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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 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.

Major investment in record linkage infrastructure in Australia and internationally reflects the strategic value of high‐quality linked datasets. Dedicated record linkage units with secure environments and specialised linkage personnel have been established to support academic research, policy development and service design by government. The challenge for units creating linked data is to maximise linkage quality using a variety of matching and management techniques. However, it is also important that researchers understand processes around both data collection and linkage to ensure that they are aware of strengths and limitations of the data and methods used to bring together records. In this way, research study design can be optimised and potential for misinterpretation is reduced.Knowing that linkage error can affect interpretation of research findings and introduce bias highlights a need for routinely measuring and reporting linkage quality.3 Unfortunately, although there are a variety of standard matching and management techniques available, the assessment of linkage results varies.

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 technical challenges associated with national data linkage, and the extent of cross-border population movements, are explored as part of a pioneering research project. The project involved linking state-based hospital admission records and death registrations across Australia for a national study of hospital related deaths. Methods The project linked over 44 million morbidity and mortality records from four Australian states between 1st July 1999 and 31st December 2009 using probabilistic methods. The accuracy of the linkage was measured through a comparison with jurisdictional keys sourced from individual states. The extent of cross-border population movement between these states was also assessed. Results Data matching identified almost twelve million individuals across the four Australian states. The percentage of individuals from one state with records found in another ranged from 3-5 %. Using jurisdictional keys to measure linkage quality, results indicate a high matching efficiency (F measure 97 to 99 %), with linkage processing taking only a matter of days. Conclusions The results demonstrate the feasibility and accuracy of undertaking cross jurisdictional linkage for national research. The benefits are substantial, particularly in relation to capturing the full complement of records in patient pathways as a result of cross-border population movements. The project identified a sizeable ‘mobile’ population with hospital records in more than one state. Research studies that focus on a single jurisdiction will under-enumerate the extent of hospital usage by individuals in the population. It is important that researchers understand and are aware of the impact of this missing hospital activity on their studies. The project highlights the need for an efficient and accurate data linkage system to support national research across Australia.

Background Geocoding, the process of converting textual information describing a location into one or more digital geographic representations, is a routine task performed at large organizations and government agencies across the globe. In a health context, this task is often a fundamental first step performed prior to all operations that take place in a spatially-based health study. As such, the quality of the geocoding system used within these agencies is of paramount concern to the agency (the producer) and researchers or policy-makers who wish to use these data (consumers). However, geocoding systems are continually evolving with new products coming on the market continuously. Agencies must develop and use criteria across a number axes when faced with decisions about building, buying, or maintaining any particular geocoding systems. To date, published criteria have focused on one or more aspects of geocode quality without taking a holistic view of a geocoding system’s role within a large organization. The primary purpose of this study is to develop and test an evaluation framework to assist a large organization in determining which geocoding systems will meet its operational needs. Methods A geocoding platform evaluation framework is derived through an examination of prior literature on geocoding accuracy. The framework developed extends commonly used geocoding metrics to take into account the specific concerns of large organizations for which geocoding is a fundamental operational capability tightly-knit into its core mission of processing health data records. A case study is performed to evaluate the strengths and weaknesses of five geocoding platforms currently available in the Australian geospatial marketplace. Results The evaluation framework developed in this research is proven successful in differentiating between key capabilities of geocoding systems that are important in the context of a large organization with significant investments in geocoding resources. Results from the proposed methodology highlight important differences across all axes of geocoding system comparisons including spatial data output accuracy, reference data coverage, system flexibility, the potential for tight integration, and the need for specialized staff and/or development time and funding. Such results can empower decisions-makers within large organizations as they make decisions and investments in geocoding systems.

Background Record linkage techniques are widely used to enable health researchers to gain event based longitudinal information for entire populations. The task of record linkage is increasingly being undertaken by specialised linkage units (SLUs). In addition to the complexity of undertaking probabilistic record linkage, these units face additional technical challenges in providing record linkage ‘as a service’ for research. The extent of this functionality, and approaches to solving these issues, has had little focus in the record linkage literature. Few, if any, of the record linkage packages or systems currently used by SLUs include the full range of functions required. Methods This paper identifies and discusses some of the functions that are required or undertaken by SLUs in the provision of record linkage services. These include managing routine, on-going linkage; storing and handling changing data; handling different linkage scenarios; accommodating ever increasing datasets. Automated linkage processes are one way of ensuring consistency of results and scalability of service. Results Alternative solutions to some of these challenges are presented. By maintaining a full history of links, and storing pairwise information, many of the challenges around handling ‘open’ records, and providing automated managed extractions are solved. A number of these solutions were implemented as part of the development of the National Linkage System (NLS) by the Centre for Data Linkage (part of the Population Health Research Network) in Australia. Conclusions The demand for, and complexity of, linkage services is growing. This presents as a challenge to SLUs as they seek to service the varying needs of dozens of research projects annually. Linkage units need to be both flexible and scalable to meet this demand. It is hoped the solutions presented here can help mitigate these difficulties.

Record linkage is the process of bringing together data relating to the same individual within and between different datasets. These integrated datasets provide diverse and rich resources for researchers without the cost associated with additional data collection. By their nature, record linkage systems deal with large volumes of data and require complex organizational and technical infrastructure. Bringing together information from different sources often requires many different organizations to collaborate and share data, which presents challenges around data privacy and confidentiality. Various processes and protocols have been developed to protect the privacy of individuals during the record linkage process. These include data governance procedures covering people, processes and information technology, role separation and restricted data flows. Combinations of these are used to mitigate risks to privacy by limiting access to certain information. In addition, privacy-preserving record linkage techniques can be utilized to further reduce the risk to privacy, by removing all personal identifying information from linkage protocols. This chapter reviews current practices, processes and developments for maintaining security and privacy as applied in existing record linkage centres. Models for role separation and data flows are outlined and evaluated, and requirements for an effective privacy-preserving record linkage protocol are described.

Volleyball players are often subject to micro-traumatisms of the heel fat pad and ankle injuries. Recently, mat-based proprioceptive training has assumed a key role in recovery from these disorders. Therefore, this proof-of-principle study aimed to assess the efficacy of proprioceptive mat training on plantar pressures and athletic performance in volleyball players. The participants included adult semi-professional volleyball players allocated into two groups: an experimental group, with mat-based proprioceptive and balance training, and a control group, with a sham protocol. For the outcome, we evaluated the barefoot plantar pressure, performing an analysis on a baropodometric resistive platform. The countermovement jump and squat jump were measured using an inertial measurement unit. Nineteen subjects were included in the two groups: the active proprioceptive group (n = 10) or the control group (n = 9). The results show a more uniform redistribution of loads with pressure hindfoot relief in the experimental group compared to the control group (p = 0.021, RBC = 0.67). Moreover, we observed a significant increase in peak landing force and high concentric power development in the experimental group compared to the controls. Focused proprioceptive management provided hindfoot load attenuation by stimulating higher peaks of concentric force in the experimental group compared to the sham group. Even though the study included a small sample, the results obtained in this proof-of-principle study suggest a positive role of proprioceptive stimulation in the inter-seasonal scenario for volleyball players to improve their jump performance and reduce the micro-traumatisms of the heel fat pad and the ankle injury rate. However, further studies performed on larger samples are needed to confirm these preliminary results.

Development of biotherapeutics is hampered by the inherent risk of immunogenicity, which requires extensive clinical assessment and possible re-engineering efforts for mitigation. The focus in the pre-clinical phase is to determine the likelihood of developing treatment-emergent anti-drug antibodies (TE-ADA) and presence of pre-existing ADA in drug-naïve individuals as risk-profiling strategies. Pre-existing ADAs are routinely identified during clinical immunogenicity assessment, but their origin and impact on drug safety and efficacy have not been fully elucidated. One specific class of pre-existing ADAs has been described, which targets neoepitopes of antibody fragments, including Fabs, VH, or VHH domains in isolation from their IgG context. With the increasing number of antibody fragments and other small binding scaffolds entering the clinic, a widely applicable method to mitigate pre-existing reactivity against these molecules is desirable. Here is described a structure-based engineering approach to abrogate pre-existing ADA reactivity to the C-terminal neoepitope of VH(H)s. On the basis of 3D structures, small modifications applicable to any VH(H) are devised that would not impact developability or antigen binding. In-silico B cell epitope mapping algorithms were used to rank the modified VHH variants by antigenicity; however, the limited discriminating capacity of the computational methods prompted an experimental evaluation of the engineered molecules. The results identified numerous modifications capable of reducing pre-existing ADA binding. The most efficient consisted of the addition of two proline residues at the VHH C-terminus, which led to no detectable pre-existing ADA reactivity while maintaining favorable developability characteristics. The method described, and the modifications identified thereby, may provide a broadly applicable solution to mitigate immunogenicity risk of antibody-fragments in the clinic and increase safety and efficacy of this promising new class of biotherapeutics.