Community factors and excess mortality in the COVID-19 pandemic in England, Italy and Sweden

Abstract

Analyses of COVID-19 suggest specific risk factors make communities more or less vulnerable to pandemic related deaths within countries. What is unclear is whether the characteristics affecting vulnerability of small communities within countries produce similar patterns of excess mortality across countries with different demographics and public health responses to the COVID-19 pandemic. We applied a two-stage Bayesian spatial model to quantify inequalities in excess mortality in people aged 40 years and older at the community level in England, Italy and Sweden during the first year of the pandemic (March 2020– February 2021). We used community characteristics measuring deprivation, air pollution, living conditions, population density and movement of people as model covariates to quantify their associations with excess mortality. We found that just under half of communities in England (48.1%) and Italy (45.8%) had an excess mortality of over 300 per 100,000 males over the age of 40, while for Sweden that covered 23.1% of the community-level areas. We showed that deprivation is a strong predictor of excess mortality across the three countries, and communities with high levels of overcrowding were associated with higher excess mortality in England and Sweden. These results highlight some international similarities in factors affecting mortality that will help policy makers target public health measures to increase the resilience of communities to the mortality impacts of this and future pandemics.

Inequalities in regional excess mortality and life expectancy during the COVID-19 pandemic in Europe

“…The present paper fits into a line of studies [17][18][19][20] examining the mortality burden of COVID-19 across several countries at the subnational level. The first novelty of our research is that we have pursued a comprehensive analysis, including subnational data from 28 European countries, covering three full years of the COVID-19 pandemic, and evaluating the mortality burden of the pandemic through the number of excess mortality and the change in life expectancy too.…”

“…This highlights the need to shift towards geographic disaggregation to gain a deeper understanding of the factors influencing the mortality burden of the pandemic. However, to the best of our knowledge, only a few studies [17][18][19][20] have been published so far that have investigated COVID-19 mortality at subnational level across multiple countries, focusing on the magnitude of regional differences in the mortality burden of the pandemic. Three studies focus on European regions or communities 17,19,20 and a meta-analysis covers 74 countries from all continents, providing intracountry regional estimations for some of the countries 18 .…”

“…However, to the best of our knowledge, only a few studies [17][18][19][20] have been published so far that have investigated COVID-19 mortality at subnational level across multiple countries, focusing on the magnitude of regional differences in the mortality burden of the pandemic. Three studies focus on European regions or communities 17,19,20 and a meta-analysis covers 74 countries from all continents, providing intracountry regional estimations for some of the countries 18 . Our first contribution to this literature is to extend the geographical coverage and the survey period, that enables us to provide a deeper insight into the changes in regional differences in mortality over successive phases of COVID-19.In this paper, we comprehensively analyze the time trends in excess mortality rates and life expectancy at birth in Europe and their differences between NUTS 2 regions.…”

A Capture–Recapture-based Ascertainment Probability Weighting Method for Effect Estimation With Under-ascertained Outcomes

“…34 Hence, one might assume that area of residence or work is the primary driver of this choice. There were also notable spatial variations in infection rates, 35 which may affect test-positivity rates. We therefore suspect that geographic factors may be important to adjust for, because they may influence test ascertainment in both data sources.…”