This article analyzes the evolution of spatial inequalities in mortality across 90 French territorial units since 1806. Using a new database, we identify a period from 1881 to 1980 when inequalities rapidly shrank while life expectancy rose. This century of convergence across territories was mainly due to the fall in infant mortality. Since 1980, spatial inequalities have levelled out or occasionally widened, due mainly to differences in life expectancy among the elderly. The geography of mortality also changed radically during the century of convergence. Whereas in the 19th century high mortality occurred mainly in larger cities and along a line from North‐west to South‐east France, it is now concentrated in the North, and Paris and Lyon currently enjoy an urban advantage.

National authorities publish COVID-19 death counts, which are extensively re-circulated and compared; but data are generally poorly sourced and documented. Academics and stakeholders need tools to assess data quality and to track data-related discrepancies for comparability over time or across countries. “The Demography of COVID-19 Deaths” database aims at bridging this gap. It provides COVID-19 death counts along with associated documentation, which includes the exact data sources and points out issues of quality and coverage of the data. The database — launched in April 2020 and continuously updated — contains daily cumulative death counts attributable to COVID-19 broken down by sex and age, place and date of occurrence of the death. Data and metadata undergo quality control checks prior to online release. As of mid-December 2021, it covers 21 countries in Europe and beyond. It is open access at a bilingual (English and French) website with content intended for expert users and non-specialists (https://dc-covid.site.ined.fr/en/; figshare: 10.6084/m9.figshare.c.5807027). Data and metadata are available for each country separately and pooled over all countries.

Introduction 2 Sources 2.1 Deaths 2.2 Births 2.3 Censuses 3 Methods 3.1 HMD protocol methods 3.1.1 Raw data adjustments 3.1.2 Splitting deaths into Lexis triangles 3.1.3 Computations of populations by age at January 1 of each year 3.1.4 Adjustment of computed mortality rates 3.1.5 Computations of lifetables 3.2 Specific departmental methods for the period 1901-2014 3.2.1 Specific methods due to deaths during the two world wars 3.2.2 Specific methods due to the two world wars 3.2.3 Specific methods due to territorial changes and missing data 3.3 Reliability of the data and comparison with other studies 4 Available results and discussion 4.1 Available results 4.

The Covid-19 pandemic did not affect sub-national regions in a uniform way. Knowledge of the impact of the pandemic on mortality at the local level is therefore an important issue for better assessing its burden. Vital statistics are now available for an increasing number of countries for 2020 and 2021 and allow the calculation of sub-national excess mortality. However, this calculation faces two important methodological challenges: (1) it requires appropriate mortality projection models; (2) small populations imply important uncertainty in the estimates, commonly neglected. We address both issues by adopting a method to forecast mortality at sub-national level and by incorporating uncertainty in the computation of mortality measures. We illustrate our approach to French departements (NUTS 3, 95 geographical units) and produce estimates for 2020 and both sexes. Nonetheless, the proposed approach is so flexibility to allow estimation of excess mortality during Covid-19 in most demographic scenarios as well as for past pandemics.

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