there was any disagreement, the reviewers went through the original articles until they reached consensus. For each included study, we extracted the following: (1) general information (e.g. ethics approval, funding and study period), (2) study design, (3) participants, (4) interventions/comparators, (5) outcomes, (6) results and (7) risk of bias information.
Assessment of risk of bias in included studies
Two reviewers (MM and AM) independently assessed the risk of bias of the included studies. Differences between the reviewers were resolved through discussion. The Cochrane risk of bias tool was used to assess bias in the included studies [
24]. Domains assessed in the risk of bias assessment included selection bias (adequacy of sequence generation and allocation concealment), performance bias (blinding of the participants and research staff) and detection bias (outcome assessors). The other domains assessed were incomplete/missing outcome data caused by attrition or loss to follow-up, publication bias or selective reporting (i.e. where unfavourable or negative outcomes are not reported) and other bias including the influence of funders and other ethical considerations.
Measure of treatment effect and data synthesis
For binary outcomes, we calculated risk ratios with their corresponding 95% confidence intervals (CI) where raw data were reported, otherwise we reported odds ratios (OR) as reported by the study authors. For continuous data, we calculated mean differences (MD) and corresponding 95% CI. Both RR and MD were calculated using Review Manager 5.3 software. Some studies reported intervention effects for continuous outcomes using Cohen’s d, and we reported the effects as such, owing to the fact that there were no sufficient data to report the effects as mean differences. As the outcome measures in individual studies were so diverse, a meta-analysis could not be performed. Analyses were done separately for the different types of interventions.
Dealing with missing data
In the case of missing data, we used the available case analysis. Where there were missing data, intention-to-treat (ITT) analysis was used.
Assessment of publication biases
We had planned to plot funnel plots to indicate the possibility of publication bias; however, since no meta-analysis was performed, we did not construct funnel plots.
Subgroup analysis, investigation of heterogeneity and sensitivity analysis
We had planned to perform a subgroup analysis to identify potential sources of heterogeneity, as well as undertake a sensitivity analysis; however, we did not undertake these as no meta-analysis was performed.
Discussion
This systematic review aimed to synthesise studies that have investigated the effectiveness of psychological interventions for AUDs in PLWH. We identified 21 studies that met our inclusion criteria. Owing to significant heterogeneity across studies in the populations studied, the interventions tested, and the outcome measures administered, no meta-analysis could be performed. The included studies were randomised controlled trials of PLWH: women only, MSM, mixed gender and adolescents and young adults. Studies aimed at reducing alcohol use in PLWH employed a variety of interventions that included motivational interviewing, CBT, brief interventions, mobile/technology aided treatments and group therapies.
Three previous systematic reviews have reported on the effectiveness of psychological/behavioural interventions on alcohol use in PLWH, published in 2010 [
55], 2013 [
16] and 2017 [
56]. All but one [
56] of these concluded that psychological/behavioural interventions were effective for problematic alcohol use in PLWH. New studies have since emerged that address alcohol use in the context of HIV treatment and have been included in this review [
2,
26,
38]. In this synthesis, there were no consistent findings of intervention effect for motivational interviewing, compared to a control, on the quantity and frequency of alcohol use, with the exception of the study by Kahler et al. [
32]. Papas et al. [
27] assessed the effects of cognitive behavioural therapy on the frequency of alcohol consumption and found a significant treatment effect [
27]. Neither studies that delivered a brief intervention nor those that administered a technology assisted intervention found significant treatment effects on the quantity and frequency of alcohol use. Of the studies that delivered group therapy, only Zule et al. [
38] found intervention effects on alcohol use. Another study documented an increase in the quantity of alcohol consumed in the intervention group [
38]. Similarly, secondary outcomes were also heterogeneous and measured in a non-uniform manner across studies, and we were not able to pool data to examine intervention effects on these outcomes.
Across the studies, populations included were diverse. Treatment response may be a function of gender, age and ART adherence. Alcohol users compared to multiple substance users may also respond differently. Some of the interventions were delivered in the community and yet others were delivered at clinics or were hospital based. The context may also affect response to an intervention. Although PROJECT Match was a large study that found that outcomes did not differ by intervention type (motivational enhancement therapy, cognitive behavioural therapy and Twelve-Step Facilitation), the findings of PROJECT Match may not be applicable to diverse HIV-infected populations [
57,
58]. Aside from the different theoretical foundations of the aforementioned interventions, differences in treatment duration, number of sessions and delivery agents may contribute to the differences in outcomes recorded in the studies included in this review.
In addition, different measures of alcohol use were employed. Some studies elected to assess the quantity of alcohol consumed within a certain timeframe while other studies assessed the frequency of alcohol consumption. All studies used self-report screeners that are limited by social desirability bias. Few of the studies assessed viral load and CD4 count change, and for those that did, they did not find intervention effects. Alcohol use is also a dynamic behaviour, and change in the pattern of use due to intervention may not have been present long enough to lead to enduring change in viral load and/or CD4 count. Further, CD4 count measured at baseline may not be a good predictor of change and CD4 nadir maybe a better predictor of future CD4 change [
59,
60]. The adherence measures in the studies reviewed were self-report or pill count, but these have also been shown to be unreliable, with antiretroviral drug levels being a better and more reliable assessment of adherence.
HIV infection is associated with other social challenges such as poverty, unemployment and isolation, and all have been shown to independently influence treatment outcomes [
61]. Apart from these social factors, mental disorders such as depression, anxiety and posttraumatic stress disorder (PTSD) are common comorbidities and can influence treatment response [
62]. Depression, anxiety and PTSD are associated with alcohol use, with research findings suggesting a shared neurobiological basis prompting recommendations for transdiagnostic interventions that target alcohol use, adherence and these mental disorders [
63]. Further, psychosocial interventions for alcohol and depression may also work for dually diagnosed patients [
64]. Complications of alcohol use include liver damage, hepatocellular carcinoma and hepatitis C, and these can all affect an individual’s ability to metabolise antiretroviral drugs and can increase the propensity to adverse effects [
65‐
67].
A number of limitations deserve mention. Most important is the lack of standardised measures of alcohol use outcomes, whether frequency or quantity. Biochemical measures of alcohol use, such as the gamma glutaryl transferase, phosphotidyl ethanol or mean corpuscular volume, are recommended, in addition to self-report instruments. Given that the effects of alcohol on PLWH are not only related to effects on adherence, risky sexual behaviours and virological control, but are also associated with immunosuppressant and deleterious effects on the liver, these outcomes need to be included as treatment targets. Further, the studies in this review included participants with different levels of alcohol use which may affect the effects of an intervention. More severe users, including those with dependency, may require different doses of an intervention and perhaps adjunctive pharmacological therapies.
Our search was recent and comprehensive and encompassed electronic searches of key databases and a search of reference lists of included studies and relevant reviews for additional studies. It is unlikely that any studies were missed. To reduce the potential for bias, two review authors independently undertook the selection of studies, extraction of data and assessment for the risk of bias. We could have obtained more data if we had contacted authors and requested additional data, but it was not possible owing to time constraints. Finally, using GRADEpro to assess the quality of evidence would have given more robust results; however, the heterogeneity in outcomes made it difficult to tease out outcomes for inclusion using GRADEpro [
65].
To our knowledge, this is the fourth systematic review to assess the evidence for psychological interventions for AUDs in PLWH. Brown et al. [
21] raised similar concerns pertaining to the selection of outcome measures and the variation in study methodologies in their review. A review by Samet et al. [
64] found limited evidence for the effectiveness of behavioural interventions, a finding replicated in this review. However, a recent review by Scott-Sheldon et al. [
56] found behavioural interventions to be effective in reducing alcohol consumption, risky sexual behaviour and viral load in PLWH. Our review compared with that of Scott-Sheldon employed a broader search strategy, included more recent studies and rigorously assessed the risk of bias assessment [
56]. We believe our findings are consistent with previous reviews in finding little evidence for effectiveness of psychological interventions for AUDs in PLWH.
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