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CC BY 4.0 · Appl Clin Inform 2022; 13(04): 901-909
DOI: 10.1055/s-0042-1757174
AIDH Summit 2022

Unlocking Potential within Health Systems Using Privacy-Preserving Record Linkage: Exploring Chronic Kidney Disease Outcomes through Linked Data Modelling

David Lim
1   Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
,
Sean Randall
1   Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
,
Suzanne Robinson
1   Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
2   Deakin Health Economics, Deakin University, Burwood, Victoria, Australia
,
Elizabeth Thomas
1   Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
3   Medical School, The University of Western Australia, Perth, Western Australia, Australia
,
James Williamson
4   WA Department of Health, Perth, Western Australia, Australia
,
Aron Chakera
3   Medical School, The University of Western Australia, Perth, Western Australia, Australia
5   Renal Unit, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
,
Kathryn Napier
6   Curtin Institute for Computation, Curtin University, Perth, Western Australia, Australia
,
Carola Schwan
7   WA Country Health Service, Perth, Western Australia, Australia
,
Justin Manuel
7   WA Country Health Service, Perth, Western Australia, Australia
,
Kim Betts
1   Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
,
Chris Kane
8   WA Primary Health Alliance, Perth, Western Australia, Australia
,
James Boyd
1   Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
9   La Trobe University, Melbourne, Bundoora, Victoria, Australia
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Abstract

Background Chronic kidney disease (CKD) is a major global health problem that affects approximately one in 10 adults. Up to 90% of individuals with CKD go undetected until its progression to advanced stages, invariably leading to death in the absence of treatment. The project aims to fill information gaps around the burden of CKD in the Western Australian (WA) population, including incidence, prevalence, rate of progression, and economic cost to the health system.

Methods Given the sensitivity of the information involved, the project employed a privacy preserving record linkage methodology to link data from four major pathology providers in WA to hospital records, to establish a CKD registry with continuous medical record for individuals with biochemical specification for CKD. This method uses encrypted personal identifying information in a probability-based linkage framework (Bloom filters) to help mitigate risk while maximizing linkage quality.

Results The project developed interoperable technology to create a transparent CKD data catalogue which is linkable to other datasets. This technology has been designed to support the aspirations of the research program to provide linked de-identified pathology, morbidity, and mortality data that can be used to derive insights to enable better CKD patient outcomes. The cohort includes over 1 million individuals with creatinine results over the period 2002 to 2021.

Conclusion Using linked data from across the care continuum, researchers are able to evaluate the effectiveness of service delivery and provide evidence for policy and program development. The CKD registry will enable an innovative review of the epidemiology of CKD in WA. Linking pathology records can identify cases of CKD that are missed in the early stages due to disaggregation of results, enabling identification of at-risk populations that represent targets for early intervention and management.

Keywords

privacy preserving record - linkage accuracy - chronic kidney disease

Note

All authors also showed consent for publication and the supplementary data that supports the findings of this review will be available upon submission and publication.


Protection of Human and Animal Subjects

This project does not involve any animal subjects, and has obtained ethics approval from Curtin's Human Research Ethics Committee (HREC) for pathology datasets (HRE2019–0303), and ethics approval from the DOH WA HREC for hospital datasets (RGS000000183).




Publication History

Received: 29 April 2022

Accepted: 28 July 2022

Article published online:
28 September 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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