Modeling and mapping the burden of disease in Kenya.
Modeling and mapping the burden of disease in Kenya.
复制标题
在肯尼亚建模和映射疾病负担。
DOI：
10.1038/s41598-018-28266-4
复制
发表时间：
2018-06-29
影响因子：
4.6
通讯作者：
Gruebner O
中科院分区：
文献类型：
--
作者：
Frings M;Lakes T;Müller D;Khan MMH;Epprecht M;Kipruto S;Galea S;Gruebner O
精确的公共卫生方法对于将卫生政策针对受疾病影响最严重的地区至关重要。我们提出了非洲第一个次国家和空间明确的疾病负担研究。我们采用了横断面研究设计，并评估了2009年肯尼亚人口和住房普查的数据，以计算由于部门水平（N = 612）过早死亡而导致的YLLs（寿命损失年数）。我们进行了空间自相关分析，以确定空间集群的YLLs和应用助推回归树，以找到位置风险因素和YLLs之间的统计关联。我们发现在肯尼亚西部，西北部和东北部地区的分区水平的高数量的YLLs的统计学显着的空间集群。种族和家庭拥挤是YLL最重要和最显著的危险因素。进一步积极和显着相关的变量是疟疾的地方性，北方的地理位置，并在邻近地区较高的YLL。与此相反，较高的结婚率和更多的降水在一个部门显着相关的YLL少。我们提供了一个证据基础和一个可转移的方法，可以指导卫生政策和干预在肯尼亚和其他地区的可比设置的疾病负担困扰的国家以下地区。
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.
登录
查看更多内容
影响因子：
6.3
作者：
Baker, M.;Das, D.;Howden-Chapman, P.
通讯作者：
Howden-Chapman, P.
影响因子：
3.6
作者：
ANSELIN, L
通讯作者：
ANSELIN, L
影响因子：
4.8
作者：
Elith, J.;Leathwick, J. R.;Hastie, T.
通讯作者：
Hastie, T.
影响因子：
5.7
作者：
Breiman, L
通讯作者：
Breiman, L
影响因子：
3.4
作者：
Cheong, Yoon Ling;Leitao, Pedro J.;Lakes, Tobia
通讯作者：
Lakes, Tobia