Cohort-resolved excess mortality in Germany (2000-2024): Patterns and implications for the SARS-CoV-2 era

Understanding the impact of the SARS-CoV-2 pandemic on mortality requires more than aggregate statistics. While whole-population indicators have informed policy, they risk concealing subgroup-specific patterns. We analysed all-cause mortality in Germany from 2000 to 2024 using a weekly, cohort-resolved framework across 15 age groups to detect excess and under-mortality before, during, and after the pandemic. Expected mortality was modelled using exponential trends from two decades of pre-pandemic data. Deviations from expectation were quantified as normalised excess all-cause mortality rates (NEAMR), enabling the identification of significant, age-specific anomalies. We found sustained NEAMR in adults aged 75-79 and 35-49 from late 2021 through 2024-patterns absent in whole-population trends. Conversely, cohorts aged 30-34 and 55-59 showed persistent under-mortality. Earlier excess peaks in older cohorts (e.g., 85-89 in 2003, 95+ in 2013) suggest generational vulnerabilities potentially linked to early-life adversity. Cross-correlation analyses indicate that associations between NEAMR and SARS-CoV-2 mRNA injection rates diverge from expected protective patterns in most age cohorts, especially during the 2021 'alpha-to-delta' transition. These findings highlight the need for further hypothesis-driven investigations as well as a high-resolution mortality surveillance. Cohort-resolved analysis reveals NEAMR signals that aggregate data obscure, offering a more accurate assessment of public health outcomes across demographic groups.

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Journal
PLoS ONE
Published
2025-10-27
DOI
https://doi.org/10.1371/journal.pone.0334884
Primary Topic
Health disparities and outcomes
Type
article
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article

Cohort-resolved excess mortality in Germany (2000-2024): Patterns and implications for the SARS-CoV-2 era

Robert Rockenfeller, Michael Günther
PLoS ONE
Health disparities and outcomes
article

Cohort-resolved excess mortality in Germany (2000-2024): Patterns and implications for the SARS-CoV-2 era

Robert Rockenfeller, Michael Günther
article en

Abstract

Understanding the impact of the SARS-CoV-2 pandemic on mortality requires more than aggregate statistics. While whole-population indicators have informed policy, they risk concealing subgroup-specific patterns. We analysed all-cause mortality in Germany from 2000 to 2024 using a weekly, cohort-resolved framework across 15 age groups to detect excess and under-mortality before, during, and after the pandemic. Expected mortality was modelled using exponential trends from two decades of pre-pandemic data. Deviations from expectation were quantified as normalised excess all-cause mortality rates (NEAMR), enabling the identification of significant, age-specific anomalies. We found sustained NEAMR in adults aged 75-79 and 35-49 from late 2021 through 2024-patterns absent in whole-population trends. Conversely, cohorts aged 30-34 and 55-59 showed persistent under-mortality. Earlier excess peaks in older cohorts (e.g., 85-89 in 2003, 95+ in 2013) suggest generational vulnerabilities potentially linked to early-life adversity. Cross-correlation analyses indicate that associations between NEAMR and SARS-CoV-2 mRNA injection rates diverge from expected protective patterns in most age cohorts, especially during the 2021 'alpha-to-delta' transition. These findings highlight the need for further hypothesis-driven investigations as well as a high-resolution mortality surveillance. Cohort-resolved analysis reveals NEAMR signals that aggregate data obscure, offering a more accurate assessment of public health outcomes across demographic groups.

PLoS ONEVol. 20(10)
University of Stuttgart (DE), Koblenz University of Applied Sciences (DE), Universität Koblenz (DE), Friedrich Schiller University Jena (DE)
Openalex Percentile: Top 61%
Health disparities and outcomes
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Cohort-resolved excess mortality in Germany (2000-2024): Patterns and implications for the SARS-CoV-2 era — Robert Rockenfeller, Michael Günther · PLoS ONE (2025) | TGRS Research Map | TGRS