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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
AIDS Care. Author manuscript; available in PMC 2010 April 21.
Published in final edited form as:
PMCID: PMC2858229
NIHMSID: NIHMS178507

Psychosocial Factors and Substance Use in High Risk Youth Living with HIV: A Multisite Study

Sylvie Naar-King, Ph.D., Karen Kolmodin, Ph.D., Jeffrey T. Parsons, Ph.D., Debra Murphy, Ph.D., and ATN 004 Protocol Team

Abstract

The purpose of the study was to test relationships between psychosocial factors and alcohol and illicit drug use among high risk youth living with HIV. 186 high risk youth with HIV (defined as those with a substance use problem, sexual risk problem OR medication adherence problem) were enrolled across 5 cities (ages 16 to 24). Alcohol and illicit drug use was measured with the ASSIST and a timeline follow-back interview. Questionnaires assessed constructs from the adapted TTM including a continuous measure of motivational readiness in response to criticisms of the stage component. Path analysis was utilized to fit cross-sectional data collected via Computer Assisted Personal Interviewing (baseline data from intervention study). Separate models were fit for each commonly used substance. In the previous month, 47% used alcohol, 37% used cannabis, and 9% used other illicit drugs. Path models fit the data well and accounted for 30% of the variance in alcohol use and 47% in cannabis use. Higher self-efficacy predicted lower alcohol and cannabis use, but motivational readiness was only directly related to cannabis use. A reduction in pros of substance use was indirectly related to use. Social support and psychological distress were associated with TTM constructs. Interventions focusing on improving motivation and self-efficacy for healthy behaviors may reduce substance use in youth living with HIV.

Keywords: HIV, Adolescents, Young Adults, Transtheoretical Model, Substance Use

Introduction

Half of new HIV infections occur in young people under the age of 25 (Department of Child and Adolescent Health, 2004). Alcohol and illicit drug use are highly prevalent among youth living with HIV (YLH; Murphy et al., 2001; Naar-King et al., 2006a) and have been associated with increased sexual risk (Naar-King et al., 2006b) and poor adherence to medications (Murphy et al., 2005). However, there have been no multi-site studies describing the substance use of YLH who represent the demographics of the epidemic and the adolescent clinics providing treatment (minority females and men who have sex with men; adolescents and young adults). Also, no multi-site studies of YLH to date have assessed substance use beyond a single likert-scale item.

Even less is known about the psychosocial factors associated with substance use in YLH. Such knowledge is critical for developing interventions to reduce substance use as well as other co-occurring risk behaviors in YLH. In the only study of social and cognitive factors, Naar-King et al. (2006b) tested components of the Transtheoretical Model (TTM; Prochaska,et al., 1994) to predict alcohol and cannabis use among YLH. Stage of change was only indirectly related to substance use through its relationship with self-efficacy. Social support specific to avoiding alcohol and other drugs was related to self-efficacy. Decisional balance, a third key construct of the TTM defined as a shift in the pros versus the cons of a behavior, was not assessed. Criticisms of the TTM have focused on the inconsistent empirical evidence for the stage component of the model (Sutton, 2001; West, 2005), and a focus on a more continuous measure of motivational readiness may be necessary. Thus, the purpose of the present study was to expand on the Naar-King et al. (2006b) study by addressing psychosocial factors associated with substance use in a multisite sample utilizing a continuous measure of motivational readiness. Because studies have suggested that the cons of risk behaviors are less relevant for behavior change in youth (Moore & Parsons, 2000; Nickoletti & Taussig, 2006), we focused on the pros of substance use. Pros of use, also termed outcome expectancies in the social cognitive literature (Bandura, 1982), have been shown to be directly related to substance use (Parsons, Siegel, & Cousins, 1997). Because readiness to change was not directly related to use in the Naar-King et al. (2006b) study, we proposed an indirect relationship. We hypothesized that motivational readiness to avoid alcohol and illicit drugs would be associated with self-efficacy and fewer pros of substance use which in turn would be associated with less substance use. Low social support and high emotional distress were expected to relate to lower levels of readiness and self-efficacy, and more pros of substance use.

Methods

Participants

YLH were participants in a randomized clinical trial examining the efficacy of a motivational intervention addressing multiple risk behaviors. Baseline data were utilized in the current analysis. Youth were recruited from four Adolescent Trials Network (ATN) sites, and one non-ATN site (see Table 1). All five sites offered multidisciplinary care including social work support, case management services, and access to mental health services. Inclusion criteria included HIV-positive status, ages 16 to 24, and ability to complete questionnaires in English. Because the study targeted high risk YLH, inclusion criteria also included having engaged in at least one of 3 behaviors – substance use problem on the CRAFFT (Car, Relax, Alone, Forget, Friends, and Trouble; Knight et al., 1999), self-report of at least one unprotected intercourse act in the previous month, or self-report of less than 90% medication adherence the last month. Exclusion criteria were having an active thought disorder, being involved in behavioral research targeting substance use, or being in a substance abuse treatment program. Only 10 YLH refused to be screened. Of the 375 participants who were screened, 205 participants enrolled, 151 were not eligible (mostly due to lack of problem level behavior or a second engaged behavior), 15 refused to enroll after being screened, and four were lost to follow-up. Of the 205 participants enrolled in the study, 19 did not complete baseline data collections. The current sample consisted of 186 participants. Of these participants, 122 (65.5%) had problem level substance use, and 64 did not (34.5%).

Table 1
Sample Demographics by Site

Procedures

The protocol was approved by each site’s Institutional Review Board and a certificate of confidentiality was obtained from the National Institutes of Health. Clinic staff gave a general description of the study to potential participants. If interested, a research assistant obtained verbal consent for screening. Upon determination of eligibility, written informed consent was obtained, and a waiver of parental consent was permitted for youth under age 18. Youth had to complete the baseline assessment within 30 days of screener completion using a computer assisted personal interviewing (CAPI) method via an internet based application. Responses were entered into the computer by the research interviewer in a confidential manner. Once entered, all responses were anonymous and no personal identifying information was recorded during the computer session. Participants received $30 compensation for the baseline visit. Transportation, snacks and childcare were available.

Measures

Demographics

Participants reported ethnicity, sexual orientation, biological sex, age, average monthly income and whether they were perinatally infected. Biological sex was recoded as female versus male with male to female transgender (N=6) recoded as male. There were no female to male transgendered youth. Ethnicity was recoded as African American versus other. A sexual minority variable categorized those not reporting a heterosexual orientation (e.g., gay, bisexual) versus those reporting a heterosexual orientation.

Alcohol and Illicit Drugs

To describe substance use patterns, youth completed the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST; WHO ASSIST Working Group, 2002). This measure was developed for the World Health Organization (WHO) to detect psychoactive substance use and related problems in primary care patients across multiple cultures. Individuals responded to 8 items assessing the frequency and consequences of substance use for the past three months. The measure yields cut-off scores based on adult samples (18 to 45 years) for each substance used from low risk (score of 0 to 3), moderate risk (4 to 26) to high risk (27+). Because adolescent cut-off scores were not available, adult categories were used for participants under age 18 (12% of sample). The authors have reported good psychometric properties, and further validity studies are underway. Actual alcohol and illicit drug use in the past 30 days was assessed using the Timeline Follow-Back Procedure (TLFB). A calendar assists participants in recalling when they used a particular substance and the amount used on each occasion. The TLFB procedure has demonstrated excellent psychometric properties in a number of studies (Carney, Tennen, Affleck, del Boca, & Kranzler, 1998) including correlation with urine drug screens (Fals-Stewart et al., 2000). Number of standard drinks (8 ounces beer, 4 ounces of wine, or 1 ounce hard liquor) and number of illicit drug use episodes were the primary outcomes in analysis.

Self-Efficacy

Youth completed an instrument consisting of a temptation measure and a confidence measure. The Alcohol and Other Drug Abstinence Temptation scale has shown good reliability in adults with HIV (Parsons et al., 2003) and in YLH (Naar-King et al., 2006b). Youth completed one version for alcohol and one for illicit drug use consisting of 20 situations that may lead them to use alcohol (20 items) or to use illicit drugs (20 items). They then rated how tempted they would be in each situation to use from 1 (not at all tempted) to 5 (extremely tempted). These items were reverse coded so that higher scores indicated greater self-efficacy. Additional items rated on a 5 point scale asked about confidence to avoid using alcohol (3 items) and illicit drugs (3 items), where higher scores indicated greater self-efficacy. These items showed good internal consistency and validity in a study of HIV+ youth (Naar-King et al., 2006b). Cronbach’s alpha for the combined 23 items in the current study was .89 for alcohol use and .92 for illicit drug use.

Motivational Readiness to Avoid Alcohol and Illicit Drugs

Rollnick’s Readiness Ruler (Stott, Rollnick, & Pill, 1995) was administered for alcohol and for illicit drug use (2 items). Youth marked their readiness to avoid alcohol and illicit drugs on a 10 point scale from 1 (not ready to avoid) to 10 (ready to avoid). The measure was more strongly correlated with substance use than a stage algorithm in a single site sample of HIV+ youth (Naar-King et al., 2006b).

Decisional balance

The Pros subscale of the Decisional Balance Scales (Prochaska et al., 1994) was used to measure individuals' perceived positive outcomes of alcohol and drug use behavior (e.g., you like yourself better when you are drinking, using drugs helps you deal with problems). Participants rate how important these outcomes are in their decision to use drugs from 1 “not at all important” to 5 “very important”. The pros of substance use are summed so that higher scores indicate greater endorsement. Youth completed separate scales for alcohol and for illicit drug use. The scales have shown good psychometric properties (Carey, Maisto, Carey, & Purnine, 2001; Prochaska, Velicer, et al., 1994), but have not been previously utilized in YLH. Cronbach’s alpha for the pros scale was .94 for alcohol and .93 for illicit drugs.

Emotional distress

The Brief Symptom Inventory (Derogatis & Spencer, 1982) has been used extensively in medical, psychiatric, and non-patient populations. Internal consistency for the sub-scales (dimensions) ranged from .71 to .85. The 5-point response scale ranges from not at all to extremely. Analysis utilized the Global Symptom Index (GSI) where higher scores indicate greater distress.

Social support

Two items asking about social support specific to avoiding alcohol and to avoiding illicit drugs were rated on a 5-point Likert scale from strongly agree to strongly disagree. Higher scores indicated more support. These items have been previously associated with self-efficacy in YLH (Naar-King et al., 2006b).

Data Analysis

Days used alcohol or cannabis were log transformed to account for skewness. Path analysis (AMOS Version 7.0; Arbuckle, 2006) with single indicators was used to examine the relationships in Figure 1. Exogenous variables were allowed to covary consistent with the correlation matrix. Model fit was assessed by model chi-square p >.05 and standardized root mean square residual (SRMR) < .08. Root mean square error of approximation (RMSEA) < .08 and comparative fit index (CFI) > .95 were used as additional fit indices as they are less sensitive to small sample size (N < 200) (Fan, Thompson, & Wang, 1999). Pros of alcohol use (N = 3) and sexual orientation (N = 4) variables had more than 1 missing data point. Regression imputation was used to replace missing values by setting model parameters equal to their maximum likelihood estimates. Linear regression then predicted unobserved values for each case based on the observed values for that same case.

Figure 1
Path Analysis for Alcohol Use.

Results

Descriptive Statistics

Table 1 demonstrates demographic information by site. On the TFLB, only 9% of the sample had used other illicit drugs. Thus, subsequent analysis focused on alcohol and cannabis use. For alcohol, 53% of the sample had used in the previous 30 days. For cannabis, 47% had used in the previous 30 days (31% 4 times or more). Alcohol and cannabis use showed a significant but low correlation (r = .20, p < .01). On the ASSIST, 35% scored in the moderate or high risk range for alcohol. For cannabis use, 53% of the sample scored in the moderate or high risk range. Total ASSIST scores ranged from 0 to 138 (M = 32.92, SD = 26.49). Over 35 percent of the sample scored at or above the clinical cut-off (T score of 65) for the global symptom index of the BSI based on published norms. Table 2 shows descriptive statistics for predictor variables.

Table 2
Descriptive Statistics of Hypothesized Predictors

Bivariate Analyses

Table 3 and Table 4 demonstrate bivariate correlations among hypothesized predictors and substance use. Age, ethnicity, and education level were not related to alcohol or cannabis use. Biological males reported more days of cannabis use (t (184) = 3.25, p < .01), but not after controlling for sexual orientation. Males were more likely than females to identify as a sexual minority (non-heterosexual) (X2 (1, 182) = 90.44, p < .001). Sexual minorities had higher levels of alcohol use (t (180) = −4.31, p < .001) and cannabis use (t (180) = −3.94, p < .001) compared to heterosexual youth. This variable was included as a covariate with paths entered based on significant correlations.

Table 3
Correlations for Alcohol Use
Table 4
Correlations for Cannabis Use

Path Analyses

Results of path analysis resulted in an adequate fit for alcohol use (X2 (4, 186) = 9.246, p = .055, RMSEA = .084, CFI = .983, SRMR = .0411) and a good fit for cannabis use (X2 (6, 186) = 4.15, p = .656, RMSEA = .000, CFI = 1.0, SRMR = .037). However, a number of paths in each model were not significant. In the interest of parsimony, paths with p ≥ .10 were trimmed from each model. Figure 1 and Figure 2 show the final models. Fit indices suggested good to excellent fit for the more parsimonious alcohol model (X2(9, 186) = 5.624, p = .229; RMSEA = .047, CFI = .995, SRMR=.021) and cannabis model (X2 (9, 186) = 9.92, p = .357, RMSEA = .024, CFI = .997, SRMR = .044). Higher self-efficacy was directly related to lower alcohol use and lower cannabis use, but motivational readiness was only directly related to lower cannabis use. Self-efficacy did not significantly mediate the relationship between motivational readiness and alcohol use using a Sobel test (z = 2.912, p > .10). A reduction in pros was only indirectly related to substance use also through self-efficacy (alcohol = .18, p < .01; cannabis = .14, p < .01). Self-efficacy was a significant mediator here (alcohol: z = 4.24, p < .001, cannabis: z = 3.08, p < .01). Social support was associated with readiness to avoid substances. Psychological distress was associated with lower self-efficacy and more pros of substance use, but was only associated with less readiness to avoid cannabis. The models accounted for 41% of the variance in cannabis use and 28% of the variance in alcohol use.

Figure 2
Path Analysis for Cannabis Use.

Discussion

This is the first study to describe the substance use of a multi-site clinical sample of youth living with HIV using validated substance use measures. Alcohol and cannabis were the most commonly used drugs, and scores on the ASSIST suggest that at least 1/3 of the sample had problem level rates of use. Youth who reported being a sexual minority appeared to struggle the most with substance use and its social cognitive predictors, as has been found in previous studies (Beatty et al., 1999; Parsons, Halkitis, & Bimbi, 2006). As the majority of these youth were men who have sex with men (MSM), the data support the need for specialized interventions targeting this subpopulation of YLH.

Although some may consider ages 16 to 24 to span several developmental periods (e.g., adolescence, late adolescence, adulthood), this age range was chosen because it represented the population seen in clinic settings. Perhaps because of the stigma associated with HIV or because of lack of parental involvement with high risk youth, younger YLH are likely to be out of school and independent from parents, similar to the young adults. These data suggest that their rates of substance use were also equivalent.

Psychosocial predictors of substance use were tested as a foundation for future interventions. Results suggested that the models were a good fit for the data, and that there are elements of the TTM model that may be relevant even without the stages as Migneault et al. (2005) suggest. Similar to a previous study of YLH (Naar-King et al., 2006b), motivational readiness was only indirectly related to alcohol use. However, cannabis use was directly predicted by both motivation and self-efficacy. It is possible that motivation to avoid alcohol is less salient in a country where social drinking is acceptable, especially when the study utilized a continuous measure of use rather than problem-level drinking or binge drinking. Alternatively, self-efficacy is the primary driver of avoiding alcohol, a legally available substance for most of this sample.

Contrary to the TTM and to some studies of adolescents and young adults (Migneault, Pallonen, & Velicer, 1997; Migneault, Velicer, Prochaska & Stevenson, 1999; Migneault et al., 2005), the pros of substance use were not associated with readiness and were only indirectly related to substance use through their relationship to self-efficacy. Most of these studies were with higher SES samples. Bandura (1982) argued that outcome expectancies do not ordinarily predict behavior independent of self-efficacy beliefs, and this appeared to be true in this sample. It is also possible that the highly thought-mediated process of weighing the pros and cons may be less relevant for high risk adolescents and young adults, whereas confidence and motivation are more proximal constructs. Studies of the neural architecture of adolescents have found less brain activation during decisional balance tasks compared to adults (Blakemore & Choudhury, 2006). It is also possible that the neurological effects of HIV on the developing brain may affect thought-mediated processes.

Limitations include the use of a clinic-based convenience sample that may not represent community samples with undiagnosed HIV infection. These community samples may show even higher rates of substance use including particularly methamphetamine (Parsons, Kelly, & Weiser, 2007). Second, this was a sample of YLH entering an intervention study because of risk behavior and results may not be generalizable to the broader population of YLH. Third, the study relied on self-report measures. Although the TFLB improves the validity of self-report, corroboration with biological data may show different rates of use. Fourth, there may have been insufficient power to include site differences as a covariate in the model. Finally, longitudinal data are necessary to truly predict substance use in this population, test alternative models, and confirm the ordering of variables as proposed in the model. As Maxwell and Cole (2007) note, cross-sectional analysis of mediation may produce biased parameter estimates.

These data suggest that interventions increasing motivation and self-efficacy may be helpful for reducing substance use with YLH. Interventions combining motivational interviewing with cognitive-behavioral treatment have shown some success in reducing substance use in adolescents (e.g., Burleson & Kaminer, 2007) and adults living with HIV (Parsons, Rosof, Punzalan, & DiMaria, 2005). Interventions to increase social support for risk reduction (e.g., popular opinion leader interventions; Kelly, 2004) and to address psychological distress (e.g., depression interventions for persons living with HIV; Safren et al., 2004) may help to increase motivational readiness and self-efficacy to avoid alcohol and illicit drugs.

Acknowledgements

This work was supported by The Adolescent Trials Network for HIV/AIDS Interventions (ATN) [U01-HD040533 from the National Institutes of Health through the National Institute of Child Health and Human Development (B. Kapogiannis, S. Lee)], with supplemental funding from the National Institutes on Drug Abuse (N. Borek) and Mental Health (P. Brouwers, S. Allison). The study was scientifically reviewed by the ATN’s Behavioral Leadership Group. Network, scientific and logistical support was provided by the ATN Coordinating Center (C. Wilson, C. Partlow) at The University of Alabama at Birmingham Network operations and data management support was provided by the ATN Data and Operations Center at Westat, Inc. (J. Korelitz, J. Davidson, B. Harris). We acknowledge the contribution of the investigators and staff at the following ATN 004 sites that participated in this study: Children’s Diagnostic and Treatment Center (Ana Puga, MD, Esmine Leonard, BSN, Zulma Eysallenne, RN); Childrens Hospital of Los Angeles (Marvin Belzer, MD, Cathy Salata, RN, Diane Tucker, RN, MSN); University of Maryland (Ligia Peralta, MD, Leonel Flores, MD, Esther Collinetti, BA); University of Pennsylvania and the Children's Hospital of Philadelphia (Bret Rudy, MD, Mary Tanney, MPH, MSN, CPNP, Adrienne DiBenedetto, BSN); University of Southern California (Andrea Kovacs, M.D.,), and Wayne State University Horizons Project (K. Wright, D.O., P. Lam, M.A., V. Conners, B.A.). We sincerely thank the youth who participated in this project.

Contributor Information

Sylvie Naar-King, Wayne State University.

Karen Kolmodin, Wayne State University.

Jeffrey T. Parsons, Hunter College and the Graduate Center of the City University of New York.

Debra Murphy, University of California – Los Angeles.

ATN 004 Protocol Team, Adolescent Trials Network for HIV/AIDS Interventions.

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