Culture, Social Networks, Risk Perception, and HIV Disparities

Culture, Social Networks,
Risk Perception, and HIV
Risk Disparities
David P. Kennedy, Ryan A. Brown
RAND Corporation
November 17, 2015

Inter-disciplinary approach to
Culture and HIV
• NIMHD R24 Grant
– NIMHDR24MD008818
– Infrastructure Building
Grant: Culture and HIV risk
– Find interdisciplinary
intersection and
synthesize:
• Theory – common
constructs
• Methods – common
techniques for measuring
systems of beliefs
• Vocabulary – common
language

• Activities
– Analysis of Existing Data
– Pilot Data Collection
– Develop new project
• R01 – HIV and
homelessness
• R21 HIV in the Deep South
• Add connections within
and outside of RAND
• Mentoring junior
researchers
Inter-disciplinary approach to
Culture and HIV

Outline
• What is Culture?
– How to operationalize, measure
– How to test the impact of culture on important outcomes
• Examples of Previous and Current Research
– Public Health Example: HIV studies of homeless populations in
Los Angeles
– Demography Example: Culture’s Impact on Fertility
• R24 Pilot Data Findings
– Explore cultural differences in perceptions of HIV in Southeast of
US
• Compare two areas of Mississippi
• Deep South culture?
• Future Directions
– Build empirical model to test several components of HIV risk
evaluation simultaneously
• Culture, social networks, sexual relationships, risk evaluation

HIV Disparities
• US Continues to have many disparities in
HIV risk
– Sexual Orientation
– Ethnicity
– Geography
– Economic status
– Housing
– Education
– Employment
• Social Determinants in Group Differences
– Culture?

Culture and Health
• Culture informs all human
behavior
• Health risk and protection behaviors
• Understanding and identifying symptoms
of disease
• Communicating with professionals
• Adhering to medications and treatment,
vaccinations
• NIH Expert Panel On Defining And
Operationalizing Culture For Health Research
– “The Cultural Framework for Health”
– “No other variable used in health research is as poorly
defined or tested as is culture”

Integrated Model of HIV Risk
Evaluation

Integrated Model of HIV Risk Evaluation
Vicarious Social Network
Risk Experience Outcomes
Positive
Negative
Neutral/None/
Unknown
Personal
Experience
with Risk
Baseline Risk
Perception
Cognition
Evaluation
of Costs/
Benefits of
Risk
Risk
Decisions
Individual
Culture 1
Sub-Culture
Culture 2

Integrated Model of HIV Risk Evaluation: Decision
Making
Personal
Experience
with Risk
Baseline Risk
Perception
Cognition
Evaluation
of Costs/
Benefits of
Risk
Risk
Decisions
Individual

Integrated Model of HIV Risk Evaluation: Decision
Making and Social Networks
Positive
Negative
Neutral/None/
Unknown
Personal
Experience
with Risk
Baseline Risk
Perception
Cognition
Evaluation
of Costs/
Benefits of
Risk
Risk
Decisions
Individual

What is Culture?
• Lack of operationalization across disciplines
– Demography, Public Health
• Hruschka, Daniel J.(2009)
– 'Culture as an explanation in population health‘
– Black box approach to Culture
• Often used as a synonym for ethnicity/nationality
/race/ etc. with no empirical justification
• Anthropology also often lacks operationalization
– Dressler (2015): “I will declare first that I belong to the
‘culture-is-too-important-a-concept-to-be-jettisoned’ wing
of anthropology.”
– Cognitive Anthropology
• Interdisciplinary theory and methods

What is Culture?
• Learned knowledge we need to function in a
given social/ecological system
– Individually/Socially Constructed
• Cognitive models / Cultural models
– Each person participates in multiple cultures
• cultural domains
• Culture has inertia
• Can be both a cause and a consequence
• Existence of a culture is empirical question
– Consensus Analysis

Consensus Analysis
• Is there a Culture?
– Formal and Informal Approaches
• If No: Are there multiple Cultures?
• If Yes: What is the Intra-Cultural Variation?
– Mixed-methods
• Does Culture Matter?
– Other factors may be more important
– Falsifiability

Measuring Culture: Latent Variable
Approach
CULTURE
I1 INI5I4I3I2 …

Approach
CULTURE
I1 INI5I4I3I2 …
Measurement of Agreement
• Factor Analysis on
Respondents
• Q methodology

Approach
CULTURE
I1 INI5I4I3I2 …
Consensus Analysis Rules
of Thumb for “a” culture:
•First Factor Large
Compared with Other
Factors
•Factor 1 3x Factor 2
•Explains a lot of
Variance
•> 50%
•No Negative Loadings
Measurement of Agreement
• Factor Analysis on
Respondents
• Q methodology

Question 1: Is there a culture?

HIV Risk among Homeless Men
• Test theory of culture and HIV risk
– “Traditional” masculinity influences
heterosexual men to take sexual risks
– Especially economically marginalized men
19

EXPLORATORY
INTERVIEWS (n=30)
● Focused on gender roles
and sexual encounters
THEME EXTRACTION
● Open-coding for themes
related to masculinity and
sex
ITEM GENERATION
● Structured items
regarding gender ideology
and relationship beliefs
STRUCTURED
INTERVIEWS (n=305)
● Sexual and relationship
outcomes, social networks,
contextual factors
CULTURAL CONSENSUS
●Extract highest loading
items regarding gender and
relationship beliefs
MULTI-LEVEL DYADIC
REGRESSION ANALYSIS
● Run model with quantitative
sample (n=305)
Qualitative Steps Quantitative StepsMixed Qual-Quant
Steps
Consensus Analysis Process

1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Component2
Component1
Masculinity Culture: First Two PCA Component Loadings
Responsibility,
Equality,
Difficulty
Traditional Masculinity

1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Component2
Component1
Responsibility,
Equality,
Difficulty
“A man’s number 1
responsibility is to protect
and provide for his family.”

1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Component2
Component1
Responsibility,
Equality,
Difficulty
“Men and women should
share decisions equally.”

1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Component2
Component1
Responsibility,
Equality,
Difficulty
“If a man pays for sex, he
should not have to use a
condom.”

1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Component2
Component1
Responsibility,
Equality,
Difficulty
“If a man pays for sex, he
should not have to use a
condom.”
“Men who have a lot of sex
with different women should
be admired.

Question 2: Do different populations
have similar or different cultures?
26

1
0
1
-1 0 1
Component2
Component 1
Iran Honduras USA
Ratio of First to
Second Factor: 2.55
Variance Explained
by First Factor: 35%
Natality Culture: Honduras, Iran, and USA
Honduran (n=200)
Iranian (n=596)
USA (n=810)
PCA of Childbearing Questionnaire (n=1606)
Cross-cultural Analysis:
Childbearing Motivation

1
0
1
-1 0 1
Component2
Component 1
Iran Honduras USA
Natality Culture: Honduras, Iran, and USA
PCA of Childbearing Questionnaire, Honduras (n=200)
Cross-cultural Analysis:
Childbearing Motivation
Ratio of First to Second
Factor Eigenvalues: 8.77
Variance Explained by
First Factor: 61%
Evidence of a culture
of pronatality

Question 3: What does culture
explain?

1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Component2
Component 1
Responsibility,
Equality,
Difficulty

Significant Variables OR (95% CI) P-value
Individual •[Overall Mental Health]
•[Condom Efficacy]
•# Male Sex Partners
.98 (.95, 1.0) *
.31 (.13, .77) *
2.10 (1.09, 4.07) *
Relationship •[Talked with about HIV]
•[Talked about Condoms]
•Met on Street
•Frequency of Contact
•Emotional Support
•Relationship Commitment
.10 (.04, .28) **
.05 (.02, .13) **
2.36 (1.01, 5.49) *
1.59 (1.06, 2.39) **
4.48 (1.68, 11.92) **
2.03 (1.29, 3.2) **
Social
Network
•Closeness Centrality 1.02 (1.0, 1.04) **
Note. p < .10 †, p < .05,* p < .01**
[Reverse Relationship]
Multi-level Predictors of Unprotected Sex
among Heterosexual Homeless Men
Kennedy DP, Wenzel SL, Brown R, Tucker JS, Golinelli D.
Unprotected sex among heterosexually active homeless men:
Results from a multi-level dyadic analysis. AIDS and Behavior.
2013;17(5):1655-1667. PMCID: PMC3593821

Significant Variables OR (95% CI) P-value
Individual •[Overall Mental Health]
•[Condom Efficacy]
•# Male Sex Partners
•Masculinity Culture
.98 (.95, 1.0) *
.31 (.13, .77) *
2.10 (1.09, 4.07) *
--
Relationship •[Talked with about HIV]
•[Talked about Condoms]
•Met on Street
•Frequency of Contact
•Emotional Support
•Relationship Commitment
.10 (.04, .28) **
.05 (.02, .13) **
2.36 (1.01, 5.49) *
1.59 (1.06, 2.39) **
4.48 (1.68, 11.92) **
2.03 (1.29, 3.2) **
Social
Network
•Closeness Centrality 1.02 (1.0, 1.04) **
Note. p < .10 †, p < .05,* p < .01**
[Reverse Relationship]
Multi-level Predictors of Unprotected Sex
among Heterosexual Homeless Men
Kennedy DP, Wenzel SL, Brown R, Tucker JS, Golinelli D.
Unprotected sex among heterosexually active homeless men:
Results from a multi-level dyadic analysis. AIDS and Behavior.
2013;17(5):1655-1667. PMCID: PMC3593821

Question 4: Is it culture
or is it something else driving
patterns of risk behavior?
33

Homeless Youth and HIV Risk: Baseline Risk
Perception Cognition
Personal
Experience
with Risk
Baseline Risk
Perception
Cognition
Evaluation
of Costs/
Benefits of
Risk
Risk
Decisions
Individual

Personal
Experience
with Risk
Baseline Risk
Perception
Cognition
Evaluation
of Costs/
Benefits of
Risk
Risk
Decisions
Individual
Probability evaluation
coherence

Individual, Relationship, and Event
Level Factors Predicting HIV Risk
among Homeless Youth
• Identified sub-population differences
• Multi-level qualitative analysis

Sexual Risk Decisions of Homeless
Youth: 3 Risk Profiles
Profile Category Risk Evaluation Characteristics
Low Risk, Risk Avoiders
N = 12 (7 female, 5 male)
Kappa = .82
• Consistently engage in risk avoidance
• Concerned about consequences
• Occasional unplanned risk events
High Risk, Risk Takers
N = 10 (3 female, 7 male)
Kappa = .79
• Consistently engage in risk
• Unconcerned about consequences
Medium Risk, Risk Reactors
N= 15 (10 female, 5 male)
Kappa = .67
• Inconsistent concerns and behaviors
• Risks often in reaction to relationship
or event circumstances

Personal
Experience
with Risk
Baseline Risk
Perception
Cognition
Evaluation
of Costs/
Benefits of
Risk
Risk
Decisions
Individual
coherence
• Q1:“What is the percent
chance that you will get
a sexually transmitted
infection the next time
you have sex?”
• Q2: “What is the percent
chance that you will not
use a condom the next
time you have sex?”
• Q3: “If you do not use a
condom the next time
you have sex, what is
the percent chance that
you will get a sexually
transmitted infection?”

Personal
Experience
with Risk
Baseline Risk
Perception
Cognition
Evaluation
of Costs/
Benefits of
Risk
Risk
Decisions
Individual
coherence
• Q1:“What is the percent
chance that you will get
a sexually transmitted
infection the next time
you have sex?”
• Q2: “What is the percent
chance that you will not
use a condom the next
time you have sex?”
• Q3: “If you do not use a
condom the next time
you have sex, what is
the percent chance that
you will get a sexually
transmitted infection?”
STI Coherence
• Conditional subjective
probability of event
• Sum of mutually
exclusive and
collectively
exhaustive subsets
• Coherence is indicated
by a logical evaluation of
conditional subjective
probability

Risk Evaluation Coherence and Risk Profile
• Consistent Logical Evaluation of Probability
– Pregnancy, HIV
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
High (N = 12)Medium (N = 15)Low (N = 10)
Coherence with STI and Pregnancy Expectations by Risk Profiles
Consistency with Both
Consistency with Pregnancy
Consistency with STI
Consistency with Neither

Pilot Data Analysis:
Is there a unique Culture of HIV
risk and treatment perception in
the Deep South?
42

Culture is often cited as a reason for high HIV diagnosis
rates and lower survival rates in the American South
• Religiosity, traditional gender roles, stigma, etc.
• A similar explanatory frame has been used to
explain patterns of violence in the South (“Culture of
Honor”)
• Needs empirical substantiation
• Start by examining local variability
– For example, compare Rankin Co. (Jackson, MS) with
Lamar Co. (Hattiesburg, MS)
– Both counties 20% Black
– Rankin Co. 5X the HIV prevalence rate for both Blacks and
Whites and has lower poverty, lower % uninsured
– Is culture involved?

We collected pilot data from Hattiesburg and Jackson
on cultural/explanatory models of HIV/AIDS
• Convenience sample
– Jackson: 25 from waiting room of clinic
– Hattiesburg : 26 undergraduates from state school
• Freelist questionnaire
1. Cause
“There are many ideas and opinions about how a person gets HIV Please list as many
behaviors, actions, situations, or other things that lead a person to get HIV.”
2. Prevention
“Now, how about ways a person can avoid getting HIV? Please list as many actions, behaviors,
or other things that someone can do to avoid getting HIV.”
3. Diagnosis
“We’ve talked about what HIV/AIDS is and how you can get it or avoid getting it. How does
someone know they have it? Please list all the ways someone could tell they have HIV/AIDS,
including signs and symptoms.”

We examine frequency of responses within and across
questions – group differences and similarities
• Freelist questionnaire (cont’d)
4. Disease course
“After someone gets HIV, what happens to them? Please list all of the outcomes and changes
that might occur.”
5. Treatment
“Let’s say someone knows they have HIV. What can they do now? Please list all of the actions,
procedures, or other things someone can do to treat HIV or manage the effects of it.”
• Analysis using Anthropac, UCINET, Netdraw

We combined open-ended responses into similar
categories – both within and across questions

Cultural consensus analysis indicates core set of
shared items but two distinct cultures
• Core shared items
– Sex as transmission pathway / avoiding
sexual contact as prevention
– Dirty needles and open wounds as
transmission pathways
– Weight change as expected sign of HIV
– Professional medical care and adherence
to HIV medication regime as essential for
successful treatment
• Higher consensus within groups

Network visualization of items X
respondents supports a 2-culture model
with a core set of shared items
54

Visualizing item nodes by size of group
differences indicates several outliers

After consolidating and collapsing responses, we
identified items with extreme group differences
“Extreme” items in Jackson
“Extreme” items in Hattiesburg

We examined broad differences between groups
as well as unique items
• Broad trends
– Jackson more focused on
social & mental health
impact of HIV
– Hattiesburg more focused
on:
o Risk of HIV transmission to
other sex partners
o Needing medical system for
diagnosis
o Intrauterine transmission
• Unique to Jackson
– Oral sex as a mode of
transmission
– Diarrhea as a physical effect
– PReP as preventive measure
• Unique to Hattiesburg
– Stigma as a social effect
– Doctor exam as a means of
knowing HIV status
– AIDS as a physical outcome
– Assuming have HIV after 1
encounter with HIV+ person

Future Work: Does Culture
Impact a Sexually Transmitted
Epidemic?

Agent Based Modeling Approach
Heard about “orange”
experience first-hand
Heard about “teal”
experience first-hand
Individual who had “teal”
and “orange” experiences
Heard about “teal”
experience second-hand
Heard about “orange”
experience second-hand
• Spread of Information
• Disease Transmission
• Simulate effect of modifying parameters
Nowak SA, Parker AM. Social network effects of
nonlifesaving early-stage breast cancer detection
on mammography rates. American Journal of
Public Health. 2014;104(12):2439-2444.

Integrated Model of HIV Risk Evaluation: Social
Networks, and Decision Making
Positive
Negative
Neutral/None/
Unknown
Personal
Experience
with Risk
Baseline Risk
Perception
Cognition
Evaluation
of Costs/
Benefits of
Risk
Risk
Decisions
Individual

Next Steps*
• Collect comprehensive data set
– Cultural, social network, behavioral, and
decision-making measures
– Parameterize and test model, run
simulations
• Deep South Culture of HIV risk
– Unique Deep South Culture?
– Compare impact of cultural measures
with other factors
• Develop Intervention *1R01MH110159-01

Thank You!
David P. Kennedy
davidk@rand.org
@qualintitative
Ryan A. Brown
rbrown@rand.org

Culture, Social Networks, Risk Perception, and HIV Disparities

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