A population-based study on mortality among Belgian immigrants during the first COVID-19 wave in Belgium. Can demographic and socioeconomic indicators explain differential mortality?

Abstract

Introduction Belgium has noted a significant excess mortality during the first COVID-19 wave. Research in other countries has shown that people with migrant origin are disproportionally affected. Belgium has an ethnically diverse and increasingly ageing population and is therefore particularly apt to study differential mortality by migrant group during this first wave of COVID-19. Data and methods We used nationwide individually-linked data from the Belgian National Register providing sociodemographic indicators and mortality; and the administrative census of 2011 providing indicators of socioeconomic position. Age-standardized all-cause mortality rates (ASMRs) were calculated during the first COVID-19 wave (weeks 11–20 in 2020) and compared with ASMRs during weeks 11–20 in 2019 to calculate excess mortality by migrant origin, age and gender. For both years, relative inequalities were calculated by migrant group using Poisson regression, with and without adjustment for sociodemographic and socioeconomic indicators. Results Among the middle-aged, ASMRs revealed increased mortality in all origin groups, with significant excess mortality for Belgians and Sub-Saharan African men. At old age, excess mortality up to 60% was observed for all groups. In relative terms, most male elderly migrant groups showed higher mortality than natives, as opposed to 2019 and to women. Adding the control variables decreased this excess mortality. Discussion This study underlined important inequalities in overall and excess mortality in specific migrant communities, especially in men. Tailor-made policy measures and communication strategies should be set-up taking into account the particular risks to which groups are exposed.

The association between area deprivation and COVID-19 incidence: a municipality-level spatio-temporal study in Belgium, 2020–2021

“…However, the use of PAFs provides us with an uncomplicated inequality measure. It is translatable to policy measures by addressing the population impact of inequalities in area deprivation and the potential gain by instating policy measures that aim to reduce these inequalities [ 15 ]. Our results demonstrate that the PAFs can present a useful instrument to gain insights into the relative burden of living in a deprived area.…”

“…Our results provide valuable insights into spatio-temporal patterns of SE inequalities in COVID-19 incidence in Belgium. The large majority of studies on COVID-19 and SE differences in Belgium are based on excess mortality and these studies indicate the existence of SE inequalities during the first Belgian COVID-19 epidemiological wave [ 13 , 15 – 17 ]. Our study is the first in Belgium that focusses on SE inequalities in COVID-19 cases over a larger period of time, also taking into account a temporal gradient.…”

Excess mortality and COVID-19 in Sweden in 2020: A demographic account

“…These studies found that mortality increased, most notably among socioeconomically disadvantaged groups (Drefahl et al, 2020), people living in crowded and multigenerational households (Brandén et al, 2020) and migrants (Aradhya et al, 2021;Rostila et al, 2021). Similar studies on mortality among foreign-born populations have been conducted in Norway (Indseth et al, 2020) and Belgium (Vanthomme et al, 2021). Calderón-Larrañaga et al (2020) showed that mortality in Stockholm was elevated in areas with lower incomes and/or in areas with more foreign-born residents.…”

COVID-19 as a social disease: qualitative analysis of COVID-19 prevention needs, impact of control measures and community responses among racialized/ethnic minorities in Antwerp, Belgium

“…With more than 8800 additional deaths during the first wave, Belgium had a high COVID-19 related excess mortality. Elderly male immigrants had higher mortality rates in 2020 than natives compared to the previous year, with striking excess mortality rates for SSA women and men at middle age (40 and 70% increase respectively), typically related to their socio-economic and demographic position [ 28 ].…”