Background The COVID-19 mortality rate in Belgium has been ranked among the highest in the world. To assess the appropriateness of the country’s COVID-19 mortality surveillance, that includes long-term care facilities deaths and deaths in possible cases, the number of COVID-19 deaths was compared with the number of deaths from all-cause mortality. Mortality during the COVID-19 pandemic was also compared with historical mortality rates from the last century including those of the Spanish influenza pandemic. Methods Excess mortality predictions and COVID-19 mortality data were analysed for the period March 10th to June 21st 2020. The number of COVID-19 deaths and the COVID-19 mortality rate per million were calculated for hospitals, nursing homes and other places of death, according to diagnostic status (confirmed/possible infection). To evaluate historical mortality, monthly mortality rates were calculated from January 1900 to June 2020. Results Nine thousand five hundred ninety-one COVID-19 deaths and 39,076 deaths from all-causes were recorded, with a correlation of 94% (Spearman’s rho, p < 0,01). During the period with statistically significant excess mortality (March 20th to April 28th; total excess mortality 64.7%), 7917 excess deaths were observed among the 20,159 deaths from all-causes. In the same period, 7576 COVID-19 deaths were notified, indicating that 96% of the excess mortality were likely attributable to COVID-19. The inclusion of deaths in nursing homes doubled the COVID-19 mortality rate, while adding deaths in possible cases increased it by 27%. Deaths in laboratory-confirmed cases accounted for 69% of total COVID-19-related deaths and 43% of in-hospital deaths. Although the number of deaths was historically high, the monthly mortality rate was lower in April 2020 compared to the major fatal events of the last century. Conclusions Trends in all-cause mortality during the first wave of the epidemic was a key indicator to validate the Belgium’s high COVID-19 mortality figures. A COVID-19 mortality surveillance limited to deaths from hospitalised and selected laboratory-confirmed cases would have underestimated the magnitude of the epidemic. Excess mortality, daily and monthly number of deaths in Belgium were historically high classifying undeniably the first wave of the COVID-19 epidemic as a fatal event.

Purpose Health-related quality of life outcomes are increasingly used to monitor population health and health inequalities and to assess the (cost-) effectiveness of health interventions. The EQ-5D-5L has been included in the Belgian Health Interview Survey, providing a new source of population-based self-perceived health status information. This study aims to estimate Belgian population norms for the EQ-5D-5L by sex, age, and region and to analyze its association with educational attainment. Methods The BHIS 2018 provided EQ-5D-5L data for a nationally representative sample of the Belgian population. The dimension scores and index values were analyzed using logistic and linear regressions, respectively, accounting for the survey design. Results More than half of respondents reported problems of pain/discomfort, while over a quarter reported problems of anxiety/depression. The average index value was 0.84. Women reported more problems on all dimensions, but particularly on anxiety/depression and pain/discomfort, resulting in significantly lower index values. Problems with mobility, self-care, and usual activities showed a sharp increase after the age of 80 years. Consequently, index values decreased significantly by age. Lower education was associated with a higher prevalence of problems for all dimensions except anxiety/depression and with a significantly lower index value. Conclusion This paper presents the first nationally representative Belgian population norms using the EQ-5D-5L. Inclusion of the EQ-5D in future surveys will allow monitoring over time of self-reported health, disease burden, and health inequalities.

Background COVID-19-related mortality in Belgium has drawn attention for two reasons: its high level, and a good completeness in reporting of deaths. An ad hoc surveillance was established to register COVID-19 death numbers in hospitals, long-term care facilities (LTCF) and the community. Belgium adopted broad inclusion criteria for the COVID-19 death notifications, also including possible cases, resulting in a robust correlation between COVID-19 and all-cause mortality. Aim To document and assess the COVID-19 mortality surveillance in Belgium. Methods We described the content and data flows of the registration and we assessed the situation as of 21 June 2020, 103 days after the first death attributable to COVID-19 in Belgium. We calculated the participation rate, the notification delay, the percentage of error detected, and the results of additional investigations. Results The participation rate was 100% for hospitals and 83% for nursing homes. Of all deaths, 85% were recorded within 2 calendar days: 11% within the same day, 41% after 1 day and 33% after 2 days, with a quicker notification in hospitals than in LTCF. Corrections of detected errors reduced the death toll by 5%. Conclusion Belgium implemented a rather complete surveillance of COVID-19 mortality, on account of a rapid investment of the hospitals and LTCF. LTCF could build on past experience of previous surveys and surveillance activities. The adoption of an extended definition of ‘COVID-19-related deaths’ in a context of limited testing capacity has provided timely information about the severity of the epidemic.

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COVID-19 became pandemic in 2020 and causes higher mortality in males (M) than females (F) and among older people. In some countries, like Belgium, more than half of COVID-19 confirmed or suspected deaths occurring in spring 2020 concerned residents of care homes. The high incidence in this population is certainly linked to its peculiar age structure but could also result from its poorer general health condition and/or from a higher contamination through the staff of care homes, while protection equipment and testing capacity were initially limited. To address these issues, we used data from Wallonia (Belgium) to characterize the distribution of death rates among care home institutions, to compare the dynamics of deaths in and outside care homes, and to analyse how age and sex affected COVID-19 death rates inside and outside care homes. We also used annual death rates as a proxy for the health condition of each population. We found that: (1) COVID-19 death rate per institution varied widely from 0‰ to 340‰ (mean 43‰) and increased both with the size of the institution (number of beds) and with the importance of medical care provided. (2) 65% of COVID-19 deaths in Wallonia concerned residents of care homes where the outbreak started after but at a faster pace than the outbreak seen in the external population. (3) The impact of age on both annual and COVID-19 mortality closely follows exponential laws (i.e. Gompertz law) but mortality was much higher for the population living in care homes where the age effect was lower (mortality rate doubling every 20 years of age increment in care homes, 6 years outside them). (4) Both within and outside care homes, the ratio of M/F death rates was 1.6 for annual mortality but reached 2.0 for COVID-19 mortality, a ratio consistent among both confirmed and suspected COVID-19 deaths. (5) When reported to the annual death rate per sex and age, the COVID-19 relative mortality was little affected by age and reached 24% (M) and 18% (F) of their respective annual rate in nursing homes, while these percentages reduced to 10% (M) and 9% (F) in homes for elderly people (with less medical assistance), and to 5% (M) and 4% (F) outside of care homes. In conclusion, a c. 130x higher COVID-19 mortality rate found in care homes compared to the outside population can be attributed to the near multiplicative combination of: (1) a 11x higher mortality due to the old age of its residents, (2) a 3.8x higher mortality due to the low average health condition of its residents, and (3) probably a 3.5x higher infection rate (1.6x in homes for elderly people) due to the transmission by its staff, a problem more acute in large institutions. Our results highlight that nursing home residents should be treated as a very specific population, both for epidemiological studies and to take preventive measures, due to their extreme vulnerability to COVID-19.

Issue Monitoring population health is crucial for policymakers. In Belgium, health monitoring only existed at regional level, with no integrated view at country level. Policy/tool The Health Status Report (HSR) project developed a tool for centralizing key health indicators. The HSR aims to support policymakers in multiple ways: as a ’warning signal’, by contributing to the planning of health policies, and as an assessment tool for those policies. Rather than being exhaustive, the HSR selects key indicators to highlight important needs. These indicators have been identified through literature and consultations with experts and stakeholders. Topics include life and health expectancies, mortality, morbidity, and lifestyles, with an important focus on socioeconomic inequalities. Good results and health gaps are underlined with international comparisons, trend analyses, and comparisons with reference values. By disaggregating the data by sex, age, geographic level or socio-economic level, specific health needs are identified. Results The main outcome of the project is a continuously updated website: www.healthybelgium.be. The report highlighted that, although the Belgian health status is rather good, there is room for improvement: for some indicators Belgium lags behind other European countries; regional disparities remain important, with most indicators revealing a better health status in Flanders than in Brussels and Wallonia. Socioeconomic disparities also remain very important, and for some indicators even tend to worsen. Comparing the Belgian health status to that of the EU-15 results in more severe conclusions than in international reports. Conclusions We developed a new tool to support public health policy in Belgium through benchmarking and trend and disparity analyses of several health indicators. The tool will be expanded in the next years, integrating for instance the results of the Belgian national burden of disease study. Key messages We developed an online health status monitoring tool to inform policymakers. The rather good health status hides important regional and socioeconomic disparities in Belgium.

Introduction No information is available in Belgium on life expectancy adjusted for health-related quality of life (HRQoL). Quality-adjusted life expectancy (QALE) captures the multidimensionality of health by accounting for losses in mortality and HRQoL linked to physical, mental, and social impairments. The objective of this study is to estimate for Belgium QALE, the changes in QALE between 2013 and 2018 and the contribution of mortality, HRQoL and its dimensions to this trend. Methods The Belgian Health Interview Survey (BHIS), a representative sample of the general population, included the EQ-5D-5L instrument in 2013 and 2018. The tool assesses HRQoL comprising five dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) using a 5-level severity scoring to define a large variety of health states. The Sullivan method was used to compute at different ages QALE by gender using mortality data from the Belgian statistical office and average EQ-5D scores from the BHIS. QALE was calculated for 2013 and 2018, and changes in QALE over time were decomposed into mortality and ill-health effect. Results In 2018, QALE at age 15 years (QALE15) was 56.3 years for women and 55.8 years for men, a decrease from 2013 by 0.7 year for women and a stagnation for men. In men, the decrease in mortality counterbalanced the decline in HRQoL. The decline in QALE in women is driven by a decrease in mortality rates that is too small to compensate for the substantial decline in HRQoL before the age of 50 years. In women at older ages, improvements in HRQoL are observed. In women, QALE15 is decreasing due to an increase in pain/discomfort, anxiety/depression and problems in usual activities. In men at age 15, the pain/discomfort and anxiety/depression domains contributed to the stagnation. QALE65 increased somewhat, due to an improvement in self-care and mobility for both genders, and usual activities and anxiety/depression in men only. Conclusion The strength of QALE as member of the family of composite indicators, the health expectancies, is the multidimensional structure of the underlying health component, including both ill-health with different health domains as levels of severity. The ability to decompose differences in the health expectancy not only into a mortality and health component but also into the different health dimensions allows to better inform on general population health trends. Next, compared to other health expectancy indicators, QALE is more sensitive to changes at younger ages.

Introduction Information on years of life lost (YLL) due to premature mortality is instrumental to assess the fatal impact of disease and necessary for the calculation of Belgian disability-adjusted life years (DALYs). This study presents a novel method to reallocate causes of death data. Materials and methods Causes of death data are provided by Statistics Belgium (Statbel). First, the specific ICD-10 codes that define the underlying cause of death are mapped to the GBD cause list. Second, ill-defined deaths (IDDs) are redistributed to specific ICD-10 codes. A four-step probabilistic redistribution was developed to fit the Belgian context: redistribution using predefined ICD codes, redistribution using multiple causes of death data, internal redistribution, and redistribution to all causes. Finally, we used the GBD 2019 reference life table to calculate Standard Expected Years of Life Lost (SEYLL). Results In Belgium, between 2004 and 2019, IDDs increased from 31% to 34% of all deaths. The majority was redistributed using predefined ICD codes (14-15%), followed by the redistribution using multiple causes of death data (10–12%). The total number of SEYLL decreased from 1.83 to 1.73 million per year. In 2019, the top cause of SEYLL was lung cancer with a share of 8.5%, followed by ischemic heart disease (8.1%) and Alzheimer’s disease and other dementias (5.7%). All results are available in an online tool https://burden.sciensano.be/shiny/mortality2019/. Conclusion The redistribution process assigned a specific cause of death to all deaths in Belgium, making it possible to investigate the full mortality burden for the first time. A large number of estimates were produced to estimate SEYLL by age, sex, and region for a large number of causes of death and every year between 2004 and 2019. These estimates are important stepping stones for future investigations on Disability-Adjusted Life Years (DALYs) in Belgium.