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Populations at Increased Risk for HIV Infection in Kenya: Results From a National Population-Based Household Survey, 2012

Githuka, George MBChB*; Hladik, Wolfgang MD, PhD; Mwalili, Samuel PhD, MSc; Cherutich, Peter MBChB, MPH*; Muthui, Mercy MSc; Gitonga, Joshua MPH§; Maina, William K. MBChB, MPH*; Kim, Andrea A. PhD, MPH; for the KAIS Study Group

JAIDS Journal of Acquired Immune Deficiency Syndromes: 1 May 2014 - Volume 66 - Issue - p S46–S56
doi: 10.1097/QAI.0000000000000137
Supplement Article

Background: Populations with higher risks for HIV exposure contribute to the HIV epidemic in Kenya. We present data from the second Kenya AIDS Indicator Survey to estimate the size and HIV prevalence of populations with high-risk characteristics.

Methods: The Kenya AIDS Indicator Survey 2012 was a national survey of Kenyans aged 18 months to 64 years which linked demographic and behavioral information with HIV results. Data were weighted to account for sampling probability. This analysis was restricted to adults aged 18 years and older.

Results: Of 5088 men and 6745 women, 0.1% [95% confidence interval (CI): 0.03 to 0.14] were persons who inject drugs (PWID). Among men, 0.6% (CI: 0.3 to 0.8) had ever had sex with other men, and 3.1% (CI: 2.4 to 3.7) were males who had ever engaged in transactional sex work (MTSW). Among women, 1.9% (CI: 1.3 to 2.5) had ever had anal sex, and 4.1% (CI: 3.5 to 4.8) were women who had ever engaged in transactional sex work (FTSW). Among men, 17.6% (CI: 15.7 to 19.6) had been male clients of transactional sex workers (TSW). HIV prevalence was 0% among men who have sex with men, 6.3% (CI: 0 to 18.1) among persons who injected drugs, 7.1% (CI: 4.8 to 9.4) among male clients of TSW, 7.6% (CI: 1.8 to 13.4) among MTSW, 12.1% (CI: 7.1 to 17.1) among FTSW, and 12.1% (CI: 5.0 to 19.2) among females who ever had engaged in anal sex.

Conclusions: Population-based data on high-risk populations can be used to set realistic targets for HIV prevention, care, and treatment for these groups. These data should inform priorities for high-risk populations in the upcoming Kenyan strategic plan on HIV/AIDS.

*National AIDS and Sexually Transmitted Infection Control (STI) Programme, Ministry of Health, Nairobi, Kenya;

Division of Global HIV/AIDS, Center for Global Health, US Centers for Disease Control and Prevention, Atlanta, GA;

Division of Global HIV/AIDS, Center for Global Health, US Centers for Disease Control and Prevention, Nairobi, Kenya; and

§National AIDS Control Council, Nairobi, Kenya.

Correspondence to: George Githuka, MBChB, National AIDS and STI Control Programme, Kenyatta Hospital Grounds, Off Hospital Road, P.O. Box 19361-00202 Nairobi, Kenya (e-mail:

Kenya AIDS Indicator Survey (KAIS) 2012 was supported by the National AIDS and STI Control Programme (NASCOP), Kenya National Bureau of Statistics (KNBS), National Public Health Laboratory Services (NPHLS), National AIDS Control Council (NACC), National Council for Population and Development (NCPD), Kenya Medical Research Institute (KEMRI), US Centers for Disease Control and Prevention (CDC/Kenya, CDC/Atlanta), United States Agency for International Development (USAID/Kenya), University of California, San Francisco (UCSF), Joint United Nations Team on HIV/AIDS, Japan International Cooperation Agency (JICA), Elizabeth Glaser Paediatric AIDS Foundation (EGPAF), Liverpool Voluntary Counselling and Testing (LVCT), African Medical and Research Foundation (AMREF), World Bank, and Global Fund. This publication was made possible by support from the US President's Emergency Plan for AIDS Relief through cooperative agreements (#PS001805, GH,000069, and PS001814) from the US Centers for Disease Control and Prevention, Division of Global HIV/AIDS. This work was also funded in part by support from the Global Fund, World Bank, and the Joint United Nations Team for HIV/AIDS.

The authors have no conflicts of interest to disclose.

The findings and conclusions in this article are those of the authors and do not necessarily represent those of the US Centers for Disease Control and Prevention and the Government of Kenya.

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Kenya has a mature HIV epidemic with HIV prevalence estimated to be 5.6% in the adult general population in 2012.1 Embedded within this epidemic are several key populations that have substantially higher risks for HIV infection. Local population-based surveys conducted between 2008 and 2011 found high levels of HIV prevalence among female sex workers (FSW), estimated to range from 29.1% in Nairobi to 56.5% in Kisumu; men who have sex with men (MSM), with prevalence of 18.2% in Nairobi and 11.1% in Kisumu; and persons who inject drugs (PWID) of whom 18.7% were HIV infected in Nairobi.2,3 In a long-standing cohort of MSM in Mombasa, not only has high HIV prevalence been reported at 43% [95% confidence interval (CI): 35 to 52] of the study population, but elevated levels of HIV incidence have been observed both among men who have sex with men exclusively and MSM who have sex with women, up to 8 times as high as the rates observed in the general population.4–6 Through mathematical modeling, the Kenya Modes of Transmission study estimated that up to 1 in 3 recently acquired HIV infections in Kenya were attributable to key populations.7

Bridging populations, defined as members of the general population that interact sexually with key population members, may further facilitate the spread of HIV infection. In particular, receptive anal intercourse has been cited as an important risk factor for HIV infection and described primarily in the context of MSM behavior in Kenya.4,5,8 However, the extent to which heterosexual anal intercourse is prevalent among women and how this behavior may bridge with key populations is unknown.

Many of the behaviors that place key populations at increased risk of acquiring and transmitting HIV are illegal in Kenya. The prevailing stigma and criminalization make these groups hidden and difficult to reach in routine HIV surveillance, impeding their access to HIV prevention, care, and treatment services. The Kenya National AIDS Strategic Plan III 2009–2013 recognized the need to prioritize access to services for key populations.9 To date, available HIV surveillance data among key populations are based on targeted sampling, using convenience samples or complex sampling designs, such as respondent-driven or time–location sampling. No nation-wide estimates for key populations, however, are available, and the lack of reliable estimates of population sizes, burden of HIV, and spatial distribution of these groups impede HIV programmatic activities and advocacy. We report on a national household-based survey that included HIV-related biomarkers and behavioral indicators relevant to key and other high-risk populations in Kenya.

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Survey Design and Study Population

The second Kenya AIDS Indicator Survey (KAIS 2012), described in detail elsewhere,10 was a nationally representative cross-sectional household survey conducted from October 2012 to February 2013 in 9 programmatic regions in the country. KAIS 2012 used the National Sample Survey and Evaluation Programme V household sampling frame, which comprised a total of 5360 clusters with county boundaries. From these, 372 clusters were selected for KAIS 2012. The study population included individuals aged 18 months to 64 years who were usual residents of or had stayed in the sampled household the night before the survey. For this article, we restricted our study population to adult participants aged 18–64 years.

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Data Instruments

Household and individual survey questionnaires were adapted from several instruments used in previous national surveys, including standard AIDS Indicator Survey tools developed by Monitoring and Evaluation to Assess and Use Results of Demographic and Health Surveys (MEASURE DHS, Calverton, MD), the HIV module used in the 2003 Kenya Demographic and Health Survey, and the adult individual adult questionnaire used in the first Kenya AIDS Indicator Survey conducted in 2007.11,12 The questionnaires were translated from English into Kiswahili and other local languages, back-translated into English, and reviewed to ensure accuracy.

The individual questionnaire covered basic sociodemographic characteristics, reproductive history, fertility preferences, family planning, marriage, sexual and drug-using behaviors, HIV knowledge and attitudes, HIV testing, access to HIV care and treatment services, medical injections, and other health-related topics. Questionnaires were administered in the participant's home by trained interviewers in a private location away from other members of the household.

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Data Collection

Data were collected by field team members using tablet computers (Mirus Innovations, Mississauga, Ontario, Canada). Survey data were electronically transferred between field team members through a secure local area wireless network for review and cleaning. The field team supervisor then transmitted the electronic data at the end of the day to a central database in Nairobi using a virtual private network.

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Laboratory Tests

Blood specimens were collected from consenting participants and tested at the National HIV Reference Laboratory in Nairobi for HIV antibodies using enzyme immunoassays (EIA) [Vironostika HIV-1/2 UNIF II Plus O EIA (bioMérieux, Marcy I'Etoile, France) as the screening assay and Murex HIV.1.2.O HIV EIA (DiaSorin, SpA, Saluggia, Italy) as the confirmatory assay]. Laboratory-based test results were not returned to participants. However, participants could learn their HIV status in privacy of their homes through home-based testing and counseling using the national HIV testing guidelines for rapid HIV testing.13 HIV testing and counseling was conducted by trained home-based testing and counseling service providers who were a part of the survey team. Referrals for follow-up care were provided where needed.

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Data Analysis

We assessed 6 populations with high-risk characteristics: PWID, MSM, females and males who had engaged in transactional sex work (FTSW and MTSW, respectively), male clients of transactional sex workers (TSW), and females who had engaged in anal sex. The 6 high-risk population groups analyzed were not mutually exclusive; based on reported behaviors, individuals could fall in 1 or more population groups. Respondents were classified as PWID if they answered “yes” to the question “Have you ever injected drugs with a needle and syringe for pleasure?” Men were classified as MSM if they replied “yes” to the question “Have you ever had sex with a man?” Women and men were classified as FTSW and MTSW, respectively, if they answered “yes” to the question “Have you ever received money, gifts, or favors in exchange for sex?” Men were classified as male clients of TSW if they answered “yes” to the question “Have you ever given money, gifts, or favors in exchange for sex?” Female respondents who replied “yes” to the question “Have you ever had anal sex?” were classified as females who ever had engaged in anal sex (FAS).

We estimated the proportion of adults who reported a history of high-risk characteristics as defined above. Using non-normalized weights based on the 2012 projected population data from the 2009 Kenya Population and Housing Census,14 we estimated the national population sizes of MSM, PWID, MTSW, FTSW, male clients of TSW, and FAS. For MTSW, FTSW, male clients of TSW, and FAS, we further estimated population sizes for individuals who had engaged in these behaviors in the past 12 months. However, due to the small sample sizes of MSM and PWID, we only ascertained population sizes for individuals who reported this behavior at any point during their lifetime. We conducted bivariate analyses to estimate the frequencies and proportions of select sociodemographic factors, risk behavior characteristics, and HIV status for the populations analyzed. Because we identified few MSM and PWID in our sample, we present a limited number of indicators for these 2 groups. Statistical significance in comparisons were assessed using the Rao–Scott χ2 test. Estimates and 95% confidence intervals (CI) were adjusted to account for the survey sampling design and nonresponse using sampling weights. All analyses were conducted in SAS version 9.3 (SAS Institute, Cary, NC) using procedures for survey sampling.

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Ethical Considerations

The survey protocol was approved by the Ethical Review Board of the Kenya Medical Research Institute, The Institutional Review Board of the US Centers for Disease Control and Prevention, and the Committee on Human Research of the University of California, San Francisco.

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From October 2012 to February 2013, 14,411 eligible persons aged 18–64 years were identified in participating households. Of these, 12,301 (85.4%) were interviewed, of whom 5088 (41.4%) were men and 7213 (58.6%) were women.

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Prevalence of High-Risk Characteristics

Among 5088 participating men, 0.6% (95% CI: 0.3 to 0.8) were MSM, 3.1% (95% CI: 2.4 to 3.7) were MTSW, and 17.6% (95% CI: 15.7 to 19.6) were male clients of TSW (Table 1). Among 7213 participating women, 1.9% (95% CI: 1.3 to 2.5) were FAS, and 4.1% (95% CI: 3.5 to 4.8) were FTSW. Among the 12,301 men and women participants combined, 0.1% (95% CI: 0.03 to 0.14) were PWID.

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Population Size Estimates of Groups With High-Risk Characteristics

Based on self-reported lifetime behaviors, we estimated that the national number of MSM was 49,000 (95% CI: 25,000 to 72,000) and the number of men and women who had ever injected drugs was 16,000 (95% CI: 6000 to 25,000). The estimated number of persons who in the past 12 months had engaged in high-risk behaviors that defined FAS, MTSW, FTSW, and male clients of TSW were 56,000 (95% CI: 28,000 to 83,000) for FAS, 63,000 (95% CI: 40,000 to 87,000) for MTSW, 103,000 (95% CI: 73,000 to 133,000) for FTSW, and 408,000 (95% CI: 333,000 to 484,000) for male clients of TSW, respectively.

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HIV Testing, HIV Prevalence, and Undiagnosed Infection

The percentage of persons who had ever been tested for HIV ranged from 68.0% (95% CI: 63.9 to 72.1) for male clients of TSW, 72.8% (95% CI: 64.8 to 80.8) for MTSW, 87.9% (95% CI: 83.8 to 92.0) for FTS, and 90.8% (95% CI: 85.4 to 96.2) for FAS (Tables 2–5). HIV testing rates were 70.7% (95% CI: 47.7 to 93.6) for PWID and 61.3% (95% CI: 45.1 to 77.5) for MSM (data not shown). The estimated HIV prevalence was 6.3% (95% CI: 0.0 to 18.1) among PWID, 7.1% (95% CI: 4.8 to 9.4) among male clients of TSW, 7.6% (95% CI: 1.8 to 13.4) among MTSW, 12.1% (95% CI: 7.1 to 17.1) among FTSW, and 12.1% (95% CI: 5.0 to 19.2) among FAS. No HIV infections were detected among MSM. Among individuals with laboratory-confirmed HIV infection, 37.7% (95% CI: 8.2 to 67.1) of FAS, 45.4% (95% CI: 16.5 to 74.2) of MTSW, 55.5% (95% CI: 34.7 to 76.4) of FTSW, and 57.7% (95% CI: 41.9 to 73.5) of male clients of TSW had been previously diagnosed with HIV infection (data not shown). Among HIV-infected PWID, none were aware of their HIV infection.

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Males Engaging in Transactional Sex Work

The median age of MTSW was 31.3 years [interquartile range (IQR), 24.9–41.2] compared with the median age of other men at 32.6 years (IQR, 24.9–43.3). One-third (33.6%, 95% CI: 25.4 to 41.9) had never been married or cohabited (Table 2). Among MTSW, 24.0% (95% CI: 16.7 to 31.4) reported engaging in transactional sex in the past 12 months, 5.4% (95% CI: 1.3 to 9.4) reported a lifetime history of anal sex, and 2.0% (95% CI: 0 to 4.5) reported ever having sex with other men. Sixty-three percent (95% CI: 45.9 to 80.1) of MTSW reported that they used a condom the last time they engaged in transactional sex in the past 12 months, and 26.8% (95% CI: 18.7 to 35.0) used a condom with their most recent partner in the past 12 months. Despite low rates of condom use, the majority perceived themselves to be at no (36.0%, 95% CI 26.3 to 45.6) to low (36.4%, 95% CI 26.4 to 46.4) risk of HIV infection. Compared with other men, MTSW were significantly more likely to report higher lifetime number of sexual partners (P < 0.001). Over one-third (36.9%) of MTSW reported 10 or more lifetime number of sexual partners, compared to 19.6% of other men. We also found significant differences in the self-perception of risk for HIV among MTSW and other men (P < 0.001): 36.4% of MTSW compared with 46.8% of other men perceived that they were at low risk of HIV infection. Furthermore, significant differences were observed in the use of condoms with the most recent sexual partner in the past 12 months (26.8% compared with 15.6%, P < 0.001), anal intercourse in the past (5.4% compared with 1.7%, P = 0.0009), and a history of sex with other men (2.0% compared with 0.5%, P = 0.008) between MTSW and other men, respectively.

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Females Engaging in Transactional Sex Work

FTSW were younger than other women, with a median age of 29.0 years (IQR, 23.9–38.2) compared with 32.7 years (IQR, 25.0–43.4) among women without a history of FTSW. Approximately one-half (51.9%, 95% CI: 45.6 to 58.3) of FTSW were married of cohabiting with a partner (Table 3). Among FTSW, 26.9% (95% CI: 21.0 to 32.8) reported engaging in transactional sex in the past 12 months, and the majority perceived themselves to be at no (23.5%, 95% CI: 17.1 to 29.9) to low (31.2%, 95% CI: 24.7 to 37.8) risk of HIV infection. A history of anal sex was reported by 7.5% (95% CI: 3.0 to 11.9) of FTSW. Marital status, lifetime number of sexual partners, and self-perception of HIV risk differed significantly between FTSW and other women (P < 0.001). Compared with other women, FTSW were less likely to be married or cohabiting (51.9% compared with 72.7%) but more likely to be separated, divorced, or widowed (25.1% compared with 13.6%), report 10 or more lifetime number of sexual partners (8.1% compared with 0.9%), and report that their risk for HIV was great (13.6% compared with 5.5%), respectively. Additionally, FTSW were more likely to report a history of anal intercourse (7.5% compared with 1.7%, P < 0.001) and condom use with their most recent sexual partner in the past 12 months (18.4% compared with 8.7%, P < 0.001) than other women, respectively.

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Male Clients of Persons Who Engaged in Transactional Sex Work

The median age of male clients of TSW (34.2 years; IQR, 26.3–46.7) was higher than the median age of other men (32.4 years; IQR, 24.8, 43.0). Over one-quarter (26.8%, 95% CI: 23.4 to 30.3) had never been married or cohabited (Table 4). Overall, 27.3% (95% CI: 23.1 to 31.5) reported having a transactional sex partner in the past 12 months. Of those, 65.9% (95% CI: 59.2 to 72.5) reported that they used a condom the last time they had sex with a transactional sex partner. Male clients of TSW differed significantly from other men with respect to marital status (P < 0.001): 10.4% of male clients of TSW reported being separated, divorced, or widowed compared with 4.5% of other men; lifetime number of sex partners (P < 0.001): 39.2% of male clients of TSW reported 10 or more lifetime number of partners compared with 16.3% of other men; and self-perception of HIV risk (P < 0.001): 21.4% of male clients of TSW reported having a moderate to great risk for HIV compared with 12.4% of other men.

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Females Engaging in Anal Sex

The median age of FAS was 30.3 years (IQR, 24.0–42.4) compared with 32.6 years (IQR, 24.9–43.3) among women who never had anal sex. Three-quarters (74.9%, 95% CI: 67.3 to 82.5) of FAS were married or cohabiting with a partner. Among FAS, 31.8% (95% CI: 22.1 to 41.5) reported engaging in anal sex in the past 12 months (Table 5). Overall, 13.2% (95% CI: 5.2 to 21.2) of FAS had used a condom with their last sexual partner; 16.4% (95% CI: 8.7 to 24.0) reported a history of transactional sex work. Over 40% of FAS (41.3%, 95% CI: 31.5 to 51.2%) reported that they had low risk for HIV infection. Compared with other women, FAS were more likely to have ever engaged in transactional sex work (16.4% compared with 3.9%, P < 0.001) and report higher numbers of lifetime partners (P < 0.001), respectively. FAS had significantly different self-perceptions of HIV risk compared with other women (P < 0.001). While 40.6% reported a moderate to high self-perceived risk for HIV, only 17.9% of other women perceived themselves to have the same level of HIV risk.

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This is the first study to provide national estimates of key and other high-risk populations in sub-Saharan Africa using data collected from a national population-based household survey. The estimated population sizes derived in this analysis were consistent with the Kenya Ministry of Health 2012 population size consensus estimates of key populations, which were based on a synthesis of available programmatic and surveillance data in the country.15

Our findings suggest that while the sizes of high-risk populations may be small, their contribution to the HIV epidemic is important. The estimated HIV prevalence among most high-risk populations was high, up to 1.8 times as high as among women and up to 2.8 times as high as among men in the general population.1 Still, the prevalence ratios for MSM and transactional sex were lower than those reported elsewhere, likely because our analysis was based on lifetime characteristics,16,17 Although it has been estimated elsewhere that 1.9% of the adult male population in sub-Saharan Africa are MSM18 we found that less than 1% of the male adult population reported lifetime male-to-male sex behavior. Furthermore, only 2% of MTSW reported MSM behavior. Although this is roughly 4 times as high as observed among other men, it is lower than men who engage in commercial sex work in Kenya.19 Our survey instrument did not collect information to determine whether partners of MTSW were women or men and whether money, as opposed to gifts or goods, were exchanged during sexual transactions. However, we suspect that some MTS were men who engaged in sexual transactions with other men based on the proportion that reported anal sex in the survey. While 98% of MTS denied a history of having sex with other men, 5.4% reported a history of anal intercourse compared with only 1.9% of women. The discrepancy in the prevalence of anal sex among men and women suggests potential reporting bias, where MTS may have been reluctant to self-report on same sex relations because of stigmatization of this behavior in Kenya.

Women who had been divorced, separated, or widowed were more likely to report a history of transactional sex. In Kenya's traditional culture, where most women depend on their male partners for financial support, the loss of a marital partnership may drive women to seek other means to quickly finance their livelihood. Programs aimed to address transactional sex should focus on understanding the cultural, social, and economic contexts that influence HIV risk and seek practical approaches for addressing HIV vulnerability, particularly for women. Promising interventions include those aimed to empower women to become self-sufficient in their economic circumstances, including microfinance opportunities and cash incentive programs, to lower the risk of becoming economically disadvantaged and relying on sexuality for survival.

Our survey findings highlight the need for expanded education campaigns to improve awareness of high-risk sexual behaviors such as anal intercourse. Although few women in our study reported ever having had anal intercourse, HIV prevalence among women who engaged in this behavior was 1.6 times as high as women who had never engaged in anal intercourse in their lifetime. Our data indicate that anal intercourse is not an exclusive practice among females who engage in transactional sex, as commonly perceived, but is also occurring among women in the general population. Anal intercourse among women is rarely discussed in HIV prevention despite evidence that shows the risk of HIV infection from unprotected anal intercourse among women can range from 20 to over 500 times as great as the risk from unprotected vaginal intercourse.20,21 Key messages on the elevated risk of HIV acquisition among persons who engage in unprotected anal intercourse should be included in the minimum package for HIV prevention for high-risk population and general population members.

Although condom use was higher among high-risk groups compared with their referent groups, the level of condom use was low with their sexual partners, with only 13%–27% of high-risk population members reporting condom use with their most recent sexual partner in the past 12 months. We found that condom use was higher in the context of transactional sex with 47% of FTSW, 63% of MTSW, and 66% of male clients of TSW reporting that they used a condom with their last transactional sex partner in the past year. Still these levels were not optimal given that consistent condom use is needed to prevent HIV infection in high-risk situations. The low rates of condom use observed may be influenced by the low level of self-perceived risk we also observed in our study, where the majority of high-risk population members reported that they were at no to low risk for HIV.22 Different findings regarding self-perceived risk were reported in a population-based study of active female sex workers in Nairobi, where self-perceived risk for HIV was great, yet condom use remained low with all sexual partners.23

We found that high-risk population members who were HIV infected were generally unaware of their HIV infection. Coupled with unsafe risk behavior, such as inconsistent condom use and high numbers of sexual partners, incorrect knowledge of HIV-positive status among HIV-infected members of high-risk groups presents a dangerous potential for rapid transmission of HIV to sexual partners. Efforts are therefore needed to help establish routine access to HIV testing for high-risk populations through targeted and tailored programs that also facilitate early linkages to care and treatment services.

This analysis of high-risk populations in Kenya was not without limitations. We present results from bivariate analyses and did not control for factors that may have confounded our observed associations or masked potential associations. Our definition of transactional sex was not limited to the monetary exchange for sex, but included broader elements of sexual transactions, such as gifts and favors. These exchanges represent aspects of more heterogenous sexual relationships compared with commercial sex relationships, which rely specifically on financial gain.24 Transactional sex partnerships tend to last longer than commercial relationships, tend toward intergenerational relationships, and the exchange of gifts and favors is often viewed as symbols of love and respect. Although persons engaging in transactional sex can be at high-risk for HIV infection,25,26 these findings should not be generalized to men and women who exclusively practice formal sex work for monetary gain.

Populations with high-risk characteristics are less likely to be included in the sampling frame of a household survey. Therefore, the number of high-risk population members identified in this study was small, resulting in lack of precision in some of the estimates presented. Because of the small numbers, it was not possible to describe the geographical distribution of high-risk population members nor were we able to characterize MSM and PWID beyond a few limited indicators. Therefore careful consideration should be used when interpreting these findings as estimates may not be reliable or generalizable due to the small sample size. The small number of affirmative responses to the high-risk behaviors of interest mandated that our data analysis be based primarily on lifetime behaviors rather than behaviors during the preceding 12 months. Because of this, high-risk population members in this analysis may have characteristics that are reflective of those expected in the general population, such as higher rates of HIV testing. The much smaller numbers of affirmative responses for high-risk behaviors in the preceding 12 months suggest reluctance to report illegal and stigmatized behaviors and selection bias, leading to the likely exclusion of population members who were actively engaged in high-risk behavior. Because of this limitation, the estimated national population sizes for high-risk populations analyzed are likely to be underestimated and should be considered as lower plausible bounds for these groups.

For the first time in a national survey in Kenya, we asked sensitive questions on anal intercourse, transactional sex, and illicit drug use. None of the survey teams reported that respondents ended their interviews because such sensitive questions were asked. With high-risk populations potentially contributing substantially to new HIV infections in Kenya,3 the reduction of risky behaviors in these groups has been defined as a priority area in the Kenyan HIV response.5 To fully understand the epidemiology of HIV and coverage of HIV prevention, care, and treatment programs among populations with high-risk characteristics, we recommend that targeted and routine surveillance approaches be designed to reach hidden and vulnerable populations at high-risk for HIV.27 Nonetheless, national data on high-risk populations provide important public health information that can complement targeted surveillance efforts to evaluate the impact and reach of current services for high-risk populations, baseline levels of risk behaviors, and the burden and awareness of HIV infection in these groups. With the current Kenya National AIDS Strategic Plan ending, these data will inform the new 5-year national strategic vision for planning, implementing, and monitoring HIV prevention, care, and treatment programs among key and other high-risk populations to help achieve an AIDS-free generation in Kenya.

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The authors acknowledge all the persons who participated in this national survey and the survey teams that helped to collect and analyze the data. The authors thank Kevin DeCock, George Rutherford, Linda Wright-Deaguero, Eddas Bennett, and Joy Mirjahangir for reviewing and providing input on the article. The authors also acknowledge the KAIS Study Group for their contribution to the design of the survey and collection of the data set: Willis Akhwale, Sehin Birhanu, John Bore, Angela Broad, Robert Buluma, Thomas Gachuki, Jennifer Galbraith, Anthony Gichangi, Beth Gikonyo, Margaret Gitau, Joshua Gitonga, Mike Grasso, Malayah Harper, Andrew Imbwaga, Muthoni Junghae, Mutua Kakinyi, Samuel Mwangi Kamiru, Nicholas Owenje Kandege, Lucy Kanyara, Yasuyo Kawamura, Timothy Kellogg, George Kichamu, Andrea Kim, Lucy Kimondo, Davies Kimanga, Elija Kinyanjui, Stephen Kipkerich, Danson Kimutai Koske, Boniface O. K'Oyugi, Veronica Lee, Serenita Lewis, William Maina, Ernest Makokha, Agneta Mbithi, Joy Mirjahangir, Ibrahim Mohamed, Rex Mpazanje, Nicolas Muraguri, Patrick Murithi, Lilly Muthoni, James Muttunga, Jane Mwangi, Mary Mwangi, Sophie Mwanyumba, Silas Mulwa, Francis Ndichu, Anne Ng'ang'a, James Ng'ang'a, John Gitahi Ng'ang'a, Lucy Ng'ang'a, Carol Ngare, Bernadette Ng'eno, Inviolata Njeri, David Njogu, Bernard Obasi, Macdonald Obudho, Edwin Ochieng, Linus Odawo, Jacob Odhiambo, Caleb Ogada, Samuel Ogola, David Ojakaa, James Kwach Ojwang, George Okumu, Patricia Oluoch, Tom Oluoch, Kenneth Ochieng Omondi, Osborn Otieno, Yakubu Owolabi, Bharat Parekh, George Rutherford, Sandra Schwarcz, Shahnaaz Sharrif, Victor Ssempijja, Lydia Tabuke, Yuko Takenaka, Mamo Umuro, Brian Eugene Wakhutu, Celia Wandera, John Wanyungu, Wanjiru Waruiru, Anthony Waruru, Paul Waweru, Larry Westerman, and Kelly Winter.

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1. Kimanga DO, Ogola S, Umuro M, et al.. Prevalence and incidence of HIV infection, trends, and risk factors among persons aged 15-64 years in Kenya: results from a nationally representative study. J Acquir Immune Defic Syndr. 2014;66:S13–S26.
2. National AIDS and Sexually Transmitted Infection Control Programme (NASCOP). 2010–2011 Integrated Biological and Behavioral Survey Among Most-at-Risk-Populations in Nairobi and Kisumu. Nairobi, Kenya: NASCOP; 2014.
3. Vandenhoudt HM, Langat L, Menten J, et al.. Prevalence of HIV and other sexually transmitted infections among female sex workers in Kisumu, Western Kenya, 1997 and 2008. PLoS One. 2013;8:e54953.
4. Sanders EJ, Graham SM, Okuku HS, et al.. HIV-1 infection in high-risk men who have sex with men in Mombasa, Kenya. AIDS. 2007;21:2513–2520.
5. Sanders EJ, Okuku HS, Smith AD, et al.. High HIV-1 incidence, correlates of HIV-1 acquisition, and high viral loads following seroconversion among MSM. AIDS. 2013;27:437–446.
6. National AIDS Control Council (NACC). The Kenya AIDS Epidemic Update 2011. Nairobi, Kenya: NACC; 2012.
7. Gelmon L, Kenya P, Oguya F, et al.. Kenya HIV Prevention Response and Modes of Transmission Analysis. Nairobi, Kenya: National AIDS Control Council; 2009.
8. Winklestein W Jr, Lyman DM, Padian N, et al.. Sexual practices and risk of infection by the human immunodeficiency virus: The San Francisco Men's Health Study. JAMA. 1987;257:321–325.
9. Kenya National AIDS Control Council (NACC). Kenya National AIDS Strategic Plan, 2009/10–2012/13. Nairobi, Kenya: NACC; 2009.
10. Waruiru W, Kim AA, Kimanga DO, et al.. The Kenya AIDS indicator survey 2012: rationale, methods, description of participants, and response rates. J Acquir Immune Defic Syndr. 2014;66(suppl 1):S3–S12.
11. Central Bureau of Statistics (CBS), Ministry of Health (MOH) and ORC Macro. Kenya Demographic and Health Survey 2003. Calverton, MD: CBS, MOH and ORC Macro; 2004.
12. National AIDS and Sexually Transmitted Infection Control Program (NASCOP). 2007 Kenya AIDS Indicator Survey Final Report. Nairobi, Kenya: NASCOP; 2009.
13. National AIDS and STI Control Programme (NASCOP). National Guidelines for HIV Testing and Counseling in Kenya. 2nd ed. Nairobi, Kenya: NASCOP; 2010.
14. Kenya National Bureau of Statistics (KNBS). Population and Housing Census Report. Nairobi, Kenya: KNBS; 2009.
15. National AIDS and STI Control Programme (NASCOP). Kenya MARPs Size Estimation Consensus Report 2012/2013. Nairobi, Kenya: NASCOP; 2013.
16. 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 meta-analysis. Lancet Infect Dis. 2012;12:538–549.
17. Baral S, Sifakis F, Cleghorn F, et al.. Elevated risk for HIV infection among men who have sex with men in low- and middle-income countries 2000–2006: a systematic review. PLoS Med. 2007;4:e339.
18. Cáceres C, Konda K, Pecheny M, et al.. Estimating the number of men who have sex with men in low and middle income countries. Sex Transm Infect. 2006;82(suppl 3):iii3–9.
19. Mannava P, Geibel S, King'ola N, et al.. Male sex workers who sell sex to men also engage in anal intercourse with women: evidence from Mombasa, Kenya. PLoS One. 2013;8:e52547. doi:10.1371/journal.pone.0052547.
20. Misegades L, Page-Shafer K, Halperin D, et al.. Anal intercourse among young low-income women in California: an overlooked risk factor for HIV? AIDS. 2001;15:534–535.
21. Leynaert B, Downs AM, de Vincenzi I. Heterosexual transmission of human immunodeficiency virus: variability of infectivity throughout the course of infection. European study group on heterosexual transmission of HIV. Am J Epidemiol. 1998;148:88–96.
22. Prata N, Morris L, Mazive E, et al.. Relationship between HIV risk perception and condom use: evidence from a population-based survey in Mozambique. Int Fam Plan Perspect. 2006;32:192–200.
23. Musyoki H, Kim AA, Geibel S, et al.. High prevalence of HIV infection among female sex workers in nairobi, Kenya: results of a respondent driven sampling study, 2010-11 (Abstract MOPE242). Presented at the XIX International AIDS Conference; 2012; Washington DC.
24. Transactional and Age-disparate Sex in Hyperendemic Countries. Available at: Accessed February 7, 2014.
25. Dunkle KL, Jewkes RK, Brown HC, et al.. Transactional sex among women in Soweto, South Africa: prevalence, risk factors and association with HIV infection. Soc Sci Med. 2004;59:1581–1592.
26. Rositch AFR, Cherutich P, Brentlinger P, et al.. HIV infection and sexual partnerships and behaviour among adolescent girls in Nairobi, Kenya. Int J STD AIDS. 2012;23:468–474.
27. Joint United Nations Programme on HIV/AIDS (UNAIDS). Guidelines on Surveillance of Population Most at Risk for HIV Infection. UNAIDS Working Group on Global HIV/AIDS and STI Surveillance. Geneva, Switzerland: UNAIDS; 2011.

high-risk populations; HIV; men who have sex with men; transactional sex; anal intercourse; Kenya

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