Better data for decision-making through Bayesian imputation of suppressed provisional COVID-19 death counts.

ABSTRACT:

Purpose

To facilitate use of timely, granular, and publicly available data on COVID-19 mortality, we provide a method for imputing suppressed COVID-19 death counts in the National Center for Health Statistic's 2020 provisional mortality data by quarter, county, and age.

Methods

We used a Bayesian approach to impute suppressed COVID-19 death counts by quarter, county, and age in provisional data for 3,138 US counties. Our model accounts for multilevel data structures; numerous zero death counts among persons aged <50 years, rural counties, early quarters in 2020; highly right-skewed distributions; and different levels of data granularity (county, state or locality, and national levels). We compared three models with different prior assumptions of suppressed COVID-19 deaths, i

SUBMITTER: Kao SZ 

PROVIDER: S-EPMC10399909 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature