%0期刊文章%@ 2369-2960 %I JMIR出版物%V 8% 卡塔尔世界杯8强波胆分析N 7% P e31306 %T因果建模以减轻基于COVID-19的互联网流行病学中的选择偏差和不可测混杂:模型开发与验证%A Stockham,Nathaniel %A Washington,Peter %A Chrisman,Brianna %A Paskov,Kelley %A Jung，在- yoon %A Wall,Dennis Paul %+神经科学跨部门项目，斯坦福大学，3145波特博士，帕罗奥图，CA, 94304，美国，1 2056021832，stockham@stanford.edu %K选择偏差%K COVID-19 %K流行病学%K因果%K敏感性分析%K公共卫生%K监测%K方法%K流行病学研究设计%K模型%K偏差%K开发%K验证%K效用%K实施%K敏感性%K设计%K研究%K流行病学%D 2022 %7 21.7.2022 %9原始论文%J JMIR公共卫生监测%G英文%X背景:选择偏差和未测混杂是流行病学中威胁研究内外效度的基本问题。这些现象在基于互联网的公共卫生监测中尤其危险，因为传统的缓解和调整方法不适用、不可用或过时。因果模型的最新理论进展可以减轻这些威胁，但这些创新尚未在流行病学界得到广泛应用。目的:本文的目的是证明因果模型在检测不可测混杂和选择偏差和指导模型选择以最小化偏差方面的实际效用。我们在纽约市2020年春季疫情的COVID-19累积感染率应用流行病学研究中实施了这种方法。方法:我们收集了Qualtrics在2020年4月11日至14日和5月8日至11日两个采样期对居住在新泽西州和纽约州的亚马逊机械土耳其人(MTurk)人群工作人员的调查中的主要数据。调查询问了被调查对象的家庭健康状况和人口特征。 We constructed a set of possible causal models of household infection and survey selection mechanisms and ranked them by compatibility with the collected survey data. The most compatible causal model was then used to estimate the cumulative infection rate in each survey period. Results: There were 527 and 513 responses collected for the 2 periods, respectively. Response demographics were highly skewed toward a younger age in both survey periods. Despite the extremely strong relationship between age and COVID-19 symptoms, we recovered minimally biased estimates of the cumulative infection rate using only primary data and the most compatible causal model, with a relative bias of +3.8% and –1.9% from the reported cumulative infection rate for the first and second survey periods, respectively. Conclusions: We successfully recovered accurate estimates of the cumulative infection rate from an internet-based crowdsourced sample despite considerable selection bias and unmeasured confounding in the primary data. This implementation demonstrates how simple applications of structural causal modeling can be effectively used to determine falsifiable model conditions, detect selection bias and confounding factors, and minimize estimate bias through model selection in a novel epidemiological context. As the disease and social dynamics of COVID-19 continue to evolve, public health surveillance protocols must continue to adapt; the emergence of Omicron variants and shift to at-home testing as recent challenges. Rigorous and transparent methods to develop, deploy, and diagnosis adapted surveillance protocols will be critical to their success. %M 35605128 %R 10.2196/31306 %U https://publichealth.www.mybigtv.com/2022/7/e31306 %U https://doi.org/10.2196/31306 %U http://www.ncbi.nlm.nih.gov/pubmed/35605128