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. 2022 Feb 14;12(2):e055562.
doi: 10.1136/bmjopen-2021-055562.

Investigating the effect of macro-scale estimators on worldwide COVID-19 occurrence and mortality through regression analysis using online country-based data sources

Sabri Erdem  1 , Fulya Ipek  2 , Aybars Bars  3 , Volkan Genç  3 , Esra Erpek  4 , Shabnam Mohammadi  3 , Anıl Altınata  3 , Servet Akar  4
Affiliations

Investigating the effect of macro-scale estimators on worldwide COVID-19 occurrence and mortality through regression analysis using online country-based data sources

Sabri Erdem et al. BMJ Open. .

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.

Trial registration number: ClinicalTrials.gov Registry (NCT04486508).

Keywords: COVID-19; health policy; public health.

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Conflict of interest statement

Competing interests: None declared.

References

    1. Steenhuysen J, Kelland K. 'When will it end?': How a changing virus is reshaping scientists’ views on COVID-19, 2021. Available: https://www.reuters.com/article/us-health-coronavirus-variants-insight-i...
    1. Carethers JM. Insights into disparities observed with COVID-19. J Intern Med 2021;289:463–73. 10.1111/joim.13199 - DOI - PMC - PubMed
    1. Wollenstein-Betech S, Cassandras CG, Paschalidis IC. Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: hospitalizations, mortality, and the need for an ICU or ventilator. Int J Med Inform 2020;142:104258. 10.1016/j.ijmedinf.2020.104258 - DOI - PMC - PubMed
    1. Ficetola GF, Rubolini D. Containment measures limit environmental effects on COVID-19 early outbreak dynamics. Sci Total Environ 2021;761:144432. 10.1016/j.scitotenv.2020.144432 - DOI - PMC - PubMed
    1. Arsalan M, Mubin O, Alnajjar F, et al. . COVID-19 global risk: expectation vs. reality. Int J Environ Res Public Health 2020;17:5592. 10.3390/ijerph17155592 - DOI - PMC - PubMed

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