HIV Among African Americans

1
HIV Among African Americans

• Nina T. Harawa, MPH, PhD
• Assistant Professor
• Charles Drew University/UCLA

2
Objectives

• To review the epidemiology of HIV among Black
  people in the US
• Discuss the role of sexual networks in HIV
  epidemics
• To discuss possible reasons for the
  disproportionate impact of HIV among African
  Americans

3
Epidemiology of HIV/AIDS among Black People in
the US
4
AIDS Data

• All 50 States

5
(No Transcript)
6
(No Transcript)
7
(No Transcript)
8
(No Transcript)
9
(No Transcript)
10
(No Transcript)
11
HIV/AIDS Data
12
(No Transcript)
13
7 times
20 times
14
Gender distribution by Race/ethnicity new
HIV/AIDS Diagnoses in 2007
34
21
16
15
(No Transcript)
16
(No Transcript)
17
NHANES Survey
18
Summary National Data for African Americans

• Make up 1 in 2 new cases but just 12 or 1 in 8
  of the US population.
• Males are more affected than females
• 2 out of every 3 new cases are male, 1 out of 3
  is female
• Greater racial disparities for females than
  males.
• MSM are the largest portion of male cases.
• Heterosexual risk is the largest portion of
  female cases.
• High prevalence in Northeast and Southeast

19
Objectives

• To understand why sexual networks may place a key
  role in the Af Am HIV epidemic
• To understand key terms for aspects of sexual
  networks
• To understand how contextual factors might
  influence the shape of Af Am sexual networks

20
The Problem

• Black people experience higher HIV/AIDS rates
  across gender, age, and behavioral risk groups.

21
The Conundrum

• Not all of these disparities explained by
• higher risk behaviors
• lower individual income/educational levels
• key papers
• Millett et al. 2006 and 2007, Malebranche 2008,
  Harawa, et al. 2004, Halfors, et al. 2007
• Although other minority groups experience
  disparities, this is not true across the board or
  across behavioral risk groups.

22
Percentage of Persons Aged 22--44 Years at
Increased Risk for Human Immunodeficiency Virus
(HIV) Infection, by Race/Ethnicity and Education
--- National Survey of Family Growth, United
States, 2002
23
Solving the Conundrum

• Looking beyond the individual level
• Couple
• Family
• Network social and sexual
• Neighborhood, zip, city, county, state, etc.
• Economic, STD prevalence, broken windows,
  criminal justice, drug, political, . .
  .environment

24
Sexual Networks

• Groups of persons who are connected to one
  another sexually. The number of persons in a
  network, how central high-risk persons are within
  it, the percentage in monogamous relationships
  and the number of links each has to others all
  determine how quickly HIV/STDs can spread through
  a network.
• Distinct from but often overlap with social
  networks.
• Who has sex with whom.
• How many and how tightly are members connected.

25
Chlamydia network from Qikiqtarjuaq,
NunavutCanada, 2003
Data courtesy of Andrea Cuschieri
26
Transmission Dynamics Model
R0 ß x c x D R0 Case reproduction
rate ß Efficiency of transmission C
Mean rate of partner change D Duration of
infectiousness Higher the value of R0, greater
spread of infection
Pamina M. Gorbach, DrPH Lecture UCLA 5/10/01
27
Aspects of Sexual Networks

• Core groups
• Mixing patterns
• Concurrency
• Size
• Connectedness
• Rates of partner change

28
Core Groups

• Critical to maintaining high transmission rates.
• Core transmitters have high levels of risky
  behaviors, contribute a disproportionate share of
  HIV/STDs cases, and can fuel sustained
  transmission in a network.
• Sex workers
• Repeatedly infected with STDs
• High numbers of sexual partners
• From core neighborhoods/networks
• Men who have sex with men
• IDUs (?crack users)

29
Chlamydia network from Qikiqtarjuaq,
NunavutCanada, 2003
Data courtesy of Andrea Cuschieri
30
Core Transmitters
31
Chlamydia network from Qikiqtarjuaq,
NunavutCanada, 2003
Data courtesy of Andrea Cuschieri
32
Partner Mixing Patterns

• Assortative
• Tendency toward partnering with similar partners
  (e.g., ISO)
• Similar race (especially Black women)
• Disassortative
• Tendency toward partnering with dissimilar
  partners.
• Dissimilar risk groups (partnering between high-
  and low-risk partners).
• Mixed

33
Disassortative Mixing

• Random spread broadens transmission. An infection
  spreads quickest when partnering is
  random.(Laumann 1994) When partners select one
  another within groups such as age, ethnicity,
  class, religion or other characteristics,
  diseases may not spread to all subgroups. When
  partnering is anonymous or random, a disease can
  spread more quickly through all groups.

34
Examples of factors encouraging disassortative
mixing

• Gender norms
• Public sex venues
• Sex-ratio imbalances
• Secrecy/lack of dialogue regarding sexual
  histories

35
Concurrency

• Overlapping sexual partnerships
• Sexual partnerships in which a new sexual
  partnership is initiated prior to the termination
  of another.
• Bacterial STDs are known to travel faster in
  populations with greater concurrency, but with
  equal rates of new partnerships.

36
Concurrency

• Increases the probability for transmission,
  because earlier partners can be infected by both
  earlier and later partners. Further, they can
  serve as nodes, connecting all persons in a
  dense cluster, creating highly connected networks
  that facilitate transmission.
• Concurrent partners can connect each of their
  respective clusters and networks as well.
• Concurrency alone can fuel an epidemic even if
  the average number of partners is relatively
  low.(Morris, 1997)

37
Centrality/Connectedness

• The degree to which an individual is near all
  other individuals in a network (directly or
  indirectly).
• How central an HIV person is to a network deeply
  influences transmission rates in a community.
• In Colorado Springs, CO, network analysts found
  that HIV persons had high levels of risk
  behavior but were located in peripheral areas of
  risk networks.(Rothenburg et al. 98). This
  network configuration may have explained the
  relatively low HIV transmission levels.
• HIV persons in New York City, NY occupied
  central positions within their needle-sharing and
  sexual risk networks, which helped explain the
  high observed levels of infection.(Friedman et
  al. 97)

38
Network Density
Two Examples 5 actors 10 possible ties
39
Summary Sexual Networks

• Networks integrate core transmitters into the
  larger population.
• Dense networks help maintain STD endemicity.
• Core transmitters are key to population-based STD
  control.

40
Sexual network structure of African American
Communities

• Factors which influence these patterns
• Male-to-female sex ratios
• Social and residential segregation
• Incarceration
• Gender and cultural norms
• Racial oppression that diminishes opportunities
  for advancement, especially for Black men

41
CONTEXT-NETWORK PATHWAYS P O V E R T
Y/SEGREGATION Pool of
Relationship marriageable
men Instability CONCURRENCY SEX RATIO
42
Male-to-female sex ratios

• Higher numbers of men than women across age
  groups.
• Caused by differential
• Mortality
• Incarceration
• Military service
• Compounded by differential
• Rates of interracial relationships
• Unemployment

43
Current Marital Status

• Black women are less likely to marry, marry
  later, and more frequently divorce than white
  women.Tucker and Mitchell-Kernan, 1995.
• Black women ages 15, are nearly half as likely
  as white women to be married and living with
  their spouse (29 vs. 54) Table A1. Marital
  Status of People 15 Years and Over, by Age, Sex,
  Personal Earnings, Race, and Hispanic Origin,
  2003 - US Census

44
Social and residential segregation

• Black people are the most racially segregated
  group in the US.
• Black/white segregation indices are still quite
  high 69.
• Blacks tend to be concentrated in metropolitan
  areas (58).
• Lower and middle-class African Ams more likely to
  live in low-income urban areas than poor and
  middle-class Whites.

45
Incarceration of Black Men

• Nearly 5 of men are incarcerated at any given
  time.
• Among men ages 20-29 years, nearly 1 in 3 are
  under criminal justice supervision.
• Projection Nearly 1 in 3 men will be imprisoned
  in lifetime.
• Nearly 60 of low-income men who did not graduate
  HS will be imprisoned.

46
Dual Epidemics
47
Impact of incarceration

• Imbalanced gender ratios
• Disrupted relationships - correctional
  concurrency
• Spread of STIs within prison
• Normalization of incarceration and effects on
  normative community values of sex, violence and
  drug use
• Diversion of human/economic resources

N.T. Harawa and A. Adimora. Incarceration,
African Americans, and HIV advancing a research
agenda. J Natl Med Assoc 100 (2008) 57-62.
48
Gender and cultural norms

• Economic/historical circumstances have altered
  some gender norms but strengthened others.
• Women historically have been employed.
• Women often play crucial decision-making roles
  within institutions.
• Masculine roles within families strongly
  upheld/defended given threats/assaults in other
  areas.

49
Racial oppression

• Diminishes opportunities for economic
  advancement, especially for Black men.
• CONTEXT - NETWORK RELATIONSHIPS
• residential segregation by race
• concentration of adverse social and economic
  influences (poverty, drugs, violence)
• selection of partners
• from neighborhood

50
Proximal/Distal Determinants

• A determinate is an element that identifies or
  determines the nature of something or that fixes
  or conditions an outcome
• PROXIMAL DETERMINATES directly affect disease
  risk.
• DISTAL DETERMINANTS help shape behavior and the
  risks associated with given behaviors.

51
Determinants of Heightened STD Risks in African
American Communities

• MAJOR PROXIMAL DETERMINATES
• ??High prevalence of STDs
• ??Sexual network patterns concurrency and mixing
  among different subpopulations
• ??Risk behaviors
• DISTAL DETERMINANTS
• ??Poverty, inequality, discrimination,
  segregation
• ??Healthcare access and utilization

52
NHANES Survey
53
African American MSM

• Higher HIV rates despite
• Similar to lower risk behaviors
• Number partners
• Unprotected sex
• Risky drug use
• . . . Compared with other MSM

54
Associations of Race/Ethnicity with HIV
Prevalence and HIV-related Behaviors among Young
MSM in Seven US Urban Centers

• Nina T. Harawa, MPH, PhD
• Los Angeles County
• HIV Epidemiology Program

55
Hypothesis

• Young Black and Latino MSM are more likely to
  report factors that are associated with high-risk
  partners and behaviors.
• These contribute to increased infection risk.

Risk level of Partner pool
Social and sexual networks
RACE
INFECTION RISK
56
Objectives

• Use the Young Mens Survey (YMS) Phase One data
  to
• examine racial/ethnic differences in SES, risk
  behaviors, and partnership types
• examine associations of these factors with HIV
  infection and
• evaluate whether differences in these factors
  explain racial/ethnic disparities in HIV
• . . . among young MSM ages 15 22 years.

57
Methods Sample Frame

• Study conducted in SF, LA, Seattle, Dallas, NYC,
  Baltimore, and Miami
• Venues identified via formative research
• Coffee house, clubs, events, parks, gyms, etc.
• Sampling frame constructed
• On-site enumeration (4-hr periods with yield 7
  men ages 15-22 years)
• Each month, 12 venues randomly selected

58
Methods - Recruitment

• During sampling events
• Potentially eligible young men sequentially
  selected and recruited
• Consent obtained, survey administered, and HIV
  CT performed in a mobile unit
• Compensation 40-50
• Eligibility
• Ages 15-22 years
• Current resident
• Spoke Spanish or English

59
Results - associations with race/ethnicity and age
60
(No Transcript)
61
Results - multivariate

• Following factors associated with infection
• Race/ethnicity
• Increasing age
• Lacking a parent who completed grad/professional
  ed.
• Being out of school/work
• Certain partner types (? with exchange, steady,
  IDU partners)
• Sharing needles
• Sex while high on crack (and poppers)
• Having any anal sex in past 6 months

62
Results - multivariate associations with
race/ethnicity
63
Discussion

• Reason for higher levels of infection among
  blacks and Latinos still unclear
• Findings provided little direct support for the
  hypothesis

Risk level of Partner pool
Social and sexual networks
RACE
INFECTION RISK
64
Other Key Issues

• Enhanced HIV testing a critical component
• 91 of HIV blacks unaware of infection in YMS
  (ages 15-24 years)
• Outness and homophobia may play key roles, but
  not always in the expected directions.
• A number of studies have shown higher levels of
  risky behavior among out and gay-identified men
  than other MSM

65
Out to health provider by race/ethnicity and
known HIV status

Source L.A. Mens Survey 2008, National HIV
Behavioral Surveillance
66
Potential alternative explanations for high HIV
rates in Black MSM

• Other indicators of partner type may better
  indicate risk (e.g., age, race, SES, etc.)
• Greater levels of concurrency
• Differential misreporting of risk behaviors
• Differences in frequency of anal sex
• Missed and delayed diagnosis of HIV infection
  among MSM of color and their partners
• Biological differences (e.g., CCR5 mutation and
  circumcision prevalence)

67
(No Transcript)