Mapping geographic clusters of new HIV diagnoses to inform granular-level interventions for HIV epidemic control in western Kenya

Abstract

Background As 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. Methods We 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. Results Of 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). Conclusion Our 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.

Etiology, Presentation, and Risk Factors for Diarrheal Syndromes in 3 Sub-Saharan African Countries After the Introduction of Rotavirus Vaccines From the Vaccine Impact on Diarrhea in Africa (VIDA) Study

“…In addition, after introduction of rotavirus vaccination, the proportional distribution of PD increased in The Gambia but decreased significantly in both Mali and Kenya. Because we used consistent methods across our sites, we suspect that the patterns reflect inherent differences in exposure to factors that may affect diarrhea duration, such as malnutrition [ 25 ]), improved sanitation and hygiene [ 26 ], treatment with antibiotics and zinc [ 27 ], reduced human immunodeficiency virus exposure [ 28 ], and AF of etiologic agents.…”

Stunting Following Moderate-to-Severe Diarrhea Among Children Aged <5 Years in Africa Before and After Rotavirus Vaccine Introduction: A Comparison of the Global Enteric Multicenter Study and the Vaccine Impact on Diarrhea in Africa (VIDA) Study

“…One possible explanation is the burden of human immunodeficiency virus (HIV) in Kenya, which may have augmented baseline stunting and created a subset of children who are more vulnerable to stunting following enteric infection. Of Kenya's 47 counties, Siaya, the location of GEMS and VIDA, had the highest HIV prevalence (21% in those aged >15 years) during the years that VIDA was conducted [ 17 ], while Mali and The Gambia have maintained low HIV prevalence rates [ 18 ].…”

Risk Screening Tools Could Potentially Miss HIV-Positive Individuals Who Seek Testing Services: A Secondary Program Data Analysis on the Performance Characteristics of an Adolescent and Adult HIV Risk Screening Tool in Uganda

“…Globally, human immune-deficiency virus (HIV) testing programs have contributed to enormous progress in identifying people living with HIV (PLHIV) and increased coverage of anti-retroviral therapy (ART) [ 1 ]. By 2020, Uganda was nearing the first of the United Nation’s 95–95–95 targets of identifying 95% of PLHIV with approximately 91% of the 1.4 million PLHIV already identified, and 94% on treatment but with a slightly lower viral suppression rate (85%) [ 2 ].…”