Revisiting Disproportionality: Prescription-Adjusted and TF-IDF-Inspired Metrics for Post-Market ADR Detection

Adverse drug reaction (ADR) detection in post-market surveillance is limited by underreporting and the absence of drug utilization data. This study proposes three signal detection metrics-including a TF-IDF-inspired method (EF-IDF) and two prescription-adjusted measures-to improve pharmacovigilance, using ADHD medications and the FDA Adverse Event Reporting System (FAERS) as a case study. We standardized drug and ADR entities, integrated prescription data from Bloomberg Intelligence, and evaluated performance across 12 ingredients using precision-at-10%. EF-IDF achieved the highest mean precision (0.56), significantly outperforming traditional PRR and prescription-adjusted metrics. Correlation analysis showed that prescription volume negatively influenced all metrics, particularly EF-IDF, underscoring the role of contextual factors in ADR detection. Despite limitations in temporal granularity and the lack of prescription data specific to ADHD use, this work demonstrates the value of bias-aware, data-integrated methods for signal detection. Future directions include temporal modeling and more targeted identification of ADHD-related prescriptions using public data.