1. Audrey J. Brooks, PhD University of Arizona CA-AZ node

2. Gender SIG Collaborators Christina S. Meade, Ph.D., NNE node Jennifer Sharpe Potter, Ph.D., M.P.H., NNE node Yuliya Lokhnygina, Ph.D., DCRI Donald A. Calsyn, Ph.D., PNW node Shelly Greenfield, M.D., M.P.H., NNE node Paul Wakim, PhD, NIDA representative

3. Background Rising rates of HIV in women highlight the need to identify unique factors associated with risk behaviors in women to help inform risk reduction interventions. Evidence of gender differences in frequency of HIV risk behaviors. Multiple risk factors associated with HIV risk behaviors have been identified in the literature. Few studies have examined whether risk factors vary by gender.

4. Purpose To examine gender differences in the rates and correlates of HIV sexual and drug risk behaviors in a sample of clients participating in 5 multi-site trials of the NIDA Clinical Trials Network. To test whether multiple risk factors for HIV risk behaviors differ by gender. Does gender moderate the impact of stimulant use, alcohol and drug severity, psychiatric severity, abuse history, family/social relationships, legal status and housing stability?

5. Methods Secondary data analysis of baseline CAB data from www.ctndatashare.org CTN-0001/ CTN-0002 - Buprenorphine/Naloxone versus Clonidine for Inpatient/ Outpatient Opiate Detoxification (Ling et al., 2005) CTN-0005 – Motivational Interviewing to Improve Treatment Engagement and Outcome in Outpatient Substance Users (Carroll et al., 2006) CTN-0006 / CTN-0007 - Motivational Incentives for Enhanced Recovery in Stimulant Users in Drug Free Methadone Maintenance Clinics (Petry et al., 2005; Pierce et al., 2006)

6. Measures HIV Risk Behavior Scale (HRBS) Sex and Drug Risk Behaviors Composites Individual sex and drug risk items ASI-Lite Composites Alcohol, Drug, and Psychiatric Symptom Severity, Family/Social Relationships, Legal Problems ASI-Lite derived variables Demographics Housing Stability (length at address) Stimulant use: stimulant only, stimulants + opioids, opioids only, other drug use Lifetime abuse: physical only, sexual only, both physical + sexual

7. Statistical Analysis Gender differences in sociodemographic characteristics and HIV risk behaviors Chi-square tests for categorical variables and Wilcoxon two-sample tests for continuous variables Gender differences in risk factors associated with HIV risk behaviors Ordinal logistic regression analysis using partial proportional odds model were conducted to identify variables associated with HIV sex risk composite Linear regression models were conducted to identify variables associated with HIV drug risk composite Models adjusted for age, gender, education, ethnicity, living arrangements Gender interaction tested first The ASI composite results are described using a clinically meaningful difference unit (0.1) as the measurement unit

8. Participant Characteristics CharacteristicMale N=790 (55%) Female N=790 (45%) Total N=1429 Age37.6 ±10.236.6 ±9.137.2 ±9.7 Education12.2 ±1.912.0 ±2.112.1 ±2.0 Ethnicity* White371 (47.0%)325 (50.9%)696 (48.7%) African-American276 (34.9%)251 (39.3%)527 (36.9%) Hispanic68 (8.6%)13 (2.0%)81 (5.6%) Other75 (9.5%)50 (7.8%)125 (8.8%) Living with Partner306 (38.7%)244 (38.2%)550 (38.5%) *p<.0001

9. Participant Characteristics CharacteristicMale N=790 (55%) Female N=790 (45%) Total N=1429 Employment** Full-time431 (54.6%)270 (42.3%)701 (49.1%) Part-time122 (15.4%)110 (17.2%)232 (16.2%) Other237 (30.0%)259 (40.5%)496 (34.7%) Primary Drug* Heroin/Opiates144 (18.2%)99 (15.5%)243 (17.0%) Stimulants144 (18.2%)161 (25.2%)305 (21.3%) Stimulants/Opiates315 (39.9%)247 (38.6%)562 (39.4%) Other drug187 (23.7%)132 (20.7%)319(22.3%) *p<.0001; +p<.01

10. HIV Sex Risk Behaviors Past 30-days *p<.008 N=790 N=639 N=504 N=388

11. Unprotected Sex *p<.016 * * N=484 N=357N=83 N=31 N=39 N=41 N=82 N=31

12. HIV Drug Risk Behaviors Past 30-days *p<.0008 * * N=790 N=639 N=250 N=151 N=221 N=129 N=227 N=132

13. HIV Risk Composites *p<.043 * * N=208 N=124 N=488 N=379

14. Sex Risk Behavior Gender Effects Variable High risk: OR (95% CI) High or moderate risk: OR (95% CI)χ 2 (df)p-value Alcohol use composite women1.11 (1.03-1.20)7.77 (1)0.005 men0.98 (0.90-1.06)0.32 (1)0.57 Psychiatric composite women 1.14 (1.06-1.23) 11.45 (1)0.0007 men0.96 (0.89-1.04) 0.84 (1)0.36 Family/social composite women 1.03 (0.92-1.14)1.01 (0.91-1.11) 0.23 (1)0.89 men 0.80 (0.70-0.93)1.01 (0.91-1.13) 11.1 (2)0.004

15. Drug Risk Behavior Gender Effects VariableLinear regression coefficient (SD) tp-value Alcohol use composite women0.56 (0.28)2.010.045 men-0.24 (0.21)-1.140.26

16. Main Effects Sex Risk Behaviors Stimulant use Drug use severity Sexual abuse history only Sexual and physical abuse history Legal problems Drug Risk Behaviors Drug use severity Sexual abuse history negatively related

17. Summary of Findings Women engaged in higher risk sexual behavior overall, were more likely to have multiple partners, and have unprotected sex with regular partners. Alcohol and psychiatric severity were associated with engaging in higher risk sexual behaviors for women. Alcohol use severity associated with engaging in higher risk drug behaviors for women. Men with impaired family/social relationships were less likely to engage in high risk sexual behavior. Men more likely to inject drugs. Confirmed relationship between stimulant use, drug severity, abuse history, and legal severity and risk behaviors in treatment-seeking sample.

18. Conclusions Findings consistent with other studies reporting higher rates of high risk sexual behavior for women. Studies incorporating gender into the analyses have found similar relationships between gender and HIV risk factors. Underscores the importance of examining the role of gender in studies of HIV risk behavior. Comprehensive assessment of HIV risk behaviors needs to occur at treatment entry. In addition to targeting women and men separately, the content of the intervention may need to reflect the unique risk factors.