Opportunities and challenges in using epidemiologic methods to monitor drug safety in the era of large automated health databases

Abstract

Healthcare databases have been used in the past four decades to identify, refine, and evaluate potential safety signals of marketed medical products. Critics have challenged this research because data are from secondary sources and because some published studies have lacked robust methods for exposure and outcome definition and failed to adequately control for biases. We review the history of healthcare databases used in pharmacovigilance for quantifying adverse outcomes associated with therapeutics, methods to improve the quality of this research, and best practices for pharmacoepidemiologic studies. Drug and vaccine safety studies increasingly use information from multiple healthcare databases, with analyses that aim to keep patient-level identifying data with local research custodians. Analytic methods, including high-dimensional exposure propensity scores, use large numbers of variables to reduce confounding and further anonymize patient data. However, due to gaps in and complexities of the available databases, the value of the research depends on experts with knowledge about the clinical context (e.g., how products are prescribed and taken, how outcomes are diagnosed and recorded, what risk factors must be considered), understanding the nuances of individual databases and the clinical practice patterns they represent, and utilizing study designs that minimize bias, particularly confounding by medication indication.