Covid-19 Mortality: A Matter of Vulnerability Among Nations Facing Limited Margins of Adaptation

Abstract

Context: The human development territories have been severely constrained under the Covid-19 pandemic. A common dynamics has been observed, but its propagation has not been homogeneous over each continent. We aimed at characterizing the non-viral parameters that were most associated with death rate. Methods: We tested major indices from five domains (demography, public health, economy, politics, environment) and their potential associations with Covid-19 mortality during the first 8 months of 2020, through a Principal Component Analysis and a correlation matrix with a Pearson correlation test. Data of all countries, or states in federal countries, showing at least 10 fatality cases, were retrieved from official public sites. For countries that have not yet finished the first epidemic phase, a prospective model has been computed to provide options of death rates evolution. Results: Higher Covid death rates are observed in the [25/65°] latitude and in the [-35/-125°] longitude ranges. The national criteria most associated with death rate are life expectancy and its slowdown, public health context (metabolic and non-communicable diseases (NCD) burden vs. infectious diseases prevalence), economy (growth national product, financial support), and environment (temperature, ultra-violet index). Stringency of the measures settled to fight pandemia, including lockdown, did not appear to be linked with death rate. Conclusion: Countries that already experienced a stagnation or regression of life expectancy, with high income and NCD rates, had the highest price to pay. This burden was not alleviated by more stringent public decisions. Inherent factors have predetermined the Covid-19 mortality: understanding them may improve prevention strategies by increasing population resilience through better physical fitness and immunity.

Keywords: COVID-19; demography; environment; lockdown; niche adaptation; public health.

Figure 1

Each point represents the Covid-19 mortality rate of a country or a state, according to its latitude. Greater mortality rates were mostly observed in the [25 /65] latitude interval.

Figure 2

PCA first factorial plane: axis 1 is horizontal; axis 2 is vertical. The more distant a variable is from the center, the more it is correlated with the first or the second factorial axis. The studied parameters regrouped the countries associated with Covid-19 highest death rates on the right of the horizontal axis. These are the high income countries with a high LE but a low progression of it, high sedentarity, obesity, high deviation from latitude 0 and low longitude (Asian countries have a high longitude, while it is negative in the Americas). Countries associated with low Covid-19 death rates have a low GDP, a low LE but a great margin of progression for it, a high prevalence of infectious diseases, a greater deviation from optimum temperature and UV index; they occupy the left part of the axis. Lockdown stringency, containment index and ambiant humidity are not correlated with Covid-19 mortality, as they are linked to the second axis. The cloud of individuals on the first factorial plane is presented in the Supplementary Figure 5.

Figure 3

Polynomial or linear regression between the logarithm of the mortality rates due to Covid-19 and the UV index, humidity, and average temperature from the local beginning of the pandemic up to the peak.

Figure 4

Coordinates of the variables on the first 3 factorial axes. The larger the circle, the more the variable is correlated with the axis. A blue circle indicates that the variable is negatively correlated, a red circle indicates that the variable is positively correlated. The scale shows the coordinates of the variables on the axis.

Figure 5

PCA third factorial plane: axis 1 is horizontal; axis 3 is vertical. The studied parameters regrouped the countries associated with Covid-19 highest death rates on the right of the horizontal axis. Longitude and obesity rates are related to the third axis.

Figure 6

Correlation matrix: a larger area in the circles indicates a stronger correlation between the row and the column variables. A blue circle indicates a positive correlation coefficient; a red circle indicates a negative one; a full circle corresponds to r = 1 or −1; an empty circle corresponds to r = 0. If the Pearson correlation test was not significant, a cross on the circle was added.