TY - JOUR AU - Stockham, Nathaniel AU - Washington, Peter AU - Chrisman, Brianna AU - Paskov, Kelley AU - Jung, Jae-Yoon AU - Wall, Dennis Paul PY - 2022 DA - 2022/7/21 TI -基于COVID-19互联网流行病学中减轻选择偏差和不可测混淆的因果模型:模型开发和验证乔- JMIR公共卫生Surveill SP - e31306六世- 8 - 7 KW -选择偏见KW - COVID-19 KW -流行病学KW -因果关系KW -灵敏度分析KW -公共卫生KW -监视KW -方法KW -流行病学研究设计KW - KW -偏见千瓦发展模式千瓦验证KW -效用KW -实现千瓦敏感性KW -设计KW -研究AB -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。我们构建了一套可能的家庭感染因果模型和调查选择机制，并根据与收集的调查数据的兼容性对其进行排序。 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. SN - 2369-2960 UR - https://publichealth.www.mybigtv.com/2022/7/e31306 UR - https://doi.org/10.2196/31306 UR - http://www.ncbi.nlm.nih.gov/pubmed/35605128 DO - 10.2196/31306 ID - info:doi/10.2196/31306 ER -