  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, i.e. 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 finite-sample 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 and Health and Human Services. The analysis
suggests that population relative risk estimates by race during the early part
of the COVID-19 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.

Methodology;Applications