Race and Age Disparities in HIV Incidence and Prevalence Among MSM in Atlanta, GA Eli Rosenberg Patrick Sullivan, Colleen Kelley, Travis Sanchez, Nicole.

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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 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: ▫ Sexually active black and white MSM in Atlanta ▫ Ages 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 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 cohort BMSM (n=260) WMSM (n=302) P-value Age at enrollment col % < – 24 years50%33% 25 + years50%67% Education<.0001 High school or less24%11% Some college40%33% College degree35%56% Sexual Identity<.0001 Homosexual, Gay76%92% Bisexual20%6% Heterosexual, Other4%2% Health insurance54%76%<.0001 Poverty29%13%<.0001

STI Incidence BMSMWMSM Rate Ratio (95% CI) Infections Incidence/ 100 PY Infections Incidence/ 100 PY Urethral Chlamydia (0.7, 3.4) Urethral Gonorrhea (1.4, 458) Rectal Chlamydia (1.04, 3.4) Rectal Gonorrhea (1.2, 4.8) Syphilis

1.7 / 100 PY 8 infections Cum. Inc. (2-yr): 3.6% 1.7 / 100 PY 8 infections Cum. Inc. (2-yr): 3.6% 6.6 / 100 PY 24 infections Cum. Inc. (2-yr): 11.3% 6.6 / 100 PY 24 infections Cum. Inc. (2-yr): 11.3% Log-Rank P = Proportion HIV Infected

Log-Rank P < Proportion HIV Infected 3.5 / 100 PY 8 infections Cum. Inc. (2-yr): 6.0% 3.5 / 100 PY 8 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 PY 7 infections Cum. Inc. (2-yr): 4.5% 1.9 / 100 PY 7 infections Cum. Inc. (2-yr): 4.5% 12.1 / 100 PY 16 infections Cum. Inc (2-yr): 16.6% 12.1 / 100 PY 16 infections Cum. Inc (2-yr): 16.6%

HIV incidence Factor Incidence /100 PY Rate Ratio (95% CI) Black participant (1.7, 9.9) White participant1.7 ref. Health Insurance2.6 ref. No health Insurance (1.2, 5.0) UAI (1.5, 24) No UAI1.1 ref. Older partners (≥10 y) (1.2, 6.1) No older partners3.1 ref. Black partners (2.1, 10) No black partners1.9 ref. Social determinants Partner pool / network

HIV incidence Covariate Health Insurance UAI Older partners (≥10 y) Black partners HR Race = 1 HR Race = 2.9 (1.3, 6.5) (no covariate adjustment) Age-scaled Cox PH models Black vs. White HR (95% CI): 2.6 HR Race = 3.3 (1.4, 7.5) (UAI) HR Race = 2.6 (1.3, 6.5) (Health Ins.) HR Race = 3.0 (1.3, 6.7) (Older partners) HR Race = 1.6 (0.6, 4.2) (Black partners) HR Race = 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 Effect Poster #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 Eli Rosenberg Thank You! R01-MH (PI: Sullivan) R01-HD (PI: Sullivan) P30 AI (Emory CFAR, PI: Curran) Supported by NIH #: