Nazia Nushrat, Ali Mohammad, Jakariya Md, Nahar Quamrun, Yunus Mohammad, Emch Michael
Department of Environmental Science & Management, North South University Plot # 15, Block # B, Bashundhara, Dhaka-1229, Bangladesh.
Johns Hopkins Bloomberg School of Public Health, USA.
Spat Spatiotemporal Epidemiol. 2018 Feb;24:1-9. doi: 10.1016/j.sste.2017.09.001. Epub 2017 Oct 16.
We identify high risk clusters and measure their persistence in time and analyze spatial and population drivers of small area incidence over time. The geographically linked population and cholera surveillance data in Matlab, Bangladesh for a 10-year period were used. Individual level data were aggregated by local 250 × 250 m communities. A retrospective space-time scan statistic was applied to detect high risk clusters. Generalized estimating equations were used to identify risk factors for cholera. We identified 10 high risk clusters, the largest of which was in the southern part of the study area where a smaller river flows into a large river. There is persistence of local spatial patterns of cholera and the patterns are related to both the population composition and ongoing spatial diffusion from nearby areas over time. This information suggests that targeting interventions to high risk areas would help eliminate locally persistent endemic areas.
我们识别高风险集群，衡量它们在时间上的持续性，并分析随时间推移小区域发病率的空间和人口驱动因素。我们使用了孟加拉国马特莱地区10年期间地理相关的人口和霍乱监测数据。个体层面的数据按当地250×250米的社区进行汇总。应用回顾性时空扫描统计量来检测高风险集群。使用广义估计方程来识别霍乱的风险因素。我们识别出10个高风险集群，其中最大的一个位于研究区域的南部，那里有一条较小的河流入一条大河流。霍乱存在局部空间模式的持续性，这些模式与人口构成以及随着时间推移来自附近地区的持续空间扩散都有关系。这些信息表明，针对高风险地区进行干预将有助于消除局部持续存在的流行地区。
