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Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey
Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey
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Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey
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Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey
Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey

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Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey
Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey
Journal Article

Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey

2016
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Overview
A recent infection testing algorithm (RITA) that can distinguish recent from long-standing HIV infection can be applied to nationally representative population-based surveys to characterize and identify risk factors for recent infection in a country. We applied a RITA using the Limiting Antigen Avidity Enzyme Immunoassay (LAg) on stored HIV-positive samples from the 2007 Kenya AIDS Indicator Survey. The case definition for recent infection included testing recent on LAg and having no evidence of antiretroviral therapy use. Multivariate analysis was conducted to determine factors associated with recent and long-standing infection compared to HIV-uninfected persons. All estimates were weighted to adjust for sampling probability and nonresponse. Of 1,025 HIV-antibody-positive specimens, 64 (6.2%) met the case definition for recent infection and 961 (93.8%) met the case definition for long-standing infection. Compared to HIV-uninfected individuals, factors associated with higher adjusted odds of recent infection were living in Nairobi (adjusted odds ratio [AOR] 11.37; confidence interval [CI] 2.64-48.87) and Nyanza (AOR 4.55; CI 1.39-14.89) provinces compared to Western province; being widowed (AOR 8.04; CI 1.42-45.50) or currently married (AOR 6.42; CI 1.55-26.58) compared to being never married; having had ≥ 2 sexual partners in the last year (AOR 2.86; CI 1.51-5.41); not using a condom at last sex in the past year (AOR 1.61; CI 1.34-1.93); reporting a sexually transmitted infection (STI) diagnosis or symptoms of STI in the past year (AOR 1.97; CI 1.05-8.37); and being aged <30 years with: 1) HSV-2 infection (AOR 8.84; CI 2.62-29.85), 2) male genital ulcer disease (AOR 8.70; CI 2.36-32.08), or 3) lack of male circumcision (AOR 17.83; CI 2.19-144.90). Compared to HIV-uninfected persons, factors associated with higher adjusted odds of long-standing infection included living in Coast (AOR 1.55; CI 1.04-2.32) and Nyanza (AOR 2.33; CI 1.67-3.25) provinces compared to Western province; being separated/divorced (AOR 1.87; CI 1.16-3.01) or widowed (AOR 2.83; CI 1.78-4.45) compared to being never married; having ever used a condom (AOR 1.61; CI 1.34-1.93); and having a STI diagnosis or symptoms of STI in the past year (AOR 1.89; CI 1.20-2.97). Factors associated with lower adjusted odds of long-standing infection included using a condom at last sex in the past year (AOR 0.47; CI 0.36-0.61), having no HSV2-infection at aged <30 years (AOR 0.38; CI 0.20-0.75) or being an uncircumcised male aged <30 years (AOR 0.30; CI 0.15-0.61). We identified factors associated with increased risk of recent and longstanding HIV infection using a RITA applied to blood specimens collected in a nationally representative survey. Though some false-recent cases may have been present in our sample, the correlates of recent infection identified were epidemiologically and biologically plausible. These methods can be used as a model for other countries with similar epidemics to inform targeted combination prevention strategies aimed to drastically decrease new infections in the population.