Connecting the Data: Population-Scale Linkage to Study Prescription Stimulants and Overdose Risk

Thomas M. Menino Convention & Exhibition Center 

Background: Evaluating the population-level impact of prescription stimulants on overdose risk requires longitudinal data spanning prescribing, healthcare utilization, and mortality-sources that are rarely integrated at scale. Legal, administrative, and technical barriers often limit linkage of prescription drug monitoring program (PDMP) data with claims, hospital, emergency department, and vital records. We describe a probabilistic linkage framework developed to support an NIH-funded study of prescription stimulants and overdose outcomes.

Linkage Methods: We used FasLink to probabilistically link records across the administrative health data sets. Individuals were assigned persistent, study-specific anonymous identifiers, enabling longitudinal follow-up across insurance transitions and care settings.

Conclusion:
The linked data support time-varying definitions of stimulant exposure and capture fatal and non-fatal overdose outcomes, including polysubstance involvement. This framework demonstrates a scalable, reproducible approach for linking administrative health data to support pharmacoepidemiologic surveillance of controlled substances beyond opioids.

Probabilistic linkage

PDMP

all-payer claims databases

prescription stimulant

overdose surveillance

population health analytics 

Mental Health Statistics Section