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. Author manuscript; available in PMC: 2021 Jan 1.
Published in final edited form as: Drug Alcohol Depend. 2019 Nov 20;206:107753. doi: 10.1016/j.drugalcdep.2019.107753

Patterns of alcohol use and associated characteristics and HIV-related outcomes among a sample of African-American women living with HIV

Lauren Lipira a,b, Deepa Rao b,c, Paul E Nevin b, Christopher G Kemp b, Susan E Cohn d, Janet M Turan e, Jane M Simoni f, Michele P Andrasik g, Audrey L French h, Joseph M Unger a,i, Patrick Heagerty j, Emily C Williams a,k
PMCID: PMC6980681  NIHMSID: NIHMS1545675  PMID: 31785536

Abstract

Background:

Alcohol use is common among people living with HIV and negatively impacts care and outcomes. African-American women living with HIV are subject to vulnerabilities that may increase risk for alcohol use and associated HIV-related outcomes.

Methods:

We used baseline data from a randomized controlled trial of an HIV-related stigma-reduction intervention among African-American women living with HIV in Chicago and Birmingham (2013-2015). Patterns of alcohol use [any use, unhealthy alcohol use (UAU), heavy episodic drinking (HED)] were measured using the AUDIT-C. We assessed demographic, social, and clinical characteristics which may influence alcohol use and HIV-related outcomes which may be influenced by patterns of alcohol use in bivariate and multivariable analyses.

Results:

Among 220 African-American women living with HIV, 54% reported any alcohol use, 24% reported UAU, and 27% reported HED. In bivariate analysis, greater depressive symptoms, low religiosity, low social support, marijuana, and crack/cocaine use were associated with patterns of alcohol use (p<0.05). Marijuana and cocaine/crack use were associated with patterns of alcohol use in adjusted analysis (p<0.05). In adjusted analysis, any alcohol use and HED were associated with lower likelihood of ART adherence (ARR = 0.72, 95% CI: 0.53-0.97 and ARR =0.65, 95% CI: 0.44-0.96, respectively), and UAU was associated with lack of viral suppression (ARR = 0.78, 95% CI: 0.63-0.96).

Conclusions:

Findings suggest any and unhealthy alcohol use is common and associated with poor HIV-related outcomes in this population. Regular alcohol screening and intervention should be offered, potentially targeted to subgroups (e.g., those with other substance use).

Keywords: African-American women, alcohol use, depression, religiosity, substance use

1. INTRODUCTION

Over 1 million people are living with HIV in the United States (U.S.), and thousands are newly infected each year (CDC, 2017). Ongoing incidence of HIV is largely attributed to social determinants of health and substance use (Fauci et al., 2019). Alcohol use is of key interest because it is highly prevalent among people living with HIV (PLWH) (Williams et al., 2016b), and is associated with increased risk for HIV transmission (Scott-Sheldon et al., 2016), decreased receipt of quality care at every phase of the HIV care continuum (Vagenas et al., 2015; Williams et al., 2016a; Williams et al., 2018b) and increased risk for complications and mortality (Justice et al., 2016).

For PLWH, no level of alcohol use may be safe (Justice et al., 2016; McGinnis et al., 2016). Furthermore, unhealthy alcohol use (UAU), and particularly, heavy episodic drinking (HED), may be especially dangerous (Williams et al., 2016a). Unhealthy alcohol use refers to a spectrum from drinking above recommended limits to meeting diagnostic criteria for alcohol use disorder (AUD) (Saitz, 2005). Heavy episodic drinking is a type of UAU that increases risk for both short- (e.g., trauma) and long-term (e.g., AUD) alcohol-related consequences (Kuntsche et al., 2017). For PLWH, all levels of UAU are associated with elevated health risks (Justice et al., 2016; Williams et al., 2018b), and alcohol-related consequences generally increase with increasing levels of alcohol use (Rubinsky et al., 2013), which may be worse for women (Matson et al., 2018).

African-American women living with HIV are subject to multiple forms of stigma, stress, and vulnerability — all of which may be associated with increased risk of any and unhealthy alcohol use (Hutton et al., 2017; Williams et al., 2016b). Specifically, African-American women are disproportionately exposed to high levels of psychosocial stressors and structural barriers such as sexism, racism, and poverty (Gilbert and Zemore, 2016; Martin et al., 2003; Mulia et al., 2008). Boyd et al. (2009) hypothesized that African-American women drink alcohol (and use other substances) as a method of coping with these stressors. For African-American women who acquire HIV, the added stress and stigma associated with living with HIV may also influence patterns of alcohol use (Cook et al., 2016; Elliott et al., 2014; Galvan et al., 2002; Warded et al., 2018). Moreover, there may be specific demographic, social, or clinical subgroups of African-American women living with HIV (e.g., older women (Epstein et al., 2007; Williams et al., 2014a), women with greater social support (Peirce et al., 2000), and women with comorbid mental health disorders (Sullivan et al., 2005)) who have higher or lower risk for any and unhealthy alcohol use.

Moreover, research indicates that racial/ethnic minorities, PLWH, and women are at increased risk of alcohol-related consequences (Matson et al., 2018; McGinnis et al., 2016; Mulia et al., 2009; Nolen-Hoeksema, 2004). First, data from a nationally representative alcohol survey demonstrated that African-American and Hispanic drinkers were more likely than White drinkers to report social consequences of drinking, even after adjusting for differences in heavy drinking, and especially at lower levels of drinking. (Mulia et al., 2009) Furthermore, in a study of HIV infected and HIV uninfected men, individuals living with HIV were able to “feel a buzz” with fewer alcoholic drinks. And finally, results from a large national cohort of PLWH suggest that at higher levels of alcohol use, women have worse HIV-related outcomes (e.g., poor ART adherence and higher viral load) than men (Matson et al., 2018).

Despite the potential for elevated levels of any and unhealthy alcohol use among African American women living with HIV, neither prevalence nor correlates of any and unhealthy alcohol use have been described specifically in this population. Similarly, associations between patterns of alcohol use and HIV-related outcomes have also not been studied specifically among African-American women living with HIV (Williams et al., 2016a). Therefore, the purpose of this study was to 1) describe patterns of alcohol use and associated demographic, social, and clinical characteristics and 2) describe associations between patterns of alcohol use and HIV-related outcomes, in a sample of African-American women living with HIV.

2. MATERIALS AND METHODS

2.1. Data source

This study is a secondary analysis of baseline data from the Unity Study, a randomized controlled trial evaluating the long-term (12 month) effectiveness of an HIV-related stigma-reduction intervention among African-American women living with HIV (Rao et al., 2018). From 2013 to 2015, African-American women living with HIV were recruited from three HIV clinical care sites: Northwestern University Infectious Diseases HIV clinic (NU) and the Ruth M. Rothstein CORE Center (CORE) in Chicago, Illinois and the University of Alabama at Birmingham 1917 HIV Clinic (UAB) in Birmingham, Alabama.

2.2. Study sample

To participate in the Unity Study, individuals needed to self-identify as African-American women, be >18 years old, and be currently receiving HIV services from a participating study clinic (NU, CORE, or UAB). Women were excluded if they were foreign born and had lived in the US for less than 10 years. Baseline data from Unity Study participants with complete alcohol-related data were included in the current analysis. As ART adherence was a critical correlate of interest, women were excluded if they were not currently prescribed ART or did not provide adherence data.

2.3. Data collection

After providing consent, participants completed baseline assessments via tablet-based audio computer assisted self-interview. Baseline assessments included questions about patterns of alcohol use, demographic, and psychosocial measures. Clinical data were abstracted from medical records.

2.4. Measures

2.4.1. Patterns of alcohol use

We used the Alcohol Use Disorders Identification Test (AUDIT) Consumption (AUDIT-C) alcohol screening questionnaire to characterize three patterns of alcohol use: 1) any alcohol use, 2) any UAU, and 3) any HED. The AUDIT-C is a brief screening tool for UAU (Jonas et al., 2012b), consisting of three items that assess frequency of drinking (How often do you have a drink containing alcohol?), quantity of drinking (How many standard drinks containing alcohol do you have on a typical day?), and frequency of HED (How often do you have 4 or more drinks on one occasion?). Each item is scored 0-4, resulting in a total score ranging from 0 to 12 (Bush et al., 1998), with higher scores indicating higher levels of alcohol-related risk (e.g., consumption, UAU severity, and probability of AUD) (Rubinsky et al., 2013; Williams et al., 2014b) and alcohol-related consequences (e.g. health status, trauma and mortality) (Bradley et al., 2016; Chavez et al., 2012; Williams et al., 2010). Any alcohol use was defined dichotomously (AUDIT-C > 0), UAU was defined dichotomously using a female-specific threshold for UAU (AUDIT-C ≥ 3) (Bradley et al., 2003) and HED was defined dichotomously using a female-specific threshold (4 drinks) for the third item of the AUDIT-C (item score >0) (Substance Abuse and Mental Health Services Administration (SAMHSA), 2016).

2.4.2. Demographic, social, and clinical characteristics

Three sets of participant characteristics (demographic, social, and clinical) were evaluated based on a review of the alcohol and HIV literature and measures available in the Unity Study (Williams et al., 2016a).

2.4.2.1. Demographic characteristics

Demographic characteristics hypothesized to be associated with patterns of alcohol use in this sample included participant age (continuous), educational level (less than high school, high school or equivalent, or greater than high school), Latina/Hispanic identity (Latina/Hispanic or other), and marital status (married/partnered or other) (Chavez et al., 2015; Curran et al., 1998; Darrow et al., 1992; Epstein et al., 2007; Klein et al., 2016; Okosun et al., 2005; Volk et al., 1996; Williams et al., 2014a).

2.4.2.2. Social characteristics

Social characteristics included characteristics hypothesized to be negatively (HIV-related stigma) or positively (religiosity, social support, and attachment and belonging to one’s ethnic identity) associated with patterns of alcohol use (Brome et al., 2000; Cotton et al., 2006; Lehavot et al., 2011; Liao et al., 2014; Mannes et al., 2016; Martin et al., 2003; Mulia et al., 2008; Peirce et al., 2000; Serovich et al., 2001; Vyavaharkar et al., 2010). HIV-related stigma was measured continuously using the 14-item Stigma Scale for Chronic Illness (SSCI), a measure validated for use with African Americans living with HIV (Rao et al., 2009; Rao et al., 2016). Referring to the past month, participants respond to statements such as, “Because of my illness, people were unkind to me,” on a 5-point Likert-type scale ranging from 1 (never) to 5 (always) resulting in scores from 14 to 70, with higher scores reflecting greater HIV-related stigma. Religiosity was measured continuously using the 7-item version of the Religious Beliefs and Behaviors survey (RBB) (Connors et al., 1996; Hawes and Berkley-Patton, 2014). The RBB has demonstrated good internal consistency in an African-American church-based sample (Hawes and Berkley-Patton, 2014). Participants are first asked to select the term that best describes their level of God consciousness from 0 (atheist) to 4 (religious) and then asked how often they engaged in formal religious activities (e.g., prayer) in the last year. Participants respond using an 8-point Likert-type scale from 0 (never) to 7 (more than once a day). Factor subscale scores are summed for an overall religiosity score ranging from 0 to 46, with higher scores reflecting greater religiosity. Social support was measured continuously using two subscales (Emotional/Informational Support and Positive Social Interaction) from the Medical Outcomes Study Social Support Survey (MOS-SSS) (Sherbourne and Stewart, 1991). Participants answer questions regarding how often types of social support are available using a 5-point Likert-type scale from 1 (none of the time) to 5 (all of the time). For this study, an overall support score was calculated by averaging all items from the two subscales. Attachment and belonging to one’s ethnic identity was measured continuously using the Commitment subscale of the revised Multigroup Ethnic Identity Measure (MEIM-R) (Phinney, 1992; Phinney and Ong, 2007). Participants respond to statements such as, “I have a strong sense of belonging to my own ethnic group” on a Likert-type scale from 1 (strongly disagree) to 5 (strongly agree). A subscale score is generated by taking an average of the individual items.

2.4.2.3. Clinical characteristics

Clinical characteristics included depressive symptom severity, post-traumatic stress disorder (PTSD), and other substance use, all known to be associated with any and unhealthy alcohol use (Braithwaite et al., 2016; Crane et al., 2017; Hasin et al., 2007; Hoggatt et al., 2015; Jasinski et al., 2000; Korthuis et al., 2012; Sullivan et al., 2005; Sullivan et al., 2011). Depressive symptom severity was measured using the 8-item Patient Health Questionnaire (PHQ-8) (Center for quality assessment and improvement in mental health, 1999; Kroenke et al., 2009). Participants answer questions about depressive symptoms in the past two weeks using a 4-point Likert-type scale from 0 (not at all) to 3 (nearly every day) resulting in scores from 0 to 24 with higher scores reflecting greater severity (based on clinical cut points, 0-4: minimal depression, 5-9: mild depression, 10-14: moderate depression, 15-19: moderately severe depression, 20-24: severe depression). Symptomatic PTSD was defined dichotomously as a score >30 on the 17-item PTSD Checklist — Civilian version (PCL-C) (Ruggiero et al., 2003). Participants answer questions about PTSD symptoms in the past month, using a 5-point Likert scale from 1 (not at all) to 5 (extremely) resulting in scores from 17 to 85.(U.S. Department of Veterans Affairs National Center for Posstraumatic Stress Disorder, 2012) A cut off of >30 is considered appropriate for women (U.S. Department of Veterans Affairs National Center for Posstraumatic Stress Disorder, 2012; Walker et al., 2002; Yeager et al., 2007). Other substance use was assessed with two questions generated by Unity Study investigators to preface a longer measure which assesses the severity of an individual’s dependence on a given substance (Gossop et al., 1995). Participants were first asked, “Do you use other drugs?” (with “other drugs” left open to the participant’s interpretation). Participants who responded affirmatively were then asked the open-ended question, “What is your drug of choice (heroin, cocaine, etc.)?” The responses were categorized as marijuana, cocaine or crack, heroin, or did not specify.

2.4.3. HIV-related outcomes

HIV-related outcomes were selected for evaluation based on prior work and included adherence to ART (100% adherent, < 100% adherent) and viral supression (Azar et al., 2010; Bryson et al., 2008; Hendershot et al., 2009; Kader et al., 2015; Kalichman et al., 2014; Williams et al., 2016a). We evaluated viral suppression using participants’ mean HIV RNA viral load during the 6 months pre- and post-entry into the Unity Study. Viral suppression was defined dichotomously as less than 200 (virally suppressed) vs. 200 or more (not virally suppressed) copies/mL of plasma HIV RNA.

2.5. Analysis

We first summarized participant demographic, social, and clinical characteristics and HIV-related outcomes for the entire analytic sample. Next, to evaluate demographic, social, and clinical characteristics associated with patterns of alcohol use, we compared participant characteristics across groups of women who did and did not report different patterns of alcohol use, testing for differences across groups using χ2 tests of independence and Kruskal-Wallis tests (with ties) for categorical and continuous variables, respectively. We then estimated the adjusted relative risks (ARR) of patterns of alcohol use associated with participant demographic, social, and clinical characteristics using generalized linear models with a log link, Poisson distribution, and robust standard errors (Cummings, Peter, 2009). Generalized linear models were used to estimate ARRs instead of logistic regression models, because we anticipated patterns of alcohol use to be common, and odds ratios may overestimate effects when outcomes are common (Cummings, P., 2009). In each model, the alcohol pattern outcome was regressed on all demographic social, and clinical characteristics, as well as study site as an additional covariate.

Similarly, to assess whether patterns of alcohol use were associated with HIV-related outcomes, we first compared patterns of alcohol use across groups based on ART adherence and viral suppression, testing for differences using χ2 tests of independence. We then fit similar generalized linear models to estimate the relative risk of ART adherence and viral suppression associated with patterns of alcohol use adjusting for demographic, social and clinical characteristics, study site and study arm. We included study arm in this analysis because a small number of participants had viral loads collected after the intervention started, and we hypothesized that participation in a HIV-related stigma reduction intervention could influence alcohol use and severity and viral suppression, thus confounding the observed association.

With our sample, we estimated sufficient power (80%) to detect a difference of 20% in patterns of alcohol use between similarly sized groups based on a 2-arm binomial design with two-sided alpha=.05 tests. The use of covariate modeling to explain additional components of variation increased power for each comparison. We set alpha=.05 with no adjustments for multiple comparisons. Thus, the examination was considered hypothesis-generating, requiring confirmation in independent studies.

3. RESULTS

3.1. Participant characteristics

Of the 239 African-American women living with HIV enrolled in the Unity Study, 220 met inclusion criteria for this analysis; 15 women did not report being prescribed ART and 4 women did not provide adherence data and were excluded. Characteristics of the sample are described in Table 1. The median age was 47 years (interquartile range [IQR] = [40, 54]), a minority (2%) identified as Hispanic/Latina, three-quarters (76%) were unmarried (single/separated/divorced/widowed), and more than half (60%) had at least a high school education.

Table 1.

Baseline characteristics of African-American women living with HIV who participated in an HIV-related stigma-reduction intervention (Unity Study, N = 220)

N (%)
Study characteristics
Study arm
 Control 107 (49%)
 Treatment 113 (51%)
Study site
 NU 44 (20%)
 CORE 77 (35%)
 UAB 99 (45%)
Demographic characteristics
Age (median years, IQR) 46 years (40-54 years)
Ethnicity
 Hispanic/Latina 4 (2%)
 Not Hispanic/Latina 212 (96%)
 Missing 4 (2%)
Marital status
 Single/Separated/Divorced/Widowed 169 (77%)
 Married/Partner 50 (23%)
 Missing 1 (<1%)
Education
 Less than high school 79 (36%)
 High school or equivalent 49 (23%)
 More than high school 86 (39%)
 Missing 6 (3%)
Social characteristics
HIV-related stigma (median SSCI, IQR)* 30 (22-41)
Religiosity (median RBB, IQR)* 34 (27-39)
Social support (median MOS-SSS, IQR)* 4.0 (3.1-4.8)
Ethnic identity (median MEIM-R, IQR)* 4.0 (3.3-5.0)
Clinical characteristics
Depression severity (median PHQ-8, IQR)* 6.0 (2.0-12.0)
Symptomatic PTSD
 Yes 148 (67%)
 No 72 (33%)
Other substance use
 Yes 44 (20%)
 No 175 (80%)
 Missing 1 (<1%)
Reported substances (N = 44)
 Marijuana 21 (48%)
 Cocaine or crack 13 (30%)
 Heroin 5 (11%)
 Did not specify 5 (11%)
HIV-related clinical characteristics
ART adherence
 100% adherent 108 (49%)
 <100% adherent 112 (51%)
Viral suppression (<200 copies/mL)
 Yes 170 (77%)
 No 50 (23%)
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*

Missing values for participants

NU – Northwestern University, CORE – Ruth M. Rothstein CORE Center, UAB – University of Alabama, Birmingham IQR – Interquartile range, AUDIT-C – Alcohol Use Disorders Identification Test – Consumption

SSCI – 14-item Stigma Scale for Chronic Illness, RBB – 7-item Religious Beliefs and Behaviors survey, MOS-SSS – Medical Outcomes Study Social Support Survey Emotional/Information Support and Positive Social Interaction subscales, MEIM-R – Multigroup Ethnic Identity Measure Commitment subscale, PHQ-8 – 8-item Patient Health Questionnaire PTSD – post-traumatic stress disorder, ART – antiretroviral therapy

The median HIV-related stigma (SSCI) score was 30 (IQR = [22,41]), and all but 9 participants reported experiencing at least some level of HIV-related stigma (SSCI > 14). The median religiosity (RBB) score was 34 (IQR = [27,39]), the median social support (MOS-SSS subscales) score was 4 (IQR = [3-5]), and the median ethnic identity (MEIM-R Commitment subscale) score was 4 (IQR = [3-5]). Median PHQ-8 score was 6 (IQR = [2-12]). Based on clinical cut points (Kroenke et al., 2009), 67% of women experienced minimal to mild depressive symptoms, 15% experienced moderate depressive symptoms, and 18% experienced moderately severe to severe depressive symptoms. Symptomatic PTSD was common in this sample (78% met diagnostic criteria), but other substance use was not. Of the 44 women who reported other substance use, 21 women reported that marijuana was their drug of choice, 13 reported cocaine or crack, 5 reported heroin, and 5 did not specify. Almost 90% of the women in this sample were currently taking ART, about half of whom (49%) reported to be 100% adherent in the past month. Nearly three-quarters of participants (73%) were virally suppressed.

3.2. Patterns of alcohol use and associated characteristics

More than half of participants (54%) reported any alcohol use; 24% screened positive for UAU, and 27% screened positive for HED. Table 2 compares women who did and did not report each of the patterns of alcohol use. Lower religiosity (p = .04), higher depressive symptom severity (p = .03), and marijuana use (p < .001) were more common among women who reported any alcohol use than women who did not. Similarly, lower religiosity (p = .01), higher depressive symptom severity (p <.01), and cocaine or crack use (p < .01) were more common among women who screened positive for UAU compared to those who did not. Finally, higher depressive symptom severity (p = .03) and cocaine or crack use (p = .02) were more common among women who reported HED than those who did not. In adjusted regression analyses (Table 3), marijuana use was significantly associated with higher likelihood of any alcohol use (adjusted relative risk [ARR] = 1.88, 95% confidence interval [CI] = 1.43, 2.50), UAU (ARR = 2.37, 95% CI: 1.13, 4.97) and HED (ARR = 2.11, 95% CI: 1.09, 4.07). Cocaine or crack use was associated with higher likelihood of UAU (ARR = 2.53, 95% CI: 1.37, 4.67) and HED (ARR = 2.61, 95% CI: 1.40, 4.85).

Table 2.

Bivariate comparisons of participant characteristics across patterns of alcohol use among a sample of African-American women living with HIV recruited to the Unity Study (N = 220)

No Alcohol Use (N = 102) Any Alcohol Use (N = 118) No Unhealthy Alcohol Use (N = 172) Any Unhealthy Alcohol Use (N = 48) No Heavy Episodic Drinking (N = 164) Any Heavy Episodic Drinking (N = 56)
N (%) N (%) N (%) N (%) N (%) N (%)
Demographic characteristics
Age (median years, IQR) 46 (40-55) 47 (40-54) 46 (40-55) 46 (37-54) 48 (41-55) 45 (36-54)
Education
 Less than high school 44 (45%) 35 (30%) 61 (36%) 18 (38%) 58 (36%) 21 (38%)
 High school or equivalent 20 (20%) 29 (25%) 40 (24%) 9 (19%) 35 (22%) 14 (25%)
 More than high school 33 (33%) 53 (45%) 66 (40%) 20 (43%) 66 (42%) 20 (36%)
Hispanic/Latina ethnicity 1 (1%) 3 (3%) 3 (2%) 1 (2%) 2 (1%) 2 (4%)
Marital status
Single/Separated/Divorced/Widowed 78 (77%) 91 (77%) 132 (77%) 37 (77%) 126 (77%) 43 (77%)
 Married/Partner 23 (23%) 27 (23%) 39 (23%) 11 (23%) 37 (23%) 13 (23%)
Social characteristics
HIV-related stigma (median SSCI, IQR) 30 (20-43) 32 (24-41) 30 (22-40) 34 (21-43) 30 (22-43) 32 (22-41)
Religiosity (median RBB, IQR) 35 (28-41) 33 (25-39) 34 (28-40) 29 (24-38) 34 (28-40) 32 (24-39)
Social support (median MOS-SSS, IQR) 4.3 (2.8-5.0) 3.9 (3.1-4.5) 4.1 (3.2-4.9) 3.6 (2.9-4.5) 4.2 (3.3-4.9) 3.6 (2.8-4.4)
Ethnic identity (median MEIM-R, IQR) 4.0 (3.3-5.0) 4.0 (3.0-4.7) 4.0 (3.3-5.0) 4.0 (3.3-4.7) 4.0 (3.3-5.0) 4.0 (3.3-4.7)
Clinical characteristics
Depression (median PHQ-8, IQR) 5.0 (1.0-12.0) 6.0 (4.0-12.0) 6.0 (2.0-12.0) 7.5 (5.5-15.0) 6.0 (2.2-12.0) 6.5 (5.0-11.0)
Symptomatic PTSD 67 (66%) 81 (69%) 111 (65%) 37 (77%) 108 (66%) 40 (71%)
Other substance use
 Marijuana 2 (2%) 19 (16%) 13 (8%) 8 (17%) 12 (7%) 9 (16%)
 Cocaine or crack 4 (4%) 9 (8%) 6 (3%) 7 (15%) 6 (4%) 7 (13%)
 Heroin 3 (3%) 2 (2%) 5 (3%) 0 (0%) 4 (2%) 1 (2%)
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Bold used to indicate p-value <0.05 based on χ2 test for independence for categorical variables and Kruskal-Wallis test (with ties) for continuous variables, IQR – Interquartile range

SSCI – 14-item Stigma Scale for Chronic Illness, RBB – 7-item Religious Beliefs and Behaviors survey, MOS-SSS – Medical Outcomes Study Social Support Survey Emotional/Information Support and Positive Social Interaction subscales, MEIM-R – Multigroup Ethnic Identity Measure Commitment subscale, PHQ-8 – 8-item Patient Health Questionnaire, PTSD – post-traumatic stress disorder

Table 3.

Estimated adjusted associations between demographic, social, and clinical characteristics and patterns of alcohol use among African-American women living with HIV recruited to the Unity Study (N = 197)*

Any Alcohol Use Unhealthy Alcohol Use Any Heavy Episodic Drinking
ARR (95% CI) ARR (95% CI) ARR (95% CI)
Demographic characteristics
Age (years) 1.01 (1.00 – 1.02) 1.01 (0.98 – 1.04) 1.00 (0.98 – 1.02)
Education
 Less than high school REF - REF - REF -
 High school or equivalent 1.07 (0.75 – 1.54) 0.79 (0.36 – 1.72) 0.79 (0.39 – 1.60)
 More than high school 1.25 (0.90 – 1.74) 1.07 (0.56 – 2.04) 0.87 (0.49 – 1.55)
Hispanic/Latina ethnicity 1.13 (0.62 – 2.06) 0.90 (0.16 – 5.22) 1.31 (0.41 – 4.14)
Marital status
Single/Separated/Divorced/Widowed REF - REF - REF -
 Married/Partner 0.95 (0.69 – 1.31) 1.05 (0.57 – 1.91) 0.99 (0.56 – 1.75)
Social characteristics
HIV-related stigma (SSCI, IQR) 1.00 (0.99 – 1.01) 0.98 (0.95 – 1.01) 0.98 (0.96 – 1.01)
Religiosity (mean RBB, IQR) 0.99 (0.97 – 1.00) 0.97 (0.94 – 1.01) 0.99 (0.96 – 1.02)
Social support (MOS-SSS, IQR) 1.03 (0.90 – 1.17) 0.93 (0.72 – 1.19) 0.84 (0.67 – 1.05)
Ethnic identity (MEIM-R, IQR) 1.06 (0.91 – 1.23) 1.03 (0.79 – 1.34) 0.99 (0.76 – 1.30)
Clinical characteristics
Depression (PHQ-8, IQR) 1.03 (1.00 – 1.05) 1.04 (0.98 – 1.10) 1.03 (0.98 – 1.09)
Symptomatic PTSD 0.95 (0.68 – 1.33) 1.64 (0.84 – 3.20) 1.18 (0.61 – 2.27)
Other substance use
 Marijuana 1.88 (1.43 – 2.50) 2.37 (1.13 – 4.97) 2.11 (1.09 – 4.07)
 Cocaine or crack 1.45 (0.96 – 2.18) 2.53 (1.37 – 4.67) 2.61 (1.40 – 4.85)
 Heroin 1.17 (0.22 – 6.08) ** ** 1.75 (0.26 – 11.68)
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Bold used to indicate p<0.05, ARR – adjusted relative risk, models adjusted for study site

*

Individuals without complete covariate data were dropped from the multivariable analysis

**

Heroin and crack use perfectly predicted no unhealthy alcohol use and were dropped from the model

SSCI – 14-item Stigma Scale for Chronic Illness, RBB – 7-item Religious Beliefs and Behaviors survey, MOS-SSS – Medical Outcomes Study Social Support Survey Emotional/Information Support and Positive Social Interaction subscales, MEIM-R – Multigroup Ethnic Identity Measure Commitment subscale, PHQ-8 – 8-item Patient Health Questionnaire, PTSD – post-traumatic stress disorder

3.3. Associations between patterns of alcohol use and HIV-related outcomes

Results of both bivariate and multivariable analyses assessing associations between patterns of alcohol use and HIV-related outcomes are presented in Table 4. Any alcohol use (p < .001), UAU (p = .03) and HED (p < .01) were all more common among women who reported suboptimal adherence, and UAU was more common among women who were not virally suppressed (p = .05). Similarly, in adjusted regression analyses, any alcohol use and HED were associated with lower likelihood of ART adherence (ARR = 0.72, 95% CI: 0.53, 0.97 and ARR = 0.65, 95% CI: 0.44, 0.96, respectively), but only UAU was significantly associated with lack of viral suppression (ARR = 0.78, 95% CI: 0.63, 0.96).

Table 4.

Bivariate and multivariable associations between patterns of alcohol use and HIV-related outcomes (ART adherence and viral suppression) among a sample of African-American women living with HIV recruited to the Unity Study (N = 220)*

ART Adherence Viral Suppression
<100% ART adherent (N = 112) 100% ART adherent (N = 108) Adjusted relative risk** (N = 197) Not virally suppresse d (N = 50) Virally suppresse d (N = 170) Adjusted relative risk*** (N = 197)
N (%) N (%) ARR* (95% CI) N (%) N (%) ARR* (95% CI)
Patterns of alcohol use
Any alcohol use 73 (67%) 45 (42%) 0.72 (0.53 – 0.97) 30 (60%) 88 (52%) 0.95 (0.82 – 1.10)
Unhealthy alcohol use 31 (28%) 17 (16%) 0.76 (0.52 – 1.10) 16 (32%) 32 (19%) 0.78 (0.63 – 0.96)
Heavy episodic drinking 39 (35%) 17 (16%) 0.65 (0.44 – 0.96) 17 (34%) 39 (23%) 0.88 (0.73 – 1.07)
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ART – antiretroviral therapy

Bold used to indicate p-value <0.05 based on χ2 test for independence or Wald test

*

Individuals without complete covariate data were dropped from the multivariable analysis

**

Adjusted for demographic, social, and clinical characteristics, study site and intervention arm

***

Adjusted for demographic, social, and clinical characteristics, study site, intervention arm, and ART adherence

4. DISCUSSION

In this cross-sectional analysis of a sample of African-American women living with HIV, approximately half reported any alcohol use, a quarter screened positive for UAU, and a quarter reported HED. In bivariate comparisons, social factors hypothesized to be protective (religiosity and social support) were more common among women who did not report any alcohol use, UAU or HED. Clinical factors, specifically depressive symptoms and other substance use were more common among women who reported any alcohol use, UAU or HED than those who did not. After accounting for other factors, only marijuana and cocaine or crack use were associated with patterns of alcohol use. Patterns of alcohol use were associated with sub-optimal ART adherence and lack of viral suppression in both bivariate and multivariable analyses.

To our knowledge, this is the first study to estimate patterns of alcohol use in a sample specifically limited to African-American women living with HIV. Estimates of any and unhealthy alcohol use in this sample were similar to estimates among Black females (Substance Abuse and Mental Health Services Administration (SAMHSA), 2015), PLWH (Williams et al., 2016b), women living with HIV (Crane et al., 2017), and older adults living with HIV (Williams et al., 2014a). While future research using large, diverse samples is needed to estimate the relative risk of any and unhealthy alcohol use associated with being an African-American woman living with HIV, relative to other populations, our results suggest the possibility that despite intersecting vulnerabilities, African-American women living with HIV may not be drinking at elevated levels. Potentially, African-American women living with HIV are practicing resilience via religiosity and social support (Earnshaw et al., 2013). Though patterns of alcohol use were not significantly associated with religiosity and social support in adjusted analysis, bivariate associations were consistent with previous studies demonstrating the protective effects of religiosity and social support among African-American women living with HIV (Grodensky et al., 2015; Himelhoch and Njie-Carr, 2016; Serovich et al., 2001).

Still, our study indicates a substantial number of African-American women living with HIV are reporting any and unhealthy alcohol use, including a slightly more than a quarter reporting HED. Further, our results suggest that African-American women living with HIV reporting these patterns of use may be at increased risk of adverse HIV-related clinical outcomes. Specifically, in adjusted analyses, women reporting any alcohol use or HED (though not the more general measure of UAU) had a lower likelihood of being adherent to ART compared to women who did not, and UAU was associated with lower likelihood of viral suppression, even after adjustment for other factors, including ART adherence. These findings are consistent with studies demonstrating the harmful effects of any and unhealthy alcohol use on HIV care and outcomes, including studies showing associations between alcohol use and HIV disease progression independent of ART adherence (Azar et al., 2010; Cook et al., 2017; Deiss et al., 2016; Lesko et al., 2019; Williams et al., 2018a).

It is unclear why any alcohol use and HED were associated with ART adherence, but UAU was not, and why UAU, but not any alcohol use or HED, was associated with viral suppression. Though one prior longitudinal study of predominately African-American women found that, at baseline, there was no association between heavy drinking (i.e., UAU) and ART adherence (Barai et al., 2017), most prior studies demonstrate a negative relationship between UAU and ART adherence (Hendershot et al., 2009), and many indicate a dose-response association (Braithwaite et al., 2005; Parsons et al., 2008). Further, prior studies have identified associations between any alcohol use and viral suppression (Williams et al., 2019) and high levels of alcohol use (though not specifically HED) and viral suppression (Williams et al., 2016a; Williams et al., 2019). Given the high correlation between ART adherence and viral suppression, it is surprising these outcomes would be differentially associated with specific patterns of unhealthy alcohol use. Findings may relate to limited power to detect associations due to the small sample size and multivariable adjustment or limitations related to the measurement, particularly measures of adherence and unhealthy alcohol use, both of which were based on self-report and could be limited by social desirability bias. Further exploration is needed to better understand associations between patterns of alcohol use and HIV-related outcomes among African-American women living with HIV.

Regardless, our findings reiterate the need for regular alcohol screening and appropriate interventions for this population (Edelman et al., 2018). While brief alcohol-related interventions have demonstrated success in reducing alcohol use in primary care patients (Jonas et al., 2012a), research indicates that PLWH who screen positive for UAU may be less likely than those without HIV to receive brief alcohol interventions (Williams et al., 2017b). Furthermore, in settings where routinely offered to PLWH, brief interventions may not be having their intended effects (Williams et al., 2017a). Still, a recent review of the literature highlights the potential for adapting existing interventions and integrating alcohol treatment into HIV care (Edelman et al., 2018). Indeed, among a sample of women living with HIV recruited from an urban HIV clinic in Baltimore, a brief intervention adapted for women living with HIV was effective in reducing drinking frequency (Chander et al., 2015). Furthermore, a meta-analysis of behavioral interventions, the majority of which addressed alcohol use as a part of comprehensive HIV-related behavior change, found that interventions were associated with reduced alcohol consumption, improved medication adherence, and decreased viral load (Scott-Sheldon et al., 2017). In combination, these studies suggest that African-American women living with HIV may benefit from targeted brief alcohol interventions integrated into broader HIV-related care initiatives.

The results from this study also indicate that alcohol-related interventions for this population should address other clinical factors (e.g. other substance use and potentially depression). Women who reported any and unhealthy alcohol use had greater depressive symptoms in bivariate analyses and were more likely to use marijuana or cocaine and crack in multivariable analyses when compared to women who did not. Research has repeatedly demonstrated that depression and cocaine use are additional risk factors for poor HIV-related care and outcomes (Baum et al., 2009; Gonzalez et al., 2011; Hartzell et al., 2008; Lesko et al., 2019; Sharpe et al., 2004). The evidence around marijuana use and HIV-related care and outcomes is less clear (Montgomery et al., 2019); as such, the correlation between marijuana use and any and unhealthy alcohol use in this study should be further investigated. Ultimately, treatment options that integrate alcohol treatment with HIV care, mental health services, and other substance treatment may be most effective in yielding positive outcomes for these individuals.

4.1. Limitations

There are several limitations to this study. First, the analysis was cross-sectional and we cannot make causal interpretation of any observed associations. Relatedly, any and unhealthy alcohol use can change over time (Cook et al., 2013; Dawson et al., 2008; Lapham et al., 2014), but patterns of alcohol use were assessed at a single time point. Regular and repeated monitoring over an extended period of time may provide a more accurate and comprehensive assessment of patterns of alcohol use. Second, we had limitations in our measures. Self-reported measures may be subject to recall and social desirability bias. Additionally, other characteristics (e.g., income or employment status) may be associated with alcohol use in this population but were not collected as part of the Unity Study measures and were therefore absent from our analysis. Also, our measure of other substance use was broad; it is possible that participants interpreted “other drugs” in different ways (e.g., some may have considered tobacco or prescription drug use). Furthermore, we only assessed other substance use as an absolute measure; evaluating frequency and/or severity among substance users may provide more nuanced findings. Third, women in the present study were currently seeking medical care and willing to participate in a study. Thus, the experiences of these women may not be generalizable to the larger population of African-American women living with HIV, many of whom are not in care (CDC, 2019).

5. CONCLUSIONS

In summary, African-American women living with HIV in this study commonly reported any and unhealthy alcohol use. However, qualitative comparison with existing literature suggests that, despite intersecting vulnerabilities, African-American women living with HIV may not have substantially higher prevalence of any alcohol use, UAU, or HED than similar populations (e.g., Black females, PLWH, and older adults living with HIV in the US). These women may be benefiting from resilience resources such as religiosity and social support. Nonetheless, over half of the women in this sample reported any alcohol use, and a quarter each screened positive for UAU and HED. with patterns more common in some subgroups (e.g., women with other substance use). Findings suggest that African-American women living with HIV should be offered regular alcohol screening and intervention. Special attention may be indicated for women who present with, or are at risk for, depression and other substance use, an approach in line with a strategic plan set forth by Fauci et al. (2019) emphasizing the importance of targeting vulnerable populations and simultaneously addressing substance use and mental health disorders as a means of ending HIV in the United States. Future research is needed to investigate longitudinal trends in larger samples of African-American women living with HIV; quantitatively compare patterns of alcohol use among African-American women to other populations, and to clarify the size and directionality of relationships observed in this population. Finally, efforts must be made to develop and test brief interventions that are targeted for African-American women living with HIV and integrated into HIV, mental health, and other substance care.

Highlights.

  • Over half of African-American women living with HIV reported any alcohol use

  • A quarter reported both unhealthy alcohol use and heavy episodic drinking

  • Religiosity and social support were associated with less alcohol use

  • Depression and other substance use were associated with greater alcohol use

  • Any and unhealthy alcohol use were associated with poor HIV-related outcomes

Acknowledgements

The authors would also like to extend a special thank you to the women who participated in the Unity Study.

Role of funding

This work was supported by the National Institutes of Mental Health [grant number R01-MH98675] with additional support from an Agency for Healthcare Research and Quality Health Services training award [grant number T32 HS013853-13] and a VA Health Services Research & Development Career Development Award [CDA 12-276].

Footnotes

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Conflict of interest

No conflicts to declare.

References

  1. Azar MM, Springer SA, Meyer JP, Altice FL, 2010. A systematic review of the impact of alcohol use disorders on HIV treatment outcomes, adherence to antiretroviral therapy and health care utilization. Drug and alcohol dependence 112(3), 178–193. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Barai N, Monroe A, Lesko C, Lau B, Hutton H, Yang C, Alvanzo A, McCaul ME, Chander G, 2017. The Association Between Changes in Alcohol Use and Changes in Antiretroviral Therapy Adherence and Viral Suppression Among Women Living with HIV. AIDS and behavior 21(7), 1836–1845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Baum MK, Rafie C, Lai S, Sales S, Page B, Campa A, 2009. Crack-cocaine use accelerates HIV disease progression in a cohort of HIV-positive drug users. Journal of acquired immune deficiency syndromes (1999) 50(1), 93–99. [DOI] [PubMed] [Google Scholar]
  4. Boyd MR, Berger KC, Baliko B, Tavakoli A, 2009. Predictors of alcohol and drug problems in rural African American women. Issues in mental health nursing 30(6), 383–391. [DOI] [PubMed] [Google Scholar]
  5. Bradley KA, Bush KR, Epler AJ, Dobie DJ, Davis TM, Sporleder JL, Maynard C, Burman ML, Kivlahan DR, 2003. Two brief alcohol-screening tests From the Alcohol Use Disorders Identification Test (AUDIT): validation in a female Veterans Affairs patient population. Archives of internal medicine 163(7), 821–829. [DOI] [PubMed] [Google Scholar]
  6. Bradley KA, Rubinsky AD, Lapham GT, Berger D, Bryson C, Achtmeyer C, Hawkins EJ, Chavez LJ, Williams EC, Kivlahan DR, 2016. Predictive validity of clinical AUDIT-C alcohol screening scores and changes in scores for three objective alcohol-related outcomes in a Veterans Affairs population. Addiction (Abingdon, England) 111(11), 1975–1984. [DOI] [PubMed] [Google Scholar]
  7. Braithwaite RS, Fang Y, Tate J, Mentor SM, Bryant KJ, Fiellin DA, Justice AC, 2016. Do Alcohol Misuse, Smoking, and Depression Vary Concordantly or Sequentially? A Longitudinal Study of HIV-Infected and Matched Uninfected Veterans in Care. AIDS and behavior 20(3), 566–572. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Braithwaite RS, McGinnis KA, Conigliaro J, Maisto SA, Crystal S, Day N, Cook RL, Gordon A, Bridges MW, Seiler JF, Justice AC, 2005. A temporal and dose-response association between alcohol consumption and medication adherence among veterans in care. Alcoholism, clinical and experimental research 29(7), 1190–1197. [DOI] [PubMed] [Google Scholar]
  9. Brome DR, Owens MD, Allen K, Vevaina T, 2000. An examination of spirituality among African American women in recovery from substance abuse. Journal of Black Psychology 26(4), 470–486. [Google Scholar]
  10. Bryson CL, Au DH, Sun H, Williams EC, Kivlahan DR, Bradley KA, 2008. Alcohol screening scores and medication nonadherence. Annals of internal medicine 149(11), 795–804. [DOI] [PubMed] [Google Scholar]
  11. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA, 1998. 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. Archives of internal medicine 158(16), 1789–1795. [DOI] [PubMed] [Google Scholar]
  12. CDC, 2017. Fact sheet: HIV in the United States: At a Glance. https://www.cdc.gov/hiv/statistics/overview/ataglance.html. (Accessed October 2017.
  13. CDC, 2019. Fact sheet: HIV and African Americans https://www.cdc.gov/hiv/group/racialethnic/africanamericans/index.html. (Accessed September 2019.
  14. Center for quality assessment and improvement in mental health, 1999. The patient health questionnaire (PHQ-9) overview STABLE resource toolkit
  15. Chander G, Hutton HE, Lau B, Xu X, McCaul ME, 2015. Brief Intervention Decreases Drinking Frequency in HIV-Infected, Heavy Drinking Women: Results of a Randomized Controlled Trial. Journal of acquired immune deficiency syndromes (1999) 70(2), 137–145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Chavez LJ, Ornelas IJ, Lyles CR, Williams EC, 2015. Racial/ethnic workplace discrimination: association with tobacco and alcohol use. American journal of preventive medicine 48(1), 42–49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Chavez LJ, Williams EC, Lapham G, Bradley KA, 2012. Association between alcohol screening scores and alcohol-related risks among female veterans affairs patients. Journal of studies on alcohol and drugs 73(3), 391–400. [DOI] [PubMed] [Google Scholar]
  18. Connors GJ, Tonigan JS, Miller WR, 1996. A measure of religious background and behavior for use in behavior change research. Psychology of Addictive Behaviors 10(2), 90. [Google Scholar]
  19. Cook RL, Cook CL, Karki M, Weber KM, Thoma KA, Loy CM, Goparaju L, Rahim-Williams B, 2016. Perceived benefits and negative consequences of alcohol consumption in women living with HIV: a qualitative study. BMC public health 16, 263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Cook RL, Zhou Z, Kelso-Chichetto NE, Janelle J, Morano JP, Somboonwit C, Carter W, Ibanez GE, Ennis N, Cook CL, Cohen RA, Brumback B, Bryant K, 2017. Alcohol consumption patterns and HIV viral suppression among persons receiving HIV care in Florida: an observational study. Addiction science & clinical practice 12(1), 22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Cook RL, Zhu F, Belnap BH, Weber KM, Cole SR, Vlahov D, Cook JA, Hessol NA, Wilson TE, Plankey M, Howard AA, Sharp GB, Richardson JL, Cohen MH, 2013. Alcohol consumption trajectory patterns in adult women with HIV infection. AIDS and behavior 17(5), 1705–1712. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Cotton S, Puchalski CM, Sherman SN, Mrus JM, Peterman AH, Feinberg J, Pargament KI, Justice AC, Leonard AC, Tsevat J, 2006. Spirituality and religion in patients with HIV/AIDS. Journal of general internal medicine 21 Suppl 5, S5–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Crane HM, McCaul ME, Chander G, Hutton H, Nance RM, Delaney JAC, Merrill JO, Lau B, Mayer KH, Mugavero MJ, Mimiaga M, Willig JH, Burkholder GA, Drozd DR, Fredericksen RJ, Cropsey K, Moore RD, Simoni JM, Christopher Mathews W, Eron JJ, Napravnik S, Christopoulos K, Geng E, Saag MS, Kitahata MM, 2017. Prevalence and Factors Associated with Hazardous Alcohol Use Among Persons Living with HIV Across the US in the Current Era of Antiretroviral Treatment. AIDS and behavior 21(7), 1914–1925. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Cummings P, 2009. Methods for estimating adjusted risk ratios. Stata Journal 9(2), 175. [Google Scholar]
  25. Cummings P, 2009. The relative merits of risk ratios and odds ratios. Archives of pediatrics & adolescent medicine 163(5), 438–445. [DOI] [PubMed] [Google Scholar]
  26. Curran PJ, Muthen BO, Harford TC, 1998. The influence of changes in marital status on developmental trajectories of alcohol use in young adults. Journal of studies on alcohol 59(6), 647–658. [DOI] [PubMed] [Google Scholar]
  27. Darrow SL, Russell M, Cooper ML, Mudar P, Frone MR, 1992. Sociodemographic correlates of alcohol consumption among African-American and white women. Women & health 18(4), 35–51. [DOI] [PubMed] [Google Scholar]
  28. Dawson DA, Stinson FS, Chou SP, Grant BF, 2008. Three-year changes in adult risk drinking behavior in relation to the course of alcohol-use disorders. Journal of studies on alcohol and drugs 69(6), 866–877. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Deiss RG, Mesner O, Agan BK, Ganesan A, Okulicz JF, Bavaro M, Lalani T, O’Bryan TA, Bebu I, Macalino GE, 2016. Characterizing the Association Between Alcohol and HIV Virologic Failure in a Military Cohort on Antiretroviral Therapy. Alcoholism, clinical and experimental research 40(3), 529–535. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Earnshaw VA, Bogart LM, Dovidio JF, Williams DR, 2013. Stigma and racial/ethnic HIV disparities: moving toward resilience. The American psychologist 68(4), 225–236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Edelman EJ, Williams EC, Marshall BDL, 2018. Addressing unhealthy alcohol use among people living with HIV: recent advances and research directions. Current opinion in infectious diseases 31(1), 1–7. [DOI] [PubMed] [Google Scholar]
  32. Elliott JC, Aharonovich E, O’Leary A, Wainberg M, Hasin DS, 2014. Drinking motives as prospective predictors of outcome in an intervention trial with heavily drinking HIV patients. Drug and alcohol dependence 134, 290–295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Epstein EE, Fischer-Elber K, Al-Otaiba Z, 2007. Women, aging, and alcohol use disorders. Journal of women & aging 19(1-2), 31–48. [DOI] [PubMed] [Google Scholar]
  34. Fauci AS, Redfield RR, Sigounas G, Weahkee MD, Giroir BP, 2019. Ending the HIV Epidemic: A Plan for the United States. Jama. [DOI] [PubMed] [Google Scholar]
  35. Galvan FH, Bing EG, Fleishman JA, London AS, Caetano R, Burnam MA, Longshore D, Morton SC, Orlando M, Shapiro M, 2002. The prevalence of alcohol consumption and heavy drinking among people with HIV in the United States: results from the HIV Cost and Services Utilization Study. Journal of studies on alcohol 63(2), 179–186. [DOI] [PubMed] [Google Scholar]
  36. Gilbert PA, Zemore SE, 2016. Discrimination and drinking: A systematic review of the evidence. Social science & medicine (1982) 161, 178–194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Gonzalez JS, Batchelder AW, Psaros C, Safren SA, 2011. Depression and HIV/AIDS treatment nonadherence: a review and meta-analysis. Journal of acquired immune deficiency syndromes (1999) 58(2), 181–187. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Gossop M, Darke S, Griffiths P, Hando J, Powis B, Hall W, Strang J, 1995. The Severity of Dependence Scale (SDS): psychometric properties of the SDS in English and Australian samples of heroin, cocaine and amphetamine users. Addiction (Abingdon, England) 90(5), 607–614. [DOI] [PubMed] [Google Scholar]
  39. Grodensky CA, Golin CE, Jones C, Mamo M, Dennis AC, Abernethy MG, Patterson KB, 2015. “I should know better”: the roles of relationships, spirituality, disclosure, stigma, and shame for older women living with HIV seeking support in the South. The Journal of the Association of Nurses in AIDS Care : JANAC 26(1), 12–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Hartzell JD, Janke IE, Weintrob AC, 2008. Impact of depression on HIV outcomes in the HAART era. The Journal of antimicrobial chemotherapy 62(2), 246–255. [DOI] [PubMed] [Google Scholar]
  41. Hasin DS, Stinson FS, Ogburn E, Grant BF, 2007. Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Archives of general psychiatry 64(7), 830–842. [DOI] [PubMed] [Google Scholar]
  42. Hawes SM, Berkley-Patton JY, 2014. Religiosity and risky sexual behaviors among an African American church-based population. Journal of religion and health 53(2), 469–482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Hendershot CS, Stoner SA, Pantalone DW, Simoni JM, 2009. Alcohol use and antiretroviral adherence: review and meta-analysis. Journal of acquired immune deficiency syndromes (1999) 52(2), 180–202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Himelhoch S, Njie-Carr V, 2016. “God loves me no matter how I am”: a phenomenological analysis of the religious and spiritual experiences of HIV-infected African-American women with depression. Mental Health, Religion & Culture, 1–14. [Google Scholar]
  45. Hoggatt KJ, Williams EC, Der-Martirosian C, Yano EM, Washington DL, 2015. National prevalence and correlates of alcohol misuse in women veterans. Journal of substance abuse treatment 52, 10–16. [DOI] [PubMed] [Google Scholar]
  46. Hutton H, Lesko CR, Chander G, Lau B, Wand GS, McCaul ME, 2017. Differential effects of perceived stress on alcohol consumption in moderate versus heavy drinking HIV-infected women. Drug and alcohol dependence 178, 380–385. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Jasinski JL, Williams LM, Siegel J, 2000. Childhood physical and sexual abuse as risk factors for heavy drinking among African-American women: a prospective study. Child abuse & neglect 24(8), 1061–1071. [DOI] [PubMed] [Google Scholar]
  48. Jonas DE, Garbutt JC, Amick HR, Brown JM, Brownley KA, Council CL, Viera AJ, Wilkins TM, Schwartz CJ, Richmond EM, Yeatts J, Evans TS, Wood SD, Harris RP, 2012a. Behavioral counseling after screening for alcohol misuse in primary care: a systematic review and meta-analysis for the U.S. Preventive Services Task Force. Annals of internal medicine 157(9), 645–654. [DOI] [PubMed] [Google Scholar]
  49. Jonas DE, Garbutt JC, Brown JM, Amick HR, Brownley KA, Council CL, Viera AJ, Wilkins TM, Schwartz CJ, Richmond EM, Yeatts J, Swinson Evans T, Wood SD, Harris RP, 2012b. AHRQ Comparative Effectiveness Reviews, Screening, Behavioral Counseling, and Referral in Primary Care To Reduce Alcohol Misuse. Agency for Healthcare Research and Quality (US), Rockville (MD). [PubMed] [Google Scholar]
  50. Justice AC, McGinnis KA, Tate JP, Braithwaite RS, Bryant KJ, Cook RL, Edelman EJ, Fiellin LE, Freiberg MS, Gordon AJ, Kraemer KL, Marshall BD, Williams EC, Fiellin DA, 2016. Risk of mortality and physiologic injury evident with lower alcohol exposure among HIV infected compared with uninfected men. Drug and alcohol dependence 161, 95–103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Kader R, Govender R, Seedat S, Koch JR, Parry C, 2015. Understanding the Impact of Hazardous and Harmful Use of Alcohol and/or Other Drugs on ARV Adherence and Disease Progression. PloS one 10(5), e0125088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Kalichman SC, Grebler T, Amaral CM, McNemey M, White D, Kalichman MO, Cherry C, Eaton L, 2014. Viral suppression and antiretroviral medication adherence among alcohol using HIV-positive adults. International journal of behavioral medicine 21(5), 811–820. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Klein H, Sterk CE, Elifson KW, 2016. The Prevalence of and Factors Associated with Alcohol-Related Problems in a Community Sample of African American Women. Journal of addiction 2016, 7513827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Korthuis PT, Fiellin DA, McGinnis KA, Skanderson M, Justice AC, Gordon AJ, Doebler DA, Asch SM, Fiellin LE, Bryant K, Gibert CL, Crystal S, Goetz MB, Rimland D, Rodriguez-Barradas MC, Kraemer KL, 2012. Unhealthy alcohol and illicit drug use are associated with decreased quality of HIV care. Journal of acquired immune deficiency syndromes (1999) 61(2), 171–178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Kroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mokdad AH, 2009. The PHQ-8 as a measure of current depression in the general population. Journal of affective disorders 114(1-3), 163–173. [DOI] [PubMed] [Google Scholar]
  56. Kuntsche E, Kuntsche S, Thrul J, Gmel G, 2017. Binge drinking: Health impact, prevalence, correlates and interventions. Psychology & health 32(8), 976–1017. [DOI] [PubMed] [Google Scholar]
  57. Lapham GT, Rubinsky AD, Heagerty PJ, Achtmeyer C, Williams EC, Hawkins EJ, Maynard C, Kivlahan DR, Au D, Bradley KA, 2014. Probability and predictors of patients converting from negative to positive screens for alcohol misuse. Alcoholism, clinical and experimental research 38(2), 564–571. [DOI] [PubMed] [Google Scholar]
  58. Lehavot K, Huh D, Walters KL, King KM, Andrasik MP, Simoni JM, 2011. Buffering effects of general and medication-specific social support on the association between substance use and HIV medication adherence. AIDS patient care and STDs 25(3), 181–189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Lesko CR, Keil AP, Fojo AT, Chander G, Lau B, Moore RD, 2019. Recent Substance Use and Probability of Unsuppressed HIV Viral Load among Persons on Antiretroviral Therapy in Continuity Care. American journal of epidemiology. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Liao M, Kang D, Tao X, Bouey JH, Aliyu MH, Qian Y, Wang G, Sun X, Lin B, Bi Z, Jia Y, 2014. Alcohol use, stigmatizing/discriminatory attitudes, and HIV high-risk sexual behaviors among men who have sex with men in China. BioMed research international 2014, 143738. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Mannes ZL, Burrell LE, Bryant VE, Dunne EM, Hearn LE, Whitehead NE, 2016. Loneliness and substance use: the influence of gender among HIV+ Black/African American adults 50+. AIDS care 28(5), 598–602. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Martin JK, Tuch SA, Roman PM, 2003. Problem drinking patterns among African Americans: the impacts of reports of discrimination, perceptions of prejudice, and “risky” coping strategies. Journal of health and social behavior 44(3), 408–425. [PubMed] [Google Scholar]
  63. Matson TE, McGinnis KA, Rubinsky AD, Frost MC, Czarnogorski M, Bryant KJ, Edelman EJ, Satre DD, Catz SL, Bensley KM, Fiellin DA, Justice AC, Williams EC, 2018. Gender and alcohol use: influences on HIV care continuum in a national cohort of patients with HIV. AIDS (London, England) 32(15), 2247–2253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. McGinnis KA, Fiellin DA, Tate JP, Cook RL, Braithwaite RS, Bryant KJ, Edelman EJ, Gordon AJ, Kraemer KL, Maisto SA, Justice AC, 2016. Number of Drinks to “Feel a Buzz” by HIV Status and Viral Load in Men. AIDS and behavior 20(3), 504–511. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Montgomery L, Bagot K, Brown JL, Haeny AM, 2019. The Association Between Marijuana Use and HIV Continuum of Care Outcomes: a Systematic Review. Current HIV/AIDS reports 16(1), 17–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Mulia N, Ye Y, Greenfield TK, Zemore SE, 2009. Disparities in alcohol-related problems among white, black, and Hispanic Americans. Alcoholism, clinical and experimental research 33(4), 654–662. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Mulia N, Ye Y, Zemore SE, Greenfield TK, 2008. Social disadvantage, stress, and alcohol use among black, Hispanic, and white Americans: findings from the 2005 U.S. National Alcohol Survey. Journal of studies on alcohol and drugs 69(6), 824–833. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Nolen-Hoeksema S, 2004. Gender differences in risk factors and consequences for alcohol use and problems. Clinical psychology review 24(8), 981–1010. [DOI] [PubMed] [Google Scholar]
  69. Okosun IS, Seale JP, Daniel JB, Eriksen MP, 2005. Poor health is associated with cepisodic heavy alcohol use: evidence from a National Survey. Public health 119(6), 509–517. [DOI] [PubMed] [Google Scholar]
  70. Parsons JT, Rosof E, Mustanski B, 2008. The temporal relationship between alcohol consumption and HIV-medication adherence: a multilevel model of direct and moderating effects. Health psychology : official journal of the Division of Health Psychology, American Psychological Association 27(5), 628–637. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Peirce RS, Frone MR, Russell M, Cooper ML, Mudar P, 2000. A longitudinal model of social contact, social support, depression, and alcohol use. Health psychology : official journal of the Division of Health Psychology, American Psychological Association 19(1), 28–38. [DOI] [PubMed] [Google Scholar]
  72. Phinney JS, 1992. The multigroup ethnic identity measure a new scale for use with diverse groups. Journal of adolescent research 7(2), 156–176. [Google Scholar]
  73. Phinney JS, Ong AD, 2007. Conceptualization and measurement of ethnic identity: Current status and future directions. Journal of Counseling Psychology 54(3), 271. [Google Scholar]
  74. Rao D, Choi SW, Victorson D, Bode R, Peterman A, Heinemann A, Cella D, 2009. Measuring stigma across neurological conditions: the development of the stigma scale for chronic illness (SSCI). Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation 18(5), 585–595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Rao D, Kemp CG, Huh D, Nevin PE, Turan J, Cohn SE, Simoni JM, Andrasik M, Molina Y, Mugavero MJ, French AL, 2018. Stigma Reduction Among African American Women with HIV: UNITY Health Study. Journal of acquired immune deficiency syndromes (1999). [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Rao D, Molina Y, Lambert N, Cohn SE, 2016. Assessing Stigma among African Americans Living with HIV. Stigma and health 1(3), 146–155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Rubinsky AD, Dawson DA, Williams EC, Kivlahan DR, Bradley KA, 2013. 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. Alcoholism, clinical and experimental research 37(8), 1380–1390. [DOI] [PubMed] [Google Scholar]
  78. Ruggiero KJ, Del Ben K, Scotti JR, Rabalais AE, 2003. Psychometric properties of the PTSD Checklist-Civilian Version. Journal of traumatic stress 16(5), 495–502. [DOI] [PubMed] [Google Scholar]
  79. Saitz R, 2005. Clinical practice. Unhealthy alcohol use. The New England journal of medicine 352(6), 596–607. [DOI] [PubMed] [Google Scholar]
  80. Scott-Sheldon LA, Carey KB, Cunningham K, Johnson BT, Carey MP, 2016. Alcohol Use Predicts Sexual Decision-Making: A Systematic Review and Meta-Analysis of the Experimental Literature. AIDS and behavior 20 Suppl 1, S19–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Scott-Sheldon LAJ, Carey KB, Johnson BT, Carey MP, 2017. Behavioral Interventions Targeting Alcohol Use Among People Living with HIV/AIDS: A Systematic Review and Meta-Analysis. AIDS and behavior 21(Suppl 2), 126–143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Serovich JM, Kimberly JA, Mosack KE, Lewis TL, 2001. The role of family and friend social support in reducing emotional distress among HIV-positive women. AIDS care 13(3), 335–341. [DOI] [PubMed] [Google Scholar]
  83. Sharpe TT, Lee LM, Nakashima AK, Elam-Evans LD, Fleming PL, 2004. Crack cocaine use and adherence to antiretroviral treatment among HIV-infected black women. Journal of community health 29(2), 117–127. [DOI] [PubMed] [Google Scholar]
  84. Sherbourne CD, Stewart AL, 1991. The MOS social support survey. Social science & medicine (1982) 32(6), 705–714. [DOI] [PubMed] [Google Scholar]
  85. Substance Abuse and Mental Health Services Administration (SAMHSA), 2015. 2015 National Survey on Drug Use and Health [PubMed] [Google Scholar]
  86. Substance Abuse and Mental Health Services Administration (SAMHSA), 2016. Binge drinking: Terminology and patterns of use. https://www.samhsa.gov/capt/tools-learning-resources/binge-drinking-terminology-patterns. (Accessed November 2017.
  87. Sullivan LE, Fiellin DA, O’Connor PG, 2005. The prevalence and impact of alcohol problems in major depression: a systematic review. The American journal of medicine 118(4), 330–341. [DOI] [PubMed] [Google Scholar]
  88. Sullivan LE, Goulet JL, Justice AC, Fiellin DA, 2011. Alcohol consumption and depressive symptoms over time: a longitudinal study of patients with and without HIV infection. Drug and alcohol dependence 117(2–3), 158–163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. U.S. Department of Veterans Affairs National Center for Posstraumatic Stress Disorder, 2012. Using the PTSD Checklist (PCL).
  90. Vagenas P, Azar MM, Copenhaver MM, Springer SA, Molina PE, Altice FL, 2015. The Impact of Alcohol Use and Related Disorders on the HIV Continuum of Care: a Systematic Review : Alcohol and the HIV Continuum of Care. Current HIV/AIDS reports 12(4), 421–436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Volk RJ, Steinbauer JR, Cantor SB, 1996. Patient factors influencing variation in the use of preventive interventions for alcohol abuse by primary care physicians. Journal of studies on alcohol 57(2), 203–209. [DOI] [PubMed] [Google Scholar]
  92. Vyavaharkar M, Moneyham L, Corwin S, Saunders R, Annang L, Tavakoli A, 2010. Relationships between stigma, social support, and depression in HIV-infected African American women living in the rural Southeastern United States. Journal of the Association of Nurses in AIDS Care 21(2), 144–152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Walker EA, Newman E, Dobie DJ, Ciechanowski P, Katon W, 2002. Validation of the PTSD checklist in an HMO sample of women. General hospital psychiatry 24(6), 375–380. [DOI] [PubMed] [Google Scholar]
  94. Wardell JD, Shuper PA, Rourke SB, Hendershot CS, 2018. Stigma, Coping, and Alcohol Use Severity Among People Living With HIV: A Prospective Analysis of Bidirectional and Mediated Associations. Annals of behavioral medicine : a publication of the Society of Behavioral Medicine 52(9), 762–772. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Williams EC, Bradley KA, Balderson BH, McClure JB, Grothaus L, McCoy K, Rittmueller SE, Catz SL, 2014a. Alcohol and associated characteristics among older persons living with human immunodeficiency virus on antiretroviral therapy. Substance abuse 35(3), 245–253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Williams EC, Hahn JA, Saitz R, Bryant K, Lira MC, Samet JH, 2016a. Alcohol Use and Human Immunodeficiency Virus (HIV) Infection: Current Knowledge, Implications, and Future Directions. Alcoholism, clinical and experimental research 40(10), 2056–2072. [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Williams EC, Joo YS, Lipira L, Glass JE, 2016b. Psychosocial Stressors and Alcohol Use, Severity, and Treatment Receipt across HIV Status in a Nationally Representative Sample of U.S. Residents. Substance abuse, 0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Williams EC, Lapham GT, Bobb JF, Rubinsky AD, Catz SL, Shortreed SM, Bensley KM, Bradley KA, 2017a. Documented brief intervention not associated with resolution of unhealthy alcohol use one year later among VA patients living with HIV. Journal of substance abuse treatment 78, 8–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  99. Williams EC, Lapham GT, Shortreed SM, Rubinsky AD, Bobb JF, Bensley KM, Catz SL, Richards JE, Bradley KA, 2017b. Among patients with unhealthy alcohol use, those with HIV are less likely than those without to receive evidence-based alcohol-related care: A national VA study. Drug and alcohol dependence 174, 113–120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Williams EC, McGinnis KA, Bobb JF, Rubinsky AD, Lapham GT, Skanderson M, Catz SL, Bensley KM, Richards JE, Bryant KJ, Edelman EJ, Satre DD, Marshall BDL, Kraemer KL, Blosnich JR, Crystal S, Gordon AJ, Fiellin DA, Justice AC, Bradley KA, 2018a. Changes in alcohol use associated with changes in HIV disease severity over time: A national longitudinal study in the Veterans Aging Cohort. Drug and alcohol dependence 189, 21–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Williams EC, McGinnis KA, Edelman EJ, Matson TE, Gordon AJ, Marshall BDL, Bryant KJ, Rubinsky AD, Lapham GT, Satre DD, Richards JE, Catz SL, Fiellin DA, Justice AC, Bradley KA, 2018b. Level of Alcohol Use Associated with HIV Care Continuum Targets in a National U.S. Sample of Persons Living with HIV Receiving Healthcare. AIDS and behavior. [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Williams EC, McGinnis KA, Edelman EJ, Matson TE, Gordon AJ, Marshall BDL, Bryant KJ, Rubinsky AD, Lapham GT, Satre DD, Richards JE, Catz SL, Fiellin DA, Justice AC, Bradley KA, 2019. Level of Alcohol Use Associated with HIV Care Continuum Targets in a National U.S. Sample of Persons Living with HIV Receiving Healthcare. AIDS and behavior 23(1), 140–151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Williams EC, Peytremann-Bridevaux I, Fan VS, Bryson CL, Blough DK, Kivlahan DR, Bradley KA, 2010. The association between alcohol screening scores and health status in male veterans. Journal of addiction medicine 4(1), 27–37. [DOI] [PubMed] [Google Scholar]
  104. Williams EC, Rubinsky AD, Lapham GT, Chavez LJ, Rittmueller SE, Hawkins EJ, Grossbard JR, Kivlahan DR, Bradley KA, 2014b. Prevalence of clinically recognized alcohol and other substance use disorders among VA outpatients with unhealthy alcohol use identified by routine alcohol screening. Drug and alcohol dependence 135, 95–103. [DOI] [PubMed] [Google Scholar]
  105. Yeager DE, Magruder KM, Knapp RG, Nicholas JS, Frueh BC, 2007. Performance characteristics of the posttraumatic stress disorder checklist and SPAN in Veterans Affairs primary care settings. General hospital psychiatry 29(4), 294–301. [DOI] [PubMed] [Google Scholar]

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