Abstract
BACKGROUND: The 2019 coronavirus (COVID-19) epidemic began in Wuhan, China in December 2019 and quickly spread to the rest of the world. This study aimed to analyse the associations between the COVID-19 mortality rate in hospitals, the availability of health services, and socio-spatial and health risk factors at department level. METHODS AND FINDINGS: This spatial cross-sectional study used cumulative mortality data due to the COVID-19 pandemic in hospitals until 30 November 2020 as a main outcome, across 96 departments of mainland France. Data concerning health services, health risk factors, and socio-spatial factors were used as independent variables. Independently, we performed negative binomial, spatial and geographically weighted regression models. Our results revealed substantial geographic disparities. The spatial exploratory analysis showed a global positive spatial autocorrelation in each wave indicating a spatial dependence of the COVID-19 deaths across departments. In first wave about 75% of COVID-19 deaths were concentrated in departments of five regions compared to a total of 13 regions. The COVID-19 mortality rate was associated with the physicians density, and not the number of resuscitation beds. Socio-spatial factors were only associated with the COVID-19 mortality rate in first wave compared to wave 2. For example, the COVID-19 mortality rate increased by 35.69% for departments densely populated. Health risk factors were associated with the COVID-19 mortality rate depending on each wave. This study had inherent limitations to the ecological analysis as ecological bias risks and lack of individual data. CONCLUSIONS: Our results suggest that the COVID-19 pandemic has spread more rapidly and takes more severe forms in environments where there is already a high level of vulnerability due to social and health factors. This study showed a different dissemination pattern of COVID-19 mortality between the two waves: a spatial non-stationarity followed by a spatial stationarity in the relationships between the COVID-19 mortality rate and its potential drivers.