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

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.

Figure 1

Evolution of notifications to the Federal Office of Public Health during the COVID-19 pandemic in Switzerland from March 1, 2020, to April 14, 2021 The counts of total tests were available only from May 23, 2020. The dashed line shows the date chosen for the separation between the first and second wave (June 8, 2020). ICU=intensive care unit.

Figure 2

Counts of notified SARS-CoV-2 tests, positive tests, hospitalisations, ICU admissions, and deaths across groups of SEP per 100 000 population, tests, or positive tests Higher SEP groups correspond to neighbourhoods of higher SEP. The study period was March 1, 2020, to April 14, 2021, except for total tests that only covered May 23, 2020, to April 14, 2021. ICU=intensive care unit. SEP=socioeconomic position.

Figure 3

Unadjusted and adjusted IRRs per increase in the group of neighbourhood SEP for the counts of SARS-CoV-2 tests, positive tests, hospitalisations, ICU admissions, and mortality per population, tests or positive tests Median posteriors and 95% credibility intervals are shown in each case. IRR estimates higher than 1 correspond to a positive association with Swiss neighbourhood index of SEP groups; estimates lower than 1 correspond to a negative association. Adjusted estimates are adjusted for age, sex, canton, and epidemic wave. The study period was March 1, 2020, to April 14, 2021, except for total tests, which covered May 23, 2020, to April 14, 2021. ICU=intensive care unit. IRR=incidence rate ratio. SEP=socioeconomic position.

Figure 4

Adjusted IRRs from sensitivity analyses (A) Adjusted IRRs per increase in group of neighbourhood SEP for COVID-19 deaths per population, per test, or per positive test in the baseline analysis or in sensitivity analyses: (1) excluding all cases geocoded from the postcode only and (2) excluding cases with a residential address corresponding to retirement or nursing homes. (B) Adjusted IRRs for COVID-19 deaths per positive test by age group, sex, and epidemic wave. IRR=incidence rate ratio. SEP=socioeconomic position. The first wave of infections was before June 8, 2020, and the second wave from June 8, 2020.