Download Gender differences in the rates and correlates of HIV risk behaviors

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