Accuracy and completeness of patient pathways – the benefits of national data linkage in Australia

Abstract

BackgroundThe technical challenges associated with national data linkage, and the extent of cross-border population movements, are explored as part of a pioneering research project. The project involved linking state-based hospital admission records and death registrations across Australia for a national study of hospital related deaths.MethodsThe project linked over 44 million morbidity and mortality records from four Australian states between 1st July 1999 and 31st December 2009 using probabilistic methods. The accuracy of the linkage was measured through a comparison with jurisdictional keys sourced from individual states. The extent of cross-border population movement between these states was also assessed.ResultsData matching identified almost twelve million individuals across the four Australian states. The percentage of individuals from one state with records found in another ranged from 3-5 %. Using jurisdictional keys to measure linkage quality, results indicate a high matching efficiency (F measure 97 to 99 %), with linkage processing taking only a matter of days.ConclusionsThe results demonstrate the feasibility and accuracy of undertaking cross jurisdictional linkage for national research. The benefits are substantial, particularly in relation to capturing the full complement of records in patient pathways as a result of cross-border population movements.The project identified a sizeable ‘mobile’ population with hospital records in more than one state. Research studies that focus on a single jurisdiction will under-enumerate the extent of hospital usage by individuals in the population. It is important that researchers understand and are aware of the impact of this missing hospital activity on their studies.The project highlights the need for an efficient and accurate data linkage system to support national research across Australia.

Linking disease registries and nationwide healthcare administrative databases: the French renal epidemiology and information network (REIN) insight

“…Moreover, it would require additional legal authorizations and, arguably, a disproportionate logistic deployment. In Australia, Boyd et al, compared their linkage of national morbidity-mortality data with the linkage produced by well-established statebased regional linkage facilities (the gold standard) that use extensive manual reviews [30]. To validate different deterministic linkage rules between a registry and Medicare administrative data, Setoguchi et al defined their gold standard as the results of a matching rule containing both direct (beneficiary ID) and indirect identifiers (hospital admission date, hospital ID) [31].…”

GUILD: GUidance for Information about Linking Data sets†

“…Transcription errors, misspellings and missing data in particular can cause false- and missed-matches. 13 , 22 , 23 Information about data cleaning rules and the extent of missing data or errors in identifiers can help identify common scenarios that cause linkage error. 13 Information should also be provided about any preprocessing of source data sets involving internal linkage of multiple records to the same entity or to remove duplicate records (Table 1 , Step 1, 1b(iii)).…”

“…However, data linkers in Wales (SAIL), Scotland (eDRIS), Australia, the US and Canada, demonstrate that probabilistic linkage is feasible at scale. 23 , 33 , 34 …”

Improving the Estimation of Risk-Adjusted Grouped Hospital Standardized Mortality Ratios Using Cross-Jurisdictional Linked Administrative Data: A Retrospective Cohort Study

“…These keys allowed the data custodians from each jurisdiction to provide relevant de-identified extractions of clinical and death data for analysis. The details of the cross-jurisdictional linkage process involved in this study are presented elsewhere ( 16 ).…”