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.

BackgroundThe 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”.MethodsThe 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.ResultsThe 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.ConclusionsThis 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.

There has been substantial growth in Data Linkage (DL) activities in recent years. This reflects growth in both the demand for, and the supply of, linked or linkable data. Increased utilisation of DL "services" has brought with it increased need for impartial information about the suitability and performance capabilities of DL software programs and packages. Although evaluations of DL software exist; most have been restricted to the comparison of two or three packages. Evaluations of a large number of packages are rare because of the time and resource burden placed on the evaluators and the need for a suitable "gold standard" evaluation dataset. In this paper we present an evaluation methodology that overcomes a number of these difficulties. Our approach involves the generation and use of representative synthetic data; the execution of a series of linkages using a pre-defined linkage strategy; and the use of standard linkage quality metrics to assess performance. The methodology is both transparent and transportable, producing genuinely comparable results. The methodology was used by the Centre for Data Linkage (CDL) at Curtin University in an evaluation of ten DL software packages. It is also being used to evaluate larger linkage systems (not just packages). The methodology provides a unique opportunity to benchmark the quality of linkages in different operational environments.

Research on diversity in offending patterns is crucial given ongoing polemical debates concerning the relationship between gender, ethnicity and crime. Competing theoretical perspectives, limited supporting evidence and inconclusive or contradictory findings from prior research point to the need for more empiricallygrounded, generalizable research which compare and contrast offending patterns across and within gender and ethnic groups. The current study applies a semiparametric group-based modelling approach to a large, longitudinal dataset of offenders to determine if, and how, offending trajectories vary across gender and ethnic sub-groups. Findings suggest that some trajectory attributes (e.g. number and shape) are shared across gender/ethnic groups, while other trajectory attributes (height, peak age) are not. An exploratory investigation of the risk factors associated with trajectory group membership finds that few of the available factors discriminate between trajectories either within or across gender/ethnic offender groups. The findings fill a knowledge gap, particularly in relation to offending patterns in Australia. Invariance in trajectory risk factors present a challenge to taxonomic theories of offending.