Association of comorbidities with COVID-19 infection rate and severity: nationwide cohort study with propensity score matching (preprint)

ABSTRACT

ABSTRACT O bjective To describe the association of comorbidities with coronavirus infection-19 (COVID-19) infection rates and severity of infection through Korean nationwide medical system. D esign Nationwide population-based retrospective cohort study. S etting Korean national health insurance claims database between January 1, 2020, and May 30, 2020. P articipants Patients with positive COVID-19 test and 12 folded controls matched by age, sex and region. M ain O utcomes M easures Outcomes were confirmation of the comorbidities affecting the infection rate and the severity of COVID-19. Patients and outcomes were propensity score matching of factors which may affect COVID-19 infection rate and severity was performed. COVID-19 infections were confirmed through laboratory testing. Severe infection was defined as those who underwent tracheostomy, continuous renal replacement therapy, intensive care unit admission, ventilator use, cardiopulmonary resuscitation, or died. R esults A total of 8070 individuals with positive covid-19 test and 12015 controls were identified. In people aged 60 or older, in those insured with Medicaid, and in the disabled, the proportion corresponding to the severe group of patients showed a tendency to increase. The infection rate of COVID-19 was highest in pulmonary disease (adjusted odds ratio 1.88, 95% confidence interval 1.70 to 2.03), and hyperlipidemia (0.73, 0.67 to 0.80) had a lower infection rate. Disease severity was highest in kidney disease (5.59, 2.48 to 12.63), and lower in hyperlipidemia (0.78, 0.60 to 1.00). C onclusions There is less bias as the government pays for all tests and treatments related to COVID-19 included in the data used in this study. Using propensity matching to reduce statistical bias, we found that most comorbidities increased the infection rate and severity of COVID-19, whereas hyperlipidemia reduced the rate and severity of infection. These results can be utilized to effectively manage COVID-19 infections.