Risks of covid-19 hospital admission and death for people with learning disability: population based cohort study using the OpenSAFELY platform

  1. Elizabeth J Williamson
  2. Helen I McDonald
  3. Krishnan Bhaskaran
  4. Alex J Walker
  5. Sebastian Bacon
  6. Simon Davy
  7. Anna Schultze
  8. Laurie Tomlinson
  9. Chris Bates
  10. Mary Ramsay
  11. Helen J Curtis
  12. Harriet Forbes
  13. Kevin Wing
  14. Caroline Minassian
  15. John Tazare
  16. Caroline E Morton
  17. Emily Nightingale
  18. Amir Mehrkar
  19. Dave Evans
  20. Peter Inglesby
  21. Brian MacKenna
  22. Jonathan Cockburn
  23. Christopher T Rentsch
  24. Rohini Mathur
  25. Angel Y S Wong
  26. Rosalind M Eggo
  27. William Hulme
  28. Richard Croker
  29. John Parry
  30. Frank Hester
  31. Sam Harper
  32. Ian J Douglas
  33. Stephen J W Evans
  34. Liam Smeeth
  35. Ben Goldacre
  36. Hannah Kuper

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Abstract

Objective

To assess the association between learning disability and risk of hospital admission and death from covid-19 in England among adults and children.

Design

Population based cohort study on behalf of NHS England using the OpenSAFELY platform.

Setting

Patient level data were obtained for more than 17 million people registered with a general practice in England that uses TPP software. Electronic health records were linked with death data from the Office for National Statistics and hospital admission data from NHS Secondary Uses Service.

Participants

Adults (aged 16-105 years) and children (<16 years) from two cohorts: wave 1 (registered with a TPP practice as of 1 March 2020 and followed until 31 August 2020); and wave 2 (registered 1 September 2020 and followed until 8 February 2021). The main exposure group consisted of people on a general practice learning disability register; a subgroup was defined as those having profound or severe learning disability. People with Down’s syndrome and cerebral palsy were identified (whether or not they were on the learning disability register).

Main outcome measure

Covid-19 related hospital admission and covid-19 related death. Non-covid-19 deaths were also explored.

Results

For wave 1, 14 312 023 adults aged ≥16 years were included, and 90 307 (0.63%) were on the learning disability register. Among adults on the register, 538 (0.6%) had a covid-19 related hospital admission; there were 222 (0.25%) covid-19 related deaths and 602 (0.7%) non-covid deaths. Among adults not on the register, 29 781 (0.2%) had a covid-19 related hospital admission; there were 13 737 (0.1%) covid-19 related deaths and 69 837 (0.5%) non-covid deaths. Wave 1 hazard ratios for adults on the learning disability register (adjusted for age, sex, ethnicity, and geographical location) were 5.3 (95% confidence interval 4.9 to 5.8) for covid-19 related hospital admission and 8.2 (7.2 to 9.4) for covid-19 related death. Wave 2 produced similar estimates. Associations were stronger among those classified as having severe to profound learning disability, and among those in residential care. For both waves, Down’s syndrome and cerebral palsy were associated with increased hazards for both events; Down’s syndrome to a greater extent. Hazard ratios for non-covid deaths followed similar patterns with weaker associations. Similar patterns of increased relative risk were seen for children, but covid-19 related deaths and hospital admissions were rare, reflecting low event rates among children.

Conclusions

People with learning disability have markedly increased risks of hospital admission and death from covid-19, over and above the risks observed for non-covid causes of death. Prompt access to covid-19 testing and healthcare is warranted for this vulnerable group, and prioritisation for covid-19 vaccination and other targeted preventive measures should be considered.

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  1. Version published to 10.1136/bmj.n1592
  2. ScreenIT

    SciScore for 10.1101/2021.03.08.21253112: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableStudy population: The first cohort comprised patients (males and females, aged up to 105 years) registered as of 1st March 2020 in a general practice which employs the TPP system and followed until 31st August, 2020.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and weaknesses: Key strengths and weaknesses of the OpenSafely platform have been outlined previously12. An important strength for the current analyses is that the study is large, including records of approximately 40% of the English population, allowing disaggregation by learning disability grouping. We had comprehensive data on participants from medical records, allowing us to adjust analyses successively to explore mechanisms for the association of learning disability and adverse COVID-19 outcomes. Furthermore, we were able to assess excess risks in both waves 1 and 2 of the COVID-19 epidemic, in terms of both hospitalization and mortality outcomes. We considered children as well as adults. There are also important limitations. It is not possible to identify everyone with a learning disability from medical records alone, which may have under-estimated hazard ratios. For instance, the most recent data, from 2015, suggests that 23% of people with learning disability are included on the registers1. Our hospitalisation data included only completed hospital admissions, thus will have under-ascertained this outcome towards the end of wave 2. Data was not available on epilepsy, which is both a COVID-19 risk factor and more common among people with learning disability11,13. We also had an incomplete measure of residential care. There was missing data, in particular for ethnicity, although we do not anticipate that this had a meaningful impact on the results. We did not h...

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.03.08.21253112 on medRxiv