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HIV testing uptake and prevalence among adolescents and adults in a large home-based HIV testing program in Western
Juddy Wachira1, Samson Ndege, Julius Koech
1*USAID-Academic Model Providing Access to Healthcare (AMPATHPlus) Partnership, Eldoret, Kenya; †Department of Epidemiology, School of Public Health, College of Health Sciences, Moi University, Eldoret, Kenya; ‡Children's Health Services Research, Indiana University School of Medicine, Indianapolis, IN; §Department of Medicine, School of Medicine, College of Health Sciences, Moi University, Eldoret, Kenya; ‖Department of Medicine, School of Medicine, Indiana University, Indianapolis, IN; ¶Regenstrief Institute, Inc, Indianapolis, IN; and #Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Objective:
To describe HIV testing uptake and prevalence among adolescents and adults in a home-based HIV counseling and testing program in western Kenya.
Methods:
Since 2007, the Academic Model Providing Access to Healthcare program has implemented home-based HIV counseling and testing on a large scale. All individuals aged ≥13 years were eligible for testing. Data from 5 of 8 catchments were included in this analysis. We used descriptive statistics and multivariate logistic regression to examine testing uptake and HIV prevalence among adolescents (13-18 years), younger adults (19-24 years), and older adults (≥25 years).
Results:
There were 154,463 individuals eligible for analyses as follows: 22% adolescents, 19% younger adults, and 59% older adults. Overall mean age was 32.8 years and 56% were female. HIV testing was high (96%) across the following 3 groups: 99% in adolescents, 98% in younger adults, and 94% in older adults (P < 0.001). HIV prevalence was higher (11.0%) among older adults compared with younger adults (4.8%) and adolescents (0.8%) (P < 0.001). Those who had ever previously tested for HIV were less likely to accept HIV testing (adjusted odds ratio: 0.06, 95% confidence interval: 0.05 to 0.07) but more likely to newly test HIV positive (adjusted odds ratio: 1.30, 95% confidence interval: 1.21 to 1.40). Age group differences were evident in the sociodemographic and socioeconomic factors associated with testing uptake and HIV prevalence, particularly, gender, relationship status, and HIV testing history.
Conclusions:
Sociodemographic and socioeconomic factors were independently associated with HIV testing and prevalence among the age groups. Community-based treatment and prevention strategies will need to consider these factors.
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