Meeting the challenge of data linkage for special populations

IntroductionA file’s overall linkage rate may hide significant bias in unlinked records. Newborns, for example, may represent a small percentage of a file but a large proportion of unlinked records. This creates challenges for researchers focused on the affected populations. Decreasing bias is a clear goal for data linkage science.
 Objectives and ApproachA standard spine-based linkage process with a typical set of identifiers was used to link perinatal records in British Columbia. This included multiple iterations and some manual linkage, stopping when there were only minor improvements. Despite high overall linkage rates, poor rates existed for babies who were stillborn or died near birth. Given research interests in stillbirth, there was a desire to use a creative approach to improve linkage for that sub-population. The solution was to expand the scope of the spine linkage to include birth and stillbirth records from other files, e.g. hospital discharge and Vital Statistics Registrations.
 ResultsOriginal linkage methods produce overall baby linkage rates of 98.1% but only 3.9% of records discharged to death/stillbirth were linked. Revised methods provide a small chang