Revisiting the link between COVID-19 incidence and infection fatality rate during the first pandemic wave

Abstract

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.

Fig. 1

Causal graph. The variables are considered at the departmental scale. The effect of X on Y can be estimated by adjusting on {Diab., Age, Beds}. X: COVID-19 incidence. Y: IFR (infection fatality rate) or IHR (infection hospitalization rate). Diab.: Prevalence of diabetes. Beds: Number of intensive care beds per inhabitant. Age: Proportion of population over 60. Dashed arrows represent the effects of unmeasured confounders C.

Fig. 2

Overview of the model. The blue and red rectangles represent the exposure and outcome of the main analysis, respectively. The numbers indicate the equations associated with the variables (see the Model section). IFR: Infection fatality rate. IHR: Infection hospitalization rate.

Fig. 3

COVID-19 departmental incidence (cumulated over the first wave) in metropolitan France.This map was created using the R package maps: https://cran.r-project.org/web/packages/maps/index.html..

Fig. 4

Effect of departmental incidence on infection fatality rate (IFR) and on infection hospitalization rate (IHR). The points represent the mean posterior departmental estimates. Black line and gray zone: Posterior mean and 95% CI of the expected adjusted departmental IFR given incidence (or expected causal effect of incidence on IFR: see Eq. 1 of the Methods). The same description applies to IHR. The covariates are represented relative to their medians. Pop. over 60: Proportion of adult population over 60. ICU beds per 1,000 inhab.: Number of intensive care beds per 1,000 inhabitants.

Fig. 5

Association between incidence in people under 60 and the proportion of people over 60 among those infected. The points represent the mean posterior departmental estimates. Black line and gray zone: Posterior mean and 95% CI of the expected proportion of persons over 60 among those infected (for a department with the same age structure as metropolitan France).