Link between COVID-19-related in-hospital mortality in continental France administrative areas and weather: an ecological study

OBJECTIVE: To assess the effect of a weather index on in-hospital COVID-19-linked deaths. DESIGN: Ecological study. SETTING: Continental France administrative areas (départements; henceforth counties). The study period, from 18 March to 30 May 2020, corresponds to the main first outbreak period in France. POPULATION: COVID-19-linked in-hospital deaths. MAIN OUTCOME MEASURES: In-hospital deaths and demographics (population, human density, male sex and population percentage >59 years old) were obtained from national and centralised public databases. County weather indexes were calculated by the French National Meteorological Agency. METHODS: In this observational ecological study, the relationship between in-hospital COVID-19-related mortality and climate zones in continental French counties were analysed, by comparing the cumulative in-hospital death tolls in France by county to other factors (population density, climate, age and sex). The study period lasted from 18 March to 30 May 2020. A multivariate linear-regression analysis of in-hospital mortality included climate zones, population density, population >59 years old and percentages of males as potential predictors. The significance level was set at 5%. RESULTS: Weather indicators and population density were factors independently associated with the COVID-19 death toll. Colder counties had significantly higher mortality rates (p<0.00001). Percentages of males and population >59 years old in counties did not affect COVID-19 in-hospital mortality. CONCLUSIONS: Many parameters influence COVID-19 outbreak-severity indicators. Population density is a strong factor but its exact importance is difficult to discern. Weather (mainly cold winter temperatures) was independently associated with mortality and could help explain outbreak dynamics, which began and were initially more severe in the coldest counties of continental France. Weather partly explains fatality-rate discrepancies observed worldwide.