Record Linkage Research Articles

Leveraging Fuzzy Match: Building an Automated Record Linkage Template in SAS Dataflux

Background ICES has conducted Probabilistic Record Linkage (PRL) for heath and non-health data for over 15 years. The PRL process has made data linkable to many ICES data holdings. However, due to the manual gray area resolution, record linkages for large datasets are time consuming with inconsistent results. ApproachAdapting from many years of experience with PRL methodology, record linkage templates were built in SAS Dataflux. By using combination of deterministic linkage and fuzzy match algorithms, the multi-pass linkage strategy is maintained by passing unlinked records in subsequent linkage comparison cycles. The new template uses personal identifiers of multiple given names, surnames, date of birth, death date (if applicable) and sex. In each pass, the pool of possible matched pairs is created by merging on low sensitive matchcodes with different conditions. Then, using a rule-based approach, possible pairs are examined, flags are assigned, and survival rules are applied to select the best matched records. ResultsBy feeding a pre-prepared linkage file along with updating the input and output file names, the record linkage job will be conducted automatically in SAS Dataflux through submitting a “run” command. It significantly reduces the turnaround time with no clerical review required, achieves a similar linkage rate as PRL consistently, and is utilized by various research projects. ConclusionThe automated SAS Dataflux record linkage template is more efficient than the traditional PRL process. It eliminates the human intervention of gray area resolution, while reducing project turnaround times, and maintaining a comparable accuracy and linkage rate.

Efficiency and Privacy in Record Linkage: Evaluating a Novel Blocking Technique Implemented on Cryptographic Longterm Keys

Efficient privacy-preserving record linkage (PPRL) is essential for integrating data from different providers without exposing personally identifiable information (PII). This study investigates the effectiveness of a novel blocking technique, implemented on Bloom filters (a space-efficient probabilistic data structure), to enhance efficiency and maintain privacy in a real-world evaluation. This methodology involves the utilization of Anonlink, an open-source Python-based PPRL system which generates a type of Bloom filter, Cryptographic Longterm Keys (CLK), for secure record linkage. Initially, the relevant PII fields of the two datasets undergo anonymization into CLKs. Utilizing the CLK’s property of similarity preservation, we create manageable blocks of records based on bits in common within the Bloom filters. This allows us to reduce the number of comparisons of non-matching records to improve linkage efficiency. Finally, record linkage is performed to identify potential matches within the blocked datasets. This blocking technique for CLKs is evaluated in terms of efficiency and record matching precision, aiming to determine the optimal balance between the two factors. Preliminary results indicate a significant reduction in computational burden, with recall minimally affected. Moreover, the implemented blocking technique poses no additional risks of privacy breaches. Preliminary evaluation of the blocking technique shows a promising avenue for secure and efficient data integration, especially in datasets with PII, warranting further investigation for validation and wider application.

From Probabilistic to Fuzzy Matching Record Linkage: A promising Transition

ObjectiveProbabilistic Record Linkage (PRL) heavily relies on manual intervention for gray area resolutions. This makes PRL extremely time and resource intensive. No matter how scientifically sound and robust PRL method is, it didn’t meet close-to-real-time data availability requirements at ICES. ApproachDuring a thorough evaluation and comparison process of three software and two methods of Record Linkage, ICES initiated a semi-design of experiment to select an optimal record linkage approach. For this experiment, a large Ontario data on 12 million individuals with required linkage variables plus valid Ontario health card numbers was selected. While the availability of the health card number enabled assessment of the accuracy of different approaches, the analysts were blinded to the correct health card numbers during the process. If manual intervention was required, it was repeated by two analysts to allow capturing human error. ResultsPRL-based software needed the most personnel time to complete the process. Human errors were identified during the manual intervention due to subjective decisions by the analysts. Fuzzy Matching approach eliminated manual intervention but achieved comparable linkage rate to PRL while maintaining the same accuracy. The Fuzzy Matching software costs were higher; however, the data timeliness was significantly improved, and the clerical review costs and human error were eliminated. ConclusionsThe Modernization of Record Linkage (MORL) project was a successful demonstration of the advantages of Fuzzy Matching over PRL method. However, the implementation of new approach at the organization level was a challenging change management.

Using Polars to Improve String Similarity Performance in Python

IntroductionString similarity is central to textual record linkage, and is often calculated with Levenshtein distance or Jaro-Winkler distance. In polars-strsim, we leverage the Polars DataFrame interface to surpass all existing Python libraries in computing these distances. Polars-strsim is especially useful for larger-than-memory datasets. Objectives and ApproachPolars is a library built to analyse and manipulate tabular data. It is written in Rust, but can be called from Python for ease of use. Polars-strsim implements the Levenshtein and Jaro-Winkler algorithms as a Polars extension. We compare performance against nine other Python libraries on three million pairs of first names. ResultsWhen computing Levenshtein/Jaro-Winkler distance for this benchmark, polars-strsim is respectively 5x/4x faster than the current fastest Python alternative and 47x/35x faster than the median. The Polars streaming engine can be seamlessly used to manipulate larger than memory datasets. One practical application of this in record linkage is to find all pairs of records in a dataset where the Levenshtein distance computed across one (or more) fields is below a certain threshold (e.g., during blocking). With polars-strsim, this calculation can be performed in Python with CPU parallelism even if the input and/or output doesn’t fit in memory. Conclusions/ImplicationsThis work introduces a powerful library for record linkage practitioners. Polars-strsim provides the convenience of Python, the performance of a strongly/statically typed compiled language, and the full power of the Polars streaming engine when computing string similarity, and can be updated to implement additional similarity algorithms.

Linkja: Open Source Privacy-Preserving Record Linkage Tool

ObjectiveTo introduce Linkja, an open-source, certified, Privacy-Preserving Record Linkage (PPRL) tool with the primary objective of facilitating secure and efficient record linkage across diverse datasets, while preserving individual privacy, providing a comprehensive and privacy-centric approach to program assessments. ApproachLinkja has been used to evaluate Chicago’s Flexible Housing Pool program by securely linking various datasets, including Homeless Management Information Systems (HMIS), Electronic Health Records, Jail records, Medical Examiner reports, and Public Health data. Data contributors used separate honest brokers for record linkage, and generation of salt and cryptography keys. ResultsLinkja enabled this multi-sector linkage by facilitating legal and data sharing agreements, avoided licensing fees and procurement processes for all parties, and provided a de-identified for analysis. The insights gleaned from the analysis motivated the FHP program to plan a follow-up evaluation that will add emergency serviced records from a separate City of Chicago department. The successful linkage provided a nuanced understanding of the association between stable housing and reduced healthcare utilization. ConclusionsLinkja's cryptographic certification ensures the highest standards of security during data linkage, safeguarding sensitive information. The matching rules, validated on hard-to-match populations, prove essential in maintaining accuracy and reliability in the linkage process. ImplicationsThe open-source nature of Linkja promotes transparency and collaboration in the development and enhancement of privacy-preserving record linkage techniques. This tool has broad implications for sectors dealing with sensitive data, fostering responsible data-sharing practices, and enabling secure collaborative research initiatives without compromising individual privacy.

The use of Supervised Learning to perform pairwise classification for Record Linkage over real world data

Supervised Learning (SL) is a subset of Machine Learning that uses labeled data to establish the learning process and perform tasks such as classification and regression. Record Linkage (RL) is the process for entity resolution and aims to determine if two records correspond to the same entity in the real world. A challenging aspect of RL involves estimating probabilities and weights for attributes and choosing the algorithm to perform the comparison. Our study aims to address this challenge and contribute in two primary ways: 1) by providing a labeled dataset comprising pairs of records, and 2) by evaluating an alternative classification method using an SL classifier. We generated more than 28.000 pairs, manually reviewed, derived from a massive Record Linkage Process on the Brazilian Public Health System (SUS). From that, we selected 5000 random stratified pairs for evaluation. The data contains demographic attributes such as sex, city, date of birth, zip code, and name. We modeled the data pairwise in a vector of latent space. Our methodology involved comparing Random Forest, LightGBM, XGBoost, and Neural Networks for classification tasks on these pairs, utilizing cross-validation to optimize hyperparameters. Our findings reveal that XGBoost had the highest performance, achieving 96% accuracy. Additionally, we conducted privacy evaluations using bloom filters, yielding an accuracy of 93%. The adoption of SL presents a promising space due to its capability for weight estimation and classification. In future endeavors, integrating these algorithms into a framework for the Record Linkage process could streamline procedures for researchers without compromising performance.

Algorithmic Accountability for Record Linkage

This session will examine record linkage approaches through the lens of principles for algorithmic accountability: responsibility, explainability, accuracy, auditability, and fairness.[1] It is easy to hypothesize cases where linkage algorithms could have disparate impacts, such as linking names more or less easily depending on their cultural background. How can researchers use careful linkage design and sensitivity testing to identify and, where necessary, mediate these impacts? What does it look like for record linkage processes to be explainable and auditable—and how can researchers invite engagement from broader groups of stakeholders in considering linkage methods? We will present on our experiences identifying and addressing these challenges in the context of linking state and local public sector administrative data sources. These data sources, which generally include populations engaging with particular programs or systems, have especially variable data quality and are often marred by structural biases. Our approach to linking these datasets emphasizes integrating policy and data context, interrogating linkage decisions for iterative improvement, and collaborating with a wide range of stakeholders for equitable, accountable data use. We will demonstrate examples of how linkage decisions and results have varied across contexts. [1] Diakopoulos, N., Friedler, S., Arenas, M., Barocas, S., Hay, M., Howe, B., Jagadish, H.V., Unsworth, K., Sahuguet, A., Venkatasubramanian, S., Wilson, C., Yu, C., and Zevenbergen, B. (2016). Principles for Accountable Algorithms and a Social Impact Statement for Algorithms. Fairness, Accountability, and Transparency in Machine Learning. https://www.fatml.org/resources/principles-for-accountable-algorithms.

Multiple imputation Procedure for Record Linkage and Causal Inference to Estimate the Effects of home-delivered meals

To assess the impact of home-delivered meals programs offered by Meals on Wheels (MOW) a dataset that includes enrollment into MOW and healthcare utilization is required. Because of confidentiality restrictions this data is dispersed over MOW programs internal systems and Medicare claims. In the absence of unique identifying identifiers, MOW beneficiaries are linked to Medicare beneficiaries using probabilistic linking algorithm. However, this procedure may erroneously link individuals across the two datasets, which may result in biased treatment effect estimates and suboptimal interval estimates. We propose a two-stage multiple imputation framework to estimate causal effects when the covariates and outcome information are stored in Medicare claims and the treatment assignment is MOW program data. In the first stage, we create multiple datasets in which MOW beneficiaries are linked to Medicare beneficiaries using a Bayesian record linkage technique. In the second stage, we match Medicare beneficiaries that were not enrolled in MOW programs to those that did. Using these matches we multiply imputed for each MOW beneficiary their unobserved healthcare utilization if they did not receive MOW. This procedure propagates the errors in the linking process and the matching process, and can be used to estimate effects of interventions in other linked datasets when there are no unique identifiers.

Privacy Preserving Evaluation of Sensitive Population Record Linkage in the Absence of Ground Truth Data

ObjectivesLinkage of data containing personal information allows extensive studies in the health and social sciences for population level studies. However, real-world linkage applications often lack ground truth (GT) data, generally due to privacy concerns, which hinders linkage quality assessment. We propose a method to assess the linkage of sensitive population data in the absence of GT data. ApproachOur method takes as input a set of sensitive and public population clusters. The sensitive clusters are generated with some record linkage algorithm, whereas the public population dataset, which is considered an approximation for the GT, is assumed to have a group identifier allowing generation of known clusters, such as households or families. We encode clusters as bit vectors and conduct cluster matching across the sensitive and public datasets using vector comparisons. Based on matched clusters, the population linkage quality of sensitive data is estimated. ResultsWe conducted experiments on a sensitive Scottish birth data set containing 17,613 records from 1861 to 1901, and four successive publicly available Scottish census datasets from 1871 to 1901. Using the quality measures precision, recall and the F-measure, we show that our estimations are close to GT quality, with a high correlation in F-measure values ranging from 88% to 99%. ConclusionWe proposed a method to estimate the linkage quality of sensitive population data for situations where public data with similar characteristics are available for approximating the GT. Our experimental evaluation showed that our method was effective in estimating true linkage quality with high accuracy.

Identifying “problematic” clusters in record linkage using graph measures

Manual review of outputs from record linkage processes remains a frequently necessary but time-consuming operation. This work outlines a potential means to reduce the manual review workload through the extraction of graph measures from the outputs of record linkage processes combined with machine learning classification methods. The outputs of a pairwise record linkage process can naturally be represented by undirected simple graphs with a vertex representing each record and an edge representing each comparison of records. One representation may be a graph which only includes edges representing pairwise comparisons that have a match score greater than a given threshold. A second representation may include match scores (suitably transformed) as the weight of the edges (pairwise record comparisons) connecting vertices (records). A third representation may include not just the overall match score as a weight on each of the edges (pairwise record comparisons) but also, for each compared attribute (e.g. names, date of birth, address, etc.), the contributing component to the overall match score as additional edge attributes. In this work, a "problematic" cluster is a set formed of three or more records which, in totality, belong to two or more true entities. Equivalently, a connected graph of three or more vertices (representing records) with at least one edge that represents a false positive link. Finally, this work evaluates the performance and generalisability of various combinations of graph measures for identifying problematic clusters across linkage processes featuring varying degrees of corruption and duplication.

Leveraging Administrative Records and Record Linkages to Enhance Health Data at the U.S. Census Bureau

Direct data collection via surveys of patients and health care providers continues to experience declining response rates and increased costs. Alternative data sources such as medical claims or electronic health records hold significant promise to complement direct data collection efforts but these records often suffer from a lack of important sociodemographic, economic, and other critical social determinants of health characteristics. On the heath care provider side, existing records often lack information needed to fully describe complex organizational relationships between providers and their places of work, employment, and service delivery. Through strategic partnerships with external data owners, the Enhancing Health Data (EHealth) program at the U.S. Census Bureau links multiple data sources of restricted administrative records covering patients, providers, and health care businesses and leverages data assets and record linkage capabilities available exclusively at the Census Bureau. This presentation will describe existing EHealth projects and share the vision to build a data infrastructure that empowers researchers, policy makers, and the public to uncover new insights on patients, providers, and population health.

The application of deterministic and probabilistic methods of data linkage methods to link population and health data in western Uganda

IntroductionThere is increasing interest in record linkage between population-level data and health facility records to support epidemiological research in Africa. However, there is a lack of unique and ubiquitous identification systems for linkage. We applied two common data linkage approaches between population-level data from a health and demographic surveillance system and hospital records in Western Uganda. MethodologyWe established a data linkage system using deterministic and probabilistic linkage on population level data collected from 23 villages with hospital records. Deterministic linkage entails connecting a pair of records from the clinic and census data using a common identifier. Probabilistic linkage classifies a pair of records from two data sources as belonging to the same individual based on the statistical probability that a common set of identifiers drawn from the two data sources belong to the same individual. The preliminary results are on the deterministic approaches. ResultsUsing deterministic linkage, we fully linked 1061 records from 12/23 villages. Further updates of linkage with characteristics of the linked patients will be summarized in the next two months. Probabilistic linkage data will be presented with the characteristics of participants after the completion of the study, and the agreement between the two methods will be compared. Note to the results: Presentations during the conference will be done using finalized data. ConclusionPreliminary findings show that the linkage of population and health data is feasible. Findings could be used by researchers to design data linkage studies and answer different types of health and population research questions using innovative data science methods.

Roadmap for Linking Registry Data with Health Services Data to Support Evidence-Informed Decision-Making.

ApproachAs part of a learning period to optimize the use of RWE for decision-making for drugs for rare diseases, the [organization name removed to allow for blind review] conducted an environmental scan to map real-world data in patient registries. Over 400 patient registries were identified, signaling the potential wealth of untapped information to support decision-making by linking registry data with health services data. To better understand the challenges faced by registry holders hoping to link registry data with health services data sources available in Canada, a series of interviews were conducted with several Canadian rare disease registries. In addition, a literature review was completed, and Canadian experts in epidemiology, privacy, record linkage, registry science, and health services research were consulted to inform the development of a roadmap to meet various stakeholder needs. ResultsThe resulting roadmap consists of 8 specific steps covering topics related to registry purpose, informed consent, ethical approval, participant privacy, governance, data linkability, participant identifiability and jurisdictional requirements. ConclusionThe roadmap is currently undergoing pilot testing by a pan-Canadian rare disease registry. A final [organization name] report and an accompanying roadmap in a checklist format to facilitate implementation will be finalized, disseminated across key stakeholders, and made publicly available. ImplicationsWhile developed for registries, the roadmap applies to the linkage of clinical trial or cohort study data, or other systematically gathered patient-level data to health services data.

Methodological Challenges when Using Routinely Collected Health Data for Research: A scoping review.

Routinely collected health data (RCD) including electronic health records, disease registries, health administrative data and wearables data are not specifically collected for research purposes. Analysis of these data poses unique methodological challenges that must be addressed when conducting research, particularly as availability and use increase. This scoping review aimed to identify methodological challenges in research using RCD from existing literature (registered protocol: https://doi.org/10.17605/OSF.IO/EBM4D). We searched 6 electronic databases, including medical, health economics, nursing and psychology research databases, between Jan 2015 and Jan 2023, combining multiple “RCD” and “research” search terms (e.g., epidemiologic, informatics, pharmaceutical research). After screening abstracts and full-texts, we doubly extracted methodological themes, categorizing them into different study stages. We screened more than 23,000 records and included 430 papers. Bias and confounding were the most common methodological issues identified, discussed in relation to both study design and data analysis. Data quality, including data accuracy, validation, completeness, timeliness and cleaning, also posed substantial challenges, particularly during data processing stage. Record linkage and conducting analyses using distributed health networks also pose unique methodological challenges. Heterogeneity, incorporating social determinants of health and statistical models that address methodological challenges are also described in the literature. External validity and reporting are important considerations for RCD research. Our review identified several methodological challenges facing researchers using RCD. These issues should be addressed to ensure methodologically sound research. These findings will inform the development of a standardized protocol template and accompanying educational platform aimed at enhancing methodological quality and transparency when conducting research using RCD.

A Bayesian Record Linkage Approach that Adjusts for Variables in One File

In many healthcare and social science applications, information about units is dispersed across multiple data files. Linking records across files is necessary to estimate associations between variables exclusive to each of the files. Common record linkage algorithms only rely on similarities between linking variables common to all the files. Moreover, analysis of linked files often ignores errors that may arise from incorrect or missed links. Bayesian record linking methods allow for natural propagation of linkage errors, by jointly sampling the linkage structure and the model parameters. We extend an existing Bayesian record linkage approach to integrate associations between variables exclusive to each file being linked. We show analytically, and using simulations, that the proposed method improves the linkage process, and results in accurate statistical inferences. We apply the proposed method to link Meals on Wheels (MOW) recipients to Medicare Enrollment records, and examine the relationship between activities of daily living and healthcare utilization among MOW recipients.

The Fundamental Role of Linkage Uncertainty in Epidemiological Analysis of Big Data

BackgroundEpidemiologists increasingly work with linked “big data”. Uncertainty in record linkage may lead to biased inferences but is often overlooked. We evaluate the impact of linkage uncertainty on statistical inference in linked big data. MethodsWe developed a graphical framework for describing linkage uncertainty when linking multiple representations of the same entity, applied to de-identified data from South Africa’s national laboratory database. Through simulation, we systematically introduced linkage errors and measured their impact on overall accuracy (sensitivity, positive predictive value (PPV)). We evaluate how linkage errors affect bias and variance in point estimates for a hypothetical parameter of interest in clinical epidemiology: 24-month retention in care for HIV patients. We compare the roles of sampling error vs. linkage error as fundamental sources of uncertainty in datasets of varying sizes. ResultsWe simulated a population of 14,393 HIV patients, with a “true” 24-month retention of 38.7%. There were 338,056 true links. Introducing 4,200 false links reduced PPV by 5%. Removing 21,500 existing links decreased sensitivity by 5%. From 10 simulation runs, a 95% sensitivity led, on average, to a 7.4% overestimate in entries to care and a 2.2% (range: 2.1-2.4%) underestimate in 24-month retention. A 95% PPV resulted, on average, in a 7.5% underestimate in entries to care and a 1.8% (range: 1.5-2.0%) overestimate in 24-month retention. ConclusionWe observe that in a large sample, linkage uncertainty minimally impacts variance in point estimates but has a potentially large influence on the magnitude and direction, distinguishing it from typical sampling errors.

Data Modernization Opportunity: Alignment between US Public Health Data Policies and Public Health Ethics

Objective and ApproachModernizing data sharing policies is key to fix deficiencies exposed by COVID-19. Decentralized US public health data collection, with varying state laws, challenges data sharing across governmental entities. Many data protection laws rest on bioethical principles that de-emphasize collective interests, creating challenges for public health data governance. Here, we analyze US state public health data laws with WHO ethical guidelines, using a modified policy surveillance method to examine laws regulating data sharing between public health partners. We assess state law alignment with WHO guidelines for (1) public health data use, (2) research use, (3) emergency use, and (4) law enforcement use. ResultsWe found 34 states with laws explicitly allowing data sharing with federal public health agencies, 16 states permitting data sharing for research, 10 with provisions for emergencies, and 14 states permitting some data sharing for law enforcement. ConclusionsEfficient US public health data modernization needs policies that enable effective data sharing with proper justification and governance. The WHO offers guidance on ethically sharing sensitive data, but many US states' laws don't align with these guidelines, based on public health ethics. Instead, state laws often reflect bioethical standards, which are increasingly seen as unsuitable for managing population data in public health. ImplicationsSupportive policies are crucial for allowing population data to be used for social benefits. Policies that go against ethical guidelines and impose unnecessary obstacles to data sharing can hinder vital activities, like record linkage, that support public health efforts and collective benefits.

A Bayesian Multi-Layered Record Linkage Procedure to Analyze Functional Status of Medicare Patients with Traumatic Brain Injury

Understanding associations between injury severity and post-acute care recovery for older adults with traumatic brain injury (TBI) is crucial to improving care. Estimating these associations requires information on patients’ injury, demographics, and long-term health outcomes. This data is despersed across two different datasets: Medicare claims data and the National Trauma Data Bank (NTDB). Because of privacy regulations, unique identifiers are not available to link records across these two data sets. Record linkage methods identify records that represent the same patient across data sets in the absence of unique identifiers. With a large number of records, these methods may result in many false links. Health providers are a natural grouping scheme for patients, because only records that receive care from the same provider can represent the same patient. In some cases, providers are defined within each data set, but they are not uniquely identified across data sets. We propose a Bayesian record linkage procedure that simultaneously links providers and patients. The procedure improves the accuracy of the estimated links compared to current methods. We implement the proposed method to examine the associations between functional status assessments and TBI patients' ability to be independent following their injury by linking records from Medicare enrollment records and the NTDB.

Matching Occupational Injury and Illness Data Using Augmented Twin Neural Networks

When record linkage efforts involve complex characteristics there is ample potential for general purpose machine learning (ML) techniques to succeed where traditional probabilistic approaches might fall short. However, there can still be pre-processing (e.g. geocoding) and hand-picked comparators that can further improve linkage outcomes using standard ML models. In this project we present a fusion of these sides we are calling an Augmented Twin Neural Network. This approach leverages the inherent flexibility of Twin Neural Networks in a record linkage context while adding additional layers to allow for hand curated comparators that may be difficult for ML optimizers to implicitly identify without sufficiently large, labeled data sets. The framework is used to match establishments from the BLS Survey of Occupational Injuries and Illnesses to establishments in the OSHA Injury Tracking Application data. The difficulties inherent in matching company names and addresses and the existence of multi-establishment firms make this a prime application for testing. Linkage outcome metrics of this augmented algorithm are compared both with results from probabilistic methods (e.g. Fellegi-Sunter) and standard machine learning methods to illustrate the added benefits.

Generation Scotland – Linking all the records we can

ObjectivesGeneration Scotland (GS) is a family-based genetic epidemiology study. Initial recruitment between 2006-11 recruited ~24,000 adults from ~7000 families across Scotland with consent for medical record linkage and re-contact. In 2022 we began recruiting another 20,000, with consent extended to administrative records, with age range now 12+. MethodsOriginal volunteers completed a baseline questionnaire, provided biological samples and underwent clinical assessment. The samples, phenotype and genotype (including methylation) data are linked to routine NHS hospital, maternity, lab test, prescriptions, dentistry, mortality, imaging, cancer screening, GP data records, Covid-19 testing and vaccinations, and follow-up questionnaires. The new wave of recruitment is all online and can be done on a smartphone, with DNA from saliva collected by post. Teenagers aged 12-15 can join with parental consent. ResultsResearchers can find prevalent and incident disease cases and controls, to test research hypotheses on a stratified population. GS is a vanguard cohort testing emerging linkage opportunities in Scotland – including neonatal bloodspots and medical imaging reports. GS has established and validated E-HR linkage with the NHS Scotland CHI Register, overcoming technical and governance issues in the process. We contribute to major international consortia, with collaborators from institutions worldwide, both academic and commercial. ConclusionWe plan to extend the linkage process to include other administrative data from national datasets as and when approvals are obtained. New types of data can also be collected by online questionnaires. The Research Tissue Bank resources are available to academic and commercial researchers through a managed access process.