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

  1. Brandon Parkes
  2. Massimo Stafoggia
  3. Daniela Fecht
  4. Bethan Davies
  5. Carl Bonander
  6. Francesca de’ Donato
  7. Paola Michelozzi
  8. Frédéric B Piel
  9. Ulf Strömberg
  10. Marta Blangiardo

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Abstract

Background

Analyses of coronavirus disease 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 pandemic. Our aim is to quantify community-level variations in excess mortality within England, Italy and Sweden and identify how such spatial variability was driven by community-level characteristics.

Methods

We applied a two-stage Bayesian 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 covariates to quantify their associations with excess mortality.

Results

We found 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 communities. 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.

Conclusion

These results highlight some international similarities in factors affecting mortality that will help policy makers target public health measures to increase resilience to the mortality impacts of this and future pandemics.

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  1. Version published to 10.1093/eurpub/ckad075
  2. Version published to 10.1101/2022.04.26.22274332v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2022.04.26.22274332: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and limitations: Our analysis aimed to use similar community-level areas and a range of covariates previously linked to excess mortality in COVID-19 studies across three European countries which experienced different pandemic dynamics and implemented different time-varying, public health interventions (29). The methodology applied combines a rigorous two-stage modelling approach, a large number areas, and expertise from each of the three countries. The large range of population sizes for Italian municipalities is likely to affect the relationship between excess mortality and the covariates because the larger municipalities are unlikely to be homogenous communities with similar characteristics. The influence of the highly populous municipalities also affected the categories of the community characteristics. For example, 59.6% of the population over 40 years was in the top category of the population density covariate (Supplementary Table 19). Some community characteristics previously reported to show associations with COVID-19 related excess mortality (5) (proportion of population that is non-white, the number of care homes) could not be used here because the data (or close equivalents) were not available for all three countries. Although covariates were selected for consistency, it was not possible to use the same measures of deprivation or overcrowding for the three countries, meaning that they will be measuring different things. Furthermore, for the covariates whic...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  4. Version published to 10.1101/2022.04.26.22274332v1 on medRxiv