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Understanding the association between injury severity and patients' potentialfor recovery is crucial to providing better care for patients with traumaticbrain injury (TBI). Estimation of this relationship requires clinicalinformation on injury severity, patient demographics, and healthcareutilization, which are often obtained from separate data sources. Because ofprivacy and confidentiality regulations, these data sources do not includeunique identifiers to link records across data sources. Record linkage is aprocess to identify records that represent the same entity across data sourcesin the absence of unique identifiers. These processes commonly rely onagreement between variables that appear in both data sources to link records.However, when the number of records in each file is large, this task iscomputationally intensive and may result in false links. Blocking is a datapartitioning technique that reduces the number of possible links that should beconsidered. Healthcare providers can be used as blocks in applications ofrecord linkage with healthcare datasets. However, providers may not be uniquelyidentified across files. We propose a Bayesian record linkage procedure thatsimultaneously performs block-level and record-level linkage. This iterativeapproach incorporates the record-level linkage within block pairs to improvethe accuracy of the block-level linkage. Subsequently, the algorithm improvesrecord-level linkage using the accurate partitioning of the linkage spacethrough blocking. We demonstrate that our proposed method provides improvedperformance compared to existing Bayesian record linkage methods that do notincorporate blocking. The proposed procedure is then used to merge registrydata from the National Trauma Data Bank with Medicare claims data to estimatethe relationship between injury severity and TBI patients' recovery.

A Bayesian Multi-Layered Record Linkage Procedure to Analyze Functional Status of Medicare Patients with Traumatic Brain Injury