Regional excess mortality in France during COVID-19 pandemic: the first three epidemic periods (March 2020-June 2021)

Marlène Faisant1, Nicolas Vincent1, Bruno Hubert1

  • 1Santé publique France, SpFrance, Saint-Maurice, France.

PubMed

Insights

France experienced significant COVID-19 mortality excess during the first three epidemic waves, with notable regional disparities. Deaths in healthcare and medico-social institutions alone did not fully explain these excess mortality trends.

Area of Science:

  • Epidemiology
  • Public Health
  • Biostatistics

Background:

  • Examining the impact of the COVID-19 pandemic on mortality.
  • Focusing on the initial three epidemic waves in France.
  • Investigating regional variations in mortality excess.

Purpose of the Study:

  • To quantify and describe excess mortality during the first three COVID-19 epidemic periods across France.
  • To compare mortality data from healthcare and medico-social institutions with all-cause mortality.
  • To identify regional disparities in mortality patterns.

Main Methods:

  • Utilized two complementary approaches: direct counting of COVID-19 deaths in institutions and estimation of all-cause mortality excess.
  • Employed a negative binomial regression model to estimate expected mortality, accounting for long-term trends and seasonality.
  • Defined three epidemic periods for each region, including overseas territories.

Main Results:

  • Observed consistency between the two methods in highly affected regions, but significant regional disparities were evident.
  • Identified an east-west gradient in relative all-cause mortality excess across epidemic periods.
  • Found that deaths within healthcare and medico-social institutions did not solely account for the observed mortality fluctuations.

Conclusions:

  • Further analysis by age group and specific causes of death is recommended for a comprehensive understanding.
  • The electronic death registration system shows potential for rapid mortality estimation of emerging diseases like COVID-19 but requires further development for real-time monitoring.
  • Regional variations in excess mortality highlight the need for geographically tailored public health interventions.
Abstract

Related Concept Videos

Pareto Chart00:52

Pareto Chart

A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
6.7K
Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
108
Life Tables01:22

Life Tables

A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
98
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
366
Pie Chart01:04

Pie Chart

A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
14.1K
Vaccinations01:51

Vaccinations

Overview
44.5K