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Editorial
Observational data during the COVID-19 pandemic: opportunity with uncertainty
  1. Sam Straw,
  2. Klaus K Witte
  1. Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, West Yorkshire, UK
  1. Correspondence to Dr Klaus K Witte, Division of Cardiovascular and Diabetes Research, University of Leeds, Leeds LS16 5AR, UK; k.k.witte{at}leeds.ac.uk

https://doi.org/10.1136/heartjnl-2020-317486

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    This editorial refers to ‘Risk of Severe COVID-19 disease associated with angiotensin converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARB) in England: population cohort study including 8.3 million people’ by Hippisley et al published in Heart (2020).1

    The exponential growth of the coronavirus disease 2019 (COVID-19) pandemic has led to an unprecedented hunger for information in the medical community, the media and the wider public. In response, there has been an explosion of publications including more than 21 000 references on PubMed to ‘COVID-19’ since January 2020. Many of these are observational data of hospitalised patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exploring demographics or proposed risk factors including pre-existent cardiovascular disease (including diabetes mellitus),2 obesity3 and non-white ethnicity.4 The low rate of testing in communities in many countries (including the UK) means that data are mostly limited to hospitalised patients such that a valid picture of risk and outcomes relevant to the wider population is difficult to establish. Specifically, there remains uncertainty about the role of medications inhibiting the renin-angiotensin-aldosterone system (RAAS), based on initial reports of their association with poor outcomes in COVID-19.5 Possible explanations for this include underlying cardiovascular comorbidities in patients taking these medications and that the entry of SARS-CoV-2 into cells is permitted by the angiotensin-converting enzyme 2 receptor.6

    Can we trust observational data?

    Observational data are challenging to work with, requiring care to avoid the pitfalls of observed and hidden interactions and biases while balancing the need to make the results accessible to non-statisticians. The reliability of conclusions drawn from observational data depends on their interpretation and on the data themselves. Uncertainty in only one aspect can …

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    Footnotes

    • Contributors Both authors have contributed equally to the manuscript and both have reviewed the contents prior to submission.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Patient consent for publication Not required.

    • Provenance and peer review Commissioned; internally peer reviewed.

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