Women

1
Womens risk networks Drugs, sex and resources

•   Maureen Miller
• Columbia University
• New York, NY USA

2
Causal Level Risk Factors

• Biological
• Behavioral
• Dyadic relationship
• Network
• Environmental

3
Biological Risk Factors

• Women more susceptible to STIs than men
• Women may acquire HIV differently than men
• HCV infrequently transmitted sexually

4
Behavioral Risk Factors

• Sex practices
• Unprotected sex with an infected partner
• Lack of women controlled alternatives
• Drug use practices
• Injecting drug use risk
• Non-injecting drug use risk

5
Dyadic Relationship Risk Factors

• Women's ability to implement risk reduction
  linked to dynamics and norms within relationships
• Women are more likely than men to have sex
  partners who inject drugs, and less likely to
  have sex partners who do not use drugs at all
• Gender differences in sex partner selection
  probabilities may account for considerable risk

6
Network Risk Factors

• Compositional network characteristics
• risk networks
• Structural network characteristics
• description of linkages between and among network
  members
• Interactional network characteristics
• social support
• social isolation

7
Compositional Network Characteristics

• Women who use drugs are very likely to have sex
  partners who also use drugs
• Women's injection risk practices occur in social
  contexts involving intimate others
• Higher proportions of risk network members
  associated with participation in sex and drug use
  risk practices

8
Structural Network Characteristics

• Size
• Density
• Multiplexity
• Turnover
• Concurrency
• Centrality

9
Size

• Size may be limited by
• problematic relationships with primary partners
• having a primary partner who injects drugs
• Size may be increased
• as a means to acquire drugs
• by participation in sex work

10
Density
Figure B
Figure A
11
Multiplexity

• Overlap between risk and social networks.
• Multiplex relations reduce ability to adopt and
  maintain risk reduction practices.
• Multiplex relations may reinforce shared risk
  reduction.

12
Network Member Turnover

• Changes in network membership over time
• Frequent turnover increases the risk of
  encountering an infectious individual
• Networks with a high rate of turnover may be
  susceptible to the epidemic spread of HIV, (e.g.,
  shooting galleries)

13
Concurrency

• Concurrency Serial Monogamy

14
Centrality
 
15
147
34
21
312
31
56
254
102
26
52
61
23
149
16
91
18
210
7
309
63
10
41
86
29
69
17
109
83
46
13
42
155
51
642
146
660
259
734
174
(Friedman et al, 1999)
15
Social Support

• Influence may stem from consensus or coercion
• Support associated with engaging in HIV risk and
  risk reduction
• Support and risk are subject to situational
  interpretation

16
Social Isolation

• Social isolates vs. involuntary isolation
• Women initiate drug use to escape loneliness
• May restrict access to resources
• Increases exposure to violence or arrest

17
Environmental Risk Factors

• Social and economic factors
• neighborhood environment
• discrimination/inequality
• access to and control over resources
• Relationship with the transmission of infectious
  pathogens not well understood

18
Sex partnership formation and HIV risk An
analysis of qualitative data

•  

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Objectives

• To explore the process of sex partnership
  formation
• in high HIV prevalence neighborhoods
• among low income women who use drugs
• at risk for infection with HIV and other
  infectious pathogens.

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Methods

• In-depth, life history interviews with 28 women
  who use drugs.
• Recruited in New York City March to November
  2000.
• Data coded for factors that motivated and
  maintained sex relationships.
• Analyses were conducted using
• a computerized text based analytic tool for
  qualitative analysis (dtSearch), and
• SAS for data that were quantified using codes
  developed for this purpose (e.g., sex partner
  age).

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Sex partner characteristics linked to infection
risk

• Disassortative mixing
• by age
• by infection status
• by race/ethnicity

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Structural features of the sex partner
relationship

• Sex partner concurrency
• Multiplexity
• Turnover

23
Economic resources

• Legal
• Employment
• Government benefits
• Illegal
• Drug industry
• Sex work / prostitution
• Stealing

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Demographics(N28)

• N ()
• Race/ethnicity
• White 9 (32)
• Black 8 (29)
• Latina 8 (29)
• Mixed race/ethnicity 3 (10)
• Education
• lt High School 15 (54)
• HS graduate/GED 13 (46)
• Mean age 30 (s.d. 7 range 19-43)

25
Drug use(N28)

• N ()
• Heroin 22 (79)
• Crack 12 (43)
• Cocaine 6 (21)
• History of IDU 18 (64)

26
Self reported infection status(N28)

• N ()
• HIV 10 (36)
• Hepatitis C 8 (29)
• Hepatitis B 3 (11)

27
Legal resources(N28)

• N ()
• Ever employed 10 (36)
• Currently employed 1 (4)
• Current govt benefits 16 (57)

28
Illegal resources(N28)

• N ()
• Work in drug trade 21 (75)
• Sex work 19 (68)
• Stealing 19 (68)
• Sugar daddies 14 (50)

29
Current sex partners(N28)

• 22 (79) women reported current sex partners.
• All current sex partners were men.
• A minimum of 31 current sex partners reported.
• 3 (11) women reported current sex work.

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Characteristics of current sex partners(N31)

• Percent
• Race/ethnicity
• White 48
• Black 26
• Latino 22
• Other 4
• gt 5 years older 65
• Drug user 65

31
Sex partner type (N31)

• Percent
• Primary 68
• Secondary 9
• Sugar daddy 13
• Sex work client 10

32
Sex partners as resources(N31)

• Sex partner resource Percent
• Acquires drugs 54
• Government benefits 50
• Employed 35
• Works in drug trade 32

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Disassortative mixing by age

• Older sex partners 17 (61)
• Younger sex partners 2 (7)

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Disassortative mixing by infection status

• Mixing patterns assortative.
• Few report having been tested.

35
Disassortative mixing by race/ethnicity

• 5 of 22 (23) women were in current sex
  partnerships that were mixed by race/ethnicity.
• All black women reported black sex partners.

36
Sex partner concurrency(N28)

• Current partners N ()
• More than one sex partner 6 (21.5)
• One sex partner 16 (57.0)
• No sex partners 6 (21.5)

37
Sex partner turnover

• Influenced by
• interpersonal relationship issues
• (e.g., love, stress, discordant drug
  use)
• environmental factors
• (e.g., arrest, death through violence or
  disease,
• job loss).

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Process of uniplex to multiplex- The story of
Renee -

• Meet trick on the stroll.
• Trick becomes sugar daddy.
• Sugar daddy becomes primary sex partner.

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Conclusions I

• Resource acquisition plays a significant role in
  the formation of sex partnerships.
• Disassortative mixing patterns by age are linked
  to both resource acquisition and partner
  availability.
• There are notable levels of sex partner
  concurrency due to womens participation in sex
  work and multiple partnerships.
• Multiplex relationships involving strong ties can
  increase the probability of unprotected sex.

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Conclusions II

• The dynamic nature of sex partnerships increases
  individual womens risk of exposure to infection.
• The high turnover in sex partnerships increases
  the rate of disease transmission at the
  population level.
• Applications of network concepts have improved
  our understand disease transmission dynamics now
  there is a need to incorporate environmental
  factors.

41
Acknowledgements

• I would like to thank the following
• The National Institute on Drug Abuse grant
  DA13135
• The Peter F. McManus Charitable Trust
• Alan Neaigus for his significant contribution to
  the theory development
• Eszter Szucs and Malin Serner for their
  assistance with data analysis and
• The women participants who generously shared
  their stories.