%0 Report %D 2023 %T Belgian National Burden of Disease Study. Guidelines for the calculation of Disability-Adjusted Life Years in Belgium, September 2023 %A Robby De Pauw %A Vanessa Gorasso %A Aline Scohy %A Laura Van den Borre %A Brecht Devleesschauwer %K burden of disease %K Burden of disease methodology %K DALYs %I Sciensano %C Brussels %P 285 %8 09/2023 %G eng %M D/2023.14.440/67 %0 Journal Article %J BMC Public Health %D 2023 %T The direct disease burden of COVID-19 in Belgium in 2020 and 2021 %A Robby De Pauw %A Brecht Devleesschauwer %A Lander Willem %A Jure Jurcevic %A Pierre Smith %A Aline Scohy %A Grant M A Wyper %A Sara Monteiro Pires %A Nina Van Goethem %A Philippe Beutels %A Nicolas Franco %A Steven Abrams %A Dieter Van Cauteren %A Niko Speybroeck %A Niel Hens %K burden of disease %K COVID-19 %K Disability-Adjusted Life Years %K Years lived with disability %K Years of life lost %X

BACKGROUND: Burden of disease estimates have become important population health metrics over the past decade to measure losses in health. In Belgium, the disease burden caused by COVID-19 has not yet been estimated, although COVID-19 has emerged as one of the most important diseases. Therefore, the current study aims to estimate the direct COVID-19 burden in Belgium, observed despite policy interventions, during 2020 and 2021, and compare it to the burden from other causes.

METHODS: Disability-adjusted life years (DALYs) are the sum of Years Lived with Disability (YLDs) and Years of Life Lost (YLLs) due to disease. DALYs allow comparing the burden of disease between countries, diseases, and over time. We used the European Burden of Disease Network consensus disease model for COVID-19 to estimate DALYs related to COVID-19. Estimates of person-years for (a) acute non-fatal disease states were calculated from a compartmental model, using Belgian seroprevalence, social contact, hospital, and intensive care admission data, (b) deaths were sourced from the national COVID-19 mortality surveillance, and (c) chronic post-acute disease states were derived from a Belgian cohort study.

RESULTS: In 2020, the total number of COVID-19 related DALYs was estimated at 253,577 [252,541 - 254,739], which is higher than in 2021, when it was 139,281 [136,704 - 142,306]. The observed COVID-19 burden was largely borne by the elderly, and over 90% of the burden was attributable to premature mortality (i.e., YLLs). In younger people, morbidity (i.e., YLD) contributed relatively more to the DALYs, especially in 2021, when vaccination was rolled out. Morbidity was mainly attributable to long-lasting post-acute symptoms.

CONCLUSION: COVID-19 had a substantial impact on population health in Belgium, especially in 2020, when COVID-19 would have been the main cause of disease burden if all other causes had maintained their 2019 level.

%B BMC Public Health %V 23 %8 2023 Sep 04 %G eng %N 1 %R 10.1186/s12889-023-16572-0 %0 Journal Article %J Arch Public Health %D 2023 %T Investigating years of life lost in Belgium, 2004-2019: A comprehensive analysis using a probabilistic redistribution approach %A Brecht Devleesschauwer %A Aline Scohy %A Robby De Pauw %A Vanessa Gorasso %A Anne Kongs %A Elias Neirynck %A Peter Verduyckt %A Grant M A Wyper %A Laura Van den Borre %K Belgium %K burden of disease %K Cause of Death %K Garbage code %K Ill-defined death %K mortality %K Redistribution %K Years of life lost %X

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.

%B Arch Public Health %V 81 %8 2023 Aug 25 %G eng %N 1 %R 10.1186/s13690-023-01163-7 %0 Government Document %D 2023 %T Women’s health report for Belgium: addressing the information gap %A Aline Scohy %A Gaelle Mogin %A Leonor Guariguata %A Sarah Nayani %A Masja Schmidt %A Laura Van den Borre %A Robby De Pauw %A Vanessa Gorasso %K inequalities %K Women's Health %X

Issue In the last years, Belgian policy makers have become increasingly concerned about health problems affecting only women (e.g. endometriosis) or affecting women in different ways than men (e.g. cardiovascular diseases). The current monitoring tools on the health status in Belgium routinely present information disaggregated by sex. Nevertheless, there is a need to go further than a mere comparison of men and women and strive for gender-sensitive health reporting. Description of the problem To address the information gap pertaining to the health of girls and women, a women's health report for Belgium was developed. The goal of this report is to identify and highlight health issues specific to women or affecting them differently and possible knowledge and data gaps. Results The report highlighted several data gaps, e.g. prevalence of endometriosis and polycystic ovary syndrome, and several opportunities to fill them. The process also uncovered available but underused data on women-specific issues, including fertility treatments, abortions, and contraception. Among the main results, an analysis on girls (11-18 years old) showed an alarming difference in health status compared to boys, starting from a young age and increasing throughout adolescence. For example, girls reported experiencing more psychosomatic symptoms more often than boys with the difference increasing with age. Girls reported more often a negative perception of their health (22%) compared to boys (15%) and more often depressive symptoms (47%) than boys (31%). Conversely, boys were twice as likely to meet WHO recommendations on physical activity. Lessons This first report on women's health in Belgium highlighted the need to collect better information on women-specific issues and the need to promote the use of existing data. Results showed that gender differences in health emerge and increase during adolescence. We strived to put results into context to produce knowledge and recommendations for policymakers. Key messages • Developing a women’s health report allowed to highlight data gaps and underuse of existing data. • Specific interventions should target teenage girls.

%B European Journal of Public Health %V 33 %8 Dec-10-2024 %G eng %N Supplement_2 %R 10.1093/eurpub/ckad160.852 %0 Journal Article %J Quality of Life Research %D 2022 %T Belgian population norms for the EQ-5D-5L, 2018 %A Lisa Van Wilder %A Rana Charafeddine %A Philippe Beutels %A Robin Bruyndonckx %A Irina Cleemput %A Stefaan Demarest %A Delphine De Smedt %A Niel Hens %A Aline Scohy %A Niko Speybroeck %A Johan Van der Heyden %A Renata T. C. Yokota %A Herman Van Oyen %A Joke Bilcke %A Brecht Devleesschauwer %K EQ-5D %K Health inequalities %K health interview survey %K Health status %K health-related quality of life %K Multi-attribute utility instrument %K Population norms %K Visual analogue scale %X

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.

%B Quality of Life Research %V 31 %8 Jan-02-2022 %G eng %N 2 %& 527 %R 10.1007/s11136-021-02971-6 %0 Journal Article %J Archives of Public Health %D 2022 %T Changes in quality-adjusted life expectancy in Belgium, 2013 and 2018 %A Aline Scohy %A Rana Charafeddine %A Lisa Van Wilder %A Herman Van Oyen %A Delphine De Smedt %A Brecht Devleesschauwer %K EQ-5D %K HEALTH EXPECTANCY %K health-related quality of life %K Life expectancy %K quality-adjusted life expectancy %X

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.

%B Archives of Public Health %V 80 %8 Jan-12-2022 %G eng %N 1 %R 10.1186/s13690-022-01011-0 %0 Report %D 2022 %T Health Status Report 2021- De gezondheidstoestand in België %A Françoise Renard %A Aline Scohy %A Robby De Pauw %A Jure Jurcevic %A Brecht Devleesschauwer %K Health monitoring %K Health status %I Sciensano %C Brussel, België %P 34 %8 15/02/2022 %G eng %M D/2022/14.440/08 %0 Report %D 2022 %T Health Status Report 2021 - L'état de santé en Belgique %A Françoise Renard %A Aline Scohy %A Robby De Pauw %A Jure Jurcevic %A Brecht Devleesschauwer %K Health monitoring %K Health status %I Sciensano %C Bruxelles, Belgique %P 34 %8 15/02/2022 %G eng %M D/2022/14.440/07 %0 Report %D 2022 %T Health Status Report 2021 - The state of health in Belgium %A Françoise Renard %A Aline Scohy %A Robby De Pauw %A Jure Jurcevic %A Brecht Devleesschauwer %K Health monitoring %K Health status %I Sciensano %C Brussels %P 33 %8 15/02/2022 %G eng %M D/2022/14.440/06 %0 Journal Article %J BMC Public Health %D 2022 %T QALY losses for chronic diseases and its social distribution in the general population: results from the Belgian Health Interview Survey %A Lisa Van Wilder %A Brecht Devleesschauwer %A Els Clays %A Johan Van der Heyden %A Rana Charafeddine %A Aline Scohy %A Delphine De Smedt %K Chronic disease %K EQ-5D %K Health inequality %K health-related quality of life %K Quality-adjusted life year %X

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.

%B BMC Public Health %V 22 %8 Jan-12-2022 %G eng %N 1 %R 10.1186/s12889-022-13675-y %0 Journal Article %J European Journal of Public Health %D 2022 %T Years of life lost for 137 causes of death in Belgium by age, sex, and region, 2004-2018 %A Aline Scohy %A Robby De Pauw %A Vanessa Gorasso %A Laura Van den Borre %A Brecht Devleesschauwer %K Cause of Death %B European Journal of Public Health %V 32 %8 Jan-10-2024 %G eng %N Supplement_3 %R 10.1093/eurpub/ckac129.622 %0 Report %D 2021 %T Atlas of cause-specific all ages mortality by district in Belgium, 2003-2009. %A Aline Scohy %A Françoise Renard %A Jure Jurcevic %I Sciensano %C Brussels %P 22 %8 2021 %G eng %0 Report %D 2021 %T Atlas of cause-specific all-ages mortality by district in Belgium, 2010- 2017 %A Aline Scohy %A Françoise Renard %A Jure Jurcevic %I Sciensano %C Brussels %P 22 %8 Dec 2021 %G eng %0 Report %D 2021 %T Atlas of cause-specific premature mortality (0-74) by districts in Belgium, 2010-2017 %A Aline Scohy %A Françoise Renard %A Jure Jurcevic %I Sciensano %C Brussels %P 20 %8 Dec 2021 %G eng %0 Report %D 2021 %T Atlas of cause-specific premature mortality (0-74) by districts in Belgium, 2003-2009 %A Aline Scohy %A Françoise Renard %A Jure Jurcevic %P 20 %8 Dec 2021 %G eng %0 Journal Article %J Eurosurveillance %D 2021 %T Establishing an ad hoc COVID-19 mortality surveillance during the first epidemic wave in Belgium, 1 March to 21 June 2020 %A Françoise Renard %A Aline Scohy %A Johan Van der Heyden %A Ilse Peeters %A Sara Dequeker %A Eline Vandael %A Nina Van Goethem %A Dominique Dubourg %A Louise De Viron %A Anne Kongs %A Naïma Hammami %A Brecht Devleesschauwer %A André Sasse %A Javiera Rebolledo %A Natalia Bustos Sierra %X

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.

%B Eurosurveillance %V 26 %8 Feb-12-2021 %G eng %N 48 %R 10.2807/1560-7917.ES.2021.26.48.2001402 %0 Report %D 2021 %T Excess mortality during the first and second waves of the COVID-19 epidemic in Belgium %A Natalia Bustos Sierra %A Nathalie Bossuyt %A Freek Haarhuis %A Ilse Peeters %A Kristiaan Proesmans %A Sébastien Fierens %A Aline Scohy %A Maarten Vanhaverbeke %A Melissa Vermeulen %A Catharina Vernemmen %A Johan Van der Heyden %K Be-MOMO %K Coronavirus %K COVID-19 mortality %K Excess mortality %K mortality %K Surveillance %X

COVID-19 mortality was highly correlated with excess all-cause mortality during the first two waves of the epidemic. Epidemiologic surveillance of COVID-19 deaths was accurately conducted during the epidemic and it is even likely that COVID-19 deaths were underreported during the ascending phases of excess mortality by 1,193 deaths. The first wave of the epidemic has a larger excess mortality than the second wave. People aged 85 and over were the most affected during the two periods of excess mortality. The year 2020 has a 17.5% excess mortality with 18,765 additional deaths, eight times the average excess mortality of the past five years. In the absence of the implementation of restrictive measures (e.g., social distancing, etc.) and non-pharmaceutical interventions (e.g., hand hygiene, personal protective equipment, etc.), it is possible that the excess mortality during this period would have been greater. Mortality analysis encompassing entire winter seasons is more accurate for flu, which often spreads over several winter months, spread over two calendar years. It provides very different results than a typical annual analysis from January to December. Even in the 21st century, epidemics of respiratory infectious diseases can be major lethal events of rapid onset in a susceptible and vulnerable population

%I Sciensano %C Belgium %P 48 %8 08/2021 %G eng %M D/2021/14.440/62 %0 Report %D 2021 %T Oversterfte tijdens de eerste en tweede golf van COVID-19-epidemie in België %A Natalia Bustos Sierra %A Nathalie Bossuyt %A Toon Braeye %A Freek Haarhuis %A Ilse Peeters %A Kristiaan Proesmans %A Sébastien Fierens %A Aline Scohy %A Maarten Vanhaverbeke %A Melissa Vermeulen %A Catharina Vernemmen %A Johan Van der Heyden %K Be-MOMO %K Coronavirus %K COVID-19 mortality %K Excess mortality %K mortality %K Surveillance %X

De COVID-19-sterfte was sterk gecorreleerd met de oversterfte door alle oorzaken tijdens de eerste twee golven van de epidemie. Epidemiologische surveillance van COVID-19- sterfgevallen werd nauwkeurig uitgevoerd. Het is waarschijnlijk dat COVID-19-sterfgevallen werden ondergerapporteerd met 1193 sterfgevallen tijdens de toenemende fasen van de oversterfte . De eerste golf van de epidemie kende een grotere oversterfte dan de tweede golf. Personen van 85 jaar en ouder werden het zwaarst getroffen tijdens de twee perioden van oversterfte. Het jaar 2020 vertoont een oversterfte van 17,5% met 18 765 extra sterfgevallen, acht keer de gemiddelde oversterfte van de afgelopen vijf jaar. Zonder de toepassing van beperkende maatregelen (b.v. sociale distantie, enz.) en niet-farmaceutische interventies (b.v. handhygiëne, persoonlijke beschermingsmiddelen, enz.) is het mogelijk dat de oversterfte gedurende deze periode groter zou zijn geweest. Analyses van mortaliteit die hele winterseizoenen omvatten zijn nauwkeuriger voor griep, gezien de jaarlijkse griepepidemie zich vaak over meerdere wintermaanden en twee kalenderjaren verspreidt. Dit levert heel andere resultaten op dan de typische jaarlijkse analyses van januari tot december. Zelfs in de 21e eeuw kunnen epidemieën van luchtweginfecties grote dodelijke gebeurtenissen zijn met een snel begin bij een vatbare en kwetsbare bevolking.

%I Sciensano %C Belgium %P 51 %8 08/2021 %G eng %M D/2021/14.440/64 %0 Report %D 2021 %T Surmortalité durant la 1re et 2e vague de l'épidémie de COVID-19 en Belgique %A Natalia Bustos Sierra %A Nathalie Bossuyt %A Freek Haarhuis %A Ilse Peeters %A Kristiaan Proesmans %A Sébastien Fierens %A Aline Scohy %A Maarten Vanhaverbeke %A Melissa Vermeulen %A Catharina Vernemmen %A Johan Van der Heyden %K Be-MOMO %K Coronavirus %K COVID-19 mortality %K Excess mortality %K mortality %K Surveillance %X

La mortalité due à la COVID-19 était fortement corrélée à l'excès de mortalité toutes causes confondues pendant les deux premières vagues de l'épidémie. La surveillance épidémiologique des décès COVID-19 a été menée avec précision pendant l'épidémie et il est même probable que les décès COVID-19 aient été sous-déclarés pendant les phases ascendantes de la surmortalité à hauteur de 1 193 décès. La première vague de l'épidémie présente une surmortalité plus importante que la deuxième vague. Les personnes âgées de 85 ans et plus ont été les plus touchées au cours des deux périodes de surmortalité. L'année 2020 présente une surmortalité de 17,5 % avec 18 765 décès supplémentaires, soit huit fois la surmortalité moyenne des cinq dernières années. En absence de mise en œuvre de mesures contraignantes (par exemple, la distanciation sociale, etc.) et d’interventions non pharmaceutiques (par exemple, l'hygiène des mains, les équipements de protection individuelle, etc.), il est possible que la surmortalité au cours de cette période, aurait été plus importante. L'analyse de la mortalité englobant des saisons hivernales entières est plus précise pour la grippe, qui se propage souvent sur plusieurs mois d'hiver, à cheval sur deux années. Elle fournit des résultats très différents de ceux d'une analyse annuelle typique allant de janvier à décembre. Même au XXIe siècle, les épidémies de maladies infectieuses respiratoires peuvent être des événeme

%P 48 %8 08/2021 %G eng %M D/2021/14.440/63 %0 Report %D 2021 %T Surveillance de la mortalité COVID-19 en Belgique, épidémiologie et méthodologie durant la 1re et 2e vague (mars 2020 - 14 février 2021) %A Ilse Peeters %A Melissa Vermeulen %A Natalia Bustos Sierra %A Françoise Renard %A Johan Van der Heyden %A Aline Scohy %A Toon Braeye %A Nathalie Bossuyt %A Freek Haarhuis %A Kristiaan Proesmans %A Catharina Vernemmen %A Maarten Vanhaverbeke %K Be-MOMO %K Coronavirus %K COVID-19 mortality %K Surveillance %X

Ce rapport fournit une analyse épidémiologique des caractéristiques des décès COVID-19 durant la première vague (1er mars 2020 – 21 juin 2020), l’entre-deux vagues (22 juin 2020 – 30 août 2020) et la seconde vague (31 août 2020 – 14 février 2021) de l’épidémie de COVID-19 en Belgique. Il s'agit de la période précédant l'évaluation des effets de la campagne de vaccination nationale qui a débuté au début de l'année 2021. Au total, 21 860 décès COVID-19 se sont produits (43,9 % durant la première vague et 54,7 % durant la seconde). La distribution par sexe est assez équilibrée (49,1 % d’hommes et 50,8 % de femmes). Presque toutes le personnes décédées avaient plus de 64 ans et environ la moitié avaient plus de 84 ans. Les données provenant de patients hospitalisés pour COVID-19 montrent que l’âge, le sexe masculin, et plusieurs comorbidités telles que les maladies cardio-vasculaires et le diabète sont des facteurs de risque de décès. De plus, l’estimation de la létalité COVID-19 en Belgique confirme qu’elle est plus importante pour la population âgée et de sexe masculin.
Au cours de la deuxième vague, plus de décès se sont produits en hôpital (61 %) que dans les maisons de repos et les maisons de repos et de soins (MR/MRS) (38 %). Lors de la
première vague, les proportions étaient quasiment identiques (50 % en hôpital et 49 % en MR/MRS). La capacité des tests a augmenté et son champ s’est élargi au cours du temps, conduisant à une augmentation de la proportion des décès COVID-19 confirmés par test moléculaire (69 % au cours de la première vague et 95 % au cours de la seconde). Les taux de mortalité COVID-19 standardisés pour l’âge (age-standardized mortality rate, ou ASMR) qui tiennent compte de la répartition par âge de la population, montrent que Bruxelles présente l’ASMR le plus élevé pour la période entière et pour la première vague, tandis que la Wallonie présente le taux le plus élevé pour la deuxième vague (provinces de Hainaut et de Liège en tête). Les taux bruts de mortalité COVID-19 pour les résidents des maisons de repos étaient plus élevés en Flandre que dans les autres régions, tant pour la période totale que pour la deuxième vague. 

Le système de surveillance de la mortalité COVID-19 a été mis sur pied au début de l’épidémie pour collecter des données de mortalité COVID-19 à une fréquence journalière.
Ce système a combiné les informations au sujet des décès COVID-19 provenant de trois types de surveillance (surveillance hospitalière, surveillance MR/MRS et notifications aux
inspecteurs de santé des autorités régionales) à travers neuf sources de données. Ces informations incluent la date du décès, la date de naissance, le sexe, la classification de
cas, les types de lieu de décès et de lieu de résidence (par exemple vivre en MR/MRS), le code postal du lieu de décès et du lieu de résidence. Des améliorations continues de la
collecte de données ont conduit à des mises à jour rétrospectives du nombre de décès.  

Les comparaisons internationales et le classement des taux de mortalité COVID-19 sont trompeurs en raison de la grande hétérogénéité des méthodes utilisées (définition de cas, stratégie de tests et de dépistage, méthode de notification, disponibilité d’une surveillance spécifique pour les MR/MRS…). Ces méthodes ont également pu évoluer au cours de l’épidémie au sein d’un même pays. Une meilleure comparaison devrait être possible quand les différents pays auront terminé d’analyser les certificats de décès officiels. La mise sur pied rapide de la surveillance COVID-19 en MR/MRS et l’inclusion des décès de cas possibles de COVID-19 ont néanmoins permis de fournir des données précises sur les décès COVID-19. Cela a permis d’évaluer la gravité de la situation épidémiologique en MR/MRS. La mortalité COVID-19 s’est révélée fortement corrélée avec la surmortalité toutes causes confondues en Belgique. La surmortalité a été un indicateur clé dans l’épidémie de COVID-19 pour valider le fait que la déclaration épidémiologique de la mortalité liée à la COVID-19 a été correctement effectuée durant l’épidémie.

%I Sciensano %C Belgium %P 42 %8 09/2021 %G eng %M D/2021/14.440/56 %0 Report %D 2021 %T Surveillance of COVID-19 mortality in Belgium, epidemiology and methodology during 1st and 2nd wave (March 2020 - 14 February 2021) %A Ilse Peeters %A Melissa Vermeulen %A Natalia Bustos Sierra %A Françoise Renard %A Johan Van der Heyden %A Aline Scohy %A Toon Braeye %A Nathalie Bossuyt %A Freek Haarhuis %A Kristiaan Proesmans %A Catharina Vernemmen %A Maarten Vanhaverbeke %K Be-MOMO %K Coronavirus %K COVID-19 mortality %K Surveillance %X

This report provides epidemiological figures about the characteristics of COVID-19 deaths during the first wave (1 March 2020 until 21 June 2020), the inter wave period (22 June until 30 August 2020) and the second wave (31 August 2020 until 14 February 2021) of the COVID-19 epidemic in Belgium. This is the period before the effects of the nationwide vaccination campaign that started early in 2021 could be assessed. In total 21,860 COVID-19 deaths occurred (43.9% in the first wave and 54.7% in the second wave). The COVID-19 mortality surveillance system was implemented at the start of the epidemic to acquire real-time COVID-19 mortality data on a daily basis. The surveillance combined information on COVID-19 related deaths from three surveillances (the hospital surveillance, the nursing home (NH) surveillance and notifications to regional health inspection authorities) through nine data sources. This information included the date of death, date of birth, sex, case classification, type of place of death, type of place of residence (e.g. living in a NH), postal code of the place of death and residence. Continuous improvements as regards the data collection resulted in retrograde adaptations of mortality numbers.

The overall sex distribution was fairly even (49.1% in male and 50.8% in female). Almost all deaths occurred in the age group over 64 years and approximately half of the deaths occurred in the age group over 84 years. Data on hospitalized COVID-19 patients showed that higher age, male sex and several comorbidities such as cardiovascular disease and diabetes were risk factors for mortality. Additionally, the estimated COVID-19 case fatality in Belgium confirmed that it was higher for the elderly and male population. In the second wave, more deaths occurred in hospitals (61%) than in nursing homes for elderly (NHs) (38%). In contrast, during the first wave, this distribution was more equal (50% in hospitals and 49% in NHs). The test capacity increased and the testing strategy broadened over time, leading to an increase in the proportion of laboratory-confirmed COVID-19 cases among deaths (69% and 95% in the first and second waves respectively). COVID-19 age-standardized mortality rates (ASMR), which take into account the age distribution of the population, showed that Brussels presented the highest ASMR for the total period and the first wave, while Wallonia has the highest ASMR for the second wave (more precisely in the provinces of Hainaut and Liège). The crude COVID-19 mortality rates for residents of NHs were higher in Flanders than in the other regions, both for the total period and for the second wave.

International comparison and ranking of COVID-19 crude mortality rates are misleading because of very heterogeneous methods used (e.g. case definition, testing and screening strategy, reporting method, availability of specific surveillance in NHs, etc.). Methods might also have changed during the course of the epidemic within the same country. A better comparison will probably be possible when countries have finished analyzing the official death certificates. The fast initiation of the COVID-19 surveillance in NHs and the inclusion of deaths of possible COVID-19 cases nevertheless allowed Belgium to provide accurate figures on COVID-19 deaths. This helped to assess the seriousness of the epidemiological situation in NHs. COVID-19 mortality was strongly correlated with excess all-cause mortality in Belgium. The excess mortality was a key indicator in the COVID-19 epidemic to validate that the epidemiological reporting of COVID-19-related mortality was correctly conducted during the epidemic

%I Sciensano %C Belgium %P 42 %8 09/2021 %G eng %M D/2021/14.440/57 %0 Report %D 2021 %T Surveillance van COVID-19 gerelateerde mortaliteit in België, epidemiologie en methodologie tijdens 1e en 2e golf (maart 2020 - 14 februari 2021) %A Ilse Peeters %A Melissa Vermeulen %A Natalia Bustos Sierra %A Françoise Renard %A Johan Van der Heyden %A Aline Scohy %A Toon Braeye %A Nathalie Bossuyt %A Freek Haarhuis %A Kristiaan Proesmans %A Catharina Vernemmen %A Maarten Vanhaverbeke %K Be-MOMO %K Coronavirus %K COVID-19 mortality %K Surveillance %X

Dit rapport geeft epidemiologische cijfers over de kenmerken van COVID-19-sterfgevallen tijdens de eerste golf (1 maart 2020 tot 21 juni 2020), de intergolf (22 juni tot 30 augustus 2020) en de tweede golf (31 augustus 2020 tot 14 februari 2021) van de COVID-19- epidemie in België. Dit is de periode voordat de effecten van de landelijke vaccinatiecampagne, die begin 2021 van start is gegaan, konden worden beoordeeld. In totaal vielen er tijdens deze periode 21 860 COVID-19 sterfgevallen (43,9% in de eerste golf en 54,7% in de tweede golf).

Het COVID-19 mortaliteitssurveillancesysteem werd aan het begin van de epidemie ingevoerd om dagelijks COVID-19-sterftegegevens te verzamelen. De surveillance combineerde informatie over COVID-19-gerelateerde sterfgevallen uit drie surveillances (de ziekenhuissurveillance, de woonzorgcentra-surveillance en meldingen aan de regionale instanties van gezondheidsinspectie) via negen gegevensbronnen. Deze informatie omvatte de datum van overlijden, de geboortedatum, het geslacht, de gevalsclassificatie, het type plaats van overlijden, het type woonplaats (bv. woonachtig in een woonzorgcentrum), de postcode van de plaats van overlijden en van de woonplaats. Voortdurende verbeteringen met betrekking tot de gegevensverzameling leidden tot retrograde aanpassingen van de sterftecijfers. De verdeling over de geslachten was gelijkmatig (49,1% bij mannen en 50,8% bij vrouwen). Bijna alle overleden personen waren ouder dan 64 jaar en ongeveer de helft was ouder dan 84 jaar.

Uit gegevens over gehospitaliseerde COVID-19-patiënten bleek dat een hogere leeftijd, mannelijk geslacht en verschillende comorbiditeiten zoals hart- en vaatziekten en diabetes risicofactoren voor sterfte waren. Bovendien bevestigde de schatting van de “case fatality ratio” van COVID-19 in België dat de letaliteit hoger was in de oudere populatie en mannen. In de tweede golf waren er meer sterfgevallen in ziekenhuizen (61%) dan in woonzorgcentra voor ouderen (38%). Tijdens de eerste golf was deze verdeling daarentegen gelijkmatiger (50% in ziekenhuizen en 49% in woonzorgcentra). In de loop van de tijd nam de testcapaciteit toe en verbreedde de teststrategie, wat leidde tot een toename van het aandeel van de labo bevestigde COVID-19-gevallen onder de sterfgevallen (69% en 95% in respectievelijk de eerste en tweede golf).

De COVID-19-sterftecijfers na directe leeftijdsstandaardisatie (age-standardized mortality rate, ASMR), die rekening houden met de leeftijdsverdeling van de bevolking, toonden aan dat Brussel de hoogste ASMR vertoonde voor de totale periode en de eerste golf, en Wallonië voor de tweede golf (meer bepaald in de provincies Henegouwen en Luik). De ruwe COVID-19-sterftecijfers voor bewoners van woonzorgcentra lagen zowel voor de totale periode als voor de tweede golf dan weer hoger in Vlaanderen dan in de andere regio’s. 5 Een internationale vergelijking en rangschikking van de ruwe COVID-19-sterftecijfers is misleidend vanwege de zeer uiteenlopende methoden die worden gebruikt (bv. gevalsdefinitie, test- en screeningstrategie, rapportagemethode, beschikbaarheid van specifieke surveillance in woonzorgcentra, enz.). De methoden gebruikt binnen éénzelfde land kunnen ook veranderd zijn in de loop van de epidemie. Een betere vergelijking zal waarschijnlijk mogelijk zijn wanneer de landen klaar zijn met de analyse van de officiële overlijdensakten.

Niettemin stelden de snelle start van de COVID-19-surveillance in woonzorgcentra en het includeren van sterfgevallen bij mogelijke COVID-19-gevallen België in staat nauwkeurige cijfers te verstrekken over de COVID-19-sterfte. Dit hielp om de ernst van de epidemiologische situatie in woonzorgcentra te kunnen inschatten. De gerapporteerde COVID-19-sterfte kwam sterk overeen met de oversterfte ten gevolge van alle oorzaken in België. Deze oversterfte was een belangrijke indicator in de COVID-19- epidemie om te valideren dat de epidemiologische rapportage van COVID-19-gerelateerde sterfte correct gebeurde tijdens de epidemie.

%I Sciensano %C Belgium %P 42 %8 09/2021 %G eng %M D/2021/14.440/55 %0 Journal Article %J Arch Public Health %D 2020 %T All-cause mortality supports the COVID-19 mortality in Belgium and comparison with major fatal events of the last century. %A Natalia Bustos Sierra %A Nathalie Bossuyt %A Toon Braeye %A Mathias Leroy %A Isabelle Moyersoen %A Ilse Peeters %A Aline Scohy %A Johan Van der Heyden %A Herman Van Oyen %A Françoise Renard %K COVID-19 mortality %K Excess mortality %K Surveillance %X

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.

%B Arch Public Health %V 78 %8 2020 Nov 13 %G eng %N 1 %R 10.1186/s13690-020-00496-x %0 Journal Article %J European Journal of Public Health %D 2020 %T Changes in life expectancy and contribution of causes of deaths and age groups in Belgium, 1997-2018 %A Aline Scohy %A Herman Van Oyen %A Françoise Renard %B European Journal of Public Health %V 30 %8 Jun-09-2022 %G eng %N Supplement_5 %R 10.1093/eurpub/ckaa165.189 %0 Report %D 2020 %T Eerste COVID-19-Gezondheidsenquête: eerste resultaten %A Stefaan Demarest %A Elise Braekman %A Rana Charafeddine %A Sabine Drieskens %A Lydia Gisle %A Lize Hermans %A Aline Scohy %8 07/05/2020 %G eng %0 Report %D 2020 %T Enquête de santé 2018 : Contacts avec des prestataires de thérapies non-conventionnelles %A Sabine Drieskens %A Aline Scohy %A Finaba Berete %G eng %0 Report %D 2020 %T Enquête de santé 2018 : Utilisation des services de santé. Résumé des résultats %A Finaba Berete %A Sabine Drieskens %A Johan Van der Heyden %A Stefaan Demarest %A Rana Charafeddine %A Lydia Gisle %A Françoise Renard %A Aline Scohy %A Lize Hermans %A Elise Braekman %G eng %0 Report %D 2020 %T Gezondheidsenquête 2018: Contacten met beoefenaars van niet-conventionele geneeswijzen %A Sabine Drieskens %A Aline Scohy %A Finaba Berete %G eng %0 Report %D 2020 %T Gezondheidsenquête 2018: Gebruik van gezondheidsdiensten. Samenvatting van de resultaten %A Sabine Drieskens %A Finaba Berete %A Johan Van der Heyden %A Stefaan Demarest %A Rana Charafeddine %A Lydia Gisle %A Françoise Renard %A Aline Scohy %A Lize Hermans %A Elise Braekman %G eng %0 Report %D 2020 %T Première enquête de santé COVID-19: résultats préliminaires %A Rana Charafeddine %A Elise Braekman %A Stefaan Demarest %A Sabine Drieskens %A Lydia Gisle %A Lize Hermans %A Aline Scohy %8 07/05/2020 %G eng %0 Report %D 2019 %T Belgian Health Examination Survey 2018 %A Johan Van der Heyden %A Diem Nguyen %A Françoise Renard %A Aline Scohy %A Stefaan Demarest %A Sabine Drieskens %A Lydia Gisle %G eng %0 Report %D 2019 %T Belgisch gezondheidsonderzoek 2018 %A Johan Van der Heyden %A Diem Nguyen %A Françoise Renard %A Aline Scohy %A Stefaan Demarest %A Sabine Drieskens %A Lydia Gisle %P 136 %8 Nov 2019 %G eng %0 Generic %D 2019 %T Monitoring of non-communicable diseases in Belgium %A Brecht Devleesschauwer %A L A Abboud %A Petronille Bogaert %A A Cornez %A L Raes %A Aline Scohy %A Johan Van der Heyden %A Françoise Renard %B European Journal of Public Health %V 29 %8 Jan-11-2020 %G eng %N Supplement_4 %R 10.1093/eurpub/ckz185.120 %0 Journal Article %J European Journal of Public Health %D 2019 %T An online health status report to support public health in Belgium %A Aline Scohy %A Brecht Devleesschauwer %A Françoise Renard %B European Journal of Public Health %V 29 %8 Jan-11-2020 %G eng %N Supplement_4 %R 10.1093/eurpub/ckz186.608 %0 Thesis %D 2018 %T Gender differences in health-related quality of life in Belgium in 2013 %A Aline Scohy %A Bruno Masquelier %A Brecht Devleesschauwer %K health-related quality of life %K quality-adjusted life expectancy %K Quality-Adjusted Life Years %I Université catholique de Louvain %C Louvain-la-Neuve, Belgium %P 88 %G eng %0 Report %D 0 %T Enquête de Santé par Examen Belge 2018 %A Johan Van der Heyden %A Diem Nguyen %A Françoise Renard %A Aline Scohy %A Stefaan Demarest %A Sabine Drieskens %A Lydia Gisle %G eng