Are we really all in this together? The social patterning of mortality during the first wave of the COVID-19 pandemic in Belgium

Area and individual level analyses of demographic and socio-economic disparities in COVID-19 vaccination uptake in Belgium

Vaccination has played a major role in overcoming the COVID-19 pandemic. However, vaccination status can be influenced by demographic and socio-economic factors at individual and area level. In the context of the LINK-VACC project, the Belgian vaccine register for the COVID-19 vaccination campaign was linked at individual level with other registers, notably the COVID-19 laboratory test results and demographic and socio-economic variables from the DEMOBEL database. The present article aims at investigating to which extent COVID-19 vaccination status is associated with area level and/or individual level demographic and socio-economic factors. From a sample of all individuals tested for SARS-CoV-2 (LINK-VACC sample) demographic and socio-economic indicators are derived and their impact on vaccination coverages at an aggregated geographical level (municipality) is quantified. The same indicators are calculated for the full Belgian population, allowing to assess the representativeness of the LINK-VACC sample with respect to the impact of demographic and socio-economic disparities on vaccination uptake. In a second step, hierarchical models are fitted to the individual level LINK-VACC data to disentangle the individual and municipality effects allowing to evaluate the added value of the availability of individual level data in this context. The most important effects observed at the individual level are reflected in the aggregated data at the municipality level. Multilevel analyses show that most of the demographic and socio-economic impacts on vaccination are captured at the individual level, although accounting for area level in individual level analyses improve the overall description.

Did the socioeconomic inequalities in avoidable and unavoidable mortality worsen during the first year of the COVID-19 pandemic in Korea?

OBJECTIVES

This study examined changes in socioeconomic inequalities in mortality in Korea before and after the outbreak of coronavirus disease 2019 (COVID-19).

METHODS

From 2017 to 2020, age-standardized mortality rates were calculated for all-cause deaths, avoidable deaths (preventable deaths, treatable deaths), and unavoidable deaths using National Health Insurance claims data and Statistics Korea's cause of death data. In addition, the slope index of inequality (SII) and the relative index of inequality (RII) by six income levels (Medical Aid beneficiary group and quintile of health insurance premiums) were computed to analyze the magnitude and change of mortality inequalities.

RESULTS

All-cause and avoidable mortality rates decreased steadily between 2017 and 2020, whereas unavoidable mortality remained relatively stable. In the case of mortality inequalities, the disparity in all-cause mortality between income classes was exacerbated in 2020 compared to 2019, with the SII increasing from 185.44 to 189.22 and the RII increasing from 3.99 to 4.29. In particular, the preventable and unavoidable mortality rates showed an apparent increase in inequality, as both the SII (preventable: 91.31 to 92.01, unavoidable: 69.99 to 75.38) and RII (preventable: 3.42 to 3.66, unavoidable: 5.02 to 5.89) increased.

CONCLUSIONS

In the first year of the COVID-19 pandemic, mortality inequality continued to increase, although there was no sign of exacerbation. It is necessary to continuously evaluate mortality inequalities, particularly for preventable and unavoidable deaths.

Distinct socioeconomic profile of patients hospitalised with severe COVID-19 and prepandemic respiratory infections in Brussels's deprived areas: a case-control study

OBJECTIVE

Belgium has been hit harder by COVID-19 than other countries in Europe. While clinical risk factors are well studied, socioeconomic risk factors remained underexplored. This study's objective was to analyse the social and clinical profile of patients hospitalised for COVID-19 during the two waves of 2020, compared with a control population in 2019 in two hospitals located in Brussels' most deprived area.

DESIGN AND METHODS

We did a case-control study by using the minimal clinical data set in two Brussels hospitals. All patients hospitalised for COVID-19 in 2020, divided into two waves (n=3220), were compared with all patients hospitalised for viral pneumonia and respiratory diseases in 2019 (control population n=2950). Multinomial regression models were used to estimate the relative risk ratios of the association between the COVID-19 hospitalised populations (waves 1 and 2) and risk factors (social and clinical) stratified by age.

RESULTS

Patients under 65 years of age and hospitalised for COVID-19 presented significantly higher rates (relative rate ratio (95% CI)), especially for the first wave, of obesity 1.6 (1.2-2.2), African nationalities 1.4 (1.0-1.8), lack of health insurance 1.6 (1.3-2.1), living in high-density population areas 1.6 (1.3-2.1) and low incomes 1.7 (1.4-2.1), compared with the control population For patients over 65 years of age, we did not observe significant excess of COVID-19 hospitalisations for any risk factors, except diabetes during for the second wave but we have a significant excess mortality rate than the control population for both waves (p<0.002).

CONCLUSIONS

The social and clinical profile of patients hospitalised for COVID-19 compared with a population hospitalised for viral respiratory diseases differed between age groups and waves. For younger patients, risk factors were linked to patients' precarious situations. This study underlines the role of selected social health determinants and the importance of routinely collecting social data, along with clinical data, particularly among vulnerable populations.

Social inequalities and long-term health impact of COVID-19 in Belgium: protocol of the HELICON population data linkage

INTRODUCTION

Data linkage systems have proven to be a powerful tool in support of combating and managing the COVID-19 pandemic. However, the interoperability and the reuse of different data sources may pose a number of technical, administrative and data security challenges.

METHODS AND ANALYSIS

This protocol aims to provide a case study for linking highly sensitive individual-level information. We describe the data linkages between health surveillance records and administrative data sources necessary to investigate social health inequalities and the long-term health impact of COVID-19 in Belgium. Data at the national institute for public health, Statistics Belgium and InterMutualistic Agency are used to develop a representative case-cohort study of 1.2 million randomly selected Belgians and 4.5 million Belgians with a confirmed COVID-19 diagnosis (PCR or antigen test), of which 108 211 are COVID-19 hospitalised patients (PCR or antigen test). Yearly updates are scheduled over a period of 4 years. The data set covers inpandemic and postpandemic health information between July 2020 and January 2026, as well as sociodemographic characteristics, socioeconomic indicators, healthcare use and related costs. Two main research questions will be addressed. First, can we identify socioeconomic and sociodemographic risk factors in COVID-19 testing, infection, hospitalisations and mortality? Second, what is the medium-term and long-term health impact of COVID-19 infections and hospitalisations? More specific objectives are (2a) To compare healthcare expenditure during and after a COVID-19 infection or hospitalisation; (2b) To investigate long-term health complications or premature mortality after a COVID-19 infection or hospitalisation; and (2c) To validate the administrative COVID-19 reimbursement nomenclature. The analysis plan includes the calculation of absolute and relative risks using survival analysis methods.

ETHICS AND DISSEMINATION

This study involves human participants and was approved by Ghent University hospital ethics committee: reference B.U.N. 1432020000371 and the Belgian Information Security Committee: reference Beraadslaging nr. 22/014 van 11 January 2022, available via https://www.ehealth.fgov.be/ehealthplatform/file/view/AX54CWc4Fbc33iE1rY5a?filename=22-014-n034-HELICON-project.pdf. Dissemination activities include peer-reviewed publications, a webinar series and a project website.The pseudonymised data are derived from administrative and health sources. Acquiring informed consent would require extra information on the subjects. The research team is prohibited from gaining additional knowledge on the study subjects by the Belgian Information Security Committee's interpretation of the Belgian privacy framework.

Association of socio-economic deprivation with COVID-19 incidence and fatality during the first wave of the pandemic in Italy: lessons learned from a local register-based study

BACKGROUND

COVID-19 has been characterised by its global and rapid spread, with high infection, hospitalisation, and mortality rates worldwide. However, the course of the pandemic showed differences in chronology and intensity in different geographical areas and countries, probably due to a multitude of factors. Among these, socio-economic deprivation has been supposed to play a substantial role, although available evidence is not fully in agreement. Our study aimed to assess incidence and fatality rates of COVID-19 across the levels of socio-economic deprivation during the first epidemic wave (March-May 2020) in the Italian Province of Foggia, Apulia Region.

METHODS

Based on the data of the regional active surveillance platform, we performed a retrospective epidemiological study among all COVID-19 confirmed cases that occurred in the Apulian District of Foggia, Italy, from March 1st to May 5th, 2020. Geocoded addresses were linked to the individual Census Tract (CT) of residence. Effects of socio-economic condition were calculated by means of the Socio-Economic and Health-related Deprivation Index (SEHDI) on COVID-19 incidence and fatality.

RESULTS

Of the 1054 confirmed COVID-19 cases, 537 (50.9%) were men, 682 (64.7%) were 0-64 years old, and 338 (32.1%) had pre-existing comorbidities. COVID-19 incidence was higher in the less deprived areas (p < 0.05), independently on age. The level of socio-economic deprivation did not show a significant impact on the vital status, while a higher fatality was observed in male cases (p < 0.001), cases > 65 years (p < 0.001), cases having a connection with a nursing home (p < 0.05) or having at least 1 comorbidity (p < 0.001). On the other hand, a significant protection for healthcare workers was apparent (p < 0.001).

CONCLUSIONS

Our findings show that deprivation alone does not affect COVID-19 incidence and fatality burden, suggesting that the burden of disease is driven by a complexity of factors not yet fully understood. Better knowledge is needed to identify subgroups at higher risk and implement effective preventive strategies.