Howard Newville 1 , James L. Sorensen 1 , Donald A. Calsyn 2

1. Relationship between substance abuse treatment outcome and sexual risk behaviors Howard Newville1, James L. Sorensen1, Donald A. Calsyn2 1 University of California, San Francisco, San Francisco, CA 2 University of Washington, Seattle, WA Funding information: Supported by the National Institute of Drug Abuse (F32 DA032446, U10 DA13714, U10 DA015815, and P50 DA09253) • Introduction • Non-injection drug users (NIDUs) have similar HIV rates as injection drug users (IDUs) (Strathdee, 2003; Des Jarlais, 2010) • 13% among IDUs and 12% among NIDUs in a drug treatment program study, 15% and 17% in a respondent-driven sampling (RDS) storefront study (Des Jarlais, 2007) • The use of stimulants is associated with increased sex risk behavior (Plankey et al., 2007) • HIV+ individuals are more likely to have sex under the influence of stimulants than HIV-negative individuals (Carey et al., 2009) • Drug treatment lessens drug use and IDU risk, but its effects on sexual practices are unknown • Sex risk behaviors are slower to change (Sorensen & Copeland, 2000) • Many substance users in treatment continue to engage in sex risk behaviors (Farrell, Gowing, Marsden, Ling & Ali, 2005) • Aims • This secondary data analysis will assess the impact of drug treatment on HIV risk behaviors • Hypothesis: decreases in drug and alcohol use at follow-up will coincide with decreases in sex risk behaviors • Methods • Setting and design • NIDA Clinical Trials Network (CTN) study testing innovative risk reduction against standard education • Participants recruited from 7 methadone maintenance (MMT) and 7 outpatient drug free (ODF) programs • Diverse in terms of region, population density, and HIV prevalence rates • Urban (e.g., Philadelphia), suburban (e.g., Norwalk, CT) and rural (e.g., Huntington, WV) • Located in the Northeast, South, Midwest, Southwest, and West • Measures • Addiction Severity Index (ASI) • Alcohol and drug composite scores • Sexual Behavior Interview (SBI) (all behaviors – past 90 days) • 1) % protected sex with main sex partner • 2) % protected sex with a casual sex partner • 3) Number of sex partners (0, 1, >1) • 4) Having ≥1 high risk sex partner (“High risk” = IDU, crack/cocaine, or thought to be HIV+) • 5) Any sex under the influence • Methods (continued) • Participants • Eligibility requirements: (1) men ≥18 years old, in substance abuse treatment, (2) reported unprotected intercourse (past 6 months) (3) willing to be randomly assigned to one of two interventions, (4) completed all study assessments, and (5) English speaking • Exclusion criteria: (1) gross mental status impairment; (2) primary sexual partner intending to become pregnant • Data analysis • Severity of drug/alcohol use and frequency of risk behaviors calculated at baseline and six month follow-up • Paired sample t-tests (for normal data) and Wilcoxon signed-rank tests (for non-normal data) to assess change over time • Changes in drug/alcohol use severity by changes in risk behaviors assessed with multinomial logistic regression • Due to non-normal distributions, risk behaviors were considered as increasing, decreasing, or stable • Intervention sessions attended, treatment modality (MMT vs. ODF), and present engagement in drug treatment (0 vs. 1-29 vs. 30 days) added as covariates Results Bivariate analyses Drug use variables Alcohol CS (Mean [SD]) Baseline – 0.09 (0.15); Follow-up – 0.07 (0.12), p<0.001 Drug CS (Mean [SD]) Baseline – 0.19 (0.14); Follow-up – 0.15 (0.13), p<0.001 Risk variables % protected sex with a regular partner Baseline – 11.6%; Follow-up – 20.7%, p<0.001 % protected sex with a casual partner Baseline – 26.7%; Follow-up – 40.4%, p=0.038 Multiple sex partners Baseline – 40.1%; Follow-up – 26.5%; p<0.001 At least one high risk sex partner Baseline – 28.7%; Follow-up – 24.8%; p=0.302 Sex under the influence Baseline – 70.8%; Follow-up – 51.3%; p<0.001 • Treatment engagement (past 30 days) • 0 days – 115 (24.9%), 1-29 days – 161 (34.9%), 30 days – 122 (26.5%) • Multivariate analyses • % protected sex w/ regular partner, % protected sex w/ casual partner • All individual items NS (Figures 1 & 3) • Results (continued) • Multivariate analyses (continued) • Number of sex partners • ASI alcohol: decreased for those whose number of partners decreased (OR [95% CI]: 8.40 [1.07, 66.67], p=0.043), increased for those whose number of partners increased (OR [95% CI]: 21.87 [1.08, 443.00], p=0.044) (Figures 1 & 3) • Sex under the influence • ASI drug: Those who discontinued SUI had greater decreases than those who had no SUI at either time (OR [95% CI]: 27.03 [1.21, 618.67], p=0.038) (Figures 2 & 4) • At least one high risk sex partner • ASI drug: Those who had high risk partners at both times had greater decreases than those without high risk partners at either time (OR [95% CI]: 90.91 [3.91, 2,040.81], p=0.005) (Figures 2 & 4) • Discussion • Drug/alcohol use severity and most sex risk behaviors decreased for individuals in drug treatment • Changes in drug/alcohol use severity associated with decreases in certain risk behaviors • As drug treatment can decrease HIV seroconversion (Farrell et al., 2005), it is viable for risk reduction • However, not all sex risk behaviors decrease with drug treatment alone, and further interventions within drug treatment are necessary • Innovative risk reduction interventions can decrease risk (Calsyn et al., 2010) • References • Calsyn DA, Hatch-Maillette M, Tross S, et al.Motivational and Skills Training HIV/STI Sexual Risk Reduction Groups for Men. J Subst Abuse Treat. 2009 September ; 37(2): 138–150. • Carey JW, Mejia R, Bingham T, et al. Drug use, high-risk sex behaviors, and increased risk for recent HIV infection among men who have sex with men. AIDS Behav 2009; 13:1084-1096. • Des Jarlais DC, Arasteh K, McKnight C, et al. Gender and age patterns in HSV-2 and HIV infection among non-injecting drug users in New York City. Sex Transm Dis 2010; 37:637– 643. • Farrell M, Gowing L, Marsden J, Ling W, Ali R. Effectiveness of drug dependence treatment in HIV prevention. Int J Drug Policy 2005; 16S:S67–75. • Plankey MW, Ostrow DG, Stall R, et al.. The relationship between methamphetamine and popper use and risk of HIV seroconversion in the multicenter AIDS cohort study. JAIDS 2007; 45: 85-92. • Sorensen JL, Copeland AL. Drug abuse treatment as an HIV prevention strategy: A review. Drug Alc Depend 2000; 59:17-31. • Strathdee SA, Sherman SG. The role of sexual transmission of HIV infection among injection and non-injection drug users. J Urban Health 2003; 80(suppl 3):iii7–iii14. Figures Figure 1 – ASI Alcohol, Condom Use, and Number of Partners * Frequency Change in ASI Alcohol CS Figure 2 – ASI Alcohol, SUI, and High Risk Partners * Status Change in ASI Alcohol CS Figure 3 – ASI Drug, Condom Use, and Number of Partners Frequency Change in ASI Drug CS Figure 4 – ASI Drug, SUI, and High Risk Partners * Status Change in ASI Drug CS Funding information: Supported by the National Institute of Drug Abuse (F32 DA032446, U10 DA13714, U10 DA015815, and P50 DA09253)