Comparing person-level matching algorithms to identify risk across disparate datasets among patients with a controlled substance prescription: retrospective analysis.

ABSTRACT:

Background

The opioid epidemic in the United States has precipitated a need for public health agencies to better understand risk factors associated with fatal overdoses. Matching person-level information stored in public health, medical, and human services datasets can enhance the understanding of opioid overdose risk factors and interventions.

Objective

This study compares approximate match versus exact match algorithms to link disparate datasets together for identifying persons at risk from an applied perspective.

Methods

This study used statewide prescription drug monitoring program (PDMP), arrest, and mortality data matched at the person-level using an approximate match and 2 exact match algorithms. Impact of matching was assessed by analyzing 3 independent concep

SUBMITTER: Ferris LM 

PROVIDER: S-EPMC9097759 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature