A comparative analysis of COVID-19 mortality rate across the globe: An extensive analysis of the associated factors

TLDR: In this paper, the authors quantified and analyzed one of the broadest set of clinical factors associated with COVID-19-related death, ranging from disease related co-morbities, socioeconomic factors, healthcare capacity and government policy and interventions.

Abstract: Background The vast variation in COVID 19 mortality across the globe draws attention to potential risk factors other than the patient characteristics that determine COVID-19 mortality. Subjects and Methods We have quantified and analyzed one of the broadest set of clinical factors associated with COVID-19-related death, ranging from disease related co-morbities, socioeconomic factors, healthcare capacity and government policy and interventions. Data for population, total cases, total COVID mortality, tests done, and GDP per capita were extracted from the worldometers database. Datasets for health expenditure by government, hospital beds, rural population, prevalence of smoking, prevalence of overweight population, deaths due to communicable disease and incidence of malaria were extracted from the World Bank website. Prevalence of diabetes was retrieved from the indexmundi rankings. The average population age, 60+ population, delay in lockdown, population density and BCG data were also included for analysis. The COVID-19 mortality per million and its associated factors were retrieved for 56 countries across the globe. Quantitative analysis was done at the global as well as continent level. All the countries included in the study were categorized continent and region wise for comparative analysis determining the correlation between COVID 19 mortality and the aforementioned factors. Results There was significant association found between mortality per million and 60+ population of country, average age, prevalence of diabetes mellitus, and case fatality rate with correlation and p value (p) of 0.422 (p 0.009), 0.386 (p 0.0186), −0.384 (p 0.019) and 0.753 (p 0.000) respectively at 95% CI. Conclusion The study observations will serve as a evidence based management strategy for generating predictive model for COVID-19 infection and mortality rate.