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Modeling and mapping the burden of disease in Kenya

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...

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Autores principales: Frings, Michael, Lakes, Tobia, Müller, Daniel, Khan, M. M. H., Epprecht, Michael, Kipruto, Samuel, Galea, Sandro, Gruebner, Oliver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6026135/
https://www.ncbi.nlm.nih.gov/pubmed/29959405
http://dx.doi.org/10.1038/s41598-018-28266-4
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author Frings, Michael
Lakes, Tobia
Müller, Daniel
Khan, M. M. H.
Epprecht, Michael
Kipruto, Samuel
Galea, Sandro
Gruebner, Oliver
author_facet Frings, Michael
Lakes, Tobia
Müller, Daniel
Khan, M. M. H.
Epprecht, Michael
Kipruto, Samuel
Galea, Sandro
Gruebner, Oliver
author_sort Frings, Michael
collection PubMed
description 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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spelling pubmed-60261352018-07-09 Modeling and mapping the burden of disease in Kenya Frings, Michael Lakes, Tobia Müller, Daniel Khan, M. M. H. Epprecht, Michael Kipruto, Samuel Galea, Sandro Gruebner, Oliver Sci Rep Article 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. Nature Publishing Group UK 2018-06-29 /pmc/articles/PMC6026135/ /pubmed/29959405 http://dx.doi.org/10.1038/s41598-018-28266-4 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Frings, Michael
Lakes, Tobia
Müller, Daniel
Khan, M. M. H.
Epprecht, Michael
Kipruto, Samuel
Galea, Sandro
Gruebner, Oliver
Modeling and mapping the burden of disease in Kenya
title Modeling and mapping the burden of disease in Kenya
title_full Modeling and mapping the burden of disease in Kenya
title_fullStr Modeling and mapping the burden of disease in Kenya
title_full_unstemmed Modeling and mapping the burden of disease in Kenya
title_short Modeling and mapping the burden of disease in Kenya
title_sort modeling and mapping the burden of disease in kenya
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6026135/
https://www.ncbi.nlm.nih.gov/pubmed/29959405
http://dx.doi.org/10.1038/s41598-018-28266-4
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