ArticleJournal of acquired immune deficiency syndromes (1999)2018
Finding Hidden HIV Clusters to Support Geographic-Oriented HIV Interventions in Kenya.
Article in Journal of acquired immune deficiency syndromes (1999), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 22 citations in OpenAlex.
- Uncovering the impact of randomness in HIV hotspot formation: A mathematical modeling study.PLoS computational biology · 2025Article
- Analysis of HIV transmission characteristics and intervention effects in Guangxi based on molecular networks.AIDS (London, England) · 2025Article
- Cluster analysis of social determinants of health and HIV/AIDS knowledge among Peruvian youths using Kohonen's self-organized maps: a data-exploration study based on a Demographic and health survey.Global health action · 2024Article
- Geographic variation in 5-year mortality following HIV diagnosis: implications for clinical interventions.AIDS care · 2023Article
- Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa.BMC infectious diseases · 2023Article
- Novel Location-Based Survey Using Cognitive Interviews to Assess Geographic Networks and Hotspots of Sex and Drug Use: Implementation and Validation Study.JMIR formative research · 2023Article
- Human immunodeficiency virus infection in pregnant women and its correlation with socioeconomic determinants.Revista da Escola de Enfermagem da U S P · 2023Article
- Article
- Mapping geographic clusters of new HIV diagnoses to inform granular-level interventions for HIV epidemic control in western Kenya.BMC public health · 2021Article
- Where Are the Newly Diagnosed HIV Positives in Kenya? Time to Consider Geo-Spatially Guided Targeting at a Finer Scale to Reach the "First 90".Frontiers in public health · 2021Article
- Molecular network-based intervention brings us closer to ending the HIV pandemic.Frontiers of medicine · 2020Review
- Improving Information and Communications Technology (ICT) Knowledge and Skills to Develop Health Research Capacity in Kenya.Online journal of public health informatics · 2019Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn a spatially well known and dispersed HIV epidemic, identifying geographic clusters with significantly higher HIV prevalence is important for focusing interventions for people living with HIV (PLHIV).
methodsWe used Kulldorff spatial-scan Poisson model to identify clusters with high numbers of HIV-infected persons 15-64 years old. We classified PLHIV as belonging to either higher prevalence or lower prevalence (HP/LP) clusters, then assessed distributions of sociodemographic and biobehavioral HIV risk factors and associations with clustering.
resultsAbout half of survey locations, 112/238 (47%) had high rates of HIV (HP clusters), with 1.1-4.6 times greater PLHIV adults observed than expected. Richer persons compared with respondents in lowest wealth index had higher odds of belonging to a HP cluster, adjusted odds ratio (aOR) 1.61 [95% confidence interval (CI): 1.13 to 2.3], aOR 1.66 (95% CI: 1.09 to 2.53), aOR 3.2 (95% CI: 1.82 to 5.65), and aOR 2.28 (95% CI: 1.09 to 4.78) in second, middle, fourth, and highest quintiles, respectively. Respondents who perceived themselves to have greater HIV risk or were already HIV-infected had higher odds of belonging to a HP cluster, aOR 1.96 (95% CI: 1.13 to 3.4) and aOR 5.51 (95% CI: 2.42 to 12.55), respectively; compared with perceived low risk. Men who had ever been clients of female sex worker had higher odds of belonging to a HP cluster than those who had never been, aOR 1.47 (95% CI: 1.04 to 2.08); and uncircumcised men vs circumcised, aOR 3.2 (95% CI: 1.74 to 5.8).
conclusionsHIV infection in Kenya exhibits localized geographic clustering associated with sociodemographic and behavioral factors, suggesting disproportionate exposure to higher HIV risk. Identification of these clusters reveals the right places for targeting priority-tailored HIV interventions.
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