REVIEW URRENT C OPINION
The epidemiology of HIV among men who have sex with men in countries with generalized HIV epidemics Stefan D. Baral, Ashley Grosso, Claire Holland, and Erin Papworth
Purpose of review Key populations at high risk for HIV acquisition and transmission, such as MSM, have long been identified as essential subpopulations for epidemiological surveillance of the HIV epidemic. However, surveillance systems in the context of generalized and widespread HIV epidemics have traditionally excluded these men. Recent findings Emerging and consistent data highlight the disproportionate burden of HIV among MSM that exists when compared with other men of reproductive age across countries with generalized epidemics. Correlates of prevalent HIV infection include individual-level determinants of HIV acquisition and transmission similar to that found in concentrated HIV epidemics and community-level structural factors, such as stigma, being blackmailed, and history of homophobic abuse. HIV incidence was only available from two countries (Kenya, Thailand) with generalized HIV epidemics, but in both settings was an order of magnitude higher than that of other populations. Summary The data presented here suggest that the dynamics of HIV infection among men are more similar across the world than they are different. Many HIV epidemics among average-risk reproductive age adults are slowing across both generalized and concentrated settings. It is in this context that high HIV incidence is observed among MSM, especially young MSM. This trend suggests a change in the trajectory of these HIV epidemics, a change that we may miss if we continue to understudy these populations based on unproved and dated assumptions. Keywords epidemiology, HIV, men who have sex with men, MSM, Sub-Saharan Africa, surveillance
INTRODUCTION The WHO and the Joint United Nations Programme on HIV/AIDS (UNAIDS) have released two primary iterations of HIV surveillance since the HIV pandemic was recognized in the early 1980s. First generation surveillance recommendations were rooted in clinical and laboratory case reporting focused on provider-level surveillance and sentinel studies [1 ,2]. In 2000, second generation surveillance guidelines identified the need to proactively evaluate the trends of the HIV epidemic in each country. ‘Know your epidemic’ became an international mantra implying that the HIV epidemiology of a given context was fluid, and surveillance measures must equally consider the risk and trends of HIV acquisition and transmission, the effectiveness of HIV programming, and the result and impact of policy strategies [1 ]. Within this framework, specific populations at heightened risk of HIV were
identified as integral groups for data collection, evaluation, and applied analysis to support policy development [1 ]. These populations included MSM, sex workers, and people who use drugs and eventually were collectively termed as key populations [1 ,2]. Although these recommendations were issued in 2000, the application of surveillance measures for &&
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Key Populations Program, Center for Public Health and Human Rights, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA Correspondence to Stefan D. Baral, Key Populations Program, Center for Public Health and Human Rights, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, E7146, 615 N. Wolfe Street, Baltimore, MD 21205, USA. Tel: +1 410 502 8975; fax: +1 410 614 8371; e-mail: [email protected] Curr Opin HIV AIDS 2014, 9:156–167 DOI:10.1097/COH.0000000000000037 Volume 9 Number 2 March 2014
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HIV among MSM in countries with generalized HIV epidemics Baral et al.
KEY POINTS HIV surveillance in generalized HIV epidemics has focused on average-risk reproductive-age adults with limited attention to key populations in these settings. The biology of the efficient transmission of HIV during anal sex with viremic serodiscordant partners is sustained across the world resulting in the disproportionate burden of HIV among MSM wherever measured. To comprehensively understand and address the high burden of HIV among MSM in generalized epidemics, data collection should include individual, community, and structural factors.
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MSM globally has been limited [3 ,4 ,5 ]. Some countries, where the epidemic is considered concentrated in key populations such as MSM, female sex workers, and people who inject drug (PWID), have led the way in sentinel surveillance among key populations, and focused programming has shown positive effects in the reduction of incidence of HIV in these countries [6–9]. However, in epidemics considered generalized by the WHO/UNAIDS, definition of greater than 1% in antenatal clinic surveillance, the focus on data, associations of risk, and targeted programming for key populations has been overshadowed by large-scale general population HIV surveillance and prevention interventions [10,11,8]. Nevertheless, many of the risks associated with the acquisition and transmission of HIV among key populations are not unique to epidemics in which key populations carry the highest burden of disease, and emerging data indicate the biological and specific behavioral factors among these populations contribute to the heightened risk of HIV regardless of the epidemiology of HIV in a given context [12 ,13,14 ]. &&
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METHODS AND RESULTS OF SEARCH The review was conducted to assess both peerreviewed and nonpeer-reviewed, also known as gray literature. The number of countries included in the review, defined as those with generalized HIV epidemics with greater than 1% adult (aged 15–49) HIV prevalence in the 2012 or 2013 UNAIDS Global Report, was 51. Countries that reported greater than 1% prevalence in 2013 but not 2012 were included in the review (the Democratic Republic of the Congo and Comoros), and countries where prevalence dropped under 1% in 2013 were also included, but no articles were identified with relevant data. Search terms included medical subject headings or
other associated terms for HIV cross referenced with ‘MSM’ or other related terms and the countries in the review. PubMed was used to search peerreviewed articles, and gray literature was obtained from the www.HIVAIDSClearinghouse.eu, United States Agency for International Development (USAID) Development Experience Clearinghouse, United Nations Educational Scientific and Cultural Organization (UNESCO) HIV and AIDS Education Clearinghouse, Google, WHO, and UNAIDS. Inclusion criteria were studies with HIV prevalence or incidence data for the samples of MSM or associations with HIV infection or unprotected anal intercourse (UAI) within samples of MSM. The search encompassed January 1, 2007 until September 9, 2013. Articles were screened by two reviewers (C.H., E.P.) for the title and abstract review. Full text data abstraction was conducted by three reviewers (A.G., C.H., E.P.), and 10% of the articles underwent double data abstraction. There were 59 studies included in the review, with data from 30 different countries and 72 HIV prevalence studies. The review identified 10 incidence studies from two countries (Kenya and Thailand). Associations of HIV and UAI included odds ratios (OR), hazard ratios, incidence rate ratios (IRR), and Chi-square statistics.
EPIDEMIOLOGY OF HIV AMONG MSM IN GENERALIZED EPIDEMICS: HIV PREVALENCE DATA AMONG MSM The lack of peer-reviewed literature measuring the burden of HIV among MSM in most countries with generalized epidemics is evident. The majority of HIV prevalence data was retrieved from Thailand, Kenya, and South Africa (52.8%, 28/53 studies). Thailand was the only country in Southeast Asia categorized as a generalized HIV epidemic, though significant regional variation has been reported in peer-reviewed literature [9]. HIV prevalence among MSM in Thailand ranged from 8.2% (n ¼ 450) to 68.2% (n ¼ 173) compared with 1.1% among adults of reproductive age, with eight out of nine studies found in peer-reviewed literature (Table 1) [3 ,4 , 13,15 – 17,18 ,19 – 22,23 ,24 – 27,28 ,29 ,30 – 37, 38 ,39–44,45 ,46 ,47–52,53 ,54–57,58 ,59,60 , 61–63,64]. In Kenya, where the reproductive-age population HIV prevalence is reported as 6.1%, studies found HIV prevalence among MSM to be between 12.3% (n ¼ 171) and 43.0% (n ¼ 114), with regional variation and mainly from peer-reviewed literature [18 ]. The most studies from any one country were obtained from Thailand and South Africa [9], and HIV prevalence among MSM in South Africa ranged from 10.0% (n ¼ 10) to 40.7% (n ¼ 285)
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Epidemiology: concentrated epidemics Table 1. Burden of HIV infection among MSM in the context of generalized HIV epidemics Year of study
Country
HIV prevalence among MSM (sample size)
Incidence (n ¼ /100 person years), 95% CI, (sample size)
General population age 15–49 HIV prevalence (2012)
References
Asia Thailand
2009
8.20% (n ¼ 450)
1.1%
[15]
Thailand
2009
21.2% (n ¼ 1125)
1.1%
[15]
Thailand
2009
17.5% (n ¼ 1575)
1.1%
[15]
Thailand
2009
17.3% (n ¼ 1121)
4.1 (n ¼ 308)b
1.1%
[23
&&
Thailand
2009
28.3% (n ¼ 399)
6.4 (n ¼ 94)b
1.1%
[23
&&
Thailand
2009
30.8% (n ¼ 400)
7.7 (n ¼ 99)b
1.1%
[23
&&
Thailand
2010
18.8% (n ¼ 414)
1.1%
[24]
Thailand
2011
12.9% (n ¼ 551)
1.1%
[25]
Thailand
2012
21.6% (n ¼ 1544)
1.1%
[26]
Thailand
2013
46.2% (n ¼ 485)
1.1%
[27]
Thailand
2013
21.3% (n ¼ 1744)
5.9, 95% CI 5.2–6.8 (n ¼ 1744)
1.1%
[28
Thailand
2013
28.3% (n ¼ 4398)
6.3 (n ¼ 4398)
1.1%
[29 ]
Thailand
2013
68.2% (n ¼ 173)
1.1%
[16]
Benin
2008
25.5%
1.1%
[30]
Benin
2009
4.9%
1.1%
[31]
Botswana
2009
19.7% (n ¼ 117)
23.0%
[4 ]
Burkina Faso
2012
0.98%
1.0%
[32]
Burkina Faso
2013
19.0%
1.0%
[33]
Burundi
2010
2.40%
1.3%
[34]
Cameroon
2012
37.0% (n ¼ 511)
4.5%
[35]
CAR
2012
17.0%
4.6%a
[36]
ˆ te D’Ivoire Co
2010
18.5%
3.2%
[37]
ˆ te D’Ivoire Co
2011
50.0% (n ¼ 96)
3.2%
[38
Gambia
2013
9.8% (n ¼ 205)
1.3%
[39]
Ghana
2008
25.0%
Kenya
2007
38.0% (n ¼ 60)
Kenya
2007
Kenya
8.2, 95% CI 3.7–18.3 (n ¼ 81)
] ] ]
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Sub-Saharan Africa
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1.4%
[30]
6.1%
[40]
43.0% (n ¼ 114)
6.1%
[18
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2007
12.3% (n ¼ 171)
6.1%
[18
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Kenya
2009
19.7% (n ¼ 259)
6.1%
[41]
Kenya
2009
25.0% (n ¼ 285)
6.1%
[42]
Kenya
2012
26.3% (n ¼ 273)
6.1%
[43]
Kenya
2012
19.8% (n ¼ 253)
6.1%
[44]
Kenya
2012
6.8 CI 4.9–9.2 (n ¼ 327)
6.1%
[45
Kenya
2013
7.5 CI 6.0–9.5
6.1%
[46 ]
Kenya
2013
8.6 CI 6.7–11.0 (n ¼ 449)
6.1%
[18
6.1%
&
[4 ]
14.3% (n ¼ 449)
20.9, 95% CI 6.7–64.9 (n ¼ 60)
] ]
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Malawi
2009
21.4% (n ¼ 201)
Mauritius
2010
8.1% (n ¼ 358)
1.2%
[47]
Mozambique
2011
8.2% (n ¼ 496)
11.1%
[21]
Mozambique
2011
9.1% (n ¼ 583)
11.1%
[21]
Mozambique
2011
3.1% (n ¼ 353)
11.1%
[21]
Namibia
2009
12.4% (n ¼ 218)
13.3%
[4 ]
Nigeria
2011
12.4% (n ¼ 879)
3.1%
[48]
Nigeria
2012
13.4% (n ¼ 1082)
3.1%
[49]
Nigeria
2013
21.1% (n ¼ 712)
3.1%
[50]
Nigeria
2013
22.1% (n ¼ 520)
3.1%
[51]
South Africa
2008
10.0% (n ¼ 10)
17.9%
[19]
South Africa
2008
35.0% (n ¼ 37)
17.9%
[52]
South Africa
2010
17.4% (n ¼ 534)
17.9%
[53
South Africa
2010
10.4% (n ¼ 539)
17.9%
[54]
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HIV among MSM in countries with generalized HIV epidemics Baral et al. Table 1 (Continued) Incidence (n ¼ /100 person years), 95% CI, (sample size)
Year of study
HIV prevalence among MSM (sample size)
South Africa
2011
25.5% (n ¼ 200)
17.9%
[13]
South Africa
2011
13.2% (n ¼ 378)
17.9%
[55]
South Africa
2011
40.7% (n ¼ 285)
17.9%
[20]
South Africa
2013
34.5% (n ¼ 171)
17.9%
[56]
South Africa
2013
31.4% (n ¼ 35)
17.9%
[57]
Swaziland
2013
17.6% (n ¼ 328)
17.9%
[58 ]
Tanzania
2011
12.3% (n ¼ 509)
5.1%
Togo
2013
20.3%
2.9%
[33]
Uganda
2012
13.7% (n ¼ 300)
7.2%
[60
Zambia
2010
33.0% (n ¼ 641)
12.7%
Country
General population age 15–49 HIV prevalence (2012)
References
&
[59] &&
]
[37]
Latin America and the Caribbean Bahamas
2011
8.00%
2.8%a
[22]
Bahamas
2012
14.0% (n ¼ 36)
2.8%a
[61]
Bahamas
2012
26.0%
2.8%a
[61]
Belize
2012
13.9% (n ¼ 131)
1.4%
[62]
Guyana
2011
20.0%
1.3%
[22]
Guyana
2013
19.4%
1.3%
[17]
Haiti
2012
18.1% (n ¼ 860)
2.1%
[63]
Jamaica
2011
32.0%
1.7%
[22]
Jamaica
2011
25.0%
1.7%
[22]
Jamaica
2013
37.6%
1.7%
[17]
Suriname
2011
7.0%
1.1%
[22]
Trinidad and Tobago
2011
20.0%
1.5%a
[22]
2012
1.7%
1.3%a
[64]
2007
12.8% (n ¼ 63 538)
Former Soviet Union/Eastern Europe Estonia Multicountry data LMICs
NA
[3
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CAR, Central African Republic; CI, confidence interval; LMIC, low- and middle-income country. a 2011 prevalence used because 2012 prevalence was unavailable. b Annual density.
compared with 17.9% among reproductive-age adults [19,20,17]. There was significant intracountry variability in HIV prevalence among MSM based on the geographic location of the study or the timing of the study. For example, in Mozambique, one study reported 8.2% (n ¼ 496), 9.1% (n ¼ 583), and 3.1% (n ¼ 353) HIV prevalence across three cities compared with a reproductive age prevalence of 11.1%, of which over 62% of infections are among women [21,17]. In Benin, two nonpeer-reviewed studies presented a disparity of results with 25.5% reported in 2008 and 4.9% in 2009 compared with 1.1% national HIV prevalence [65,17]. In Latin America and the Caribbean, where reproductive-age adult HIV prevalence remains under 3%, HIV prevalence reported among MSM ranged from 7.0% in Suriname to 37.6% in Jamaica, and the majority of prevalence results were around
20% [22,17]. This finding is consistent with other systematic reviews that showed globally the highest burden of HIV among MSM was found in the Caribbean [14 ]. &&
HIV INCIDENCE DATA AMONG MSM IN GENERALIZED EPIDEMICS Incidence data among MSM in generalized epidemics were obtained from eight unique studies conducted in only two countries, Kenya and Thailand (Table 1). Kenya studies described HIV incidence between 6.8 [95% confidence interval (CI) 4.9– 9.2; n ¼ 327] and 20.9 (95% CI 6.7–64.9; n ¼ 60) per 100 person years [40,45 ]. Thailand data were more consistent, and annual incidence density reports showed 4.1 (n ¼ 308), 6.4 (n ¼ 94), and 7.7 (n ¼ 99), respectively, with incidence in 100 person years reported as 8.2 (95% CI 3.7–18.3; n ¼ 81), 5.9
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Epidemiology: concentrated epidemics
(95% CI 5.2–6.8; n ¼ 1744), and 6.3 (n ¼ 4398) (Table 1). There is a dearth of incidence studies among MSM in countries with generalized epidemics; however, it is important to note the rates described in the studies suggested a consistently high HIV incidence in these two countries (Table 1).
IDENTIFIED HIV RISKS AMONG MSM IN GENERALIZED EPIDEMICS In Thailand, associations of HIV were similar across studies, with lower education levels, unprotected sex with a male partner, and co-infections of other sexual transmitted infections (STI) consistently reported as associated with HIV infection (Table 2) [3 ,4 ,13,15,18 ,20,25,27,28 ,39,44,48,50,54,56, 59,60 ,66–71]. Associations between HIV and age varied in Thailand; however, individuals between 22 and 29 years of age were generally reported as more likely to be living with HIV compared with those aged 18–21, though HIV incidence was highest among the youngest group. In Kenya, one study found significant associations with HIV infection to be having had male-only sex partners in last 3 months [adjusted odds ratio (aOR) 6.3, 95% CI 2.3–17.0; n ¼ 285]; perianal condylomata on examination (aOR 5.1, 95% CI 1.1–24.0; n ¼ 285) and the per year increase in age from 18 to 24 (aOR 1.1, 95% CI 1.0–1.2; n ¼ 285) [67]. In South Africa, social factors and socio-economic status were associated with HIV infection, such as having been raped, having been blackmailed, unemployment, types of sexual partners, and limited use of condoms during the last sexual act with a male partner. Having two or more sexual partners was reported as associated with HIV in Thailand, Kenya, and South Africa studies, and commercial sex was reported as associated with HIV in Kenya, Nigeria, South Africa, and Tanzania. Notably, in the countries where a substantial body of data exists (South Africa, Nigeria, Thailand, and Kenya) results show cultural and countryspecific trends in demographics of MSM at risk for disease acquisition and transmission; however, biological risk factors remain consistent among MSM in these countries with generalized epidemics. Every country with more than one study among MSM retrieved for this analysis reported receptive or receptive and insertive anal intercourse as associated with HIV. In Thailand, two studies reported having receptive only or both receptive and insertive anal sex as associated with HIV infection 2007: aOR 2.5, 95% CI 1.7–3.6; n ¼ 1125; 2013: aOR 1.8, 95% CI 1.3–2.3; n ¼ 1744) [15,28 ]. In Kenya, individuals who reported receptive and insertive anal intercourse in the last 3 months were eight times &&
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more likely to be infected with HIV (aOR 8.0, 95% CI 2.9–22.0; n ¼ 285) [67]. Also, in Kenya, individuals who reported receptive anal intercourse were at 10 times higher risk of HIV infection [adjusted incidence rate ratio (aIRR) 9.7, 95% CI 3.8–25.1; n ¼ 449] [18 ]. In Nigeria, MSM participants who reported receptive anal intercourse in the last 6 months were four times more likely to be infected with HIV (aOR 4.2, 95% CI 1.5–12.3; n ¼ 154) [48]. MSM in South Africa who reported receptive anal intercourse were 10 times more likely to be infected with HIV (aOR 10.0, 95% CI 4.2–24.0; n ¼ 285) and those that reported unprotected receptive anal intercourse were four times more like to be living with HIV (aOR 4.3, 95% CI 2.4–7.6; n ¼ 285) [20]. Few studies have reported structural determinants of HIV risk, though studies among MSM in Uganda, Malawi, Namibia, and Botswana highlighted the significance of experienced and perceived stigma in potentiating HIV risks [4 ,60 ]. &&
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HIV prevention needs of MSM in generalized epidemics The HIV prevention needs of MSM in generalized epidemics may be different from those in concentrated epidemics. In generalized epidemics, HIV prevalence is higher in the population as a whole and usually disproportionately affects women. Thus, in generalized epidemics, MSM have higher HIV acquisition risks secondary to their heterosexual partnerships that needs to be considered in HIV prevention programs for these men [53 ]. Separately, HIV prevention, treatment, and care services have been designed for the general populations with limited competency in addressing the needs of specific populations resulting in exclusion of MSM or self-exclusion by MSM secondary to perceived and enacted stigma. The outcome is the common misconception among MSM that HIV transmission is more likely through vaginal intercourse than through anal intercourse [72]. Given the significantly higher HIV transmission probability during anal intercourse with a serodiscordant viremic partner as compared with that of vaginal intercourse, these misconceptions reinforce the need for tailored prevention messages and HIV service delivery for MSM in both generalized and concentrated epidemics globally [14 ]. Implementation science studies will facilitate characterizing appropriate service delivery models ranging from stand-alone to integrated for MSM in the context of widespread and generalized epidemics, stigma, and limited financial and health resources [73,74]. &&
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HIV among MSM in countries with generalized HIV epidemics Baral et al. Table 2. Significant associations of prevalent HIV infection and unprotected anal intercourse among MSM in the context of generalized HIV epidemics Country
Year of study
Association of HIV infection
Association of UAI
OR/RR (95% CI)
Sample size
Ref.
Asia Thailand
2009
Self-reported history of STI symptoms in the past 6 months
aOR 3.6 (1.5–8.4)
450
[15]
Thailand
2009
Having high levels of concern about contracting HIV in the future
aOR 3.0 (1.2–8.0)
450
[15]
Thailand
2009
Not having a family confidant
aOR 2.0 (1.1–3.7)
450
[15]
Thailand
2009
Lower than university education
OR 2.5 (1.5–4.0)
1125
[15]
Thailand
2009
Living alone or with a roommate vs. living with family
aOR 1.5 (1.1–2.0)
1125
[15]
Thailand
2009
Recruitment from a sauna
aOR 2.3 (1.5–3.7)
1125
[15]
Thailand
2009
Having receptive anal sex
aOR 2.0 (1.4–3.0)
1125
[15]
Thailand
2009
Having both receptive and insertive anal sex roles
aOR 2.5 (1.7–3.6)
1125
[15]
Thailand
2011
Self-identify as gay vs. bisexual
aOR 2.8 (1.1–7.3)
551
[25]
Thailand
2011
Age 25–29 vs. 18–19
aOR 2.7 (1.1–6.5)
551
[25]
Thailand
2011
More than 10 casual male sexual partners compared with 0
aOR 4.1 (1.3–12.7)
551
[25]
Thailand
2012
Less than 25 years of age
OR 3.8 (1.4–10.4)
92
[66]
Thailand
2012
Self-identify as gay
OR 0.1 (0.0–0.3)
92
[66]
Thailand
2012
Self-identify as transgender
OR 0.1 (0.0–0.7)
92
[66]
Thailand
2012
Exchanged sex for money
OR 0.3 (0.1–0.9)
92
[66]
Thailand
2012
More than 4 sexual partners
OR 0.3 (0.1–1.0)
92
[66]
Thailand
2013
Treponema pallidum positivity
aHR 1.8 (1.1–3.2)
1744
[28
Thailand
2013
HSV-2 antibody
aHR 1.5 (1.1–2.1)
1744
[28
Thailand
2013
HSV-1 antibody
aHR 1.5 (1.1–1.9)
1744
[28
Thailand
2013
Group sex
aHR 1.5 (1.1–2.1)
1744
[28
Thailand
2013
Receptive only or both receptive and insertive anal sex
aHR 1.7 (1.2–2.3)
1744
[28
Thailand
2013
Not always using a condom with male partners
aHR 4.8 (1.8–13.2)
1744
[28
Thailand
2013
Drug use for sexual pleasure
aHR 2.3 (1.5–3.4)
1744
[28
Thailand
2013
Living alone or with a roommate vs. with family or a partner
aHR 1.5 (1.1–2.1)
1744
[28
Thailand
2013
Age 22–29 vs. 30 or over
aHR 1.7 (1.2–2.4)
1744
[28
Thailand
2013
Age 18–21 vs. 30 or over
aHR 2.5 (1.6–3.8)
1744
[28
Thailand
2013
Treponema pallidum positivity
aOR 2.1 (1.3–3.4)
1744
[28
Thailand
2013
HSV-2 antibody
aOR 3.0 (2.8–4.0)
1744
[28
Thailand
2013
HSV-1 antibody
aOR 1.5 (1.1–1.9)
1744
[28
Thailand
2013
No prior HIV test
aOR 1.6 (1.3–2.1)
1744
[28
Thailand
2013
Coerced into sex
aOR 1.6 (1.2–2.1)
1744
[28
Thailand
2013
Receptive only or both receptive and insertive anal sex
aOR 1.8 (1.3–2.3)
1744
[28
Thailand
2013
Nitrate inhalation
aOR 1.6 (1.0–2.5)
1744
[28
Thailand
2013
Drug use for sexual pleasure
aOR 1.5 (1.0–2.2)
1744
[28
Thailand
2013
Being employed vs. studying
aOR 1.5 (1.1–2.1)
1744
[28
Thailand
2013
Being unemployed vs. studying
aOR 2.0 (1.1–3.5)
1744
[28
Thailand
2013
Secondary/vocational school vs. university or higher
aOR 1.8 (1.3–2.4)
1744
[28
Thailand
2013
22–29 vs. 18-21
aOR 1.8 (1.2–2.7)
1744
[28
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Epidemiology: concentrated epidemics Table 2 (Continued) Country
Year of study
Thailand
2013
30 or over vs. 18-21
Thailand
2013
HBV infection
Gambia
2013
Age 22 or older
aOR 4.0 (1.4–11.1)
205
[39]
Kenya
2007
Paid someone for sex in last 3 months
aOR 0.4 (0.2–1.0)
285
[67]
Kenya
2007
Receptive anal intercourse only in last 3 months
aOR 3.9 (1.4–11.0)
285
[67]
Kenya
2007
Receptive and insertive anal intercourse in last 3 months
aOR 8.0 (2.9–22.0)
285
[67]
Kenya
2007
Male only sex partners in last 3 months
aOR 6.3 (2.3–17.0)
285
[67]
Kenya
2007
Perianal condylomata on examination
aOR 5.1 (1.1–24.0)
285
[67]
Kenya
2007
Per year increase in age from 18-24
aOR 1.1 (1.0–1.2)
285
[67]
Kenya
2012
Less likely to be impoverished
Chi-square 4.23
253
[44]
Kenya
2013
Receptive anal intercourse
aIRR 9.7 (3.8–25.1)
449
[18
Nigeria
2011
Receptive anal intercourse in last 6 months
aOR 4.2 (1.5–12.3)
154
[48]
Nigeria
2011
Feeling at risk of HIV
aOR 4.4 (1.6–12.1)
122
[48]
Nigeria
2012
Internalized homophobia
aOR 1.8 (1.2–2.7)
1125
[49]
Nigeria
2013
Age 26–40 vs. 18-25
aOR 3.8 (1.5–9.7)
308
[50]
Nigeria
2013
Being Christian vs. Muslim
aOR 3.9 (1.4–10.9)
308
[50]
Nigeria
2013
Self identify as straight/ bisexual vs. homosexual
aOR 9.1 (1.2–70.6)
210
[50]
Nigeria
2013
Unprotected anal sex
aOR 2.9 (1.1–8.3)
194
[50]
Nigeria
2013
Unprotected anal sex
aOR 5.1 (1.5–17.8)
210
[50]
Nigeria
2013
Being paid for sex
aOR 4.9 (1.8–13.6)
194
[50]
Nigeria
2013
Ever testing for HIV
aOR 5.6 (1.9–16.6)
194
[50]
Nigeria
2013
Being from Ibadan vs. Abuja
aOR 1.8 (1.1–2.9)
712
[68]
Nigeria
2013
Living with or married to a woman
aOR 1.5 (1.0–2.2)
712
[68]
Nigeria
2013
Self-identify as straight/ bisexual vs. homosexual
aOR 1.5 (1.0–2.1)
712
[68]
Nigeria
2013
Never testing for HIV
aOR 1.9 (1.3–2.7)
712
[68]
Nigeria
2013
Living with HIV
aOR 1.8 (1.2–2.8)
712
[68]
South Africa
2008
Regular drinking
aOR 4.1 (1.4–12.6)
147
[69]
South Africa
2008
Regular drinking to intoxication
aOR 2.6 (1.0–6.8)
147
[69]
South Africa
2008
Reporting symptoms of rectal trauma resulting from anal intercourse
aOR 4.3 (1.8–10.4)
147
[69]
South Africa
2010
Transactional anal intercourse
aOR 2.8 (1.0–8.3)
537
[54]
South Africa
2010
Self-identify as gay, homosexual, or queer
aOR 4.5 (1.0–20.0)
537
[54]
South Africa
2010
Ever been diagnosed with an STI
aOR 4.3 (2.1–8.3)
537
[54]
South Africa
2011
Self-perceived high risk of HIV
[20]
South Africa
2011
Receptive anal intercourse
South Africa
2011
South Africa
Association of HIV infection
Association of UAI
Sample size
Ref.
aOR 1.6 (1.0–2.6)
1744
[28
aOR 3.1 (2.2–4.3)
1299
[27]
OR/RR (95% CI)
&&
]
Sub-Saharan Africa
&&
aOR 2.3 (1.1–3.3)
285
aOR 10.0 (4.2–24.0)
285
[20]
Unprotected anal intercourse
aOR 2.5 (1.5–4.4)
285
[20]
2011
Unprotected receptive anal intercourse
aOR 4.3 (2.4–7.6)
285
[20]
South Africa
2011
Sex with someone known to be living with HIV
aOR 2.3 (1.1–4.9)
285
[20]
South Africa
2011
STI diagnosis
aOR 2.4 (1.1–5.2)
285
[20]
South Africa
2011
Older than 25
aOR 3.8 (3.2–4.6)
363
[55]
South Africa
2011
Self-identify as gay
aOR 2.3 (1.8–3.0)
350
[55]
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Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
HIV among MSM in countries with generalized HIV epidemics Baral et al. Table 2 (Continued) Country
Year of study
Association of HIV infection
Association of UAI
OR/RR (95% CI)
Sample size
Ref.
South Africa
2011
Monthly income less than ZAR500
aOR 1.4 (1.2–1.7)
363
[55]
South Africa
2011
Purchasing alcohol of drugs in exchange for sex with another man
aOR 3.9 (3.2–4.7)
363
[55]
South Africa
2011
Any reported unprotected receptive anal intercourse
aOR 4.4 (3.5–5.7)
363
[55]
South Africa
2011
2 sexual partners in last 6 months
aOR 2.8 (2.1–3.8)
364
[55]
South Africa
2011
3–5 sexual partners in the last 6 months
aOR 1.9 (1.4–2.6)
365
[55]
South Africa
2011
6–9 sexual partners in the last 6 months
aOR 5.7 (4.0–8.2)
363
[55]
South Africa
2011
10 or more sexual partners in the last 6 months
aOR 2.2 (1.5–3.3)
366
[55]
South Africa
2011
Not always wearing condoms with men
aOR 2.3 (1.0–5.4)
200
[13]
South Africa
2011
Having been blackmailed
aOR 4.4 (1.3–15.4)
200
[13]
South Africa
2011
Having been raped
aOR 5.7 (1.6–20.2)
200
[13]
South Africa
2011
More than 26 years old
aOR 4.2 (1.6–10.6)
200
[13]
South Africa
2011
Unemployed
aOR 3.7 (1.5–9.3)
200
[13]
South Africa
2011
Rural origin
aOR 6.0 (2.2–16.7)
200
[13]
South Africa
2011
Bisexual practices
aOR 0.3 (0.1–0.9)
200
[13]
South Africa
2013
Casual neighborhood partner vs. similar partner
aOR 3.3 (1.3–8.1)
239
[70]
South Africa
2013
Familiar partner vs. similar partner
aOR 2. 3 (1.0–5.0)
239
[70]
South Africa
2013
At least mild depression based on CES-D
aOR 2.5 (1.6–4.2)
316
[56]
South Africa
2013
High self-efficacy of condom use compared to very high
aOR 4.9 (2.3–10.4)
316
[56]
South Africa
2013
Low self-efficacy of condom use compared to very high
aOR 7.0 (3.2–15.4)
316
[56]
South Africa
2013
Very low self-efficacy of condom use compared to very high
aOR 6.1 (2.4–15.4)
316
[56]
South Africa
2013
3 or more sexual partners in the last 3 months
aOR 2.9 (1.8–4.8)
316
[56]
South Africa
2013
Been paid for sex in last 3 months
aOR 2.5 (1.4–4.5)
316
[56]
South Africa
2013
Alcohol 2 h prior to sex
aOR 2.0 (1.1–3.4)
316
[56]
South Africa
2013
Drugs 2 h prior to sex
aOR 3.2 (1.5–6.7)
316
[56]
South Africa
2013
Number of anal intercourse episodes
IRR 1.0 (1.0–1.0)
377
[71]
South Africa
2013
Number of months the dyadic sexual relationship lasted
IRR 1.2 (1.1–1.3)
377
[71]
South Africa
2013
Partner identified as a regular male partner
IRR 1.8 (1.4–2.3)
377
[71]
South Africa
2013
Receptive anal intercourse only
IRR 0.5 (0.3–0.8)
377
[71]
South Africa
2013
Experiences of homonegativity
IRR 3.9 (1.0–15.3)
377
[71]
South Africa
2013
Safer sex self-efficacy
IRR 0.2 (0.1–0.3)
377
[71]
South Africa
2013
Income
IRR 0.6 (0.4–0.9)
377
[71]
South Africa
2013
IRR 1.9 (1.3–2.7)
377
[71]
Tanzania
2011
Paid someone for sex in last year
aOR 4.6 (1.0–21.4)
509
[59]
Tanzania
2011
Injecting drug use in last 3 months
aOR 2.4 (1.1–5.3)
307
[59]
Tanzania
2011
HCV positive test result
aOR 4.7 (2.1–10.6)
509
[59]
Uganda
2012
Over 25 years old
aOR 4.3 (1.3–14.0)
300
[60
Circumcised
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&&
]
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Epidemiology: concentrated epidemics Table 2 (Continued) Country
Year of study
Uganda
2012
Illicit drug consumption ever
aOR 0.2 (0.0–0.7)
300
[60
&&
Uganda
2012
History of homophobic abuse ever
aOR 5.4 (2.0–14.8)
300
[60
&&
LMICs
2007
MSM vs. background populations
OR 19.3 (18.8–19.8)
Malawi, Namibia, Botswana
2009
Had transactional sex
OR 1.7 (1.1–2.7)
93
[4 ]
Malawi, Namibia, Botswana
2009
Older than 25
OR 4.0 (2.0–8.0)
93
[4 ]
Malawi, Namibia, Botswana
2009
Not always wearing condoms during sex
OR 2.6 (1.3–4.9)
93
[4 ]
Malawi, Namibia, Botswana
2009
Increasing age groups
OR 2.6 (1.6–4.2)
93
[4 ]
Malawi, Namibia, Botswana
2009
Being employed
OR 1.7 (1.1–2.6)
93
[4 ]
Malawi, Namibia, Botswana
2009
Not always wearing condoms with men
OR 2.2 (1.3–3.8)
93
[4 ]
Malawi, Namibia, Botswana
2009
Not always wearing condoms with casual partners
OR 4.9 (2.1–11.4)
93
[4 ]
Malawi, Namibia, Botswana
2009
Not always wearing condoms with regular partners
OR 4.3 (1.7–10.9)
93
[4 ]
Malawi, Namibia, Botswana
2009
Ever been diagnosed with an STI
OR 2.7 (1.5–4.8)
93
[4 ]
Association of HIV infection
Association of UAI
Sample size
OR/RR (95% CI)
Ref. ] ]
Latin America and the Caribbean No data Former Soviet Union/ Eastern Europe No data Multicountry data [3
&&
]
&
&
&
&
&
&
&
&
&
aHR, adjusted hazard ratio; CI, confidence interval; HBV, hepatitis B virus; HR, hazard ratios; IRR, incidence rate ratios; LMIC, low- and middle-income country; OR, odds ratio; RR, relative risk; STI, sexual transmitted infections; UAI, unprotected anal intercourse.
WAYS FORWARD
&&
There are a number of determinants of the limited data available characterizing MSM in generalized HIV epidemics. The data that are available appear to be highly concentrated among younger MSM probably because of these men having larger sexual and social networks, making them more likely to be accrued into studies. Younger men have had less opportunity for the acquisition of HIV and thus have lower prevalence of HIV, though the Thai data on MSM highlight that these young men also have the highest incidence of HIV [17,75,76 ]. Separately, many of the countries with generalized HIV epidemics also criminalize same-sex sexual practices that has been shown to negatively affect uptake of services, and participation in organized research studies [77 ,78 ,79]. There has also been limited willingness to fund HIV surveillance on MSM in these settings because of the assumptions that these populations are small and largely irrelevant in the context of broader HIV epidemics. A recent model has characterized five levels of HIV risks: individual, network, community, policy, &&
&&
164
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and stage of the HIV epidemic [80 ]. In the literature reviewed on MSM in generalized HIV epidemics through this study, factors at almost every level of risk were found to be associated with HIV. Correlates of living with HIV included individual factors such as having symptoms of or being diagnosed with other STI [4 ,15,20,27,28 ,54,59,67], high numbers of partners [25,55], having receptive anal sex [15,20,28 ,55,67], not always using condoms [4 ,13,20,28 ,50,55], drug use [28 ,59,60 ], and transactional sex [4 ,50,54,55,59,67]. Furthermore, literature in this review identified network-level factors associated with living with HIV including not having a family confidant [15] and living alone or with a roommate compared to living with family [15]. Community-level factors found to be associated with living with HIV included forms of stigma, specifically having been blackmailed [13] and history of homophobic abuse [60 ]. The studies in this review did not identify public policy factors that were significantly related to HIV status, highlighting the dearth of data characterizing the relationship between justice and health in &
&&
&&
&
&&
&&
&&
&
&&
Volume 9 Number 2 March 2014
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HIV among MSM in countries with generalized HIV epidemics Baral et al.
these contexts. Out of the 51 countries in this review, 31 criminalize sex between men [81]. Out of the 22 countries for which no HIV prevalence data for MSM could be found, slightly more than half [12 ] criminalize sex between men. A study of six of the countries included in this review (Botswana, Malawi, Namibia, Swaziland, Zambia, and Zimbabwe) found that less than US$2000 from the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund) was allocated by these countries for HIV surveillance and services for MSM, and the percentage of total possible funding from the President’s Emergency Plan For AIDS Relief (PEPFAR) for surveillance and services for MSM ranged from 0.04% to a high of 9.1% [82]. A study that included four of the countries in this review (Ethiopia, Guyana, Mozambique, and Nigeria) found that less than 1% of funding from the Global Fund in these countries was allocated to surveillance services for MSM, and 10% or less of PEPFAR country budgets (excluding treatment costs) were allocated for services for MSM [83,84]. To date, the assumptions about the epidemiology of HIV in the context of generalized HIV epidemics of risk being evenly distributed in these populations have led the design of HIV surveillance systems. Consequently, limited investments have been made into data collection among MSM as explored here, PWID, but also among female, male, and transgender sex workers, and among transgender populations more broadly [85,86]. Furthermore, epidemiologic surveillance and research characterizing the actual burden of prevalent and incident HIV infections among MSM and other key populations in generalized HIV epidemics will facilitate evidence-based HIV policy decisions including funding allocations for specific populations in the context of generalized HIV epidemics. &&
CONCLUSION The data presented here suggest that the dynamics of HIV infection among men are more similar across the world than they are different. As data continue to emerge, the consistency of the burden of HIV among MSM across regions is striking and is a manifestation of the biology of HIV that transcends politics, economics, and borders. Fortunately, many HIV epidemics among average-risk reproductive-age adults are slowing across both generalized and concentrated settings. It is in this context that the high HIV incidence among MSM, and especially young MSM needs to be characterized, understood, and ultimately addressed. This trend suggests a change in the trajectory of generalized HIV epidemics, a change that we may miss if we continue to
understudy these populations based on unproved and dated assumptions. Acknowledgements None. Conflicts of interest The authors have no conflicts of interest to declare.
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40. Geibel S, van der Elst EM, King’ola N, et al. ‘Are you on the market?’ A capture-recapture enumeration of men who sell sex to men in and around Mombasa, Kenya. AIDS 2007; 21:1349–1354. 41. van der Elst EM, Okuku HS, Nakamya P, et al. Is audio computer-assisted selfinterview (ACASI) useful in risk behaviour assessment of female and male sex workers, Mombasa, Kenya? PLoS One 2009; 4:e5340. 42. Geibel S, King’ola N, Okal J. Male sex work in Mombasa, Kenya: Driven by poverty or identity. South Africa: International AIDS Society; 2009. 43. Muraguri N, Okal J, Broz D. Burden of HIV and sexual behavior among men who have sex with men and male sex workers in Nairobi, Kenya. XIX International AIDS Conference; 22–27 July 2012; Washington, DC; 2012. 44. Singh K, Brodish P, Mbai F, et al. A venue-based approach to reaching MSM, IDUs and the general population with VCT: a three study site in Kenya. AIDS Behav 2012; 16:818–828. 45. Price MA, Rida W, Mwangome M, et al. Identifying at-risk populations in Kenya && and South Africa: HIV incidence in cohorts of men who report sex with men, sex workers, and youth. J Acquir Immune Defic Syndr 2012; 59:185–193. This study reviewed existing data from cohorts of MSM in South Africa and Kenya. 46. Wahome E, Fegan G, Okuku HS, et al. Evaluation of an empiric risk & screening score to identify acute and early HIV-1 infection among MSM in Coastal Kenya. AIDS (London, England) 2013. [Epub ahead of print] This study followed a cohort to identify acute and early HIV-1 infection in MSM to help reduce transmission during this highly infectious stage. 47. AIDS Unit, Ministry of Health & Quality of Life, Mauritius. Integrated behavioral and biological surveillance survey among men who have sex with men. Mauritius National AIDS Secretariat; 2010 http://health.gov.mu/English/ Documents/msmreport.pdf. 48. Merrigan M, Azeez A, Afolabi B, et al. HIV prevalence and risk behaviours among men having sex with men in Nigeria. Sex Transm Infect 2011; 87: 65–70. 49. Adebajo SB, Eluwa GI, Allman D, et al. Prevalence of internalized homophobia and HIV associated risks among men who have sex with men in Nigeria. Afr J Reprod Health 2012; 16:21–28. 50. Vu L, Adebajo S, Tun W, et al. High HIV prevalence among men who have sex with men in Nigeria: implications for combination prevention. J Acquir Immune Defic Syndr 2013; 63:221–227. 51. Sheehy M, Tun W, Vu L, et al. High levels of bisexual behavior and factors associated with bisexual behavior among men having sex with men (MSM) in Nigeria. AIDS Care 2013. [Epub ahead of print] 52. Parry C, Petersen P, Carney T, et al. Rapid assessment of drug use and sexual HIV risk patterns among vulnerable drug-using populations in Cape Town, Durban and Pretoria, South Africa. SAHARA J 2008; 5:113–119. 53. Beyrer C, Trapence G, Motimedi F, et al. Bisexual concurrency, bisexual && partnerships, and HIV among Southern African men who have sex with men. Sex Transm Infect 2010; 86:323–327. This study demonstrated that MSM often had safer risk practices during sex with women as compared with other men given the limited knowledge about anal sex as a risk factor for HIV transmission. 54. Burrell E, Mark D, Grant R, et al. Sexual risk behaviours and HIV-1 prevalence among urban men who have sex with men in Cape Town, South Africa. Sex Health 2010; 7:149–153. 55. Lane T, Raymond HF, Dladla S, et al. High HIV prevalence among men who have sex with men in Soweto, South Africa: results from the Soweto Men’s Study. AIDS Behav 2011; 15:626–634. 56. Tucker A, Liht J, de Swardt G, et al. An exploration into the role of depression and self-efficacy on township men who have sex with men’s ability to engage in safer sexual practices. AIDS Care 2013. [Epub ahead of print] 57. Dunkle KL, Jewkes RK, Murdock DW, et al. Prevalence of consensual malemale sex and sexual violence, and associations with HIV in South Africa: a population-based cross-sectional study. PLoS Med 2013; 10:e1001472. 58. Kennedy C. BC, Donastory Y, Perez M, et al. Exploring the positive health, & dignity and prevention needs of female sex workers, men who have sex with men and transgender women in the Dominican Republic and Swaziland. Baltimore: USAID: Project Search – Research to Prevention; 2013. This study was the first to demonstrate the HIV prevention needs of MSM living with HIV in Swaziland. 59. Dahoma M, Johnston LG, Holman A, et al. HIV and related risk behavior among men who have sex with men in Zanzibar, Tanzania: results of a behavioral surveillance survey. AIDS Behav 2011; 15:186–192. 60. Hladik W, Barker J, Ssenkusu JM, et al. HIV infection among men who have && sex with men in Kampala, Uganda: a respondent driven sampling survey. PLoS One 2012; 7:e38143. This study included a HIV prevalence estimate among MSM in Uganda but importantly also demonstrated the significance of experienced homophobia among these men. 61. Global AIDS Response Progress Reporting. The Bahamas: UNAIDS; 2012. 62. HIV Behavioral Surveillance Study. Belize: Ministry of Health; 2012. 63. Declaration of Engagement against HIV/AIDS UNGASS: Report on the National Situation Port-au-Prince, Haiti. UNAIDS; 2012. 64. amfAR. MSM, HIV, and the Road to Universal Access: how far have we come. New York: amfAR; 2008.
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HIV among MSM in countries with generalized HIV epidemics Baral et al. 65. Ru¨u¨tel K, Aire T, Salekesˇin M, Pervilhac C. HIV epidemic in Estonia: analysis of strategic information. Denmark: World Health Organization; 2011. 66. Newman PA, Lee SJ, Roungprakhon S, Tepjan S. Demographic and behavioral correlates of HIV risk among men and transgender women recruited from gay entertainment venues and community-based organizations in Thailand: implications for HIV prevention. Prev Sci 2012; 13:483–492. 67. Sanders EJ, Graham SM, Okuku HS, et al. HIV-1 infection in high risk men who have sex with men in Mombasa, Kenya. AIDS (London, England) 2007; 21:2513–2520. 68. Vu L, Andrinopoulos K, Tun W, Adebajo S. High levels of unprotected anal intercourse and never testing for HIV among men who have sex with men in Nigeria: evidence from a cross-sectional survey for the need for innovative approaches to HIV prevention. Sex Transm Infect 2013. [Epub ahead of print] 69. Lane T, Shade SB, McIntyre J, Morin SF. Alcohol and sexual risk behavior among men who have sex with men in South African township communities. AIDS Behav 2008; 12:78–85. 70. Sandfort T, Yi H, Knox J, Reddy V. Sexual partnership types as determinant of HIV risk in South African MSM: an event-level cluster analysis. AIDS Behav 2013; 17 (Suppl 1):S23–S32. 71. Arnold MP, Struthers H, McIntyre J, Lane T. Contextual correlates of per partner unprotected anal intercourse rates among MSM in Soweto, South Africa. AIDS Behav 2013; 17 (Suppl 1):S4–S11. 72. Baral S, Grosso AL, Adams D, Kennedy C. Examining prevalence of HIV infection and risk factors among female sex workers (FSW) and men who have sex with men (MSM) in Swaziland. Washington, DC, USA: USAID; 2013; http://www.jiasociety.org/index.php/jias/article/view/18768. 73. Beyrer C, Sullivan P, Sanchez J, et al. The global HIV epidemics in MSM: time to act. AIDS (London, England) 2013. [Epub ahead of print] 74. Baral S, Papworth E, Logie C, Beyrer C. In: Makadon H, Mayer K, Goldhammer H, Potter J, editors. LGBT Health: global perspectives and experiences American College Of Physicians – The Fenway Guide to LGBT Health Textbook; in press. 75. Baral S, Phaswana-Mafuya N. Rewriting the narrative of the epidemiology of HIV in sub-Saharan Africa. SAHARA J 2012; 9:127–130. 76. Beyrer C, Wirtz A, Walker D, et al. The global HIV epidemics among men who && have sex with men: epidemiology, prevention. In: Bank W, editor. Access to Care and Human Rights. Washington, DC: World Bank Publications; 2011. This study reviewed all available data and highlighted the cost–effectiveness of HIV prevention approaches for MSM in low and middle-income countries.
77. Poteat T, Diouf D, Drame FM, et al. HIV risk among MSM in Senegal: a qualitative rapid assessment of the impact of enforcing laws that criminalize same sex practices. PLoS One 2011; 6:e28760. This study explored how the manifestations of criminalization of same sex practices including arrests of MSM results in the disruption of health services. 78. Fay H, Baral SD, Trapence G, et al. Stigma, healthcare access, and HIV & knowledge among men who have sex with men in Malawi, Namibia, and Botswana. AIDS Behav 2011; 15:1088–1097. This study explored the importance of stigma in limiting uptake of health services among MSM in three Southern African countries. 79. Henry E, Marcellin F, Yomb Y, et al. Factors associated with unprotected anal intercourse among men who have sex with men in Douala, Cameroon. Sex Transm Infect 2010; 86:136–140. 80. Baral S, Logie CH, Grosso A, et al. Modified social ecological model: a tool to && guide the assessment of the risks and risk contexts of HIV epidemics. BMC Public Health 2013; 13:482. This study proposed a framework for data collection for HIV risks among MSM in generalized HIV epidemics. 81. International Lesbian, Gay, Bisexual, Trans and Intersex Association. Male to Male Relationships; 2009. Available http://ilga.org/. [Accessed 8 November 2013] 82. amfAR, Johns Hopkins Bloomberg School of Public Health. Achieving an AIDS-Free generation for gay men and other MSM in Southern Africa. New York: amfAR; 2013. http://www.amfar.org/uploadedFiles/_amfarorg/Articles/ Around_The_World/GMT/2013/MSM Global Report 051613.pdf. [Accessed 15 October 2013] 83. amfAR, Johns Hopkins Bloomberg School of Public Health. Achieving an AIDS-free generation for gay men and other MSM: financing and implementation of HIV programs targeting MSM. NY: amfAR; 2012. http://www.amfar. org/uploadedFiles/_amfar.org/In_The_Community/Publications/MSM-Global Rept2012.pdf. [Accessed 13 June 2013] 84. Grosso AL, Tram KH, Ryan O, Baral S. Countries where HIV is concentrated among most-at-risk populations get disproportionally lower funding from PEPFAR. Health Aff (Millwood) 2012; 31:1519–1528. 85. Baral SD, Poteat T, Stromdahl S, et al. Worldwide burden of HIV in transgender women: a systematic review and meta-analysis. Lancet Infect Dis 2013; 13:214–222. 86. Baral S, Beyrer C, Muessig K, et al. Burden of HIV among female sex workers in low-income and middle-income countries: a systematic review and metaanalysis. Lancet Infect Dis 2012; 12:538–549. &&
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The epidemiology of HIV among men who have sex with men in countries with generalized HIV epidemics Stefan D. Baral, Ashley Grosso, Claire Holland, and Erin Papworth
Purpose of review Key populations at high risk for HIV acquisition and transmission, such as MSM, have long been identified as essential subpopulations for epidemiological surveillance of the HIV epidemic. However, surveillance systems in the context of generalized and widespread HIV epidemics have traditionally excluded these men. Recent findings Emerging and consistent data highlight the disproportionate burden of HIV among MSM that exists when compared with other men of reproductive age across countries with generalized epidemics. Correlates of prevalent HIV infection include individual-level determinants of HIV acquisition and transmission similar to that found in concentrated HIV epidemics and community-level structural factors, such as stigma, being blackmailed, and history of homophobic abuse. HIV incidence was only available from two countries (Kenya, Thailand) with generalized HIV epidemics, but in both settings was an order of magnitude higher than that of other populations. Summary The data presented here suggest that the dynamics of HIV infection among men are more similar across the world than they are different. Many HIV epidemics among average-risk reproductive age adults are slowing across both generalized and concentrated settings. It is in this context that high HIV incidence is observed among MSM, especially young MSM. This trend suggests a change in the trajectory of these HIV epidemics, a change that we may miss if we continue to understudy these populations based on unproved and dated assumptions. Keywords epidemiology, HIV, men who have sex with men, MSM, Sub-Saharan Africa, surveillance
INTRODUCTION The WHO and the Joint United Nations Programme on HIV/AIDS (UNAIDS) have released two primary iterations of HIV surveillance since the HIV pandemic was recognized in the early 1980s. First generation surveillance recommendations were rooted in clinical and laboratory case reporting focused on provider-level surveillance and sentinel studies [1 ,2]. In 2000, second generation surveillance guidelines identified the need to proactively evaluate the trends of the HIV epidemic in each country. ‘Know your epidemic’ became an international mantra implying that the HIV epidemiology of a given context was fluid, and surveillance measures must equally consider the risk and trends of HIV acquisition and transmission, the effectiveness of HIV programming, and the result and impact of policy strategies [1 ]. Within this framework, specific populations at heightened risk of HIV were
identified as integral groups for data collection, evaluation, and applied analysis to support policy development [1 ]. These populations included MSM, sex workers, and people who use drugs and eventually were collectively termed as key populations [1 ,2]. Although these recommendations were issued in 2000, the application of surveillance measures for &&
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Key Populations Program, Center for Public Health and Human Rights, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA Correspondence to Stefan D. Baral, Key Populations Program, Center for Public Health and Human Rights, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, E7146, 615 N. Wolfe Street, Baltimore, MD 21205, USA. Tel: +1 410 502 8975; fax: +1 410 614 8371; e-mail: [email protected] Curr Opin HIV AIDS 2014, 9:156–167 DOI:10.1097/COH.0000000000000037 Volume 9 Number 2 March 2014
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HIV among MSM in countries with generalized HIV epidemics Baral et al.
KEY POINTS HIV surveillance in generalized HIV epidemics has focused on average-risk reproductive-age adults with limited attention to key populations in these settings. The biology of the efficient transmission of HIV during anal sex with viremic serodiscordant partners is sustained across the world resulting in the disproportionate burden of HIV among MSM wherever measured. To comprehensively understand and address the high burden of HIV among MSM in generalized epidemics, data collection should include individual, community, and structural factors.
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MSM globally has been limited [3 ,4 ,5 ]. Some countries, where the epidemic is considered concentrated in key populations such as MSM, female sex workers, and people who inject drug (PWID), have led the way in sentinel surveillance among key populations, and focused programming has shown positive effects in the reduction of incidence of HIV in these countries [6–9]. However, in epidemics considered generalized by the WHO/UNAIDS, definition of greater than 1% in antenatal clinic surveillance, the focus on data, associations of risk, and targeted programming for key populations has been overshadowed by large-scale general population HIV surveillance and prevention interventions [10,11,8]. Nevertheless, many of the risks associated with the acquisition and transmission of HIV among key populations are not unique to epidemics in which key populations carry the highest burden of disease, and emerging data indicate the biological and specific behavioral factors among these populations contribute to the heightened risk of HIV regardless of the epidemiology of HIV in a given context [12 ,13,14 ]. &&
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METHODS AND RESULTS OF SEARCH The review was conducted to assess both peerreviewed and nonpeer-reviewed, also known as gray literature. The number of countries included in the review, defined as those with generalized HIV epidemics with greater than 1% adult (aged 15–49) HIV prevalence in the 2012 or 2013 UNAIDS Global Report, was 51. Countries that reported greater than 1% prevalence in 2013 but not 2012 were included in the review (the Democratic Republic of the Congo and Comoros), and countries where prevalence dropped under 1% in 2013 were also included, but no articles were identified with relevant data. Search terms included medical subject headings or
other associated terms for HIV cross referenced with ‘MSM’ or other related terms and the countries in the review. PubMed was used to search peerreviewed articles, and gray literature was obtained from the www.HIVAIDSClearinghouse.eu, United States Agency for International Development (USAID) Development Experience Clearinghouse, United Nations Educational Scientific and Cultural Organization (UNESCO) HIV and AIDS Education Clearinghouse, Google, WHO, and UNAIDS. Inclusion criteria were studies with HIV prevalence or incidence data for the samples of MSM or associations with HIV infection or unprotected anal intercourse (UAI) within samples of MSM. The search encompassed January 1, 2007 until September 9, 2013. Articles were screened by two reviewers (C.H., E.P.) for the title and abstract review. Full text data abstraction was conducted by three reviewers (A.G., C.H., E.P.), and 10% of the articles underwent double data abstraction. There were 59 studies included in the review, with data from 30 different countries and 72 HIV prevalence studies. The review identified 10 incidence studies from two countries (Kenya and Thailand). Associations of HIV and UAI included odds ratios (OR), hazard ratios, incidence rate ratios (IRR), and Chi-square statistics.
EPIDEMIOLOGY OF HIV AMONG MSM IN GENERALIZED EPIDEMICS: HIV PREVALENCE DATA AMONG MSM The lack of peer-reviewed literature measuring the burden of HIV among MSM in most countries with generalized epidemics is evident. The majority of HIV prevalence data was retrieved from Thailand, Kenya, and South Africa (52.8%, 28/53 studies). Thailand was the only country in Southeast Asia categorized as a generalized HIV epidemic, though significant regional variation has been reported in peer-reviewed literature [9]. HIV prevalence among MSM in Thailand ranged from 8.2% (n ¼ 450) to 68.2% (n ¼ 173) compared with 1.1% among adults of reproductive age, with eight out of nine studies found in peer-reviewed literature (Table 1) [3 ,4 , 13,15 – 17,18 ,19 – 22,23 ,24 – 27,28 ,29 ,30 – 37, 38 ,39–44,45 ,46 ,47–52,53 ,54–57,58 ,59,60 , 61–63,64]. In Kenya, where the reproductive-age population HIV prevalence is reported as 6.1%, studies found HIV prevalence among MSM to be between 12.3% (n ¼ 171) and 43.0% (n ¼ 114), with regional variation and mainly from peer-reviewed literature [18 ]. The most studies from any one country were obtained from Thailand and South Africa [9], and HIV prevalence among MSM in South Africa ranged from 10.0% (n ¼ 10) to 40.7% (n ¼ 285)
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Epidemiology: concentrated epidemics Table 1. Burden of HIV infection among MSM in the context of generalized HIV epidemics Year of study
Country
HIV prevalence among MSM (sample size)
Incidence (n ¼ /100 person years), 95% CI, (sample size)
General population age 15–49 HIV prevalence (2012)
References
Asia Thailand
2009
8.20% (n ¼ 450)
1.1%
[15]
Thailand
2009
21.2% (n ¼ 1125)
1.1%
[15]
Thailand
2009
17.5% (n ¼ 1575)
1.1%
[15]
Thailand
2009
17.3% (n ¼ 1121)
4.1 (n ¼ 308)b
1.1%
[23
&&
Thailand
2009
28.3% (n ¼ 399)
6.4 (n ¼ 94)b
1.1%
[23
&&
Thailand
2009
30.8% (n ¼ 400)
7.7 (n ¼ 99)b
1.1%
[23
&&
Thailand
2010
18.8% (n ¼ 414)
1.1%
[24]
Thailand
2011
12.9% (n ¼ 551)
1.1%
[25]
Thailand
2012
21.6% (n ¼ 1544)
1.1%
[26]
Thailand
2013
46.2% (n ¼ 485)
1.1%
[27]
Thailand
2013
21.3% (n ¼ 1744)
5.9, 95% CI 5.2–6.8 (n ¼ 1744)
1.1%
[28
Thailand
2013
28.3% (n ¼ 4398)
6.3 (n ¼ 4398)
1.1%
[29 ]
Thailand
2013
68.2% (n ¼ 173)
1.1%
[16]
Benin
2008
25.5%
1.1%
[30]
Benin
2009
4.9%
1.1%
[31]
Botswana
2009
19.7% (n ¼ 117)
23.0%
[4 ]
Burkina Faso
2012
0.98%
1.0%
[32]
Burkina Faso
2013
19.0%
1.0%
[33]
Burundi
2010
2.40%
1.3%
[34]
Cameroon
2012
37.0% (n ¼ 511)
4.5%
[35]
CAR
2012
17.0%
4.6%a
[36]
ˆ te D’Ivoire Co
2010
18.5%
3.2%
[37]
ˆ te D’Ivoire Co
2011
50.0% (n ¼ 96)
3.2%
[38
Gambia
2013
9.8% (n ¼ 205)
1.3%
[39]
Ghana
2008
25.0%
Kenya
2007
38.0% (n ¼ 60)
Kenya
2007
Kenya
8.2, 95% CI 3.7–18.3 (n ¼ 81)
] ] ]
&&
]
&
Sub-Saharan Africa
&
&&
]
1.4%
[30]
6.1%
[40]
43.0% (n ¼ 114)
6.1%
[18
&&
2007
12.3% (n ¼ 171)
6.1%
[18
&&
Kenya
2009
19.7% (n ¼ 259)
6.1%
[41]
Kenya
2009
25.0% (n ¼ 285)
6.1%
[42]
Kenya
2012
26.3% (n ¼ 273)
6.1%
[43]
Kenya
2012
19.8% (n ¼ 253)
6.1%
[44]
Kenya
2012
6.8 CI 4.9–9.2 (n ¼ 327)
6.1%
[45
Kenya
2013
7.5 CI 6.0–9.5
6.1%
[46 ]
Kenya
2013
8.6 CI 6.7–11.0 (n ¼ 449)
6.1%
[18
6.1%
&
[4 ]
14.3% (n ¼ 449)
20.9, 95% CI 6.7–64.9 (n ¼ 60)
] ]
&&
]
&
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]
Malawi
2009
21.4% (n ¼ 201)
Mauritius
2010
8.1% (n ¼ 358)
1.2%
[47]
Mozambique
2011
8.2% (n ¼ 496)
11.1%
[21]
Mozambique
2011
9.1% (n ¼ 583)
11.1%
[21]
Mozambique
2011
3.1% (n ¼ 353)
11.1%
[21]
Namibia
2009
12.4% (n ¼ 218)
13.3%
[4 ]
Nigeria
2011
12.4% (n ¼ 879)
3.1%
[48]
Nigeria
2012
13.4% (n ¼ 1082)
3.1%
[49]
Nigeria
2013
21.1% (n ¼ 712)
3.1%
[50]
Nigeria
2013
22.1% (n ¼ 520)
3.1%
[51]
South Africa
2008
10.0% (n ¼ 10)
17.9%
[19]
South Africa
2008
35.0% (n ¼ 37)
17.9%
[52]
South Africa
2010
17.4% (n ¼ 534)
17.9%
[53
South Africa
2010
10.4% (n ¼ 539)
17.9%
[54]
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Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
HIV among MSM in countries with generalized HIV epidemics Baral et al. Table 1 (Continued) Incidence (n ¼ /100 person years), 95% CI, (sample size)
Year of study
HIV prevalence among MSM (sample size)
South Africa
2011
25.5% (n ¼ 200)
17.9%
[13]
South Africa
2011
13.2% (n ¼ 378)
17.9%
[55]
South Africa
2011
40.7% (n ¼ 285)
17.9%
[20]
South Africa
2013
34.5% (n ¼ 171)
17.9%
[56]
South Africa
2013
31.4% (n ¼ 35)
17.9%
[57]
Swaziland
2013
17.6% (n ¼ 328)
17.9%
[58 ]
Tanzania
2011
12.3% (n ¼ 509)
5.1%
Togo
2013
20.3%
2.9%
[33]
Uganda
2012
13.7% (n ¼ 300)
7.2%
[60
Zambia
2010
33.0% (n ¼ 641)
12.7%
Country
General population age 15–49 HIV prevalence (2012)
References
&
[59] &&
]
[37]
Latin America and the Caribbean Bahamas
2011
8.00%
2.8%a
[22]
Bahamas
2012
14.0% (n ¼ 36)
2.8%a
[61]
Bahamas
2012
26.0%
2.8%a
[61]
Belize
2012
13.9% (n ¼ 131)
1.4%
[62]
Guyana
2011
20.0%
1.3%
[22]
Guyana
2013
19.4%
1.3%
[17]
Haiti
2012
18.1% (n ¼ 860)
2.1%
[63]
Jamaica
2011
32.0%
1.7%
[22]
Jamaica
2011
25.0%
1.7%
[22]
Jamaica
2013
37.6%
1.7%
[17]
Suriname
2011
7.0%
1.1%
[22]
Trinidad and Tobago
2011
20.0%
1.5%a
[22]
2012
1.7%
1.3%a
[64]
2007
12.8% (n ¼ 63 538)
Former Soviet Union/Eastern Europe Estonia Multicountry data LMICs
NA
[3
&&
]
CAR, Central African Republic; CI, confidence interval; LMIC, low- and middle-income country. a 2011 prevalence used because 2012 prevalence was unavailable. b Annual density.
compared with 17.9% among reproductive-age adults [19,20,17]. There was significant intracountry variability in HIV prevalence among MSM based on the geographic location of the study or the timing of the study. For example, in Mozambique, one study reported 8.2% (n ¼ 496), 9.1% (n ¼ 583), and 3.1% (n ¼ 353) HIV prevalence across three cities compared with a reproductive age prevalence of 11.1%, of which over 62% of infections are among women [21,17]. In Benin, two nonpeer-reviewed studies presented a disparity of results with 25.5% reported in 2008 and 4.9% in 2009 compared with 1.1% national HIV prevalence [65,17]. In Latin America and the Caribbean, where reproductive-age adult HIV prevalence remains under 3%, HIV prevalence reported among MSM ranged from 7.0% in Suriname to 37.6% in Jamaica, and the majority of prevalence results were around
20% [22,17]. This finding is consistent with other systematic reviews that showed globally the highest burden of HIV among MSM was found in the Caribbean [14 ]. &&
HIV INCIDENCE DATA AMONG MSM IN GENERALIZED EPIDEMICS Incidence data among MSM in generalized epidemics were obtained from eight unique studies conducted in only two countries, Kenya and Thailand (Table 1). Kenya studies described HIV incidence between 6.8 [95% confidence interval (CI) 4.9– 9.2; n ¼ 327] and 20.9 (95% CI 6.7–64.9; n ¼ 60) per 100 person years [40,45 ]. Thailand data were more consistent, and annual incidence density reports showed 4.1 (n ¼ 308), 6.4 (n ¼ 94), and 7.7 (n ¼ 99), respectively, with incidence in 100 person years reported as 8.2 (95% CI 3.7–18.3; n ¼ 81), 5.9
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Epidemiology: concentrated epidemics
(95% CI 5.2–6.8; n ¼ 1744), and 6.3 (n ¼ 4398) (Table 1). There is a dearth of incidence studies among MSM in countries with generalized epidemics; however, it is important to note the rates described in the studies suggested a consistently high HIV incidence in these two countries (Table 1).
IDENTIFIED HIV RISKS AMONG MSM IN GENERALIZED EPIDEMICS In Thailand, associations of HIV were similar across studies, with lower education levels, unprotected sex with a male partner, and co-infections of other sexual transmitted infections (STI) consistently reported as associated with HIV infection (Table 2) [3 ,4 ,13,15,18 ,20,25,27,28 ,39,44,48,50,54,56, 59,60 ,66–71]. Associations between HIV and age varied in Thailand; however, individuals between 22 and 29 years of age were generally reported as more likely to be living with HIV compared with those aged 18–21, though HIV incidence was highest among the youngest group. In Kenya, one study found significant associations with HIV infection to be having had male-only sex partners in last 3 months [adjusted odds ratio (aOR) 6.3, 95% CI 2.3–17.0; n ¼ 285]; perianal condylomata on examination (aOR 5.1, 95% CI 1.1–24.0; n ¼ 285) and the per year increase in age from 18 to 24 (aOR 1.1, 95% CI 1.0–1.2; n ¼ 285) [67]. In South Africa, social factors and socio-economic status were associated with HIV infection, such as having been raped, having been blackmailed, unemployment, types of sexual partners, and limited use of condoms during the last sexual act with a male partner. Having two or more sexual partners was reported as associated with HIV in Thailand, Kenya, and South Africa studies, and commercial sex was reported as associated with HIV in Kenya, Nigeria, South Africa, and Tanzania. Notably, in the countries where a substantial body of data exists (South Africa, Nigeria, Thailand, and Kenya) results show cultural and countryspecific trends in demographics of MSM at risk for disease acquisition and transmission; however, biological risk factors remain consistent among MSM in these countries with generalized epidemics. Every country with more than one study among MSM retrieved for this analysis reported receptive or receptive and insertive anal intercourse as associated with HIV. In Thailand, two studies reported having receptive only or both receptive and insertive anal sex as associated with HIV infection 2007: aOR 2.5, 95% CI 1.7–3.6; n ¼ 1125; 2013: aOR 1.8, 95% CI 1.3–2.3; n ¼ 1744) [15,28 ]. In Kenya, individuals who reported receptive and insertive anal intercourse in the last 3 months were eight times &&
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more likely to be infected with HIV (aOR 8.0, 95% CI 2.9–22.0; n ¼ 285) [67]. Also, in Kenya, individuals who reported receptive anal intercourse were at 10 times higher risk of HIV infection [adjusted incidence rate ratio (aIRR) 9.7, 95% CI 3.8–25.1; n ¼ 449] [18 ]. In Nigeria, MSM participants who reported receptive anal intercourse in the last 6 months were four times more likely to be infected with HIV (aOR 4.2, 95% CI 1.5–12.3; n ¼ 154) [48]. MSM in South Africa who reported receptive anal intercourse were 10 times more likely to be infected with HIV (aOR 10.0, 95% CI 4.2–24.0; n ¼ 285) and those that reported unprotected receptive anal intercourse were four times more like to be living with HIV (aOR 4.3, 95% CI 2.4–7.6; n ¼ 285) [20]. Few studies have reported structural determinants of HIV risk, though studies among MSM in Uganda, Malawi, Namibia, and Botswana highlighted the significance of experienced and perceived stigma in potentiating HIV risks [4 ,60 ]. &&
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HIV prevention needs of MSM in generalized epidemics The HIV prevention needs of MSM in generalized epidemics may be different from those in concentrated epidemics. In generalized epidemics, HIV prevalence is higher in the population as a whole and usually disproportionately affects women. Thus, in generalized epidemics, MSM have higher HIV acquisition risks secondary to their heterosexual partnerships that needs to be considered in HIV prevention programs for these men [53 ]. Separately, HIV prevention, treatment, and care services have been designed for the general populations with limited competency in addressing the needs of specific populations resulting in exclusion of MSM or self-exclusion by MSM secondary to perceived and enacted stigma. The outcome is the common misconception among MSM that HIV transmission is more likely through vaginal intercourse than through anal intercourse [72]. Given the significantly higher HIV transmission probability during anal intercourse with a serodiscordant viremic partner as compared with that of vaginal intercourse, these misconceptions reinforce the need for tailored prevention messages and HIV service delivery for MSM in both generalized and concentrated epidemics globally [14 ]. Implementation science studies will facilitate characterizing appropriate service delivery models ranging from stand-alone to integrated for MSM in the context of widespread and generalized epidemics, stigma, and limited financial and health resources [73,74]. &&
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HIV among MSM in countries with generalized HIV epidemics Baral et al. Table 2. Significant associations of prevalent HIV infection and unprotected anal intercourse among MSM in the context of generalized HIV epidemics Country
Year of study
Association of HIV infection
Association of UAI
OR/RR (95% CI)
Sample size
Ref.
Asia Thailand
2009
Self-reported history of STI symptoms in the past 6 months
aOR 3.6 (1.5–8.4)
450
[15]
Thailand
2009
Having high levels of concern about contracting HIV in the future
aOR 3.0 (1.2–8.0)
450
[15]
Thailand
2009
Not having a family confidant
aOR 2.0 (1.1–3.7)
450
[15]
Thailand
2009
Lower than university education
OR 2.5 (1.5–4.0)
1125
[15]
Thailand
2009
Living alone or with a roommate vs. living with family
aOR 1.5 (1.1–2.0)
1125
[15]
Thailand
2009
Recruitment from a sauna
aOR 2.3 (1.5–3.7)
1125
[15]
Thailand
2009
Having receptive anal sex
aOR 2.0 (1.4–3.0)
1125
[15]
Thailand
2009
Having both receptive and insertive anal sex roles
aOR 2.5 (1.7–3.6)
1125
[15]
Thailand
2011
Self-identify as gay vs. bisexual
aOR 2.8 (1.1–7.3)
551
[25]
Thailand
2011
Age 25–29 vs. 18–19
aOR 2.7 (1.1–6.5)
551
[25]
Thailand
2011
More than 10 casual male sexual partners compared with 0
aOR 4.1 (1.3–12.7)
551
[25]
Thailand
2012
Less than 25 years of age
OR 3.8 (1.4–10.4)
92
[66]
Thailand
2012
Self-identify as gay
OR 0.1 (0.0–0.3)
92
[66]
Thailand
2012
Self-identify as transgender
OR 0.1 (0.0–0.7)
92
[66]
Thailand
2012
Exchanged sex for money
OR 0.3 (0.1–0.9)
92
[66]
Thailand
2012
More than 4 sexual partners
OR 0.3 (0.1–1.0)
92
[66]
Thailand
2013
Treponema pallidum positivity
aHR 1.8 (1.1–3.2)
1744
[28
Thailand
2013
HSV-2 antibody
aHR 1.5 (1.1–2.1)
1744
[28
Thailand
2013
HSV-1 antibody
aHR 1.5 (1.1–1.9)
1744
[28
Thailand
2013
Group sex
aHR 1.5 (1.1–2.1)
1744
[28
Thailand
2013
Receptive only or both receptive and insertive anal sex
aHR 1.7 (1.2–2.3)
1744
[28
Thailand
2013
Not always using a condom with male partners
aHR 4.8 (1.8–13.2)
1744
[28
Thailand
2013
Drug use for sexual pleasure
aHR 2.3 (1.5–3.4)
1744
[28
Thailand
2013
Living alone or with a roommate vs. with family or a partner
aHR 1.5 (1.1–2.1)
1744
[28
Thailand
2013
Age 22–29 vs. 30 or over
aHR 1.7 (1.2–2.4)
1744
[28
Thailand
2013
Age 18–21 vs. 30 or over
aHR 2.5 (1.6–3.8)
1744
[28
Thailand
2013
Treponema pallidum positivity
aOR 2.1 (1.3–3.4)
1744
[28
Thailand
2013
HSV-2 antibody
aOR 3.0 (2.8–4.0)
1744
[28
Thailand
2013
HSV-1 antibody
aOR 1.5 (1.1–1.9)
1744
[28
Thailand
2013
No prior HIV test
aOR 1.6 (1.3–2.1)
1744
[28
Thailand
2013
Coerced into sex
aOR 1.6 (1.2–2.1)
1744
[28
Thailand
2013
Receptive only or both receptive and insertive anal sex
aOR 1.8 (1.3–2.3)
1744
[28
Thailand
2013
Nitrate inhalation
aOR 1.6 (1.0–2.5)
1744
[28
Thailand
2013
Drug use for sexual pleasure
aOR 1.5 (1.0–2.2)
1744
[28
Thailand
2013
Being employed vs. studying
aOR 1.5 (1.1–2.1)
1744
[28
Thailand
2013
Being unemployed vs. studying
aOR 2.0 (1.1–3.5)
1744
[28
Thailand
2013
Secondary/vocational school vs. university or higher
aOR 1.8 (1.3–2.4)
1744
[28
Thailand
2013
22–29 vs. 18-21
aOR 1.8 (1.2–2.7)
1744
[28
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Epidemiology: concentrated epidemics Table 2 (Continued) Country
Year of study
Thailand
2013
30 or over vs. 18-21
Thailand
2013
HBV infection
Gambia
2013
Age 22 or older
aOR 4.0 (1.4–11.1)
205
[39]
Kenya
2007
Paid someone for sex in last 3 months
aOR 0.4 (0.2–1.0)
285
[67]
Kenya
2007
Receptive anal intercourse only in last 3 months
aOR 3.9 (1.4–11.0)
285
[67]
Kenya
2007
Receptive and insertive anal intercourse in last 3 months
aOR 8.0 (2.9–22.0)
285
[67]
Kenya
2007
Male only sex partners in last 3 months
aOR 6.3 (2.3–17.0)
285
[67]
Kenya
2007
Perianal condylomata on examination
aOR 5.1 (1.1–24.0)
285
[67]
Kenya
2007
Per year increase in age from 18-24
aOR 1.1 (1.0–1.2)
285
[67]
Kenya
2012
Less likely to be impoverished
Chi-square 4.23
253
[44]
Kenya
2013
Receptive anal intercourse
aIRR 9.7 (3.8–25.1)
449
[18
Nigeria
2011
Receptive anal intercourse in last 6 months
aOR 4.2 (1.5–12.3)
154
[48]
Nigeria
2011
Feeling at risk of HIV
aOR 4.4 (1.6–12.1)
122
[48]
Nigeria
2012
Internalized homophobia
aOR 1.8 (1.2–2.7)
1125
[49]
Nigeria
2013
Age 26–40 vs. 18-25
aOR 3.8 (1.5–9.7)
308
[50]
Nigeria
2013
Being Christian vs. Muslim
aOR 3.9 (1.4–10.9)
308
[50]
Nigeria
2013
Self identify as straight/ bisexual vs. homosexual
aOR 9.1 (1.2–70.6)
210
[50]
Nigeria
2013
Unprotected anal sex
aOR 2.9 (1.1–8.3)
194
[50]
Nigeria
2013
Unprotected anal sex
aOR 5.1 (1.5–17.8)
210
[50]
Nigeria
2013
Being paid for sex
aOR 4.9 (1.8–13.6)
194
[50]
Nigeria
2013
Ever testing for HIV
aOR 5.6 (1.9–16.6)
194
[50]
Nigeria
2013
Being from Ibadan vs. Abuja
aOR 1.8 (1.1–2.9)
712
[68]
Nigeria
2013
Living with or married to a woman
aOR 1.5 (1.0–2.2)
712
[68]
Nigeria
2013
Self-identify as straight/ bisexual vs. homosexual
aOR 1.5 (1.0–2.1)
712
[68]
Nigeria
2013
Never testing for HIV
aOR 1.9 (1.3–2.7)
712
[68]
Nigeria
2013
Living with HIV
aOR 1.8 (1.2–2.8)
712
[68]
South Africa
2008
Regular drinking
aOR 4.1 (1.4–12.6)
147
[69]
South Africa
2008
Regular drinking to intoxication
aOR 2.6 (1.0–6.8)
147
[69]
South Africa
2008
Reporting symptoms of rectal trauma resulting from anal intercourse
aOR 4.3 (1.8–10.4)
147
[69]
South Africa
2010
Transactional anal intercourse
aOR 2.8 (1.0–8.3)
537
[54]
South Africa
2010
Self-identify as gay, homosexual, or queer
aOR 4.5 (1.0–20.0)
537
[54]
South Africa
2010
Ever been diagnosed with an STI
aOR 4.3 (2.1–8.3)
537
[54]
South Africa
2011
Self-perceived high risk of HIV
[20]
South Africa
2011
Receptive anal intercourse
South Africa
2011
South Africa
Association of HIV infection
Association of UAI
Sample size
Ref.
aOR 1.6 (1.0–2.6)
1744
[28
aOR 3.1 (2.2–4.3)
1299
[27]
OR/RR (95% CI)
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aOR 2.3 (1.1–3.3)
285
aOR 10.0 (4.2–24.0)
285
[20]
Unprotected anal intercourse
aOR 2.5 (1.5–4.4)
285
[20]
2011
Unprotected receptive anal intercourse
aOR 4.3 (2.4–7.6)
285
[20]
South Africa
2011
Sex with someone known to be living with HIV
aOR 2.3 (1.1–4.9)
285
[20]
South Africa
2011
STI diagnosis
aOR 2.4 (1.1–5.2)
285
[20]
South Africa
2011
Older than 25
aOR 3.8 (3.2–4.6)
363
[55]
South Africa
2011
Self-identify as gay
aOR 2.3 (1.8–3.0)
350
[55]
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HIV among MSM in countries with generalized HIV epidemics Baral et al. Table 2 (Continued) Country
Year of study
Association of HIV infection
Association of UAI
OR/RR (95% CI)
Sample size
Ref.
South Africa
2011
Monthly income less than ZAR500
aOR 1.4 (1.2–1.7)
363
[55]
South Africa
2011
Purchasing alcohol of drugs in exchange for sex with another man
aOR 3.9 (3.2–4.7)
363
[55]
South Africa
2011
Any reported unprotected receptive anal intercourse
aOR 4.4 (3.5–5.7)
363
[55]
South Africa
2011
2 sexual partners in last 6 months
aOR 2.8 (2.1–3.8)
364
[55]
South Africa
2011
3–5 sexual partners in the last 6 months
aOR 1.9 (1.4–2.6)
365
[55]
South Africa
2011
6–9 sexual partners in the last 6 months
aOR 5.7 (4.0–8.2)
363
[55]
South Africa
2011
10 or more sexual partners in the last 6 months
aOR 2.2 (1.5–3.3)
366
[55]
South Africa
2011
Not always wearing condoms with men
aOR 2.3 (1.0–5.4)
200
[13]
South Africa
2011
Having been blackmailed
aOR 4.4 (1.3–15.4)
200
[13]
South Africa
2011
Having been raped
aOR 5.7 (1.6–20.2)
200
[13]
South Africa
2011
More than 26 years old
aOR 4.2 (1.6–10.6)
200
[13]
South Africa
2011
Unemployed
aOR 3.7 (1.5–9.3)
200
[13]
South Africa
2011
Rural origin
aOR 6.0 (2.2–16.7)
200
[13]
South Africa
2011
Bisexual practices
aOR 0.3 (0.1–0.9)
200
[13]
South Africa
2013
Casual neighborhood partner vs. similar partner
aOR 3.3 (1.3–8.1)
239
[70]
South Africa
2013
Familiar partner vs. similar partner
aOR 2. 3 (1.0–5.0)
239
[70]
South Africa
2013
At least mild depression based on CES-D
aOR 2.5 (1.6–4.2)
316
[56]
South Africa
2013
High self-efficacy of condom use compared to very high
aOR 4.9 (2.3–10.4)
316
[56]
South Africa
2013
Low self-efficacy of condom use compared to very high
aOR 7.0 (3.2–15.4)
316
[56]
South Africa
2013
Very low self-efficacy of condom use compared to very high
aOR 6.1 (2.4–15.4)
316
[56]
South Africa
2013
3 or more sexual partners in the last 3 months
aOR 2.9 (1.8–4.8)
316
[56]
South Africa
2013
Been paid for sex in last 3 months
aOR 2.5 (1.4–4.5)
316
[56]
South Africa
2013
Alcohol 2 h prior to sex
aOR 2.0 (1.1–3.4)
316
[56]
South Africa
2013
Drugs 2 h prior to sex
aOR 3.2 (1.5–6.7)
316
[56]
South Africa
2013
Number of anal intercourse episodes
IRR 1.0 (1.0–1.0)
377
[71]
South Africa
2013
Number of months the dyadic sexual relationship lasted
IRR 1.2 (1.1–1.3)
377
[71]
South Africa
2013
Partner identified as a regular male partner
IRR 1.8 (1.4–2.3)
377
[71]
South Africa
2013
Receptive anal intercourse only
IRR 0.5 (0.3–0.8)
377
[71]
South Africa
2013
Experiences of homonegativity
IRR 3.9 (1.0–15.3)
377
[71]
South Africa
2013
Safer sex self-efficacy
IRR 0.2 (0.1–0.3)
377
[71]
South Africa
2013
Income
IRR 0.6 (0.4–0.9)
377
[71]
South Africa
2013
IRR 1.9 (1.3–2.7)
377
[71]
Tanzania
2011
Paid someone for sex in last year
aOR 4.6 (1.0–21.4)
509
[59]
Tanzania
2011
Injecting drug use in last 3 months
aOR 2.4 (1.1–5.3)
307
[59]
Tanzania
2011
HCV positive test result
aOR 4.7 (2.1–10.6)
509
[59]
Uganda
2012
Over 25 years old
aOR 4.3 (1.3–14.0)
300
[60
Circumcised
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Epidemiology: concentrated epidemics Table 2 (Continued) Country
Year of study
Uganda
2012
Illicit drug consumption ever
aOR 0.2 (0.0–0.7)
300
[60
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2012
History of homophobic abuse ever
aOR 5.4 (2.0–14.8)
300
[60
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LMICs
2007
MSM vs. background populations
OR 19.3 (18.8–19.8)
Malawi, Namibia, Botswana
2009
Had transactional sex
OR 1.7 (1.1–2.7)
93
[4 ]
Malawi, Namibia, Botswana
2009
Older than 25
OR 4.0 (2.0–8.0)
93
[4 ]
Malawi, Namibia, Botswana
2009
Not always wearing condoms during sex
OR 2.6 (1.3–4.9)
93
[4 ]
Malawi, Namibia, Botswana
2009
Increasing age groups
OR 2.6 (1.6–4.2)
93
[4 ]
Malawi, Namibia, Botswana
2009
Being employed
OR 1.7 (1.1–2.6)
93
[4 ]
Malawi, Namibia, Botswana
2009
Not always wearing condoms with men
OR 2.2 (1.3–3.8)
93
[4 ]
Malawi, Namibia, Botswana
2009
Not always wearing condoms with casual partners
OR 4.9 (2.1–11.4)
93
[4 ]
Malawi, Namibia, Botswana
2009
Not always wearing condoms with regular partners
OR 4.3 (1.7–10.9)
93
[4 ]
Malawi, Namibia, Botswana
2009
Ever been diagnosed with an STI
OR 2.7 (1.5–4.8)
93
[4 ]
Association of HIV infection
Association of UAI
Sample size
OR/RR (95% CI)
Ref. ] ]
Latin America and the Caribbean No data Former Soviet Union/ Eastern Europe No data Multicountry data [3
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aHR, adjusted hazard ratio; CI, confidence interval; HBV, hepatitis B virus; HR, hazard ratios; IRR, incidence rate ratios; LMIC, low- and middle-income country; OR, odds ratio; RR, relative risk; STI, sexual transmitted infections; UAI, unprotected anal intercourse.
WAYS FORWARD
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There are a number of determinants of the limited data available characterizing MSM in generalized HIV epidemics. The data that are available appear to be highly concentrated among younger MSM probably because of these men having larger sexual and social networks, making them more likely to be accrued into studies. Younger men have had less opportunity for the acquisition of HIV and thus have lower prevalence of HIV, though the Thai data on MSM highlight that these young men also have the highest incidence of HIV [17,75,76 ]. Separately, many of the countries with generalized HIV epidemics also criminalize same-sex sexual practices that has been shown to negatively affect uptake of services, and participation in organized research studies [77 ,78 ,79]. There has also been limited willingness to fund HIV surveillance on MSM in these settings because of the assumptions that these populations are small and largely irrelevant in the context of broader HIV epidemics. A recent model has characterized five levels of HIV risks: individual, network, community, policy, &&
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and stage of the HIV epidemic [80 ]. In the literature reviewed on MSM in generalized HIV epidemics through this study, factors at almost every level of risk were found to be associated with HIV. Correlates of living with HIV included individual factors such as having symptoms of or being diagnosed with other STI [4 ,15,20,27,28 ,54,59,67], high numbers of partners [25,55], having receptive anal sex [15,20,28 ,55,67], not always using condoms [4 ,13,20,28 ,50,55], drug use [28 ,59,60 ], and transactional sex [4 ,50,54,55,59,67]. Furthermore, literature in this review identified network-level factors associated with living with HIV including not having a family confidant [15] and living alone or with a roommate compared to living with family [15]. Community-level factors found to be associated with living with HIV included forms of stigma, specifically having been blackmailed [13] and history of homophobic abuse [60 ]. The studies in this review did not identify public policy factors that were significantly related to HIV status, highlighting the dearth of data characterizing the relationship between justice and health in &
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HIV among MSM in countries with generalized HIV epidemics Baral et al.
these contexts. Out of the 51 countries in this review, 31 criminalize sex between men [81]. Out of the 22 countries for which no HIV prevalence data for MSM could be found, slightly more than half [12 ] criminalize sex between men. A study of six of the countries included in this review (Botswana, Malawi, Namibia, Swaziland, Zambia, and Zimbabwe) found that less than US$2000 from the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund) was allocated by these countries for HIV surveillance and services for MSM, and the percentage of total possible funding from the President’s Emergency Plan For AIDS Relief (PEPFAR) for surveillance and services for MSM ranged from 0.04% to a high of 9.1% [82]. A study that included four of the countries in this review (Ethiopia, Guyana, Mozambique, and Nigeria) found that less than 1% of funding from the Global Fund in these countries was allocated to surveillance services for MSM, and 10% or less of PEPFAR country budgets (excluding treatment costs) were allocated for services for MSM [83,84]. To date, the assumptions about the epidemiology of HIV in the context of generalized HIV epidemics of risk being evenly distributed in these populations have led the design of HIV surveillance systems. Consequently, limited investments have been made into data collection among MSM as explored here, PWID, but also among female, male, and transgender sex workers, and among transgender populations more broadly [85,86]. Furthermore, epidemiologic surveillance and research characterizing the actual burden of prevalent and incident HIV infections among MSM and other key populations in generalized HIV epidemics will facilitate evidence-based HIV policy decisions including funding allocations for specific populations in the context of generalized HIV epidemics. &&
CONCLUSION The data presented here suggest that the dynamics of HIV infection among men are more similar across the world than they are different. As data continue to emerge, the consistency of the burden of HIV among MSM across regions is striking and is a manifestation of the biology of HIV that transcends politics, economics, and borders. Fortunately, many HIV epidemics among average-risk reproductive-age adults are slowing across both generalized and concentrated settings. It is in this context that the high HIV incidence among MSM, and especially young MSM needs to be characterized, understood, and ultimately addressed. This trend suggests a change in the trajectory of generalized HIV epidemics, a change that we may miss if we continue to
understudy these populations based on unproved and dated assumptions. Acknowledgements None. Conflicts of interest The authors have no conflicts of interest to declare.
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