A new framework for disentangling different components of excess mortality applied to Dutch care home residents during Covid-19

BMC Medical Research Methodology, vol. 25, pp. 126

Abstract

BACKGROUND: Vulnerable subgroups of the population, such as care home residents, often face elevated mortality risks during crises like pandemics or wars. To correctly model and interpret the excess mortality of vulnerable groups during crises, a distinction must be made between the pre-existing heightened mortality of the vulnerable group, the general population's excess mortality during the crisis, and the crisis-specific excess mortality unique to the vulnerable group. METHODS: We introduce the concept of "excess excess" mortality, which captures the extra excess mortality experienced by vulnerable groups during crises, beyond what can be explained by their excess mortality due to being vulnerable and general population excess mortality. Using individual-level data from Statistics Netherlands, we model the excess excess mortality of Dutch care home residents aged 70 and older during the Covid-19 pandemic. We extend standard relative survival methods by incorporating multiple excess mortality components and use an additive hazards model to accommodate periods of negative excess hazard. RESULTS: The findings confirm the severe impact of the Covid-19 pandemic on care home residents. In general, men and older age groups experienced higher excess excess mortality, both in absolute and relative terms. CONCLUSIONS: Our approach offers a new perspective on how to model and interpret excess mortality in vulnerable groups during a crisis and provides a methodological foundation for investigating excess excess mortality in other contexts.

Authors 6

1. Marije H. Sluiskes corresponding Aachen Medical Statistics

   Leiden University Medical Center

   Affiliation as printed

   Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands. m.h.sluiskes@lumc.nl

2. Eva A. S. Koster Aachen Medical Statistics

   Leiden University Medical Center

   Affiliation as printed

   Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

3. Jelle J. Goeman Aachen Medical Statistics

   Leiden University Medical Center

   Affiliation as printed

   Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

4. Mar Rodríguez‐Girondo Aachen Medical Statistics

   Leiden University Medical Center

   Affiliation as printed

   Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

5. Hein Putter Aachen Mathematical Institute Medical Statistics

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Mathematical Institute, Leiden University, Leiden, The Netherlands

   Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

6. Liesbeth C. de Wreede corresponding Aachen Medical Statistics

   Leiden University Medical Center

   Affiliation as printed

   Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands. l.c.de_wreede@lumc.nl