COVID-19 case-fatality rate and demographic and socioeconomic influencers: worldwide spatial regression analysis based on country-level data

Abstract

Objective: To investigate the influence of demographic and socioeconomic factors on the COVID-19 case-fatality rate (CFR) globally.

Design: Publicly available register-based ecological study.

Setting: Two hundred and nine countries/territories in the world.

Participants: Aggregated data including 10 445 656 confirmed COVID-19 cases.

Primary and secondary outcome measures: COVID-19 CFR and crude cause-specific death rate were calculated using country-level data from the Our World in Data website.

Results: The average of country/territory-specific COVID-19 CFR is about 2%-3% worldwide and higher than previously reported at 0.7%-1.3%. A doubling in size of a population is associated with a 0.48% (95% CI 0.25% to 0.70%) increase in COVID-19 CFR, and a doubling in the proportion of female smokers is associated with a 0.55% (95% CI 0.09% to 1.02%) increase in COVID-19 CFR. The open testing policies are associated with a 2.23% (95% CI 0.21% to 4.25%) decrease in CFR. The strictness of anti-COVID-19 measures was not statistically significantly associated with CFR overall, but the higher Stringency Index was associated with higher CFR in higher-income countries with active testing policies (regression coefficient beta=0.14, 95% CI 0.01 to 0.27). Inverse associations were found between cardiovascular disease death rate and diabetes prevalence and CFR.

Conclusion: The association between population size and COVID-19 CFR may imply the healthcare strain and lower treatment efficiency in countries with large populations. The observed association between smoking in women and COVID-19 CFR might be due to the finding that the proportion of female smokers reflected broadly the income level of a country. When testing is warranted and healthcare resources are sufficient, strict quarantine and/or lockdown measures might result in excess deaths in underprivileged populations. Spatial dependence and temporal trends in the data should be taken into account in global joint strategy and/or policy making against the COVID-19 pandemic.

Keywords: COVID-19; epidemiology; public health.

Figure 1

Pairwise scatter plots and Pearson’s correlation coefficients of the variables. *P<0.05, **P<0.01, ***P<0.001.

Figure 2

Global COVID-19 case-fatality rates (%). nan, no data available.

Figure 3

Residuals of the common (non-spatial) multivariate linear regression.

Figure 4

Strength and decay of the spatial autocorrelation between pair of locations.

Figure 5

Contour plot of estimated COVID-19 case-fatality rate (%).

Figure 6

Contour plot of projected (A) proportion of female smoker, (B) population, (C) GDP per capita and (D) Stringency Index. GDP, gross domestic product.