Abstract
BackgroundAs countries make progress towards HIV epidemic control, there is increasing need to identify finer geographic areas to target HIV interventions. We mapped geographic clusters of new HIV diagnoses, and described factors associated with HIV-positive diagnosis, in order to inform targeting of HIV interventions to finer geographic areas and sub-populations.MethodsWe analyzed data for clients aged > 15 years who received home-based HIV testing as part of a routine public health program between May 2016 and July 2017 in Siaya County, western Kenya. Geospatial analysis using Kulldorff’s spatial scan statistic was used to detect geographic clusters (radius < 5 kilometers) of new HIV diagnoses. Factors associated with new HIV diagnosis were assessed in a spatially-integrated Bayesian hierarchical model.ResultsOf 268,153 clients with HIV test results, 2906 (1.1%) were diagnosed HIV-positive. We found spatial variation in the distribution of new HIV diagnoses, and identified nine clusters in which the number of new HIV diagnoses was significantly (1.56 to 2.64 times) higher than expected. Sub-populations with significantly higher HIV-positive yield identified in the multivariable spatially-integrated Bayesian model included: clients aged 20–24 years [adjusted relative risk (aRR) 3.45, 95% Bayesian Credible Intervals (CI) 2.85–4.20], 25–35 years (aRR 4.76, 95% CI 3.92–5.81) and > 35 years (aRR 2.44, 95% CI 1.99–3.00); those in polygamous marriage (aRR 1.84, 95% CI 1.55–2.16), or separated/divorced (aRR 3.36, 95% CI 2.72–4.08); and clients who reported having never been tested for HIV (aRR 2.35, 95% CI 2.02–2.72), or having been tested > 12 months ago (aRR 1.53, 95% CI 1.41–1.66).ConclusionOur study used routine public health program data to identify granular geographic clusters of higher new HIV diagnoses, and sub-populations with higher HIV-positive yield in the setting of a generalized HIV epidemic. In order to target HIV testing and prevention interventions to finer granular geographic areas for maximal epidemiologic impact, integrating geospatial analysis into routine public health programs can be useful.
Highlights
As countries make progress towards Human immunodeficiency virus (HIV) epidemic control, there is increasing need to identify finer geographic areas to target HIV interventions
From the 161 Siaya administrative sub-locations included in the analysis, 365,798 clients aged > 15 years from 136,607 households were enumerated for homebased HIV testing (Fig. 1)
Our study uniquely demonstrates the use of geospatial analysis in a routine public health program to assess geospatial patterns of new HIV diagnoses, and identify geographic areas where HIV interventions could be targeted with finer granularity
Summary
As countries make progress towards HIV epidemic control, there is increasing need to identify finer geographic areas to target HIV interventions. In 2014, the Joint United Nations Programme on HIV/AIDS (UNAIDS) set ambitious global targets towards achieving HIV epidemic control, recommending programs aim for 90% of people living with HIV (PLHIV) to know their HIV status, 90% of people with diagnosed HIV infection to receive sustained ART, and 90% of people receiving ART to achieve viral suppression [6]. Of Kenya’s 47 counties, Siaya had the highest HIV prevalence of 21%, with an estimated 123,000 PLHIV, and 4000 new HIV infections [7]. In order to accelerate progress towards HIV epidemic control, programs in Siaya intensified implementation of multiple county-wide HIV prevention interventions and testing approaches, including community home-based HIV testing
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