Audrey J. Brooks, PhD
University of Arizona
CA-AZ node
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
Background
Rising rates of HIV in women highlight the need toidentify unique factors associated with risk behaviorsin women to help inform risk reduction interventions.
Evidence of gender differences in frequency of HIVrisk behaviors.
Multiple risk factors associated with HIV riskbehaviors have been identified in the literature.
Few studies have examined whether risk factors varyby gender.
Purpose
To examine gender differences in the rates and correlates ofHIV sexual and drug risk behaviors in a sample of clientsparticipating in 5 multi-site trials of the NIDA ClinicalTrials Network.
To test whether multiple risk factors for HIV risk behaviorsdiffer by gender.
Does gender moderate the impact of stimulant use, alcoholand drug severity, psychiatric severity, abuse history,family/social relationships, legal status and housing stability?
Methods
Secondary data analysis of baseline CAB data fromwww.ctndatashare.org
CTN-0001/ CTN-0002 - Buprenorphine/Naloxoneversus Clonidine for Inpatient/ Outpatient OpiateDetoxification (Ling et al., 2005)
CTN-0005 – Motivational Interviewing to ImproveTreatment Engagement and Outcome in OutpatientSubstance Users (Carroll et al., 2006)
CTN-0006 / CTN-0007 - Motivational Incentives forEnhanced Recovery in Stimulant Users in Drug FreeMethadone Maintenance Clinics (Petry et al., 2005;Pierce et al., 2006)
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
Statistical Analysis
Gender differences in sociodemographic characteristics and HIVrisk behaviors
Chi-square tests for categorical variables and Wilcoxon two-sampletests for continuous variables
Gender differences in risk factors associated with HIV riskbehaviors
Ordinal logistic regression analysis using partial proportional oddsmodel were conducted to identify variables associated with HIV sexrisk composite
Linear regression models were conducted to identify variablesassociated with HIV drug risk composite
Models adjusted for age, gender, education, ethnicity, livingarrangements
Gender interaction tested first
The ASI composite results are described using a clinicallymeaningful difference unit (0.1) as the measurement unit
Participant Characteristics
Characteristic
Male
N=790 (55%)
Female
N=790 (45%)
Total
N=1429
Age
37.6 ±10.2
36.6 ±9.1
37.2 ±9.7
Education
12.2 ±1.9
12.0 ±2.1
12.1 ±2.0
Ethnicity*
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%)
Living with Partner
306 (38.7%)
244 (38.2%)
550 (38.5%)
*p<.0001
Participant Characteristics
Characteristic
Male
N=790 (55%)
Female
N=790 (45%)
Total
N=1429
Employment**
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%)
Primary Drug*
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
187 (23.7%)
132 (20.7%)
319(22.3%)
*p<.0001; +p<.01
HIV Sex Risk Behaviors Past 30-days
*p<.008
N=790
N=639
N=504
N=388
Unprotected Sex
*p<.016
*
*
N=484
N=357
N=83
N=31
N=39
N=41
N=82
N=31
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
HIV Risk Composites
*p<.043
*
*
N=208
N=124
N=488
N=379
Sex Risk Behavior Gender Effects
Variable
High risk:
OR (95% CI)
High or moderaterisk: OR (95% CI)
χ2 (df)
p-value
Alcohol use composite
women
1.11 (1.03-1.20)
7.77 (1)
0.005
men
0.98 (0.90-1.06)
0.32 (1)
0.57
Psychiatric composite
women
1.14 (1.06-1.23)
11.45 (1)
0.0007
men
0.96 (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
Drug Risk Behavior Gender Effects
Variable
Linear regressioncoefficient (SD)
t
p-value
Alcohol use composite
women
0.56 (0.28)
2.01
0.045
men
-0.24 (0.21)
-1.14
0.26
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
Summary of Findings
Women engaged in higher risk sexual behavior overall,were more likely to have multiple partners, and haveunprotected sex with regular partners.
Alcohol and psychiatric severity were associated withengaging in higher risk sexual behaviors for women.
Alcohol use severity associated with engaging in higher riskdrug behaviors for women.
Men with impaired family/social relationships were lesslikely to engage in high risk sexual behavior.
Men more likely to inject drugs.
Confirmed relationship between stimulant use, drugseverity, abuse history, and legal severity and risk behaviorsin treatment-seeking sample.
Conclusions
Findings consistent with other studies reportinghigher rates of high risk sexual behavior for women.
Studies incorporating gender into the analyses have foundsimilar relationships between gender and HIV risk factors.
Underscores the importance of examining the role ofgender in studies of HIV risk behavior.
Comprehensive assessment of HIV risk behaviors needs tooccur at treatment entry.
In addition to targeting women and men separately, thecontent of the intervention may need to reflect the uniquerisk factors.