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Purpose: Medications are commonly used during pregnancy to manage pre-existing conditions and conditions that arise during pregnancy. However, not all medications are safe to use in pregnancy. This study utilized privacy-preserving record linkage (PPRL) to examine medications dispensed under the national Pharmaceutical Benefits Scheme (PBS) to pregnant women in Western Australia (WA) overall and by medication safety category., Methods: In this retrospective, cross-sectional, population-based study, state perinatal records (Midwives Notification Scheme) were linked with national PBS dispensing data using PPRL. Live and stillborn neonates born between 2012 and 2019 in WA were included. The proportion of pregnancies during which the mother was dispensed a PBS medication was calculated, overall and by medication safety category. Factors associated with PBS medication dispensing were examined using logistic regression., Results: PPRL linkage identified matching records for 97.4% of women with perinatal records. A total of 271 739 pregnancies were identified, with 158 585 (58.4%) pregnancies involving the dispensing of at least one PBS medication. Category A medications (those considered safe in pregnancy) were the most commonly dispensed (n = 119 126, 43.8%) followed by B3 (n = 51 135, 18.8%) and B1 (n = 42 388, 15.6%) medication (those with unknown safety). Over the study period, the dispensing of PBS medications in pregnancy increased (OR: 1.06, 95%CI: 1.06, 1.07). The strongest predictor of medication dispensing in pregnancy was pre-pregnancy dispensing (OR: 3.61, 95%CI: 3.54, 3.68). Other factors associated with medication use in pregnancy were smoking, older maternal age, obesity, and prior pregnancies., Conclusion: Privacy preserving record linkage provides a way to link cross-jurisdictional data while preserving patient confidentiality and data security. The dispensing of PBS medication in pregnancy was common and increased over time, with approximately 60% of women dispensed at least one medication during pregnancy., (© 2024 The Author(s). Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Background: Record linkage is an important tool for epidemiologists and health planners. Record linkage studies will generally contain some level of residual record linkage error, where individual records are either incorrectly marked as belonging to the same individual, or incorrectly marked as belonging to separate individuals. A key question is whether errors in linkage quality are distributed evenly throughout the population, or whether certain subgroups will exhibit higher rates of error. Previous investigations of this issue have typically compared linked and un-linked records, which can conflate bias caused by record linkage error, with bias caused by missing records (data capture errors)., Methods: Four large administrative datasets were individually de-duplicated, with results compared to an available 'gold-standard' benchmark, allowing us to avoid methodological issues with comparing linked and un-linked records. Results were compared by gender, age, geographic remoteness (major cities, regional or remote) and socioeconomic status., Results: Results varied between datasets, and by sociodemographic characteristic. The most consistent findings were worse linkage quality for younger individuals (seen in all four datasets) and worse linkage quality for those living in remote areas (seen in three of four datasets). The linkage quality within sociodemographic categories varied between datasets, with the associations with linkage error reversed across different datasets due to quirks of the specific data collection mechanisms and data sharing practices., Conclusions: These results suggest caution should be taken both when linking younger individuals and those in remote areas, and when analysing linked data from these subgroups. Further research is required to determine the ramifications of worse linkage quality in these subpopulations on research outcomes.

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.

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., (© The Author(s) 2016.)

Background: Record linkage techniques allow different data collections to be brought together to provide a wider picture of the health status of individuals. Ensuring high linkage quality is important to guarantee the quality and integrity of research. Current methods for measuring linkage quality typically focus on precision (the proportion of incorrect links), given the difficulty of measuring the proportion of false negatives., Objectives: The aim of this work is to introduce and evaluate a sampling based method to estimate both precision and recall following record linkage., Methods: In the sampling based method, record-pairs from each threshold (including those below the identified cut-off for acceptance) are sampled and clerically reviewed. These results are then applied to the entire set of record-pairs, providing estimates of false positives and false negatives. This method was evaluated on a synthetically generated dataset, where the true match status (which records belonged to the same person) was known., Results: The sampled estimates of linkage quality were relatively close to actual linkage quality metrics calculated for the whole synthetic dataset. The precision and recall measures for seven reviewers were very consistent with little variation in the clerical assessment results (overall agreement using the Fleiss Kappa statistics was 0.601)., Conclusions: This method presents as a possible means of accurately estimating matching quality and refining linkages in population level linkage studies. The sampling approach is especially important for large project linkages where the number of record pairs produced may be very large often running into millions.

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.)

Ensuring high linkage quality is important in many record linkage applications. Current methods for ensuring quality are manual and resource intensive. This paper seeks to determine the effectiveness of graph theory techniques in identifying record linkage errors. A range of graph theory techniques was applied to two linked datasets, with known truth sets. The ability of graph theory techniques to identify groups containing errors was compared to a widely used threshold setting technique. This methodology shows promise; however, further investigations into graph theory techniques are required. The development of more efficient and effective methods of improving linkage quality will result in higher quality datasets that can be delivered to researchers in shorter timeframes., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

Background: Within the field of record linkage, numerous data cleaning and standardisation techniques are employed to ensure the highest quality of links. While these facilities are common in record linkage software packages and are regularly deployed across record linkage units, little work has been published demonstrating the impact of data cleaning on linkage quality., Methods: A range of cleaning techniques was applied to both a synthetically generated dataset and a large administrative dataset previously linked to a high standard. The effect of these changes on linkage quality was investigated using pairwise F-measure to determine quality., Results: Data cleaning made little difference to the overall linkage quality, with heavy cleaning leading to a decrease in quality. Further examination showed that decreases in linkage quality were due to cleaning techniques typically reducing the variability - although correct records were now more likely to match, incorrect records were also more likely to match, and these incorrect matches outweighed the correct matches, reducing quality overall., Conclusions: Data cleaning techniques have minimal effect on linkage quality. Care should be taken during the data cleaning process.

Background: The Centre for Data Linkage (CDL) has been established to enable national and cross-jurisdictional health-related research in Australia. It has been funded through the Population Health Research Network (PHRN), a national initiative established under the National Collaborative Research Infrastructure Strategy (NCRIS). This paper describes the development of the processes and methodology required to create cross-jurisdictional research infrastructure and enable aggregation of State and Territory linkages into a single linkage "map"., Methods: The CDL has implemented a linkage model which incorporates best practice in data linkage and adheres to data integration principles set down by the Australian Government. Working closely with data custodians and State-based data linkage facilities, the CDL has designed and implemented a linkage system to enable research at national or cross-jurisdictional level. A secure operational environment has also been established with strong governance arrangements to maximise privacy and the confidentiality of data., Results: The development and implementation of a cross-jurisdictional linkage model overcomes a number of challenges associated with the federated nature of health data collections in Australia. The infrastructure expands Australia's data linkage capability and provides opportunities for population-level research. The CDL linkage model, infrastructure architecture and governance arrangements are presented. The quality and capability of the new infrastructure is demonstrated through the conduct of data linkage for the first PHRN Proof of Concept Collaboration project, where more than 25 million records were successfully linked to a very high quality., Conclusions: This infrastructure provides researchers and policy-makers with the ability to undertake linkage-based research that extends across jurisdictional boundaries. It represents an advance in Australia's national data linkage capabilities and sets the scene for stronger government-research collaboration.

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. [ABSTRACT FROM AUTHOR]