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The Population Health Research Network (PHRN) is an Australian national data linkage infrastructure that links a wide range of health and human services data in privacy-preserving ways. The data linkage infrastructure enables researchers to apply for access to routinely collected, linked, administrative data from the six states and two territories which make up the Commonwealth of Australia, as well as data collected by the Australian Government. The PHRN is a distributed network where data is collected and managed at the respective jurisdictional and/or cross-jurisdictional levels. As a result, access to linked data from multiple jurisdictions requires complex approval processes. This paper describes Australia's approach to enabling access to linked data from multiple jurisdictions. It covers the identification of, and agreement to, a minimum set of data items to be included in a unified national application form, the development and implementation of a national online application system and the harmonisation of business processes for cross-jurisdictional research projects. Utilisation of the online application system and the ongoing challenges of data linkage across jurisdictions are discussed. Changes to the data custodian and ethics committee approval criteria were out of scope for this project.

Introduction The Tasmanian Data Linkage Unit (TDLU) undertook a complex data linkage project in 2019 linking public and private pathology data to five disparate health datasets. Having linked pathology data previously, the unit was aware of the challenges it faced linking a large dataset covering a fourteen-year time span. The aim of this study was to use data-linkage to develop a Tasmanian dataset to quantify the burden and distribution of chronic kidney disease, including identifying barriers to dialysis treatment services. Objectives and Approach A cohort was selected from public and private providers of pathology services in Tasmania from 2004-2017 to support the establishment of a comprehensive researchable dataset. A linkage plan was developed that included detailed processes for cleaning and de-duplicating the pathology data prior to linkage. The larger private pathology dataset comprised 3.9 million records and data cleaning strategies were implemented. De-duplication created extensive clerical review and methods to reduce this work were devised and implemented as part of the linkage process. Results De-duplication based on exact matches reduced the size of the dataset from 3.9 million to just over 520,000 individuals. Internal linkage of the dataset resulted in approximately 47,000 ‘groups’ eligible for review. Structured Query Language (SQL) queries were constructed and the number of groups eligible for review decreased by 42%. Further analysis was conducted, which resulted in an appropriate ‘cut off’ threshold being determined for clerical review and an estimate of false positive links remaining was calculated. Conclusion / Implications Methods of reducing the amount of manual clerical review can be incorporated into a linkage design when there is a thorough understanding of the characteristics and content of the dataset to be linked. The methods used for this linkage project will be utilised for future projects using pathology data.

As a member of the Population Health Research Network Australia, being an Australian collaboration established to support the use of linked data for research and other purposes, the Tasmanian Data Linkage Unit [TDLU) provides linked-data services in Australia’s smallest state, and as part of the Menzies Institute for Medical Research at the state’s only University, the University of Tasmania. The TDLU works in close collaboration with the Tasmanian Government Department of Health and other key stakeholders both in Tasmania and Australia representing government, education, research, and the community sector. The TDLU is one of the newest data linkage services in Australia, and the smallest node of the PHRN having operated for almost nine years by less than three full time equivalent staff. However, despite its size and relative maturity as a provider of linked-data services, the TDLU continues to grow the number of datasets linked on a routine and ad-hoc basis, the number of projects completed, the size of its Master Linkage Map and number of ‘keys’ stored in this Map. The TDLU places high-emphasis on security, privacy preservation, innovation, quality assurance, stakeholder engagement and providing responsive and exemplary services to users of linked-data.}

Background with rationale The burden of chronic kidney disease (CKD) has increased rapidly in Australia over recent years. The financial cost of treating people with end-stage kidney failure by conventional models of dialysis or transplantation represents a substantial healthcare cost and is likely to continue to increase in coming years. Tasmania, the smallest state of Australia and its only island state, has the highest burden of chronic disease nationally, including kidney disease. The aim of this study was to use data-linkage to develop a state-wide dataset to quantify the burden and distribution of CKD, including identifying barriers to dialysis treatment services. Methods/Approach The Tasmanian Data Linkage Unit (TDLU) used a complex data linkage design comprising seven disparate datasets representing public and private pathology, public hospital admitted patient and emergency department data, cancer records, dialysis and transplant records and death notifications. A cohort was selected from public and private providers of pathology services in the state to support the establishment of a comprehensive researchable dataset. The datasets spanned the period 2004-2017 and included linkage of both state and national data. Results The study cohort comprised just under 490,000 individuals in the Tasmanian population from the two pathology datasets, with a combined total of 1,347,00 total links made across all datasets. Individual unit records were geocoded according to the Australian Statistical Geographic Standard (2011) with over 92% of the 374,000 unique addresses identified in the public pathology dataset geocoded to address level. Conclusion The final researchable dataset compiled by the research team following linkage is providing an enormously powerful asset to help answer questions specific to CKD in Tasmania, and that in turn is anticipated to result in greater access to services, improved care and better patient outcomes.

Background with rationale In recent years, rapid advancements in Geographic Information Systems (GIS) software has provided a range of users with an array of options to visually represent multiple digital data assets and for a range of purposes. The research community is increasingly able to represent complex data assets spatially to identify patterns in the population specific to burden of disease or for other reasons. Main Aim To develop a methodology and software solution to effectively attribute spatial coordinates at a unit record level to data held in the Tasmanian Data Linkage Units (TDLU) Master Linkage Map (MLM). A further requirement was to support an integrated process of clerical review to enable incorrect or missing address information to be corrected. Methods/Approach The Australian government makes available a geocoded address database for end users including Australian businesses, education and government. Known as the G-NAF (Geocoded National Address File), the database contains in excess of 30 tables for over 13 million principal Australian addresses. The TDLU developed software that extracts data held in its MLM and processes using the G-NAF to derive and store multiple spatial data elements. Results The TDLU has achieved in excess of 92% automated geocoding to address level for all records held in its MLM. Extensive metadata is produced, analysed and stored and used for many reasons including quality assurance. The release of spatial information, together with linkage keys, is strictly managed in accordance with Human Research Ethic Committee and custodian approvals. Conclusion The TDLU’s custom designed spatial information system has proven effective in both automatically allocating spatial coordinates for records held in its MLM, and for supporting review of missing or incorrect address information. Generating and storing spatial data at a unit record level enables the effective allocation of a range of geographic elements, population data and indices including measures of socio-economic disadvantage.

ABSTRACT Objectives The Tasmanian Data Linkage Unit (TDLU) was established through the University of Tasmania in 2011 with the first dataset imported to its Master Linkage Map (MLM) during 2014. Tasmania an island state of Australia, has a population of approximately 516,000. From the TDLU’s earliest inception, it was deemed important to build a high quality linkage spine comprising key administrative data representative of significant state health and related datasets to support quality population level research. Approach The TDLU has embraced a model of continual quality and process enhancement as a determined strategy to support ongoing business improvement. Initial linkage approaches utilised ‘traditional’ methods of reviewing record pairs within an upper and lower confidence range. This approach resulted in false record pairs with high confidence levels being linked (false positives) and true record pairs at lower confidence levels not linked (false negatives). To improve linkage quality, the TDLU has continually refined and modified its clerical review methodology with a specialist software module developed to identify specific record attributes within groups that require the group to be manually reviewed and resolved. A range of SQL queries have also been developed to identify incorrect links and further enhance the linkage quality of the MLM. Results The linkage quality tools implemented have led to improved clerical review and quality assurance processes which in turn have increased the overall quality of the linkage spine. The ‘targeted’ method of clerical review provides easy identification of false positive records, particularly those with high confidence scores such as twins and husband/wife combinations. The review of groups at lower confidence levels has minimised the rate of false negative pairs however further refinement of tools is required to minimise the time spent on reviewing these groups. The clerical review software module has equipped staff with the necessary information to make informed and timely decisions when reviewing groups of records. Detailed documentation is maintained for each linkage project providing continual feedback for system and process improvements as the linkage spine increases in size. Conclusion The process of clerical review and quality assurance requires a commitment to continual refinement of tools and techniques resulting in a higher quality linkage spine and a reduction in the total time and resource required to link datasets.

ABSTRACT Objectives The Tasmanian Data Linkage Unit (TDLU) routinely collects detailed statistical data specific to time spent preparing datasets for linkage, performing clerical review and quality assurance for all datasets added to its Master Linkage Map (MLM). This information is used for a range of functions including resource planning, performance monitoring, preparation of data linkage quotes, data quality reporting and identifying potential process improvements. Approach Detailed metadata for each dataset imported to the MLM is collected to determine likely linkage quality and to estimate clerical review resource requirements. Attributes including the time period covered, data completeness, data quality and accuracy, the population characteristics and an understanding of how the data was collected are documented. In addition to this metadata, clerical reviewer staff record the number of record pairs and groups reviewed within confidence ranges and the time taken to complete review. Results The data collected provides strong evidence that administrative datasets with more problematic characteristics take longer to prepare, clerically review and quality assure. Tasmanian public hospital emergency presentations and admitted patient datasets, which are high in quality and completeness, have required less clerical review (8% and 6% respectively of total records) compared with perinatal baby (30% of total records clerically reviewed and significant additional time spent on quality assurance) and a small cohort of ambulance patients where 78% of all records were manually reviewed. The ambulance and perinatal baby datasets contained incomplete name, address and hospital identifier fields, and spelling and data errors were evident in the ambulance dataset as a result of the data capture method (over the phone with critical time constraints). Conclusion The underlying dataset quality and completeness impacts to a significant degree the time taken to clerically review a dataset, and in turn the quality of links made in a MLM. Additionally, each new dataset added to the MLM introduces an additional layer of complexity necessitating additional processing time due to the number of record comparisons made. Having an agreed set of formal processes to prepare administrative data prior to data linkage, and understanding how source data is collected and processed, is crucial to obtaining high levels of quality within the MLM.