Spatiotemporal health surveillance accounting for risk factors and spatial correlation
Spatiotemporal health surveillance accounting for risk factors and spatial correlation
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DOI：
10.1002/qre.3335
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发表时间：
2023-03
影响因子：
2.3
通讯作者：
O. A. Vanli;Nour Alawad
中科院分区：
文献类型：
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作者：
O. A. Vanli;Nour Alawad
目前流行病学研究中用于识别疾病热点的公共卫生监测方法大多假设各地区的疾病病例数是独立分布的，缺乏对混杂协变量进行调整的能力。本文提出了一种新的方法，该方法在经典的时空累积和(CUSUM)框架内使用同时自回归(SAR)模型，该模型是一种流行的空间回归方法，用于检测计数数据的空间分布的变化，同时考虑了风险因素和空间相关性。基于带有协变量的SAR模型，我们推导了似然比检验监测统计量的表达式，从而得到了所提出的空时累积和检验统计量。通过模拟区域计数中的各种移位场景，研究了该监控方法在检测和识别步进移位方面的有效性。通过一个监测区域新冠肺炎感染人数同时调整社会脆弱性(通常与社区对疾病感染的易感性相关)的案例研究，说明了所提方法在公共卫生监测中的应用。
Most of the current public health surveillance methods used in epidemiological studies to identify hotspots of diseases assume that the regional disease case counts are independently distributed and they lack the ability of adjusting for confounding covariates. This article proposes a new approach that uses a simultaneous autoregressive (SAR) model, a popular spatial regression approach, within the classical space‐time cumulative sum (CUSUM) framework for detecting changes in the spatial distribution of count data while accounting for risk factors and spatial correlation. We develop expressions for the likelihood ratio test monitoring statistics based on a SAR model with covariates, leading to the proposed space‐time CUSUM test statistic. The effectiveness of the proposed monitoring approach in detecting and identifying step shifts is studied by simulation of various shift scenarios in regional counts. A case study for monitoring regional COVID‐19 infection counts while adjusting for social vulnerability, often correlated with a community's susceptibility towards disease infection, is presented to illustrate the application of the proposed methodology in public health surveillance.