Psychosocial Factors Associated with Problem Drinking Among Substance Users with Poorly Controlled HIV Infection

Psychosocial Factors Associated with Problem Drinking Among Substance Users with Poorly... Abstract Aims We aimed to identify psychosocial factors related to problem drinking among patients with poorly controlled human immunodeficiency virus (HIV) infection. Short Summary We aimed to identify psychosocial factors related to problem drinking among those with poorly controlled HIV infection. Increased levels of interpersonal conflict were associated with greater severity of alcohol problems. Poorer mental health, medical mistrust and less satisfaction with one’s physician related to excessive drinking. Methods This secondary analysis used baseline data from a large multisite randomized controlled trial of substance users whose HIV infection was currently poorly controlled, from 11 urban hospitals across the USA. Participants were HIV-infected adult inpatients (n = 801; 67% male, 75% African American) with substance use histories. Participants self-reported on their drinking, perceived health, mental health, social relationships and patient–provider relationship. Structural equation models examined psychosocial factors associated with problem drinking, controlling for demographic covariates. Results Increased levels of interpersonal conflict were associated with greater severity of alcohol problems. Poorer mental health, medical mistrust and less satisfaction with one’s physician were associated with excessive drinking. Conclusions Several psychosocial factors, including interpersonal conflict, poor mental health (i.e. anxiety, depression and somatization), medical mistrust and less satisfaction with one’s provider, were associated with problem drinking among HIV-infected substance users with poorly controlled HIV infection. The co-occurrence of these concerns highlights the need for comprehensive services (including attention to problem drinking, social services, mental health and quality medical care) in this at-risk group. INTRODUCTION Antiretroviral therapy has led to substantial improvements in the morbidity and mortality of human immunodeficiency virus (HIV; World Health Organization, 2017). However, some patients fail to achieve and maintain viral suppression (Castel et al., 2016). Poorly controlled HIV infection has consequences for personal health and infectivity and thus has important public health implications. Problem drinking poses risks to the health of all individuals with HIV, through reduced adherence to antiretroviral medication (Azar et al., 2010) and less engagement in care (Vagenas et al., 2015). These consequences of problem drinking can be particularly detrimental for those whose infection is already poorly controlled. It is, therefore, important to understand why some individuals with poorly controlled HIV infection engage in problem drinking. Some studies have shown that factors related to personal well-being (including perceived health and mental health) and social support (in personal relationships and from one’s HIV provider) relate to problem drinking among individuals with HIV generally. Yet, factors underlying problem drinking for individuals with poorly controlled infection in particular (who are arguably of greatest concern) are rarely studied. Whether these factors relate to problem drinking among individuals with poorly controlled HIV infection is important to know, so that efforts to intervene with problem drinking among these individuals do so in a relevant and useful way. Aspects of personal well-being—including perceived health and mental health—may relate to problem drinking among individuals with poorly controlled HIV infection. Specifically, poor perceived health may relate to problem drinking if perceived health fosters hopelessness (leading to increased drinking) or if drinking makes one feel less well. Poor perceived health has been found to relate to heavier drinking among substance users with HIV in prior research (Elliott et al., 2017), although we know of no such studies among those with poorly controlled HIV infection. Poor mental health may also relate to problem drinking among individuals with poorly controlled HIV infection, either due to comorbidity between mental health and substance use disorders or due to drinking to cope with mental distress. This hypothesis is supported by past studies that have demonstrated that drinking is associated with emotional distress (Naar-King et al., 2010), anxiety/depression (Garey et al., 2015) and Posttraumatic stress disorder (Devieux et al., 2013) in mixed-gender HIV-infected samples, and with depression (Cook et al., 2009) in female HIV-infected samples. Social relationships, including both personal relationships and relationships with providers, may also relate to problem drinking. Those whose social support from friends, family or significant others is insufficient or even stressful may seek comfort through alternate means such as alcohol; excessive alcohol use may in turn have isolating effects. Some past research suggests that poor social support is associated with drinking in HIV-infected adults (Hansen et al., 2009) and youth (Naar-King et al., 2010). However, a third study found that social support was not associated with drinking in HIV primary care (Lehavot et al., 2011). Whether social support (or in contrast, the presence of negative/conflictual social interactions) relates to problem drinking among persons with poorly controlled HIV infection is unknown. Relationship with one’s HIV provider may also relate to problem drinking, as poor relationship quality may interfere with the provision of alcohol risk information and/or patients’ receptivity to this information. Problem drinking may also introduce barriers such as stigma or guilt into the patient–provider relationship. Although most HIV patients and providers report comfort discussing substance use (Ray et al., 2013), most HIV patients do not actually discuss drinking with their providers (Metsch et al., 2008), and provider communication has been found to be poorer with HIV-infected drinkers than drug users (Korthuis et al., 2011). Medical mistrust is one specific aspect of the patient–provider relationship that can relate to patients’ engagement in health/risk behaviors (Eaton et al., 2014) that may also relate to problem drinking. These psychological and social characteristics may relate to problem drinking among patients with poorly controlled HIV infection but to our knowledge, these relationships have yet to be tested in this group. We, therefore, examined whether these four domains of psychosocial well-being (perceived health, mental health, social relationships and the patient–provider relationship) are associated with problem drinking in a large sample of inpatients with poorly controlled HIV infection. To investigate this question, this study used baseline data from a large multisite clinical trial testing interventions intended to increase viral suppression (Metsch, 2016; Metsch et al., 2016). METHODS Participants and procedures This study is a secondary analysis of data from Project HOPE—Hospital Visit as Opportunity for Prevention and Engagement for HIV-Infected Drug Users (Metsch, 2016; Metsch et al., 2016), a multisite clinical trial sponsored by the National Drug Abuse Treatment Clinical Trials Network. Patients were recruited from 11 hospitals in major urban areas with high HIV prevalence across the USA: Boston, New York, Philadelphia, Baltimore, Pittsburgh, Chicago, Atlanta, Miami, Birmingham, Dallas and Los Angeles. The protocol was approved by institutional review boards at all participating institutions. A total of 2291 patients were assessed for eligibility. Clinical eligibility criteria required individuals to (a) be an HIV-infected inpatient at a participating hospital, (b) be an adult (18 years and older), (c) have poorly controlled HIV infection (have a current AIDS-defining illness or meet CD4 and viral load cutoffs [<350 cells/μl and >200 copies/ml, respectively, in past 6 months; or <500 cells/μl and >200 copies/ml or unknown in past year but likely still unsuppressed and inadequately medicated]) and (d) have self-reported or documented opioid or stimulant use or exceed alcohol cutoffs (>3 for women or >4 for men on the Alcohol Use Disorders Identification Test [AUDIT]-C) in the past 12 months. Other eligibility criteria required provision of informed consent, locator information and medical record release; ability to communicate in English and to return for follow-up visits; and a Karnofsky performance level of >60 (omitting those severely disabled). A sample of 801 eligible patients completed the baseline assessment, and were then assigned to one of three intervention conditions designed to attain virologic suppression and to link and retain patients in HIV and substance abuse care: Patient Navigation (n = 266), Patient Navigation with Contingency Management (n = 271) and Treatment as Usual (n = 264). Current analyses utilize baseline data, collected prior to intervention, with the full sample of 801 patients. Measures Problem drinking We include two measures of problem drinking, encompassing both severity of alcohol problems (primary outcome) and excessive drinking (secondary outcome). Severity of alcohol problems was assessed using items from the Addiction Severity Index-Lite (ASI-Lite). The full ASI-Lite assesses problem behaviors regarding alcohol and drug use as well as other related (medical, employment, legal, family/social, psychiatric) domains (McGahan et al., 1986; Mclellan et al., 1999). ASI-Lite items have demonstrated construct validity (Newcombe et al., 2005; Humeniuk et al., 2008) and been used previously in research with HIV patients (Palmer et al., 2003). The current study used a latent model of severity of alcohol problems, created from the six alcohol indicators from the ASI-Lite that most directly measured intensity of alcohol use and misuse: (a) alcohol use in the past 30 days, (b) alcohol intoxication in the past 30 days, (c) money spent on alcohol in the past 30 days, (d) number of days experienced alcohol problems in the past 30 days, (e) level of trouble/bother in the past 30 days due to alcohol problems and (f) rated importance of treatment for alcohol problems (McGahan et al., 1986). The ASI-Lite was used as the primary outcome because it is designed to measure severity of alcohol problems (McGahan et al., 1986), an important clinical outcome. In addition to the ASI-Lite, the Alcohol Use Disorders Identification Test (AUDIT) was analyzed as a measure of excessive drinking, for sensitivity analyses, to assess consistency of findings across different aspects of problem drinking. The AUDIT is a screening measure of excessive drinking in the past year, designed to identify hazardous and harmful drinkers (Babor et al., 2001). Its 10 items assess recent alcohol use, alcohol dependence symptoms and alcohol-related problems, with higher scores indicating more excessive drinking (Babor et al., 2001). This widely-used measure has demonstrated reliability and validity across many studies (Reinert and Allen, 2007). This measure was used as a secondary outcome for sensitivity analyses because it is a brief screening measure and because its focus on excessive drinking was deemed secondary to the ASI-Lite’s focus on severity of alcohol problems. However, due to substantial overlap in item content between the ASI-Lite and AUDIT, overall findings are discussed in terms of ‘problem drinking.’ Perceived health Perceived health was assessed using the 12-item Short-Form Health Survey (SF-12), a reliable and valid abbreviation of the 36-Item Short-Form Health Survey (SF-36; Ware et al., 1996). The SF-12 measures subjective characterization of health and well-being, limitations in engagement in daily activities, as well as specific interference from physical and emotional difficulties, yielding a composite score of perceived health and disability. It has demonstrated reliability and validity among individuals with HIV (Han et al., 2002), and has been shown to be responsive to initiation of and adherence to antiretroviral therapy (Mannheimer et al., 2005). Cronbach’s alpha in this sample was 0.86. Mental health The 18-item Brief Symptom Inventory (BSI-18) was used to measure mental health (Derogatis, 2000). The BSI-18 is an abbreviated version of the original 53-item scale (Derogatis, 1975). This measure assesses symptoms in the domains of anxiety, depression and somatization, and yields an overall global severity index with higher scores indicating poorer mental health. Patients are asked to consider symptoms present in the past seven days, and to rate their experience using the following response options: Not at all (0), A little bit (1), Moderately (2), Quite a bit (3) and Extremely (4) (Derogatis, 1975). The BSI-18 has demonstrated reliability and validity across several medical populations (Zabora et al., 2001; Recklitis et al., 2006; Meachen et al., 2008). Cronbach’s alpha in this sample was 0.92. Social relationships Positive social support and social conflict were measured using subscales from the Seek, Test, Treat and Retain Data Harmonization Measure, adapted from earlier surveys in HIV samples where its validity was supported (The RAND Corporation, 1994–2016; Hays et al., 1995; Fleishman et al., 2000). The Short Social Support Scale is a five-item measure of the presence of others who meet emotional needs and provide assistance when needed. The Conflictual Social Interaction Scale is a three-item measure of disagreements and pressure to change from significant others. Participants rated items from both subscales using response options: None of the time (1), A little of the time (2), Some of the time (3), Most of the time (4) and All of the time (5). Higher scores on the Social Support Scale indicate higher levels of perceived support. Higher scores on the Conflictual Social Interaction Scale indicate higher levels of interpersonal conflict. Cronbach’s alphas were 0.75 and 0.86 on the Conflictual Social Interactions and Social Support scales, respectively, in this sample. Relationship with provider Two aspects of patients’ relationships with their providers were assessed: satisfaction with one’s physician and medical mistrust. For satisfaction with one’s physician, the Overall Satisfaction with Care scale was used, consisting of four items taken from a larger set of scales assessing a range of components of the physician–patient relationship (Schneider et al., 2004). For the Overall Satisfaction with Care subscale, patients rated personal manner, communication skills, technical skills and overall care from their HIV provider using response options: excellent, very good, good, fair and poor. Higher scores indicated greater satisfaction with care. This scale has been shown to be reliable and to relate to medication adherence among patients with HIV (Schneider et al., 2004). Cronbach’s alpha in this sample was 0.94. The Medical Mistrust Scale assesses patient mistrust of health care professionals and systems. This scale prompts participants to respond to 12 statements regarding the medical treatment of their ethnic group, using response options ranging from Strongly Agree (1) to Strongly Disagree (5) (Thompson et al., 2004). This scale has demonstrated reliability and validity in prior research on cancer screening (Thompson et al., 2004). Cronbach’s alpha was 0.86 in this sample. Covariates Patient reported age (in years), gender (male or female), race/ethnicity (White, Black, Hispanic, other) and education (high school graduate versus non-graduate). Drug use was measured as the number of days of use in the past 30 days, for whichever drug was used most frequently (heroin, methadone [illicit], opiates, barbiturates, sedatives, cocaine, amphetamines, cannabis, hallucinogens and inhalants). These five variables were included as control covariates in structural equation models. Analysis plan Our first step was a preliminary examination of our variables. Specifically, univariate descriptive examination of the observed variables was conducted, followed by a confirmatory factor analysis for the latent severity of alcohol problems variable (using the six items from the ASI-Lite as indicators of the latent construct). Second, bivariate associations of the ASI-Lite (the primary outcome variable) with the psychosocial and demographic factors were generated. Third, structural equation models were conducted. The first model regressed the latent severity of alcohol problems variable from the ASI-Lite on the psychosocial factors (physical health, interpersonal conflict, social support, satisfaction with physician, medical mistrust and mental health) and demographic covariates (age, race/ethnicity, education, gender and drug use). As a sensitivity analysis, observed AUDIT score was regressed on the same psychosocial factors and demographic covariates. All analyses were conducted in Mplus 7.0 using full information maximum likelihood (Muthén and Muthén, 1998–2012). For variables with substantial skew, we used count specification with Poisson distribution (for the ASI-Lite use and intoxication indicators; AUDIT) or log transformation (for the ASI-Lite money spent indicator), as indicated by the variables’ distributions. RESULTS Descriptive information The sample (n = 801) was predominantly African American (75%; White: 12%; Hispanic: 11%; Other: 1%), male (67%) and had at least a high school education (60%). Mean age of the sample was 45 years (SD = 9.99) with a range from 18 to 68 years. Slightly more than half (57.2%) reported taking antiretroviral medication in the past month (although not necessarily consistently). Of the full sample of 801 participants, 188 (23.5%) were eligible for the trial on the basis of scoring >3 (for women) or >4 (for men) on the Alcohol Use Disorders Identification Test (AUDIT)-C, 330 (41.2%) on the basis of report or medical record documentation of any opioid or stimulant (cocaine, ecstasy or amphetamine) use and 283 (35.3%) on the basis of both AUDIT-C and drug use. The mean AUDIT score was 9.04 (SD = 9.54). Nearly half of participants (43.7%) had AUDIT scores of 8 or above, indicating hazardous and harmful alcohol use of at least a medium level of severity. A substantial proportion (22.6%) scored 16 or above, indicating hazardous and harmful alcohol use of a high level of severity (Babor et al., 2001). Participants reported drinking on an average of 5.93 (SD = 8.93) days and intoxication on an average of 3.26 (SD = 7.34) days in the past 30 days, with an average of $47.35 (SD = $185.55) spent on alcohol during this time. Participants reported alcohol problems on an average of 1.91 (SD = 6.30) days in the past 30 days. Despite their relatively high AUDIT scores, participants reported low levels of being troubled/bothered by alcohol problems (M = 0.48, SD = 1.15) and low importance of treatment (M = 0.90, SD = 1.53) for their alcohol use during this time (0 = Not at all, 1 = Slightly). In the confirmatory factor analysis for the latent severity of alcohol problems variable, all indicator variables loaded significantly on the latent variable, P < 0.001. Bivariate associations Table 1 shows the bivariate correlations between the latent severity of alcohol problems (ASI-Lite) score with the primary psychosocial factors and demographic covariates. In this bivariate analysis, poor mental health (r = 0.12, P = 0.002) and interpersonal conflict (r = 0.13, P < 0.001) were positively and significantly correlated with severity of alcohol problems. Individuals identifying as ‘other’ race/ethnicity (i.e. participants who did not identify as Hispanic, White or Black) had significantly higher severity of alcohol problems scores compared with non-Hispanic White participants (b = 1.15, P = 0.03), and drug use positively correlated with severity of alcohol problems, r = 0.12, P < 0.01. Table 1. Bivariate associations between severity of alcohol problems (ASI-Lite) with demographic/control and psychosocial variables Correlation with severity of alcohol problems Mean severity of alcohol problems scorea P-value Demographic/Control variables  Age 0.06 0.09  Race   Black, non-Hispanic   (reference group: White) 0.24 0.27   Hispanic (reference group:   White) −0.22 0.47   Other non-White race   (reference group: White) 1.15 0.03  High school graduate  (reference group: non- graduates) −0.06 0.14  Male (reference group:  female) 0.03 0.42  Drug use 0.12 <0.01 Primary psychosocial factors  Physical health 0.01 0.86  Poor mental health 0.12 0.002  Satisfaction with physician −0.05 0.76  Medical mistrust 0.06 0.16  Interpersonal conflict 0.13 <0.001  Social support −0.06 0.10 Correlation with severity of alcohol problems Mean severity of alcohol problems scorea P-value Demographic/Control variables  Age 0.06 0.09  Race   Black, non-Hispanic   (reference group: White) 0.24 0.27   Hispanic (reference group:   White) −0.22 0.47   Other non-White race   (reference group: White) 1.15 0.03  High school graduate  (reference group: non- graduates) −0.06 0.14  Male (reference group:  female) 0.03 0.42  Drug use 0.12 <0.01 Primary psychosocial factors  Physical health 0.01 0.86  Poor mental health 0.12 0.002  Satisfaction with physician −0.05 0.76  Medical mistrust 0.06 0.16  Interpersonal conflict 0.13 <0.001  Social support −0.06 0.10 Note. ASI-Lite, Addiction Severity Index-Lite. Bold indicates significance at α = 0.05. aMeasured as a latent variable with mean = 0 for the non-Hispanic White reference group. Means are presented to elucidate direction of effects for multiple racial groups in comparison with the non-Hispanic White reference group. Statistical comparisons between the mean ASI-Lite scores for each group and the reference group are Wald tests resulting from a multiple regression with dummy variables for each of the three race categories. Table 1. Bivariate associations between severity of alcohol problems (ASI-Lite) with demographic/control and psychosocial variables Correlation with severity of alcohol problems Mean severity of alcohol problems scorea P-value Demographic/Control variables  Age 0.06 0.09  Race   Black, non-Hispanic   (reference group: White) 0.24 0.27   Hispanic (reference group:   White) −0.22 0.47   Other non-White race   (reference group: White) 1.15 0.03  High school graduate  (reference group: non- graduates) −0.06 0.14  Male (reference group:  female) 0.03 0.42  Drug use 0.12 <0.01 Primary psychosocial factors  Physical health 0.01 0.86  Poor mental health 0.12 0.002  Satisfaction with physician −0.05 0.76  Medical mistrust 0.06 0.16  Interpersonal conflict 0.13 <0.001  Social support −0.06 0.10 Correlation with severity of alcohol problems Mean severity of alcohol problems scorea P-value Demographic/Control variables  Age 0.06 0.09  Race   Black, non-Hispanic   (reference group: White) 0.24 0.27   Hispanic (reference group:   White) −0.22 0.47   Other non-White race   (reference group: White) 1.15 0.03  High school graduate  (reference group: non- graduates) −0.06 0.14  Male (reference group:  female) 0.03 0.42  Drug use 0.12 <0.01 Primary psychosocial factors  Physical health 0.01 0.86  Poor mental health 0.12 0.002  Satisfaction with physician −0.05 0.76  Medical mistrust 0.06 0.16  Interpersonal conflict 0.13 <0.001  Social support −0.06 0.10 Note. ASI-Lite, Addiction Severity Index-Lite. Bold indicates significance at α = 0.05. aMeasured as a latent variable with mean = 0 for the non-Hispanic White reference group. Means are presented to elucidate direction of effects for multiple racial groups in comparison with the non-Hispanic White reference group. Statistical comparisons between the mean ASI-Lite scores for each group and the reference group are Wald tests resulting from a multiple regression with dummy variables for each of the three race categories. Structural equation models Table 2 details the results of the structural equation models. The primary structural equation model regressed the latent severity of alcohol problems (ASI-Lite) score on all psychosocial factors and demographic covariates (for depiction of primary structural equation model highlighting significant associations, see Fig. 1). Increased age (b = 0.02, P = 0.02), endorsement of ‘other’ ethnicity (b = 1.31, P = 0.02), male gender (b= 0.32, P = 0.048) and drug use (b= 0.02, P = 0.004) were associated with higher severity of alcohol problems. Increased levels of interpersonal conflict (b = 0.05, P = 0.04) was the only psychosocial factor associated with higher severity of alcohol problems in the primary model (Fig. 1). In the second structural equation model, using the observed AUDIT score for sensitivity analyses, we found male gender (b = 0.21, P = 0.03), poor mental health (b = 0.01, P < 0.001), less satisfaction with one’s physician (b = −0.14, P < 0.001) and more medical mistrust (b = 0.01, P = 0.03) to be associated with excessive drinking. Table 2. Results for structural equation models: associations between problem drinking with psychosocial factors and demographic covariates Severity of Alcohol Problems: (ASI-Litea, Primary outcome) Excessive drinking: (AUDITb, Secondary outcome) b SE(b) P b SE(b) P Demographic control covariates  Age 0.017 0.007 0.020 0.005 0.004 0.172  Race   Black, non-Hispanic (reference group: White) 0.384 0.227 0.091 0.083 0.139 0.551   Hispanic (reference group: White) −0.212 0.327 0.517 −0.205 0.183 0.263   Other non-White race (reference group: White) 1.313 0.561 0.019 0.163 0.289 0.573  High school graduate (reference group: non-graduates) −0.130 0.149 0.383 −0.036 0.085 0.671  Male (reference group: female) 0.319 0.162 0.048 0.209 0.097 0.031  Drug use 0.020 0.007 0.004 −0.008 0.004 0.054 Primary psychosocial factors  Physical health 0.009 0.007 0.170 −0.002 0.004 0.639  Poor mental health 0.009 0.005 0.065 0.010 0.003 <0.001  Satisfaction with physician 0.022 0.037 0.560 −0.139 0.014 <0.001  Medical mistrust 0.001 0.010 0.949 0.012 0.005 0.032  Interpersonal conflict 0.049 0.023 0.035 0.025 0.014 0.074  Social support −0.009 0.011 0.409 −0.002 0.006 0.742 Severity of Alcohol Problems: (ASI-Litea, Primary outcome) Excessive drinking: (AUDITb, Secondary outcome) b SE(b) P b SE(b) P Demographic control covariates  Age 0.017 0.007 0.020 0.005 0.004 0.172  Race   Black, non-Hispanic (reference group: White) 0.384 0.227 0.091 0.083 0.139 0.551   Hispanic (reference group: White) −0.212 0.327 0.517 −0.205 0.183 0.263   Other non-White race (reference group: White) 1.313 0.561 0.019 0.163 0.289 0.573  High school graduate (reference group: non-graduates) −0.130 0.149 0.383 −0.036 0.085 0.671  Male (reference group: female) 0.319 0.162 0.048 0.209 0.097 0.031  Drug use 0.020 0.007 0.004 −0.008 0.004 0.054 Primary psychosocial factors  Physical health 0.009 0.007 0.170 −0.002 0.004 0.639  Poor mental health 0.009 0.005 0.065 0.010 0.003 <0.001  Satisfaction with physician 0.022 0.037 0.560 −0.139 0.014 <0.001  Medical mistrust 0.001 0.010 0.949 0.012 0.005 0.032  Interpersonal conflict 0.049 0.023 0.035 0.025 0.014 0.074  Social support −0.009 0.011 0.409 −0.002 0.006 0.742 Notes. ASI-Lite, Addiction Severity Index-Lite; AUDIT, Alcohol Use Disorders Identification Test. SE, standard error. All regression coefficients are unstandardized. Bold parameter estimates are statistically significant at α = 0.05. aLatent output: ASI-Lite on all psychosocial factors and covariates. bAUDIT output: AUDIT score (count) on all psychosocial factors and covariates. Table 2. Results for structural equation models: associations between problem drinking with psychosocial factors and demographic covariates Severity of Alcohol Problems: (ASI-Litea, Primary outcome) Excessive drinking: (AUDITb, Secondary outcome) b SE(b) P b SE(b) P Demographic control covariates  Age 0.017 0.007 0.020 0.005 0.004 0.172  Race   Black, non-Hispanic (reference group: White) 0.384 0.227 0.091 0.083 0.139 0.551   Hispanic (reference group: White) −0.212 0.327 0.517 −0.205 0.183 0.263   Other non-White race (reference group: White) 1.313 0.561 0.019 0.163 0.289 0.573  High school graduate (reference group: non-graduates) −0.130 0.149 0.383 −0.036 0.085 0.671  Male (reference group: female) 0.319 0.162 0.048 0.209 0.097 0.031  Drug use 0.020 0.007 0.004 −0.008 0.004 0.054 Primary psychosocial factors  Physical health 0.009 0.007 0.170 −0.002 0.004 0.639  Poor mental health 0.009 0.005 0.065 0.010 0.003 <0.001  Satisfaction with physician 0.022 0.037 0.560 −0.139 0.014 <0.001  Medical mistrust 0.001 0.010 0.949 0.012 0.005 0.032  Interpersonal conflict 0.049 0.023 0.035 0.025 0.014 0.074  Social support −0.009 0.011 0.409 −0.002 0.006 0.742 Severity of Alcohol Problems: (ASI-Litea, Primary outcome) Excessive drinking: (AUDITb, Secondary outcome) b SE(b) P b SE(b) P Demographic control covariates  Age 0.017 0.007 0.020 0.005 0.004 0.172  Race   Black, non-Hispanic (reference group: White) 0.384 0.227 0.091 0.083 0.139 0.551   Hispanic (reference group: White) −0.212 0.327 0.517 −0.205 0.183 0.263   Other non-White race (reference group: White) 1.313 0.561 0.019 0.163 0.289 0.573  High school graduate (reference group: non-graduates) −0.130 0.149 0.383 −0.036 0.085 0.671  Male (reference group: female) 0.319 0.162 0.048 0.209 0.097 0.031  Drug use 0.020 0.007 0.004 −0.008 0.004 0.054 Primary psychosocial factors  Physical health 0.009 0.007 0.170 −0.002 0.004 0.639  Poor mental health 0.009 0.005 0.065 0.010 0.003 <0.001  Satisfaction with physician 0.022 0.037 0.560 −0.139 0.014 <0.001  Medical mistrust 0.001 0.010 0.949 0.012 0.005 0.032  Interpersonal conflict 0.049 0.023 0.035 0.025 0.014 0.074  Social support −0.009 0.011 0.409 −0.002 0.006 0.742 Notes. ASI-Lite, Addiction Severity Index-Lite; AUDIT, Alcohol Use Disorders Identification Test. SE, standard error. All regression coefficients are unstandardized. Bold parameter estimates are statistically significant at α = 0.05. aLatent output: ASI-Lite on all psychosocial factors and covariates. bAUDIT output: AUDIT score (count) on all psychosocial factors and covariates. Fig. 1. View largeDownload slide Structural equation model regressing latent severity of alcohol problems variable (with six alcohol indicator variables) on demographic and psychosocial covariates. Solid lines indicate statistical significance at α = 0.05. Fig. 1. View largeDownload slide Structural equation model regressing latent severity of alcohol problems variable (with six alcohol indicator variables) on demographic and psychosocial covariates. Solid lines indicate statistical significance at α = 0.05. DISCUSSION Using a large sample of hospitalized patients with poorly controlled HIV infection across the USA, we found several psychosocial variables to relate to problem drinking. Increased levels of interpersonal conflict were associated with greater severity of alcohol problems. Poorer mental health, less satisfaction with one’s physician and more medical mistrust related to excessive drinking. To our knowledge, this was the first examination of psychosocial factors related to problem drinking among individuals with poorly controlled HIV. The relevance of psychosocial factors to problem drinking is generally consistent with research in the HIV literature as a whole, as well as in other medical populations for whom drinking is dangerous (e.g. liver disease patients after transplantation (Rustad et al., 2015)). Past research has demonstrated that poor mental health is associated with problem drinking in the HIV-infected population (Naar-King et al., 2010; Devieux et al., 2013; Garey et al., 2015). The co-occurrence of poor mental health and excessive drinking in our study of individuals with poorly controlled HIV suggests the potential value of coordinated HIV care, mental health care and substance use care for these individuals, ideally through co-located or integrated care or at least attentive care coordination. When the full BSI measure is replaced by its constituent subscales, it becomes clear that anxiety drives this association in our sample, demonstrating a particular need for services that target anxiety for these individuals (exploratory results available upon request). Perceived health did not relate to problem drinking in the current study. This association was significant in a previous study of HIV-infected substance abusers (Elliott et al., 2017). Perhaps perceived health is less relevant to problem drinking for those with poorly controlled HIV or perhaps more restricted variability in health tempered the effect. Regarding social relationships, our results found social conflict (but not social support) to relate to severity of alcohol problems. It is therefore, possible that counselors or social workers who help patients with conflictual relationships to better manage or remove themselves from these situations, in addition to helping patients manage their lives, could also reduce severity of alcohol problems. However, this is speculative given that current data cannot conclude causality or even directionality. Social conflict was most related to ASI-Lite items assessing trouble/bother from alcohol problems and importance of treatment for alcohol problems (Supplemental data Table 1), suggesting that social conflict relates most saliently to problematic aspects of drinking as opposed to simple drinking intensity. Excessive drinkers (identified using the AUDIT) rated their care from their HIV physician more poorly. This is consistent with other research (Korthuis et al., 2011) suggesting that HIV-infected excessive drinkers may perceive their care to be substandard. Excessive drinkers may truly receive poorer quality care (perhaps due to provider stigma toward excessive drinkers) or there may be something else occurring that leads excessive drinkers to rate the interaction as poorer, requiring further study. Ensuring quality care, perhaps by providing training to doctors on working with problem drinkers (on topics such as enhancing empathy, screening and brief intervention) may help re-engage problem drinkers with poorly controlled HIV in care, and/or may help them reduce their drinking. However, cross-sectional findings prevent concluding a causative effect based on our findings, requiring further study. Interestingly, medical mistrust also related to excessive drinking, suggesting a possible factor undermining the patient–provider relationship. It is worth noting that older individuals evidenced greater severity of alcohol problems. This is different from previous findings of higher alcohol use disorder among younger individuals in the general population (Grant et al., 2015), and of more hazardous drinking (Crane et al., 2017) and substance use disorders (Hartzler et al., 2017) among younger individuals with HIV. Future research with individuals with poorly controlled HIV should incorporate age into their analyses, to confirm this finding. Those reporting a race other than Hispanic, White or Black also reported greater severity of alcohol problems. The ‘other’ group in our sample collapsed Asian, American Indian, Alaska Native and others, with small sample sizes prohibiting analyses among these groups, making it difficult to make meaningful conclusions. However, high prevalence of problem drinking in some of these populations has been documented (Heart et al., 2016). Other covariates were less surprising, including the association of male gender with both measures of problem drinking, and the association of drug use with severity of alcohol problems. This current study has certain limitations. First, this is a secondary analysis of baseline data from a trial of selected patients hospitalized in urban settings; our findings may not apply to non-hospitalized HIV-infected populations or those in less urban regions. Second, analyses are cross-sectional. Therefore, although the current study identifies associations, prospective data are needed to determine if they are true predictors. Third, the selection criteria requiring all patients to be substance users may have limited variability in problem drinking. Yet, drug users needed not drink to be included. Fourth, models of the ASI-Lite and AUDIT yielded different results. One potential reason is that the ASI-Lite is designed to measure severity of alcohol problems (McGahan et al., 1986), while the AUDIT proports to measure excessive drinking (Babor et al., 2001). That social conflict related to severity of alcohol problems may reflect the disruptive nature of alcohol problems to relationships. The modeling of this latent variable accounts for measurement error, enhancing confidence in this result. In contrast, the associations between aspects of the patient–provider relationship and mental health with excessive drinking may reflect problems correlated with more subtle elevations in drinking. Fifth, use of brief screening measures is a limitation. More extensive measurement of these constructs would be informative. Future research should also measure alcohol use disorder, to see whether these psychosocial factors relate to actual disorder and whether alcohol use disorder explains (mediates) associations found in this study. Sixth, measures of model fit are limited in structural equation models with categorical outcomes. The only option for a global fit measure was the chi-square test, which is known to demonstrate poor model fit in samples >400 participants where there are high correlations. Finally, there are numerous other psychosocial and control variables that could have been examined. In choosing our psychosocial factors, we chose several that had demonstrated relevance in individuals with HIV in prior research. Future research should include others such as pain, peer norms, attitudes toward drinking and perceived behavioral control. We limited our control variables to age, sex, race, education and drug use. Others could be argued to be relevant (e.g. time since HIV infection, HIV care status, antiretroviral status, income, homelessness, geographic region and marital status) but we chose only the most basic control covariates used most commonly in the alcohol literature. In conclusion, interpersonal conflict, poor mental health, less satisfaction with one’s physician and medical mistrust were associated with different aspects of problem drinking among substance users with poorly controlled HIV. These results advance our understanding of psychosocial factors associated with problem drinking in this particularly high-risk group. Prospective work is needed to more clearly elucidate directionality (e.g. whether psychosocial factors lead to problem drinking or vice versa), and to evaluate potential moderating or mediating effects among these psychosocial factors. If these factors are found to be prospectively predictive of problem drinking among individuals with poorly controlled HIV, this would suggest that helping patients address harmful/conflictual relationships, poor mental health, and poor/mistrusting relationships with providers, while worthwhile goals themselves, may also reduce problem drinking. This could be accomplished through comprehensive care services in social organizations for HIV patients and/or in HIV primary care. Continued research in this area could help to better address the complex needs of a population where preventing problem drinking is important to health and well-being. SUPPLEMENTARY MATERIAL Supplementary data are available at Alcohol And Alcoholism online. Funding This work was supported by the National Institutes of Health (NIH) grants ([U10DA013720 (Project HOPE–Hospital Visit as Opportunity for Prevention and Engagement for HIV-Infected Drug Users to LRM)], [K23AA023753 to JCE], [R01AA023163 to DSH], [K24DA035684 to GML], [K23AI112477 to AEN]); and the New York State Psychiatric Institute (DSH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. CONFLICT OF INTEREST STATEMENT None declared. REFERENCES Azar MM , Springer SA , Meyer JP , et al. . 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Psychosomatics 42 : 241 – 6 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Medical Council on Alcohol and Oxford University Press. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Alcohol and Alcoholism Oxford University Press

Psychosocial Factors Associated with Problem Drinking Among Substance Users with Poorly Controlled HIV Infection

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Oxford University Press
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© The Author(s) 2018. Medical Council on Alcohol and Oxford University Press. All rights reserved.
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0735-0414
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1464-3502
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10.1093/alcalc/agy021
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Abstract

Abstract Aims We aimed to identify psychosocial factors related to problem drinking among patients with poorly controlled human immunodeficiency virus (HIV) infection. Short Summary We aimed to identify psychosocial factors related to problem drinking among those with poorly controlled HIV infection. Increased levels of interpersonal conflict were associated with greater severity of alcohol problems. Poorer mental health, medical mistrust and less satisfaction with one’s physician related to excessive drinking. Methods This secondary analysis used baseline data from a large multisite randomized controlled trial of substance users whose HIV infection was currently poorly controlled, from 11 urban hospitals across the USA. Participants were HIV-infected adult inpatients (n = 801; 67% male, 75% African American) with substance use histories. Participants self-reported on their drinking, perceived health, mental health, social relationships and patient–provider relationship. Structural equation models examined psychosocial factors associated with problem drinking, controlling for demographic covariates. Results Increased levels of interpersonal conflict were associated with greater severity of alcohol problems. Poorer mental health, medical mistrust and less satisfaction with one’s physician were associated with excessive drinking. Conclusions Several psychosocial factors, including interpersonal conflict, poor mental health (i.e. anxiety, depression and somatization), medical mistrust and less satisfaction with one’s provider, were associated with problem drinking among HIV-infected substance users with poorly controlled HIV infection. The co-occurrence of these concerns highlights the need for comprehensive services (including attention to problem drinking, social services, mental health and quality medical care) in this at-risk group. INTRODUCTION Antiretroviral therapy has led to substantial improvements in the morbidity and mortality of human immunodeficiency virus (HIV; World Health Organization, 2017). However, some patients fail to achieve and maintain viral suppression (Castel et al., 2016). Poorly controlled HIV infection has consequences for personal health and infectivity and thus has important public health implications. Problem drinking poses risks to the health of all individuals with HIV, through reduced adherence to antiretroviral medication (Azar et al., 2010) and less engagement in care (Vagenas et al., 2015). These consequences of problem drinking can be particularly detrimental for those whose infection is already poorly controlled. It is, therefore, important to understand why some individuals with poorly controlled HIV infection engage in problem drinking. Some studies have shown that factors related to personal well-being (including perceived health and mental health) and social support (in personal relationships and from one’s HIV provider) relate to problem drinking among individuals with HIV generally. Yet, factors underlying problem drinking for individuals with poorly controlled infection in particular (who are arguably of greatest concern) are rarely studied. Whether these factors relate to problem drinking among individuals with poorly controlled HIV infection is important to know, so that efforts to intervene with problem drinking among these individuals do so in a relevant and useful way. Aspects of personal well-being—including perceived health and mental health—may relate to problem drinking among individuals with poorly controlled HIV infection. Specifically, poor perceived health may relate to problem drinking if perceived health fosters hopelessness (leading to increased drinking) or if drinking makes one feel less well. Poor perceived health has been found to relate to heavier drinking among substance users with HIV in prior research (Elliott et al., 2017), although we know of no such studies among those with poorly controlled HIV infection. Poor mental health may also relate to problem drinking among individuals with poorly controlled HIV infection, either due to comorbidity between mental health and substance use disorders or due to drinking to cope with mental distress. This hypothesis is supported by past studies that have demonstrated that drinking is associated with emotional distress (Naar-King et al., 2010), anxiety/depression (Garey et al., 2015) and Posttraumatic stress disorder (Devieux et al., 2013) in mixed-gender HIV-infected samples, and with depression (Cook et al., 2009) in female HIV-infected samples. Social relationships, including both personal relationships and relationships with providers, may also relate to problem drinking. Those whose social support from friends, family or significant others is insufficient or even stressful may seek comfort through alternate means such as alcohol; excessive alcohol use may in turn have isolating effects. Some past research suggests that poor social support is associated with drinking in HIV-infected adults (Hansen et al., 2009) and youth (Naar-King et al., 2010). However, a third study found that social support was not associated with drinking in HIV primary care (Lehavot et al., 2011). Whether social support (or in contrast, the presence of negative/conflictual social interactions) relates to problem drinking among persons with poorly controlled HIV infection is unknown. Relationship with one’s HIV provider may also relate to problem drinking, as poor relationship quality may interfere with the provision of alcohol risk information and/or patients’ receptivity to this information. Problem drinking may also introduce barriers such as stigma or guilt into the patient–provider relationship. Although most HIV patients and providers report comfort discussing substance use (Ray et al., 2013), most HIV patients do not actually discuss drinking with their providers (Metsch et al., 2008), and provider communication has been found to be poorer with HIV-infected drinkers than drug users (Korthuis et al., 2011). Medical mistrust is one specific aspect of the patient–provider relationship that can relate to patients’ engagement in health/risk behaviors (Eaton et al., 2014) that may also relate to problem drinking. These psychological and social characteristics may relate to problem drinking among patients with poorly controlled HIV infection but to our knowledge, these relationships have yet to be tested in this group. We, therefore, examined whether these four domains of psychosocial well-being (perceived health, mental health, social relationships and the patient–provider relationship) are associated with problem drinking in a large sample of inpatients with poorly controlled HIV infection. To investigate this question, this study used baseline data from a large multisite clinical trial testing interventions intended to increase viral suppression (Metsch, 2016; Metsch et al., 2016). METHODS Participants and procedures This study is a secondary analysis of data from Project HOPE—Hospital Visit as Opportunity for Prevention and Engagement for HIV-Infected Drug Users (Metsch, 2016; Metsch et al., 2016), a multisite clinical trial sponsored by the National Drug Abuse Treatment Clinical Trials Network. Patients were recruited from 11 hospitals in major urban areas with high HIV prevalence across the USA: Boston, New York, Philadelphia, Baltimore, Pittsburgh, Chicago, Atlanta, Miami, Birmingham, Dallas and Los Angeles. The protocol was approved by institutional review boards at all participating institutions. A total of 2291 patients were assessed for eligibility. Clinical eligibility criteria required individuals to (a) be an HIV-infected inpatient at a participating hospital, (b) be an adult (18 years and older), (c) have poorly controlled HIV infection (have a current AIDS-defining illness or meet CD4 and viral load cutoffs [<350 cells/μl and >200 copies/ml, respectively, in past 6 months; or <500 cells/μl and >200 copies/ml or unknown in past year but likely still unsuppressed and inadequately medicated]) and (d) have self-reported or documented opioid or stimulant use or exceed alcohol cutoffs (>3 for women or >4 for men on the Alcohol Use Disorders Identification Test [AUDIT]-C) in the past 12 months. Other eligibility criteria required provision of informed consent, locator information and medical record release; ability to communicate in English and to return for follow-up visits; and a Karnofsky performance level of >60 (omitting those severely disabled). A sample of 801 eligible patients completed the baseline assessment, and were then assigned to one of three intervention conditions designed to attain virologic suppression and to link and retain patients in HIV and substance abuse care: Patient Navigation (n = 266), Patient Navigation with Contingency Management (n = 271) and Treatment as Usual (n = 264). Current analyses utilize baseline data, collected prior to intervention, with the full sample of 801 patients. Measures Problem drinking We include two measures of problem drinking, encompassing both severity of alcohol problems (primary outcome) and excessive drinking (secondary outcome). Severity of alcohol problems was assessed using items from the Addiction Severity Index-Lite (ASI-Lite). The full ASI-Lite assesses problem behaviors regarding alcohol and drug use as well as other related (medical, employment, legal, family/social, psychiatric) domains (McGahan et al., 1986; Mclellan et al., 1999). ASI-Lite items have demonstrated construct validity (Newcombe et al., 2005; Humeniuk et al., 2008) and been used previously in research with HIV patients (Palmer et al., 2003). The current study used a latent model of severity of alcohol problems, created from the six alcohol indicators from the ASI-Lite that most directly measured intensity of alcohol use and misuse: (a) alcohol use in the past 30 days, (b) alcohol intoxication in the past 30 days, (c) money spent on alcohol in the past 30 days, (d) number of days experienced alcohol problems in the past 30 days, (e) level of trouble/bother in the past 30 days due to alcohol problems and (f) rated importance of treatment for alcohol problems (McGahan et al., 1986). The ASI-Lite was used as the primary outcome because it is designed to measure severity of alcohol problems (McGahan et al., 1986), an important clinical outcome. In addition to the ASI-Lite, the Alcohol Use Disorders Identification Test (AUDIT) was analyzed as a measure of excessive drinking, for sensitivity analyses, to assess consistency of findings across different aspects of problem drinking. The AUDIT is a screening measure of excessive drinking in the past year, designed to identify hazardous and harmful drinkers (Babor et al., 2001). Its 10 items assess recent alcohol use, alcohol dependence symptoms and alcohol-related problems, with higher scores indicating more excessive drinking (Babor et al., 2001). This widely-used measure has demonstrated reliability and validity across many studies (Reinert and Allen, 2007). This measure was used as a secondary outcome for sensitivity analyses because it is a brief screening measure and because its focus on excessive drinking was deemed secondary to the ASI-Lite’s focus on severity of alcohol problems. However, due to substantial overlap in item content between the ASI-Lite and AUDIT, overall findings are discussed in terms of ‘problem drinking.’ Perceived health Perceived health was assessed using the 12-item Short-Form Health Survey (SF-12), a reliable and valid abbreviation of the 36-Item Short-Form Health Survey (SF-36; Ware et al., 1996). The SF-12 measures subjective characterization of health and well-being, limitations in engagement in daily activities, as well as specific interference from physical and emotional difficulties, yielding a composite score of perceived health and disability. It has demonstrated reliability and validity among individuals with HIV (Han et al., 2002), and has been shown to be responsive to initiation of and adherence to antiretroviral therapy (Mannheimer et al., 2005). Cronbach’s alpha in this sample was 0.86. Mental health The 18-item Brief Symptom Inventory (BSI-18) was used to measure mental health (Derogatis, 2000). The BSI-18 is an abbreviated version of the original 53-item scale (Derogatis, 1975). This measure assesses symptoms in the domains of anxiety, depression and somatization, and yields an overall global severity index with higher scores indicating poorer mental health. Patients are asked to consider symptoms present in the past seven days, and to rate their experience using the following response options: Not at all (0), A little bit (1), Moderately (2), Quite a bit (3) and Extremely (4) (Derogatis, 1975). The BSI-18 has demonstrated reliability and validity across several medical populations (Zabora et al., 2001; Recklitis et al., 2006; Meachen et al., 2008). Cronbach’s alpha in this sample was 0.92. Social relationships Positive social support and social conflict were measured using subscales from the Seek, Test, Treat and Retain Data Harmonization Measure, adapted from earlier surveys in HIV samples where its validity was supported (The RAND Corporation, 1994–2016; Hays et al., 1995; Fleishman et al., 2000). The Short Social Support Scale is a five-item measure of the presence of others who meet emotional needs and provide assistance when needed. The Conflictual Social Interaction Scale is a three-item measure of disagreements and pressure to change from significant others. Participants rated items from both subscales using response options: None of the time (1), A little of the time (2), Some of the time (3), Most of the time (4) and All of the time (5). Higher scores on the Social Support Scale indicate higher levels of perceived support. Higher scores on the Conflictual Social Interaction Scale indicate higher levels of interpersonal conflict. Cronbach’s alphas were 0.75 and 0.86 on the Conflictual Social Interactions and Social Support scales, respectively, in this sample. Relationship with provider Two aspects of patients’ relationships with their providers were assessed: satisfaction with one’s physician and medical mistrust. For satisfaction with one’s physician, the Overall Satisfaction with Care scale was used, consisting of four items taken from a larger set of scales assessing a range of components of the physician–patient relationship (Schneider et al., 2004). For the Overall Satisfaction with Care subscale, patients rated personal manner, communication skills, technical skills and overall care from their HIV provider using response options: excellent, very good, good, fair and poor. Higher scores indicated greater satisfaction with care. This scale has been shown to be reliable and to relate to medication adherence among patients with HIV (Schneider et al., 2004). Cronbach’s alpha in this sample was 0.94. The Medical Mistrust Scale assesses patient mistrust of health care professionals and systems. This scale prompts participants to respond to 12 statements regarding the medical treatment of their ethnic group, using response options ranging from Strongly Agree (1) to Strongly Disagree (5) (Thompson et al., 2004). This scale has demonstrated reliability and validity in prior research on cancer screening (Thompson et al., 2004). Cronbach’s alpha was 0.86 in this sample. Covariates Patient reported age (in years), gender (male or female), race/ethnicity (White, Black, Hispanic, other) and education (high school graduate versus non-graduate). Drug use was measured as the number of days of use in the past 30 days, for whichever drug was used most frequently (heroin, methadone [illicit], opiates, barbiturates, sedatives, cocaine, amphetamines, cannabis, hallucinogens and inhalants). These five variables were included as control covariates in structural equation models. Analysis plan Our first step was a preliminary examination of our variables. Specifically, univariate descriptive examination of the observed variables was conducted, followed by a confirmatory factor analysis for the latent severity of alcohol problems variable (using the six items from the ASI-Lite as indicators of the latent construct). Second, bivariate associations of the ASI-Lite (the primary outcome variable) with the psychosocial and demographic factors were generated. Third, structural equation models were conducted. The first model regressed the latent severity of alcohol problems variable from the ASI-Lite on the psychosocial factors (physical health, interpersonal conflict, social support, satisfaction with physician, medical mistrust and mental health) and demographic covariates (age, race/ethnicity, education, gender and drug use). As a sensitivity analysis, observed AUDIT score was regressed on the same psychosocial factors and demographic covariates. All analyses were conducted in Mplus 7.0 using full information maximum likelihood (Muthén and Muthén, 1998–2012). For variables with substantial skew, we used count specification with Poisson distribution (for the ASI-Lite use and intoxication indicators; AUDIT) or log transformation (for the ASI-Lite money spent indicator), as indicated by the variables’ distributions. RESULTS Descriptive information The sample (n = 801) was predominantly African American (75%; White: 12%; Hispanic: 11%; Other: 1%), male (67%) and had at least a high school education (60%). Mean age of the sample was 45 years (SD = 9.99) with a range from 18 to 68 years. Slightly more than half (57.2%) reported taking antiretroviral medication in the past month (although not necessarily consistently). Of the full sample of 801 participants, 188 (23.5%) were eligible for the trial on the basis of scoring >3 (for women) or >4 (for men) on the Alcohol Use Disorders Identification Test (AUDIT)-C, 330 (41.2%) on the basis of report or medical record documentation of any opioid or stimulant (cocaine, ecstasy or amphetamine) use and 283 (35.3%) on the basis of both AUDIT-C and drug use. The mean AUDIT score was 9.04 (SD = 9.54). Nearly half of participants (43.7%) had AUDIT scores of 8 or above, indicating hazardous and harmful alcohol use of at least a medium level of severity. A substantial proportion (22.6%) scored 16 or above, indicating hazardous and harmful alcohol use of a high level of severity (Babor et al., 2001). Participants reported drinking on an average of 5.93 (SD = 8.93) days and intoxication on an average of 3.26 (SD = 7.34) days in the past 30 days, with an average of $47.35 (SD = $185.55) spent on alcohol during this time. Participants reported alcohol problems on an average of 1.91 (SD = 6.30) days in the past 30 days. Despite their relatively high AUDIT scores, participants reported low levels of being troubled/bothered by alcohol problems (M = 0.48, SD = 1.15) and low importance of treatment (M = 0.90, SD = 1.53) for their alcohol use during this time (0 = Not at all, 1 = Slightly). In the confirmatory factor analysis for the latent severity of alcohol problems variable, all indicator variables loaded significantly on the latent variable, P < 0.001. Bivariate associations Table 1 shows the bivariate correlations between the latent severity of alcohol problems (ASI-Lite) score with the primary psychosocial factors and demographic covariates. In this bivariate analysis, poor mental health (r = 0.12, P = 0.002) and interpersonal conflict (r = 0.13, P < 0.001) were positively and significantly correlated with severity of alcohol problems. Individuals identifying as ‘other’ race/ethnicity (i.e. participants who did not identify as Hispanic, White or Black) had significantly higher severity of alcohol problems scores compared with non-Hispanic White participants (b = 1.15, P = 0.03), and drug use positively correlated with severity of alcohol problems, r = 0.12, P < 0.01. Table 1. Bivariate associations between severity of alcohol problems (ASI-Lite) with demographic/control and psychosocial variables Correlation with severity of alcohol problems Mean severity of alcohol problems scorea P-value Demographic/Control variables  Age 0.06 0.09  Race   Black, non-Hispanic   (reference group: White) 0.24 0.27   Hispanic (reference group:   White) −0.22 0.47   Other non-White race   (reference group: White) 1.15 0.03  High school graduate  (reference group: non- graduates) −0.06 0.14  Male (reference group:  female) 0.03 0.42  Drug use 0.12 <0.01 Primary psychosocial factors  Physical health 0.01 0.86  Poor mental health 0.12 0.002  Satisfaction with physician −0.05 0.76  Medical mistrust 0.06 0.16  Interpersonal conflict 0.13 <0.001  Social support −0.06 0.10 Correlation with severity of alcohol problems Mean severity of alcohol problems scorea P-value Demographic/Control variables  Age 0.06 0.09  Race   Black, non-Hispanic   (reference group: White) 0.24 0.27   Hispanic (reference group:   White) −0.22 0.47   Other non-White race   (reference group: White) 1.15 0.03  High school graduate  (reference group: non- graduates) −0.06 0.14  Male (reference group:  female) 0.03 0.42  Drug use 0.12 <0.01 Primary psychosocial factors  Physical health 0.01 0.86  Poor mental health 0.12 0.002  Satisfaction with physician −0.05 0.76  Medical mistrust 0.06 0.16  Interpersonal conflict 0.13 <0.001  Social support −0.06 0.10 Note. ASI-Lite, Addiction Severity Index-Lite. Bold indicates significance at α = 0.05. aMeasured as a latent variable with mean = 0 for the non-Hispanic White reference group. Means are presented to elucidate direction of effects for multiple racial groups in comparison with the non-Hispanic White reference group. Statistical comparisons between the mean ASI-Lite scores for each group and the reference group are Wald tests resulting from a multiple regression with dummy variables for each of the three race categories. Table 1. Bivariate associations between severity of alcohol problems (ASI-Lite) with demographic/control and psychosocial variables Correlation with severity of alcohol problems Mean severity of alcohol problems scorea P-value Demographic/Control variables  Age 0.06 0.09  Race   Black, non-Hispanic   (reference group: White) 0.24 0.27   Hispanic (reference group:   White) −0.22 0.47   Other non-White race   (reference group: White) 1.15 0.03  High school graduate  (reference group: non- graduates) −0.06 0.14  Male (reference group:  female) 0.03 0.42  Drug use 0.12 <0.01 Primary psychosocial factors  Physical health 0.01 0.86  Poor mental health 0.12 0.002  Satisfaction with physician −0.05 0.76  Medical mistrust 0.06 0.16  Interpersonal conflict 0.13 <0.001  Social support −0.06 0.10 Correlation with severity of alcohol problems Mean severity of alcohol problems scorea P-value Demographic/Control variables  Age 0.06 0.09  Race   Black, non-Hispanic   (reference group: White) 0.24 0.27   Hispanic (reference group:   White) −0.22 0.47   Other non-White race   (reference group: White) 1.15 0.03  High school graduate  (reference group: non- graduates) −0.06 0.14  Male (reference group:  female) 0.03 0.42  Drug use 0.12 <0.01 Primary psychosocial factors  Physical health 0.01 0.86  Poor mental health 0.12 0.002  Satisfaction with physician −0.05 0.76  Medical mistrust 0.06 0.16  Interpersonal conflict 0.13 <0.001  Social support −0.06 0.10 Note. ASI-Lite, Addiction Severity Index-Lite. Bold indicates significance at α = 0.05. aMeasured as a latent variable with mean = 0 for the non-Hispanic White reference group. Means are presented to elucidate direction of effects for multiple racial groups in comparison with the non-Hispanic White reference group. Statistical comparisons between the mean ASI-Lite scores for each group and the reference group are Wald tests resulting from a multiple regression with dummy variables for each of the three race categories. Structural equation models Table 2 details the results of the structural equation models. The primary structural equation model regressed the latent severity of alcohol problems (ASI-Lite) score on all psychosocial factors and demographic covariates (for depiction of primary structural equation model highlighting significant associations, see Fig. 1). Increased age (b = 0.02, P = 0.02), endorsement of ‘other’ ethnicity (b = 1.31, P = 0.02), male gender (b= 0.32, P = 0.048) and drug use (b= 0.02, P = 0.004) were associated with higher severity of alcohol problems. Increased levels of interpersonal conflict (b = 0.05, P = 0.04) was the only psychosocial factor associated with higher severity of alcohol problems in the primary model (Fig. 1). In the second structural equation model, using the observed AUDIT score for sensitivity analyses, we found male gender (b = 0.21, P = 0.03), poor mental health (b = 0.01, P < 0.001), less satisfaction with one’s physician (b = −0.14, P < 0.001) and more medical mistrust (b = 0.01, P = 0.03) to be associated with excessive drinking. Table 2. Results for structural equation models: associations between problem drinking with psychosocial factors and demographic covariates Severity of Alcohol Problems: (ASI-Litea, Primary outcome) Excessive drinking: (AUDITb, Secondary outcome) b SE(b) P b SE(b) P Demographic control covariates  Age 0.017 0.007 0.020 0.005 0.004 0.172  Race   Black, non-Hispanic (reference group: White) 0.384 0.227 0.091 0.083 0.139 0.551   Hispanic (reference group: White) −0.212 0.327 0.517 −0.205 0.183 0.263   Other non-White race (reference group: White) 1.313 0.561 0.019 0.163 0.289 0.573  High school graduate (reference group: non-graduates) −0.130 0.149 0.383 −0.036 0.085 0.671  Male (reference group: female) 0.319 0.162 0.048 0.209 0.097 0.031  Drug use 0.020 0.007 0.004 −0.008 0.004 0.054 Primary psychosocial factors  Physical health 0.009 0.007 0.170 −0.002 0.004 0.639  Poor mental health 0.009 0.005 0.065 0.010 0.003 <0.001  Satisfaction with physician 0.022 0.037 0.560 −0.139 0.014 <0.001  Medical mistrust 0.001 0.010 0.949 0.012 0.005 0.032  Interpersonal conflict 0.049 0.023 0.035 0.025 0.014 0.074  Social support −0.009 0.011 0.409 −0.002 0.006 0.742 Severity of Alcohol Problems: (ASI-Litea, Primary outcome) Excessive drinking: (AUDITb, Secondary outcome) b SE(b) P b SE(b) P Demographic control covariates  Age 0.017 0.007 0.020 0.005 0.004 0.172  Race   Black, non-Hispanic (reference group: White) 0.384 0.227 0.091 0.083 0.139 0.551   Hispanic (reference group: White) −0.212 0.327 0.517 −0.205 0.183 0.263   Other non-White race (reference group: White) 1.313 0.561 0.019 0.163 0.289 0.573  High school graduate (reference group: non-graduates) −0.130 0.149 0.383 −0.036 0.085 0.671  Male (reference group: female) 0.319 0.162 0.048 0.209 0.097 0.031  Drug use 0.020 0.007 0.004 −0.008 0.004 0.054 Primary psychosocial factors  Physical health 0.009 0.007 0.170 −0.002 0.004 0.639  Poor mental health 0.009 0.005 0.065 0.010 0.003 <0.001  Satisfaction with physician 0.022 0.037 0.560 −0.139 0.014 <0.001  Medical mistrust 0.001 0.010 0.949 0.012 0.005 0.032  Interpersonal conflict 0.049 0.023 0.035 0.025 0.014 0.074  Social support −0.009 0.011 0.409 −0.002 0.006 0.742 Notes. ASI-Lite, Addiction Severity Index-Lite; AUDIT, Alcohol Use Disorders Identification Test. SE, standard error. All regression coefficients are unstandardized. Bold parameter estimates are statistically significant at α = 0.05. aLatent output: ASI-Lite on all psychosocial factors and covariates. bAUDIT output: AUDIT score (count) on all psychosocial factors and covariates. Table 2. Results for structural equation models: associations between problem drinking with psychosocial factors and demographic covariates Severity of Alcohol Problems: (ASI-Litea, Primary outcome) Excessive drinking: (AUDITb, Secondary outcome) b SE(b) P b SE(b) P Demographic control covariates  Age 0.017 0.007 0.020 0.005 0.004 0.172  Race   Black, non-Hispanic (reference group: White) 0.384 0.227 0.091 0.083 0.139 0.551   Hispanic (reference group: White) −0.212 0.327 0.517 −0.205 0.183 0.263   Other non-White race (reference group: White) 1.313 0.561 0.019 0.163 0.289 0.573  High school graduate (reference group: non-graduates) −0.130 0.149 0.383 −0.036 0.085 0.671  Male (reference group: female) 0.319 0.162 0.048 0.209 0.097 0.031  Drug use 0.020 0.007 0.004 −0.008 0.004 0.054 Primary psychosocial factors  Physical health 0.009 0.007 0.170 −0.002 0.004 0.639  Poor mental health 0.009 0.005 0.065 0.010 0.003 <0.001  Satisfaction with physician 0.022 0.037 0.560 −0.139 0.014 <0.001  Medical mistrust 0.001 0.010 0.949 0.012 0.005 0.032  Interpersonal conflict 0.049 0.023 0.035 0.025 0.014 0.074  Social support −0.009 0.011 0.409 −0.002 0.006 0.742 Severity of Alcohol Problems: (ASI-Litea, Primary outcome) Excessive drinking: (AUDITb, Secondary outcome) b SE(b) P b SE(b) P Demographic control covariates  Age 0.017 0.007 0.020 0.005 0.004 0.172  Race   Black, non-Hispanic (reference group: White) 0.384 0.227 0.091 0.083 0.139 0.551   Hispanic (reference group: White) −0.212 0.327 0.517 −0.205 0.183 0.263   Other non-White race (reference group: White) 1.313 0.561 0.019 0.163 0.289 0.573  High school graduate (reference group: non-graduates) −0.130 0.149 0.383 −0.036 0.085 0.671  Male (reference group: female) 0.319 0.162 0.048 0.209 0.097 0.031  Drug use 0.020 0.007 0.004 −0.008 0.004 0.054 Primary psychosocial factors  Physical health 0.009 0.007 0.170 −0.002 0.004 0.639  Poor mental health 0.009 0.005 0.065 0.010 0.003 <0.001  Satisfaction with physician 0.022 0.037 0.560 −0.139 0.014 <0.001  Medical mistrust 0.001 0.010 0.949 0.012 0.005 0.032  Interpersonal conflict 0.049 0.023 0.035 0.025 0.014 0.074  Social support −0.009 0.011 0.409 −0.002 0.006 0.742 Notes. ASI-Lite, Addiction Severity Index-Lite; AUDIT, Alcohol Use Disorders Identification Test. SE, standard error. All regression coefficients are unstandardized. Bold parameter estimates are statistically significant at α = 0.05. aLatent output: ASI-Lite on all psychosocial factors and covariates. bAUDIT output: AUDIT score (count) on all psychosocial factors and covariates. Fig. 1. View largeDownload slide Structural equation model regressing latent severity of alcohol problems variable (with six alcohol indicator variables) on demographic and psychosocial covariates. Solid lines indicate statistical significance at α = 0.05. Fig. 1. View largeDownload slide Structural equation model regressing latent severity of alcohol problems variable (with six alcohol indicator variables) on demographic and psychosocial covariates. Solid lines indicate statistical significance at α = 0.05. DISCUSSION Using a large sample of hospitalized patients with poorly controlled HIV infection across the USA, we found several psychosocial variables to relate to problem drinking. Increased levels of interpersonal conflict were associated with greater severity of alcohol problems. Poorer mental health, less satisfaction with one’s physician and more medical mistrust related to excessive drinking. To our knowledge, this was the first examination of psychosocial factors related to problem drinking among individuals with poorly controlled HIV. The relevance of psychosocial factors to problem drinking is generally consistent with research in the HIV literature as a whole, as well as in other medical populations for whom drinking is dangerous (e.g. liver disease patients after transplantation (Rustad et al., 2015)). Past research has demonstrated that poor mental health is associated with problem drinking in the HIV-infected population (Naar-King et al., 2010; Devieux et al., 2013; Garey et al., 2015). The co-occurrence of poor mental health and excessive drinking in our study of individuals with poorly controlled HIV suggests the potential value of coordinated HIV care, mental health care and substance use care for these individuals, ideally through co-located or integrated care or at least attentive care coordination. When the full BSI measure is replaced by its constituent subscales, it becomes clear that anxiety drives this association in our sample, demonstrating a particular need for services that target anxiety for these individuals (exploratory results available upon request). Perceived health did not relate to problem drinking in the current study. This association was significant in a previous study of HIV-infected substance abusers (Elliott et al., 2017). Perhaps perceived health is less relevant to problem drinking for those with poorly controlled HIV or perhaps more restricted variability in health tempered the effect. Regarding social relationships, our results found social conflict (but not social support) to relate to severity of alcohol problems. It is therefore, possible that counselors or social workers who help patients with conflictual relationships to better manage or remove themselves from these situations, in addition to helping patients manage their lives, could also reduce severity of alcohol problems. However, this is speculative given that current data cannot conclude causality or even directionality. Social conflict was most related to ASI-Lite items assessing trouble/bother from alcohol problems and importance of treatment for alcohol problems (Supplemental data Table 1), suggesting that social conflict relates most saliently to problematic aspects of drinking as opposed to simple drinking intensity. Excessive drinkers (identified using the AUDIT) rated their care from their HIV physician more poorly. This is consistent with other research (Korthuis et al., 2011) suggesting that HIV-infected excessive drinkers may perceive their care to be substandard. Excessive drinkers may truly receive poorer quality care (perhaps due to provider stigma toward excessive drinkers) or there may be something else occurring that leads excessive drinkers to rate the interaction as poorer, requiring further study. Ensuring quality care, perhaps by providing training to doctors on working with problem drinkers (on topics such as enhancing empathy, screening and brief intervention) may help re-engage problem drinkers with poorly controlled HIV in care, and/or may help them reduce their drinking. However, cross-sectional findings prevent concluding a causative effect based on our findings, requiring further study. Interestingly, medical mistrust also related to excessive drinking, suggesting a possible factor undermining the patient–provider relationship. It is worth noting that older individuals evidenced greater severity of alcohol problems. This is different from previous findings of higher alcohol use disorder among younger individuals in the general population (Grant et al., 2015), and of more hazardous drinking (Crane et al., 2017) and substance use disorders (Hartzler et al., 2017) among younger individuals with HIV. Future research with individuals with poorly controlled HIV should incorporate age into their analyses, to confirm this finding. Those reporting a race other than Hispanic, White or Black also reported greater severity of alcohol problems. The ‘other’ group in our sample collapsed Asian, American Indian, Alaska Native and others, with small sample sizes prohibiting analyses among these groups, making it difficult to make meaningful conclusions. However, high prevalence of problem drinking in some of these populations has been documented (Heart et al., 2016). Other covariates were less surprising, including the association of male gender with both measures of problem drinking, and the association of drug use with severity of alcohol problems. This current study has certain limitations. First, this is a secondary analysis of baseline data from a trial of selected patients hospitalized in urban settings; our findings may not apply to non-hospitalized HIV-infected populations or those in less urban regions. Second, analyses are cross-sectional. Therefore, although the current study identifies associations, prospective data are needed to determine if they are true predictors. Third, the selection criteria requiring all patients to be substance users may have limited variability in problem drinking. Yet, drug users needed not drink to be included. Fourth, models of the ASI-Lite and AUDIT yielded different results. One potential reason is that the ASI-Lite is designed to measure severity of alcohol problems (McGahan et al., 1986), while the AUDIT proports to measure excessive drinking (Babor et al., 2001). That social conflict related to severity of alcohol problems may reflect the disruptive nature of alcohol problems to relationships. The modeling of this latent variable accounts for measurement error, enhancing confidence in this result. In contrast, the associations between aspects of the patient–provider relationship and mental health with excessive drinking may reflect problems correlated with more subtle elevations in drinking. Fifth, use of brief screening measures is a limitation. More extensive measurement of these constructs would be informative. Future research should also measure alcohol use disorder, to see whether these psychosocial factors relate to actual disorder and whether alcohol use disorder explains (mediates) associations found in this study. Sixth, measures of model fit are limited in structural equation models with categorical outcomes. The only option for a global fit measure was the chi-square test, which is known to demonstrate poor model fit in samples >400 participants where there are high correlations. Finally, there are numerous other psychosocial and control variables that could have been examined. In choosing our psychosocial factors, we chose several that had demonstrated relevance in individuals with HIV in prior research. Future research should include others such as pain, peer norms, attitudes toward drinking and perceived behavioral control. We limited our control variables to age, sex, race, education and drug use. Others could be argued to be relevant (e.g. time since HIV infection, HIV care status, antiretroviral status, income, homelessness, geographic region and marital status) but we chose only the most basic control covariates used most commonly in the alcohol literature. In conclusion, interpersonal conflict, poor mental health, less satisfaction with one’s physician and medical mistrust were associated with different aspects of problem drinking among substance users with poorly controlled HIV. These results advance our understanding of psychosocial factors associated with problem drinking in this particularly high-risk group. Prospective work is needed to more clearly elucidate directionality (e.g. whether psychosocial factors lead to problem drinking or vice versa), and to evaluate potential moderating or mediating effects among these psychosocial factors. If these factors are found to be prospectively predictive of problem drinking among individuals with poorly controlled HIV, this would suggest that helping patients address harmful/conflictual relationships, poor mental health, and poor/mistrusting relationships with providers, while worthwhile goals themselves, may also reduce problem drinking. This could be accomplished through comprehensive care services in social organizations for HIV patients and/or in HIV primary care. Continued research in this area could help to better address the complex needs of a population where preventing problem drinking is important to health and well-being. SUPPLEMENTARY MATERIAL Supplementary data are available at Alcohol And Alcoholism online. Funding This work was supported by the National Institutes of Health (NIH) grants ([U10DA013720 (Project HOPE–Hospital Visit as Opportunity for Prevention and Engagement for HIV-Infected Drug Users to LRM)], [K23AA023753 to JCE], [R01AA023163 to DSH], [K24DA035684 to GML], [K23AI112477 to AEN]); and the New York State Psychiatric Institute (DSH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. CONFLICT OF INTEREST STATEMENT None declared. REFERENCES Azar MM , Springer SA , Meyer JP , et al. . 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Alcohol and AlcoholismOxford University Press

Published: Mar 27, 2018

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