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The BARI-LIFESTYLE trial is a randomized controlled trial evaluating the efficacy of a post-surgery nutritional and behavioural tele-counselling, and supervised exercise programme to maximize the health benefits of bariatric surgery. Due to the coronavirus disease 2019 (COVID-19) pandemic, the in-person supervised exercise component had to be converted to remote tele-exercise. However, patients' acceptability of this method of exercise provision is unknown. Between 3 and 6 months following bariatric surgery, 13 adults participated in weekly, structured, 60-min supervised exercise classes delivered via Zoom by a trained exercise therapist. A total of 12 participants (n = 8 female), with a mean age of 46.3 (range 33-63) years, who had undergone either sleeve gastrectomy (n = 8) or Roux-en-Y gastric bypass (n = 4) surgery, participated in one-to-one semi-structured interviews following the tele-exercise classes. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis. Participants described how the tele-exercise classes helped them to cope with the changes to their lives brought about by the COVID-19 pandemic. Participants found the tele-exercise schedule, content and intensity to be acceptable, and were satisfied with the privacy, security and safety of the technology and classes. Professional supervision and guidance from an exercise therapist were described as central to the tele-exercise provision. Importantly, participation in the tele-exercise provided physical, emotional and social benefits. Few participants reported barriers to participation. Overall, the tele-exercise classes were deemed acceptable and compared favourably to in-person exercise classes.

IntroductionWhile the quantity and type of datasets used by data linkage projects is growing, there remain some datasets that are ‘not available’ or ‘hard to access’ by researchers and linkers, either due to legal/regulatory constraints restricting the release of personally identifying information or because of privacy or reputational concerns. Advances in privacy-preserving record linkage methods (e.g. PPRL-Bloom) have made it possible to overcome this impasse. These techniques aim to provide strong privacy protection while still maintaining high linkage quality. PPRL-Bloom methods are being used in practice. The Centre for Data Linkage (CDL) at Curtin University has been involved in several PPRL linkage and evaluation projects using real-world data. As the methods are relatively new, published information on achievable linkage quality in real-world scenarios is limited. Objectives and ApproachWe present and describe several real-world applications of privacy preserving record linkage (PPRL-Bloom) where the quality of the linkage could be ascertained. In each case, data was linked ‘blind’; that is, without linkers having access to the original personal identifiers at any stage, or having any additional information about the records. Evaluations include a linkage of state-based morbidity and mortality records, a linkage of a number of general practice datasets to morbidity and emergency records, and a linkage of a range of state-based non-health administrative data, including education, police, housing, birth and child protection records. ResultsThe privacy preserving record linkage performed admirably, with very high-quality results across all evaluations. Conclusion / ImplicationsPrivacy preserving linkage is a useful and innovative methodology that is currently being used in real world projects. The results of these evaluation suggest it can be an appropriate linkage tool when legal or other constraints block release of personally identifying information to third party linkage units.

IntroductionDuring 2019, the Western Australian (WA) government and Curtin University’s Centre for Data Linkage (CDL) created a large, de-identified researchable database – the Social Investment Data Resource (SIDR) – to support government in delivering targeted early interventions to young offenders and their families to reduce the likelihood of re-offending (the Target 120 program). Objectives and ApproachSIDR brings together administrative data from health, education, justice, child protection, disability and housing sectors. The linked, de-identified data provides an invaluable resource for actuarial assessment and social investment analytics to assess long-term costs and benefits of the Target 120 program. SIDR also provides an invaluable tool for academic research. SIDR adopted a distributed linkage model where linkage workload was shared between the Department of Health Data Linkage Branch who create and maintain the WA Data Linkage System (WADLS) and the CDL. Design elements of the model included a common spine (embedded into the infrastructure of both groups), methods for leveraging quality from WADLS, and inclusion of family relationships data from the WA Family Connections database. The linkage model within SIDR uses a combination of traditional and privacy-preserving record linkage (PPRL) methods. PPRL does not require release of personal identifiers; instead, data is irreversibly hashed prior to release for probabilistic linkage. ResultsThrough cooperation (distributed linkage) and innovation (a mix of traditional and PPRL linkage), the project has delivered a large, linked, cross-sectoral data resource for policymakers and researchers. Sharing of the linkage workload maximised the capacity and unique capabilities of each linkage unit. PPRL enabled ‘hard to get’ datasets from justice to be included. SIDR is being updated in 2020. Conclusion / ImplicationsSIDR provides a resource for whole-of-government policy development, service evaluation, academic research and social investment analytics for T120 and beyond. The SIDR linkage model has potential for adaptation and use elsewhere.

IntroductionPrivacy-preserving Record Linkage (PPRL) is a record linkage technique that can increase the security of personal information. PPRL uses techniques of either hashing identifiers (where exact matches are required) or Blooming identifiers (where partial matches are of interest before they are provided for linkage. Objectives and ApproachWe use LinXmart software to evaluate performance of PPRL linkage compared to linkage using clear text identifiers. The test linkage dataset is one that is routinely linked (N=2,672,257) at our linkage centre. The population spine (N=8,440,442) includes a record for every person who has resided in British Columbia, Canada over the past 30 years. Weights were determined using LinXmart’s implementation of the Expectation Maximization (EM) algorithm. For both linkages, accepted links were the highest-weighted candidate link with a weight above the threshold suggested by EM estimation. We compare linkage rates and quality and differences in weight and threshold estimations between clear-text and PPRL linkages ResultsClear-text and PPRL methods resulted in 97% and 90% linkage rates, respectively. Approximately 67% of records in the linked datasets contained a nominally unique ID. Records with a unique ID linked at higher rates (>99% for both clear-text and PPRL) while the linkage rate for records missing the ID differed substantially (92% /70% for clear-text/PPRL). Comparing PPRL linkage to the clear-text linkage, we obtain F-measures of 0.99 and 0.80 for records with and without the unique ID, respectively. Conclusion / ImplicationsLinkage performance may be attributable to differences in comparison operators between the two methods. Bloomed fields compared with Dice coefficient allow for partial matching but may not be as sensitive as clear-text string comparisons. Numerical comparisons in PPRL are exact matches while clear-text comparisons allow for more sophisticated matching. Further refinements in PPRL are being explored to improve these results.

The Centre for Data Linkage (CDL) was established at Curtin University, Western Australia, to develop infrastructure to enable cross-jurisdictional record linkage in Australia. The CDL’s operating model makes use of the ‘separation principle’, with content data typically provided to researchers directly by the data custodian; jurisdictional linkage where available are used within the linkage process. Along with conducting record linkage, the team has also invested in establishing a research programme in record linkage methodology and in developing modern record linkage software which can handle the size and complexity of today’s workloads. The Centre has been instrumental in the development of practical methods for privacy-preserving record linkage, with this methodology now regularly used for real-world linkages. While the promise of a nation-wide linkage system in Australia has yet to be met, distributed models provide a potential solution.

BackgroundGeneral practice is a rich source of health data for research. It is an important resource which can be used to improve patient management, reduce costs and improve patient outcomes. Traditionally, the challenge has been around access to general practice data which remains hard to ‘join up’. This abstract describes technology developed to support aspirations of the MedicineInsight program to provide linked de-identified general practice data that can be used to derive insights to enable better patient outcomes. Main AimThe aim of this project was to use real-world data to identify technical, logistical and analytical requirements throughout the linkage process. Logistical aims covered the negotiation, approval and data acquisition processes, as well as data linkage and data delivery aspects performed by technical and data service stakeholders. Methods/Approach Given the sensitivity of the information involved, the project employed a privacy preserving record linkage methodology. This method uses encrypted personal identifying information (Bloom filters) in a probability-based linkage framework to help mitigate risk while maximising linkage quality. Existing MedicineInsight systems were extended to automatically generate encoded linkage data at each general practice. Pilot linkages were then used to validate the capability/capacity of CDL infrastructure to create secure extensible linked general practice datasets. ResultsThe project has successfully developed interoperable technology to create a transparent data catalogue which is linkable to other datasets. This technology has been embedded with MedicineInsight systems and results of the pilot linkages are being evaluated. The project will make recommendations to enable consistent delivery of linkage services across care settings. ConclusionOutcomes from the project will improve delivery of record linkage services to the health and broader research community. Using linked data from across the care continuum, researchers will be able to evaluate the effectiveness of service delivery and provide evidence for policy and programme development.

Background/rationaleThe Western Australian (WA) government and the Centre for Data Linkage (CDL) at Curtin University are creating a large, de-identified researchable database – the Social Investment Data Resource (SIDR) – to support a key government initiative called Target 120 (T120). T120 delivers targeted early interventions to young offenders and their families to reduce the likelihood of re-offending. Main AimThe SIDR brings together de-identified data from across government to be used for actuarial assessment and social investment analytics to assess long-term costs and benefits of T120 interventions. MethodsSIDR adopts a distributed linkage model where linkage workload is shared between the Department of Health Data Linkage Branch who curate WA Data Linkage System (WADLS) and the CDL. Design elements of the model included a common spine (embedded into the infrastructure of both groups), methods for leveraging quality from WADLS, and inclusion of family relationships data from the WA Family Connections database. The linkage model uses a combination of traditional and privacy-preserving record linkage (PPRL) methods. PPRL does not require release of personal identifiers; instead, data is irreversibly hashed prior to release for probabilistic linkage. The resultant SIDR repository has been designed to be securely and strictly managed. Access is by authorised, approved users only. ResultsUse of a distributed linkage model, coupled with traditional and PPRL methods, is an innovative yet pragmatic way of delivering data linkage services to a large, cross-sectoral research project. PPRL methods enable inclusion of otherwise excluded datasets in the project. Sharing of workload harnesses linkage capacity and capabilities across the state. The SIDR includes health data, education records, justice, child protection, disability and housing data. ConclusionSIDR provides a resource for whole-of-government policy development, service evaluation, academic research and social investment analytics for T120 and beyond. The SIDR distributed linkage model has potential for adaptation and use elsewhere.

ObjectivesThe management of patients with long-standing type 2 diabetes and obesity receiving insulin therapy (IT) is a substantial clinical challenge. Our objective was to examine the effect of a low-energy total diet replacement (TDR) intervention versus standardized dietetic care in patients with long-standing type 2 diabetes and obesity receiving IT.Research design and methodsIn a prospective randomized controlled trial, 90 participants with type 2 diabetes and obesity receiving IT were assigned to either a low-energy TDR (intervention) or standardized dietetic care (control) in an outpatient setting. The primary outcome was weight loss at 12 months with secondary outcomes including glycemic control, insulin burden and quality of life (QoL).ResultsMean weight loss at 12 months was 9.8 kg (SD 4.9) in the intervention and 5.6 kg (SD 6.1) in the control group (adjusted mean difference −4.3 kg, 95% CI −6.3 to 2.3, pConclusionsPatients with advanced type 2 diabetes and obesity receiving IT achieved greater weight loss using a TDR intervention while also reducing or stopping IT and improving glycemic control and QoL. The TDR approach is a safe treatment option in this challenging patient group but requires maintenance support for long-term success.Trial registration numberISRCTN21335883.

Introduction The need for increased privacy protection in data linkage has driven the development of privacy-preserving record linkage (PPRL) techniques. A popular technique using Bloom filters with cryptographic analyses, modifications, and hashing variations to optimise privacy has been the focus of much research in this area. With few applications of Bloom filters within a probabilistic framework, there is limited information on whether approximate matches between Bloom filtered fields can improve linkage quality. Objectives In this study, we evaluate the effectiveness of three approximate comparison methods for Bloom filters within the context of the Fellegi-Sunter model of recording linkage: Sørensen–Dice coefficient, Jaccard similarity and Hamming distance. Methods Using synthetic datasets with introduced errors to simulate datasets with a range of data quality and a large real-world administrative health dataset, the research estimated partial weight curves for converting similarity scores (for each approximate comparison method) to partial weights at both field and dataset level. Deduplication linkages were run on each dataset using these partial weight curves. This was to compare the resulting quality of the approximate comparison techniques with linkages using simple cut-off similarity values and only exact matching. Results Linkages using approximate comparisons produced significantly better quality results than those using exact comparisons only. Field level partial weight curves for a specific dataset produced the best quality results. The Sørensen-Dice coefficient and Jaccard similarity produced the most consistent results across a spectrum of synthetic and real-world datasets. Conclusion The use of Bloom filter similarity comparisons for probabilistic record linkage can produce linkage quality results which are comparable to Jaro-Winkler string similarities with unencrypted linkages. Probabilistic linkages using Bloom filters benefit significantly from the use of similarity comparisons, with partial weight curves producing the best results, even when not optimised for that particular dataset.

Information and communication technologies (ICTs) promise to increase government transparency, accountability, and civic engagement by providing information about government activities and enabling electronic interaction with community members. Drawing from a content analysis of 500 U.S. city websites from two points in time, we examine the prevalence and growth of ICTs used b y municipalities ranging in population from 25,000 to 250,000 from 2010 to 2014. We present the results of the content analysis, outlining the prevalence of five categories of ICTs – information tools, e-services, utility, transparency, and civic engagement features - and investigating how type of ICT adoption is related to city size, form of government, and technical capacity. We then rank the 500 cities based on 27 ICT features and note how those rankings have changed over the four-year period. We conclude with a discussion of municipal use of ICTs and what this means for governments and the communities they serve.

IntroductionRecord linkage is inherently uncertain, with all linkages containing some amount of false positive and false negative errors. Previous results have suggested that linkage error may not be evenly distributed throughout the population, with particular subgroups exhibiting higher rates of linkage error. Record linkage is inherently uncertain, with all linkages containing Objectives and ApproachThis study investigated the distribution of linkage error using four large-scale Australian administrative datasets; hospital admissions datasets from Western Australia and New South Wales, and emergency presentation datasets from New South Wales and South Australia. Each dataset had been previously de-duplicated to a very high standard, with large scale manual review taking place; these results were used as our truth set. Each dataset was linked using probabilistic record linkage with results (precision and recall) compared by gender, age, geographic indices of remoteness and socioeconomic status. ResultsResults were highly dataset dependent. Consistent findings were lower linkage quality found for individuals living in remote locations, and lower linkage quality in those in the youngest category (those born after 1980). Some datasets showed lower linkage quality for females, for those in middle age as compared to the elderly, and for those with lower socioeconomic status. The differences in linkage quality found were typically small. Changes in threshold settings had generally no effect on the relationship between sociodemographic characteristics and linkage quality. Conclusion/ImplicationsLinkage studies focussing on younger individuals and those in remote areas may have greater uncertainty regarding their results. Targeting efforts by linkage units may be required to ensure even distribution of linkage errors. Further research is required into investigating how linkage errors effect research outcomes.

IntroductionBusinesses worldwide are increasingly adopting the storage, compute and analytical services provided by cloud computing. Yet, few operational linkage units are keeping pace with this world of technological change - most use legacy systems approaching their limits with the rapidly increasing size and range of datasets now required for linkage. Objectives and ApproachTo meet the demands of linkage for the near future, it is important that new solutions for linkage consider the services provided by public cloud infrastructure for compute, storage and analytics. We examined Platform as a Service (PaaS) offerings for use in the development of a cost-effective cloud model for scalable, privacy-preserving record linkage (PPRL). PPRL techniques were adapted to maximise the quality of linkage and to automate as much of the process as possible. Finally, a prototype was created to demonstrate the capabilities and potential of the model. ResultsWe present our cloud model for PPRL, a platform for record linkage that provides rapid scaling of resources to meet demand, and the results of how our prototype performed on massive datasets. Conclusion/ImplicationsThe application of record linkage using relatively inexpensive cloud infrastructure represents a significant step towards providing an efficient and scalable record linkage service to researchers and government. Larger datasets can be linked efficiently, including national or cross-jurisdictional datasets, with little investment in private infrastructure, and improved turnaround times for researchers.

IntroductionRecord linkage units around the world use probabilistic linkage techniques for routine linkage of large datasets. It is widely known how probabilities are converted to agreement and disagreement weights for each field, yet there has been little exploration of the methodology to optimally convert field similarity scores into partial weights. Objectives and ApproachString similarity comparators such as Jaro-Winkler are commonly used in traditional linkage, other comparators such as the Sorenson Dice coefficient, Jaccard similarity and Hamming distance are used in alternative privacy-preserving record linkage techniques. Determining partial weights to apply at each level of similarity is a non-trivial task. However, both types of linkages would greatly benefit from similarity to weight functions for each field that maximises the accuracy of the linkage. We evaluated several methods for computing partial agreement weights and applied these to synthetic datasets with varying levels of corruption. We then evaluated the methods on real administrative datasets. ResultsExact comparisons can miss matches where typographical errors or misspellings produce small changes in value. Similarity comparisons can reduce the number of missed matches, but may also increase the number of incorrect matches. Various results of the partial agreement methods on Jaro-Winkler, Sorenson Dice coefficient, Jaccard similarity and Hamming distance comparators will be presented. A generic function to convert similarity values to weights, created from synthetic data, can be used on most datasets with a greatly improved result in linkage quality. However, maximising the linkage quality requires the creation of similarity-to-weight functions that are optimised for each dataset. Conclusion/ImplicationsAccuracy in record linkage is vital for the correct analysis of linked data. It is even more critical in privacy-preserving record linkage where the ability for clerical review is limited. Optimised functions for converting similarities to partial weights can significantly improve the quality of linkage and should not be overlooked.

Background The CRADLE-3 trial is a stepped-wedge randomised controlled trial aiming to reduce maternal mortality and morbidity by implementing a novel vital sign device (CRADLE Vital Sign Alert) and training package into routine maternity care in 10 low-income sites. The MRC Guidance on complex interventions proposes that interventions and implementation strategies be shaped by early phase piloting and development work. We present the findings of a three-month mixed-methodology feasibility study for this trial, describe how this was informed by the MRC guidance and the study design was refined. Methods The fidelity, dose, feasibility and acceptability of implementation and training materials were assessed in three representative non-trial sites (Zimbabwe, Ethiopia, India) using multiple-choice questionnaires, evaluation of clinical management (action log), healthcare provider (HCP) semi-structured interviews and focus groups 4–10 weeks after implementation. Simultaneously, the 10 sites included in the main trial (eight countries) collected primary outcome data to inform the power calculation and randomisation allocation and assess the feasibility of data collection. Results The package was implemented with high fidelity (85% of HCP trained, n = 204). The questionnaires indicated a good understanding of device use with 75% of participants scoring > 75% (n = 97; 90% of those distributed). Action logs were inconsistently completed but indicated that the majority of HCP responded appropriately to abnormal results. From 18 HCP interviews and two focus groups it was widely reported that the intervention improved capacity to make clinical decisions, escalate care and make appropriate referrals. Nine of the ten main trial sites achieved ethical approval for pilot data collection. Intensive care was an inconsistent marker of morbidity and stroke an infrequent outcome and therefore they were removed from the main trial composite outcome. Tools and methods of data collection were optimized and event rates used to inform randomisation. Conclusions This feasibility study demonstrates that the components of the intervention were acceptable, methods of implementing were successful and the main trial design would be feasible. Qualitative work identified key moderators that informed the main trial process evaluation. Changes to the training package, implementation strategy, study design and processes were identified to refine the implementation in the main trial. Trial registration ISRCTN41244132; Registered 24/11/2015. Electronic supplementary material The online version of this article (10.1186/s12884-018-1737-x) contains supplementary material, which is available to authorized users.

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.

ObjectivesThe grouping of record-pairs to determine which administrative records belong to the same individual is an important process in record linkage. A variety of grouping methods are used but the relative benefits of each are unknown. We evaluate a number of grouping methods against the traditional merge based clustering approach using large scale administrative data. ApproachThe research aimed to both describe current grouping techniques used for record linkage, and to evaluate the most appropriate grouping method for specific circumstances. A range of grouping strategies were applied to three datasets with known truth sets. Conditions were simulated to appropriately investigate one-to-one, many-to-one and ongoing linkage scenarios. ResultsResults suggest alternate grouping methods will yield large benefits in linkage quality, especially when the quality of the underlying repository is high. Stepwise grouping methods were clearly superior for one-to-one linkage. There appeared little difference in linkage quality between many-to-one grouping approaches. The most appropriate techniques for ongoing linkage depended on the quality of the population spine and the underlying dataset. ConclusionsThese results demonstrate the large effect that the choice of grouping strategy can have on overall linkage quality. Ongoing linkages to high quality population spines provide large improvements in linkage quality compared to merge based linkages. Procuring or developing such a population spine will provide high linkage quality at far lower cost than current methods for improving linkage quality. By improving linkage quality at low cost, this resource can be further utilised by health researchers.

ObjectivesLinkage of “big data” can provide the answers to a variety of health questions that benefit the delivery of patient care, impact of policies, system planning and evaluation. In some jurisdictions, legal and operational barriers may prevent data linkage for research and system evaluation. Collaboration between international research institutions in Canada, Australia and Wales was formed at the Farr Institute International Conference in 2015. This partnership will test privacy-preserving record linkage (PPRL) techniques for linkage accuracy on real datasets held in a Canadian data repository. ApproachBloom filter PPRL techniques have been incorporated into a prototype linkage system. Evaluations on probabilistic linkage using Bloom filters method have shown potential for large-scale record linkage, performing both accurately and efficiently under experimental conditions. The prototype will be used to evaluate the Bloom filter PPRL techniques in 3 phases. Phase 1: 3 tests using simulated data relating to 20 million individuals will be matched to a sub-cohort of 1 million individuals. Phase 2: 100,000 people from hospital inpatient records will be matched to 18 million people in a health system registration file. These tests will inform whether the method can achieve high levels of privacy protection without negatively impacting performance and linkage quality. Performance indicators include match rate and processing efficiency based on record volumes. ResultsLinkage quality will be assessed by the number of true matches and non matches identified as links and non-links. This method will be evaluated using synthetic and real-world datasets, where the true match status is known. Initial performance testing linked a file of 3,000 records to 30,000 with a 100% match result. Subsequent test phases as above will continue to be evaluated and these results will be presented. ConclusionCompletion of the phased tests will confirm the ability to link datasets while preserving privacy. This international collaboration will expand the utility of this prototype linkage system and expand the global knowledge bank focusing on PPRL methods in general. It will also inform how to adapt to local requirements by providing a solution to many common legal and administrative challenges.

ObjectivesRecord linkage is a powerful technique which transforms discrete episode data into longitudinal person-based records. These records enable the construction and analysis of complex pathways of health and disease progression, and service use. Achieving high linkage quality is essential for ensuring the quality and integrity of research based on linked data. The methods used to assess linkage quality will depend on the volume and characteristics of the datasets involved, the processes used for linkage and the additional information available for quality assessment. This paper proposes and evaluates two methods to routinely assess linkage quality. ApproachLinkage units currently use a range of methods to measure, monitor and improve linkage quality; however, no common approach or standards exist. There is an urgent need to develop “best practices” in evaluating, reporting and benchmarking linkage quality. In assessing linkage quality, of primary interest is in knowing the number of true matches and non-matches identified as links and non-links. Any misclassification of matches within these groups introduces linkage errors. We present efforts to develop sharable methods to measure linkage quality in Australia. This includes a sampling-based method to estimate both precision (accuracy) and recall (sensitivity) following record linkage and a benchmarking method - a transparent and transportable methodology to benchmark the quality of linkages across different operational environments. ResultsThe sampling-based method achieved estimates of linkage quality that were very close to actual linkage quality metrics. This method presents as a feasible means of accurately estimating matching quality and refining linkages in population level linkage studies. The benchmarking method provides a systematic approach to estimating linkage quality with a set of open and shareable datasets and a set of well-defined, established performance metrics. The method provides an opportunity to benchmark the linkage quality of different record linkage operations. Both methods have the potential to assess the inter-rater reliability of clerical reviews. ConclusionsBoth methods produce reliable estimates of linkage quality enabling the exchange of information within and between linkage communities. It is important that researchers can assess risk in studies using record linkage techniques. Understanding the impact of linkage quality on research outputs highlights a need for standard methods to routinely measure linkage quality. These two methods provide a good start to the quality process, but it is important to identify standards and good practices in all parts of the linkage process (pre-processing, standardising activities, linkage, grouping and extracting).

In an era where the volume of structured and unstructured digital data has exploded, there has been an enormous growth in the creation of data about individuals that can be used for understanding and treating disease. Joining these records together at an individual level provides a complete picture of a patient’s interaction with health services and allows better assessment of patient outcomes and effectiveness of treatment and services. Record linkage techniques provide an efficient and cost-effective method to bring individual records together as patient profiles. These linkage procedures bring their own challenges, especially relating to the protection of privacy. The development and implementation of record linkage systems that do not require the release of personal information can reduce the risks associated with record linkage and overcome legal barriers to data sharing. Current conceptual and experimental privacy-preserving record linkage (PPRL) models show promise in addressing data integration challenges. Enhancing and operationalizing PPRL protocols can help address the dilemma faced by some custodians between using data to improve quality of life and dealing with the ethical, legal, and administrative issues associated with protecting an individual’s privacy. These methods can reduce the risk to privacy, as they do not require personally identifying information to be shared. PPRL methods can improve the delivery of record linkage services to the health and broader research community.

BackgroundThe UK national diabetic eye screening (DES) programme invites diabetic patients aged > 12 years annually. Simple and cost-effective methods are needed to increase screening uptake. This trial tests the impact on uptake of two financial incentive schemes, based on behavioural economic principles.ObjectivesTo test whether or not financial incentives encourage screening attendance. Secondarily to understand if the type of financial incentive scheme used affects screening uptake or attracts patients with a different sociodemographic status to regular attenders. If financial incentives were found to improve attendance, then a final objective was to test cost-effectiveness.DesignThree-armed randomised controlled trial.SettingDES clinic within St Mary’s Hospital, London, covering patients from the areas of Kensington, Chelsea and Westminster.ParticipantsPatients aged ≥ 16 years, who had not attended their DES appointment for ≥ 2 years.Interventions(1) Fixed incentive – invitation letter and £10 for attending screening; (2) probabilistic (lottery) incentive – invitation letter and 1% chance of winning £1000 for attending screening; and (3) control – invitation letter only.Main outcome measuresThe primary outcome was screening attendance. Rates for control versus fixed and lottery incentive groups were compared using relative risk (RR) and risk difference with corresponding 95% confidence intervals (CIs).ResultsA total of 1274 patients were eligible and randomised; 223 patients became ineligible before invite and 1051 participants were invited (control,n = 435; fixed group,n = 312; lottery group,n = 304). Thirty-four (7.8%, 95% CI 5.29% to 10.34%) control, 17 (5.5%, 95% CI 2.93% to 7.97%) fixed group and 10 (3.3%, 95% CI 1.28% to 5.29%) lottery group participants attended. Participants offered incentives were 44% less likely to attend screening than controls (RR 0.56, 95% CI 0.34 to 0.92). Examining incentive groups separately, the lottery group were 58% less likely to attend screening than controls (RR 0.42, 95% CI 0.18 to 0.98). No significant differences were found between fixed incentive and control groups (RR 0.70, 95% CI 0.35 to 1.39) or between fixed and lottery incentive groups (RR 1.66, 95% CI 0.65 to 4.21). Subgroup analyses showed no significant associations between attendance and sociodemographic factors, including gender (female vs. male, RR 1.25, 95% CI 0.77 to 2.03), age (≤ 65 years vs. > 65 years, RR 1.26, 95% CI 0.77 to 2.08), deprivation [0–20 Index of Multiple Deprivation (IMD) decile vs. 30–100 IMD decile, RR 1.12, 95% CI 0.69 to 1.83], years registered [mean difference (MD) –0.13, 95% CI –0.69 to 0.43], and distance from screening location (MD –0.18, 95% CI –0.65 to 0.29).LimitationsDespite verification, some address details may have been outdated, and high ethnic diversity may have resulted in language barriers for participants.ConclusionsThose receiving incentives were not more likely to attend a DES than those receiving a usual invitation letter in patients who are regular non-attenders. Both fixed and lottery incentives appeared to reduce attendance. Overall, there is no evidence to support the use of financial incentives to promote diabetic retinopathy screening. Testing interventions in context, even if they appear to be supported by theory, is important.Future workFuture research, specifically in this area, should focus on identifying barriers to screening and other non-financial methods to overcome them.Trial registrationCurrent Controlled Trials ISRCTN14896403.FundingThis project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full inHealth Services and Delivery Research; Vol. 5, No. 15. See the NIHR Journals Library website for further project information.

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.

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.