LAUSR

Objective To investigate macro-scale estimators of the variations in COVID-19 cases and deaths among countries. Design Epidemiological study. Setting Country-based data from publicly available online databases of international organisations. Participants The study involved 170 countries/territories, each of which had complete COVID-19 and tuberculosis data, as well as specific health-related estimators (obesity, hypertension, diabetes and hypercholesterolaemia). Primary and secondary outcome measures The worldwide heterogeneity of the total number of COVID-19 cases and deaths per million on 31 December 2020 was analysed by 17 macro-scale estimators around the health-related, socioeconomic, climatic and political factors. In 139 of 170 nations, the best subsets regression was used to investigate all potential models of COVID-19 variations among countries. A multiple linear regression analysis was conducted to explore the predictive capacity of these variables. The same analysis was applied to the number of deaths per hundred thousand due to tuberculosis, a quite different infectious disease, to validate and control the differences with the proposed models for COVID-19. Results In the model for the COVID-19 cases (R2=0.45), obesity (β=0.460), hypertension (β=0.214), sunshine (β=−0.157) and transparency (β=0.147); whereas in the model for COVID-19 deaths (R2=0.41), obesity (β=0.279), hypertension (β=0.285), alcohol consumption (β=0.173) and urbanisation (β=0.204) were significant factors (p<0.05). Unlike COVID-19, the tuberculosis model contained significant indicators like obesity, undernourishment, air pollution, age, schooling, democracy and Gini Inequality Index. Conclusions This study recommends the new predictors explaining the global variability of COVID-19. Thus, it might assist policymakers in developing health policies and social strategies to deal with COVID-19. Trial registration number ClinicalTrials.gov Registry (NCT04486508).