Unhealthy alcohol use carries a risk of adverse health and social consequences and is a major public health issue.1,2 It is common among individuals infected with HIV (HIV+) and/or hepatitis C virus (HCV+) and may be particularly detrimental in these populations.3–6 The potentially high susceptibility to harm from unhealthy alcohol use may be related to its known association and negative impact on HIV medication adherence,4 disease progression,7,8 risk of hepatic disorders,3,9 and exacerbation of other effects of HIV infection.7,10 Unhealthy alcohol use is linked to harmful health consequences and mortality.9–12 A clear dose-dependent relationship between levels of heavy drinking and all-cause mortality has been reported.12,13
Despite the known health impacts of unhealthy alcohol use, accurate characterization of the spectrum of alcohol exposure is challenging.14,15 To assess alcohol consumption among patients and study participants, health care providers and researchers typically rely on self-reported measures. However, likely because of social desirability bias, alcohol consumption is frequently under-reported, especially among populations such as HIV+ or HCV+ individuals for whom alcohol use is discouraged.16,17 Reliance on self-report is an important limitation in many alcohol studies14,18; however, it remains the standard method for characterizing exposure in clinical settings.14 The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire is widely used to screen for self-reported alcohol consumption19,20 and has been validated in multiple settings.19–21
To address the biases associated with self-report, several alcohol biomarkers have been evaluated to objectively assess alcohol use.22 Phosphatidylethanol (PEth) is a direct metabolite of alcohol consumption formed from phosphatidylcholine by the action of the enzyme phospholipase D and can detect alcohol exposure up to 3 weeks after consumption.22–25 PEth performs relatively well compared with other established alcohol biomarkers because it is both highly sensitive (ranging from 85% to 99%) and specific (almost 100%).26,27 It does not seem to be affected by age, sex, other ingested substances, or nonalcohol-associated diseases.24 However, it can vary between persons consuming the same amount of alcohol, likely because of differences in alcohol metabolism.28 In settings in which under-reported alcohol use is common, the high specificity of both PEth and self-report has been used in combination (considered positive if positive on either measure) to increase sensitivity.29
Our primary objective was to evaluate the association between alcohol consumption, determined using a combination of AUDIT-C and PEth, and all-cause mortality, among a cohort of HIV-infected and HIV-uninfected comparators. The secondary objectives were to: (1) evaluate the level of agreement between the alcohol measures; and (2) determine whether HIV or HCV status was associated with under-reporting of alcohol.
Study Design, Setting, and Participants
We used data from the Veterans Aging Cohort Study (VACS), an ongoing longitudinal cohort study with the aim of understanding the role of alcohol use on health. All HIV+ individuals receiving care in the Veterans Affairs (VA) Healthcare System are included plus an age-, race-, and site-of-care matched control group without HIV infection. In addition to full electronic health record data, a subset of consented patients also provide survey data on alcohol use and other behaviors. Detailed information about VACS has been reported elsewhere.30,31 The institutional review boards of the participating VA sites and the coordinating center approved the VACS.
This study was restricted to 2344 VACS survey participants (1513 HIV+ and 831 uninfected) who consented to be included in a tissue repository substudy [VACS-Biomarker Cohort (VACS-BC)], provided blood specimen between 2005 and 2007, as previously described,32,33 and for whom PEth assay was available. Analyses were performed only among participants for whom complete AUDIT-C survey data characterizing self-reported alcohol consumption in the past year were available. Participant baseline was defined as the date of collection of the blood specimen.
The primary outcome was all-cause 5-year mortality. Participants were followed for 5 years after their baseline date or until death within 5 years. Deaths during follow-up were ascertained from the VA vital status file which uses multiple sources including: (1) the Patient Treatment File, which records hospital deaths in the VA health care system; (2) the Beneficiary Identification Records Locating System, which tracks VA death benefits; (3) the Medicare Vital Status File, containing vital statistics records including death information on all Medicare beneficiaries; and (4) the Social Security Death Master File, containing information on deceased persons which is typically collected in connection with filing of benefits by family members.
Primary Explanatory Variable
Our primary explanatory variable was alcohol exposure, which was characterized using 2 different alcohol exposure measures, separately and combined: (1) self-reported AUDIT-C; and (2) PEth. AUDIT-C data were collected as part of a confidential self-administered survey conducted at VACS enrollment and during annual follow-ups. PEth assays were performed on dried blood spot samples by United States Drug Testing Laboratories (USDTL), in Des Plaines, IL, using a previously reported method.34 A positive PEth (measuring PEth species 16:0/18:1) indicates alcohol exposure for up to approximately 21 days before sample collection.22,35
Alcohol Exposure Categories
AUDIT-C consists of the first 3 items on the 10-item AUDIT survey that asks about quantity and frequency of alcohol consumption as well as the frequency of heavy episodic drinking. Details of the questions and possible responses have been described previously.14 Responses to each of the 3 items are assigned 0–4 points. Summing points for the 3 items in AUDIT-C result in a score total ranging from 0 to 12, with higher scores reflecting greater severity of alcohol use.36 Because the recommended AUDIT-C threshold for unhealthy alcohol use differs between men (≥4) and women (≥3),20 we categorized self-reported alcohol consumption in the past year according to AUDIT-C using the following cutoffs: 0 (no alcohol use/“abstinence”); 1–3 for men/1–2 for women (lower-risk drinking); 4–7 for men/3–7 for women (at-risk drinking); and 8–12 (high-risk drinking).
There is currently no established PEth level for acceptable lower-risk alcohol intake,24,26 although a level of ≥8 ng/mL, the lower limit of quantitation is commonly used as an indicator of any alcohol use in the previous 21 days.16,37–39 As PEth 8 ng/mL represents the lower threshold at which the assay can reliably quantify alcohol exposure,34 we considered a PEth <8 ng/mL to represent no alcohol use and created 2 PEth levels (<8 vs. ≥8 ng/mL) accordingly. We also considered a PEth variable with 3 levels (<8, 8–49, and ≥50), as PEth of ≥50 has been considered to indicate unhealthy alcohol use.
We further categorized alcohol exposure combining AUDIT-C and PEth measures. We show characteristics and mortality rates by all AUDIT-C/PEth categories in tabular form. In our approach to modeling, we prioritized any alcohol use based on self-reported AUDIT-C over PEth because AUDIT-C assesses alcohol use over the past year, whereas PEth detects alcohol use for only up to 3 weeks. We therefore used a combined AUDIT-C/PEth measure only among individuals who self-reported abstinence (AUDIT-C = 0). The final alcohol exposure categories were: (1) AUDIT-C = 0 and PEth <8 (self-reported abstinence with negative PEth); (2) AUDIT-C = 0 and PEth ≥8 (self-reported abstinence with positive PEth); (3) AUDIT-C = 1–3 for men/1–2 for women (lower-risk drinking); (4) AUDIT-C = 4–7 for men/3–7 for women (at-risk drinking); and (5) AUDIT-C = 8–12 (high-risk drinking).
Primary covariates included age, sex, race/ethnicity, HIV, HCV antibody status, and HIV viral suppression. Age at blood draw was used as both a categorical (<50, 50–64, and ≥65 years) and a continuous variable. Race was classified as African American, white, and Hispanic/other (includes other race not already classified). HIV+ status was confirmed during enrollment at the VA sites. HCV infection was defined based on a positive HCV antibody test, detectable HCV RNA, or at least 1 inpatient and/or 2 outpatient ICD-9 code.40 Plasma HIV RNA of ≤500 copies per milliliter was used to define viral suppression. Additional covariates included smoking status (never/past/current), injection drug use in the past year (yes/no), and having at least some college education (yes/no), all of which were collected on the VACS surveys.
Sample characteristics were assessed descriptively using χ2 tests for categorical variables and t tests or Wilcoxon rank-sum tests for continuous variables. We compared agreement between self-report and PEth-detected alcohol exposure by summarizing PEth (proportion ≥8 ng/mL, mean and median) by AUDIT-C for HIV+ and uninfected. Of those self-reporting abstinence (AUDIT-C = 0), we compared the proportion with positive PEth by HIV and HCV status, using χ2 tests.
We calculated cumulative incidence of mortality over the 5-year follow-up period and compared across alcohol measures. We fit Cox proportional hazards models for time to death and estimated mortality hazard ratios with 95% confidence intervals (CIs) first using AUDIT-C as the only alcohol metric, adjusted for age, sex, race/ethnicity, HIV, HCV, and viral suppression (these variables were identified a priori and are commonly associated with mortality). We used the lower-risk drinking category (AUDIT-C = 1–3 for men/1–2 for women) as the reference group, as this has been suggested to be an ideal comparison group for studying the effect of alcohol on health outcomes.41 We, then, refit the model using the combined AUDIT-C/PEth alcohol exposure categories and used the likelihood ratio test to determine whether model fit improved. We further adjusted for smoking status, injection drug use in the past year, and having at least some college education.
To address potential concern about differences in timing between the PEth blood draw date and the AUDIT-C date, we restricted the data set to 682 individuals with blood draw on the same day or within 21 days before the AUDIT-C date and reran all analyses on this restricted sample.
All statistical analyses were performed using Stata, version 14.2 (StataCorp, College Station, TX).
Demographics and Sample Characteristics
Of 2656 VACS-BC participants (1721 HIV+; 935 uninfected) with PEth data (1529 HIV+; 836 uninfected), a total of 2344 participants (1513 HIV+ and 831 uninfected) also had complete AUDIT-C data and were included in the final sample of this study. The median age at blood draw was 52 and 53 years among HIV+ and uninfected individuals, respectively. Over half of the sample were aged 50–64 years, 95% were men, 70% had at least some college education, 36% had hepatitis C infection, 27% were current smokers, and 9% reported using intravenous drugs in the past year (Table 1). Approximately two-thirds of the 1513 HIV+ participants had suppressed viral load (≤500 copies/mL) at blood collection date. Characteristics of the restricted sample were very similar to those of the full sample. The median time between blood draw (PEth measurement) and AUDIT-C survey date was 46 days (25th, 75th percentile: 0, 222) for the entire sample (n = 2344). For the restricted sample (n = 682), the median time between dates was 0 days (25th, 75th percentile: 0, 0), and 91% had a blood draw and AUDIT-C survey on the same day.
For both HIV+ and uninfected, AUDIT-C characterized a higher proportion of individuals as alcohol exposed (AUDIT-C > 0) compared with PEth (≥8 ng/mL) (58% vs. 36% for HIV+; 55% vs. 38% for uninfected). In addition, AUDIT-C characterized a higher proportion of individuals as drinking unhealthy levels of alcohol (AUDIT-C ≥4 for men/3 for women) compared with (PEth ≥50) (23% vs. 17% for HIV+; 26% vs. 21% for uninfected) (Table 1). Comparing AUDIT-C and PEth, the proportion with positive PEth (ie, ≥8 ng/mL) increased as AUDIT-C increased. Among those who reported alcohol use (AUDIT-C > 0), median PEth increased with increasing AUDIT-C (Table 2). Approximately 43% (1015/2344) of participants self-reported past-year abstinence (AUDIT-C = 0). However, 15% (149/1015) of these individuals had PEth ≥8 ng/mL, suggesting very recent alcohol consumption (Table 2). Similarly, in the restricted sample, 43% (291/682) reported abstinence, and 12% of these (33/291) had PEth ≥8 (Table 2), thus corroborating results from the full sample.
Patterns of AUDIT-C and PEth by HIV and HCV status are shown in Figure 1. Among those who self-reported abstinence, the proportion with PEth ≥8 ng/mL was similar by HIV status (14.5% for HIV+; 15.0% for uninfected) but differed by HCV status; a higher proportion of those with HCV had PEth ≥8 ng/mL compared with those without HCV (18% vs. 11%, P = 0.009 for HIV+ and 21% vs. 13%, P = 0.039 for uninfected) (Fig. 1A). Results were similar in the restricted sample, although not statistically significant. Among those who self-reported abstinence, a higher proportion of those with HCV had PEth ≥8 ng/mL compared with those without HCV (16% vs. 9% for HIV+ and 17% vs. 9% for uninfected) (Fig. 1B).
Association of Alcohol Exposure With Mortality According to AUDIT-C and PEth
During a mean of 4.7 years of the follow-up time, 13% of the sample died, for cumulative incidence of 2.71, 95% CI: 2.42 to 3.03 per 100 person-years (PY). Mortality was higher among HIV+ (3.13 per 100 PY, 95% CI: 2.74 to 3.57) compared with uninfected individuals (1.96 per 100 PY, 95% CI: 1.57 to 2.45) (Table 3). Mortality among HIV+ individuals self-reporting abstinence (3.68 per 100 PY, 95% CI: 3.05 to 4.44) was higher than that of those with lower-risk (2.05 per 100 PY, 95% CI: 1.56 to 2.70) and at-risk drinking (3.44 per 100 PY, 95% CI: 2.55 to 4.64), but lower than that of those with high-risk drinking (5.35 per 100 PY, 95% CI: 3.33 to 8.61) (Table 3). A similar pattern was observed among uninfected individuals, except where power was limited; the lowest mortality was among those with high-risk drinking (2 deaths with wide CIs). In both HIV+ and uninfected individuals, mortality rates were higher among individuals with PEth ≥8 ng/mL compared with those with PEth <8 ng/mL; and highest among those with PEth ≥50 ng/mL compared with those with PEth <8 and PEth 8–49 (Table 3). Among HIV+, the highest mortality was among those reporting abstinence but with PEth ≥8 ng/mL (5.69 per 100 PY, 95% CI: 3.78 to 8.56) and among those with high-risk AUDIT-C and PEth ≥8 ng/mL (6.12 per 100 PY, 95% CI: 3.82 to 9.85). Among the uninfected, those reporting abstinence but having PEth ≥8 ng/mL had the highest mortality rate (3.03 per 100 PY, 95% CI: 1.52 to 6.07). Patterns are similar among the restricted sample (Table 3).
The association between alcohol exposure and mortality persisted after adjusting for age, sex, race, HIV status, HCV, and viral suppression in Cox models. First, using only AUDIT-C alcohol characterization, individuals self-reporting abstinence had higher mortality [adjusted hazard ratio (aHR) 1.45, 95% CI: 1.10 to 1.92] compared with those with lower-risk drinking (reference group). Adding PEth improved model fit based on the likelihood ratio test (P = 0.027). Compared with those with lower-risk drinking, individuals self-reporting abstinence but with PEth ≥8 ng/mL had higher mortality (aHR 2.15, 95% CI: 1.40 to 3.29) (Fig. 2A). Those with both self-report and PEth suggesting abstinence, at-risk, and high-risk drinking groups also had increased mortality risk, but the association was not statistically significant. Further adjustment for smoking, injection drug use, and education results minimally attenuated the association: (aHR 2.04, 95% CI: 1.33 to 3.12) comparing individuals self-reporting abstinence but with PEth ≥8 ng/mL to those with lower-risk drinking (data not otherwise shown). Results were similar in the restricted sample; specifically, compared with those with lower-risk drinking, significantly higher mortality was observed only among those individuals with self-reported abstinence but with a PEth ≥8 ng/mL (aHR: 2.74, 95% CI: 1.10 to 6.83) (Fig. 2B).
To the best of our knowledge, this is the first study to evaluate the association between alcohol use determined through a combined self-report/biomarker-detected measure, and risk of mortality among a cohort of HIV+ and uninfected individuals. Several important findings emerged from our study. Likely because of the difference in the encompassed time period, more individuals overall were characterized as using alcohol and having unhealthy alcohol use with AUDIT-C than with PEth. However, a substantial proportion of those self-reporting abstinence in the past year had biomarker evidence of alcohol consumption in the past 3 weeks. These individuals seem to be at highest risk of mortality. In addition, individuals infected with HCV including those coinfected with HIV were more likely to under-report exposure and may be at particularly high risk.
Because PEth detects alcohol use only in the 3 weeks before blood draw, we also conducted analysis in a subsample restricted to those with PEth blood draw within 21 days before or on the same day that AUDIT-C was measured, thus ensuring complete overlap of the 3 weeks covered by PEth measurement with the 1 year encompassed by AUDIT-C. This analysis yielded results consistent with our findings in the larger sample. It is important to emphasize that patients who self-reported abstinence are likely a heterogeneous group, which could include those with lifetime abstinence, those who are currently abstinent but previously consumed alcohol, and those who do not abstain but report nondrinking because of social desirability bias or other unknown factors. Taken together, these results underscore the need for further research evaluating health outcomes among patients who self-report abstinence. This will help to better understand the drivers of under-reporting alcohol exposure as well as to fully elucidate the mortality risk associated with this behavior, especially among HCV+ and HIV+/HCV+–coinfected individuals who are prone to greater risk of harm from unhealthy alcohol use.3–5
Consistent with previous findings of under-reporting alcohol exposure among HIV+ individuals,16,17,38,39,42,43 about 15% of HIV+ individuals self-reporting abstinence in our sample had positive PEth. Three studies among HIV+ individuals in Uganda reported higher level (25%, 25%, 27%) of disagreement between AUDIT-C and PEth among those self-reporting abstinence.16,38,43 These studies assessed self-report alcohol use in the past 3016 and 90 days.38,43 Another study among young people in San Francisco who inject drugs reported positive PEth results among 6% of those self-reporting abstinence.42 Our findings corroborate those from other settings,16,29,38,42,43 that combining AUDIT-C and PEth can improve detection of alcohol exposure. Our work extends previous findings by demonstrating an association between under-report and increased risk of mortality.
Our results should be interpreted in light of some limitations. First, our sample is predominantly male, so our results may not generalize to women. Second, one should note that AUDIT-C assesses drinking over the past year, whereas PEth can only detect alcohol exposure only over approximately 3 weeks.44 As observed, despite reporting alcohol use ranging from lower-risk to high-risk drinking, some individuals self-reporting alcohol use had PEth <8 ng/mL in both the full sample and the sample restricted to the PEth time window. This may be a reflection of the difference in the timing between PEth measurement and AUDIT-C survey date but may also be because PEth is not 100% sensitive, especially for drinking below cutoffs for excessive use.25 In the restricted sample, positive PEth results could not be attributed to alcohol use that occurred after AUDIT-C survey date. In these analyses, a similar proportion of patients with self-reported abstinence had positive PEth as in the full sample (15% vs. 12%) and this group also showed significantly increased risk of mortality, corroborating results from the full sample. In addition, previous research using AUDIT-C trajectory models indicates that self-report of alcohol use varies little over the time in this population.45 We were also limited by sample size in our ability to use the combined alcohol measure in the modeling of mortality risk among patients who self-reported any alcohol use. Finally, because of the observational nature of our study, we cannot establish causality.
Because of the known adverse effects of unhealthy alcohol use on liver-related and all-cause morbidity and mortality, it is critical to accurately measure the spectrum of alcohol consumption. Although PEth measures alcohol use in a shorter timeframe, we found that it enhanced detection of alcohol use when used in conjunction with AUDIT-C. In high-risk groups such as those infected with HCV or HIV+/HCV+ (coinfected), a group in which our data suggest greater likelihood of under-report of alcohol use, using PEth in combination with AUDIT-C can improve alcohol detection.
The authors acknowledge the veterans who participate in the Veterans Aging Cohort Study and the study coordinators and staff at each of our sites and at the West Haven Coordinating Center. Without the commitment and care of these individuals, this research would not be possible. They also acknowledge the substantial in-kind support that they receive from the Veterans Affairs Healthcare System.
1. World Health Organization (WHO). Global status report on alcohol
and health. 2014. Available at: http://http://www.who.int
/substance_abuse/publications/global_alcohol_report/en/. Accessed December 17, 2016.
2. Saitz R. Clinical practice. Unhealthy alcohol
use. N Engl J Med. 2005;352:596–607.
3. Conigliaro J, Gordon AJ, McGinnis KA, et al; Veterans Aging Cohort 3-Site S. How harmful is hazardous alcohol
use and abuse in HIV
infection: do health care providers know who is at risk? J Acquir Immune Defic Syndr. 2003;33:521–525.
4. Braithwaite RS, Bryant KJ. Influence of alcohol
consumption on adherence to and toxicity of antiretroviral therapy and survival. Alcohol
Res Health 2010;33:280–287.
5. Peters MG, Terrault NA. Alcohol
use and hepatitis C. Hepatology. 2002;36(5 suppl 1):S220–S225.
6. 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.
7. Samet JH, Cheng DM, Libman H, et al. Alcohol
consumption and HIV
disease progression. J Acquir Immune Defic Syndr. 2007;46:194–199.
8. Poynard T, Bedossa P, Opolon P. Natural history of liver fibrosis progression in patients with chronic hepatitis C. The OBSVIRC, METAVIR, CLINIVIR, and DOSVIRC groups. Lancet. 1997;349:825–832.
9. Muga R, Sanvisens A, Fuster D, et al. Unhealthy alcohol
infection and risk of liver fibrosis in drug users with hepatitis C. PLoS One. 2012;7:e46810.
10. Rentsch C, Tate JP, Akgun KM, et al. Alcohol
-related diagnoses and all-cause Hospitalization among HIV
-infected and uninfected patients: a longitudinal analysis of United States veterans from 1997 to 2011. AIDS Behav. 2016;20:555–564.
11. Braithwaite RS, Conigliaro J, Roberts MS, et al. Estimating the impact of alcohol
consumption on survival for HIV
+ individuals. AIDS Care. 2007;19:459–466.
12. Di Castelnuovo A, Costanzo S, Bagnardi V, et al. Alcohol
dosing and total mortality
in men and women: an updated meta-analysis of 34 prospective studies. Arch Intern Med. 2006;166:2437–2445.
13. Plunk AD, Syed-Mohammed H, Cavazos-Rehg P, et al. Alcohol
consumption, heavy drinking, and mortality
: rethinking the j-shaped curve. Alcohol
Clin Exp Res. 2014;38:471–478.
14. Justice AC, McGinnis KA, Tate JP, et al. Risk of mortality
and physiologic injury evident with lower alcohol
exposure among HIV
infected compared with uninfected men. Drug Alcohol
15. Turner BJ, McLellan AT. Methodological challenges and limitations of research on alcohol
consumption and effect on common clinical conditions: evidence from six systematic reviews. J Gen Intern Med. 2009;24:1156–1160.
16. 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.
17. Hormes JM, Gerhardstein KR, Griffin PT. Under-reporting of alcohol
and substance use versus other psychiatric symptoms in individuals living with HIV
. AIDS Care. 2012;24:420–423.
18. Gaziano JM, Gaziano TA, Glynn RJ, et al. Light-to-moderate alcohol
consumption and mortality
in the Physicians' Health Study enrollment cohort. J Am Coll Cardiol. 2000;35:96–105.
19. Bush K, Kivlahan DR, McDonell MB, et al. The AUDIT alcohol
consumption questions (AUDIT-C
): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol
Use Disorders Identification Test. Arch Intern Med. 1998;158:1789–1795.
20. 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.
21. Aertgeerts B, Buntinx F, Ansoms S, et al. Screening properties of questionnaires and laboratory tests for the detection of alcohol
abuse or dependence in a general practice population. Br J Gen Pract. 2001;51:206–217.
22. Wurst FM, Thon N, Yegles M, et al. Ethanol metabolites: their role in the assessment of alcohol
Clin Exp Res. 2015;39:2060–2072.
23. Winkler M, Skopp G, Alt A, et al. Comparison of direct and indirect alcohol
markers with PEth in blood and urine in alcohol
dependent inpatients during detoxication. Int J Leg Med. 2013;127:761–768.
24. Viel G, Boscolo-Berto R, Cecchetto G, et al. Phosphatidylethanol
in blood as a marker of chronic alcohol
use: a systematic review and meta-analysis. Int J Mol Sci. 2012;13:14788–14812.
25. 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.
26. Aradottir S, Asanovska G, Gjerss S, et al. PHosphatidylethanol
(PEth) concentrations in blood are correlated to reported alcohol
intake in alcohol
-dependent patients. Alcohol Alcohol
27. Hahn JA, Dobkin LM, Mayanja B, et al. Phosphatidylethanol
(PEth) as a biomarker of alcohol
consumption in HIV
-positive patients in sub-Saharan Africa. Alcohol
Clin Exp Res. 2012;36:854–862.
28. Javors MA, Hill-Kapturczak N, Roache JD, et al. Characterization of the pharmacokinetics of phosphatidylethanol
16:0/18:1 and 16:0/18:2 in human whole blood after alcohol
consumption in a clinical laboratory study. Alcohol
Clin Exp Res. 2016;40:1228–1234.
29. Hahn JA, Emenyonu NI, Fatch R, et al. Declining and rebounding unhealthy alcohol
consumption during the first year of HIV
care in rural Uganda, using phosphatidylethanol
to augment self-report. Addiction. 2016;111:272–279.
30. Justice AC, Dombrowski E, Conigliaro J, et al. Veterans Aging Cohort Study (VACS): overview and description. Med Care. 2006;44(8 suppl 2):S13–S24.
31. Conigliaro J, Madenwald T, Bryant K, et al. The Veterans Aging Cohort Study: observational studies of alcohol
use, abuse, and outcomes among human immunodeficiency virus-infected veterans. Alcohol
Clin Exp Res. 2004;28:313–321.
32. Armah KA, McGinnis K, Baker J, et al. HIV
status, burden of comorbid disease, and biomarkers of inflammation, altered coagulation, and monocyte activation. Clin Infect Dis. 2012;55:126–136.
33. So-Armah KA, Tate JP, Chang CC, et al. Do biomarkers of inflammation, monocyte activation, and altered coagulation explain excess mortality
infected and uninfected people? J Acquir Immune Defic Syndr. 2016;72:206–213.
34. 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;3:1101–1106.
35. United States Drug Testing Laboratories Inc. PEth Testing. 2016. Available at: http://http://www.usdtl.com
-test-labs. Accessed December 9, 2016.
36. Rubinsky AD, Dawson DA, Williams EC, et al. AUDIT-C
scores as a scaled marker of mean daily drinking, alcohol
use disorder severity, and probability of alcohol
dependence in a U.S. general population sample of drinkers. Alcohol
Clin Exp Res. 2013;37:1380–1390.
37. Stewart SH, Koch DG, Willner IR, et al. Validation of blood phosphatidylethanol
as an alcohol
consumption biomarker in patients with chronic liver disease. Alcohol
Clin Exp Res. 2014;38:1706–1711.
38. Asiimwe SB, Fatch R, Emenyonu NI, et al. Comparison of traditional and novel self-report measures to an alcohol
biomarker for quantifying alcohol
consumption among HIV
-infected adults in Sub-Saharan Africa. Alcohol
Clin Exp Res. 2015;39:1518–1527.
39. 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.
40. Goulet JL, Fultz SL, McGinnis KA, et al. Relative prevalence of comorbidities and treatment contraindications in HIV
-mono-infected and HIV
-co-infected veterans. AIDS. 2005;19(suppl 3):S99–S105.
41. Rehm J, Irving H, Ye Y, et al. Are lifetime abstainers the best control group in alcohol
epidemiology? On the stability and validity of reported lifetime abstention. Am J Epidemiol. 2008;168:866–871.
42. Jain J, Evans JL, Briceno A, et al. Comparison of phosphatidylethanol
results to self-reported alcohol
consumption among young injection drug users. Alcohol Alcohol
43. Hahn JA, Fatch R, Kabami J, et al. Self-report of alcohol
use increases when specimens for alcohol
biomarkers are collected in persons with HIV
in Uganda. J Acquir Immune Defic Syndr. 2012;61:e63–64.
44. Justice AC, McGinnis KA, Tate JP, et al. Validating harmful alcohol
use as a phenotype for genetic discovery using phosphatidylethanol
and a polymorphism in ADH1B. Alcohol
Clin Exp Res. 2017;41:998–1003.
45. Marshall BDL, Operario D, Bryant KJ, et al. Drinking trajectories among HIV
-infected men who have sex with men: a cohort study of United States veterans. Drug Alcohol