George Githuka1, Wolfgang Hladik, Samuel Mwalili, Peter Cherutich, Mercy Muthui, Joshua Gitonga, William K Maina, Andrea A Kim. 1. *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.
Abstract
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.
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.
Authors: Stefan Baral; Chris Beyrer; Kathryn Muessig; Tonia Poteat; Andrea L Wirtz; Michele R Decker; Susan G Sherman; Deanna Kerrigan Journal: Lancet Infect Dis Date: 2012-03-15 Impact factor: 25.071
Authors: Kristin L Dunkle; Rachel K Jewkes; Heather C Brown; Glenda E Gray; James A McIntryre; Siobán D Harlow Journal: Soc Sci Med Date: 2004-10 Impact factor: 4.634
Authors: W Winkelstein; D M Lyman; N Padian; R Grant; M Samuel; J A Wiley; R E Anderson; W Lang; J Riggs; J A Levy Journal: JAMA Date: 1987-01-16 Impact factor: 56.272
Authors: Eduard J Sanders; Susan M Graham; Haile S Okuku; Elise M van der Elst; Allan Muhaari; Alun Davies; Norbert Peshu; Matthew Price; R Scott McClelland; Adrian D Smith Journal: AIDS Date: 2007-11-30 Impact factor: 4.177
Authors: Paul J Fleming; Thomas L Patterson; Claudia V Chavarin; Shirley J Semple; Carlos Magis-Rodriguez; Eileen V Pitpitan Journal: AIDS Behav Date: 2017-08
Authors: Matthew J Akiyama; Charles M Cleland; John A Lizcano; Peter Cherutich; Ann E Kurth Journal: Lancet Infect Dis Date: 2019-09-17 Impact factor: 25.071
Authors: William K Maina; Andrea A Kim; George W Rutherford; Malayah Harper; Boniface O K'Oyugi; Shahnaaz Sharif; George Kichamu; Nicholas M Muraguri; Willis Akhwale; Kevin M De Cock Journal: J Acquir Immune Defic Syndr Date: 2014-05-01 Impact factor: 3.731
Authors: Ruth S Mwatelah; Raphael M Lwembe; Saida Osman; Bernhards R Ogutu; Rashid Aman; Rose C Kitawi; Laura N Wangai; Florence A Oloo; Gilbert O Kokwaro; Washingtone Ochieng Journal: PLoS One Date: 2015-07-24 Impact factor: 3.240
Authors: Jerry Okal; Henry F Raymond; Waimar Tun; Helgar Musyoki; Sufia Dadabhai; Dita Broz; Joan Nyamu; David Kuria; Nicholas Muraguri; Scott Geibel Journal: BMC Res Notes Date: 2016-03-11
Authors: Donna S Jones; Richard C Dicker; Robert E Fontaine; Amy L Boore; Jared O Omolo; Rana J Ashgar; Henry C Baggett Journal: Emerg Infect Dis Date: 2017-12 Impact factor: 6.883