eli rosenberg
DESCRIPTION
Race and Age Disparities in HIV Incidence and Prevalence Among MSM in Atlanta, GA. Eli Rosenberg. Patrick Sullivan, Colleen Kelley, Travis Sanchez, Nicole Luisi, Carlos del Rio, Laura Salazar, Paula Frew, John Peterson Center for AIDS Research Emory University Atlanta, GA - PowerPoint PPT PresentationTRANSCRIPT
Race and Age Disparities in HIV Incidence and Prevalence Among
MSM in Atlanta, GA
Eli RosenbergPatrick Sullivan, Colleen Kelley, Travis Sanchez, Nicole Luisi, Carlos del Rio, Laura Salazar, Paula Frew, John Peterson
Center for AIDS ResearchEmory University Atlanta, GA
CROI 2014 March 4, 2014
Emory University
Center for AIDS Research
Dr. Rosenberg has no financial relationships with commercial entities to disclose.
Disclosures
• HIV prevalence among MSM is high and MSM continue to bear the burden of new infections in the US and Atlanta, GA
• Black MSM (BMSM), particularly young BMSM, continue to be overrepresented among new HIV infections
• Similar patterns for other sexually transmitted infections (STI)
• Reasons for these racial disparities remain unclear
• Prospective, racially comparative studies are needed
HIV and MSM
Study Design• Prospective HIV/STI incidence cohort study: 2009-2014
▫ Sexually active black and white MSM in Atlanta▫ Ages 18 - 39
• Recruitment▫ Venue-time-space sampling, Facebook
• Procedures▫ Testing: HIV, Chlamydia, Gonorrhea, Syphilis▫ Behavioral questionnaire
• Enrollment▫ 803 men enrolled▫ 30% HIV-positive (BMSM: 44%, WMSM: 13%)
▫ 562 HIV-negative MSM followed for 832 person-years▫ 79% retention at 24-months
Baseline
Month 3
Month 6
Month 12
Month 18
Month 24
HIV/STI testing,Questionnaire
HIV/STI testing,Questionnaire
HIV/STI testing,Questionnaire
HIV/STI testing,Questionnaire
HIV/STI testing,Questionnaire
HIV/STI testing,Questionnaire
Demographic characteristics of cohortBMSM (n=260) WMSM (n=302) P-value
Age at enrollment col % col % < .000118 – 24 years 50% 33%25 + years 50% 67%
Education < .0001High school or less 24% 11%Some college 40% 33%College degree 35% 56%
Sexual Identity < .0001Homosexual, Gay 76% 92%Bisexual 20% 6%Heterosexual, Other 4% 2%
Health insurance 54% 76% < .0001Poverty 29% 13% < .0001
STI Incidence
1.7 / 100 PY8 infections
Cum. Inc. (2-yr): 3.6%
1.7 / 100 PY8 infections
Cum. Inc. (2-yr): 3.6%
6.6 / 100 PY24 infections
Cum. Inc. (2-yr): 11.3%
6.6 / 100 PY24 infections
Cum. Inc. (2-yr): 11.3%
Log-Rank P = 0.0005Log-Rank P = 0.0005P
rop
ort
ion
HIV
In
fect
ed
Log-Rank P < 0.0001Log-Rank P < 0.0001P
rop
ort
ion
HIV
In
fect
ed
3.5 / 100 PY8 infections
Cum. Inc. (2-yr): 6.0%
3.5 / 100 PY8 infections
Cum. Inc. (2-yr): 6.0%
1.0 / 100 PY 1 infection
Cum. Inc. (2-yr): 1.6%
1.0 / 100 PY 1 infection
Cum. Inc. (2-yr): 1.6%
1.9 / 100 PY7 infections
Cum. Inc. (2-yr): 4.5%
1.9 / 100 PY7 infections
Cum. Inc. (2-yr): 4.5%
12.1 / 100 PY16 infections
Cum. Inc (2-yr): 16.6%
12.1 / 100 PY16 infections
Cum. Inc (2-yr): 16.6%
HIV incidence
FactorIncidence/100 PY
Rate Ratio (95% CI)
Black participant 6.6 3.8 (1.7, 9.9)
White participant 1.7 ref.
Health Insurance 2.6 ref.
No health Insurance 6.3 2.4 (1.2, 5.0)
UAI 5.3 4.8 (1.5, 24)
No UAI 1.1 ref.
Older partners (≥10 y) 8.6 2.8 (1.2, 6.1)
No older partners 3.1 ref.
Black partners 8.6 4.5 (2.1, 10)
No black partners 1.9 ref.
Social determinants
Social determinants
Partner pool / network
Partner pool / network
Individual risk behaviors
Individual risk behaviors
HIV incidenceCovariateHealth InsuranceUAIOlder partners (≥10 y)Black partners
HRRace = 1
HRRace = 2.9 (1.3, 6.5) (no covariate adjustment)
Age-scaled Cox PH modelsBlack vs. White HR (95% CI):
2.6
HRRace = 3.3 (1.4, 7.5)(UAI)
HRRace = 2.6 (1.3, 6.5)(Health Ins.)
HRRace = 3.0 (1.3, 6.7)(Older partners)
HRRace = 1.6 (0.6, 4.2)(Black partners)
HRRace = 1.5 (0.6, 3.9)(Black P, Health Ins.)
Conclusions
• In Atlanta, MSM and BMSM face multiple high-incidence epidemics of HIV/STI ▫>1 in 10 YBMSM acquire HIV per year
• Individual behavioral risk factors associated with HIV incidence, but do not account for race disparity
•Partner pool/network and structural factors help to explain HIV race disparity
STI-HIV EffectPoster #1028
Thursday, P-W9
STI-HIV EffectPoster #1028
Thursday, P-W9
Sexual network factors and social determinants may
supersede individual characteristics and behaviors as
drivers of HIV disparities.
Relevance
The InvolveMENt Team:•Investigators•Recruiters•Event staff•Retention specialists•Data team
•Our participants
Thank You!
Supported by NIH #: