COVID-19 Mortality in the Colorado Center for Personalized Medicine Biobank

Amanda N. Brice, Lauren A. Vanderlinden, Katie M. Marker, David Mayer, Meng Lin, Nicholas Rafaels, Jonathan A. Shortt, Alex Romero, Jan T. Lowery, Christopher R. Gignoux and Randi K. Johnson ()
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Amanda N. Brice: Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA
Lauren A. Vanderlinden: Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA
Katie M. Marker: Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
David Mayer: Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
Meng Lin: Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
Nicholas Rafaels: Colorado Center for Personalized Medicine, Aurora, CO 80045, USA
Jonathan A. Shortt: Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
Alex Romero: Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
Jan T. Lowery: Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA
Christopher R. Gignoux: Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
Randi K. Johnson: Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA

IJERPH, 2023, vol. 20, issue 3, 1-12

Abstract: Over 6.37 million people have died from COVID-19 worldwide, but factors influencing COVID-19-related mortality remain understudied. We aimed to describe and identify risk factors for COVID-19 mortality in the Colorado Center for Personalized Medicine (CCPM) Biobank using integrated data sources, including Electronic Health Records (EHRs). We calculated cause-specific mortality and case-fatality rates for COVID-19 and common pre-existing health conditions defined by diagnostic phecodes and encounters in EHRs. We performed multivariable logistic regression analyses of the association between each pre-existing condition and COVID-19 mortality. Of the 155,859 Biobank participants enrolled as of July 2022, 20,797 had been diagnosed with COVID-19. Of 5334 Biobank participants who had died, 190 were attributed to COVID-19. The case-fatality rate was 0.91% and the COVID-19 mortality rate was 122 per 100,000 persons. The odds of dying from COVID-19 were significantly increased among older men, and those with 14 of the 61 pre-existing conditions tested, including hypertensive chronic kidney disease (OR: 10.14, 95% CI: 5.48, 19.16) and type 2 diabetes with renal manifestations (OR: 5.59, 95% CI: 3.42, 8.97). Male patients who are older and have pre-existing kidney diseases may be at higher risk for death from COVID-19 and may require special care.

Keywords: COVID-19; COVID-19 related mortality; causes of death; cardiovascular disease; diabetes; respiratory disease (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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