Evidence map›Paper›PMID 39334191›Full record

ArticlePopulation health metrics2024

Population age structure dependency of the excess mortality P-score.

Niklas Ullrich-Kniffka, Jonas Schöley

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Article in Population health metrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Short-Term Effects of Extreme Heat, Cold, and Air Pollution Episodes on Excess Mortality in Luxembourg.International journal of environmental research and public health · 2025
    Article
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Niklas Ullrich-KniffkaUniversity of Rostock, Ulmenstr. 69, 18057, Rostock, Germany. niklas.ullrich@uni-rostock.de.
Jonas SchöleyMax Planck Institute for Demographic Research, Konrad-Zuse-Str. 1, 18057, Rostock, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince the outbreak of the COVID-19 pandemic, the excess mortality P-score has gained prominence as a measure of pandemic burden. The P-score indicates the percentage by which observed deaths deviate from expected deaths. As the P-score is regularly used to compare excess mortality between countries, questions arise regarding the age dependency of the measure. In this paper we present formal and empirical results on the population structure bias of the P-score with a special focus on cross-country comparisons during the COVID-19 pandemic in Europe.

methodsP-scores were calculated for European countries for 2021, 2022, and 2023 using data from the 2024 revision of the United Nations' World Population Prospects and the HMDs Short Term Mortality Fluctuations data series. The expected deaths for 2021, 2022, and 2023 were estimated using a Lee-Carter forecast model assuming pre-pandemic conditions. P-score differences between countries were decomposed using a Kitagawa-type decomposition into excess-mortality and structural components. To investigate the sensitivity of P-score cross-country rankings to differences in population structure we calculated the rank-correlation between age-standardized and classical P-scores.

resultsThe P-score is an average of age-specific percent excess deaths weighted by the age-distribution of expected deaths. It can be shown that the effect of differences in the distribution of deaths only plays a marginal role in a European comparison. In most cases, the excess mortality effect is the dominant effect. P-score rankings among European countries during the COVID-19 pandemic are similar under both age-standardized and classical P-scores.

conclusionsAlthough the P-score formally depends on the age-distribution of expected deaths, this structural component only plays a minor role in a European comparison, as the distribution of deaths across the continent is similar. Thus, the P-score is suitable as a measure of excess mortality in a European comparison, as it mainly reflects the differences in excess mortality. However, this finding should not be extrapolated to global comparisons, where countries could have very different death distributions. In situations were P-score comparisons are biased age-standardization can be applied as a solution.

Indexed as

COVID-19MortalityPandemicsSARS-CoV-2AdolescentAdultAgedAged, 80 and overAge DistributionAge FactorsEuropeHumansMiddle AgedCOVID-19DecompositionExcess mortalityP-scoreStandardization

Identifiers

PMID39334191
PMCPMC11428885

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.