Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/182013
Authors: 
Frings, Michael
Lakes, Tobia Maike
Müller, Daniel
Khan, M. M. H.
Epprecht, Michael
Kipruto, Samuel
Galea, Sandro
Gruebner, Oliver
Year of Publication: 
2018
Citation: 
[Journal:] Scientific Reports [ISSN:] 2045-2322 [Volume:] 8 [Year:] 2018 [Issue:] (Article no. 9826) [Pages:] 1-9
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.
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
Document Version: 
Published Version
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