Last week the EPIBEL team attended the XXIe colloque international de L’Association Internationale des Démographes de Langue Française (AIDELF) in Athens. We presented no less than 3 papers on COVID-19 and the Spanish Flu (1918-19) in Belgium:
- Mélanie Bourguignon , Emmanuel Debruyne, Isabelle Devos, Yoann Doignon, Thierry Eggerickx, Hilde Greefs, Jord Hanus, Philippe Paeps, Wouter Ronsijn, Jean-Paul Sanderson, Tim Soens: La grippe espagnole (1918-19) en Belgique : une maladie socialement neutre ?
- In Châtelet (province of Hainaut), the Spanish flu epidemic led to an important surplus of deaths during the years 1918 and 1919. This paper examines the relationship between dying of Spanish flu and the social affiliation of the population: is it a socially neutral disease? It is based on individual data from cause of death registers, civil status registers and population registers. The first results show that the probability of dying from Spanish flu or a related cause during the years 1918-1919 (versus dying from another cause) is largely determined by the sex and age of individuals. Men have a higher probability than women, as do children and young adults (30-50 years). Social category has a much weaker explanatory power: the probability of death is higher among unskilled workers (compared to low-skilled workers and farmers), the hypothesis being that unskilled workers (essentially workers in mines, collieries, etc.), are more at risk because of a fragility linked to their job and their work environment (promiscuity and interpersonal contacts, fragility of the respiratory system, etc.) These results are preliminary. Similar analyses will be performed on a larger set of deaths and municipalities.
- Mélanie Bourguignon , Emmanuel Debruyne, Isabelle Devos, Yoann Doignon, Thierry Eggerickx, Hilde Greefs, Jord Hanus, Philippe Paeps, Wouter Ronsijn, Jean-Paul Sanderson, Tim Soens: La pandémie de grippe espagnole (1918-19) en Belgique. Les disparités spatiales de mortalité
- This paper questions whether in Belgium, were some regions more severely affected than others? Are there spatial differences in mortality between men and women? Is the spatial distribution of the different waves similar? To answer these questions, we used an indicator of excess mortality at the level of the municipalities that relates the deaths observed in 1918 and 1919 to a reference period representing ‘normal’ mortality, i.e. the average of the years 1909, 1910, 1912 and 1913. Using a spatial smoothing method, we were able to identify areas of very high excess mortality. We found, firstlt, that the spatial pattern of mortality in 1918 and 1919 does not correspond to that of ‘normal’ mortality (1909-1913), nor to that of mortality during the war years (1914-1917). Secondly, the spatial patterns of female and male mortality in 1918 and 1919 are different. Finally, the spatial patterns of the 1918 and 1919 mortality waves do not really correspond, which does not allow the hypothesis of inter-wave immunity to be confirmed or refuted.
- Mélanie Bourguignon , Yoann Doignon, Thierry Eggerickx, Jean-Paul Sanderson: Les déterminants individuels et spatiaux de la mortalité pendant la pandémie de Covid- 19 en Belgique
- Since March 2020, the Covid-19 pandemic has resulted in more than 26,000 deaths in Belgium, mainly divided into two waves: March to May 2020 and October 2020 to January 2021. Exploratory studies have uncovered significant inequalities in death during this pandemic period in Belgium. The existence of a geography of excess mortality is not surprising given the transmissible and contagious nature of Covid-19. Thus, the determinants of mortality during Covid-19 periods appear to be both individual and spatial. The objective is twofold: to identify the determinants of the variability of mortality since March 2020, both in terms of the various socio-demographic characteristics of the individual and the characteristics of his or her environment of residence; and then to estimate models specific to the two waves of mortality in Belgium, in order to compare the weight of individual and spatial determinants for each of them. Overall, from one wave to the next, the same mortality determinants are found as those observed in a non-crisis reference year (2019). The difference lies more in the weight of these determinants. Thus, age and household type have a greater weight in the first wave, which is reduced in the second wave when it is more similar to the situation outside the crisis. Conversely, the social group has a lower weight in the first wave when we observe a decrease in the gaps between social groups. These initial analyses highlight a particular pattern for the first wave, whereas the second wave is much closer to the non-crisis situation. This tends to demonstrate a ‘surprise’ effect in the first wave that is largely avoided in the second.