Spatial determinants of excess all-cause mortality during the first wave of the COVID-19 epidemic in France

Abstract

Background The first wave of the COVID-19 pandemic in France was associated with high excess mortality, and anecdotal evidence pointed to differing excess mortality patterns depending on social and environmental determinants. In this study we aimed to investigate the spatial distribution of excess mortality during the first wave of the COVID-19 pandemic in France and relate it at the subnational level to contextual determinants from various dimensions (socioeconomic, population density, overall health status, healthcare access etc.). We also explored whether the determinants identified at the national level varied depending on geographical location. Methods We used available national data on deaths in France to calculate excess mortality by department for three age groups: 0–49, 50–74 and > 74 yrs. between March 1st and April 27th, 2020. We selected 15 variables at the department level that represent four dimensions that may be related to overall mortality at the ecological level, two representing population-level vulnerabilities (morbidity, social deprivation) and two representing environmental-level vulnerabilities (primary healthcare supply, urbanization). We modelled excess mortality by age group for our contextual variables at the department level. We conducted both a global (i.e., country-wide) analysis and a multiscale geographically weighted regression (MGWR) model to account for the spatial variations in excess mortality. Results In both age groups, excess all-cause mortality was significantly higher in departments where urbanization was higher (50–74 yrs.: β = 15.33, p < 0.001; > 74 yrs.: β = 18.24, p < 0.001) and the supply of primary healthcare providers lower (50–74 yrs.: β = − 8.10, p < 0.001; > 74 yrs.: β = − 8.27, p < 0.001). In the 50–74 yrs. age group, excess mortality was negatively associated with the supply of pharmacists (β = − 3.70, p < 0.02) and positively associated with work-related mobility (β = 4.62, p < 0.003); in the > 74 yrs. age group our measures of deprivation (β = 15.46, p < 0.05) and morbidity (β = 0.79, p < 0.008) were associated with excess mortality. Associations between excess mortality and contextual variables varied significantly across departments for both age groups. Conclusions Public health strategies aiming at mitigating the effects of future epidemics should consider all dimensions involved to develop efficient and locally tailored policies within the context of an evolving, socially and spatially complex situation.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

“…14 out of 164 papers included this criterion in their analysis on state, county, and city scales. The result of our review showed that the urban population in France ( Pilkington et al, 2021 ; Tchicaya et al, 2021 ), Iran ( Ramírez-Aldana et al, 2020 ), Bangladesh ( Alam, 2021 ), and the US ( Qeadan et al, 2021 ) increased the number of cases. However, reports from Italy ( Perone, 2021 ), the Netherlands ( Boterman, 2020 ), and China ( Chu et al, 2021 ) found an insignificant effect of urbanization on COVID-19 spread.…”

“…Based on our review, many studies conducted in countries such as the US ( Sarmadi et al, 2021 ; White and Hébert-Dufresne, 2020 ), Italy ( Ilardi et al, 2020 ), Iran ( Ahmadi et al, 2020 ), Bangladesh ( Sharif et al, 2021 ; Alam, 2021 ; Rahman et al, 2021 ), Oman ( Al Kindi et al, 2021 ), France ( Tchicaya et al, 2021 ), Nigeria ( Bayode et al, 2022 ) and Scotland ( Rideout et al, 2021 ) found population density as a positive and significant predictor of COVID-19 cases on state scale. However, about 40 % of studies reported that population density is not a significant factor to explain the difference in COVID-19 cases in different states ( Ramírez-Aldana et al, 2020 ; Gupta et al, 2020 ; Perone, 2021 ; Gargiulo et al, 2020 ; Basellini and Camarda, 2021 ; Pilkington et al, 2021 ; Sen-Crowe et al, 2021 ).…”

“…Internal and external mobility of population on state, county, and urban scales is another common variable to analyze the spread of COVID-19. The result of studies in Iran, France, and Mexico shows that population mobility, internally and externally, plays a significant role in a higher number of cases on the state scale ( Ahmadi et al, 2020 ; Ramírez-Aldana et al, 2021 ; Pilkington et al, 2021 ). As was expected, the higher rate of mobility is a significant predictor of COVID-19 cases on the County scale ( Jamshidi et al, 2020 ; Niu et al, 2020 ; Tokey, 2021 ).…”

“… Moosa and Khatatbeh (2021) found that the healthcare system of countries plays a critical role in mortality rate even in the countries with a low rate of infection. In general states and counties with better health services such as hospital beds, doctors, ICU beds, and primary medical services have a lower rate of mortality ( Lee et al, 2021 ; Basellini and Camarda, 2021 ; Pilkington et al, 2021 ). However, this effect is mixed for the number of cases.…”

Sociodemographic inequalities and excess non-COVID-19 mortality during the COVID-19 pandemic: A data-driven analysis of 1,069,174 death certificates in Mexico

“…Other high-income countries have demonstrated the association between hospital occupancy and excess mortality during periods of peak COVID-19 infections (21–23). Explanations related to this phenomenon rely on data on restricted access to healthcare services in places which experienced hospital overload due to COVID-19, reduced out-of-hospital attention due to severely restricted healthcare services and personnel availability, lower insurance coverage, and lower number of healthcare personnel per-capita (24–26). Other reported non-related healthcare contributors were social stigma for being treated in hospitals due to potentially acquiring COVID-19 infection and increased unhealthy lifestyle habits which could have exacerbated complications due to chronic health conditions (27).…”