Investigating the effect of macro-scale estimators on worldwide COVID-19 occurrence and mortality through regression analysis using online country-based data sources.
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引用次数: 0
Abstract
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.
期刊介绍：
BMJ Open is an online, open access journal, dedicated to publishing medical research from all disciplines and therapeutic areas. The journal publishes all research study types, from study protocols to phase I trials to meta-analyses, including small or specialist studies. Publishing procedures are built around fully open peer review and continuous publication, publishing research online as soon as the article is ready.
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