Chen Yuan, Fei Wenbo, Wang Qinxia, Zeng Donglin, Wang Yuanjia
Department of Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10017.
Department of Biostatistics, Columbia University, New York, NY 10032.
Adv Neural Inf Process Syst. 2021 Dec;34:27747-27760.
COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data in New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where time-varying effects of community risk factors and significant interactions between individual- and community-level risk factors are detected. By examining the socioeconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decision-making and facilitate better clinical management of COVID patients.
新冠疫情对我们的社会造成了前所未有的负面影响，包括进一步暴露了公共卫生领域的不公平和差异。为了研究社会经济因素对新冠病毒传播的影响，我们首先提出一个时空模型，以检验社区层面新冠病毒传播的社会经济异质性和空间相关性。其次，为了评估新冠病毒检测呈阳性后出现严重后果的个体风险，我们提出一个动态、变系数模型，该模型将电子健康记录（EHR）中的个体层面风险因素与社区层面风险因素相结合。将根据先前的时空模型预测的感染（有症状和症状前）的潜在邻里患病率纳入个体风险评估，以便更好地捕捉每个个体接触病毒的背景风险。我们设计了一种加权方案，以减轻新冠患者电子健康记录中固有的多重选择偏差。我们分析了纽约市（美国首次疫情高峰的震中）的新冠病毒传播数据以及纽约市医院的电子健康记录，在这些数据中检测到了社区风险因素的时变效应以及个体层面和社区层面风险因素之间的显著相互作用。通过研究感染风险的社会经济差异以及风险因素之间的相互作用，我们的方法可以协助公共卫生决策，并促进对新冠患者更好的临床管理。
