Citation

Shan M, Thomas KS, Gutman R. Biostatistics 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Oxford University Press)

DOI

10.1093/biostatistics/kxac016

PMID

35579386

Abstract

Understanding associations between injury severity and postacute care recovery for patients with traumatic brain injury (TBI) is crucial to improving care. Estimating these associations requires information on patients' injury, demographics, and healthcare utilization, which are dispersed across multiple data sets. Because of privacy regulations, unique identifiers are not available to link records across these data sets. Record linkage methods identify records that represent the same patient across data sets in the absence of unique identifiers. With a large number of records, these methods may result in many false links. Health providers are a natural grouping scheme for patients, because only records that receive care from the same provider can represent the same patient. In some cases, providers are defined within each data set, but they are not uniquely identified across data sets. We propose a Bayesian record linkage procedure that simultaneously links providers and patients. The procedure improves the accuracy of the estimated links compared to current methods. We use this procedure to merge a trauma registry with Medicare claims to estimate the association between TBI patients' injury severity and postacute care recovery.

Language: en

Keywords

Risk factors; Bayesian analysis; Blocking; File linkage; Multiple imputation