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Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?
Jeffrey C Cegan1, Benjamin D Trump1, Susan M Cibulsky2
1US Army Engineer Research and Development Center, US Army Corps of Engineers, Vicksburg, MS, USA.
Risk Management and Healthcare Policy
|July 16, 2021
Summary
COVID-19 risk prediction using age and comorbidities is unreliable. These factors alone do not explain variations in hospitalization, intensive care unit (ICU) admission, or death rates across regions.
Area of Science:
- Epidemiology
- Public Health
- Health Informatics
Background:
- COVID-19 impact prediction relies heavily on patient age and comorbidities.
- Understanding these factors is crucial for public health risk assessment and pandemic planning.
- Existing models often use these variables for hospitalization, ICU, and mortality predictions.
Purpose of the Study:
- To assess the predictive power of age and comorbidities for COVID-19 outcomes.
- To evaluate if individual-level relationships explain population-level variations in outcomes.
- To identify limitations in current predictive models for public health policy.
Main Methods:
- Utilized a US government database of 1.4 million patient records from May 2020.
- Analyzed the relationship between age and comorbidity count at the individual level.
- Applied derived predictive relationships to US states and selected countries to compare with observed rates.
Main Results:
- Age and comorbidity data explained minimal variation in hospitalization, ICU admission, or death rates.
- Significant international and within-country disparities in outcomes were not accounted for by these factors.
- The predictive models based on age and comorbidities showed limited efficacy at a population level.
Conclusions:
- Age and comorbidities alone are insufficient predictors of COVID-19 outcomes at a population level.
- Further research is needed to identify alternative factors influencing regional outcome variations.
- Rethinking predictive modeling is essential for effective public health risk management during pandemics.
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