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Audrey J. Brooks, PhD University of Arizona Western States Node Gender SIG Collaborators • Christina S. Meade, PhD, NNE node • Jennifer Sharpe Potter, PhD, MPH, NNE node • Yuliya Lokhnygina, PhD, DCRI • Donald A. Calsyn, PhD, PNW node • Shelly Greenfield, MD, MPH, NNE node • Paul Wakim, PhD, NIDA representative Significance First CTN study utilizing CTN datashare website. Utilized CAB measures across protocols. First GSIG examination of gender-related issues across multiple protocols in the CTN. Unique opportunity to systematically examine multiple HIV risk factors with a sufficient sample size to examine gender differences. Replicated analyses to examine racial/ethnic differences. Two Examples Gender Differences in the Rates and Correlates of HIV Risk Behaviors Among Drug Dependent Individuals Brooks, AJ, Meade, CS, Sharpe Potter, J, Lokhnygina, Y, Calsyn, DA, Greenfield, SF. Gender differences in the rates and correlates of HIV risk behaviors among drug abusers. Subst. Use Misuse. 2010; 45:2444-69. Racial/Ethnic Differences in the Rates and Correlates of HIV Risk Behaviors Among Drug Abusers Brooks, AJ, Lokhnygina, Y, Meade, CS, Sharpe Potter, J, Calsyn, DA, Greenfield, SF. Racial/Ethnic differences in the rates and correlates of HIV risk behaviors among drug abusers. Am. J. Addict, 2013; 22(2):136-147. 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. Background Few studies have examined whether risk factors vary by gender Inconclusive gender findings due to: Variation in samples across studies Number of women too small to analyze separately Women-only samples Variation in definitions of HIV risk behaviors Variation in time-frames Similar issues examining racial/ethnic differences Combining across protocols utilizing common assessments allows us address some of these limitations in large, heterogeneous sample 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? Hypotheses Women will engage in higher rates of risky sexual behavior. Risk factors will be associated with greater sexual risk behavior for women. Stimulant use will be associated with greater sexual risk for both women and men. Men will be more likely to inject drugs. Women will be more likely to engage in high risk drug behaviors. 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) CTN 004 & 021: Motivational Enhancement Treatment to Improve Treatment Engagement and Outcome – English & Spanish-speaking * Measures HIV Risk Behavior Scale (HRBS) Sex Risk Behaviors Composite Drug Risk Behaviors Composite Individual sex risk items Sex with 2 or more partners Any unprotected sex with regular partner, casual partner, when trading sex, and during anal intercourse Individual drug risk items Any injection use Any needle sharing Inconsistent needle cleaning Measures ASI-Lite Composites Alcohol Severity Drug Severity Family/Social Relationships Psychiatric Symptom Severity Legal Problems ASI-Lite derived variables Stimulant use: stimulant only stimulants + opioids opioids only other drug use Lifetime abuse physical only sexual only both physical + sexual Housing stability – length at present address Demographics Considerations in Analyzing Across Protocols Data discrepancies present in the data set required initial data cleaning prior to combining datasets and running analyses. A cross-tab conducted between questionnaires collecting the same data (the Demographic form and ASI), revealed response discrepancies. Solution: Chose ASI data in most instances due to the rigorous interviewer training provided for the ASI Missing data Solution: Checked whether missing data on one form was available on the other to maximize the number of participants included in the analyses Considerations – cont’d Variation between data management centers in the CTN decentralized data management system in how skip out patterns were coded Solution: Recoded missing data and skip out patterns A determination of primary drug was also integral to our hypotheses, however, the necessary variable for this from the ASI is not included in the public dataset Solution: Created an algorithm for assigning primary drug based on our hypotheses Considerations – cont’d Used apriori approach to hypothesis development; however once frequencies were obtained the target variables had lower frequencies than anticipated Solution: Dependent variables were redefined and analyses re-run to allow for sufficient power to detect any differences. Demographic differences between protocols Controlled for demographic characteristics in all models Added Spanish MET to increase Hispanic N Protocol sample targeted a specific group, e.g., female, Hispanic Re-ran models with protocol as a variable in model 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, employment status, living arrangements Gender interaction tested first Sex Risk Composite 60 54 54 Percent of Sample 50 40 31 30 Males 26 20 20 15 10 0 Low Risk < 6 Moderate Risk = 6 High Risk > 6 Females Participant Characteristics Characteristic Age Male N=790 (55%) Female N=790 (45%) Total N=1429 37.6 ±10.2 36.6 ±9.1 37.2 ±9.7 12.2 ±1.9 12.0 ±2.1 12.1 ±2.0 White 371 (47.0%) 325 (50.9%) 696 (48.7%) African-American 276 (34.9%) 251 (39.3%) 527 (36.9%) Hispanic 68 (8.6%) 13 (2.0%) 81 (5.6%) Other 75 (9.5%) 50 (7.8%) 125 (8.8%) 306 (38.7%) 244 (38.2%) 550 (38.5%) Education Ethnicity* Living with Partner *p<.0001 Participant Characteristics Male N=790 (55%) Female N=790 (45%) Total N=1429 Full-time 431 (54.6%) 270 (42.3%) 701 (49.1%) Part-time 122 (15.4%) 110 (17.2%) 232 (16.2%) Other 237 (30.0%) 259 (40.5%) 496 (34.7%) Heroin/Opiates 144 (18.2%) 99 (15.5%) 243 (17.0%) Stimulants 144 (18.2%) 161 (25.2%) 305 (21.3%) Stimulants/Opiates 315 (39.9%) 247 (38.6%) 562 (39.4%) Other drug 132 (20.7%) 319(22.3%) Characteristic Employment** Primary Drug* 187 (23.7%) *p<.0001; +p<.01 HIV Sex Risk Behaviors Percent of Sample 70 64 61 60 50 40 Males 30 20 20 Females 13 10 0 Sexually Active N=892 Multiple Partners N=144* *p<.008 Unprotected Sex 90 80 70 60 50 40 30 20 10 0 75 84 82 77 64 49 49 54 Males Females Regular Partner N=659* Casual Trading Sex Partner N=81 N=47 Anal Intercourse N=50 *p<.016 HIV Drug Risk Behaviors 80 68 Percent of Sample 70 62 60 60 54 50 40 30 33 36 32 Males 24 20 Females 10 0 Any IDU* N=401 Daily IDU N=264 Needle Sharing N=118 Inconsistent Cleaning N=206 *p<.0008 HIV Risk Composites 10 9 8 7 6 5 4 3 2 1 0 8.7 8.4 5.8 6.1 Males Females Drug Risk Sex Risk* *p<.043 Sex Risk Behavior Gender Effects Variable High risk: High or moderate 2 OR (95% CI) risk: OR (95% CI) χ (df) p-value Alcohol use composite women men 1.11 (1.03-1.20) 7.77 (1) 0.005 0.98 (0.90-1.06) 0.32 (1) 0.57 1.14 (1.06-1.23) 11.45 (1) 0.0007 0.96 (0.89-1.04) 0.84 (1) 0.36 Psychiatric composite women men Family/social composite women men 1.03 (0.92-1.14) 1.01 (0.91-1.11) 0.23 (2) 0.89 0.80 (0.70-0.93) 1.01 (0.91-1.13) 11.1(2) 0.004 Sex Risk Behavior Main Effects Variable High risk: OR (95% CI) High or moderate risk: OR (95% CI) 2 χ (df) p-value Stimulant use 1.57 (1.16-2.31) 0.83 (0.61-1.14) 9.23 (2) 0.01 Drug use composite 1.30 (1.15-1.47) 1.05 (0.95-1.17) 18.02 (2) 0.0001 sex abuse only 4.10 (2.07-8.15) 1.29 (0.65-2.56) 9.99 (2) 0.007 physical abuse only 1.30 (0.89-1.88) 1.86 (1) 0.17 sex & physical abuse 1.82 (1.21-2.75) 8.14 (1) 0.004 Legal status 1.11 (1.03-1.18) 8.29 (1) 0.004 1.00 (0.98-1.01) 0.19 (1) 0.66 Trauma/abuse Housing stability Drug Risk Behavior Gender Effects Variable Linear regression coefficient (SD) Alcohol use composite women 5.6 (2.8) men -2.4 (2.1) t p-value 2.01 -1.14 0.045 0.26 Drug Risk Behavior Main Effects Variable Psychiatric composite Linear regression coefficient (SD) -0.017 (0.12) t p-value -0.14 0.89 -0.038 (0.13) -0.50 0.76 -0.73 (0.63) -1.16 0.25 0.67 (0.26) 2.63 0.009 sex abuse only -2.37 (1.2) -2.01 0.046 physical abuse only 0.36 (0.84) 0.42 0.67 sex & physical abuse 0.53 (0.79) 0.67 0.50 0.18 (0.13) 1.38 0.17 -0.004 (0.032) -0.13 0.89 Family/social composite Stimulant use Drug use composite Trauma/abuse Legal status composite Housing stability Summary of Gender Findings Gender Differences in Rates Women: Engaged in higher risk sexual behavior overall More likely to have multiple partners More likely to have unprotected sex with regular partners Men: More likely to inject drugs Summary of Gender Findings Gender Differences in Risk Factors Associated with Engaging in High Risk Behaviors Women: Alcohol use severity associated with engaging in higher risk sexual behaviors Psychiatric severity associated with engaging in higher risk sexual behaviors Alcohol use severity associated with engaging in higher risk drug behaviors Men: More impaired family/social relationships associated with engaging in less risk sexual behavior Summary of Non-gender Related Findings Stimulant use, drug use severity, sexual abuse alone, combined sexual and physical abuse, and legal severity were associated with greater likelihood to engage in high risk sexual behaviors. Drug severity was associated with higher level of engagement in drug risk behaviors. Sexual abuse history was associated with lower levels of engagement in drug risk behaviors. Summary of Non-gender Related Findings Greater Likelihood to Engage in High Risk Sexual Behaviors Associated with: Stimulant use Drug use severity Sexual abuse alone Combined sexual and physical abuse Legal severity Higher Level of Engaging in Drug Risk Behaviors Associated with: Drug use severity Lower Level of Engaging in Drug Risk Behaviors Associated with: Sexual abuse history Summary - Racial/Ethnic Differences HIV Sex Risk Behaviors African-Americans: Engaged in less HIV sexual risk behaviors overall than Whites Reported more specific high risk sexual encounters but greater use of protection Alcohol severity was related to engaging in higher sex risk behaviors for African-American Whites: Alcohol use and psychiatric severity was related to engaging in higher sex risk behaviors for Whites Hispanics: Less likely to engage in high risk sexual behaviors, less likely to use protection in high risk sexual behaviors Other risk factors operated equally across racial/ethnic groups Summary - Racial/Ethnic Differences in HIV Drug Risk Behaviors Whites were most likely to be IDUs Hispanics least likely to be IDUs but engaged in more high risk HIV drug risk behaviors Drug use severity was associated with engaging in higher risk drug behaviors for Hispanics, and to a lesser degree, Whites Limitations Secondary data analysis not designed specifically to address these questions. Short time-frame may have influenced findings. Sample is treatment seeking participants. Some variables not available in de-identified public data set; e.g., ethnicity subgroup, geographic location. Strengths A sufficient sample size to allow for systematic examination of potential gender differences in the relationship between psychosocial risk factors and HIV risk behaviors. Findings highlighted specific gender differences in prevalence and correlates of HIV risk behaviors in persons entering treatment. The relationship between multiple risk factors and HIV risk behaviors was confirmed in a large, ethnically and geographically diverse sample of drug user treatment participants