Body mass index and risk of COVID-19 diagnosis, hospitalisation, and death: a population-based multi-state cohort analysis including 2,524,926 people in Catalonia, Spain

  1. Martina Recalde
  2. Andrea Pistillo
  3. Sergio Fernandez-Bertolin
  4. Elena Roel
  5. Maria Aragon
  6. Heinz Freisling
  7. Daniel Prieto-Alhambra
  8. Edward Burn
  9. Talita Duarte-Salles

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Abstract

Objective

To investigate associations between body mass index (BMI) and risk of COVID-19 diagnosis, hospitalisation with COVID-19, and COVID-19-related death, accounting for potential effect modification by age and sex.

Design

Population-based cohort study.

Setting

Primary care records covering >80% of the Catalonian population (Spain), linked to region-wide testing, hospital, and mortality records from March to May 2020.

Participants

People aged ≥18 years with at least one measurement of weight and height from the general population and with at least one year of prior medical history available.

Main outcome measures

Cause-specific hazard ratios (HR) with 95% confidence intervals for each outcome.

Results

Overall, 2,524,926 participants were followed up for a median of 67 days. A total of 57,443 individuals were diagnosed with COVID-19, 10,862 were hospitalised with COVID-19, and 2,467 had a COVID-19-related death. BMI was positively associated with being diagnosed as well as hospitalised with COVID-19. Compared to a BMI of 22kg/m 2 , the HR (95%CI) of a BMI of 31kg/m 2 was 1.22 (1.19-1.24) for COVID-19 diagnosis, and 1.88 (1.75-2.03) and 2.01 (1.86-2.18) for hospitalisation without and with a prior outpatient diagnosis, respectively. The relation between BMI and risk of COVID-19 related death was J-shaped. There was a modestly higher risk of death among individuals with BMIs≤19 kg/m 2 and a more pronounced increasing risk for BMIs ≥37 kg/m 2 and ≥40 kg/m 2 among those who were previously hospitalised with COVID-19 and diagnosed with COVID-19 in outpatient settings, respectively. The increase in risk for COVID-19 outcomes was particularly pronounced among younger patients.

Conclusions

There is a monotonic association between BMI and COVID-19 infection and hospitalisation risks, but a J-shaped one with mortality. More research is needed to unravel the mechanisms underlying these relationships.

Summary boxes

Section 1: What is already known on this topic

  • A high body mass index (BMI) has previously been associated in a linear and non-linear fashion with an increased risk of multiple health outcomes; these associations may vary by individual factors such as age and sex.

  • Obesity has been identified as a risk factor for COVID-19 severity and mortality. However, the role of general adiposity in relation to COVID-19 outcomes has mostly been studied by dichotomizing BMI (below or above 30 kg/m 2 ) or by a diagnostic code indicating obesity.

  • Two studies have investigated BMI (as a continuous variable) in relation to COVID-19 outcomes, accounting for non-linearity: one conducted in a tested population sample of the UK Biobank found BMI is related in a dose-response manner with the risk of testing positive for COVID-19; another conducted in a hospital setting in New York reported a J-shaped association between BMI and the risk of intubation or death. These studies were limited in sample size and were prone to collider bias due to the participant’s restriction to tested and hospitalised patients. No studies have described the association between BMI and COVID-19 outcomes across the natural history of the disease (from no disease to symptomatic disease, hospitalisation, and mortality) using data from diverse health settings.

Section 2: What this study adds

  • We provide a comprehensive analysis of the association between BMI and the course of the COVID-19 disease in the general population of a Spanish region during the first wave of the pandemic, using linked data capturing outpatient clinical diagnoses, RT-PCR test results, hospitalisations, and mortality (inside and outside of the hospital setting).

  • We found that BMI is positively associated with being diagnosed as well as hospitalised with COVID-19, and is linked in a J-shaped fashion with the risk of COVID-19 related death.

  • The association between BMI and COVID-19 related outcomes is modified by age and sex; particularly, the risk of COVID-19 outcomes related to increased BMI is higher for those aged between 18 and 59 years, compared to those in older age groups.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.11.25.20237776: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Clinical Research Ethics Committee of the IDIAPJGol (project code: 20/070-PCV).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableWe extracted participants’ sex (female, male), age (in years) at index date and smoking status (never, former or current smoker).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We selected these conditions based on their relevance to the obesity and COVID-19 research fields and their availability in the OMOP-CDM mapped version of the SIDIAP database.[
    SIDIAP
    suggested: None
    We compared the baseline characteristics of the included individuals to those of the excluded due to unavailability of BMI, smoking status or the MEDEA deprivation index information using standardized mean differences (SMDs).
    MEDEA
    suggested: (MEDEA, RRID:SCR_013356)

    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 of this study: This study has several strengths. To our knowledge, this is the first longitudinal study to investigate the association between BMI and the course of the COVID-19 disease containing individual detailed BMI information and incident COVID-19 outcomes recorded in diverse healthcare settings from a large and representative population. The possibility to investigate COVID-19 trajectories in a single and sufficiently powered dataset, including systematic investigation of non-linearity and effect modification, is also a major strength. Further, the SIDIAP is representative of the Catalan general population, which suggests our findings are generalisable to Catalonia as well as to comparable regions. The results were robust when we explored the violation of the models’ assumptions, the possibility of selection bias due to missing data and exposure misclassification which is also a strong asset. This study also has weaknesses. Firstly, this observational study only includes COVID-19 diagnoses of individuals who interacted with the health system. Especially in the first wave of the pandemic, testing was mainly restricted to severe cases of COVID-19. Although we aimed to reduce this bias by also including clinical diagnoses of COVID-19 (i.e., symptomatic individuals who were diagnosed with COVID-19 by general practitioners but were not confirmed by a positive test for SARS-CoV-2), we could not avoid missing asymptomatic or paucisymptomatic patients...

    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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  2. Version published to 10.1101/2020.11.25.20237776v1 on medRxiv