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. MethodsWe 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 healthcare systems should address this pandemic of inequality by taking measures to reduce health inequalities in response to the SARS-CoV-2 pandemic.

Germany’s low SARS-CoV-2 seroprevalence confirms effective containment in 2020: Results of the nationwide RKI-SOEP study

“…Rapid antigen tests started to become available towards the end of the fieldwork of our study. The social gradient in utilization of tests found in our study is in line with ecological evidence from Switzerland and from Massachusetts, where testing was associated with neighborhood socioeconomic position 65 or neighborhood socioeconomic vulnerability index. 66 Our findings of a lower test frequency and a higher underascertainment of cases in the socioeconomically most deprived districts of Germany suggest that testing-related disparities may have masked the magnitude of the social gradient in SARS-CoV-2 infections as found in previous ecological analyses of notification data.…”

“…Rapid antigen tests started to become available towards the end of the fieldwork of our study. The social gradient in utilization of tests found in our study is in line with ecological evidence from Switzerland and from Massachusetts, where testing was associated with neighborhood socioeconomic position 65 or neighborhood socioeconomic vulnerability index. 66…”

Regional excess mortality during the 2020 COVID-19 pandemic in five European countries

“…Studying these variations may help our understanding of the transmission patterns and the effectiveness of policies and measures to contain the pandemic. Other factors that may have also contributed to the varying impact on mortality across regions include differences in demographics 13 , the prevalence of comorbidities 13 and environmental factors 12 , 14 , 15 .…”

SARS-CoV-2 induces human endogenous retrovirus type W envelope protein expression in blood lymphocytes and in tissues of COVID-19 patients

“…The human "coronavirus disease 2019" (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is associated with high morbidity and mortality [1][2][3][4][5][6]. It has generated a worldwide pandemic, the evolution of which is still uncertain due to the emergence of variants [7][8][9][10][11].…”