audrey j. brooks, phd university of arizona ca-az node
TRANSCRIPT
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
BackgroundRising 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.
PurposeTo 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?
MethodsSecondary data analysis of baseline CAB data
from www.ctndatashare.orgCTN-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)
MeasuresHIV Risk Behavior Scale (HRBS)
Sex and Drug Risk Behaviors CompositesIndividual sex and drug risk items
ASI-Lite CompositesAlcohol, Drug, and Psychiatric Symptom Severity,
Family/Social Relationships, Legal ProblemsASI-Lite derived variables
DemographicsHousing 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 HIV risk behaviors Chi-square tests for categorical variables and Wilcoxon
two-sample tests for continuous variablesGender 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
Participant Characteristics Characteristic Male
N=790 (55%)
FemaleN=790 (45%)
TotalN=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 CharacteristicsCharacteristic Male
N=790 (55%)
FemaleN=790 (45%)
TotalN=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
64
13
61
20
010203040506070
Sexually Active N=892 ≥ 2 Partners N=144*
Perc
ent o
f Sam
ple
Males
Females*
*p<.008
N=790
N=639N=504
N=388
Unprotected Sex75
49
64
8482
4954
77
0102030405060708090
Regular Partner N=659*
Casual Partner N=81
Trading Sex N=47
Anal Intercourse
N=50
Males
Females
*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
32
68
33
60
24
62
36
54
0
10
20
30
40
50
60
70
80
Any IDU* N=401
Daily IDU N=264
Needle Sharing N=118
Inconsistent Cleaning N=206
Perc
ent o
f Sam
ple
Males
Females
*p<.0008
**
N=790
N=639
N=250
N=151
N=221
N=129
N=227
N=132
HIV Risk Composites 8.7
5.8
8.4
6.1
0123456789
10
Drug Risk N=332 Sex Risk* N=867
MalesFemales
*p<.043
**N=208
N=124
N=488
N=379
Sex Risk Behavior Gender EffectsVariable
High risk:OR (95% CI)
High or moderate risk: 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 compositewomen 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 compositewomen 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 regression
coefficient (SD)
t p-value
Alcohol use compositewomen 0.56 (0.28) 2.01 0.045
men -0.24 (0.21) -1.14 0.26
Main EffectsSex Risk Behaviors
Stimulant useDrug use severitySexual abuse history onlySexual and physical abuse historyLegal problems
Drug Risk BehaviorsDrug use severitySexual abuse history negatively related
Summary of FindingsWomen 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.
ConclusionsFindings 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.