Toward a fine-scale population health monitoring system
Toward a fine-scale population health monitoring system
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DOI：
10.1016/j.cell.2021.03.034
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发表时间：
2021-04-15
期刊：
影响因子：
64.5
通讯作者：
Kenny, Eimear E.
中科院分区：
文献类型：
--
作者：
Belbin, Gillian M.;Cullina, Sinead;Kenny, Eimear E.
了解人口健康差异是公平精准卫生工作的重要组成部分。流行病学研究通常依赖于种族和民族的定义，但这些人口标签可能无法充分捕捉影响特定亚人群的疾病负担和环境因素。在这里，我们提出了一个框架，用于重新利用电子健康记录（EHRs）数据与基因组数据，以探索可能影响疾病负担的人口统计学联系。利用来自纽约市不同生物库的数据，我们确定了17个共享近期遗传祖先的社区。我们观察了1177个健康结果，这些结果在统计学上与特定群体相关，并证明了导致孟德尔疾病的遗传变异分离的显著差异。我们还证明了精细尺度的人口结构可以影响群体内复杂疾病风险的预测。这项工作加强了将基因组数据与电子病历联系起来的效用，并为人口健康的精细监测提供了一个框架。
Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.