Time-series comparison of COVID-19 case fatality rates across 21 countries with adjustment for multiple covariates

Abstract

Objectives Although it is widely used as a measure for mortality, the case fatality rate (CFR) of coronavirus disease 2019 (COVID-19) can vary over time and fluctuate for many reasons other than viral characteristics. To compare the CFRs of different countries in equal measure, we estimated comparable CFRs after adjusting for multiple covariates and examined the main factors that contributed to variability in the CFRs among 21 countries. Methods For statistical analysis, time-series cross-sectional data were collected from Our World in Data, CoVariants.org, and GISAID. Biweekly CFRs of COVID-19 were estimated by pooled generalized linear squares regression models for the panel data. Covariates included the predominant virus variant, reproduction rate, vaccination, national economic status, hospital beds, diabetes prevalence, and population share of individuals older than age 65. In total, 21 countries were eligible for analysis. Results Adjustment for covariates reduced variation in the CFRs of COVID-19 across countries and over time. Regression results showed that the dominant spread of the Omicron variant, reproduction rate, and vaccination were associated with lower country-level CFRs, whereas age, the extreme poverty rate, and diabetes prevalence were associated with higher country-level CFRs. Conclusion A direct comparison of crude CFRs among countries may be fallacious, especially in a cross-sectional analysis. Our study presents an adjusted comparison of CFRs over time for a more proper comparison. In addition, our findings suggest that comparing CFRs among different countries without considering their context, such as the epidemic phase, medical capacity, surveillance strategy, and socio-demographic traits, should be avoided.

Keywords

• covid-19
• least-squares analysis
• linear models
• mortality