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Anal Intercourse and HIV Among MSM Epidemiological Realities and Ways forward

Anal Intercourse and HIV Among MSM Epidemiological Realities and Ways forward. Stefan Baral MD MPH CCFP FRCPC Johns Hopkins School of Public Health, USA . Overview. Epidemiology of HIV among MSM Epidemic Scenarios of HIV among MSM Assessment of Data Quality Molecular Epidemiology

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Anal Intercourse and HIV Among MSM Epidemiological Realities and Ways forward

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  1. Anal Intercourse and HIV Among MSMEpidemiological Realities and Ways forward Stefan Baral MD MPH CCFP FRCPC Johns Hopkins School of Public Health, USA

  2. Overview • Epidemiology of HIV among MSM • Epidemic Scenarios of HIV among MSM • Assessment of Data Quality • Molecular Epidemiology • Ecological Model of Risk Factors for HIV among MSM • Anal Intercourse as a Risk factor for HIV • Moving Forward • Human Rights-Affirming HIV Prevention Strategies • Conclusions

  3. Introduction • Epidemiology • Ongoing epidemics among MSM in multiple LMIC • Newly identified epidemics in previously unstudied areas • Resurgent epidemics among MSM in high income countries (HIC) • Responses • Inadequate coverage and access for prevention, treatment, and care • Inadequate “toolkit” of prevention services for MSM

  4. Epidemic Scenarios Algorithm HIV prevalence in any high-risk subgroup >5% Unavailable Data HIV prevalence ratio (MSM/gen pop) Ratio ≥ 10 Ratio < 10 HIV prevalence ratio (IDU/gen pop) HIV prevalence ratio (IDU/gen pop) Ratio < 10 Ratio ≥ 10 Ratio ≥ 10 Ratio < 10 % population IDU SCENARIO 3 SCENARIO 4 < 1% ≥ 1% Source: Beyrer et al, Epidemiological Reviews, 2010 % population MSM < 10% SCENARIO 2 SCENARIO 1

  5. Epidemic Scenarios for MSM • Evidence suggested four epidemic scenarios for LMIC MSM epidemics • Scenario 5 will come from MENA region: now largely “unavailable data” Beyrer C, et al, Epidemiology Reviews, 2010.

  6. Scenario 1 - MSM risks are the predominant exposure mode for HIV infection in the population 

  7. SCENARIO1MSM are the predominant exposure group for HIV Beyrer C, et al, Epidemiology Reviews, 2010.

  8. Scenario 2- MSM risks occur within established HIV epidemics driven by injecting drug use (IDU)

  9. SCENARIO 2: Same sex practices are evaluated in the context of established HIV epidemics among IDU Beyrer C, et al, Epidemiology Reviews, 2010.

  10. Scenario 3 - MSM risks occur in the context of mature and widespread HIV epidemics among heterosexuals

  11. SCENARIO 3: Same sex practices are evaluated in the context of high prevalence and mature HIV epidemics among heterosexuals Beyrer C, et al, Epidemiology Reviews, 2010.

  12. HIV Prevalence among MSM in Africa 6.2% (267) Egypt 21.5% (463) 21.8% (501) 9.3% (713) 4.9% (1,778) 7.3% (406) 4.4% (90) Tunisia Morocco 25.0% (N/A) 13.4% (1,125) Senegal Sudan 5.7% (259) 5.9% (262) Nigeria 24.6% (285) Ghana 17.2% (1,291) Legend 13.3% (215) Kenya The Gambia 13.2% (306) Tanzania 19.0% (563) 12.3% (509) Uganda 21.4% (201) 2002 2003 12.4% (218) Malawi 2004 19.7% (117) 2005 Namibia 28.9% (249) 40.7% (285) 2006 Botswana 2007 25.0% (200) Soweto 2008 10.6% (538) Cape Town (Township) 2009 2010 Cape Town 2011 Modified From : van Griensven, Baral, et al. The Global Epidemic of HIV Infection among Men who have Sex with Men. Curr Opinion on HIV/AIDS, 2009

  13. SCENARIO 4: MSM, heterosexual, and IDU transmission all contribute significantly to the HIV epidemic

  14. SCENARIO 4: MSM, heterosexual, and IDU transmission all contribute significantly to the HIV epidemic Beyrer C, et al, Epidemiology Reviews, 2010.

  15. EPIDEMIC SCENARIOS: Unavailable Data • Algeria • Azerbaijan • Djibouti • Iran • Iraq • Jordan • Kazakhstan • 94 other Countries • Kyrgyzstan • Lebanon • Libya • Syria • West Bank and Gaza

  16. Assessment of Data Quality • Disease burden among MSM in LMIC • Data is predominantly Prevalence Data from Convenience Samples • May not be generalizable to general population of MSM • Samples are among young MSM—so likely very conservative estimates of disease burden • HIV Incidence has been characterized in • Cohort studies in Kenya, Peru, Brazil, Thailand • RCT in South Africa

  17. HIV-1 incidence in MSM cohort, Kilifi, Kenya 2006-2011, 479 MSM in follow-up; 733.8 person years 9.1 per 100 person-years (95% CI; 7.2 – 11.6) Provided by Sanders, E. Kenyan Medical Research Institute, 2011

  18. HIV among MSM in High Income Countries Source: Sullivan, et al, 2009. Reemergence of the HIV Epidemic Among Men Who Have Sex With Men in North America, Western Europe, and Australia, 1996–2005

  19. Number of newly diagnosed HIV infections among men who have sex with men, Hong Kong, Singapore, Taiwan and Japan, 2002 - 2007 TaiwanJapan Hong KongSingapore HK 56 50 67 96 118 168 SG 38 54 94 101 108 145 TW - 336 503 584 743 1075 JP 305 340 449 514 571 690 Source: van Griensven, Baral, et al. 2009. The Global Epidemic of HIV Infection among Men who have Sex with Men. Current Opinion in HIV/AIDS

  20. Phylogenetic Analysis of HIV among MSM Source: Beyrer, et al 2012. The Epidemiology of HIV among MSM. Lancet. 2012.

  21. Changing Patterns of MSM Subtype in Cape Town(Note: cohorts not matched) Heterosexual MSM 1990s 2010 B C B C C Source: Middelkoop, Williamson, .., Bekker, HIV Subtypes in MSM in Cape Town: evidence of bridging between epidemics, MOPE034 IAS 2011

  22. HIV Clade by Race among MSM in Cape Town SA White SA Black SA Coloured Source: Middelkoop, Williamson, .., Bekker, HIV Subtypes in MSM in Cape Town: evidence of bridging between epidemics, MOPE034 IAS 2011

  23. Level of Risks Stage of Epidemic Public Policy Community Network Individual Ecological Model for HIV Risk in MSM HIV Epidemic Stage Exclusion from National Surveillance, Criminalization, Human Rights Contexts, Sexual Health Education Access to preventive services, Stigma, VCT Access, ARV Access STI Prevalence, Condom knowledge, IDUs, MSW, Transgenders Unprotected Receptive Anal Intercourse, GUD, frequency of male partners, high lifetime partners, IDU, NIDU Source: Baral and Beyrer, 2006

  24. Anal Intercourse • Highest Risk form of Sexual Transmission • 1.4% Per Sexual Act Probability of Transmission • No significant difference between heterosexual and same-sex risk of anal intercourse • Approximately 14 times higher than penile-vaginal per-act probability Source: Baggaley, et al. HIV transmission risk through anal intercourse: systematic review, meta-analysis and implications for HIV prevention. Int Journal of Epidemiology, 2010

  25. Anal Intercourse is not limited to MSM • In Cape Town, South Africa: • Anonymous surveys of 2593 men and 1818 women: • Anal intercourse (past 3 months): Men = 14%; Women = 10% • Condom use during anal intercourse: Men = 67%; Women = 50% Kalichman et al (2009) • In KwaZulu-Natal, South Africa: • 42% of truck drivers (n=320) reported anal sex with female sex workers Ramjee et al (2002) • In Kenya: • Survey among FSW (n=147): • 40.8% reported ever practising anal intercourse, 30% reported never or rarely using condoms during anal intercourse • consistent condom use lower in anal sex than peno-vaginal intercourse Schwandt et al (2006) • In Nigeria: • anal sex practiced by 12% of public secondary schools students (N= 521) Bamidele et al (2009) Modified from: SalimKarim, Does Africa need a rectal microbicide?, 2011

  26. Anal Intercourse – Per Partner Source: Baggaley, et al. HIV transmission risk through anal intercourse: systematic review, meta-analysis and implications for HIV prevention. Int Journal of Epidemiology, 2010

  27. Anal Intercourse • Biological Drivers of HIV Risk Among MSM • Anal Intercourse is far higher per-act and per-partner risk of HIV transmission • Reasons: • HIV is a gut-tropic virus • Increased trauma during intercourse • Sexual Positioning • In penile-vaginal intercourse, sexual positioning is biologically determined • In penile-anal intercourse among men, sexual positioning is versatile

  28. Rights-Affirming HIV Prevention Programs • Combination HIV Prevention Interventions (CHPI) • Behavioural Interventions • Increasing condom and lubricant use during sex • Eg. Peer Education, Risk Reduction Counselling, Adherence Counselling • Biomedical Interventions • Biomedical interventions aim to decrease transmission and acquisition risk of sex • Eg. Oral or topical antiviral chemoprophylaxis, Treatment as Prevention • Structural Interventions • Rarely been appropriately evaluated because of complexity in study design to characterize efficacy and effectiveness of these interventions • Eg. Decriminalization, Government-sponsored anti-stigma policy, Mass media engagement, Gender engagement programs, Community systems strengthening, Health Sector Interventions

  29. Prerequisites for Effective HIV Prevention Programs • Identification • Must be able to Identify MSM • Willing to Self-Disclose • Risk Assessment • Must be able to appropriately stratify MSM according to risk • Asked about risks in a competent and sensitive manner • Follow Up • Must be able to follow up participants to assess adherence and efficacy of intervention • Safe Environment • Client trust in health care facility

  30. Research Priorities for Structural Interventions

  31. Research Priorities for Structural Interventions

  32. HIV Research Priorities among MSM in Africa

  33. Prevention Expenditures for MARPS • Concentrated Epidemics • MSM and SW predominant risk groups • 3.3% of non-treatment expenditures supporting MSM • 2% of non-treatment expenditures support FSW • Generalized Epidemics • Emerging evidence of risk among MSM and SW • < 0.1% of non-treatment expenditures supporting MSM and SW • With few exceptions, most African States have invested 0% of national expenditures for prevention needs for MSM and SW Source: Global HIV Prevention Working Group: Global HIV Prevention: The Access, Funding, and Leadership Gaps. 2009

  34. Conclusions • HIV continues to disproportionately affect MSM in high and low income settings • The exclusion of MSM from national responses has not been a decision based in evidence • In every setting where MSM have been studied, they have been found to carry disproportionate burden of HIV compared to other age-matched general population men • Data quality is sub-optimal with limited: • HIV incidence data • Population-based prevalence data • HIV risk factors include individual level and structural drivers of risk including stigma, criminalization, and human rights violations • Molecular epidemiology demonstrates that these epidemics are not separated from prevalent strains in each country

  35. Moving Forward • Epidemiology • Filling in the map • We have studies in several countries including Swaziland, Malawi, The Gambia, Cameroon, Togo, and Burkina Faso • Characterizing Incidence Data, Phylogenetic Analysis • Prevention • Combination HIV Prevention Research • Biomedical, Behavioral, and Structural Approaches

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