MODELING RACIAL/ETHNIC DIFFERENCES IN COVID-19 INCIDENCE WITH COVARIATES SUBJECT TO NONRANDOM MISSINGNESS

• 成果类型: Article
• 署名作者: Trangucci, Rob; Chen, Yang; Zelner, Jon
• 署名单位: University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1711
• 发表日期: 2023
• 页码: 2723-2758
• 关键词: drop-out; disease; inference; binary; disparities

摘要

Characterizing the cumulative burden of COVID-19 by race/ethnicity is of the utmost importance for public health researchers and policy makers in order to design effective mitigation measures. This analysis is hampered, however, by surveillance case data with substantial missingness in race and ethnicity covariates. Worse yet, this missingness likely depends on the values of these missing covariates; that is, they are not-missing-at-random (NMAR). We propose a Bayesian parametric model that leverages joint information on spatial variation in the disease and covariate missingness processes and can accommodate both MAR and NMAR missingness. We show that the model is locally identifiable when the spatial distribution of the population covariates is known and observed cases can be associated with a spatial unit of observation. We also use a simulation study to investigate the model's finitesample performance. We compare our model's performance on NMAR data against complete-case analysis and multiple imputation (MI), both of which are commonly used by public health researchers when confronted with missing categorical covariates. Finally, we model spatial variation in cumulative COVID-19 incidence in Wayne County, Michigan, using data from the Michigan Department of Health and Human Services. The analysis suggests that population relative risk estimates by race during the early part of the COVID19 pandemic in Michigan were understated for non-white residents, compared to white residents, when cases missing race were dropped or had these values imputed using MI.