Changing patterns of sickness absence among healthcare workers in England during the COVID-19 pandemic

  1. Rhiannon Edge
  2. Diana A van der Plaat
  3. Vaughan Parsons
  4. David Coggon
  5. Martie van Tongeren
  6. Rupert Muiry
  7. Ira Madan
  8. Paul Cullinan

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Abstract

Background

Patterns of sickness absence shed useful light on disease occurrence and illness-related behaviours in working populations.

Methods

We analysed prospectively collected, pseudonymized data on 959 356 employees who were continuously employed by National Health Service trusts in England from 1 January 2019 to 31 July 2020, comparing the frequency of new sickness absence in 2020 with that at corresponding times in 2019.

Results

After exclusion of episodes directly related to COVID-19, the overall incidence of sickness absence during the initial 10 weeks of the pandemic (March–May 2020) was more than 20% lower than in corresponding weeks of 2019. Trends for specific categories of illness varied substantially, with a fall by 24% for cancer, but an increase for mental illness. A doubling of new absences for pregnancy-related disorders during May–July of 2020 was limited to women with earlier COVID-19 sickness absence.

Conclusions

Various factors will have contributed to the large and divergent changes that were observed. The findings reinforce concerns regarding delays in diagnosis and treatment of cancers and support a need to plan for a large backlog of treatment for many other diseases. Further research should explore the rise in absence for pregnancy-related disorders among women with earlier COVID-19 sickness absence.

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  1. Version published to 10.1093/pubmed/fdab341
  2. ScreenIT

    SciScore for 10.1101/2021.04.08.21255128: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

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  3. Version published to 10.1101/2021.04.08.21255128v1 on medRxiv