Factors associated with COVID‐19 related hospitalisation, critical care admission and mortality using linked primary and secondary care data

  1. Lisa Cummins
  2. Irene Ebyarimpa
  3. Nathan Cheetham
  4. Victoria Tzortziou Brown
  5. Katie Brennan
  6. Jasmina Panovska‐Griffiths

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Abstract

Background

It is important that population cohorts at increased risk of hospitalisation and death following a COVID‐19 infection are identified and protected.

Objectives

We identified risk factors associated with increased risk of hospitalisation, intensive care unit (ICU) admission and mortality in inner North East London (NEL) during the first UK COVID‐19 wave.

Methods

Multivariate logistic regression analysis on linked primary and secondary care data from people aged 16 or older with confirmed COVID‐19 infection between 01/02/2020 and 30/06/2020 determined odds ratios (OR), 95% confidence intervals (CI) and P ‐values for the association between demographic, deprivation and clinical factors with COVID‐19 hospitalisation, ICU admission and mortality.

Results

Over the study period, 1781 people were diagnosed with COVID‐19, of whom 1195 (67%) were hospitalised, 152 (9%) admitted to ICU and 400 (23%) died. Results confirm previously identified risk factors: being male, or of Black or Asian ethnicity, or aged over 50. Obesity, type 2 diabetes and chronic kidney disease (CKD) increased the risk of hospitalisation. Obesity increased the risk of being admitted to ICU. Underlying CKD, stroke and dementia increased the risk of death. Having learning disabilities was strongly associated with increased risk of death (OR = 4.75, 95% CI = [1.91, 11.84], P  = .001). Having three or four co‐morbidities increased the risk of hospitalisation (OR = 2.34, 95% CI = [1.55, 3.54], P  < .001; OR = 2.40, 95% CI = [1.55, 3.73], P  < .001 respectively) and death (OR = 2.61, 95% CI = [1.59, 4.28], P  < .001; OR = 4.07, 95% CI = [2.48, 6.69], P  < .001 respectively).

Conclusions

We confirm that age, sex, ethnicity, obesity, CKD and diabetes are important determinants of risk of COVID‐19 hospitalisation or death. For the first time, we also identify people with learning disabilities and multi‐morbidity as additional patient cohorts that need to be actively protected during COVID‐19 waves.

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  1. Version published to 10.1111/irv.12864
  2. ScreenIT

    SciScore for 10.1101/2021.01.19.20241844: (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 variablenot detected.

    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:
    Our study has some limitations. Firstly, in terms of data quality and availability, data on con-firmed COVID-19 cases is highly likely to be an underestimation of the true number of cases and may also be skewed toward more severe cases given that. For example, testing was only done on people hospitalised with suspected COVID-19 up to 1 April 2020 with wider testing in the community available from May 2020 and capacity increasing throughout the year. Hence the data is limited to those who were recorded as having confirmed COVID-19 in primary or secondary care; and as such may omit many milder and asymptomatic cases that were unreported and untested. Secondly, there were fewer deaths recorded in primary and secondary care data compared to the number of deaths reported by the ONS – 400 compared to 721 over the closest equivalent time period (18). Furthermore, the proportion of in-hospital deaths were disproportionately higher than the number of deaths taking place elsewhere. According to ONS data (19), 73% of COVID-19 deaths took place in hospital whereas 91% of deaths included in our analysis took place in a hospital. Also, it may be that a small number of COVID-19-related deaths were misclassified as non-COVID-19, particularly in the early stages of the pandemic when access to testing was limited. Accounting for this underestimation of mortality may alter our results. We also note that while primary care records are detailed and longitudinal, they may contain incomplete or out...

    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

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  3. Version published to 10.1101/2021.01.19.20241844 on medRxiv