Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2018 Nov 1.
Published in final edited form as: Drug Alcohol Depend. 2017 Aug 24;180:62–67. doi: 10.1016/j.drugalcdep.2017.07.034

Alcohol and substance use diagnoses among HIV-positive patients receiving care in NYC clinic settings

Sitaji Gurung 1,2, Ana Ventuneac 1,6, Demetria Cain 1, Chloe Mirzayi 1,2, Christopher Ferraris 5, H Jonathon Rendina 1,3,4, Martha A Sparks 7, Jeffrey T Parsons 1,3,4
PMCID: PMC5648608  NIHMSID: NIHMS901416  PMID: 28881318

Abstract

Background

Substance use among HIV-positive persons exacerbates health problems. This study sought to estimate the prevalence of alcohol and drug-use diagnoses and examined hypothesized predictors associated with alcohol and drug-use diagnoses among HIV-positive patients in New York City (NYC).

Methods

This cohort study reviewed electronic medical records (EMRs) of 4,965 HIV-positive patients based on diagnostic codes. These patients attended a comprehensive care clinic in NYC in 2012. Multinomial logistic regression was used to predict the odds of classification into substance use diagnosis grouping.

Results

Of the full sample, only 12.7% of patients had an alcohol use diagnosis documented in their EMR compared with more than one-quarter (26.4%) of patients having a recorded drug use diagnosis (p < 0.001). Compared with the No Alcohol or Drugs group, the regression model showed that older age and having a recent inpatient hospital stay independently predicted being in the Alcohol Only group; years living with HIV, having an unsuppressed viral load, and having a recent inpatient hospital stay were associated with higher odds of being in the Drugs Only and Alcohol and Drugs groups; and being women and men who have sex with men (MSM) were associated with decreased odds of being in the Drugs Only and Alcohol and Drugs groups.

Conclusions

Substance use diagnosis was associated with viremia and low CD4 counts and hospital stays. This implies that providers should screen for substance use in HIV-positive patients with poor health. Further examination of the extent of such comorbidity is instrumental for intervention efforts.

Keywords: alcohol use, drug use, HIV

1. Introduction

Alcohol (Chander et al., 2008; Cook et al., 2009; Galvan et al., 2002) and drug use (Korthuis et al., 2008) is prevalent among HIV-positive individuals with reported rates exceeding United States (U.S.) national averages by as much as two-fold (Justice et al., 2006; Petry, 1999). Reported rates among HIV-positive individuals have varied, with 32%– 66% currently consuming alcohol (Galvan et al., 2002; Petry, 1999; Samet et al., 2004) and 9–29% meeting criteria for hazardous drinking or an alcohol use diagnosis (Cook et al., 2009; Pence et al., 2008; Petry, 1999; Samet et al., 2004). For illicit drug use, rates have been found to be 25–40% among HIV-positive individuals (Chander et al., 2006; Korthuis et al., 2008; Sohler et al., 2007). Across studies, cocaine and marijuana were the most common drug types (Pence et al., 2008; Sohler et al., 2007; Turner et al., 2001) and poly drug use was often prevalent (Proeschold-Bell et al., 2010; Sohler et al., 2007; Turner et al., 2001).

Substance use exacerbates health problems and has implications for HIV transmission. Heavy alcohol use (Baum et al., 2010; Kader et al., 2015) and drug use (Baum et al., 2009; Chander et al., 2009; Lucas et al., 2006; Murray et al., 2012) accelerate HIV disease progression (Hahn and Samet, 2010) by hindering viral suppression (Chander et al., 2009; Conigliaro et al., 2003; Conigliaro et al., 2006) and decreasing immunologic functioning (Bagasra et al., 1996; Bagasra et al., 1993), resulting in clinical indicators of higher viral load (VL) and lower CD4 counts. A meta-analysis examining 40 studies found that drinkers were half as likely to take antiretroviral (ARV) medication as prescribed compared to those who were abstinent or who consumed alcohol infrequently (Hendershot et al., 2009). Adherence was particularly problematic among those who drank heavily or met criteria for an alcohol use diagnosis (Hendershot et al., 2009). Disease progression associated with substance use has also been found to be independent of medication non-adherence, pointing to additional clinical challenges for these patients (Baum et al., 2009, 2010; Carrico, 2011; Malbergier et al., 2015). In addition to the elevated risk of HIV transmission among injection drug users (Kaplan and Heimer, 1992), the sexual transmission of HIV is also of concern given that drug use and heavy alcohol consumption is associated with condomless sex (Cohen and Gay, 2010; Parsons et al., 2012; Parsons et al., 2003a; Parsons et al., 2003b; Parsons et al., 2008; Parsons et al., 2004; Ramirez-Valles et al., 2008; Velasquez et al., 2009).

Since much of the effects of alcohol and drugs on the health outcomes of HIV-positive individuals are closely related to medication adherence and have implications for medical care, continued assessment of substance use prevalence in HIV medical care clinics remains critical to provide substance use treatment within medical care. Clinic settings present an opportunity to understand the health needs of HIV-positive patients in the general population (Chander et al., 2009; Chander et al., 2006; Cofrancesco et al., 2008; Masson et al., 2004; Weaver et al., 2008), including women (Justice et al., 2006), and those who may not have sought substance use treatment. Additionally, the cumulative effect of alcohol and drug use among HIV-positive patients could present additional barriers to HIV medical care, but few studies have examined the prevalence of co-morbid alcohol and drug-use diagnoses. Cook et al. (2009) found rates of 30% cocaine use, 39.5% crack use, 42% marijuana use, and 21.5% heroin use among hazardous drinkers and Chander et al. (2008) found hazardous alcohol use 2.67 times more likely among illicit drug users. Co-occurring alcohol and drug use present unique challenges for HIV disease progression and has implications for the utilization of substance use treatment services (Weaver et al., 2008).

As part of a larger study to conduct a comparative effectiveness trial to test the effectiveness of an intervention, Positive Living through Understanding and Support (PLUS), on treatment adherence and substance use, this study sought to examine alcohol and drug-use diagnoses among HIV-positive patients receiving care in New York City (NYC). Utilizing electronic medical records (EMRs) from HIV-positive patients at the Spencer Cox Center for Health (SCCH) in NYC, we conducted retrospective analyses. Our goals were to estimate the prevalence of alcohol and drug-use diagnoses and to examine hypothesized predictors associated with alcohol and drug-use diagnoses.

2. Methods

2.1. Participants and Procedures

The Spencer Cox Center for Health (now part of the Mount Sinai Institute for Advanced Medicine) comprised three clinics at St. Luke’s-Roosevelt Hospital in NYC providing HIV primary care to over 5000 active HIV-positive patients. All three SCCH clinics utilized a unified EMR, the Clinical Information Management of Ambulatory Care Services (CLIMACS) system, since 1996 which facilitates the analysis of data for research and clinical purposes. CLIMACS documents insurance information, clinic visit information (scheduled, kept and missed visits), International Classification of Diseases (ICD) diagnoses, laboratory results, clinical notes, and patient-reported medication adherence and substance use, in terms of onset, last use, quantity and frequency. A computer in each exam room allows providers to document services and clinical notes at point of care, review diagnostic information, monitor treatment, prescribe medication, and schedule appointments. Providers track and document all patient activity in CLIMACS for each patient visit.

Analyses were conducted on patient records that contained complete data for age, race/ethnicity, gender, risk category, CD4 count, HIV-1 RNA (i.e., viral load), and years since HIV diagnosis. Based on these criteria, EMR data from 4,965 HIV-positive patients who attended the SCCH in 2012 were extracted for these analyses. Only those with VL tests indicated in EMR were included for analyses. A sample of 619 cases were not extracted due to a missing viral load lab result in 2012, which was one of the variables we used in our models. The data elements described below were extracted into a useable SPSS format from the EMR database. The electronic data were securely transferred in an Access database to our research site after identifying information was removed. The Institutional Review Boards of both collaborating groups approved the use of these data.

2.2. Measures

EMR data included diagnoses of alcohol and/or drug use using the International Statistical Classification of Disease and Related Health Problems, Ninth and Tenth Revisions (ICD-9/10) system. Using ICD-9/10 codes, we developed algorithms to search records of both outpatient and inpatient diagnoses for substance use.

We analyzed both demographic and clinical covariates hypothesized to be associated with alcohol and drug-use diagnoses and relevant to an HIV-positive population. Age was calculated using year of birth. Race/ethnicity was categorized as Black, Latino, White, and other. EMR data indicated men who have sex with men (MSM) as the primary HIV risk behavior. Whether the patient was a MSM or not, combined with the data on gender, we created four mutually exclusive HIV risk category groups – MSM, men who have sex with women (MSW), women, and transgender. Years since HIV diagnosis were calculated using the HIV diagnosed year. Most recent viral load values were recoded into virally suppressed (VL ≤ 200) or not virally suppressed (VL > 200) and most recent CD4 cell count was recoded into not immunocompromised (CD4 > 200) or immunocompromised (CD4 ≤ 200) based on current guidelines (Agins et al., 2013; Panel on Antiretroviral Guidelines for Adults and Adolescents, 2016; Vajpayee et al., 2005). Finally, a dichotomous indicator was created for whether or not there was any record of an inpatient hospital visit over the 12-month period.

2.3. Data Analysis

We first conducted chi-square tests to examine bivariate associations between individual demographic and clinical variables and substance use diagnosis. To examine the prevalence of substance use diagnosis among HIV-positive patients in NYC, we divided the study sample into four groups based on diagnoses documented in the EMR system: 1) patients who had not been diagnosed with any alcohol or drug use; 2) patients who had an alcohol use diagnosis only; 3) patients who had a drug use diagnosis only; and 4) patients who had both alcohol and drug-use diagnoses. Following significant chi-squared tests, post-hoc tests were examined with Bonferroni adjustment as well as standardized residuals greater than or equal to ±2 to find groups where the proportion differed from what was expected by chance (i.e., areas in which we observed the greatest sources of non-independence).

We next ran a multinomial logistic regression model, with age, years since HIV diagnosis, race/ethnicity, HIV risk category, virally unsuppressed, immunocompromised, and inpatient hospitalization predicting substance use diagnosis grouping. Adjusted odds ratios (AOR) from the model are presented for the odds of classification with an alcohol-only, drug-only, or both alcohol and drug-use diagnosis relative to no substance use diagnosis as the reference group.

3. Results

Table 1 shows the demographic and clinical characteristics of the sample in each group of substance use diagnosis. The sample was predominantly male (78.1%) and of minority race/ethnicity − 78.1% identified as Black or Latino. More than half of the sample was MSM. The mean age was 46 (range: 19– 80 years) and the mean time since HIV diagnosis was 12.5 years (range: 0–32 years). Across all four groups, nearly one-quarter (23.8%) of the sample was not virally suppressed, approximately 10% was immunocompromised, and roughly 9% had an inpatient hospital stay during the course of the year.

Table 1.

Demographic and Clinical Characteristics of Sample by Alcohol and Drug-Use Diagnoses (N = 4,965)

Total
Sample
N = 4,965		 No
Alcohol
or Drugs
n = 3,456		 Alcohol
Only
n = 199		 Drugs
Only
n = 877		 Alcohol
and Drugs
n = 433
n % n % n % n % n % Test Statistic
Race/Ethnicity
  Black 2063 41.6 1434 69.5 74 3.6 349 16.9 206 10.0 χ²(9) = 28.300***
  Latino 1811 36.5 1279 70.6 85 4.7 321 17.7 126 7.0
  White 911 18.3 603 66.2 35 3.8 188 20.6 85 9.3
  Other 180 3.6 140 77.8 5 2.8 19 10.6 16 8.9
HIV Risk Category
  MSW 1176 23.7 736 62.6 45 3.8 250 21.3 145 12.3 χ2(9) = 64.733***
  MSM 2701 54.4 1961 72.6 124 4.6 421 15.6 195 7.2
  Female 1019 20.5 708 69.5 29 2.8 190 18.6 92 9.0
  Transgender 69 1.4 51 73.9 1 1.4 16 23.2 1 1.4
Viral Load
  Not Virally Suppressed (VL > 200) 1183 23.8 770 65.1 47 4.0 228 19.3 138 11.7 χ2(3) = 22.274***
  Virally Suppressed (VL ≤ 200) 3782 76.2 2686 71.0 152 4.0 649 17.2 295 7.8
CD4 Count
  Immunocompromised (CD4 ≤ 200) 528 10.6 321 60.8 23 4.4 103 19.5 81 15.3 χ2(3) = 37.595***
  Not Immunocompromised (CD4 > 200) 4437 89.4 3135 70.7 176 4.0 774 17.4 352 7.9
Hospital Stay
  Yes 441 8.9 200 45.4 30 6.8 119 27.0 92 20.9 χ2(3) = 155.942***
  No 4524 91.1 3256 72.0 169 3.7 758 16.8 341 7.5
M SD M SD M SD M SD M SD
Age (range 19–80) 45.6 11.6 44.7a 12.1 47.2b,c 11.2 47.1b 10.3 48.9c 8.8 F(3, 4961) = 25.033***
Years Living with HIV (range 0–32) 12.5 7.8 11.6a 7.8 12.3a 7.7 14.5b 7.4 15.6c 7.0 F(3, 4961) = 59.586***
Open in a new tab

Note: *p < 0.05.

**

p < 0.01.

***

p < 0.001.

Means with differing superscripts within rows differed significantly (p < 0.05) in LSD-adjusted post hoc analyses. Within row means that share a common superscript did not differ significantly.

Of the full sample, only 12.7% of patients had an alcohol use diagnosis documented in their EMR compared with more than one-quarter (26.4%) of patients having a record of drug use diagnosis. An alcohol use diagnosis was significantly associated with having a drug use diagnosis. The odds of having an alcohol use diagnosis with a drug use diagnosis was 8.6 times the odds relative to those without a drug use diagnosis. In fact, 8.7% of all patients had co-morbid alcohol and drug-use diagnoses – this was 68.5% of patients with a documented alcohol use diagnosis and 33.0% of patients with a drug use diagnosis. The two substance use diagnoses are highly interrelated in this sample and, as a result, all later analyses utilize a four-group classification of the two diagnoses.

We found that all demographic and clinical indicators examined were significantly associated with substance use diagnosis groupings (p < 0.001). Examining the standardized residuals (not presented) and post-hoc results displayed in Table 1, several general trends emerged. A higher proportion of Black and lower proportion of Hispanic patients were in the Alcohol and Drugs group and a higher proportion of White and a lower proportion of other race patients were in the Drugs Only group than expected by chance. A lower proportion of MSM and a higher proportion of MSW were in the Drugs Only and Alcohol and Drugs groups than expected by chance, while a lower proportion of transgender were in the Alcohol and Drugs groups than expected. Among those not virally suppressed, a greater proportion were observed in the Alcohol and Drugs group than expected, while the opposite was true among those virally suppressed. Among those who were immunocompromised, we observed a lower proportion in the No Alcohol or Drugs group and a higher proportion in the Alcohol and Drugs group than expected. Finally, among those who did not have an inpatient hospital stay, we observed a lower proportion in the Alcohol and Drugs group than expected while among those who had an inpatient stay we observed a substantially lower proportion in the No Alcohol or Drugs group and a substantially higher proportion in all three other groups than expected. Based on the results of the ANOVA, we found that age and years living with HIV were significantly associated with substance use groups. Overall, the No Alcohol or Drugs group was significantly younger than the other three groups and the No Alcohol or Drugs and Alcohol Only groups had been living with HIV for significantly less time than the other two groups.

Table 2 presents two views of the same multinomial logistic regression model of 4,965 patients examining alcohol and drug-use diagnoses. Model 1A uses no alcohol or drug-use diagnosis as the reference group while model 1B uses both alcohol and drug-use diagnosis as the reference group. In model 1A, older age (p < 0.01) and having a recent inpatient hospital stay (p < 0.001) significantly increased the odds of being in the Alcohol Only group compared with the No Alcohol or Drugs group. No other variables independently predicted being in the Alcohol Only group versus the No Alcohol or Drugs group. Years living with HIV (p < 0.001), having an unsuppressed viral load (p < 0.05), and having a recent inpatient hospital stay (p < 0.001) significantly increased the odds of being in the Drugs Only group compared with the No Alcohol or Drugs group. Conversely, being Black (p < 0.001), Latino (p < 0.001), and other race (p < 0.01) compared to White race were associated with a significant decrease in the odds of being in the Drugs Only group compared with the No Alcohol or Drugs group. Similarly, being MSM (p < 0.001) and female (p < 0.05) compared with MSW were associated with decreased odds of being in the Drugs Only group compared with the No Alcohol or Drugs group. Finally, the results comparing the Alcohol and Drug group with the No Alcohol or Drugs group were generally consistent with those for the Drug Only analyses with the exception of race. Specifically, years living with HIV (p < 0.001), having an unsuppressed viral load (p < 0.001), and having a recent inpatient hospital stay (p < 0.001) significantly increased the odds of being in the Alcohol and Drugs group compared with the No Alcohol or Drugs group. Conversely, being Latino (p < 0.01) compared with White was associated with decreased odds of being in the Alcohol and Drugs group rather than the No Alcohol or Drugs group. Similarly, being MSM (p < 0.01) and female (p < 0.01) compared with MSW were associated with decreased odds of being in the Alcohol and Drugs group compared with the No Alcohol or Drugs group.

Table 2.

Multinomial Logistic Regression Predicting Alcohol and Drug-Use Diagnoses†

Model 1A (Ref. Group: No Alcohol or Drugs)
Model 1B (Ref. Group: Alcohol
and Drugs)a
Alcohol Only Drugs Only Alcohol and
Drugs		 Alcohol
Only
Drugs Only
B SE AOR B SE AOR B SE AOR B SE AOR B SE AOR
Age 0.02 0.01 1.02** 0.00 0.00 1.00 0.01 0.01 1.01 0.02 0.01 1.02 −0.01 0.01 0.99
HIV Diagnosed Years 0.00 0.01 1.00 0.05 0.01 1.05*** 0.06 0.01 1.06*** −0.06 0.01 0.94*** −0.01 0.01 0.99
Race/Ethnicity (Ref: White)
  Black 0.01 0.22 1.00 −0.42 0.11 0.66*** −0.20 0.15 0.82 0.21 0.26 1.23 −0.22 0.17 0.80
  Latino 0.26 0.21 1.30 −0.34 0.11 0.71*** −0.47 0.16 0.63** 0.73 0.25 2.07** 0.12 0.17 1.13
  Other −0.37 0.49 0.70 −0.73 0.26 0.48** 0.00 0.30 1.00 −0.37 0.56 0.69 −0.73 0.37 0.48*
HIV Risk Category (Ref: MSW)
  MSM 0.28 0.19 1.32 −0.38 0.10 0.69*** −0.41 0.13 0.67** 1.05 1.43 2.86 2.02 1.04 7.53
  Female −0.36 0.25 0.70 −0.23 0.11 0.79* −0.38 0.15 0.69** 0.02 0.28 1.02 0.14 0.17 1.15
  Transgender −0.91 1.03 0.40 0.06 0.30 1.06 −1.96 1.02 0.14 0.68 0.22 1.98** 0.03 0.15 1.03
Viral Load (Ref: Virally Suppressed)
  Not Virally Suppressed 0.13 0.19 1.14 0.23 0.10 1.26* 0.42 0.13 1.52*** −0.29 0.21 0.75 −0.19 0.14 0.83
CD4 Count (Ref: Not Immunocompromised)
  Immunocompro mised 0.07 0.25 1.08 −0.06 0.13 0.94 0.28 0.15 1.32 −0.20 0.28 0.82 −0.34 0.18 0.71
Hospital Stay (Ref: No)
  Yes 1.01 0.22 2.75*** 0.84 0.13 2.31*** 1.21 0.15 3.35*** −0.20 0.24 0.82 −0.37 0.16 0.69*
Open in a new tab

Note: aBetas and AORs for the No Alcohol or Drugs category are excluded from the results for Model 1B as they are the inverse of the Alcohol and Drugs category from Model 1a.

*

p < 0.05.

**

p < 0.01.

***

p < 0.001.

4. Discussion

In a clinical sample of 4,965 urban HIV-positive patients, one-third had alcohol and/or drug use diagnoses. In contrast, the World Health Organization (WHO) surveillance data estimates prevalence of drug use diagnoses at 1.83% of the U.S. adult population and alcohol use diagnoses at 5.48% (WHO, 2004). Rates are substantially higher in this clinic sample and that the magnitude is reversed, with higher prevalence of drug use diagnoses.

Alcohol and drug diagnoses were associated with poorer clinical outcomes including higher viral loads, lower CD4 counts and more inpatient stays. Among virally unsuppressed patients, a greater proportion was observed in the Alcohol and Drugs group. Among those who were immunocompromised, we observed a lower proportion in the No Alcohol or Drugs group and a higher proportion in the Alcohol and Drugs group than expected. Diagnosis of an alcohol use was significantly associated with having a diagnosis of a drug use. Finally, among those who had an inpatient stay we observed a substantially lower proportion in the No Alcohol or Drugs group and a substantially higher proportion in all three other groups than expected. This is also an important finding, essentially that inpatient admissions are correlated with alcohol/drug use groups.

The most common substance use diagnosis was Drugs Only irrespective of HIV risk category. Contrary to previous studies showing higher rates of substance use among HIV-positive MSM (Parsons et al., 2014; Parsons and Starks, 2014; Rendina et al., 2015) and transgender populations (Mayer et al., 2016), our results found these two groups had lower percentages of substance use overall (27.4% and 26.1%, respectively) compared to men who have sex with women (37.4%) and female (30.5%). It is worth noting that the information on sexual orientation being an essential element of self-identification was not recorded in the EMRs used for analyzing this study. The importance of routine collection of sexual orientation and gender identity information and its inclusion in EMRs as noted in a 2011 Institute of Medicine report is supported by studies focusing on identifying the unique health needs and health disparities of lesbian, gay, bisexual and transgender (LGBT) patients (Cahill et al., 2015; Cahill and Makadon, 2014; Callahan et al., 2014). The No Alcohol or Drugs group was significantly younger than the other three groups. This may not reflect a difference in prevalence but differences in screening practices.

Our results showed that alcohol and drug-use diagnoses are highly intercorrelated in this sample. Though fewer patients with a drug use diagnosis had a co-morbid alcohol use diagnosis, this outcome could be a result of the ways in which they are reported and documented in the EMR databases. This suggests that medical providers are more likely to document drug than alcohol use diagnoses – and highlights the need for more routine screening.

Although our study is based on large, comprehensive clinic-based data analyses and uses a sophisticated analytical model to predict the relationships between the independent variables and outcomes, it has potential limitations. First, we are unable to make any causal inferences based on a cross-sectional study design looking at one point in time retrospectively. While we are able to see associations between individual demographic and clinical indicators examined and substance use diagnosis groupings, observational studies are not intended to prove causation, and causal inferences should be interpreted with causation (Aschengrau and Seage, 2014). Our findings do, however, suggest changes in organizational level practices that may enhance screening for substance use for those who are not virally suppressed in prospective studies.

Second, the results of our study are not generalizable to all HIV-positive patients because the sample is not designed to provide nationally representative estimates of the prevalence of alcohol and drug-use diagnoses. However, our design of a multisite component affords greater generalizability than single-site studies, and the use of EMRs in a large urban, diverse cohort of three related clinics does encompass a broad geographic distribution. Third, although we consider it strength to use electronic diagnoses, there is documented variation in the way substance use questions and response options are administered in the EMR databases. Our use of diagnostic codes as a proxy for substance use diagnoses likely underestimates the prevalence of alcohol and drug-use diagnoses. To document a diagnosis, providers must first ask about substance use, then determine whether a patient’s use meets criteria for a diagnosis, and finally hand-enter the diagnostic code in the ICD tab of the medical record. In the context of a 20-minute primary care visit, one or more of these steps may be skipped, especially given the sensitive nature of substance use diagnoses. Furthermore, medical providers may not feel equipped to inquire or respond to patients in a way that opens up a longer conversation about treatment for substance use diagnoses.

Fourth, the electronic records did not specify the drug types used among the sample. They were mostly coded as drug use diagnosis. Different drugs and methods of use would likely have differential effects on patients’ disease progression, level of clinic engagement, and possibly the levels of viral suppression. Therefore, understanding which drugs are associated with disengagement and viremia would be incredibly valuable. Future research could look at subsamples of which drugs and methods of use were most correlated with poorer outcomes. Finally, our data are limited to older individuals with HIV, impacting the generalizability of our results. The average age of our sample was 45. Future research should investigate if younger adults would present differently on the prevalence and interacting effects of comorbid alcohol and drug-use diagnoses.

Despite these limitations, our study has several clinical implications for substance use diagnosis among HIV-positive patients receiving care. Alcohol and drugs contribute to worse outcomes so providers should be highly aware of an increased likelihood of substance use diagnoses in viremic patients. Enhanced, routine screening for substance use is indicated for all HIV-positive patients. Our findings also suggest promoting up-to-date knowledge of referral processes to appropriate alcohol/other drug services for patients with alcohol and/or substance use diagnoses among clinicians. Alcohol and drug use varies over time so screening should take place on a regular basis especially if a previously stable patient presents with an unsuppressed viral load. Enhanced screening for immunocompromised patients-with CD4’s lower than 200–400 may be an effective strategy for improving substance use diagnosis and retaining patients in substance use treatment. Finally, given the relationship between inpatient hospitalizations and alcohol and drug use diagnoses, the inpatient hospital is an optimal setting for screening and engagement in substance use treatment.

In conclusion, the aim of this retrospective cohort analysis was to estimate the prevalence of alcohol and drug-use diagnoses based on ICD-9/10 codes of substance use and examine interacting effects of comorbid alcohol and drug use among HIV-positive people. The results indicate that nearly three-quarters of patients with a documented alcohol use diagnosis also had a co-morbid drug use diagnosis and compared with patients with no alcohol use diagnosis, had nearly 9 times the odds of having a drug use diagnosis. In particular, when comparing the three groups with the No Alcohol or Drugs group, we found that older age and having a recent inpatient hospital stay independently predicted being in the Alcohol Only group; years living with HIV, having an unsuppressed viral load, and having a recent inpatient hospital stay significantly increased the odds of being in the Drugs Only and Alcohol and Drugs groups; and being a MSM and female were associated with decreased odds of being in the Drugs Only and Alcohol and Drugs groups. Future research should distinguish the data between severity of alcohol and drug-use diagnoses as indicated by the Alcohol Use Disorders Identification Test (AUDIT) and Drug Abuse Screening Test (DAST) scores.

Highlights.

  • Immunocompromised patients were found to be lower in the reference group.

  • The reference group was significantly younger than the three comparison groups.

  • Diagnosis of alcohol and drug use was significantly associated.

  • Higher proportion of patients in all three comparison groups had an inpatient stay.

  • HIV treatment providers should routinely screen for alcohol and substances.

Acknowledgments

Role of Funding Source

This project was supported by a research grant from the National Institute on Alcohol Abuse and Alcoholism (R01-AA022302; Jeffrey T. Parsons, Principal Investigator). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

The authors would like to acknowledge the contributions of the PLUS Research Team: Juline Koken, Laurie Spacek, Tyrel Starks, and Evie Arroyo. Finally, we thank the participants who volunteered their time for this study.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Author Disclosures

Contributors

Sitaji Gurung and Demetria Cain were primarily responsible for drafting text, reviewing content, and approving the final manuscript. Ana Ventuneac, Chloe Mirzayi and H. Jonathon Rendina were primarily responsible for designing and executing analyses, reviewing content, and approving the final manuscript. Martha A. Sparks and Christopher Ferraris were primarily responsible for confirming issues with the data, reviewing content, and approving the final manuscript. Jeffrey T. Parsons was the principal investigator on the grant, and primarily responsible for project conceptualization, reviewing content, and approving the final manuscript.

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. Agins BD, Ng D, Steinbock C, Belanger D, Palumbo M, Felder A, Wells CG, Geis M, Tietz D, Dai H, Savitsky L. HIVQUAL-US Coaching and Mentoring Peer Learning Patient Involvement HIVQUAL-US Annual Data Report. New York State Department of Health AIDS Institute HIVQUAL-US; 2013. [Google Scholar]
  2. Aschengrau A, Seage GR. Essentials of Epidemiology in Public Health. 3. Jones and Bartlett; Sudbury, MA: 2014. [Google Scholar]
  3. Bagasra O, Bachman SE, Jew L, Tawadros R, Carter J, Boden G, Ryan I, Pomerantz RJ. Increased human immunodeficiency virus type 1 replication in human peripheral blood mononuclear cells induced by ethanol: potential immunopathogenic mechanisms. J. Infect. Dis. 1996;173:550–558. doi: 10.1093/infdis/173.3.550. [DOI] [PubMed] [Google Scholar]
  4. Bagasra O, Kajdacsy-Balla A, Lischner HW, Pomerantz RJ. Alcohol intake increases human immunodeficiency virus type 1 replication in human peripheral blood mononuclear cells. J. Infect. Dis. 1993;173:789–797. doi: 10.1093/infdis/167.4.789. [DOI] [PubMed] [Google Scholar]
  5. Baum MK, Rafie C, Lai S, Sales S, Page B, Campa A. Crack-cocaine use accelerates HIV disease progression in a cohort of HIV-positive drug users. J. Acquir. Immune Defic. Syndr. 2009;50:93–99. doi: 10.1097/QAI.0b013e3181900129. [DOI] [PubMed] [Google Scholar]
  6. Baum MK, Rafie C, Lai S, Sales S, Page B, Campa A. Alcohol use accelerates HIV disease progression. AIDS Res. Hum. Retroviruses. 2010;26:511–518. doi: 10.1089/aid.2009.0211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cahill S, Baker K, Deutsch MB, Keatley J, Makadon H. Inclusion of sexual orientation and gender identity in stage 3 meaningful use guidelines: A huge step forward for LGBT Health. LGBT Health. 2015;3:100–102. doi: 10.1089/lgbt.2015.0136. [DOI] [PubMed] [Google Scholar]
  8. Cahill S, Makadon H. Sexual orientation and gender identity data collection in clinical settings and in electronic health records: A key to ending LGBT health disparities. LGBT Health. 2014;1:34–41. doi: 10.1089/lgbt.2013.0001. [DOI] [PubMed] [Google Scholar]
  9. Callahan EJ, Hazarian S, Yarborough M, Sánchez JP. Eliminating LGBTIQQ health disparities: The associated roles of electronic health records and institutional culture. Hastings Cent. Rep. 2014;44:S48–S52. doi: 10.1002/hast.371. [DOI] [PubMed] [Google Scholar]
  10. Carrico AW. Substance use and HIV disease progression in the HAART era: Implications for the primary prevention of HIV. Life Sci. 2011;88:940–947. doi: 10.1016/j.lfs.2010.10.002. [DOI] [PubMed] [Google Scholar]
  11. Chander G, Himelhoch S, Fleishman JA, Hellinger J, Gaist P, Moore RD, Gebo KA. HAART receipt and viral suppression among HIV-infected patients with co-occurring mental illness and illicit drug use. AIDS Care. 2009;21:655–663. doi: 10.1080/09540120802459762. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chander G, Himelhoch S, Moore RD. Substance abuse and psychiatric disorders in HIV-positive patients: epidemiology and impact on antiretroviral therapy. Drugs. 2006;66:769–789. doi: 10.2165/00003495-200666060-00004. [DOI] [PubMed] [Google Scholar]
  13. Chander G, Josephs J, Fleishman JA, Korthuis PT, Gaist P, Hellinger J, Gebo K, Network HIVR. Alcohol use among HIV-infected persons in care: Results of a multi-site survey. HIV Med. 2008;9:196–202. doi: 10.1111/j.1468-1293.2008.00545.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cofrancesco J, Scherzer R, Tien PC, Gibert CL, Southwell H, Sidney S, Dobs A, Grunfeld C. Illicit drug use and HIV treatment outcomes in a US cohort. AIDS (London, England) 2008;22:357. doi: 10.1097/QAD.0b013e3282f3cc21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cohen MS, Gay CL. Treatment to prevent transmission of HIV-1. Clin. Infect. Dis. 2010;50(Suppl. 3):S85–S95. doi: 10.1086/651478. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Conigliaro J, Gordon AJ, McGinnis KA, Rabeneck L, Justice AC Veterans Aging Cohort 3-Site Study. How harmful is hazardous alcohol use and abuse in HIV infection: do health care providers know who is at risk? J. Acquir. Immune Defic. Syndr. 2003;33:521–525. doi: 10.1097/00126334-200308010-00014. [DOI] [PubMed] [Google Scholar]
  17. Conigliaro J, Justice AC, Gordon AJ, Bryant K, Alcohol V Behavior Change Research, G. Role of alcohol in determining human immunodeficiency virus (HIV)-relevant outcomes: A conceptual model to guide the implementation of evidence-based interventions into practice. Med. Care. 2006;44(Suppl. 2):S1–6. doi: 10.1097/01.mlr.0000223659.36369.cf. [DOI] [PubMed] [Google Scholar]
  18. Cook RL, Zhu F, Belnap BH, Weber K, Cook JA, Vlahov D, Wilson TE, Hessol NA, Plankey M, Howard AA, Cole SR, Sharp GB, Richardson JL, Cohen MH. Longitudinal trends in hazardous alcohol consumption among women with human immunodeficiency virus infection, 1995–2006. Am. J. Epidemiol. 2009;169:1025–1032. doi: 10.1093/aje/kwp004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Galvan FH, Bing EG, Fleishman JA, London AS, Caetano R, Burnam MA, Longshore D, Morton SC, Orlando M, Shapiro M. 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. J. Stud. Alcohol. 2002;63:179–186. doi: 10.15288/jsa.2002.63.179. [DOI] [PubMed] [Google Scholar]
  20. Hahn JA, Samet JH. Alcohol and HIV disease progression: Weighing the evidence. Curr. HIV/AIDS Rep. 2010;7:226–233. doi: 10.1007/s11904-010-0060-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hendershot CS, Stoner SA, Pantalone DW, Simoni JM. Alcohol use and antiretroviral adherence: Review and meta-analysis. J. Acquir. Immune Defic. Syndr. 2009;52:180–202. doi: 10.1097/QAI.0b013e3181b18b6e. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Justice AC, Lasky E, McGinnis KA, Skanderson M, Conigliaro J, Fultz SL, Crothers K, Rabeneck L, Rodriguez-Barradas M, Weissman SB, Bryant K Veterans Aging Cohort Study Project Team. Medical disease and alcohol use among veterans with human immunodeficiency infection: A comparison of disease measurement strategies. Med. Care. 2006;44(Suppl 2):S52–60. doi: 10.1097/01.mlr.0000228003.08925.8c. [DOI] [PubMed] [Google Scholar]
  23. Kader R, Govender R, Seedat S, Koch JR, Parry C. Understanding the impact of hazardous and harmful use of alcohol and/or other drugs on ARV adherence and disease progression. PLoS One. 2015;10:e0125088. doi: 10.1371/journal.pone.0125088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kaplan EH, Heimer R. A model-based estimate of HIV infectivity via needle sharing. JAIDS J. Acquir. Immune Defic. Syndr. 1992;5:1116–1118. [PubMed] [Google Scholar]
  25. Korthuis PT, Zephyrin LC, Fleishman JA, Saha S, Josephs JS, McGrath MM, Hellinger J, Gebo KA, Network HIVR. Health-related quality of life in HIV-infected patients: The role of substance use. AIDS Patient Care STDS. 2008;22:859–867. doi: 10.1089/apc.2008.0005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lucas GM, Griswold M, Gebo KA, Keruly J, Chaisson RE, Moore RD. Illicit drug use and HIV-1 disease progression: A longitudinal study in the era of highly active antiretroviral therapy. Am. J. Epidemiol. 2006;163:412–420. doi: 10.1093/aje/kwj059. [DOI] [PubMed] [Google Scholar]
  27. Malbergier A, Amaral RA, Cardoso LD. Alcohol dependence and CD4 cell count: Is there a relationship? AIDS Care. 2015;27:54–58. doi: 10.1080/09540121.2014.947235. [DOI] [PubMed] [Google Scholar]
  28. Masson CL, Sorensen JL, Phibbs CS, Okin RL. Predictors of medical service utilization among individuals with co-occurring HIV infection and substance abuse disorders. AIDS Care. 2004;16:744–755. doi: 10.1080/09540120412331269585. [DOI] [PubMed] [Google Scholar]
  29. Mayer KH, Grinsztejn B, El-Sadr WM. Transgender people and HIV prevention: What we know and what we need to know, a call to action. J. Acquir. Immune Defic. Syndr. 2016;15 doi: 10.1097/QAI.0000000000001086. 0000000000001086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Murray M, Hogg RS, Lima VD, May MT, Moore DM, Abgrall S, Bruyand M, D’Arminio Monforte A, Tural C, Gill MJ, Harris RJ, Reiss P, Justice AC, Kirk O, Saag M, Smith CJ, Weber R, Rockstroh J, Khaykin P, Sterne JA Antiretroviral Therapy Cohort Collaboration. The effect of injecting drug use history on disease progression and death among HIV-positive individuals initiating combination antiretroviral therapy: Collaborative cohort analysis. HIV Med. 2012;13:89–97. doi: 10.1111/j.1468-1293.2011.00940.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Pane on Antiretroviral Guidelines for Adults Adolescents. Guidelines for the Use of Antiretroviral Agents in HIV-1 Infected Adults and Adolescents. Department of Health and Human Services; 2016. [Google Scholar]
  32. Parsons JT, Grov C, Golub SA. Sexual compulsivity, co-occurring psychosocial health problems, and HIV risk among gay and bisexual men: Further evidence of a syndemic. Am. J. Public Health. 2012;102:156–162. doi: 10.2105/AJPH.2011.300284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Parsons JT, Halkitis PN, Wolitski RJ, Gomez CA. Correlates of sexual risk behaviors among HIV-positive men who have sex with men. AIDS Educ. Prev. 2003a;15:383–400. doi: 10.1521/aeap.15.6.383.24043. [DOI] [PubMed] [Google Scholar]
  34. Parsons JT, Halkitis PN, Wolitski RJ, Gómez CA SUM Study Team. Correlates of sexual risk behaviors among HIV-positive men who have sex with men. AIDS Educ. Prev. 2003b;15:383–400. doi: 10.1521/aeap.15.6.383.24043. [DOI] [PubMed] [Google Scholar]
  35. Parsons JT, Lelutiu-Weinberger C, Botsko M, Golub SA. A randomized controlled trial utilizing motivational interviewing to reduce HIV risk and drug use in young gay and bisexual men. J. Consult. Clin. Psychol. 2014;82:9–18. doi: 10.1037/a0035311. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Parsons JT, Rosof E, Mustanski B. The temporal relationship between alcohol consumption and HIV-medication adherence: a multilevel model of direct and moderating effects. Health Psychol. 2008;27:628–637. doi: 10.1037/a0012664. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Parsons JT, Starks TJ. Drug use and sexual arrangements among gay couples: Frequency, interdependence, and associations with sexual risk. Arch. Sex. Behav. 2014;43:89–98. doi: 10.1007/s10508-013-0237-3. [DOI] [PubMed] [Google Scholar]
  38. Parsons JT, Vicioso KJ, Punzalan JC, Halkitis PN, Kutnick A, Velasquez MM. The impact of alcohol use on the sexual scripts of HIV-positive men who have sex with men. J. Sex. Res. 2004;41:160–172. doi: 10.1080/00224490409552224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Pence BW, Thielman NM, Whetten K, Ostermann J, Kumar V, Mugavero MJ. Coping strategies and patterns of alcohol and drug use among HIV-infected patients in the United States Southeast. AIDS Patient Care STDs. 2008;22:869–877. doi: 10.1089/apc.2008.0022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Petry NM. Alcohol use in HIV patients: What we don’t know may hurt us. Int. J.STD AIDS. 1999;10:561–570. doi: 10.1258/0956462991914654. [DOI] [PubMed] [Google Scholar]
  41. Proeschold-Bell RJ, Heine A, Pence BW, McAdam K, Quinlivan EB. A cross-site, comparative effectiveness study of an integrated HIV and substance use treatment program. AIDS Patient Care STDs. 2010;24:651–658. doi: 10.1089/apc.2010.0073. [DOI] [PubMed] [Google Scholar]
  42. Ramirez-Valles J, Garcia D, Campbell RT, Diaz RM, Heckathorn DD. HIV infection, sexual risk behavior, and substance use among Latino gay and bisexual men and transgender persons. Am. J. Public Health. 2008;98:1036. doi: 10.2105/AJPH.2006.102624. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Rendina HJ, Moody RL, Ventuneac A, Grov C, Parsons JT. Aggregate and event-level associations between substance use and sexual behavior among gay and bisexual men: Comparing retrospective and prospective data. Drug Alcohol Depend. 2015;154:199–207. doi: 10.1016/j.drugalcdep.2015.06.045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Samet JH, Phillips SJ, Horton NJ, Traphagen ET, Freedberg KA. Detecting alcohol problems in HIV-infected patients: Use of the CAGE questionnaire. AIDS Res. Hum. Retroviruses. 2004;20:151–155. doi: 10.1089/088922204773004860. [DOI] [PubMed] [Google Scholar]
  45. Sohler NL, Wong MD, Cunningham WE, Cabral H, Drainoni M, Cunningham CO. Type and pattern of illicit drug use and access to health care services for HIV-infected people. AIDS Patient Care STDs. 2007;21:S-68–S-76. doi: 10.1089/apc.2007.9985. [DOI] [PubMed] [Google Scholar]
  46. Turner BJ, Fleishman JA, Wenger N, London AS, Burnam MA, Shapiro MF, Bing EG, Stein MD, Longshore D, Bozzette SA. Effects of drug abuse and mental disorders on use and type of antiretroviral therapy in HIV-infected persons. J. Gen. Intern. Med. 2001;16:625–633. doi: 10.1046/j.1525-1497.2001.016009625.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Vajpayee M, Kaushik S, Sreenivas V, Wig N, Seth P. CDC staging based on absolute CD4 count and CD4 percentage in an HIV-1-infected Indian population: Treatment implications. Clin. Exp. Immunol. 2005;141:485–490. doi: 10.1111/j.1365-2249.2005.02857.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Velasquez MM, von Sternberg K, Johnson DH, Green C, Carbonari JP, Parsons JT. Reducing sexual risk behaviors and alcohol use among HIV-positive men who have sex with men: A randomized clinical trial. J. Consult. Clin. Psychol. 2009;77:657. doi: 10.1037/a0015519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Weaver MR, Conover CJ, Proescholdbell RJ, Arno PS, Ang A, Ettner SL Cost Subcommittee of the HIV/AIDS Treatment Adherence, H.O., Cost Study Group. Utilization of mental health and substance abuse care for people living with HIV/AIDS, chronic mental illness, and substance abuse disorders. J. Acquir. Immune Defic. Syndr. 2008;47:449–458. doi: 10.1097/QAI.0b013e3181642244. [DOI] [PubMed] [Google Scholar]
  50. WHO. [Accessed September 22, 2016];Global Health Observatory (GHO) data. 2004 http://www.who.int/gho/substance_abuse/burden/drug_prevalence/en/

RESOURCES