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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., Competing Interests: None declared., (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/).)

Background: Privacy preserving record linkage (PPRL) methods using Bloom filters have shown promise for use in operational linkage settings. However real-world evaluations are required to confirm their suitability in practice., Methods: An extract of records from the Western Australian (WA) Hospital Morbidity Data Collection 2011-2015 and WA Death Registrations 2011-2015 were encoded to Bloom filters, and then linked using privacy-preserving methods. Results were compared to a traditional, un-encoded linkage of the same datasets using the same blocking criteria to enable direct investigation of the comparison step. The encoded linkage was carried out in a blinded setting, where there was no access to un-encoded data or a 'truth set'., Results: The PPRL method using Bloom filters provided similar linkage quality to the traditional un-encoded linkage, with 99.3% of 'groupings' identical between privacy preserving and clear-text linkage., Conclusion: The Bloom filter method appears suitable for use in situations where clear-text identifiers cannot be provided for linkage., (© 2022. The Author(s).)

Background: Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters., Methods: Our method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data., Results: Linkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher than the F-measure using calculated probabilities. Further, the threshold estimation yielded results for F-measure that were only slightly below the highest possible for those probabilities., Conclusions: The method appears highly accurate across a spectrum of datasets with varying degrees of error. As there are few alternatives for parameter estimation, the approach is a major step towards providing a complete operational approach for probabilistic linkage of privacy-preserved datasets.

Background: Integrating medical data using databases from different sources by record linkage is a powerful technique increasingly used in medical research. Under many jurisdictions, unique personal identifiers needed for linking the records are unavailable. Since sensitive attributes, such as names, have to be used instead, privacy regulations usually demand encrypting these identifiers. The corresponding set of techniques for privacy-preserving record linkage (PPRL) has received widespread attention. One recent method is based on Bloom filters. Due to superior resilience against cryptographic attacks, composite Bloom filters (cryptographic long-term keys, CLKs) are considered best practice for privacy in PPRL. Real-world performance of these techniques using large-scale data is unknown up to now., Methods: Using a large subset of Australian hospital admission data, we tested the performance of an innovative PPRL technique (CLKs using multibit trees) against a gold-standard derived from clear-text probabilistic record linkage. Linkage time and linkage quality (recall, precision and F-measure) were evaluated., Results: Clear text probabilistic linkage resulted in marginally higher precision and recall than CLKs. PPRL required more computing time but 5 million records could still be de-duplicated within one day. However, the PPRL approach required fine tuning of parameters., Conclusions: We argue that increased privacy of PPRL comes with the price of small losses in precision and recall and a large increase in computational burden and setup time. These costs seem to be acceptable in most applied settings, but they have to be considered in the decision to apply PPRL. Further research on the optimal automatic choice of parameters is needed.

Record linkage typically involves the use of dedicated linkage units who are supplied with personally identifying information to determine individuals from within and across datasets. The personally identifying information supplied to linkage units is separated from clinical information prior to release by data custodians. While this substantially reduces the risk of disclosure of sensitive information, some residual risks still exist and remain a concern for some custodians. In this paper we trial a method of record linkage which reduces privacy risk still further on large real world administrative data. The method uses encrypted personal identifying information (bloom filters) in a probability-based linkage framework. The privacy preserving linkage method was tested on ten years of New South Wales (NSW) and Western Australian (WA) hospital admissions data, comprising in total over 26 million records. No difference in linkage quality was found when the results were compared to traditional probabilistic methods using full unencrypted personal identifiers. This presents as a possible means of reducing privacy risks related to record linkage in population level research studies. It is hoped that through adaptations of this method or similar privacy preserving methods, risks related to information disclosure can be reduced so that the benefits of linked research taking place can be fully realised., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Background: The statistical linkage key (SLK-581) is a common tool for record linkage in Australia, due to its ability to provide some privacy protection. However, newer privacy-preserving approaches may provide greater privacy protection, while allowing high-quality linkage. Objective: To evaluate the standard SLK-581, encrypted SLK-581 and a newer privacy-preserving approach using Bloom filters, in terms of both privacy and linkage quality. Method: Linkage quality was compared by conducting linkages on Australian health datasets using these three techniques and examining results. Privacy was compared qualitatively in relation to a series of scenarios where privacy breaches may occur. Results: The Bloom filter technique offered greater privacy protection and linkage quality compared to the SLK-based method commonly used in Australia. Conclusion: The adoption of new privacy-preserving methods would allow both greater confidence in research results, while significantly improving privacy protection. [ABSTRACT FROM AUTHOR]