Improving patient safety and pharmacovigilance: Methods using observational data and cohort studies
Background: The Medicare Prescription Drug, Improvement and Modernization Act of 2003 introduced the Part D benefit for outpatient medications for Medicare. Anticipated increases in use of prescription drugs, coupled with the concern for drug safety, fuels the need for drug safety data beyond those from randomized controlled trials or voluntary reporting schemes. Objective: To improve methods for using claims data to examine patient safety and pharmacovigilance issues, we developed a data analytic framework and methods for pharmacoepidemiologic research on adverse drug events (ADEs) using population-based claims and administrative data sources. We tested our framework and methods by performing pilot analyses using drugs for dementia, including Alzheimer’s disease, as the illustrative case. Design: We used an open cohort design with data structured in a longitudinal format to measure exposure accurately. We adjusted for confounding using logistic regression and for treatment selection using inverse probability weights. Setting: Because Medicare prescription drug claims are not yet available, we used pharmacy and medical claims and death records from the State of Utah Medicaid programs. Patients: Medicaid patients had to be ages 50 and older, be identified in the Medicaid enrollment table, and have at least one pharmacy or medical claim recorded between January 1, 2003, and December 31, 2005. Measurements: We reconstructed patients’ drug regimens and established therapeutic course through drug claims. We measured ADEs through the medical claims and death records for three types of outcomes: death, expected adverse events (gastrointestinal and psychological disorders), and novel events that are rare but serious events (hematological and hepatic disorders). Results: We were able to develop a database that allowed us to characterize drug exposure and evaluate the association between drug exposure and three types of adverse drug effects; these included death, expected events, and idiosyncratic events. Analysis of early versus late exposure within the treated cohort demonstrated a highly significant early risk for episodes of care for hematological diagnoses (incidence rate ratio, 2.86; 95 percent confidence interval, 1.6-5.11). Conclusions: Researchers can easily apply our framework for working with observational data, particularly pharmacoepidemiologic databases; they can readily adopt or adapt our methods and stepwise approach (i.e., data integrity, exposure and persistence, and ADE analysis). Data from Medicaid, employer, insurer, and (eventually) Medicare claims can be used to examine specific drug classes and individual drugs for known and unknown ADEs. The ADE framework of initially examining mortality, expected events, and then novel reactions that are potentially severe but unlikely events will foster understanding of drug safety and generate hypotheses for future investigations. The clinical findings concerning antidementia drugs, because of their limited nature (e.g., one state, relatively small numbers of ADEs), should be used for generating hypotheses and signals for further investigation, not for clinical decisionmaking.