bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing a generalisable stratification approach for clerical review of linked data
Authors: Maizey Leah , Platcha Josie , Gammon Tim , Wray Matt , Thompson Gavin , Antal Laszlo , Archer Rosaland 
Published: September 2024

ObjectiveData linkage is a vital process in the creation of many national statistics, but understanding the quality of linked data is currently highly inefficient. To find errors, data must be reviewed by humans which is costly and lengthy. Sampling is used to reduce the clerical burden. This research aims to develop a method for stratifying links to create representative samples while reducing the number reviewed. The final method will enable nuanced stratification of data for review whilst optimising resource efficiency.
The objectives are to:

ensure that the method is adaptable across diverse datasets,
achieve full automation,
ensure scalability to accommodate large datasets.

ApproachOur approach centres on designing an algorithm that responds to the variability in the data distribution of probabilistic scores and stratify accordingly. The intention is for the developed method to automatically adjust its parameters, such as strata threshold and numbers based on the data’s characteristics.
The research involves a comparative analysis of the performance of dynamic- and percentile-based stratification against the current standard practice of static threshold stratification.
ResultsTests are ongoing to compare the above methods on a variety of metrics including homogeneity of strata, total variance, and between-strata distance. Findings will be presented at the conference.
ConclusionsWe hope to design a robust, generalisable and scalable stratification method that can be integrated into a Linkage pipeline.
Implications Implementing the method will help to improve the quality of national statistics, ensuring more accurate, reliable and timely outputs are produced in a resource efficient manner.
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.
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.
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 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.
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.