Open AccessCan Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?
Author(s) -
Jeffrey C. Cegan,
Benjamin D. Trump,
Susan M. Cibulsky,
Zachary A. Collier,
Christopher Cummings,
Scott L. Greer,
Holly Jarman,
Katarzyna Klasa,
Gary Kleinman,
Melissa A. Surette,
Emily Wells,
Igor Linkov
Publication year - 2021
Publication title -
risk management and healthcare policy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.828
H-Index - 22
ISSN - 1179-1594
DOI - 10.2147/rmhp.s313312
Subject(s) - comorbidity , intensive care unit , medicine , government (linguistics) , covid-19 , pandemic , population , health care , mortality rate , predictive power , public health , demography , intensive care medicine , disease , environmental health , psychiatry , nursing , infectious disease (medical specialty) , linguistics , philosophy , epistemology , sociology , economics , economic growth
Many efforts to predict the impact of COVID-19 on hospitalization, intensive care unit (ICU) utilization, and mortality rely on age and comorbidities. These predictions are foundational to learning, policymaking, and planning for the pandemic, and therefore understanding the relationship between age, comorbidities, and health outcomes is critical to assessing and managing public health risks. From a US government database of 1.4 million patient records collected in May 2020, we extracted the relationships between age and number of comorbidities at the individual level to predict the likelihood of hospitalization, admission to intensive care, and death. We then applied the relationships to each US state and a selection of different countries in order to see whether they predicted observed outcome rates. We found that age and comorbidity data within these geographical regions do not explain much of the international or within-country variation in hospitalization, ICU admission, or death. Identifying alternative explanations for the limited predictive power of comorbidities and age at the population level should be considered for future research.