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

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

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. We used a Bayesian approach to i...

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Veröffentlicht in:PloS one 2023-08, Vol.18 (8), p.e0288961-e0288961
Hauptverfasser: Kao, Szu-Yu Zoe, Tutwiler, M Shane, Ekwueme, Donatus U, Truman, Benedict I
Format: Artikel
Sprache:eng
Schlagworte:
Age
Age groups
Algorithms
Bayesian analysis
Bayesian statistical decision theory
Biology and Life Sciences
COVID-19
Data structures
Datasets
Death
Decision making
Disease control
Estimates
Evaluation
Fatalities
Health aspects
Mathematical models
Medicine and Health Sciences
Modelling
Mortality
People and Places
Public health
Research and Analysis Methods
Science Policy
Skewed distributions
Social Sciences
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Beschreibung
Zusammenfassung: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. 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
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0288961