COVID-19 Total Mortality and Relative Waves: the Reasons for the Differences among European Countries

Background: Several mortality waves (between 1 to 5) have been observed over time in the 47 European countries. Material and Methods: The data on mortality rate shown on the WHO dashboard were used, and twelve LEEDELS variables (life expectancy, ecological, economic, demographic and lifestyle) during the period between March 2020 and September 2021. WMVs (weighted median values) were used to calculate the mortality rates and their respective waves. The partition model was used to identify which LEEDELS variables correlated with the mortality rates (predictors). Results: In the partition model four LEEDELS predictors were considered relevant: number of cars per 103 inhabitants, GDP, percentage of population over 65 years old, and life expectancy. The remaining eight LEEDELS variables (total population aged over 65 years old, population density, urban population, education expenditure, number of hospital beds, particulate matter, number of mobile phones, and number of internet connections) were irrelevant. The analysis revealed four pathways (from P1 to P4), which limit or increase the WMVs respectively. The P3 pathway was shown to be at the highest risk of death while P1 was more protective. Conclusions: High GDP, a high percentage of the population over 65 years of age and life expectancy are crucial for WMV containment, represented by P1. The pathway P3, characterized by high GDP, low percentage of population over 65 is at higher risk. Population density, particulate matter, number of hospital beds and expenditure on education, cell phone x 103 inhabitants were not found to be causal variables