Exploring Causal relationship between risk factors and vulnerability to COVID-19 Cases of Italy, Spain, France, Greece, Portugal, Morocco and South Africa

Objective

We aim to find correlation between the identified risk factors and vulnerability to COVID-19 in seven different countries from Europe and Africa: Most affected countries Italy, Spain, France, moderately affected: Portugal, and less affected countries: Greece, Morocco and South Africa.

Data sources

WHO, Worldometers, Ourworldindata

Population

all reported COVID-19 total in-hospital infected and death cases in Italy, Spain, France, Portugal, Greece, Morocco, and South Africa.

Time period

15^th March 2020 to 15^th May 2020

Methods

We used Multiple linear regression in our approach to modeling the relationship between the dependent variable (DV): vulnerability to COVID19 (which we presented by number of totals in-hospital infected cases per million for each country) and the independent variables (IV) scores: median age, aged 65+, population density, international arrivals, BMI, diabetes prevalence, and temperature. We used SPSS software to generate multiple linear regression; Pearson correlation factor: r, ANOVA table, Coefficients table, as well as bar charts and scatter plots.

The multiple linear regression equation of our study model is: <disp-formula id="ueqn1"><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20139121v1_ueqn1.gif" position="float" orientation="portrait"/></alternatives></disp-formula>

Where:

Y: Predicted score on total cases per million or Vulnerability to COVID19; a: intercept; b1: regression coefficient or weight for median age; X1: median age; b2: regression coefficient or weight for population density; X2: population density; b3: regression coefficient or weight for international arrivals; X3: international arrivals in millions; b4: regression coefficient or weight average temperature; X4: average temperature in Celsius; b5: regression coefficient or weight for BMI; X5: BMI Body Mass Index in Kg/m; b6: regression coefficient or weight for diabetes prevalence percentage; X6: diabetes prevalence percentage; B7: regression coefficient or weight for aged 65+; X7: aged 65+ percentage

Results

Till 15th May 2020 There were in Spain: 229540 total infected cases and 27321 total death attributed to COVID-19, France: 141356 total cases and 27425 total deaths, Italy: 223096 total cases and 31368 total deaths, Portugal: 28319 total cases and 1184 total deaths, Greece: 2770 total cases and 156 total deaths, Morocco: 6607 total cases and 190 total deaths, and South Africa: 12739 total cases and 238 total deaths. In summary after full adjustment, total death cases were strongly associated with total infected cases for the population of all the seven chosen countries combined: correlation factor r= 0.921 with a P-value= .000: P<0.05 (Sig.(2-tailed). Population density was significantly correlated with total infected cases for all the seven countries combined: r= 0.478 with a P-value= .000: P<0.05. the median age was moderately associated with infected cases: r= 0.563 with P<0.05. However, diabetes prevalence was less associated with infected cases: r= 0.146 with P<0.05. International exposure was significantly associated with infected cases: r= 0.609 with P<0.05. Median temperature was negatively correlated with total infected cases in the seven countries combined.

Conclusions

We have quantified a range of risk factors for infection and death from COVID-19, in seven highly, moderate and less affected countries, in a large correlation-regression study. People from countries with high international exposure, high population density, are at markedly risk of getting infected such as France, Spain, and Italy. Moreover, people from countries with high median age are at increased risk of in-hospital death from COVID19. Furthermore, by conducting multiple linear regression analysis we have found that the overall regression model is significant (as shown in the tables). 66.2% (r²=.662) of the vulnerability is explained by the model which includes all predictor variables or risk factors.

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