Socioeconomic position and the COVID-19 care cascade from testing to mortality in Switzerland: a population-based analysis

Julien Riou*, Radoslaw Panczak*, Christian L Althaus, Christoph Junker, Damir Perisa, Katrin Schneider, Nicola G Criscuolo, Nicola Low, Matthias Egger

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

23 Citations (Scopus)
31 Downloads (Pure)

Abstract

BACKGROUND: The inverse care law states that disadvantaged populations need more health care than advantaged populations but receive less. Gaps in COVID-19-related health care and infection control are not well understood. We aimed to examine inequalities in health in the care cascade from testing for SARS-CoV-2 to COVID-19-related hospitalisation, intensive care unit (ICU) admission, and death in Switzerland, a wealthy country strongly affected by the pandemic.

METHODS: We analysed surveillance data reported to the Swiss Federal Office of Public Health from March 1, 2020, to April 16, 2021, and 2018 population data. We geocoded residential addresses of notifications to identify the Swiss neighbourhood index of socioeconomic position (Swiss-SEP). The index describes 1·27 million small neighbourhoods of approximately 50 households each on the basis of rent per m2, education and occupation of household heads, and crowding. We used negative binomial regression models to calculate incidence rate ratios (IRRs) with 95% credible intervals (CrIs) of the association between ten groups of the Swiss-SEP index defined by deciles (1=lowest, 10=highest) and outcomes. Models were adjusted for sex, age, canton, and wave of the epidemic (before or after June 8, 2020). We used three different denominators: the general population, the number of tests, and the number of positive tests.

FINDINGS: Analyses were based on 4 129 636 tests, 609 782 positive tests, 26 143 hospitalisations, 2432 ICU admissions, 9383 deaths, and 8 221 406 residents. Comparing the highest with the lowest Swiss-SEP group and using the general population as the denominator, more tests were done among people living in neighbourhoods of highest SEP compared with lowest SEP (adjusted IRR 1·18 [95% CrI 1·02-1·36]). Among tested people, test positivity was lower (0·75 [0·69-0·81]) in neighbourhoods of highest SEP than of lowest SEP. Among people testing positive, the adjusted IRR was 0·68 (0·62-0·74) for hospitalisation, was 0·54 (0·43-0·70) for ICU admission, and 0·86 (0·76-0·99) for death. The associations between neighbourhood SEP and outcomes were stronger in younger age groups and we found heterogeneity between areas.

INTERPRETATION: The inverse care law and socioeconomic inequalities were evident in Switzerland during the COVID-19 epidemic. People living in neighbourhoods of low SEP were less likely to be tested but more likely to test positive, be admitted to hospital, or die, compared with those in areas of high SEP. It is essential to continue to monitor testing for SARS-CoV-2, access and uptake of COVID-19 vaccination and outcomes of COVID-19. Governments and health-care systems should address this pandemic of inequality by taking measures to reduce health inequalities in response to the SARS-CoV-2 pandemic.

FUNDING: Swiss Federal Office of Public Health, Swiss National Science Foundation, EU Horizon 2020, Branco Weiss Foundation.

Original languageEnglish
Pages (from-to)e683-e691
JournalLancet Public Health
Volume6
Issue number9
Early online date9 Jul 2021
DOIs
Publication statusPublished - Sep 2021

Bibliographical note

Funding Information:
This study would not have been possible without the extraordinary efforts of the data science team at the SFOPH: Samuel Colin, Jeffrey Keller, Anne Laube, Urs Mayr, and Serge Zaugg. We are also grateful to Thomas Van Boeckel and Carole Dupont for helpful comments on an earlier draft of this paper. This study was funded by the SFOPH and the Swiss National Science Foundation (189498). NL and CLA acknowledge funding from the EU's Horizon 2020 research and innovation programme (project EpiPose, 101003688). Nicola Criscuolo was supported by the Branco Weiss Foundation. Calculations were performed on UBELIX, the high performance computing cluster at the University of Bern, Bern, Switzerland. We also thank Steven J Korzeniewski and three anonymous reviewers for their helpful comments.

Funding Information:
This study would not have been possible without the extraordinary efforts of the data science team at the SFOPH: Samuel Colin, Jeffrey Keller, Anne Laube, Urs Mayr, and Serge Zaugg. We are also grateful to Thomas Van Boeckel and Carole Dupont for helpful comments on an earlier draft of this paper. This study was funded by the SFOPH and the Swiss National Science Foundation (189498). NL and CLA acknowledge funding from the EU's Horizon 2020 research and innovation programme (project EpiPose, 101003688). Nicola Criscuolo was supported by the Branco Weiss Foundation. Calculations were performed on UBELIX, the high performance computing cluster at the University of Bern, Bern, Switzerland. We also thank Steven J Korzeniewski and three anonymous reviewers for their helpful comments.

Publisher Copyright:
© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

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