Healthcare system and social trust in the fight against COVID-19: the case of France.

BACKGROUND: COVID-19, like all pandemics, has territorial specificities that need to be considered: the impact of the COVID-19 crisis strongly differs not only across countries, but also across regions, districts and municipalities within countries. There are several factors that, potentially, can contribute to the differentiated impact of COVID-19, and explain the disparities seen among areas. This study aims to contribute to this debate by analyzing the role of health system and social trust in lessening the impact of the COVID-19 pandemic in French 'départements'. METHODS: The data used in this study have been provided by the INSEE and the French Ministry of Health. Database is made up of the 96 'départements' of metropolitan France. We use spatial analysis techniques to identify the groups of areas that are particularly affected, and to test the influence of local socio-economic factors on the spread of the epidemic. RESULTS: Our exploratory spatial analysis reveals the heterogeneity and spatial autocorrelation of the disease. The use of spatial econometric models, then, allows us to highlight the impact of emergency services, and social capital in reducing the exposition to COVID-19. Our results also report on the role of spillover effects between neighbouring areas. CONCLUSIONS: This research shows that, although individual characteristics are important factors in explaining the probability of contracting COVID-19 disease, health care services and social trust factors also play a significant role in curbing the epidemic's outbreak. These findings should have an interest for policy makers in the prevention of future waves of COVID-19 pandemic.

Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe.

Often presented as a global pandemic spreading all over the world, COVID-19, however, hit not only countries but also regions differently. The objective of this paper is to focus on the spatial heterogeneity in the spread of the COVID-19 pandemic and to contribute to an understanding of the channels by which it spread, focusing on the regional socioeconomic dimension. For this, we use a dataset covering 125 European regions in 12 countries. Considering that the impact of the COVID-19 crisis differed sharply not only across countries but also across regions within the same country, the empirical strategy is based, on the one hand, on an exploratory analysis of spatial autocorrelations, which makes it possible to identify regional clusters of the disease. On the other hand, we use spatial regression models to capture the diffusion effect and the role of different families of regional factors in this process. We find that the share of older people in the population, GDP per capita, distance from achieving EU objectives, and the unemployment rate are correlated with high COVID-19 death rates. In contrast, the number of medical practitioners and hospital beds and the level of social trust are correlated with low COVID-19 death rates.