Back

Comorbidity Risk Factors Contributing to COVID-19 Related Deaths in Florida, March 1, 2020-January 16, 2021

Weiss, U. K.; Maynard, J. D.; McDaniel, K.; Cohen, A.; Bailey, M.; Rivkees, S. A.

2021-04-16 infectious diseases
10.1101/2021.04.14.21255434 medRxiv
Show abstract

ObjectivesTo assess the association of specific comorbid conditions to COVID-19 deaths in Florida among decedents 16 to 64 years of age. MethodsThis report uses Florida vital statistics death data over the period of March 1, 2020 through January 16, 2021, to estimate the effects of comorbid conditions on COVID-19 mortality for decedents 16 to 64 years of age. All cases of COVID-19 death occurring in Florida, regardless of resident status, were evaluated. The comorbidities, or contributing causes of death, identified in this report include Down syndrome, asthma, diabetes, pulmonary fibrosis, obesity, dementia, immunodeficiency, kidney disease, chronic obstructive pulmonary disease, hypertension, heart disease, and chronic liver disease and cirrhosis. The study uses a binary logistic regression to examine the relationship between COVID-19 and non-COVID-19 death and contributing causes of death based on information in the death record. Odds ratios were calculated as a residual of the logistic regression. ResultsAmong COVID-19 deaths, Down syndrome was 15.26 times more likely to be a contributing cause of death compared to non-COVID-19 deaths followed by asthma (OR 7.74), diabetes (OR 6.11), pulmonary fibrosis (OR 5.13), obesity (OR 4.66), dementia (OR 4.51), immunodeficiency (OR 2.49), and kidney disease (OR 2.13). Chronic liver disease and cirrhosis (OR 0.95) and cancer (OR 0.79) had lower odds of being a contributing cause of death. ConclusionsHeart disease, chronic liver disease and cirrhosis, and cancer were not risk factors for death from COVID-19 among decedents. Additional studies are needed to elucidate associations between race/ethnicity, socioeconomic status, and behavioral factors.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.