Linkage of Australian national registry data using a statistical linkage key

Tim G Coulson,Gil Shardey,Chris Reid,Sue Huckson,Michael Bailey,David Pilcher,Jenni Williams-Spence,Shaila Chavan

doi:10.1186/s12911-021-01393-1

Abstract

BackgroundData from clinical registries may be linked to gain additional insights into disease processes, risk factors and outcomes. Identifying information varies from full names, addresses and unique identification codes to statistical linkage keys to no direct identifying information at all. A number of databases in Australia contain the statistical linkage key 581 (SLK-581). Our aim was to investigate the ability to link data using SLK-581 between two national databases, and to compare this linkage to that achieved with direct identifiers or other non-identifying variables.MethodsThe Australian and New Zealand Society of Cardiothoracic Surgeons database (ANZSCTS-CSD) contains fully identified data. The Australian and New Zealand Intensive Care Society database (ANZICS-APD) contains non-identified data together with SLK-581. Identifying data is removed at participating hospitals prior to central collation and storage. We used the local hospital ANZICS-APD data at a large single tertiary centre prior to deidentification and linked this to ANZSCTS-CSD data. We compared linkage using SLK-581 to linkage using non-identifying variables (dates of admission and discharge, age and sex) and linkage using a complete set of unique identifiers. We compared the rate of match, rate of mismatch and clinical characteristics between unmatched patients using the different methods.ResultsThere were 1283 patients eligible for matching in the ANZSCTS-CSD. 1242 were matched using unique identifiers. Using non-identifying variables 1151/1242 (92.6%) patients were matched. Using SLK-581, 1202/1242 (96.7%) patients were matched. The addition of non-identifying data to SLK-581 provided few additional patients (1211/1242, 97.5%). Patients who did not match were younger, had a higher mortality risk and more non-standard procedures vs matched patients. The differences between unmatched patients using different matching strategies were small.ConclusionAll strategies provided an acceptable linkage. SLK-581 improved the linkage compared to non-identifying variables, but was not as successful as direct identifiers. SLK-581 may be used to improve linkage between national registries where identifying information is not available or cannot be released.

Highlights

Data from clinical registries may be linked to gain additional insights into disease processes, risk factors and outcomes
In this study our aim was to investigate the ability to link data using statistical linkage key (SLK)-581 between the ANZICS-Adult patient database (APD) and ANZSCTS-Cardiac surgery database (CSD), and to compare this linkage to that achieved with direct identifiers or other non-identifying variables
There were a total of 1283 patient procedures contained within the corresponding ANZSCTS-CSD. 47/5179 patients in the Intensive care unit (ICU) database had missing medical record number and names due to data error

Summary

Introduction

Data from clinical registries may be linked to gain additional insights into disease processes, risk factors and outcomes. There are an increasing number of clinical quality registries in Australia, with at least 40 currently in operation [1]. Data is collated with identifying information at the source hospitals, but identifiers are removed before transfer to the central registry. Data from these registries may be linked using other variables. Primarily during data entry, often with single digit errors in the resulting database This precludes matching accurately on all variables. The probability of achieving a match increases, the probability of a mismatch increases

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