The COVID-19 pandemic has had a significant impact on the mortality rates of the elderly population. During the first and second wave of the epidemic, their DSMRs were notably higher compared to the reference period, across all educational and income groups. During wave 3 there was some respite, as excess mortality was generally no longer significant. The most significant mortality increase was observed among care home residents, particularly during the first wave of the epidemic, with a gradual decrease in the second wave and even a mortality deficit in the third wave. According to a previous decomposition of excess mortality in Walloon care homes during the Spring of 2020, these significant increases in care homes were most likely the result of a combination of factors: the age composition of care home residents, their pre-COVID frail health condition, the high contamination rate in care homes, and the organization of care home resources and staff [26]. The COVID-19 virus was already present in care homes in Belgium at the early start of the epidemic [28, 29] and its spread in Belgian care homes was intensified due to the government’s delayed response [3, 30, 31]. The evolution through consecutive waves might be attributed in part to the successful implementation of prevention and vaccinations campaigns in Belgium, especially among the elderly [32, 33]. The mortality deficit among care home residents observed in the third wave can be explained by several factors. First, care home residents and staff were prioritised in the vaccination campaign meaning they could receive their first dose of the COVID-19 vaccine from January 5th, 2021 onward. Approximately 6% of persons older than 65 years completed their full primary vaccination before the start of wave 3 [34]. All other elderly persons without prespecified health risks were invited to get vaccinated from March 1st, 2021 onward and that was widely embraced in Belgium with complete primary vaccination rates of 93% among the 65 + population on October 31st, 2021 [32]. Second, Vandael and colleagues [29] report that care homes increasingly implemented infection prevention and control measures and gained additional support by the Regional health authorities during the second wave. It is highly likely that these good practices also contributed to reduced mortality among care home residents during wave 3. Third, the observed mortality during wave 3 may be influenced by difficulties in identifying care home residents in 2021. The most recently available information on care home residency dates from January 1st, 2020, in contrast to the reference years 2015–2019 for which annual information is available. From this group of identified care home residents, the frailest individuals died early in the epidemic, resulting in a relatively healthier population in care homes during wave 3. In addition, the epidemic had an influence on the composition of the care home population, as an unknown proportion of residents left their care home to be with their family and the influx of new residents was restricted by COVID-19 control measures or fear of contagion [26].
Regarding temporal patterns in mortality excess by socioeconomic variables, DSMRs for the elderly population showed that all educational groups experienced significantly higher absolute mortality during the first and second COVID-wave. In wave 3, only elderly men with a primary or lower secondary degree experienced excess mortality. Results by income group showed similar findings. Among the middle-aged, excesses were generally not significant, except for the primary-educated men in wave 2, the (lower) secondary educated men and women in wave 3 and the high-income men during wave 1 and 3. When considering relative mortality inequalities by socioeconomic group over time, results for the elderly population demonstrate that COVID-19 did not fundamentally alter the traditional pattern of higher mortality rates among lower socioeconomic groups. In contrast, for the middle-aged population, educational inequalities intensified during COVID-waves 2 and 3 compared to the reference periods. While controlling for age, income, sociodemographic background and the pre-existing health situation, middle-aged women and men with a primary degree or less experienced significantly higher mortality in COVID-wave 2, expanding the education mortality gap with their high-educated peers. In wave 3, the educational mortality gradients became even steeper for middle-aged women and men with a primary education or less, and for those with a lower secondary education. Notably, middle-aged men with an upper secondary degree also experienced a significant mortality excess during wave 3. Given that COVID-19 is a syndemic pandemic, there are numerous underlying mechanisms that contribute to this vulnerability [1]. Lower educational levels may be associated with a lower accessibility to sound coping mechanisms, a more limited knowledge about how to implement COVID-19 lockdown and hygiene measures, and with poorer healthcare [9, 10, 35]. Clear educational differences have been observed in COVID-19 vaccination [33]. However, this may only partly explain this finding because of the timing late in the third wave for the population 18 to 64 years old [32]. Lower educated socioeconomic classes typically work in occupations that increase exposure to the virus, especially in sectors characterised by constant human contact (bus drivers, retail staff, cleaners, etc.) compared to those working in sectors that allow working from home or in a more protected environment [36]. Overall, these findings for education are consistent with previous findings that pre-existing socioeconomic inequalities in all-cause and COVID-19 specific mortality have deepened due to the outbreak itself, lockdown measures and the disruption of daily life during the epidemic [15, 37].
Whereas relative educational mortality inequalities seem to have widened during the COVID-epidemic for the middle-aged population, the results regarding income mortality inequalities differ. MRRs for middle-aged women and men show a stable or even decreasing pattern of income inequalities over time, when controlling for age, educational attainment, sociodemographic background and the pre-existing health situation. Notably, mortality deficits were observed for the vulnerable group of women (wave 2) and men (wave 1 and 3) with no declared income, as well as for middle-income men in wave 1. However, it is important to acknowledge that the most recently available income data dates from 2017. In an evaluation of the effectiveness of Federal and Regional social policy measures in Belgium during 2020, Wizan, Neelen and Marchal found that –especially federal- income support measures were successful in mitigating a significant proportion of income volatility induced by the pandemic [38]. It is possible that COVID-19 emergency government support measures may have contributed to lower mortality rates for these vulnerable groups than expected from the reference period.
The increasing relative risks of the lower educated in successive waves align with the Fundamental Cause Theory [16, 39] and the SDTh [14, 15]. As COVID-19 was a new infectious disease, insights in risk factors and prevention initially lacked. These gradually came to development when public health control and prevention measures were put in place. One could thus expect that mortality inequalities were generally smallest during the first wave and highest during the second and third wave. This trend was observable in middle-aged people for educational inequalities. For income, however, no such changes could be observed, nor for the elderly population. These findings clearly demonstrate the need for further research into the underlying mechanisms and interplays between education, income, age and health status. In our view, educational attainment and the effective use of the associated resources play a powerful role in understanding Belgian excess mortality during COVID-19. We hypothesise that the implemented COVID emergency government measures may have been effective in controlling income mortality inequalities (and even decreasing them for some groups in some waves). Specifically for elderly persons, the timing and broad roll-out of the vaccination campaign seem powerful explanations for the presented findings. Regarding the elderly population, there was a noticeable contrast between those residing in care homes and those living independently.
Our study has several limitations. First, during the time of our analysis, it was not feasible to distinguish deaths specifically caused by COVID-19. Therefore, we were unable to investigate the underlying factors contributing to the excess mortality observed during the different waves of the epidemic. Excess mortality can result from a variety of factors, including a reduction in mortality for specific causes, such as traffic accidents, and an increase in mortality due to other causes. Hence, excess mortality should be interpreted as mortality related to the COVID-19 epidemic rather than people dying from COVID-19. Excess mortality is however a useful indicator to estimate the total impact of the COVID-19 epidemic, as it considers not only deaths directly caused by COVID-19, but also deaths that may have been indirectly caused by the pandemic due to factors such as delayed medical care and disrupted healthcare systems [40].
Second, it is also important to note that we did not dispose of data on occupation, health seeking behaviour, uptake of government guidelines, and other determinants of mortality differences, which could influence this study’s findings. In addition, information on educational attainment was based on data of the 2011 census, and personal income for the years 2019–2021 was based on 2017 income information. Yet, the age groups involved are not the most mobile in terms of educational attainment. Income information was grouped into three categories based on income deciles, which may limit the variability over time. The available income information may not capture extent of income differences fully, especially among care home residents. Differences in mortality rates by income were generally not significant among the elderly in care homes, possibly due to a selection effect, as entering a care home is relatively expensive in Belgium, and only people with higher incomes can afford it. The variable used in the study only measures taxable income and not wealth, which includes assets such as property, investments, and savings.
Third, the absence of data on incidence and survival also restricts the ability to fully examine the socioeconomic inequalities related to COVID-19. The study by Angelici and colleagues on educational patterns in COVID incidence in Rome followed a clear pattern through successive waves, with higher incidence initially seen among the higher-educated, which later shifted to being higher among the less-educated [9]. This finding is consistent with the extended Fundamental Cause Theory. In order to better understand the direct effects of the spread of COVID-19 and its implication on excess mortality in the Belgian situation, further analyses using incidence data and/or cause-specific data to disentangle COVID-19 as a cause of excess mortality [40]. Future studies should explore how different causes of death interact with COVID-19 mortality to generate excess mortality and how this is patterned by socioeconomic and – demographic characteristics.
Our study’s main strength is the comprehensive dataset that allowed for a detailed analysis of subgroups. We were able to include all deaths in the country, and a wide range of sociodemographic and socioeconomic determinants. Our study is also among the first to assess trends across different waves in socioeconomic inequalities in COVID-19 excess mortality using individual-level data.
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