  Understanding the association between injury severity and patients' potential
for recovery is crucial to providing better care for patients with traumatic
brain injury (TBI). Estimation of this relationship requires clinical
information on injury severity, patient demographics, and healthcare
utilization, which are often obtained from separate data sources. Because of
privacy and confidentiality regulations, these data sources do not include
unique identifiers to link records across data sources. Record linkage is a
process to identify records that represent the same entity across data sources
in the absence of unique identifiers. These processes commonly rely on
agreement between variables that appear in both data sources to link records.
However, when the number of records in each file is large, this task is
computationally intensive and may result in false links. Blocking is a data
partitioning technique that reduces the number of possible links that should be
considered. Healthcare providers can be used as blocks in applications of
record linkage with healthcare datasets. However, providers may not be uniquely
identified across files. We propose a Bayesian record linkage procedure that
simultaneously performs block-level and record-level linkage. This iterative
approach incorporates the record-level linkage within block pairs to improve
the accuracy of the block-level linkage. Subsequently, the algorithm improves
record-level linkage using the accurate partitioning of the linkage space
through blocking. We demonstrate that our proposed method provides improved
performance compared to existing Bayesian record linkage methods that do not
incorporate blocking. The proposed procedure is then used to merge registry
data from the National Trauma Data Bank with Medicare claims data to estimate
the relationship between injury severity and TBI patients' recovery.
