Trends and associated factors for Covid-19 hospitalisation and fatality risk in 2.3 million adults in England

  1. T. Beaney
  2. A. L. Neves
  3. A. Alboksmaty
  4. H. Ashrafian
  5. K. Flott
  6. A. Fowler
  7. J. R. Benger
  8. P. Aylin
  9. S. Elkin
  10. A. Darzi
  11. J. Clarke

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Abstract

The Covid-19 mortality rate varies between countries and over time but the extent to which this is explained by the underlying risk in those infected is unclear. Using data on all adults in England with a positive Covid-19 test between 1st October 2020 and 30th April 2021 linked to clinical records, we examined trends and risk factors for hospital admission and mortality. Of 2,311,282 people included in the study, 164,046 (7.1%) were admitted and 53,156 (2.3%) died within 28 days of a positive Covid-19 test. We found significant variation in the case hospitalisation and mortality risk over time, which remained after accounting for the underlying risk of those infected. Older age groups, males, those resident in areas of greater socioeconomic deprivation, and those with obesity had higher odds of admission and death. People with severe mental illness and learning disability had the highest odds of admission and death. Our findings highlight both the role of external factors in Covid-19 admission and mortality risk and the need for more proactive care in the most vulnerable groups.

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  1. Version published to 10.1038/s41467-022-29880-7
  2. ScreenIT

    SciScore for 10.1101/2021.11.24.21266818: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: The work was conducted as part of a wider service evaluation, approved by Imperial College Healthcare Trust on December 3rd 2020.
    Sex as a biological variablenot detected.
    RandomizationMixed effects logistic regression was conducted for each outcome, with a two-level hierarchical model incorporating Clinical Commissioning Group (CCG) of residence as a random intercept.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Analyses were conducted in the Big Data and Analytics Unit Secure Environment, Imperial College, using Python version 3.9.5 and Stata version 17.0 (StataCorp).
    Python
    suggested: (IPython, RRID:SCR_001658)
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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: A strength of this study is the inclusion of routine national laboratory data for positive Covid-19 test results in adults in England with only 1.5% unable to be linked to EHR data, and as a result has lower risk of sampling bias.24 To our knowledge, this is the largest such study including individual level data at a national level bias. Previous studies in England on predictors of mortality are reported on a smaller cohort of patients with 40% national coverage.6 Use of multiple imputation assumes that data are missing at random, and we cannot rule out non-random missing patterns, particularly for data on ethnicity and deprivation, where more marginalised groups are less likely to be registered in the primary care record. However, sensitivity analyses showed inferences were similar between the complete case analysis and imputed results, suggesting limited impact of the missing data on model estimates. Data represented here include only those who died within 28 days of a positive test result, in line with estimates reported by PHE. Deaths mentioning Covid-19 on a death certificate are an alternative metric used widely in many countries as recommended by the World Health Organisation25 and have tended to give a larger estimate of deaths in England, due to those attributable to Covid-19 after 28 days.4 Through use of linked EHR data, we were able to incorporate detailed medical factors for the study cohort. However, we were unable to explore the relat...

    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.

    About SciScore

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  3. Version published to 10.1101/2021.11.24.21266818v1 on medRxiv