LAUSR

Abstract Objectives Existing findings regarding the relationship between comorbidities and Covid-19 severity is inconsistent and insufficient. The present study aimed to evaluate the association between different comorbidities and the severity of Covid-19. Methods PubMed, EMBASE, and the Cochrane Library were searched to identify studies reporting on the rate of comorbidities in Covid-19 patients with severe/fatal outcomes. Subgroup analyses were conducted according to disease severity, and the country of residence. Odds ratio (OR) with 95% confidence intervals (CI) were pooled using random-effects models. Results A total of 34 eligible studies were identified. In patients with severe/fatal Covid-19, the most prevalent chronic comorbidity was obesity (42%, 95CI 34-49%) and hypertension (40%, 95%CI 35-45%), followed by diabetes (17%, 95%CI 15-20%), cardiovascular disease (13%, 95%CI 11-15%), respiratory disease (8%, 95%CI 6-10%), cerebrovascular disease (6%, 95%CI 4-8%), malignancy (4%, 95% CI 3-6%), kidney disease (3%, 95%CI 2-4%), and liver disease (2%, 95%CI 1-3%). In order of the prediction, the pooled ORs of the chronic respiratory disease, hypertension, cardiovascular disease, kidney disease, cerebrovascular disease, malignancy, diabetes, and obesity in patients with severe or fatal Covid-19 were (OR 3.56, 95%CI 2.87-4.41), (OR 3.17, 95%CI 2.46-4.08), (OR 3.13, 95%CI 2.65-3.70), (OR 3.02, 95%CI 2.23-4.08), (OR 2.74, 95%CI 1.59-4.74), (OR 2.73, 95%CI 1.73-4.21), (OR 2.63, 95%CI 2.08-3.33), and (OR 1.72, 95%CI 1.04-2.85), respectively, compared with patients with non-severe/fatal Covid-19. No correlation was observed between liver disease and Covid-19 aggravation (OR 1.54, 95%CI 0.95-2.49). Conclusions Chronic comorbidities, including obesity, hypertension, diabetes, cardia-cerebrovascular disease, respiratory disease, kidney disease, and malignancy, are clinical risk factors of severe or fatal outcomes associated with Covid-19, with obesity being the most prevalent, and respiratory disease being the most strongly predictive. Knowledge of these risk factors can help clinicians better identify and guide the high-risk populations.