Demographic and territorial characteristics of COVID-19 cases and excess mortality in the European Union during the first wave

Abstract

This article explores for a large number of countries in the European Union (plus the United Kingdom) the main demographic differentials in positive tested COVID-19 cases and excess mortality during the first wave in 2020, accounting for differences at territorial level, where population density and size play a main role in the diffusion and effects of the disease in terms of morbidity and mortality. This knowledge complements and refines the epidemiological information about the spread and impact of the virus. For this analysis, we rely on the descriptive exploration of (1) data from The European Surveillance System (TESSy) database developed at the European Centre for Disease Prevention and Control (ECDC) on the number of cases and fatality rates and (2) of weekly mortality data collected by Eurostat. The analysis at territorial level studies the changes in R0—the basic reproduction number—and median excess mortality, across territories with different levels of urbanization. The unique findings of this study encompassing most European Union Member States confirm and define the demographic and territorial differential impacts in terms of infections and fatalities during the first wave of the pandemic in 2020. The information is important for stakeholders at European Union, national and sub-national levels in charge of designing containment measures for COVID-19 and adaptation policies for the future by anticipating the rebound for certain segments of the population with differential medical and economic needs.

Demographics of COVID-19 hospitalisations and related fatality risk patterns

“…Hospitalisation rates in the nine countries included in the analysis (see Appendix A , Table A2) are highest in Italy (177 per 100 000 for men and 120 per 100 000 for women), and lowest in Latvia (13 per 100 000 for men and 8 per 100 000 for women). Figure 1 shows that, across age groups, hospitalisation rates are highest for individuals older than 70 years, consistently with the existing literature [9] , [10] , [11] , [12] , [13] . Adults aged 50-69 years are the second largest group of SARS-CoV-2 positive individuals who required hospitalisation, especially men.…”

“…Hospital-based studies have found that age, sex, and individuals’ pre-existing conditions are strongly associated with an increased risk of mortality among patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [2] , [3] , [4] , [5] , [6] , [7] . Nonetheless, the demographic characteristics of individuals hospitalised because of COVID-19 and related fatality patterns have rarely been studied at the population level, especially in comparative perspective [8] , [9] , [10] , [11] , [12] . Contrarily, most international cross-country studies have rather focused on COVID-19 fatality and mortality burden [13] , notably excess mortality [14] .…”

“… 7 The date of hospitalisation is not available for Italy in our TESSy database. To calculate the probabilities of hospitalisation in Figure B2 , we draw from the existing literature [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] and make the assumption that there is an average delay of five days from the date of symptoms’ onset to the date of hospitalisation. Studies have confirmed that the average delay between symptom onset and diagnosis in the first month of the outbreak in Italy was indeed 5.3 days [30] .…”

Assessing excess mortality in Vienna and Austria after the first year of the COVID-19 pandemic

“…To compare the impact of COVID-19 on mortality in Vienna with that in other metropolises, it would be necessary to look beyond Austria. Regional comparative analyses at the European level (Goujon et al, 2021;Schöley, 2021;EuroMOMO, 2021) are still limited, since Eurostat does not (yet) provide up-to-date mortality data for European cities and regions with a level of demographic detail similar to the level we used in our analysis (based on data provided by Statistik Austria). With respect to further research, we are currently investigating the availability of such data from European cities, which would allow us to analyse the specific characteristics of COVID-19-related excess mortality in the urban context.…”

Socio-economic determinants of SARS-CoV-2 infection: results from a population-based serosurvey in Geneva, Switzerland

“…While this may also be the case in rural areas, higher commuting times and a potentially lower sense of danger posed by the infection in these areas may explain the lack of significant difference in seropositivity between rural and urban areas. It has been suggested that a lower population density outside the urban areas might have contributed to lower incidence at the beginning of the pandemic in some regions in Europe (21) and some studies have shown lower seroprevalence in municipalities of less than 100.000 inhabitants (22). Further work is needed to uncover the potential mechanisms explaining the association of the residential area with a seropositive result in the population of Geneva, as considering the small size of the canton, the difference between urban and suburban areas is not clearly established and the distribution of SARS-CoV-2 infections might not follow a similar pattern as the one found in other places.…”