Channeling in the Use of Nonprescription Paracetamol and Ibuprofen in an Electronic Medical Records Database: Evidence and Implications

Abstract

Introduction: Over-the-counter analgesics such as paracetamol and ibuprofen are among the most widely used, and having a good understanding of their safety profile is important to public health. Prior observational studies estimating the risks associated with paracetamol use acknowledge the inherent limitations of these studies. One threat to the validity of observational studies is channeling bias, i.e. the notion that patients are systematically exposed to one drug or the other, based on current and past comorbidities, in a manner that affects estimated relative risk.

Objectives: The aim of this study was to examine whether evidence of channeling bias exists in observational studies that compare paracetamol with ibuprofen, and, if so, the extent to which confounding adjustment can mitigate this bias.

Study design and setting: In a cohort of 140,770 patients, we examined whether those who received any paracetamol (including concomitant users) were more likely to have prior diagnoses of gastrointestinal (GI) bleeding, myocardial infarction (MI), stroke, or renal disease than those who received ibuprofen alone. We compared propensity score distributions between drugs, and examined the degree to which channeling bias could be controlled using a combination of negative control disease outcome models and large-scale propensity score matching. Analyses were conducted using the Clinical Practice Research Datalink.

Results: The proportions of prior MI, GI bleeding, renal disease, and stroke were significantly higher in those prescribed any paracetamol versus ibuprofen alone, after adjusting for sex and age. We were not able to adequately remove selection bias using a selected set of covariates for propensity score adjustment; however, when we fit the propensity score model using a substantially larger number of covariates, evidence of residual bias was attenuated.

Conclusions: Although using selected covariates for propensity score adjustment may not sufficiently reduce bias, large-scale propensity score matching offers a novel approach to consider to mitigate the effects of channeling bias.

Trial registration: ClinicalTrials.gov NCT02830178.

Conflict of interest statement

Jesse A. Berlin, Daniel Fife, Amy Matcho, Kayur Patel, Martijn Schuemie, Joel Swerdel, Patrick Ryan, and Rachel Weinstein were full-time employees of Johnson & Johnson, or a subsidiary, at the time the study was conducted. Johnson & Johnson manufactures several medications that contain paracetamol or ibuprofen. Jesse A. Berlin, Daniel Fife, Amy Matcho, Kayur Patel, Martijn Schuemie, Joel Swerdel, Patrick Ryan, and Rachel Weinstein own stock, stock options and pension rights from the company.

Figures

Fig. 1

Flow of patients from Clinical Practice Research Datalink to analytic study population. CPRD Clinical Practice Research Datalink, yo years old, combo combination

Fig. 2

Distribution of propensity scores from a publication covariates and b the full set of covariates for any paracetamol compared with ibuprofen

Fig. 3

Absolute value standardized difference of the mean of the a publication covariates and b full set of data covariates available prior to and after matching on publication variable propensity scores, and c full set of data covariates available prior to matching and after matching on propensity scores using the full set of data covariates available for melanocytic nevus of skin