bluuebunny/crossref_metadata_2025 huggingface dataset:
Modelling the joint accuracy of two diagnostic tests using evidence synthesis methods incorporating linked electronic health records
Authors: Sheppard Athena , Quinn Terry , Bujkiewicz Sylwia , Owen Rhiannon
Published: September 2024

Background and ObjectivesTo compare the accuracy of different diagnostic pathways comprising of multiple diagnostic tests, statistical methods that evaluate combined or sequential test accuracy are required. This can only be achieved through joint synthesis of diagnostic accuracy data on all tests of interest, using models that account for dependencies between multiple tests. The aim of this research is to develop joint models to assess the diagnostic accuracy of two tests, using real-world evidence to inform within-study dependencies between tests.
ApproachUsing a case study in Alzheimer’s disease dementia (ADD), we will develop Bayesian meta-analysis models to evaluate the combined diagnostic accuracy of two tests. A novel application of trivariate copulas will be used to capture within-study dependencies between two tests assessed in the same patients. Individual participant data from Dementias Platform UK (DPUK) Data Portal – a repository of linked electronic health record data optimised for dementia research – will be used to estimate dependencies between diagnostic tests for ADD.
ResultsThe DPUK Data Portal includes records of over 3 million people from 51 long-term cohort studies, including individual-level test results for ADD verified against confirmed diagnoses. Analysis will focus on modelling the diagnostic accuracy of different testing strategies for ADD, combining cognitive function, imaging, and biomarker tests.
Conclusions and ImplicationsResults of this study have the potential to inform healthcare decision-making regarding optimal diagnostic pathways by enabling the comparison of different testing strategies. The incorporation of real-world data from the DPUK Data Portal makes better use of the available evidence.
Evaluation of an Optimal Method for Ordering Hierarchical Matchkeys in Data Linkage at the Office for National Statistics
Authors: Wray Matthew , McLaughlin Donal , Vellanki Pratibha , White Zoe , Plachta Josie , Maizey Leah
Published: September 2024

The quality of linkage techniques has a direct impact on the quality of statistics produced on the linked data, and as such, ensuring linkage algorithms are of the highest quality is a vital mission of the Office for National Statistics (ONS).
Deterministic hierarchical matchkeys are a linkage technique that applies a list of conditions to classify links, where records can only be linked once. The aim of the hierarchy is to classify correct links by running higher precision matchkeys first, removing linked records from the matching pool, and then running lower quality keys so that they are less likely to make incorrect links. Currently, no optimal method for ordering match-keys exists. Here we present novel research on a generalisable algorithmic approach to finding optimal matchkey orderings.
A deterministic loop over a consistent set of matchkeys was used to link ONS’s 2021 Census to the 2021 Census Coverage Survey (CCS). A range of experimental ordering methods were implemented. Gold standard linked data was used for quality determination to produce metrics such as precision and recall for grading and comparing the experimental ordering methods. The required expertise of the user and computational efficiency were considered when comparing and recommending ordering methods.
Removing historically subjective matchkey ordering methods and replacing with a deterministic ordering algorithm, can lead to better data linkage outcomes with reductions in linkage error. This can provide greater confidence to users of official statistics that linked data underpinning them are more reliable, trustworthy, and meaningful.
Understanding educational attainment disparities in Northern Ireland: the impact of pupils’ demographic characteristics and health status on post-primary outcomes.
Authors: Early Erin , Miller Sarah , Dunne Laura
Published: September 2024

Across the UK, attainment disparities remain at the forefront of education and policy debates. This is particularly evident in Northern Ireland (NI), which although is a jurisdiction of the UK, reflects a different context due to its transition to a post-conflict society and its dually selective education system (academically and religiously). In NI, post-primary attainment disparities have been reported according to pupil characteristics such as gender and socio-economic background. However, due to the lack of available data, there is limited evidence examining the influence of the health status of pupils on their educational outcomes.  
This study examines the influence of a pupil’s socio-demographic profile, health status, and school-level factors on post-primary attainment, using a linked administrative dataset that combined the household Census (2011), School Census (2010-2014) and School Leavers Survey (2010-2014) for the first time. Multilevel modelling and interactions terms were conducted to consider the relative effects of physical health and mental health on attainment. In addition, the study presents a holistic approach to understanding the association between health status and education outcomes by considering self-reported health status, the presence of an illness that limits daily activities, and the presence of a physical and mental health condition.
The findings highlight that poor physical and mental health have negative associations with post-primary attainment in NI. More specifically, pupils reporting bad health, a physical or mental health condition and a limiting illness that affects their daily activities have lower educational attainment outcomes than those with no markers of ill-health (physically or mentally).
Exploring Disparities in Opioid Utilization: An Analysis Comparing Red River Métis to All Other Manitobans
Authors: Nault Kyler , Nickel Nathan , Poitras Colton , Chartrand Frances , Driedger Michelle , Katz Alan , Kloss Olena
Published: September 2024

Objective and ApproachTo address the opioid crisis within the Red River Métis (RRM) Community, understanding opioid use is crucial to empower regional health authorities to adapt health programs, services, and policies to meet their unique needs effectively.
The investigation utilized focus groups with a Community-Based Participatory Research and Collective Consensual Data Analytics Procedure (CBPR/CCDAP) approach. Additionally, a population-based retrospective cross-sectional study for fiscal years 2006/07–2018/19 was conducted using administrative data from a population research data repository. Rates of prescription opioid dispensing (RPOD) and mean morphine equivalents (MEQ) were compared between RRM and all other Manitobans (AOM) aged 10 years or older.
ResultsThe rate of prescription opioid dispensing and MEQ/person were found to be consistently higher among RRM compared to AOM in each study year (p < 0.001). While the RPOD declined among AOM over the study period, it did not change among RRM. Key findings revealed RRM were concerned about how opioids impacted their communities, and felt the need for increased addiction treatment resources, including Red River Métis culture-specific programs.
ConclusionThe evidence demonstrates higher RPOD and MEQ among RRM compared to AOM, suggesting elevated risk of opioid-related harms. Focus group feedback reinforces the need for tailored interventions.
ImplicationsFuture policies and programs targeting the opioid crisis should prioritize the unique needs of populations like the RRM, requiring tailored, culturally appropriate interventions for effective crisis management.
Practical approaches for engaging the public in a population-level data analysis project.
Authors: Yussuf-Homenauth Sabella , Ferreira-Legere Laura , Leong-Sit Elise , Schull Michael J. , Campitelli Michael , Paterson J Michael , The ICES Public Advisory Council
Published: September 2024

ObjectiveStewards of population-level data have an obligation to serve the public interest. As one such publicly funded data steward, our Public Advisory Council (PAC) co-led and determined the focus of an analysis project using population-level administrative health data. We describe our engagement strategies herein.
ApproachThe 20-member PAC was involved in each stage of the project, from research question formulation to knowledge translation planning over 18 months, consisting of 12 meetings with both large and smaller groups. Our approach to engagement applied the International Association for Public Participation’s Framework as the foundation for an ‘empowerment’ level of engagement and adapted approaches from the James Lind Alliance and the ‘Plan-Do-Study-Act’ cycle.
ResultsThe PAC chose to focus their analysis project on factors related to mental health and addiction service use. Four strategies were used and co-designed by members to foster engagement throughout: providing education and guidance, shared and guided brainstorming, building consensus, and responsiveness to feedback and evaluation. PAC members directed how and when these strategies were used, with challenges and lessons learned currently being co-developed into a publicly accessible report.
ConclusionsOur work demonstrates the importance and value of public-driven research outputs and the feasibility of integrating public members in the work of data stewards.
ImplicationsThe insights and practical strategies generated from this project will be used to guide effective engagement of the public for future analysis projects and to improve trust and social license for other initiatives using population-level data.
Are there systematic differences in Teacher Assessment and Test Scores by Exclusion Status?
Authors: Rowland Neil , McVicar Duncan , Jahanshahi Babak , Miller Corina
Published: September 2024

Objective and ApproachThe educational performance of school pupils in England is assessed in many ways, including through formal testing and teacher assessment. It is important to understand whether certain types of pupils perform better in certain forms of assessment, and although this has been studied within the context of ethnicity, amidst a recent increase in the number of school exclusions in England there is a growing need to understand whether excluded pupils perform differently than never-excluded pupils in different forms of assessment, especially given that excluded pupils are characterized by multiple forms of disadvantage. This study investigates this question using the Longitudinal Educational Outcomes (LEO) dataset, focusing on a cohort of pupils who undertook both forms of assessment at the end of Key Stage 3 (age 14).
ResultsWe first explore whether, compared with never-excluded pupils, excluded pupils receive a teacher assessment level below their achieved test level, conditional on prior attainment. These estimates reveal no differences by exclusion record, and this result holds for all assessed subjects (English, Maths and Science). We also explore whether, conditional on the Key Stage 3 test score, excluded pupils have a higher chance of being assessed below the expected level by their teacher. These estimates indicate that, compared with measurably similar pupils who are never excluded, some excluded pupils underachieve in teacher assessment.
Conclusions and ImplicationsOverall, the results show that what can be learned about systematic discrepancies between teacher assessment and test measures depends on how such discrepancies are estimated.
Health and education needs of primary school children with Down Syndrome in England: What can we learn from linked administrative data?
Authors: Shumway Julia , Gilbert Ruth , De Stavola Bianca , Zylbersztejn Ania
Published: September 2024

ObjectiveChildren with Down syndrome (DS) have high rates of chronic health conditions (CHCs) which can affect special education needs and primary school placement (mainstream or special). We assessed timing and type of CHC by year-specific primary school placement (ages 5 to 11 years).
ApproachWe followed two cohorts: first, a birth cohort of all children with DS born from 2003 to 2020 in England using hospital administrative data; second, a sub-cohort with linked hospital and education records who enrolled in state-funded primary schools in England at age 5 from 2009/10 to 2013/14. We calculated the cumulative incidence of CHCs and multimorbidity (defined as distinct CHCs affecting two or more body systems) from birth through age 11, accounting for variable follow-up and the competing risk of death. We assessed multimorbidity by school placement over time.
ResultsThe birth cohort included 11,478 children with DS (prevalence: 0.11%). By age 5, 6.2% of children had died, 80.8% had at least one recorded CHC, and 53.4% had multiple CHCs. Corresponding figures by age 11 were 6.9%, 87.4% and 65.9%, respectively.
85% of children with DS alive at school entry linked to a school record. 72% were enrolled in mainstream primary school at age 5, decreasing to 62% by age 7, and 54% by age 11.
ConclusionsMost children with DS have multiple hospital-recorded CHCs. Nearly three-quarters start mainstream primary school, but just over half remain by age 11.
ImplicationsParents, clinicians, and educators will benefit from understanding relationships between multimorbidity and school placement.
Save A Leg – Exploring Disparities in Community Characteristics Between Regions of High and Low Leg Amputation Rates
Authors: Ni Chuanfen , Holtzman Kyle , Minardi Laura
Published: September 2024

ObjectiveTo explore the disparities in community characteristics between the regions with the highest and lowest leg amputation rates.
ApproachThis study used an exploratory method to describe the disparities between community characteristics of these two regions. The study team used several data sources: 1) We used the Dartmouth Atlas Healthcare Map to identify the highest and lowest leg amputation areas. This data was based on leg amputations per 1,000 Medicare enrollees by Hospital referral regions (HRRs) in 2015. 2) We used population estimates and income data from The Census Bureau. 3) We gathered climate, crime index, walk score, bike lanes, and park data from numerous websites.
ResultsFlorence, SC has the highest leg amputation rate and Bridgeport, CT has the lowest across the nation. Florence also shows higher cardiovascular disease and diabetes discharge rates. Florence’s population has a slightly higher percentage of persons 65 years and over, a higher percentage of bachelor’s degrees, a higher per capita income, and a lower poverty rate. Florence also has fewer parks, is less walkable or bikeable, and has worse crime. It is worth noting that Bridgeport is close to Yale New Haven Hospital, which has a Limb Preservation Program that could contribute to the region’s extremely low leg amputation rate.
ConclusionsThere are disparities in community characteristics between the regions with the highest and lowest leg amputation rates.
ImplicationsFuture work needs to investigate the association between community characteristics and leg amputation rates and assess the impact of the Limb Preservation Program.
The application of deterministic and probabilistic methods of data linkage methods to link population and health data in western Uganda
Authors: Kusuna Magdaline , Maina Daniel , Mulindwa Herbert , Kapuru Asinja , Karugu Caroline , Mwanga Daniel , Asiki Gershim
Published: September 2024

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.
Stimulant treatment for attention-deficit/hyperactivity disorder and risk of first and repeat criminal offending: a population data linkage cohort study
Authors: Nielsen Timothy , Nanan Ralph , Butler Tony , Nassar Natasha , Poulton Alison
Published: September 2024

ObjectiveTo examine the effect of stimulant treatment on first and repeat criminal offending among children and young adults with attention deficit-hyperactivity disorder (ADHD).
ApproachA population data linkage cohort study of individuals born in New South Wales (NSW), Australia in 1990-2005 and followed until May 2016. Individuals linked to stimulant treatment authorizations for ADHD in the NSW Ministry of Health controlled substance database were frequency matched (1:10) to controls without ADHD. Proven criminal offenses were linked from statewide court records. First and repeat offenses were examined separately using modified cox regression and Prentice, Williams, Peterson (PWP) models, respectively.
ResultsThe cohort included 75,650 individuals with ADHD and 745,634 controls. A total of 59,658 individuals (7.3%) committed an offense and individuals with ADHD committed 3 times as many offenses. For all age and sex groups, the risk of a first offense was increased among individuals with ADHD but reduced by treatment (Males 10-17 years, untreated Hazard Ratio (HR) 2.02 95% confidence interval (CI) 1.95, 2.10, treated HR 1.52 95% CI 1.41, 1.62). The effect estimates were reduced for repeat offenses (Males 10-17 years, untreated HR 1.09 95% CI 1.05, 1.13, treated HR 0.97 95% CI 0.90, 1.04) with mixed results for the effect of treatment.
ConclusionsIndividuals with ADHD had increased risk of offending, but stimulant treatment reduced the risk of first offenses. This association and benefit of treatment were reduced among prior offenders.
ImplicationsAdequate treatment may help keep young people with ADHD out of the criminal justice system.
Extracting Social Determinants of Health from Inpatient Electronic Medical Records
Authors: Martin Elliot , D'Souza Adam , Saini Vineet , Tang Karen , Quan Hude , Eastwood Cathy
Published: September 2024

ObjectiveSocial determinants of health (SDOH) have been shown to be important predictors of health outcomes. Here we assess how best to extract SDOH variables from inpatient electronic medical record (EMR) data. 
ApproachFour social determinants were targeted: patient language barriers, employment status, education, and whether the patient lives alone. Inpatients aged 18 and older with records in the Calgary-wide EMR system were studied. Algorithms were developed on January 2019 hospital admissions (n=8,999), and validated on January 2018 hospital admissions (n=8,839). SDOH documented as structured data, which can be easily queried, were compared against those extracted from unstructured free-text notes.
ResultsMore than twice as many patients had an unstructured note documenting a language barrier than in the structured data; 12% of patients indicated by notes to be living alone had a partner in their structured marital status. The Positive Predictive Value (PPV) of the elements extracted from notes was high, at 99% (95% CI 94.0%-100.0%) for language barriers, 98% (95% CI 92.6%-99.9%) for living alone, 96% (95% CI 89.8%-98.8%) for unemployment, and 88% (95% CI 80.0%-93.1%) for retirement.
ConclusionsIt is possible to extract SDOH elements from free text notes with high PPV. SDOH documentation was largely missing in structured data, and sometimes misleading. 
ImplicationsFree text notes can be a fruitful source of information for projects using SDOH variables, such as machine learning/AI or health services research, and can offer insights not available from the structured data elements.
Quality of care for community-dwelling older adults living with dementia in BC: an interrupted time-series analysis to examine the effect of the COVID-19 pandemic
Authors: Helmer-Smith Mary , McGrail Kimberlyn , Wong Sabrina , Law Michael
Published: September 2024

ObjectiveQuality care for older adults living with dementia (OALwD) is a priority in Canada, nationally, and British Columbia (BC), provincially. The COVID-19 pandemic likely changed care quality, but this has not been assessed. We are examining the effect of pandemic-related policy changes on quality of care for community-dwelling OALwD in BC.
ApproachThis population-based retrospective cohort study uses linked administrative data to examine trends in population-level indicators of quality care from 2016-2023. We will compare effects among community-dwelling BC residents aged 65+ with dementia and those without dementia. Indicators including hospitalization rate and medication use were selected, based on the Institute of Medicine’s six dimensions of quality (e.g., effectiveness, safety). Interrupted time series (ITS) analysis will examine changes between “pre-pandemic” and “in-pandemic” periods. Equity will be assessed through stratification by sociodemographic variables.
Anticipated ResultsPercent change in the level and slope of each indicator’s trend will be reported. Single ITS will identify the effect of the pandemic on each outcome in the dementia group only. Controlled ITS will identify whether changes differ from those observed in older adults without dementia. Together, observed changes in the indicators and differences across equity factors will inform an interpretation of the overall effect of the pandemic on quality of care for community-dwelling OALwD in BC.
ImplicationsWe will determine whether quality of care changed for older adults because of the pandemic and whether changes differed for OALwD. This knowledge will inform practice and policy for delivery of high quality, proactive dementia care.
A Population-Based Linked Cohort Study of Maternal Exposure to Bioclimatic Stress and Hypertensive Disorders of Pregnancy in Australia.
Authors: Gebremedhin Amanuel , Nyadanu Sylvester , Hanigan Ivan , Pereira Gavin
Published: September 2024

ObjectiveTo identify critical susceptible periods for the associations between bioclimatic exposure (Universal Thermal Climate Index, UTCI) and the risks of gestational hypertension and preeclampsia.
ApproachWe linked 415,091 singleton pregnancies of 20–42 weeks’ gestation between 1st January 2000 and 31st December 2015 to UTCI exposure in Western Australia. Distributed lag nonlinear logistic regressions were used to estimate weekly-specific adjusted odds of gestational hypertension and preeclampsia for UTCI exposure from twelve weeks of preconception through pregnancy.
ResultsThe analysed pregnancies included 3.7% and 2.8% cases of gestational hypertension and preeclampsia, respectively. Exposures from early pregnancy up to 30th gestational weeks were associated with greater odds of hypertensive disorders of pregnancy (HDPs) with critical susceptible periods at all exposure thresholds, particularly elevated at the 1st (10.2 °C) and 99th (26.0 °C) exposure centiles as compared to the median (14.2 °C). The most elevated weekly OR and 95% CI estimated at 99th exposure centile was 1.07 (95% CI 1.06, 1.08) in gestational weeks 8–18 for gestational hypertension and 1.10 (95% CI 1.08, 1.11) in gestational weeks 11–16 for preeclampsia. Cumulative exposures associated with HDPs with relatively higher ORs, but less precise than weekly exposures. The effects of high exposure were disproportionately elevated in both HDPs for Caucasians, non-smokers, multiparous, and mothers in moderate/low SES residences. 
Conclusions and ImplicationsMaternal bioclimatic exposure increases the risk of HDPs, suggesting potentially susceptible periods and populations for climate-related targeted interventions. It highlights the need for advising women on environmental risks in early pregnancy.
The SAIL MELD-B e-cohort (SMC) and SAIL MELD-B Young adult e-cohort (SMYC) – a reproducible methodological pipeline for dynamic cohort curation and research
Authors: Chiovoloni Roberta , Alwan Nisreen , Berrington Ann , Boniface Michael , Dylag Jakub , Fair Nic , Fraser Simon DS , Holland Emilia , Owen Rhiannon K , Stannard Sebastian , Zlatev Zlatko , Akbari Ashley
Published: September 2024

ObjectivesThrough the Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) project, we established the SAIL MELD-B e-cohort (SMC) and the SAIL MELD-B Young adults e-cohort (SMYC), with the aim to enhance the understanding of ‘burdensomeness’ in individuals living with multimorbidity, including identifying new clusters of burdensome indicators, exploring early life risk factors and modelling potential preventative scenarios.
ApproachWe use routinely-collected anonymised linked demographic, health and administrative data sources available within the SAIL Databank to define SMC and SMYC. These cohorts were developed using a reproducible, maintainable, methodological pipeline that allows for dynamic updates as data coverage expands. The pipeline efficiently processes new burdensomeness concepts, facilitating the extraction of relevant records associated with the concepts identified for use in the SMC and SMYC.
ResultsSMC and SMYC comprises of 5,180,602 and 896,155 individuals registered with a Welsh General Practice at any time between 1st January 2000 and 31st December 2022 respectively. Analysis of primary and secondary care health data reveals that the most common conditions in SMC were depression (21.6%), anxiety (21.1%), asthma (17.5%), hypertension (16.2%), and atopic eczema (14.1%). In SMYC, the most common conditions were atopic eczema (21.2%), asthma (11.6%), anxiety (6.0%), deafness (4.6%), and depression (4.3%).
Conclusions and ImplicationsSMC and SMYC provide two generalisable population samples, which can be used to address various research questions across MELD-B. The adaptability of the methodological pipeline allows cohort curation to be repurposed for other projects accessing population-scale data sources and trusted research environments.
Exposure to PM2.5 air pollution and mortality in Northern Ireland
Authors: Rowland Neil , McVicar Duncan , Jahanshahi Babak , Milller Corina
Published: September 2024

Objective and ApproachThis study assesses the relationship between PM2.5 air pollution exposure and mortality in Northern Ireland. It draws on a rich array of individual-level mortality and Census data from the Northern Ireland Longitudinal Study linked to finely grained neighbourhood-level modelled air pollution data.
ResultsCox proportional hazards models reveal associations between mortality risk and PM2.5 exposure, though the magnitude of the effects depends on the specification. Without adjustment for measured characteristics, an interquartile range increase in the five-year moving average of PM2.5 exposure is associated with a relatively large increase in the hazard of mortality. This effect is reduced after adjusting for individual-level demographic, socioeconomic and health-related factors as well as household-level factors; however, the hazard ratio remains above one and comparable with estimates from other studies. In further analysis focusing on changes in exposure resulting from address changes, propensity score matching is used to assess whether, among those with similar levels of pre-move exposure, those who move to areas with different pollution levels face subsequently different mortality risks. The results show a higher (lower) mortality risk for those moving to a higher (lower) pollution neighbourhood.
Conclusions and ImplicationsThis study provides evidence of mortality effects of PM2.5 from a setting in which annual mean PM2.5 concentrations are low by international standards (averaging roughly 8 micrograms per cubic metre during the study period) and offers timely evidence to inform the delivery of Northern Ireland’s first-ever Clean Air Strategy.
Citizen Panel: Novel method for Public Involvement in data access review of linked longitudinal data
Authors: Garbovan Lidis , Flaig Robin
Published: September 2024

UK Longitudinal Linkage Collaboration’s (UK LLC) Citizen Panel pilot research project aims to embed public feedback and perceptions into data access decision-making and Trusted Research Environment (TRE) design. The Citizen Panel is a concept developed by Understanding Patient Data as a new methodological approach to decisions about data via conversations with the public.
The Citizen Panel has three key objectives: involving the public in decisions around the acceptance and suitability of data use, assessing the scope and benefit of research questions, and creating a learning feedback loop for decision-making.
The approach taken in the Citizen Panel is intended to enhance and complement the existing ways in which public involvement is conducted in longitudinal studies, especially focused on maximizing diversity and inclusion of seldom-heard communities which are underrepresented in longitudinal studies. The Citizen Panel is conceptualized as a ‘learning model’ which will embed a diverse public voice in planning and decision-making, enabling UK LLC to be responsive to new opportunities and changes in context. 
The project will start with a small number (15-20) of public contributors and will explore approaches to scale up in size (panel with >100 members), and geography (e.g. Citizen Panel groups in four UK nations). The project is currently in the design stage (March 2024), which will be followed by recruitment, delivery, evaluation and producing and sharing outputs. The results will be shared widely with academic communities and public networks.
Exploitation of Children in England and Wales: Barriers to understanding child-centred support service outcomes using two sources of administrative data
Authors: Huxley Katy , Skeels Anna
Published: September 2024

Across the UK, the National Referral Mechanism (NRM) is the framework by which those affected, or those at risk of, modern slavery, are referred by ‘first-responders’ (such as the police) to other support services. For children, a key support mechanism is the Independent Child Trafficking Guardianship (ICTG) service, run by Barnardo’s for the Home Office.
Administrative data from the NRM and ICTG were utilised in a novel mixed-method study to evaluate child-centred outcomes for children receiving ICTG support. We utilised publicly available NRM data to look at the nature of reported modern slavery between 2017 and 2022. We also negotiated access to Home Office held ICTG service data for the same period.
Whilst our findings reveal patterns in the extent of reported exploitation and the nature of service support, examination of the two data sets revealed a number of issues related to categorisation of children, exploitation experiences, and geographical representation that hinder a comprehensive understanding of outcomes for child exploitation and trafficking.  Qualitative ICTG data, however, revealed a ‘pyramid’ of support areas that were crucial to supporting positive outcomes, whilst participatory research methods revealed significant positive outcomes for children.
The research identified a failure of administrative data to directly collect information relevant to positive child-centred outcomes. The recommendations, detailed within the final report, suggest that there is further work that could be done to improve administrative data collection, and highlight the potential of linked administrative data sources to understand related outcomes, such as health, education and crime/justice.
Using Polars to Improve String Similarity Performance in Python
Authors: Foxcroft Jeremy , Antonie Luiza
Published: September 2024

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.
Using Administrative Data to Identify Factors Associated with Healthy Development After Experiencing Household Challenge Adversity in Early Childhood
Authors: Durksen Anita , Phillips-Beck Wanda , McGavock Jon , Afifi Tracie , Brownell Marni , Nickel Nathan
Published: September 2024

This study aimed to identify factors associated with healthy development after experiencing household challenge adversity in early childhood.
A cohort of 42,505 children born in Manitoba, Canada was created using linkable health, social, and education administrative data.  Children were divided into 5 groups according to whether they experienced adversity between birth and four years of age in the following categories: 1) having a parent diagnosed with a mental health disorder, 2) having a parent diagnosed with a substance use disorder, 3) having a parent who received income assistance for at least 2 months, 4) experiencing at least one of the 3 adversities, and 5) having none of the adversities listed. For each group, multiple linear regression models were computed to determine the association of the independent variables (age, sex, birth order, team parenting, neighbourhood-level socio-economic status, parent high school completion, being an established resident in Canada, not having a major illness, residential stability, program participation, and connectedness to health care services) on child development, represented by their Early Development Instrument scores.  
In each group, factors associated with healthy development included at minimum: older age, being female, being firstborn, team parenting, higher neighbourhood-level socio-economic status, parent high school completion, being an established resident in Canada, and not having a major illness. Several of these factors are associated with increased access to material resources. As such, policies that facilitate the delivery of resources to lower-income families and neighbourhoods with lower socioeconomic status could translate into improved developmental outcomes for children.
The CanPath-HDRN Canada Collaboration: Enabling Multi-jurisdictional Research in Canada
Authors: Brooks Jennifer , Hayes Anne , Alvi Riaz , Azimaee Mahmoud , Bhatti Parveen , Cheema Sheraz , Choi Tim , ElKhair Nouar , Fleming Megan , Frank Noah , Frizzell Katelyn , Gatley Jodi , Gilbert Lindsey , Gravel Simon , Harman Shandra , Hicks Jason , Ho Vikki , Hunt Jordan , Jovanovska Stefana , Kirsh Victoria , Kydd Robyn , La Carmen , Lester Kendra , Lettre Guillaume , Nunes De Melo Magda , Standing Mary-Ann , Stewart Lindsay , Turner Donna , Urquhart Robin , Vena Jennifer , Youngson Eric , Awadalla Philip
Published: September 2024

ObjectiveTo highlight the partnership between the Canadian Partnership for Tomorrow’s Health (CanPath) and Health Data Research Network (HDRN Canada), which enables researchers to link CanPath’s health and lifestyle survey data to health records and related data across multiple regions.
BackgroundCanPath is a population health study of over 350,000 Canadians from seven regional cohorts across all ten provinces, making it one of the world's largest population cohorts. HDRN Canada is a network of member organizations, including provincial, territorial, and pan-Canadian data centres.
ApproachThis partnership leveraged collective expertise and resources to facilitate the linkage of CanPath’s harmonized data to multi-regional health and health-related administrative data held at HDRN Canada’ s data centres. Researchers can access these data for multi-regional projects through HDRN Canada’s Data Access Support Hub, which provides a single access portal.
ResultsResearchers were able to successfully link CanPath’s survey data to provincial administrative health data. The collaboration’s streamlined process for data access enhances efficiency and facilitates pan-Canadian population health research.
ConclusionThis collaboration demonstrates the feasibility and value of linking population health datasets while demonstrating the challenges and opportunities associated with accessing national administrative health data within a federated health data system.
ImplicationsThe partnership between CanPath and HDRN Canada has significant implications for advancing population health research in Canada. By providing researchers with access to linked data from diverse sources, the partnership enables comprehensive investigations into health determinants, disease patterns, and clinical outcomes. This enhances the scope and depth of population health research in Canada, thereby leading to a better understanding of the most pressing health challenges.
Describing the kidney health of ARDAC study participants using data linkage
Authors: Hiyare Amandi , Dal Grande1 Eleonora , Stephens Jacqueline , Mallitt Kylie-Ann , Kim Siah , Teixeria-Pinto Armando , Sinka Victoria , Dickson Michelle , Jaure Allison , Craig Jonathan
Published: September 2024

ObjectivesThe Antecedents of Renal Disease in Aboriginal Children and Young Adults (“ARDAC”) study is the largest data linkage study on the kidney health of First Nations Peoples internationally. Using this data linkage this study aimed to describe the kidney health of ARDAC study participants (both Indigenous and non-Indigenous) aged under 25 years old.
MethodsAdministrative data from hospital and community healthcare utilisation, pharmaceutical claims, and biomedical measurements were used to create a hybrid case definition for CKD. Using this definition, descriptive statistics and bivariate analysis were used to describe sociodemographic characteristics of ARDAC participants with CKD. A ‘strengths-based’ regression analysis was also undertaken to challenge past ways of reporting kidney health outcomes and improve equity among young Aboriginal peoples.
ResultsFrom the 3758 participants enrolled at baseline, approximately 1.5% of young people developed CKD between 2002 to 2019 (follow-up: 2-4 years). The current median age of the cohort is 26.7 years. The majority of those with CKD were identified through biomedical and pharmaceutical data combined, followed by community healthcare claims, and hospitalizations. Findings from the regression analysis will provide insight into the socioeconomic factors that lead to CKD in young people and will be the focus of this presentation.
Conclusions/ImplicationsOur findings report for the first time the socioeconomic drivers of kidney health equity for young Aboriginal peoples. Understanding these drivers provides opportunities to work in partnership with communities to develop culturally safe health promotion.
Coordinating a growing data enterprise: strategies for facilitating multiple research-practice relationships for the public good.
Authors: Frederick Carl
Published: September 2024

It takes time, energy, and goodwill to start a data partnership. The corollary to this truism is that maintaining and expanding such partnerships will demand as much, if not more, of these resources. Learn about the strategies we are using to expand our ability to produce rigorous, policy-relevant evidence to combat poverty and inequality while staying committed to addressing the needs of our long-term partners.
Developed by the University of Wisconsin-Madison’s Institute for Research on Poverty (IRP), the Wisconsin Administrative Data Core (WADC) evolved from a series of large-scale evaluation projects conducted for the state starting in the 1980s. This data enterprise, built by linking administrative data from multiple agencies, allows for cross-program analyses that are otherwise impossible.
Increased turnover since the pandemic coupled with more formalized data governance structures has highlighted the importance of maintaining good relationships and providing value back to partners who devote scarce bandwidth to sharing data. At the same time, we are adding new agency partners and working to increase usage of the WADC. These changes multiply the complexity involved in coordinating the data enterprise. We are adapting to this new world by focusing on relationships: creating formal structures that deepen the network of direct relationships among agency staff and researchers, taking advantage of increased turnover by hiring staff who have worked in state agencies, and seeking agency partners who want to be active partners versus data providers. More solid relationships will yield more policy-relevant research that agencies can use to better serve the state.
Exploring Locality Sensitive Hashing as a Blocking Method for Large-Scale Administrative Datasets
Authors: Plachta Josie , Shipsey Rachel , Quinn Leah
Published: September 2024

ObjectivesLinking large-scale datasets is challenging due to the computational power required. This research explores using Locality-Sensitive-Hashing (LSH) as a novel blocking method to facilitate linking large administrative data. LSH uses an efficient technique of hashing data while preserving similarity, reducing the search space and processing power required to find links.
ApproachSamples were taken from a gold-standard dataset and blocked using LSH. Various parameters were tested to establish LSH’s optimal performance at retrieving linked pairs and reducing the search space size.
ResultsTesting the method on small datasets gave promising results, with the LSH method creating ~9,000 candidate pairs. This is a improvement over our traditional blocking method or cartesian product, which created ~70,000 and 23.4 million candidate pairs, respectively. We have therefore shown that LSH can significantly reduce the search-space size. A further advantage of the method is its capability in handling alternative variables such as those that may be present in longitudinal or composite data, without needing to manually anticipate the different combinations of variables which may occur. Variable formats to simulate agreement weighting were also considered, with encouraging results.
Conclusion and ImplicationsOur research shows that LSH can be used to drastically reduce the search space when blocking for data linkage This suggests developing LSH as a blocking method could result in more effective blocking, quicker linkage, and no loss of quality compared to traditional methods. However, further research on increasing the method’s scale is necessary.
Exploring Text Classification Systems for Automatically Coding Historical Occupations and Causes of Death
Authors: Antonie Luiza , Christen Peter , Dibben Chris , Foxcroft Jeremy , Williamson Lee
Published: September 2024

ObjectivesText classification models can be used to automatically categorize occupations and causes of death within historical documents. It is important to classify/code these categories as different words or textual descriptions could refer to the same occupation or cause of death. Given the many historical documents that are becoming available for research, accurate classification systems can be valuable resources.
ApproachWe explore different text classification techniques, from traditional machine learning to deep learning, and investigate methodologies that transform occupations and causes of death into a vectorial space and use these representations as features to train text classification systems. Our data come from IPUMS USA/International, and SCADR.
ResultsHistorians have coded occupations and causes of death for some census collections (e.g., US, Canada), but not yet for others (e.g., Scotland). We train and evaluate our classification systems using data from the US and Canada and then deploy it on data from Scotland. We quantitatively measure the performance of the classification systems for historical documents that have codes available. Additionally, once we deploy the model to data that does not yet have codes, we qualitatively evaluate our results by engaging with historians working on those data. We report and discuss these results to understand where the models are performing well and where the models are underperforming.
ConclusionsResults suggest that there is value in building and deploying these classification models. We recommend the use of such models in conjunction with engaging with domain experts.
Exploring variations in patient safety events across equity-deserving populations
Authors: Riley Dana , De Prophetis Eric , Mahendran Mayuri , Hynes Geoffrey , Hart Ezra , Akinnibosun Rukayyah , Denny Keith
Published: September 2024

The objective of this analysis was to examine variations in rates of hospital harm (HH), an important indicator of patient safety events, across population subgroups in Canada.
We conducted a descriptive study of HH in provinces and territories using three years (2016 to 2018) of CIHI’s Discharge Abstract Database (DAD) linked to the 2016 Canadian Census, also known as Statistics Canada’s Canadian Census Health and Environment Cohorts (CanCHEC). We investigated the influence of sociodemographic variables not generally available in hospital administrative data.
We calculated age-standardized rates (ASR) per 100 hospital discharges and 95% confidence intervals for HH, stratified by sex, income (area and individual), racialized group, education, immigration status, language, disability status, social and material deprivation, and geography (urban vs. rural/remote).
Despite males experiencing higher rates of harm (ASR 4.36 vs. 4.23), overall females experienced 54% of all HH events, with an average age of 64 (vs age 49 for those discharged without a HH event). For neighbourhood income quintile, the highest rates of HH were observed in the lowest quintile (4.08, 4.04 – 4.11). The rate of HH decreased as neighbourhood income increased. Patients from rural/remote neighbourhoods had lower rates of HH (3.7, 3.66-3.73) than those living in urban neighbourhoods (4.47, 4.44-4.49). Additional equity-stratified results will be presented.
Our analysis revealed that marginalized populations were more likely to experience a harmful event during their hospital stay. Measuring and monitoring inequalities in patient safety provides health systems with information to better understand, target, and evaluate healthcare quality improvement initiatives.