Country Level Socioeconomic and Health System Indicators Explain COVID-19 Mortality Worldwide
Abstract
Background: COVID-19 mortality rates differ across countries. We aimed to construct a model that predicts mortality worldwide, by including only country-level socioeconomic and health system indicators and excluding variables related to short-term measures for pandemic management.
Methods: COVID-19 mortality data was collected from Johns Hopkins University resource center. Additional sources were public reports from the United Nations, the World Bank and the Heritage Foundation. We implemented multiple linear regression with backward elimination on the selected predictors.
Results: The final model constructed on seven Independent variables, significantly predicted COVID-19 mortality rate by country (F-statistic: 29.2, p<0.001). Regression coefficients (95% CI) in descending order of standardized effects: Annual tourist arrivals: 5.43 (4.03, 6.83); health expenditure per capita: 4.43 (2.92, 5.96); GDP (PPP): -4.60 (-6.81, -2.38); specialist surgical workforce per 100000: 2.63 (0.67, 4.59); number of physicians per 1000: -2.32 (-4.3, -0.28); economic freedom score: -1.35 (-2.60, -0.10); and total population: 1.66 (-0.19, 3.52). All VIF values were below 5, showing acceptable collinearity. R-squared (52.65%), adjusted R-squared (50.25%) and predicted R-squared (42.33%) showed strong model fit.
Conclusion: limited country-level socioeconomic and health system indicators can explain COVID-19 mortality worldwide; emphasizing the priority of attending to these fundamental structures when planning for pandemic preparedness.
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2. Bygbjerg IC. Double burden of noncommunicable and infectious diseases in developing countries. Science. 2012;337(6101):1499-501.
3. Loayza NV, Pennings S. Macroeconomic policy in the time of covid-19: A primer for developing countries. World Bank; 2020.
4. Dowd JB, Andriano L, Brazel DM, Rotondi V, Block P, Ding X, et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proceedings of the National Academy of Sciences. 2020;117(18):9696-8.
5. Legido-Quigley H, Asgari N, Teo YY, Leung GM, Oshitani H, Fukuda K, et al. Are high-performing health systems resilient against the COVID-19 epidemic? The Lancet. 2020;395(10227):848-50.
6. Kupferschmidt K, Cohen J. Can China's COVID-19 strategy work elsewhere? : American Association for the Advancement of Science; 2020.
7. Anderson RM, Heesterbeek H, Klinkenberg D, Hollingsworth TD. How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet. 2020;395(10228):931-4.
8. Gibney E. Whose coronavirus strategy worked best? Scientists hunt most effective policies. Nature. 2020.
9. Singh D, Kumar V, Kaur M. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks. European Journal of Clinical Microbiology & Infectious Diseases. 2020:1-11.
10. Koo JR, Cook AR, Park M, Sun Y, Sun H, Lim JT, et al. Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study. The Lancet Infectious Diseases. 2020.
11. Enserink M, Kupferschmidt K. With COVID-19, modeling takes on life and death importance. American Association for the Advancement of Science; 2020.
12. Armocida B, Formenti B, Ussai S, Palestra F, Missoni E. The Italian health system and the COVID-19 challenge. The Lancet Public Health. 2020.
13. Onder G, Rezza G, Brusaferro S. Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. Jama. 2020.
14. Martelletti L, Martelletti P. Air pollution and the novel Covid-19 disease: a putative disease risk factor. SN Comprehensive Clinical Medicine. 2020:1-5.
15. Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020;368(6489):395-400.
16. Coronavirus recourse center at Johns Hopkins University (2020). Mortality analysis. Retrieved April 25, 2020 from: https://coronavirus.jhu.edu/data/mortality
17. The World Bank. Open data. https://data.worldbank.org/ Accessed April 25, 2020.
18. The United Nations. World population prospects. https://population.un.org/wpp/ Accessed April 25, 2020.
19. The Heritage Foundation. Index of economic freedom. https://www.heritage.org/index/ Accessed April 25, 2020.
20. Green KC, Armstrong JS. Simple versus complex forecasting: The evidence. Journal of Business Research. 2015;68(8):1678-85.
21. Heinze G, Wallisch C, Dunkler D. Variable selection–A review and recommendations for the practicing statistician. Biometrical Journal. 2018;60(3):431-49.
22. Derksen S, Keselman HJ. Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. British Journal of Mathematical and Statistical Psychology. 1992;45(2):265-82.
23. Miles J. R squared, adjusted R squared. Wiley StatsRef: Statistics Reference Online. 2014.
24. Hawkins DM. The problem of overfitting. Journal of chemical information and computer sciences. 2004;44(1):1-12.
25. O’brien RM. A caution regarding rules of thumb for variance inflation factors. Quality & quantity. 2007;41(5):673-90.
26. Michaud C. Global Burden of Infectious Diseases. Encyclopedia of Microbiology. 2009:444.
27. Hale T, Petherick A, Phillips T, Webster S. Variation in government responses to COVID-19. Blavatnik School of Government Working Paper. 2020;31.
28. Bhutta ZA, Sommerfeld J, Lassi ZS, Salam RA, Das JK. Global burden, distribution, and interventions for infectious diseases of poverty. Infectious diseases of poverty. 2014;3(1):21.
29. Osterholm MT. Preparing for the next pandemic. New England Journal of Medicine. 2005;352(18):1839-42.
30. Gordon RA. Issues in multiple regression. American Journal of Sociology. 1968;73(5):592-616.
31. Burns PM, Novelli M. Tourism and mobilities: local-global connections: CABI; 2008.
32. Farzanegan MR, Feizi M, Gholipour HF. Globalization and outbreak of COVID-19: An empirical analysis. Joint Discussion Paper Series in Economics; 2020.
33. Peeri NC, Shrestha N, Rahman MS, Zaki R, Tan Z, Bibi S, et al. The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? International journal of epidemiology. 2020.
34. Nixon J, Ulmann P. The relationship between health care expenditure and health outcomes. The European Journal of Health Economics. 2006;7(1):7-18.
35. Barrett DH, Ortmann LH, Dawson A, Saenz C, Reis A, Bolan G. Public health ethics: cases spanning the globe: Springer International Publishing; 2016.
36. Esposto AG, Zaleski PA. Economic freedom and the quality of life: an empirical analysis. Constitutional political economy. 1999;10(2):185-97.
37. Stroup MD. Economic freedom, democracy, and the quality of life. World Development. 2007;35(1):52-66.
38. Hardiess G, Meilinger T, Mallot HA. The International Encyclopedia of the Social and Behavioral Sciences. 2015.
39. Vetter TR, Schober P. Regression: the apple does not fall far from the tree. Anesthesia & Analgesia. 2018;127(1):277-83.
| Files | ||
| Issue | Vol 6 No 2 (2020) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jbe.v6i2.4871 | |
| Keywords | ||
| COVID-19 Health care Mortality Public health Socioeconomic factors | ||
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How to Cite
1.
Noorchenarboo M, Mousavi SA, Moheimani H. Country Level Socioeconomic and Health System Indicators Explain COVID-19 Mortality Worldwide. JBE. 2020;6(2):93-100.
