Abstract TP374: Data Linkage is Effective for Improving the Available Data for Stroke: An Example from the Australian Stroke Clinical Registry.
Introduction: Multiple data collections can be a burden for clinicians. In 2009, the Australian Stroke Clinical Registry (AuSCR) was established by non-government and research organizations to provide quality of care data unavailable for acute stroke admissions. We show here the reliability of linking complimentary registry data with routinely collected hospital discharge data submitted to governmental bodies. Hypothesis: A high quality linkage with a > 90% rate is possible, but requires multiple personal identifiers common to each dataset. Methods: AuSCR identifying variables included date of birth (DoB), Medicare number, first name, surname, postcode, gender, hospital record number, hospital name and admission date. The Victorian Department of Health emergency department (ED) and hospital discharge linked dataset has most of these, with first name truncated to the first 3 digits, but no surname. Common data elements of AuSCR patients registered at a large hospital in Melbourne, Victoria (Australia) between 15 June 2009 and 31 December 2010 were submitted to undergo stepwise deterministic linkage. Results: The Victorian AuSCR sample had 818 records from 788 individuals. Three steps with 1) Medicare number, postcode, gender and DoB (80% matched); 2) hospital number/admit date; and 3) ED number/visit date were required to link AuSCR data with the ED and hospital discharge data. These led to an overall high quality linkage of >99% (782/788) of AuSCR patients, including 731/788 for ED records and 736/788 for hospital records. Conclusion: Multiple personal identifiers from registries are required to achieve reliable linkage to routinely collected hospital data. Benefits of these linked data include the ability to investigate a broader range of research questions than with a single dataset. Characters with spaces= 1941 (limit is 1950)