Alcohol Use and HIV Disease Progression in an Antiretroviral Naive Cohort : JAIDS Journal of Acquired Immune Deficiency Syndromes

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Alcohol Use and HIV Disease Progression in an Antiretroviral Naive Cohort

Hahn, Judith A. PhD, MA*,†; Cheng, Debbie M. ScD; Emenyonu, Nneka I. DrPH, MPH*; Lloyd-Travaglini, Christine MPH; Fatch, Robin MPH*; Shade, Starley B. PhD, MPH; Ngabirano, Christine MA, MPH§; Adong, Julian MBChB§; Bryant, Kendall PhD§,‖; Muyindike, Winnie R. MBChB, MMed§; Samet, Jeffrey H. MD, MA, MPH‡,¶

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JAIDS Journal of Acquired Immune Deficiency Syndromes 77(5):p 492-501, April 15, 2018. | DOI: 10.1097/QAI.0000000000001624
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A substantial proportion (between 8% and 42%) of persons living with HIV/AIDS worldwide has been reported to drink at unhealthy levels (ie, at risk drinking or meeting criteria for an alcohol use disorder).1 Alcohol use has been shown to be a consistent independent risk factor for HIV acquisition,2 and among persons with HIV, alcohol use is associated with decreased retention in care3 and worse antiretroviral treatment (ART) adherence,4 with a dose–response relationship.5,6

Chronic alcohol use impacts both innate and adaptive immune functioning,7 and chronic alcohol use and HIV independently damage the intestinal mucosa, enabling increased microbial translocation with subsequent increased inflammation.8 Experimental studies in which high doses of alcohol were administered to macaques before and after infection with simian immunodeficiency virus (SIV) found increased levels of SIV viremia and mortality compared with control macaques who were infected with SIV but who received a sucrose control.9–12 Thus alcohol use might be an important factor in HIV disease progression.

Despite the high biologic plausibility of an effect of alcohol use on HIV disease progression, the results of human observational studies have been mixed. No prospective study conducted in the period before the advent of ART found an association between alcohol consumption and the onset of AIDS,13 and a retrospective analysis of persons not yet on ART participating in a large clinical HIV cohort found no association between risky alcohol use and CD4 cell count.14 However, 2 studies conducted since the advent of ART suggested a detrimental effect of alcohol use before ART use, with one study reporting a difference in mean CD4 cell count of 49 cells/mm3 among those reporting heavy drinking compared with those abstaining15 and another reporting a strong association between frequent alcohol use (≥2 drinks daily) and time to CD4 cell count below 200 cells/mm.3,16 Neither study found an association of alcohol use measures with HIV viral load.

Among longitudinal studies of persons on ART, the findings have been mixed as well. Several studies among persons on ART have found no association between high levels of alcohol use (variously defined as heavy, hazardous, problem, or severe risk alcohol use) and CD4 cell count and/or HIV viral load after controlling for ART adherence.14,15,17–19 Two recent studies conducted mediation analyses to separate out effects of alcohol use on CD4 cell counts because of reduced adherence vs. other pathways. One found direct effects of heavy alcohol use on CD4 cell counts,20 whereas the other found only indirect effects of alcohol use through adherence.21

Several methodological considerations might explain these inconsistent findings. First, inability to accurately measure alcohol use, due to recall bias, especially social desirability bias, may impact the results.2 Biomarkers of alcohol use, such as phosphatidylethanol (PEth), a direct metabolite of alcohol use that is highly specific and reasonably sensitive for measuring previous 2–3 weeks' alcohol use,22 can provide an objective measure of alcohol intake. Second, in some populations, alcohol use may be associated with illicit drug use, which may be associated with more rapid HIV progression (ie, illicit stimulant use23,24), and thus a spurious association of alcohol use with HIV progression may occur. A solution to this is to exclude other substance use, or conduct studies in settings with very little substance use, such as in Uganda.25 Third, studies of persons on ART may be susceptible to residual confounding because of imperfect measurement of adherence, such as in the case of exaggerated self-reported adherence.26 Thus, restricting the sample to those who are not yet on ART avoids this potential pitfall. Last, the relationship between unhealthy alcohol use and HIV disease progression may be confounded over time, if individuals who engage in unhealthy drinking experience declines in their health and thus reduce their subsequent drinking. This circumstance may spuriously reduce the apparent relationship between unhealthy drinking and HIV disease progression27; previous analyses of this issue have not accounted for this possibility.

The main goal of our study was to determine the biological impact of Unhealthy alcohol consumption was defined as on HIV disease progression, to clarify the previous inconsistent results. We conducted a prospective cohort study among persons not yet on ART in southwestern Uganda, a population with little other substance use, and used PEth, an objective measure of alcohol consumption, to augment self-reported unhealthy alcohol consumption.


Study Participants

This was a longitudinal prospective cohort study conducted in Mbarara, Uganda. Participants were recruited from the Immune Suppression Syndrome Clinic of the Mbarara Regional Referral Hospital of the Mbarara University of Science and Technology. Study enrollment was conducted from September 2011 to August 2014. Eligibility criteria were adult (age ≥18) patient of the Mbarara Immune Suppression Syndrome Clinic; living within 60 km (or 120 km for men to increase male enrollment); fluent in either Runyankole (the local language) or English; and not yet meeting eligibility criteria for ART [ie, CD4 cell count <350 cells/mm3 (cutoff changed to <500 cells/mm3 beginning March 1, 2014), World Health Organization disease stage III or IV, or AIDS-defining illnesses]. We aimed to include equal numbers of persons drinking at unhealthy levels to those not drinking at such levels. To increase the proportion of unhealthy drinkers recruited, reporting any previous year alcohol consumption became a further eligibility criterion beginning in September 2013; the definition of unhealthy drinking used for analysis (see below) was unchanged.

Study Procedures

Study visits (baseline and follow-up) included a structured interviewer-administered assessment and phlebotomy for laboratory testing. Follow-up visits were conducted every 6 months, until loss to follow-up, death, ART initiation, study withdrawal, or the end of the study period (December 2015). Those who became eligible to start ART received a final interview and blood draw before initiating ART. All procedures were approved by the institutional review boards of the Boston University/Boston Medical Center, Mbarara University of Science and Technology, and the University of California, San Francisco, as well as the Uganda National Council for Science and Technology.

Laboratory Testing

Whole blood specimens were tested from all study visits to determine the CD4 cell count, and baseline specimens were tested to determine HIV viral RNA level (<40 copies/mL) in batches from frozen (−80°C) plasma. For PEth testing, whole blood was pipetted on the day of collection onto Whatman 903 cards and stored at −80°C before shipment in batches at room temperature to the United States Drug Testing Laboratories. PEth testing was performed measuring the most common PEth homologue, PEth 16:0/18:1, as previously described.28 The limit of quantification was 8 ng/mL. All baseline dried blood spots were tested for PEth level. No further PEth testing was conducted for participants whose baseline PEth level was <8 ng/mL and who denied current (previous 3 months) alcohol use at all study visits; the PEth level was assumed to be <8 ng/mL for all visits. PEth was tested at all visits for participants who tested PEth positive (≥8 ng/mL) at any visit or who reported any alcohol consumption at any visit.


The study assessment included demographics, alcohol consumption [using the Alcohol Use Disorders Identification Test—Consumption (AUDIT-C),29 modified to measure alcohol consumption in the previous 3 months]. Although the interval between study visits was 6 months to maximize available funds and minimize participant fatigue, we chose a 3-month interval for self-report of alcohol use because recall for frequent behaviors may be better for shorter recall periods.30 This period was also chosen to be roughly comparable with the maximum time period PEth can be detected after drinking ceases.31 We also measured physical health functioning (using the Medical Outcomes Study HIV Survey32,33), symptoms of HIV, and symptoms of depression (using the Center for Epidemiologic Studies Depression Scale34).

We defined unhealthy alcohol use, the primary independent variable, as unhealthy drinking detected through self-report (AUDIT-C ≥3 for women and ≥4 for men) and/or PEth, as follows. We used a cutoff of PEth ≥50 ng/mL to indicate unhealthy drinking, a cutoff that was highly sensitive (93%) and reasonably specific (83%) for detecting daily drinking of 2 or more drinks per day on average (S. Stewart, personal communication). Thus, our primary measure of unhealthy alcohol use was either AUDIT-C positive or PEth ≥50 ng/mL.

Statistical Analysis

We calculated descriptive statistics to characterize study participants overall and by unhealthy drinking status. We used χ2 or Fisher exact tests for categorical variables and t-tests or the Wilcoxon rank-sum test for continuous variables, as appropriate, to compare baseline characteristics between groups.

We evaluated the association between unhealthy alcohol consumption and CD4 cell count over time as our primary analysis. We used linear mixed effects models with subject-specific random intercepts and slopes (to account for within-subject correlation over time) and included a time by unhealthy alcohol use (as a time-dependent variable) interaction term to evaluate the main hypothesis that unhealthy alcohol use is associated with the rate of HIV disease progression. The primary outcome variable was CD4 cell count, assessed every 6 months from baseline to the final study visit (just before ART initiation or the end of the study). We adjusted for baseline age, religion, sex, time since HIV diagnosis, and log10 HIV RNA viral load as potential confounders. Before regression modeling, we calculated Spearman correlation coefficients for independent variables and covariates (all correlations r < 0.40).

We conducted several confirmatory analyses. These included analyses limited to those who were recruited and followed before the change in ART eligibility (from CD4 cell count <350 to <500 cells/mm3) to assess the impact of this change. In an analysis to assess the impact of possible “sick quitters,” we included only those participants reporting any previous 3-month alcohol use or PEth ≥8 ng/mL at baseline, and lifetime abstainers (ie, excluding past drinkers). In another analysis, we restricted the sample to those diagnosed with HIV in the past year, to be more comparable with the macaque models that focused on early infection.9 We also reran the primary analysis excluding log10 HIV RNA viral load to assess potential overfitting by including viral load as a covariate. We also examined using alternative measures of alcohol consumption, such as using PEth alone, as a 3-level variable (PEth <50 ng/mL, PEth ≥50 to <210 ng/mL, and PEth ≥210 ng/mL; 210 ng/mL is a suggested PEth cutoff for excessive drinking35), as a continuous variable (log PEth), and modeling self-report, using 3 AUDIT-C categories (low-level drinking: <3 for women, <4 for men; medium-level drinking: ≥3 and <6 for women, ≥4 and <6 for men; and high-level drinking: ≥6 for men and women). We also applied pattern mixture models to the main analyses to explore departures from the assumption that data are missing at random.36 For these analyses, we classified participants' visit patterns as complete (completing all scheduled study visits), monotone (missing one and all after visits), or intermittent (returning at least once after a missed visit), and we assessed interactions between these patterns and the parameters in the mixed effects model.

We additionally conducted analyses using marginal structural models (MSMs) to account for time-dependent covariates that may potentially be both confounders and mediators of the relationship between unhealthy alcohol consumption and HIV disease progression,27,37,38 to determine whether an effect of unhealthy alcohol use on HIV disease progression could be masked if participants reduced their drinking in response to disease progression that was the result of previous unhealthy alcohol use. The parameters of the MSM were estimated using a Generalized Estimating Equation model of CD4 cell count using inverse probability of treatment weights, to balance the joint distribution of all covariables at each time point, thus eliminating both time-independent and time-dependent confounding. Weights were estimated using a logistic GEE model and accounted for time-independent and time-dependent variables (age, sex, marital status, education, literacy, overall health status, nausea, physical functioning, number of HIV symptoms, depression, and months since HIV diagnosis).

We examined a secondary outcome, time from enrollment to CD4 cell count below the threshold for ART initiation, using the Cox proportional hazards model using the exact method for handling tied event times.39 Participants were censored at the earliest of the following: ART start, loss to follow-up, study withdrawal, or end of study. For this model, we conducted the additional analyses described for the mixed models above, and also conducted a sensitivity analysis by including a time-varying covariate for date of the guideline change for the CD4 threshold to initiate ART (ie, before vs. after March 1, 2014). In addition, we fit a separate model with time to CD4 <500 as the outcome, including only those with CD4 ≥500 at enrollment. Last, we used an MSM approach to examine the association of unhealthy alcohol use with time to CD4 cell count below the threshold of ART initiation, accounting for time-dependent confounding. All analyses were conducted using 2-sided tests and a significance level of 0.05.

Sample Size

A priori, we estimated the sample size needed to detect the expected differences in CD4 cell count decline between unhealthy vs. no unhealthy drinking, with 80% power and a 2-sided test with a significance level of 0.05. We considered the change in CD4 cell count from baseline to the 12-month time point (corresponding to testing an alcohol by time interaction), which is a conservative approach, given our analyses based on repeated measures. The SD of change in CD4 cell count over 1 year was previously 166 cells/mm3,15 with an expected retention rate of 90%, and a sample size of 450 would detect a difference in the 1-year decline in CD4 count between the groups of 50 cells/mm3 or greater, similar to the difference in CD4 count previously found.15


Of 1096 persons approached for enrollment, 484 persons enrolled, 445 persons were initially deemed ineligible, and 167 persons declined enrollment. Reasons for declining enrollment included not having time (n = 70), not wanting to have blood drawn (n = 30), not wanting to participate (n = 20), worries about stigma or disclosure (n = 15), feeling too weak to participate (n = 13), needing permission from someone to participate (n = 11), and other unspecified reasons (n = 8). Declining participation did not differ by sex. After enrollment and baseline testing, we found that several participants were not eligible for this study, based on testing of stored specimens of participants whose HIV viral load was found to be low or undetectable (<500 copies/mL). We thus excluded 32 participants who were not HIV antibody positive, 5 who tested positive for the presence of nevirapine or efavirenz (the 2 most commonly used HIV drugs in Uganda), and one participant who was missing alcohol use data, leaving 446 participants for analysis.

At baseline, 30 percent (30%) of the participants were AUDIT-C positive and 35% had PEth level ≥50 ng/mL (Table 1). Most were concordant on AUDIT-C and PEth (57% concordant negative, 21% concordant positive), 13% were AUDIT-C negative but PEth ≥50 ng/mL, and 9% were AUDIT-C positive but PEth <50 ng/mL. Thus 43% of the cohort were defined as drinking at unhealthy levels (AUDIT-C positive and/or PEth ≥50 ng/mL). Two-thirds (68%) of participants were women; all reported lifetime abstention from heroin, methamphetamine, and cocaine, and 7 (1.6%) persons reported any lifetime marijuana use and 7 reported any lifetime khat use (1.6%). The median CD4 cell count at baseline was 550 cells/mm3 [interquartile range (IQR) 416–685], and the median time since HIV diagnosis was 18.5 months (IQR 1.9–64.6).

ART Naive HIV-Infected Persons in Southwestern Uganda: Participant Characteristics at Baseline (N = 446)

The median duration of follow-up was 12.4 months (IQR 6.5–22.5), and the median number of study visits per participant was 3 (IQR 2–5). Over the course of the study, two-thirds (67%) graduated from the cohort because of starting ART, 25% were followed until the end of the study, whereas 8% were lost to follow-up or withdrew from the study.

Primary Outcome: CD4 Cell Count

The unadjusted and adjusted mixed models showed declines in CD4 cell count over time, but no statistically significant difference in the rate of the decline by unhealthy drinking (Table 2). The estimated decline in CD4 cell count from baseline over 1 year was −14.5 cells/mm3 (95% confidence interval: −38.6 to 9.5) for unhealthy drinking vs. −24.0 cells/mm3 (95% confidence interval: −43.6 to −4.5) for not drinking at unhealthy levels in the adjusted model (Table 3), and the P value for interaction was not statistically significant (P = 0.54). We did not find a significant relationship between level of drinking and CD4 cell count decline over time in any of the additional analyses (limiting visits to those that occurred before ART eligibility changes, excluding past drinkers, excluding those diagnosed more than 1 year before enrollment, not including HIV viral load in the model, examining alternative measures for unhealthy alcohol use, and using MSM techniques to account for potential time-dependent confounding) (Table 3). We did not detect significant differences across patterns of missing data in the pattern mixture models (P value for interaction: 0.68) nor did we detect a relationship between unhealthy alcohol use and HIV disease progression within any missing data pattern group (all P > 0.30).

Unadjusted and Adjusted Models of CD4 Cell Count in ART Naive HIV-Infected Persons in Southwestern Uganda
Estimated 12-Month Change in CD4 Cell Count by Drinking Status for Primary and Additional Analyses of CD4 Cell Count in ART Naive HIV-Infected Persons in Southwestern Uganda

Secondary Outcome: Time to ART Eligibility

The unadjusted and adjusted hazard ratios for unhealthy drinking in examining time to ART eligibility (ie, CD4 cell count <350 cells/mm3 before March 1, 2014, CD4 cell count <500 cells/mm3 thereafter) were 1.17 (0.91–1.49) and 1.10 (0.84–1.43), respectively (Table 4). When we included a time-dependent indicator to reflect when the threshold for starting ART changed, when we examined time to CD4 cell count to 500 cells/mm3, and when we limited the data to visits completed before the change in ART eligibility, the hazard ratios for unhealthy alcohol use were not substantially different from above. The additional analyses of time to ART eligibility (eg, excluding past drinkers, as above) yielded similar results (Table 5).

Cox Proportional Hazard Models of Time to CD4 Count Below Level for ART Eligibility in ART Naive HIV-Infected Persons in Southwestern Uganda
Cox Proportional Hazards Models for Time to CD4 Count Below ART Threshold, for Secondary Variable and Additional Analyses in ART Naive HIV-Infected Persons in Southwestern Uganda


We conducted a large prospective study of CD4 cell count over time in persons with HIV who were not yet on ART and found no significant difference in the rate of CD4 decline for unhealthy vs. no unhealthy drinking in the primary analysis, nor in any of the extensive confirmatory analyses. The clinical significance of this is that unhealthy alcohol use does not seem to have a direct short-term impact on CD4 cell counts among persons with HIV who are not on ART. This is consistent with several prospective studies conducted in East Africa18,19,21 and elsewhere14,17 but differs from some others.15,16,20 The gender distribution (68% women) was representative of that of HIV in eastern and southern Africa.40 The PEth levels revealed some discordance with self-report, consistent with previous studies of persons with HIV.41–45 The methodological strengths of this study are notable. We recruited a large number of unhealthy drinkers, incorporated biological measures of drinking, restricted the sample to persons not on ART, and did not include persons using other substances. In addition, we conducted multiple confirmatory analyses (ie, examining alternative measures of alcohol use, including using higher AUDIT-C and PEth cutoffs to examine very heavy drinking, key subgroups of participants, and accounting for potential time-dependent confounding) that were consistent with the primary results, arguing for the robustness of these findings.

Despite finding no impact on CD4 cell count decline, there is evidence that alcohol use negatively impacts the HIV epidemic and individuals with HIV in ways other than CD4 decline. First, alcohol use has been a consistent risk factor for HIV acquisition,46–48 and thus drinkers make up a disproportionate number of those infected with HIV. Alcohol use can impact the risk of onward transmission of HIV as well, presumably as a consequence of increased sexual risk behavior. The literature strongly suggests that persons with HIV who consume alcohol have lower ART adherence,4 putting viral suppression and HIV outcomes at risk. Drinkers also may be more likely to transmit HIV due to increased vaginal shedding.49–51 In addition to acquisition and transmission of HIV, some evidence suggests that alcohol use adversely impacts gut factors related to microbial translocation, despite the absence of consequences of the latter on CD4 cell count decline.52

This study was limited by the changes in the ART initiation criterion that occurred during the course of the study. These changes restricted our ability to examine CD4 cell count decline to a narrow range of starting values (ie, above 500 cells/mm3) and decreased the amount of follow-up time, because, by design, we were following participants only until they started ART. The relatively short follow-up duration (median 12.5 months) may have limited our ability to find associations of alcohol use with longer-term outcomes, such as a multifactor measure of HIV morbidity.53 Because this study was observational, unmeasured confounding may have obscured our results. For example, we did not measure exercise or nutritional status.

We conclude from these results, despite some past literature to the contrary, that there is no clinically meaningful biological impact of unhealthy alcohol use on the CD4 cell count decline among persons with HIV not yet on ART. However, unhealthy alcohol use has been previously shown to decrease ART adherence and increase HIV transmission, thus adversely impacting both individual- and population-level outcomes. In addition, there is suggestive evidence that unhealthy alcohol use may negatively impact inflammation; for these reasons, unhealthy alcohol use should be strongly discouraged for persons with HIV. However, unhealthy alcohol use does not seem to have a short-term direct biological impact on CD4 cell count.


1. Williams EC, Hahn JA, Saitz R, et al. Alcohol use and human immunodeficiency virus (HIV) infection: current knowledge, implications, and future directions. Alcohol Clin Exp Res. 2016;40:2056–2072.
2. Baliunas D, Rehm J, Irving H, et al. Alcohol consumption and risk of incident human immunodeficiency virus infection: a meta-analysis. Int J Public Health. 2010;55:159–166.
3. Monroe AK, Lau B, Mugavero MJ, et al. Heavy alcohol use is associated with worse retention in HIV care. J Acquir Immune Defic Syndr. 2016;73:419–425.
4. Hendershot CS, Stoner SA, Pantalone DW, et al. Alcohol use and antiretroviral adherence: review and meta-analysis. J Acquir Immune Defic Syndr. 2009;52:180–202.
5. Sileo KM, Simbayi LC, Abrams A, et al. The role of alcohol use in antiretroviral adherence among individuals living with HIV in South Africa: event-level findings from a daily diary study. Drug Alcohol Depend. 2016;167:103–111.
6. Braithwaite RS, McGinnis KA, Conigliaro J, et al. A temporal and dose-response association between alcohol consumption and medication adherence among veterans in care. Alcohol Clin Exp Res. 2005;29:1190–1197.
7. Szabo G, Saha B. Alcohol's effect on host defense. Alcohol Res. 2015;37:159–170.
8. Bagby GJ, Amedee AM, Siggins RW, et al. Alcohol and HIV effects on the immune system. Alcohol Res. 2015;37:287–297.
9. Bagby GJ, Stoltz DA, Zhang P, et al. The effect of chronic binge ethanol consumption on the primary stage of SIV infection in rhesus macaques. Alcohol Clin Exp Res. 2003;27:495–502.
10. Bagby GJ, Zhang P, Purcell JE, et al. Chronic binge ethanol consumption accelerates progression of simian immunodeficiency virus disease. Alcohol Clin Exp Res. 2006;30:1781–1790.
11. Kumar R, Perez-Casanova AE, Tirado G, et al. Increased viral replication in simian immunodeficiency virus/simian-HIV-infected macaques with self-administering model of chronic alcohol consumption. J Acquir Immune Defic Syndr. 2005;39:386–390.
12. Poonia B, Nelson S, Bagby GJ, et al. Chronic alcohol consumption results in higher simian immunodeficiency virus replication in mucosally inoculated rhesus macaques. AIDS Res Hum Retroviruses. 2006;22:589–594.
13. Hahn JA, Samet JH. Alcohol and HIV disease progression: weighing the evidence. Curr HIV/AIDS Rep. 2010;7:226–233.
14. Conen A, Wang Q, Glass TR, et al. Association of alcohol consumption and HIV surrogate markers in participants of the Swiss HIV Cohort Study. J Acquir Immune Defic Syndr. 2013;64:472–478.
15. Samet JH, Cheng DM, Libman H, et al. Alcohol consumption and HIV disease progression. J Acquir Immune Defic Syndr. 2007;46:194–199.
16. Baum MK, Rafie C, Lai S, et al. Alcohol use accelerates HIV disease progression. AIDS Res Hum Retroviruses. 2010;26:511–518.
17. Kowalski S, Colantuoni E, Lau B, et al. Alcohol consumption and CD4 T-cell count response among persons initiating antiretroviral therapy. J Acquir Immune Defic Syndr. 2012;61:455–461.
18. Weiser SD, Palar K, Frongillo EA, et al. Longitudinal assessment of associations between food insecurity, antiretroviral adherence and HIV treatment outcomes in rural Uganda. AIDS. 2014;28:115–120.
19. Cagle A, McGrath C, Richardson BA, et al. Alcohol use and immune reconstitution among HIV-infected patients on antiretroviral therapy in Nairobi, Kenya. AIDS Care. 2017;29:1192–1197.
20. Kahler CW, Liu T, Cioe PA, et al. Direct and indirect effects of heavy alcohol use on clinical outcomes in a longitudinal study of HIV patients on ART. AIDS Behav. 2017;21:1825–1835.
21. Wandera B, Tumwesigye NM, Nankabirwa JI, et al. Hazardous alcohol consumption is not associated with CD4+ T-cell count decline among PLHIV in Kampala Uganda: a prospective cohort study. PLoS One. 2017;12:e0180015.
22. Wurst FM, Thon N, Yegles M, et al. Ethanol metabolites: their role in the assessment of alcohol intake. Alcohol Clin Exp Res. 2015;39:2060–2072.
23. Carrico AW, Shoptaw S, Cox C, et al. Stimulant use and progression to AIDS or mortality after the initiation of highly active antiretroviral therapy. J Acquir Immune Defic Syndr. 2014;67:508–513.
24. Cook JA, Burke-Miller JK, Cohen MH, et al. Crack cocaine, disease progression, and mortality in a multicenter cohort of HIV-1 positive women. AIDS. 2008;22:1355–1363.
25. John-Langba J, Ezeh A, Georges G, et al. Alcohol, drug use, and sexual-risk behaviors among adolescents in four sub-saharan african countries. Available at: Accessed December 7, 2008.
26. Stirratt MJ, Dunbar-Jacob J, Crane HM, et al. Self-report measures of medication adherence behavior: recommendations on optimal use. Transl Behav Med. 2015;5:470–482.
27. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550–560.
28. Jones J, Jones M, Plate C, et al. The detection of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanol in human dried blood spots. Anal Methods. 2011;3:1101.
29. Bradley KA, DeBenedetti AF, Volk RJ, et al. AUDIT-C as a brief screen for alcohol misuse in primary care. Alcohol Clin Exp Res. 2007;31:1208–1217.
30. Napper LE, Fisher DG, Reynolds GL, et al. HIV risk behavior self-report reliability at different recall periods. AIDS Behav. 2010;14:152–161.
31. Hahn JA, Anton RF, Javors MA. The formation, elimination, interpretation, and future research needs of phosphatidylethanol for research studies and clinical practice. Alcohol Clin Exp Res. 2016;40:2292–2295.
32. Stangl AL, Bunnell R, Wamai N, et al. Measuring quality of life in rural Uganda: reliability and validity of summary scores from the medical outcomes study HIV health survey (MOS-HIV). Qual Life Res. 2012;21:1655–1663.
33. Wu AW, Hays RD, Kelly S, et al. Applications of the Medical Outcomes Study health-related quality of life measures in HIV/AIDS. Qual Life Res. 1997;6:531–554.
34. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. J Appl Psychol Meas. 1977;1:385–401.
35. Helander A, Hansson T. National harmonization of the alcohol biomarker PEth [in Swedish]. Lakartidningen. 2013;110:1747–1748.
36. Hedeker D, Gibbons RD. Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychol Methods. 1997;2:64.
37. Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology. 2000;11:561–570.
38. Hernan MA, Brumback BA, Robins JM. Estimating the causal effect of zidovudine on CD4 count with a marginal structural model for repeated measures. Stat Med. 2002;21:1689–1709.
39. Kalbfleisch JD, Prentice RL. The Statistlcal Analysis of Failure Time Data. 2nd ed. Hoboken, NJ: Wiley; 2002.
40. UNAIDS [Internet]. AIDSInfo; [Accessed October 6, 2017]. Available from:
41. Muyindike WR, Lloyd-Travaglini C, Fatch R, et al. Phosphatidylethanol confirmed alcohol use among ART-naive HIV-infected persons who denied consumption in rural Uganda. AIDS Care. 2017;29:1442–1447.
42. Bajunirwe F, Haberer JE, Boum Y II, et al. Comparison of self-reported alcohol consumption to phosphatidylethanol measurement among HIV-infected patients initiating antiretroviral treatment in southwestern Uganda. PLoS One. 2014;9:e113152.
43. Papas RK, Gakinya BN, Mwaniki MM, et al. Associations between the phosphatidylethanol alcohol biomarker and self-reported alcohol use in a sample of HIV-infected outpatient drinkers in Western Kenya. Alcohol Clin Exp Res. 2016;40:1779–1787.
44. Littlefield AK, Brown JL, DiClemente RJ, et al. Phosphatidylethanol (PEth) as a biomarker of alcohol consumption in HIV-infected Young Russian women: comparison to self-report assessments of alcohol use. AIDS Behav. 2017;21:1938–1949.
45. Wang Y, Chen X, Hahn JA, et al. Phosphatidylethanol (PEth) in comparison to self-reported alcohol consumption among HIV-infected women in a randomized controlled trial of naltrexone for reducing hazardous drinking. Alcohol Clin Exp Res. 2018;42:128–134.
46. Joseph Davey D, Kilembe W, Wall KM, et al. Risky sex and HIV acquisition among HIV serodiscordant couples in Zambia, 2002–2012: what does alcohol have to do with it?. AIDS Behav. 2018;21:1892–1903.
47. Chersich MF, Bosire W, King'ola N, et al. Effects of hazardous and harmful alcohol use on HIV incidence and sexual behaviour: a cohort study of Kenyan female sex workers. Glob Health. 2014;10:22.
48. Kiwanuka N, Ssetaala A, Ssekandi I, et al. Population attributable fraction of incident HIV infections associated with alcohol consumption in fishing communities around Lake Victoria, Uganda. PLoS One. 2017;12:e0171200.
49. Theall KP, Amedee A, Clark RA, et al. Alcohol consumption and HIV-1 vaginal RNA shedding among women. J Stud Alcohol Drugs. 2008;69:454–458.
50. Homans J, Christensen S, Stiller T, et al. Permissive and protective factors associated with presence, level, and longitudinal pattern of cervicovaginal HIV shedding. J Acquir Immune Defic Syndr. 2012;60:99–110.
51. Loganantharaj N, Nichols WA, Bagby GJ, et al. The effects of chronic binge alcohol on the genital microenvironment of simian immunodeficiency virus-infected female rhesus macaques. AIDS Res Hum Retroviruses. 2014;30:783–791.
52. Carrico AW, Hunt PW, Emenyonu NI, et al. Unhealthy alcohol use is associated with monocyte activation prior to starting antiretroviral therapy. Alcohol Clin Exp Res. 2015;39:2422–2426.
53. Marshall BDL, Tate JP, McGinnis KA, et al. Long-term alcohol use patterns and HIV disease severity. AIDS. 2017;31:1313–1321.

HIV progression; phosphatidylethanol; Uganda; antiretroviral treatment adherence

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