In pharmacoepidemiologic studies of COVID-19, there were concerns about bias from residual confounding. We applied high-dimensional propensity scores (HDPS) to a case study investigating the role of inhaled corticosteroids (ICS) in COVID-19 to adjust for unmeasured confounding. We selected patients with chronic obstructive pulmonary disease on 01 March 2020 from Clinical Practice Research Datalink (CPRD) Aurum, comparing ICS/LABA/(+-LAMA) and LABA/LAMA users. ICS effects on the outcomes COVID-19 hospitalisation and death were assessed through weighted and unweighted Cox proportional hazards models. HDPS were estimated from primary care clinical records, prescriptions and hospitalisations. SNOMED-CT codes and dictionary of medicines and devices codes from CPRD Aurum were mapped to International Classification of Disease 10th revision codes and British National Formulary paragraphs respectively. We estimated propensity scores (PS) combining prespecified and HDPS covariates, selecting the top 100, 250, 500, 750 and 1000 covariates ranked by confounding potential. When excluding triple therapy users, the conventional PS-weighted estimates showed weak evidence of increased risk of COVID-19 hospitalisation among ICS users (HR 1.19 (95% CI 0.92-1.54)). Results varied slightly based on the number of covariates included in HDPS (HR using 100 HDPS covariates 1.01 (95% CI 0.76-1.33), HR using 250 HDPS covariates 1.24 (95% CI 0.83-1.87)). For COVID-19 death, conventional PS-weighted models showed weak evidence of harm of ICS when excluding triple therapy users (HR 1.24 (95% CI 0.87-1.75)). HDPS-weighting moved estimates toward the null, suggesting no effect of ICS (HR using 250 HDPS covariates excluding triple therapy 1.08 (95% CI 0.73- 1.59)). HDPS may have provided better confounding control for COVID-19 deaths and may be able to partially compensate for suboptimal comparison groups. HDPS results can be sensitive to the number of covariates included, highlighting the importance of sensitivity analyses.

MPB is funded by a GSK PhD studentship to investigate the application of quantitative bias analysis in observational studies of COVID-19. IJD has unrestricted grants from and shares in GSK. AS is employed by LSHTM on a fellowship funded by GSK. CTR and JQ report no conflicts of interest.

https://catalogues.ema.europa.eu/node/3194/administrative-details

https://www.cprd.com/approved-studies/investigating-biases-observational-studies-inhaled-corticosteroids-and-risk-covid

MB is funded by a GSK PhD studentship to undertake this work.

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The study was approved by the London School of Hygiene and Tropical Medicine Research Ethics Committee (Reference: 27896) and the Independent Scientific Advisory Committee of the UK Medicines and Healthcare Products Regulatory Agency (approval number: 22_001876).

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