Several studies found an association between COVID-19 incidence, cumulated over the first pandemic wave, and the risk of death for infected individuals. They attributed this association to hospital overload. We studied this association across the French departments using 82,467 serological samples and a hierarchical Bayesian model with spatial smoothing. In high-incidence areas, we hypothesized that hospital overload would increase infection fatality rate (IFR) without increasing infection hospitalization rate (IHR). The analyses were adjusted for intensive care beds per capita, age of the population, and diabetes prevalence (as a surrogate for obesity). We found that increasing departmental incidence from 3 to 9% rose IFR from 0.42 to 1.14% (difference 0.72%, 95% CI 0.49-1.01%), and IHR from 1.66 to 3.61% (difference 1.94%, 95% CI 1.18-2.80%). An increase in incidence from 6 to 12% in people under 60 was associated with an increased proportion of people over 60 among those infected, from 11.6 to 17.4% (difference 5.8%, 95% CI 2.9-8.8%). Higher incidence increased the risk of death for infected individuals and their risk of hospitalization by the same magnitude. These findings could be explained by a higher age among infected individuals in high-incidence areas, rather by than hospital overload.
Keywords: Bayesian statistics; COVID-19; Causal graph; Hierarchical modeling; Hospital overload; Spatial modeling.
© 2025. The Author(s).