bluuebunny/crossref_metadata_2025 huggingface dataset:
Using machine learning to gain insights into chronic disease multimorbidity: trends and patterns in British Columbia, Canada
Authors: Ferris Jennifer , Choi Alexandra , Wagar Brandon , Simkin Jonathan , Woods Ryan , Djurdjev Ognjenka , Sbihi Hind , Harder Kari , Smolina Kate
Published: September 2024

ObjectiveThe goal of this project is to explore novel ways to assess chronic disease multimorbidity (co-occurrence of two or more conditions) trends and patterns in the population of British Columbia (BC), Canada.
ApproachThis study included linked data from the BC population (~5M individuals) from 2001/02 to 2019/20.  We analysed 25 chronic conditions, including 7 primary cancer subtypes. We report multimorbidity (MM) incidence, prevalence, and most common disease combinations. Further we explore temporal MM disease patterns using directed network analyses and extracted data-driven disease clusters with an unsupervised machine learning algorithm.
ResultsThe age-standardized incidence of MM stayed relatively stable over the study period for males and decreased for females, while prevalence increased to approximately 1 in 4 individuals in BC in 2019/20 (from 19% to 27% of females, from 15% to 22% of males). Disease networks and clusters varied significantly by sex and age group, this presentation will highlight select disease network and cluster findings and discuss their implications for chronic disease surveillance.
ConclusionsThe prevalence of multimorbidity continues to rise in BC. Using advanced analytics to understand disease co-occurrence patterns provides new insights above and beyond traditional epidemiological metrics to support health system planning and prevention efforts.
ImplicationsChronic disease surveillance and research have historically operated with a single disease focus, which is not patient-centered and does not adequately account for the reality of multimorbidity for many people. This project is laying the foundation for enhanced chronic disease surveillance and monitoring in BC.
Understanding Patterns of Care for Older Adults: Data to Action
Authors: Acan Osman Beliz , Maldonado Fernando , Goes Suelen , Sidhu Nirmal , Hutton Amanda
Published: September 2024

ObjectiveOlder adult population is growing rapidly, the current system is not adequately designed to meet the needs of individuals to live safely and independently in the place of their choice. 
To understand the variations in care and identify where to intervene, we examined the transitions in care of community-dwelling older adults (COA) leading up to 24-hour assisted living at special care homes (SCH). 
ApproachWe conducted a retrospective-cohort study using health administrative data from a Canadian province with a long history of comprehensive, longitudinal data. Using process mining techniques, health service utilization of COA, 65 and older, was mapped as a journey rather than independent care events. We examined the common patterns experienced leading up to SCH admission by sequencing transitions between care settings. Consideration was given to geography that the individual resides in and their health status to understand variations.
ResultsOverall health utilization patterns revealed predominant reliance on acute care and SCH admissions. However, geographic variations were identified (regions and urban/rural). More than 2000 transition patterns between emergency department, hospital, community, and SCH were observed.
ConclusionThe ability for COA to age in place is dependent on the individual, where they live, and the availability of and access to services to meet their needs. To minimize undesirable and early admissions to SCH, a multifaceted, collaborative approach to align community-based supports for COA is required. The results of these analyses will help inform areas in which to focus interventions and serve as a baseline while evaluating impact.
Establishing a linked administrative data sampling frame to recruit families into a new UK-wide birth cohort study: findings from the Early Life Cohort Feasibility (ELC-FS) Study
Authors: McBride Orla , Calderwood Lisa , Dennison Karen , Wong Erica , Raybould Alyce , Silverwood Richard , Hardelid Pia , Griffiths Lucy , Fearon Pasco , Goodman Alissa
Published: September 2024

Objective and ApproachFunded by the Economic and Social Research Council (2021-2024), a core aim of the ELC-FS was to establish a sampling frame by linking birth registrations and maternity records in the four UK nations (England, Wales, Scotland and Northern Ireland) to facilitate recruitment of ~2000 families into a new birth cohort study. An opt-out recruitment approach was essential. Widespread public dialogue work was undertaken to glean the attitudes and perspectives of stakeholders including data controllers, data users, and parents of young children, as to the acceptability of this recruitment model.   
ResultsAchieving the sampling frame required nation-specific approaches due to differences in existing legislation governing secondary use of administrative data. Complex and protracted negotiations with data controller approval panels were necessary. Public dialogue work revealed that stakeholders were: (i) excited by the prospect of a new UK-wide birth cohort study to provide invaluable insights into the lives of a new generation; and (ii) broadly accepting of the proposed use of administrative data for generating a sampling frame, particularly in terms of ensuring that the ELC-FS study has diverse and inclusive representation of family life in the UK today.
ConclusionsELC-FS fieldwork will be completed by mid-2024. Preliminary analyses to be presented at the conference will assess the suitability of the sampling frame for achieving the target ELC-FS sample.
ImplicationsWith careful engagement with stakeholders at a national level, access to linked administrative data as a sampling frame for a UK-wide birth cohort study is achievable, but not straightforward.
Temporal Trends and Determinants of Antibiotic Prescribing in Primary Care: Insights from Northern Ireland
Authors: Bucholc Magda , Redican Enya , Menting Stein GP , Ferry Finola , Shevlin Mark , Murphy Jamie
Published: September 2024

ObjectiveAntimicrobial resistance (AMR) has become one of the most serious global public health threats. Concerns about AMR resulted in increased monitoring and evaluation of antibiotic prescribing, with primary care responsible for over 80% of these prescriptions. Through this study, we aim to analyse temporal trends and variations in antibiotic prescribing at GP practice level and investigate the association between antibiotic prescribing and demographic, clinical, geographic, and socio-economic characteristics in Northern Ireland. 
MethodThe descriptive analyses are complemented by performing the multilevel modelling analysis to identify practice-level determinants of prescribing.
ResultsChanges in standardised antibiotic prescribing rates are evaluated using a linear mixed-effects model with a random intercept for each GP practice. We discuss the changes in antibiotic prescribing rates over time, between-GP practice variations in prescribing, differences in prescribing between urban and urban practices, and the impact of demographic and socio-economic factors on antibiotic prescribing rates
Conclusions and ImplicationsThis study contributes to the debate on determinants of antibiotic prescribing in primary care and provides actionable insights to policy makers responsible for antimicrobial stewardship and public health campaigns.
The Kids’ Environment and Health Cohort: a novel administrative data resource for research on the environmental determinants of child health in England
Authors: Akaraci Selin , Macfarlane Alison , Rammah Amal , Courtin Emily , Miller Faith , Mitchell Jessica , Cruz Joana , Lilliman Matthew , Shoari Niloofar , Hajna Samantha , Cummins Steven , Nafilyan Vahe , Hardelid Pia
Published: September 2024

Objective and ApproachThe environment in and around children’s homes and schools can influence their health and educational outcomes. Better understanding of how these potentially modifiable environmental risk factors can affect children is crucial in enabling the creation of healthier and more equitable places. We aim to establish the Kids’ Environment and Health Cohort, a research-ready, de-identified, national longitudinal birth cohort of approximately 11 million children born in England from 2006 to 2023, updated annually. The cohort will link vital statistics, census, health, education, and environmental data, via unique property identifiers from longitudinal health service address records for children and their mothers during pregnancy. Data on environmental exposures around schools will be linked to the cohort via education records. The cohort will be held and accessed in a secure research environment at the Office for National Statistics (ONS). All geographical identifiers will be encrypted and stored separately from the main cohort by the ONS to ensure privacy and security.
ResultsWe have received ethics approval and have agreed the legal bases for establishing the cohort. We are now setting up data sharing agreements with each data provider. Delivery of the cohort is scheduled for late 2025.
ConclusionThe Kid’s Environment and Health Cohort will support policy-relevant research in exploring associations between environmental factors and children's health and educational outcomes, and assessing the effectiveness of policy interventions. It will also support interdisciplinary collaboration, guiding evidence-based decision-making for environmental, planning, and public health policies aimed at promoting children’s health and well-being.
Automated Translation of Chronic Disease Diagnosis Codes using the ChatGPT Large Language Model
Authors: Monchka Barret , Maleki Golandouz Hassan , Lix Lisa , Hamad Amani
Published: September 2024

BackgroundThe International Classification of Diseases (ICD) is revised over time and there are region-specific versions, including ICD-10-CA (Canada) and ICD-9-CM (USA). Studies spanning multiple ICD versions require crosswalks to translate diagnosis codes across versions, but manual crosswalk development is costly and requires clinical expertise.
ObjectiveTo evaluate the accuracy of a pre-trained large language model (LLM) to automatically translate chronic disease diagnosis codes from ICD-10-CA to ICD-9-CM.
ApproachEight prompts were developed to instruct the OpenAI Generative Pre-trained Transformer 4 (GPT-4) LLM to translate 1,272 ICD-10-CA codes for the Elixhauser Comorbidity Index to ICD-9-CM. Prompt accuracy (%) was measured against a crosswalk developed by the Canadian Institute of Health Information. Variability was assessed by replicating each prompt three times. Mean accuracy ± standard deviation was reported for each prompt across replications, for both five-digit and truncated three-digit codes.
ResultsThe highest prompt performance was observed when assigning a persona of a medical coding specialist (40.8% ± 0.9%), requesting justification for the selected code (41.4% ± 1.1%), and providing diagnosis code labels (47.5% ± 0.7%). For truncated three-digit codes, these prompts achieved accuracy of 82.0% ± 0.5%, 80.8% ± 0.9%, and 82.7% ± 0.1%, respectively. Combining these three prompting techniques marginally improved accuracy to 48.6% ± 0.7% for five-digit codes and 84.3% ± 0.2% for truncated three-digit codes.
ConclusionGeneral-purpose LLMs are currently not sufficiently accurate at automating ICD code translation for chronic diseases.
ImplicationsAdditional experiments with fine-tuning, task-specific training, and prompt engineering are needed to improve accuracy and reduce variability.
Reporting on the establishment of a Privacy Preserving Record Linkage to Facilitate an Ongoing Crosswalk Between Research and Health Administrative Databases
Authors: Behan Brendan , Sparks Alana , Cheema Heena , Naska Sibel , Jeanson Francis , Basque Shalane , Ma Charlotte , Chai-Onn Jason , Javadi Mojib , Ho Minnie , Mikkelsen Tom
Published: September 2024

IntroductionThe Ontario Brain Institute (OBI) is a provincially funded, not-for-profit organization that accelerates discovery and innovation, benefiting both patients and the economy (Stuss, 2014). OBI has established a large-scale neuroinformatics platform - Brain-CODE - to support the collection, storage, federation, sharing, and analysis of different data types across several brain disorders (Behan et al., 2023; Vaccarino et al., 2018). A privacy preserving record linkage protocol was developed to allow for the linkage of research data at Brain-CODE with health administrative data holdings at the Institute for Clinical Evaluative Sciences (ICES) (Gee et al., 2018).
Objective and ApproachThe methodology related to an ongoing crosswalk linkage between OBI and ICES, to allow for more seamless integration between the respective data holdings, has been previously presented (Behan et al., 2020). This methodology has since been operationalized leading to the establishment of an ongoing crosswalk linkage that is updated on an annual basis. 
ResultsSince the initial development of this ongoing crosswalk, two updates have been successfully completed leading to the linkage of over 7,000 study participants between the two platforms. This has led to a more efficient utilization of human and computational resources, compared to earlier data linkage projects completed on a project-by-project basis. Analysis projects in the areas of neurodegeneration, concussion, and neurodevelopmental disorders have already leveraged this crosswalk linkage process. 
Conclusions/ImplicationsThe establishment of this ongoing crosswalk linkage has supported a more streamlined approach of data linkage activities between OBI and ICES allowing for enhanced neuroscience-focused research activities.
Challenges in Early Years data and their implications on programme evaluations
Authors: Colonna Silvia , Helliwell Kathryn
Published: September 2024

The Welsh Government funds a wide range of Early Years programmes to both support parents to remain in work and children’s social, emotional and educational development. The interest of Welsh Ministers in this policy area has grown in recent years with expansions in this area carefully considered. The potential for expanding the offer has generated a greater need for evidence to understand the effectiveness of the delivery against the proposed objectives.
Data linking is a valuable and efficient way to provide complex evaluations of policy programmes as it maximises the use of existing data sources without increasing the burdens on data providers and provides comprehensive information that would otherwise be limited with a single dataset.
However, Welsh Government research on programme evaluation in Early Years is facing challenges and this work aims at providing an overview of these issues, their impact on Early Years analytical work and progress made to address them. In particular, the main challenges are related to data quality and data availability that stem from lack of metadata and contextual information, poor technological infrastructure and, at times, a disconnect between policy / operational delivery and data collection. Progress is being made to address these challenges, such as acquiring new datasets or improving the quality of existing ones but these developments take time. At the same time as helping to tackle these structural challenges, the Early Years analytical team is exploring a range of novel approaches to provide robust evaluation of Early Years interventions despite the data issues.
Creating a Cleaned Dataset for Ease of Use
Authors: Ahmed Saira , Thayer Daniel S , Elmessary Muhammad A , Scanlon Ieuan , Badoche Etienne
Published: September 2024

ObjectiveThe Welsh Longitudinal General Practice (WLGP) dataset contains over 4 billion records. Due to the size and the need to filter the dataset to get the necessary general practice interaction results, query performance is poor. To overcome this, a ‘cleaned’ version of the dataset needed to be created.
ApproachAn R package was created that would be run when a new refresh of the WLGP data is provided. Two new tables would be created based on the original WLGP Events table.
ResultsA WLGP Cleaned Dataset R Package was produced and creates a reformatted events table and events look up table. The reformatted GP events table reduces the original events table from 14 columns to 8 key columns. The events look up table takes distinct events from the original WLGP events table and produces a new table including columns such as the event description, type and hierarchy levels. Both tables are then linked using an event code ID.
ConclusionThe R package now creates a new reformatted events table as well as an events look up table which is run after a WLGP refresh is provided. The new tables are then provisioned to any projects that have access to the dataset.
ImplicationsThe new tables has improved query performance for our researchers, providing them with a table with all the necessary information and an easy way to decipher events codes. This has improved the amount of time researchers have spent manipulating and querying the tables.
Differences in prescribing and dispensing at end-of-life between people who died at home before and during the Covid-19 pandemic in Scotland.
Authors: Savinc Jan , Atherton Iain
Published: September 2024

ObjectivesTo compare levels of palliative care need that were met through prescriptions and dispensing of medicines at end-of-life for people who died at home in Scotland during and prior to the Covid-19 pandemic.
ApproachRetrospective observational study of linked routinely collected data: death registrations in Scotland during 12-month pandemic period (starting 23rd March 2020) and pre-pandemic period (5 years prior), linked to inpatient hospital admissions and community prescribing & dispensing data. Prescribing/dispensing will be summarised for pandemic & pre-pandemic cohorts and palliative care needs estimated using death registrations and inpatient admissions.
ResultsAn estimated N=59,152 individuals died with palliative care needs during the first year of the pandemic, or approximately 11% more than in 2019/20. Community prescribing & dispensing will be compared between the pandemic and pre-pandemic periods as a proxy measure of primary care use and to investigate shortages. Anticipatory medication use will be compared to estimate quality of death at home for decedents.
ConclusionsDeaths at home increased by about a third during the first year of the pandemic in Scotland and continue at similar levels currently. Understanding the effect of the pandemic on prescriptions and dispensing at end-of-life will help explain the shift of deaths to home settings in addition to demographic and service contact changes early in the pandemic.
ImplicationsStudying the changes in end-of-life medication use will inform policy to better continue supporting people dying at home, especially where there are unmet needs or gaps between prescribed and dispensed medications.
Linked administrative health data on prehospital olanzapine administration by paramedics in Winnipeg, Canada: Challenges and opportunities
Authors: Detillieux Gilles , McDonald Neil , Enns Jennifer , McDougall Chelsey , Nickel Nathan
Published: September 2024

ObjectiveOlanzapine is an antipsychotic drug used in emergency departments to treat methamphetamine intoxication. Our study aim is to examine whether prehospital olanzapine administration by paramedics in Winnipeg, Canada, improves outcomes for individuals experiencing methamphetamine intoxication. However, the ‘real-world’ nature of the administrative data has presented several challenges.
ApproachFirst, we needed to determine whether individuals experiencing methamphetamine intoxication received or did not receive olanzapine. The paramedic records identify whether olanzapine was administered but not whether individuals were considered for olanzapine without receiving it. To address this, we manually reviewed the unstructured narratives from the paramedic assessments. 8000+ records were independently evaluated by two experienced paramedics to determine eligibility. Disagreements were resolved by a paramedic-educator.
Second, we discovered that ~40% of paramedic records for 2019 did not link correctly to other health data. We conducted a sensitivity analysis to assess the impact of excluding records with incorrect linkage, but the loss of records in the most critical year of assessment led us to reject this approach. Instead, we revisited the original data linkage to identify and correct the cause of the errors.
ResultsWe have constructed a cohort that allows us to compare treated (n=222) and untreated (n=205) individuals experiencing methamphetamine intoxication and provides enough statistical power to assess the impact of prehospital olanzapine treatment on hospital outcomes. 
Conclusion & ImplicationsAdministrative data from the real world are powerful tools for research with potential to show important health impacts, but their use requires creative thinking to overcome unexpected data challenges.
Linkage error and ethnicity information source affect estimates of ethnic disparities in non-communicable disease rates
Authors: Petrović-van der Deen Frederieke , Gibb Sheree , McLeod Melissa , Teng Andrea
Published: September 2024

Objective and ApproachThis study examined the impact of two sources of bias on differences in ethnic disparities in non-communicable disease rates in New Zealand (NZ). Data were sourced from Stats NZ’s Integrated Data Infrastructure (IDI), a collection of deidentified whole-population administrative (eg, health, justice, housing) and survey datasets (eg, NZ Census) linked at the individual level using probabilistic linkage procedures. Linking several datasets reduces the size of the population available for study because not all individuals can be linked. In addition, there are several sources of ethnicity information that may disagree with each other. We will illustrate the impact of population loss due to linkage and ethnicity data source on Māori-European gaps in lung cancer and cardiovascular disease.
ResultsOur results showed that the choice of ethnicity information source and the population used had an impact on the size of ethnic disparities in lung cancer and cardiovascular disease. For lung cancer the age standardised rate ratio for Māori:European ranged from 2.88 to 3.21, and for CVD 1.70 to 1.87. Population loss and ethnicity data source each had independent effects on the size of ethnic differences.
ConclusionsDifferent combinations of population and ethnicity information source produced different estimates of ethnic gaps in lung cancer and CVD prevalence. Population and source of ethnicity data both had independent effects on the size of ethnic differences.
ImplicationsComparisons of ethnic differences in disease prevalence between studies, or over time, may be misleading if they do not use the same population and ethnicity data source.
Investigating variation in reported location of death: A comparison of administrative data sources
Authors: King James , Sinnarajah Aynharan , Bakal Jeffrey
Published: September 2024

BackgroundLocation of death is an important outcome in health research. Accordingly, the collection and assembly of these data is complex. In Canadian administrative healthcare data, the gold standard for mortality has been Vital Statistics (VS), which is based on death certificates.
MethodsAll mortality records in the province of Alberta, Canada (population 4.6M) were extracted from VS between 2016 and 2021. These were deterministically linked on unique patient identifiers to databases including hospitalizations, emergency department (ED) visits, long-term care (LTC) and designated supportive living records. The primary outcome was location of death reported in administrative sources.
ResultsThere were 162,835 mortality records identified in VS. Of deaths labeled as hospital, en route, or nursing in VS (113,554), 85.3% were linked to administrative data on inpatient, ED, LTC, and/or designated supporting living deaths. Of the 52.2% VS records reporting hospital deaths, 80.4% linked to a record of death to the inpatient database. Few records were linked and reported as occurring in the ED if not already classified through inpatient records (<0.5%). Of the 15.0% of VS deaths in nursing homes, 64.5% were linked to a record of death in LTC data, and a further 5.3% linked to a death record in designated supportive living records.
ConclusionThere is considerable variation in the reported location of death across data sources in Alberta, with VS reporting lower than expected, confirmed numbers. These discrepancies require further validation in determining the ‘gold standard’ for location of death in subsequent research and quality improvement endeavors.
Evolution of ad hoc and routine data linkage in Australia
Authors: Hagemann Erika , Dombrovskaya Mikhalina , Flack Felicity
Published: September 2024

Australia has a rich history of utilising linked administrative data for health and human services research. The Population Health Research Network (PHRN), established in 2009, has developed national population-based data linkage capability and infrastructure. Australia-wide, specialist data linkage units have successfully linked their core population health data for over a decade, and within the network over 200 data collections are routinely linked. Data collections are typically classified as either routinely linked or linked on a project-by-project, ad hoc basis. Over time, linkage of data collections that were originally considered ad hoc, for example some longitudinal cohorts and clinical registries, has become ‘routine’.
This paper reports on findings of a study that evaluated the volume and proportion of data linkage requests that have been facilitated through the PHRN over the past 5 years that include ad hoc linkage. The paper also reports on the type of data collections that have been linked on an ad hoc basis and details the type of research projects and fields of research currently supported via ad hoc linkage. Quantifying and characterising the type and most sought-after data collections for ad hoc linkage and the breadth of research being undertaken informs future planning and resource allocation for expanding the Australian national linkage capability. This paper explores the integration of ad hoc data linkage within the evolving landscape of expanded master linkage files and national linked data assets, responding to a growing range of data sources and research that could benefit from population-based data linkage.
Uncharted territory in linking population-based laboratory data for the epidemiology of celiac disease in Canada
Authors: King James , Whitten Tara , Li Bing , Youngson Erik , Bakal Jeffrey , Kaplan Gilaad , Williamson Tyler
Published: September 2024

IntroductionWe previously investigated the frequency of screening for celiac disease (CD) based on tissue transglutaminase antibody testing (tTG-IgA) and developed the first incident cohort of celiac autoimmunity in Canada.
MethodsAdministrative data sources used in this study were population-based and covered the entire province of Alberta (~4.3M residents during study period). Various approaches to querying data were employed within a diverse team of health information managers, data analysts, clinical scientists, and gastroenterologists. This process involved a broad search for CD screening tests, thorough data inspection/cleaning, and numerous discussions to determine potential explanations for the findings.
ResultsApproximately ~950,000 records for tTG-IgA were first identified. Records were then excluded due to missing/invalid patient identifiers or test results (0.8%), non-Alberta residency (0.4%), and duplicate records (1.1%). A final dataset included ~920,000 tTG-IgA tests on ~680,000 unique patients, which was also validated through a separate query performed by an analyst external to the study team. A conservative approach to excluding as many potential prevalent cases of CD was applied given a robust algorithm for CD has not yet been established in Canada. The final rate of celiac autoimmunity (34 per 100,000) offered further face validity based on prior estimates of diagnosed CD in Alberta and other countries reporting on celiac autoimmunity.
ConclusionWhen developing a novel case definition or investigating unfamiliar outcomes using routinely collected data, collaboration across several disciplines is highly recommended. Certain stages of the project may require additional scrutiny and discussion to ensure findings are valid and reliable.
Novel applications of linked administrative data – adding longitudinal capability and additional variables to a nationally representative survey of an indigenous population.
Authors: Diamond Tori , Edwards Matt , Sporle Andrew
Published: September 2024

Can linked administrative data be used to transform New Zealand's only sample survey on indigenous wellbeing into a longitudinal study?
This project extends the usefulness of an important survey dataset by linkage to admin data, effectively adding longitudinal capability within a linked administrative data source. This created robust statistical processes to transform an official statistics survey into a nationally representative cohort study.
NZ's Integrated Data Infrastructure (IDI) is a research database of administrative and survey datasets containing a range of variables linkable at the individual level. Te Kupenga is a large nationally representative post-censal survey of NZ's indigenous population (Māori) and is the only official survey with Māori culturally-informed variables. However, it is under-utilised in research.
The Te Kupenga survey was used as a foundational cohort linking to outcomes and determinants in different datasets at different time periods. Outcomes included hospitalisations and COVID-19 vaccinations, while determinants included individual, household and geographic variables. 
Linking a representative survey to admin data created issues of loss to follow-up and missing data, so the original sample is not maintained after linkage. Loss to follow-up and missingness differed depending on variable selection and time periods. So, new universally applicable weights were not possible. However, we created a robust, generally applicable process for re-weighting survey data to account for missingness and loss to follow-up in admin data. 
This project demonstrates the approach for turning a sample survey into a longitudinal cohort using admin data and creates methods that can be used for other official statistics surveys.
Expanding data resources beyond “health care”: exploring options and implications
Authors: McGrail Kimberlyn , McDonald Ted , Nickel Nathan
Published: September 2024

BackgroundPopulation data science has a long history with health care data. Some data centres are expanding to include “health-related” data, including information on other publicly funded services (e.g. education, income and housing supports), and systems (e.g. criminal justice, children in care). We describe what enabled this expansion through three examples from Health Data Research Network Canada. 
MethodsHDRN Canada members include data centres that provide access to linkable data sets for approved research projects. We use case studies to identify similarities, differences, and lessons learned from the process of expansion in the provinces of Manitoba, New Brunswick and British Columbia. 
ResultsThe Manitoba Centre for Health Policy has 30+ years of experience with linked data, with a consistent focus on population health. Data sets expanded incrementally, through partnerships across government agencies. Population Data BC has a nearly 30-year history, with  recent expansion to health-related data through partnership with a program of one government ministry that operates under distinct legislative authority. The New Brunswick Institute for Research, Data and Training is newer, and helped advocate for legislation changes that enabled an expanded set of linked data to be made available for research. All embed Five Safes principles, and commitments to inclusion, diversity, equity and accessibility and Indigenous data sovereignty in data governance. 
ConclusionA greater variety of data enables research to answer complex research questions. This kind of expansion can be accomplished through different mechanisms, but in all cases requires attention to ethical and legal principles of population data science.
Integrating Precision Training for Building a Sustainable National Data Infrastructure in Wales: A Datacise Open Learning (DOL) Based Case Study
Authors: Wang Ting , Lee Stephanie , Thayer Daniel , Palmer Jack , Roberts Chris , Bale Michael , Brophy Sinead , Chee Adam , Smart Jonathan , Ford David
Published: September 2024

ObjectiveConstruct an innovative open-learning solution that provides comprehensive training specific to Trusted Research Environments (TRE) and the broader research community of administrative data users, irrespective of their proficiency levels. DOL offers training opportunities tailored to meet each user's unique learning needs, enabling them to utilise complex, linked administrative datasets confidently and effectively for meaningful research outcomes, thereby building capacity for sustainable national data infrastructure.
ApproachDOL’s innovative open-learning solution offers two learning formats: adaptive and experiential. Adaptive learning provides registered users with bitesize self-paced training based on Administrative Data Research UK's priorities. Experiential learning involves online workgroups with real-world context and practical application. They meet twice a year and are designed around specific topics with frequent guest speakers who are experts in their fields.
ConclusionDOL’s innovative open-learning solution empowers TREs, such as SAIL Databank, to provide well-rounded learning that fosters community support, knowledge-sharing, and networking opportunities for its users, while gathering valuable user feedback. Users can personalise learning and test their knowledge in a flexible training environment, allowing them to take charge of their learning journey.
ImplicationIn response to the increasing demand for training services from novice to advanced users, SAIL Databank adopted DOL's dual learning approach by (1) developing training courses that cover access, process, methodology, integration, and analytical tools for SAIL TRE users, (2) engaging users in a series of workgroups focused on themed datasets including Justice, Environmental, Maternal, Education, and Core Health.
Round 'Ere: Developing a novel method to engage community on the design of wellbeing data hubs
Authors: Rempel Emily , Polizzi Gianfranco
Published: September 2024

The promise of community benefit from health data linked at scale is often missing the perspective of communities themselves. This paper outlines a recent project on developing a peer research and public engagement methodology to build community-designed linked and open wellbeing data hubs. Drawing on action research and appreciative inquiry, we aimed to develop a novel method to engage community on the use of linked local data.
In Spring 2023 we recruited and trained 15 community researchers in Widnes, UK to co-design and run a project on what feeling well meant for their community. We trained our community researchers in listening skills, methods, and data concepts. Over Summer 2023, they interviewed over 200 of their fellow residents. We then ran three design workshops to analyse their findings and co-design a data and community insights hub with local policymakers and voluntary sector representatives.
Through repeated, comparative semi-structured interviews we tracked the community researchers’ progress. We used thematic analysis to explore the benefits and challenges at each stage of the project: recruitment, training, and field work. Benefits ranged from the practical value of capacity building through the research training to the key role of confidence in how community researchers perceived their own success during the project. Challenges ranged from more esoteric concepts like managing expectations of impact to practical concerns around diversity and interview recruitment. Overall, the project evidenced the value of peer research methodologies in involving public in data science both for the participants but also to wider community.
Family Courts in England & Wales: A population-level evidence base about court users who experience domestic abuse
Authors: Garside Ludivine
Published: September 2024

ObjectivesThe caseload of family courts in England and Wales includes a growing share of domestic abuse cases. To increase knowledge about such cases, the research uses new administrative datasets and linkages. Trends between 2011-2021 are examined in the context of policy and operational changes.
MethodThe complete set of family court cases is held in HM Courts & Tribunals Services (HMCTS) systems. Some family court data are collected by the Children and Families Court Advisory Support Service (Cafcass), insofar as courts refer the case to Cafcass. Using their new linkage, we conduct a descriptive analysis of domestic abuse cases in England and Wales over the ten years from 2011. This project is one of the first to gain approval from HMCTS to use their de-identified family court data.
ResultsThe data linkage allows to describe how court users approach domestic abuse matters, drawing distinctions between: emergency cases; lack of legal representation in court (litigants in person); repeat applications; presence of children in the household. Furthermore, the data show how courts respond through case management, timeliness of decisions and legal outcomes made. The policy context reveals how court users adapted to changes in eligibility for civil legal aid and to changes over how courts operated under lockdown measures.
ConclusionData linkage research is providing an evidence base at population level about court users in England and Wales who experience domestic abuse. This can help inform practice to support families, and policies to ease overburdened courts and avoid returns to court.
Automating production of de-identified linkable data
Authors: Iova Madalina , Huck Rachel
Published: September 2024

ObjectiveAn innovative large-scale automated method has been developed to produce de-identified linkable data. The objective is to create a wide pool of ready-to-use data to enable faster and wider collaborative analysis for the public good.
ApproachA configurable automated pipeline prepares data for onward linkage at location, person, business and classification level through:

Big data profiling pre- and post-processing, for overview of variables and characteristics
Flagging potentially sensitive/identifiable variables
Generalisable linkage methods for large-scale data, to enable the addition of unique IDs for onward linkage of de-identified data
De-identification, hashing and redaction mechanisms, to remove and/or obscure sensitive/identifiable variables
Automated production of metadata, capturing linkage quality and transformations across the data journey
Quality assurance checks, including measure of linkage quality, assurance of variable derivations and redactions, and consistency checks on remaining data.

ResultsThe pipeline enables a configurable automated approach to producing de-identified, linkable, ready-to-use data in a traceable and fully documented manner. To achieve this we have: overcome scalability issues in working with big data; implemented automation at various levels; and enabled standardisation across data types and data structures to deliver a consistent recognisable final product.
Conclusions and ImplicationsBuilding an automated pipeline to enable onward linkage of de-identified administrative data was a complex process that has resulted in positive change around how our organisation operates and distributes data. This represents an important step towards future integration of linkage within the platform and the basis of future innovation in the area.
Advancements in Pan-Canadian Data Access and Analysis Facilitation: Insights from Collaborative Health Research supported in Alberta, British Columbia, and Ontario
Authors: Jovanovska Stefana , Ou Joanna , Youngson Erik , Choi Tim , La Carmen , Dvorani Erind , Saskin Refik , Paterson Michael , Kirsh Victoria , Awadalla Philip , Brooks Jennifer , Cheema Sheraz , Elkhair Nouar , Vena Jennifer , Harman Shandra , McDonald Kelly , Bhatti Parveen , Skvirsky Dina
Published: September 2024

ObjectiveTo highlight progress in facilitating and supporting pan-Canadian data analysis research that is supported by a central coordinating center. This process will be illustrated with the use of a recently completed project that has been supported using data from Alberta, British Columbia, and Ontario.
ApproachThe project represents a significant endeavor within the central coordinating center, as it necessitates coordination for data access, data importation and analytical support across three provinces. The focus will be on the administrative processes refined to support collaborative research endeavors. While specific project details will remain undisclosed, procedural enhancements within the central coordinating center framework will be highlighted. Key discussion points include standardized protocols for data access, collaborative efforts to streamline data importation, and facilitation of analytical support across jurisdictions. The type of cross-jurisdictional analytic support will as well be highlighted; data variables harmonization and sharing of data algorithms cohorts across the participating provincial data centers in support of the meta-analysis.
ResultsThe successful completion of the final data analysis underscores the effectiveness of a unified data access coordination center for researchers seeking multi-regional health data in Canada. By highlighting advancements enabled by the central coordinating center and provincial data centers, this submission aims to inform researchers about the current landscape of pan-Canadian research and foster opportunities for future collaboration.
ImplicationsSharing insights and lessons from this project emphasizes the advancements facilitated by the central coordinating center and provincial data centers, informing researchers about the potential of pan-Canadian research, and encouraging future collaborative endeavors.
Linkja: Open Source Privacy-Preserving Record Linkage Tool
Authors: Doshi Kruti , Trick William , Hinami Keiki
Published: September 2024

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.
Algorithmic Accountability for Record Linkage
Authors: Wiegand Emily , Liu Aya , Sedovic Sabrina
Published: September 2024

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.
Concurrent Opioid and Psychotropic Drug Use During Pregnancy: Descriptive Study from Two Canadian Provinces
Authors: Dahl Lindsey , Singal Deepa , Brownell Marni , Dormuth Colin , Chateau Dan , Dahl Matthew , Kim Jason , Nickel Nathan
Published: September 2024

ObjectivesThis study aims to describe the patterns of concurrent opioid and psychotropic drug use (CU) during pregnancy and the characteristics of the mothers dispensed these drugs.
MethodsPregnancies between 2000-2020 were identified using administrative data from Manitoba and British Columbia, Canada. Prenatal opioid and psychotropic drug dispensations were assessed, and exposure groups created based on the number of dispensations within each drug category. Descriptive statistics are reported.
ResultsApproximately 1.4% of 1,040,021 pregnancies involved CU. Odds of CU were greater for 2+ opioid pregnancies compared to pregnancies with only 1 opioid (OR=4.05). Among CU pregnancies, it was more likely for 2+ types of psychotropics to be dispensed for those involving 2+ opioids compared to 1 (OR=2.23). Percent of CU pregnancies did not change over time, while opioid pregnancies decreased, and psychotropic pregnancies increased. Average morphine equivalent dose of opioids/pregnancy was higher in CU than opioid only pregnancies. Compared to mothers without exposure, CU exposed mothers were of similar age, more likely to live rurally, in lower income areas, and have lower care continuity.
ConclusionCU pregnancies remained stable over time. It was more likely for mothers dispensed 2+ opioids to be CU than mothers dispensed only 1 opioid, and more likely for CU mothers dispensed 2+ opioids to be dispensed 2+ psychotropic types. Overall strength of opioids was higher for CU mothers compared to opioid only exposed mothers.
ImplicationsStudies measuring the impact of CU on maternal and child outcomes should be conducted to inform safe prescribing practices.