Evidence map›Paper›PMID 39844284›Full record

ArticleArchives of public health = Archives belges de sante publique2025

Understanding excess mortality during COVID in Belgium: the influence of pre-existing health status and social factors.

Laura Van den Borre, Brecht Devleesschauwer, Sylvie Gadeyne, Katrien Vanthomme, Didier Willaert

Abstract read
In one paragraph

Article in Archives of public health = Archives belges de sante publique, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Laura Van den BorreDepartment of Epidemiology and Public Health, Sciensano, Brussels, Belgium. laura.vandenborre@sciensano.be.
Brecht DevleesschauwerDepartment of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
Sylvie GadeyneBrussels Institute for Social and Population Studies, Vrije Universiteit Brussel, Brussels, Belgium.
Katrien VanthommeDepartment of Public Health and Primary Care, Ghent University, Ghent, Belgium.
Didier WillaertSolidaris - Socialist Health Insurance Fund, Brussels, Belgium.

Funding

Belgian Federal Science Policy Office B2/202/P3/HELICON
6 · The paper itself

Abstract

backgroundThis study aims to investigate how pre-existing health status and social background contribute to excess mortality during the COVID-19 crisis in Belgium.

methodsThe study population consists of almost 1.4 million adult members of Solidaris, the second largest health insurance fund in Belgium. Pre-existing health status was identified using health care reimbursement data, including medication use. Social characteristics included a proxy for low socio-economic status, nationality of origin, and living arrangement. Excess mortality during the COVID-19 crisis was measured by computing the relative difference between all-cause mortality in 2020 or 2021 and the average yearly mortality in 2015-2019. Directly Standardised Mortality Rates (DSMRs) were calculated to investigate absolute mortality inequalities. Mortality Rate Ratios (MRRs) were computed using Poisson regression analyses to investigate relative mortality inequalities.

resultsDSMRs show that persons with no previous disease experienced significant excess mortality in 2020, like men with one, two or three diseases and women with various numbers of pre-existing diseases. Results by specific disease show heterogenous results. After adjusting for age, sex and social characteristics, persons with cancer experienced a significant mortality deficit of 17% in 2020 and of 9% in 2021. For persons with cancer and asthma or COPD, significant mortality deficits of 10% and  3% were observed in 2020 and 2021, respectively.

conclusionThe study provides insights into the complex dynamics of mortality during the COVID-19 crisis, emphasising the need to consider individual-level information on pre-existing health and social background jointly.

Indexed as

All-cause mortalityChronic diseasesCOVID-19MultimorbiditySocio-economic status

Identifiers

PMID39844284
PMCPMC11756169

What Socratic holds

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LicenceCC BY
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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 Socratic graph.