A new study shows the relationship between preexisting comorbidities of COVID-19 and mortality in privately insured patients from FAIR Health and the West Health Institute and Marty Makary, MD, MPH, from Johns Hopkins University School of Medicine.
The study has been released as a white paper entitled Risk Factors for COVID-19 Mortality among Privately Insured Patients: A Claims Data Analysis.
Across all age groups, the top three comorbidity risk factors for death from COVID-19 were, in order from highest to lowest risk, developmental disabilities (e.g., developmental disabilities of speech and language, developmental disabilities of scholastic skills, central auditory processing disorders), lung cancer, and intellectual disabilities and related conditions (e.g., Down syndrome and other chromosomal anomalies; mild, moderate, severe and profound intellectual disabilities; congenital malformations, such as certain disabilities that cause microcephaly). As detailed in the white paper, these findings are supported by recent scientific literature.
There are several possible reasons for the high COVID-19 mortality risk in people with developmental and intellectual disabilities. These include the greater prevalence of comorbid chronic conditions, disproportionate representation as workers in essential services, and increased COVID-19 transmission in group residential settings.
In patients under age 70, lung cancer conferred the highest risk of COVID-19 mortality. In that age cohort, patients with COVID-19 and lung cancer were nearly seven times more likely to die than patients who had COVID-19 but not lung cancer.
The findings were based on an analysis of data from the nation’s largest private healthcare claims database, the FAIR Health National Private Insurance Claims repository. Evaluating all patients in FH NPIC’s longitudinal dataset, FAIR Health identified 467,773 patients diagnosed with COVID-19 from April 1, 2020, through August 31, 2020. Relationships were examined between the outcome of mortality (dependent variable) and the following independent variables: age, gender, and preexisting comorbidities. The results of this analysis could help inform protocols for vaccine distribution as well as prevention and treatment protocols.
