Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Challenge TB Project, Interactive Research & Development (IRD), Dhaka, Bangladesh.
Epidemiol Infect. 2021 Sep 2;149:e209. doi: 10.1017/S0950268821001679.
We developed a novel method to align two data sources (TB notifications and the Demographic Health Survey, DHS) captured at different geographic scales. We used this method to identify sociodemographic indicators - specifically population density - that were ecologically correlated with elevated TB notification rates across wards (~100 000 people) in Dhaka, Bangladesh. We found population density was the variable most closely correlated with ward-level TB notification rates (Spearman's rank correlation 0.45). Our approach can be useful, as publicly available data (e.g. DHS data) could help identify factors that are ecologically associated with disease burden when more granular data (e.g. ward-level TB notifications) are not available. Use of this approach might help in designing spatially targeted interventions for TB and other diseases in settings of weak existing data on disease burden at the subdistrict level.
我们开发了一种新的方法来对齐两个数据源(结核病报告和人口健康调查,DHS),这些数据是在不同的地理尺度上捕获的。我们使用这种方法来识别社会人口学指标 - 特别是人口密度 - 这些指标与孟加拉国达卡各行政区(约 10 万人)升高的结核病报告率具有生态相关性。我们发现人口密度与行政区级结核病报告率最密切相关(斯皮尔曼等级相关系数为 0.45)。我们的方法可能很有用,因为当缺乏更细粒度的数据(例如行政区级结核病报告)时,公共可用数据(例如 DHS 数据)可以帮助识别与疾病负担具有生态关联的因素。在现有关于区县级疾病负担数据薄弱的情况下,这种方法的使用可能有助于设计针对结核病和其他疾病的空间靶向干预措施。