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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.

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.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.In 2018, QALE at age 15 years (QALEThe 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.

Background The burden of chronic diseases is rapidly rising, both in terms of morbidity and mortality. This burden is disproportionally carried by socially disadvantaged population subgroups. Quality-adjusted life years (QALYs) measure the impact of disease on mortality and morbidity into a single index. This study aims to estimate the burden of chronic diseases in terms of QALY losses and to model its social distribution for the general population. Methods The Belgian Health Interview Survey 2013 and 2018 provided data on self-reported chronic conditions for a nationally representative sample. The annual QALY loss per 100,000 individuals was calculated for each condition, incorporating disease prevalence and health-related quality of life (HRQoL) data (EQ-5D-5L). Socioeconomic inequalities, based on respondents’ socioeconomic status (SES), were assessed by estimating population attributable fractions (PAF). Results For both years, the largest QALY losses were observed in dorsopathies, arthropathies, hypertension/high cholesterol, and genitourinary problems. QALY losses were larger in women and in older individuals. Individuals with high SES had consistently lower QALY loss when facing a chronic disease compared to those with low SES. In both years, a higher PAF was found in individuals with hip fracture and stroke. In 2013, the health inequality gap amounts to 33,731 QALYs and further expanded to 42,273 QALYs in 2018. Conclusion Given that chronic diseases will rise in the next decades, addressing its burden is necessary, particularly among the most vulnerable (i.e. older persons, women, low SES). Interventions in these target groups should get priority in order to reduce the burden of chronic diseases.

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.