ArticleScientific reports2018
Modeling and mapping the burden of disease in Kenya.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Spatial heterogeneity of Rift Valley fever burden in Kenya: a 10-year One Health zDALY evaluation.Science in One Health · 2026Article
- Mapping the burden of COVID-19 infections and estimating the contribution of socioeconomic factors: a cross-sectional study.Archives of public health = Archives belges de sante publique · 2025Article
- Burden and risk factors of mental and substance use disorders among adolescents and young adults in Kenya: results from the Global Burden of Disease Study 2019.EClinicalMedicine · 2024Article
- Availability of essential medicines during the COVID-19 pandemic: A qualitative study examining experiences and level of preparedness in Kenya.PLOS global public health · 2024Article
- Profile: The Kenya Multi-Site Serosurveillance (KEMIS) collaboration.Gates open research · 2024Article
- Article
- The impact of child health interventions and risk factors on child survival in Kenya, 1993-2014: a Bayesian spatio-temporal analysis with counterfactual scenarios.BMC medicine · 2021Article
- Determinants of subnational disparities in antenatal care utilisation: a spatial analysis of demographic and health survey data in Kenya.BMC health services research · 2020Article
- Sub national variation and inequalities in under-five mortality in Kenya since 1965.BMC public health · 2019Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision public health approaches are crucial for targeting health policies to regions most affected by disease. We present the first sub-national and spatially explicit burden of disease study in Africa. We used a cross-sectional study design and assessed data from the Kenya population and housing census of 2009 for calculating YLLs (years of life lost) due to premature mortality at the division level (N = 612). We conducted spatial autocorrelation analysis to identify spatial clusters of YLLs and applied boosted regression trees to find statistical associations between locational risk factors and YLLs. We found statistically significant spatial clusters of high numbers of YLLs at the division level in western, northwestern, and northeastern areas of Kenya. Ethnicity and household crowding were the most important and significant risk factors for YLL. Further positive and significantly associated variables were malaria endemicity, northern geographic location, and higher YLL in neighboring divisions. In contrast, higher rates of married people and more precipitation in a division were significantly associated with less YLL. We provide an evidence base and a transferable approach that can guide health policy and intervention in sub-national regions afflicted by disease burden in Kenya and other areas of comparable settings.
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