Preexisting Comorbidities Predicting COVID-19 and Mortality in the UK Biobank Community Cohort

  1. Janice L Atkins
  2. Jane A H Masoli
  3. Joao Delgado
  4. Luke C Pilling
  5. Chia-Ling Kuo
  6. George A Kuchel
  7. David Melzer

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Abstract

Background

Hospitalized COVID-19 patients tend to be older and frequently have hypertension, diabetes, or coronary heart disease, but whether these comorbidities are true risk factors (ie, more common than in the general older population) is unclear. We estimated associations between preexisting diagnoses and hospitalized COVID-19 alone or with mortality, in a large community cohort.

Methods

UK Biobank (England) participants with baseline assessment 2006–2010, followed in hospital discharge records to 2017 and death records to 2020. Demographic and preexisting common diagnoses association tested with hospitalized laboratory-confirmed COVID-19 (March 16 to April 26, 2020), alone or with mortality, in logistic models.

Results

Of 269 070 participants aged older than 65, 507 (0.2%) became COVID-19 hospital inpatients, of which 141 (27.8%) died. Common comorbidities in hospitalized inpatients were hypertension (59.6%), history of fall or fragility fractures (29.4%), coronary heart disease (21.5%), type 2 diabetes (type 2, 19. 9%), and asthma (17.6%). However, in models adjusted for comorbidities, age group, sex, ethnicity, and education, preexisting diagnoses of dementia, type 2 diabetes, chronic obstructive pulmonary disease, pneumonia, depression, atrial fibrillation, and hypertension emerged as independent risk factors for COVID-19 hospitalization, the first 5 remaining statistically significant for related mortality. Chronic kidney disease and asthma were risk factors for COVID-19 hospitalization in women but not men.

Conclusions

There are specific high-risk preexisting comorbidities for COVID-19 hospitalization and related deaths in community-based older men and women. These results do not support simple age-based targeting of the older population to prevent severe COVID-19 infections.

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  1. Version published to 10.1093/gerona/glaa183
  2. ScreenIT

    SciScore for 10.1101/2020.05.06.20092700: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: UK Biobank ethical approval was from the North West Multi-Centre Research Ethics Committee.
    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:
    Other limitations include the lack of details of the hospitalization and degrees of clinical severity within cases, although at the peak of the epidemic, access to intensive care units for older patients may partly reflect resource scarcity rather than severity of illness. Only a small proportion of the English population has thus far been exposed to the virus, but the group studied here were exposed and developed severe enough COVID-19 to be tested during hospitalization. Our case group is therefore relevant for assessing risk factors for severe COVID-19 in this older population. Our diagnostic data is derived from participant’s baseline interviews plus hospital discharge data until March 2017, so under-ascertainment of disease is likely, especially for recently diagnosed conditions, but the similarity to previous reports of the common conditions seen in COVID-19 patients suggests that our data are valid. Our results should have implications for preventive interventions, encouraging a more targeted approach prioritizing those older adults with specific risk factors, rather than adopting policies that use chronological age as a blanket indicator of risk. Our cohort evidence of specific risk factors may also help with avoiding potentially ‘ageist’ approaches to setting clinical priorities in over-stretched health systems [26]. Our findings of risks associated with less prominent conditions such as atrial fibrillation and CKD could help focus clinical research, as patients thes...

    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.
    • Thank you for including a protocol registration statement.

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  3. Version published to 10.1101/2020.05.06.20092700v1 on medRxiv