bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing a High Velocity Dataset Quality Checking Pipeline
Authors: Davies Hannah , Thayer Daniel S , Elmessary Muhammad A , Coldea Alex-Ioan , Jones Carys , Evans Lorna , Makovics Alexander , Howell-Wright Owen , Hughes Lee 
Published: September 2024

ObjectiveThe volume and frequency of refreshed data within the [organisation] has increased significantly since the beginning of the COVID-19 pandemic. Therefore, a more efficient data quality (DQ) checking process was necessary.
Approach Having previously developed an automated DQ checking tool, the focus was on re-engineering the process of DQ task allocation and communication of results.
Results 5 analysts were trained in DQ checking. A JIRA workflow tracks the management of data loading. When a dataset is ready for DQ, the Data Manager allocates a ticket to the DQ Lead who then allocates it onto one of the 5 analysts. Via a DQ Slack channel, the analyst is informed and acknowledges receipt of the task. On completion of DQ, the analyst updates the ticket and transfers it to the appropriate workflow stage. Passed DQ tickets are transferred to the Data Manager for data release, whereas failed ones are placed “On Hold”. The DQ Lead triages the issues and liaises with relevant parties for resolution, which may require data amendments. On receipt of amended data, the DQ ticket is transferred back to the queue and the analyst is notified to re-check the data.
ConclusionsThe team now complete a high volume of DQ checks efficiently. In 2023, 405 datasets, containing 1716 tables, were quality checked, with the initial DQ taking, on average, 2.6 days.
Implications The improved speed of DQ checking ensures projects access the latest available data whilst maintaining the expected DQ levels, integrity and reputation of the TRE.
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.
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.
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.
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.
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.
The use of Supervised Learning to perform pairwise classification for Record Linkage over real world data
Authors: Pereira Ramon , Junior Wagner , Guerra Junior Augusto Afonso 
Published: September 2024

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.
Visuaalisen etnografian mahdollisuuksia sosiaalilääketieteellisessä tutkimuksessa
Authors: Lunkka Nina , Hakola Outi , Kivivirta Ville , Rantakari Anniina 
Published: September 2024

Visuaalinen etnografia tarjoaa vähän hyödynnetyn metodologian terveyden, sairauden tai hyvinvoinnin tutkimiseen. Visuaalinen etnografia on laadullinen tutkimusmetodologia, jossa tutkimusaineistona hyödynnetään visuaalista materiaalia, kuten valokuvia, piirustuksia, videotallenteita, graafista verkkomateriaalia tai erilaista visuaalista digitaalista mediasisältöä. Visuaalinen etnografia ammentaa antropologiasta mahdollistaen visuaalisten elementtien ja kulttuuristen, teknologisten, ruumiillisten tai kokemuksellisten elementtien välisten suhteiden tutkimisen. Vaikka visuaalisen etnografian käyttö on kansainvälisessä sosiaalilääketieteellisessä tutkimuksessa yleistynyt, kotimaisessa tutkimuksessa sen hyödyntäminen on ollut vielä vähäistä ja käyttöperiaatteet vasta muodostumassa. Tässä artikkelissa kuvaamme visuaalisen etnografian mahdollisuuksia ja käytännön soveltamista sosiaalilääketieteellisten kysymysten tutkimuksessa. Aineistoesimerkkien avulla havainnollistamme visuaalisen etnografian hyödyntämistä, huomioitavia seikkoja sekä rajoituksia. Nostamme esimerkkejä sekä itse tuotetun että valmiin visuaalisen aineiston hyödyntämisestä. Pohdimme myös tutkimuseettisiä kysymyksiä, joiden erityispiirteiden huomiointi on välttämätöntä visuaalisen etnografian käytännön toteuttamisessa.