bluuebunny/crossref_metadata_2025_split huggingface dataset:
Conceptualizing community data governance for race-related, population data: a scoping review and key informant interviews
Authors: Leong-Sit Elise , Ferreira-Legere Laura , Yussuf-Homenauth Sabella , Guttmann Astrid , Shah Baiju R , Schull Michael J , Ratnasingham Sujitha , Paterson J Michael 
Published: September 2024

ObjectiveThere is growing recognition of the importance of community data governance to build accountability of research institutions to communities. Our organization, a steward of health and administrative population-level data, has previously implemented community governance structures for Indigenous data. This scoping initiative explores development and implementation of an additional community governance structure for race-related data.
ApproachWe conducted a scoping review of peer-reviewed and grey literature to identify existing practices of community data governance. We also conducted key informant interviews with thirteen racialized community stakeholders, who addressed open-ended questions on potential co-design processes as well as governance mandates, scopes, barriers, and facilitators.
ResultsThe scoping review identified eight community data governance examples. Two of these pertained to race-related data, while the remaining six pertained to other data that identified “community” geographically, by disease condition, life stage, and/or economic circumstance. Governance structures were diverse, ranging from one-time crowd-design of a data-sharing agreement to quarterly meetings of a governing board to review project-level data requests. Key informant interviews provided four themes to guide implementation in the context of our organization: exploring organizational readiness, considering who should be involved, defining the scope and mandate, and drafting an approach and process.
ConclusionWe are committed to implementing a community governance structure for race-related, population-level data. However, there are limited examples of similar structures in the existing literature.
ImplicationsThe identified examples and the advice of community stakeholders will guide co-design of a preliminary structure, scope, and mandate for community governance of race-related, population-level data.
Exploring vaccine hesitancy among citizens of the Métis Nation of Ontario: a population-based data linkage study
Authors: Edwards Sarah , King Keith , Cripss Shelley , Chaurasia Ashok , Kim Eliane , Khan Hira , Kwong Kwong , Walker Jennifer , Cooke Martin 
Published: September 2024

Objective and ApproachUnderstanding vaccine hesitancy among distinct communities is crucial to ensuring equitable uptake of vaccination. The objective was to examine the influence of psychological antecedents of vaccine uptake, known as the “5Cs”, on COVID-19 vaccination among Métis Nation of Ontario (MNO) citizens. The Métis people are one of three Indigenous peoples in Canada. A population-based online survey was implemented by the MNO, including the short version of the “5C” (confidence, complacency, constraint, calculation, collective responsibility) psychological antecedents of vaccination scale. Census sampling achieved a 39% response rate and respondents were linked to the COVID-19 vaccine database (COVAX) in Ontario (n=4,012). Analyses included logistic regression models (adjusted for sociodemographics) and exploratory latent class analyses.
ResultsIn logistic regression models, MNO citizens who were less confident that COVID-19 vaccines were safe (OR=0.04; 95% CI=0.03-0.06) and did not agree vaccination was a collective action to prevent the spread of disease (OR=0.11, 95% CI: 0.07-0.17) had lower odds of being vaccinated. MNO citizens who disagreed the risk of COVID-19 was small (OR=14.87, 95% CI: 9.99-22.13) had a higher odds of being vaccinated. Vaccine hesitancy profiles with (i) lower confidence in availability/safety of COVID-19 vaccines, (ii) lower perceptions about severity of COVID-19 and (iii) higher everyday life constraints/stressors were associated with lower vaccination rates (males, OR: 4.69; CI: 3.18–6.92; females, OR: 4.77, CI: 3.03–7.51) in latent class analyses.
Conclusions and ImplicationsMétis-specific analyses enabled a community-driven response, resulting in low vaccine hesitancy and high uptake.
Prenatal and early life maternal substance use prevalence among Australian children born 2007-2018: a data linkage study using health, death, and child protection data
Authors: Powell Madeleine , Pilkington Rhiannon , Ahmed Tasnia , Hanly Mark , Newton BJ , Lynch John , Dobbins Timothy , Stewart Jessica , Cretikos Michelle , Havard Alys , Kathleen Falster 
Published: September 2024

ObjectiveQuantify the scale and type of maternal substance use from conception to the child’s second birthday (First 1000 days) to inform screening and support services that may reduce associated harm or risk for children.
ApproachWe used mother and child records from whole-population health, death, and child protection datasets to ascertain maternal substance use during the First 1000 days for children born in NSW, Australia, from 2008-2017. The primary outcome - maternal substance use - included use of illicit substances, alcohol, opioid-agonist treatment, organic compounds, solvents, and misuse of prescription medicines. ICD-10 and SNOMED-CT diagnosis codes were used.
ResultsThe birth cohort included 970,470 children and 625,856 mothers. 32,000 children (3.4%) had a record of maternal substance use during the First 1000 days of life, including 13,647 (1.4%) with alcohol and 23,485 (2.4%) other drug use. Ascertainment was highest from child protection records (26,045 children), followed by mother’s (12,956 children) then children’s hospital records (3,826 children). 18,672 (1.9%) children had a record of carer substance use only in child protection records. Combining data increased the prevalence estimates; adding child protection records increased the prevalence estimate to 3.4%, compared with 1.2% in health and death records alone.
ConclusionMore than 3 in every 100 Australian children had a record of maternal substance use in administrative data during the First 1000 days of life in this decade-long study. In addition to health and death data, child protection data offers public health insights into the scale of maternal substance use among whole-population cohorts of children.
Enhancing the usability of health data for inequalities research: a UK quality improvement project and code list curation.
Authors: Perchyk Tetyana , Lemanska Agnieszka , Whitaker Katriina L. , Kerrison Robert 
Published: September 2024

Objective and ApproachPrimary care datasets provide a rich source of information for inequalities research; however, the value derived from these depends largely on the comprehensiveness of the code lists used by researchers. To date, no standardised code list for inequalities research has been developed.
The aim of this project was to develop a comprehensive code list for groups of interest to inequalities research, namely: people with learning disabilities (LDs), people with severe mental illness (SMI), people from ethnic minority groups, and people who are transgender.
Existing code lists were extracted from the Clinical Practice Research Datalink (CPRD) Bibliography (the largest research dataset in the UK), and four UK code list repositories: OpenCodelists, Health Data Research UK, University of Cambridge, and London School of Hygiene and Tropical Medicine. Comprehensive code lists were then curated through collation and removal of duplicates.
Results16 code lists were identified for LDs, 18 for SMI, 16 for ethnicity, and 2 for transgender. From these, 661, 733, 346 and 68 unique codes were identified, respectively. Preliminary testing of the curated code lists, in a CPRD dataset, indicated that the number of individuals belonging to these groups was increased by 90% (compared to the original code list).
Conclusions and ImplicationsOur findings suggest that the curated code lists improved data capture. These lists now need to be validated by healthcare specialists, before being made publicly available. The inclusion of code-specific definitions will enable international researchers to adapt the code lists to their medical systems.
Maternal characteristics and infant and child mortality: A cohort study in England linking Census and health data
Authors: Ward Isobel , Barrat Sarah , Standeven Charlotte , Razieh Cameron , John Emry , Ayoubkhani Daniel , Nafilyan Vahe 
Published: September 2024

Child mortality is a common measure of the overall health of society. Maternal characteristics, such as ethnicity and socioeconomic status are known to contribute to inequalities in health and mortality in babies and children. Overall, in the UK babies born to non-white mothers have a higher risk of mortality, and there is also a strong association between deprivation and risk of death. However, population data is currently lacking for England and Wales disentangling the various socioeconomic risk factors which are known to contribute to increased risk. We utilise population level data to assess the association between socioeconomic status, ethnicity, and child mortality.
Our cohort consisted of all live singleton births in England and Wales between 2011 and 2016. We linked birth notifications to mothers’ 2011 Census records using NHS number, which provides person-level sociodemographic information. Death registration data was linked to identify all-cause and cause-specific deaths in babies. Babies were followed from date-of-birth for up to 10-years, or date-of-death. We report cumulative incidence of all-cause mortality for a 10-year follow-up for neonatal, infant and child deaths based on maternal socioeconomic and ethnic groups. Cox proportional hazard models are used to estimate hazard ratios for neonatal, infant and child deaths, with both minimally adjusted (maternal age and baby sex) and fully-adjusted models (accounting for other maternal, household and birth characteristics).
This presentation will give an overview of our unique linked data sources, the statistical techniques employed, the latest available analytical results, and the emerging implications of the research.
Using linked data to inform multidimensional real-world issues: Canadian examples
Authors: Chan Winnie , Xue Li 
Published: September 2024

Issues at the top of global and national policy agendas, such as the COVID-19 pandemic, climate emergency, and energy and cost of living crises, are illustrating the interconnectivity of the economy, society, and the environment. Moreover, it is clear, that impacts of these issues are not uniformly felt across societies. Greater granularity of information is required to address inequalities. As a result, policy makers are taking a more holistic view of issues to address the interlinkages across domains. This is driving the demand for Statistics Canada to provide statistical insights that address these cross-cutting policy issues and provide more granular information across multiple domains.
Linked data are necessary for understanding the interrelated nature of these issues and the scope of their impact. For example, recent Canadian research based on linked data has shown that COVID-19 led to changes in work arrangements that have implications for public transit use and greenhouse gas emissions. The employer-employee linked administrative data augmented with a further linkage to census data provide opportunities for Canadian researchers to produce multidimensional insights on a portrait of racialized groups and immigrants including refugees’ presence in the Canadian economy, their sociodemographic characteristics, and their performance over time relative to other Canadians, particularly post the COVID pandemic.
Rapid growth in data availability for research also poses new challenges ranging from technical issues needed for the feasibility of data linkage to stewardship issues related to governance, access, and oversight. The presentation will also discuss the lessons learned and reflections from the Canadian experiences.
Educational Attainment Dynamics in Wales: Insights through Data Linkage and Geographically Weighted Regression
Authors: Sandu Alexandra , Keating Jennifer , Huxley Katy , French Rob , Whiffen Anthony 
Published: September 2024

Quantifying the determinants of educational attainment requires a multifaceted approach encompassing various domains, including individual, family, household, neighbourhood, and school. This study examines the importance of socio-economic status for educational attainment within Wales, shedding light on the ways this can be measured, the importance across domains, and how these relationships vary across geographic areas.
Two main research questions guide this analysis. Firstly, it asks, what is the correlation between educational attainment and a range of socio-economic indicators derived from linked data, including free school meals and household measures? Secondly, is there variability of these associations across different geographical levels within Wales?
Employing Geographically Weighted Regression, the study investigates the effects of spatial differences of socio-economic indicators’ on pupils' attainment. It achieves this by integrating multiple Wales-specific administrative datasets, such as pupil administrative records, school-level data, family/household demographics and deprivation measures through data linkage. This comprehensive approach provides a nuanced understanding and underscores the importance of integrated datasets in uncovering insights into pupils' attainment.
Our findings reveal disparities in how socio-economic factors and family/household characteristics are associated with pupils’ attainment across distinct geographies in Wales. These disparities are visually depicted through maps generated in ArcGIS Pro. This emphasis on geographical variations not only underscores the pivotal role of local contexts in understanding educational disparities but also highlights the complex interplay between these factors and pupils' attainment. Incorporating such visual representation enriches our analysis and provides a compelling rationale for targeted interventions to mitigate disparities in educational outcomes.
Exploring Multimorbidity Through the Use of Deterministic and Probabilistic Record Linkage of Data in Five Health Facilities in Nairobi
Authors: Nderitu Daniel , Kisiangani Isaac , Masilela Nkosinathi 
Published: September 2024

IntroductionData collected by Health Demographic Surveillance Systems (HDSSs) at the community level can support very powerful research questions if it was linked to records at health facilities. In particular, understanding multimorbidity and building predictive models for key health outcomes requires linked data collected both in the community and health facilities. Additionally, the linked data can assist health management teams at the national, county, and local levels in making evidence-based choices.
Problemthe inability to link population level data with records at health facilities affects the ability to use data to solve key health research questions. lack of common identifiers affects the possibility of integrating clinic records with population-based data.
MethodologyWe intend to link data from five health facilities in Nairobi County serving the population in the Nairobi Urban Health Demographic Surveillance System (NUHDSS) where longitudinal data was collected between 2001 and 2018. The longitudinal data is from a database of about 260,000 individuals. This will result in a merged dataset and will also provide an opportunity to assess the accuracy of the deterministic and probabilistic record linkage algorithm compared with other methods.
Note: We will have results by the time the conference is happening as groundwork has been put.
ConclusionThe growing burden of multimorbidity on the African continent requires creative and effective strategies of addressing the problem through utilization of existing longitudinal data. By merging clinic records with population-based data, this initiative will promote the use of data evidence informed decision making for interventions to address multimorbidity.
Profiling births via Caesarean delivery in Northern Ireland (2011-2019) using the Robson classification: a population-based cohort study using administrative maternity system data.
Authors: McBride Orla , Redican Enya , Murphy Jamie 
Published: September 2024

Objective and ApproachCaesarean delivery (CD) rates are increasing globally. Over one-third of babies born in Northern Ireland (NI) during 2021-22 were via CD. Reducing the CD rate was cited as an important public health priority in the Strategy for Maternity Care in Northern Ireland (2012-2018). In 2015, WHO advocated that the Robson Ten Group Classification System (TGCS) be used to audit, analyse, and compare CD rates across countries. TGCS is currently not implemented in NI.
ResultsAnalysis of NI’s administrative maternity system (NIMATS) is on-going (accessed via Honest Broker Service Project P045) to address this study’s objectives, which will be completed for presentation: (1) describe the characteristics (maternal, obstetric practice, and fetal/infant features) of all deliveries during 2011-2019; (2) group all births over this period into TGCS categories and calculate the relative size of each group and the absolute contribution of each group to the overall CD rate; (3) compare the NI population distribution of TGCS group-specific CD rates against WHO-recommended benchmarks; (4) test for temporal trends in CD rates here over time; and (5) estimate the ability of a range of maternal, infant, and obstetric factors in predicting the overall CD rate and by TGCS group in NI over time.
ConclusionsApplication of the Robson TGCS to NIMATS data has potential to address a critical knowledge gap relating to the use of CD in NI.
ImplicationsAvenues to extend this work to investigate longer-term outcomes (via possible linkage to the NI Child Health System) will be considered.
An innovative linked Electronic Health Record derived data ecosystem for understanding the complexities of health and ageing.
Authors: Andrew Nadine , Beare Richard , Carver Alison , Ravipati Tanya , Parker Emily , Collyer Taya , Ung David , Srikanth Velandai 
Published: September 2024

ObjectiveWe describe the creation of an Electronic Health Record (EHR) derived linked data platform within the Australian National Centre for Healthy Ageing (NCHA).
ApproachOur approach involved establishing a core set of EHR data suitable for research from the sole public health provider within a defined geographic region. Relevant items were selected based on published literature and consensus processes and curated within a specialised research data warehouse. Curation involved: data validation, quality assessment, internal linkage between episodes and data harmonisation/merging. An AI supported natural language processing pipeline was implemented to extract text-based data. Approvals were obtained from government agencies to link commonwealth claims data (primary care, medication, aged care, death) and state administrative hospital data, for all residents aged ≥60 years. Publicly available datasets were scoped for inclusion and collaborations established to incorporate local council data.
ResultsA core of 113 curated items for >900,000 people over 10 years have been established across 4 hospitals and 10 community/outpatient services. Linked state and commonwealth data were obtained for 179,089 residents aged >60 years (Jan 2010-May 2021, linkage accuracy 98.4%). Environmental (greenery, air pollution, walkability) and Census data have been incorporated for each neighbourhood (Statistical Area 1). Data access, extraction and release has been tested through over 30 use-case projects in: dementia, residential aged care, medication use, homelessness, environmental impacts on health and ageing and health service redesign.
ImplicationsThe NCHA Data Platform provides an international exemplar for the use of linked EHR data in advancing population health research.
Data Quality Implications in Research when Transitioning to a New Electronic Medical Record
Authors: Hsu Zoe , Chisholm Cassandra , Lategan Conné , Lang Eddy , Wang Xiaoming , Youngson Erik 
Published: September 2024

The province of Alberta has been implementing a new hospital based electronic medical record (EMR) over the past several years based on the Epic system. With this implementation comes challenges related to consistent mapping of data to ensure consistency of data over time before and after implementation, even for standardized and routinely used elements.
In a research study evaluating potential disparities in access to Emergency Department (ED) care, particularly in wait times and patient boarding (the time spent in the ED awaiting transfer to an inpatient bed), between mental health (MH) and non-MH patients during the COVID-19 pandemic. An interrupted time series analysis was used to evaluate the impact of the pandemic on boarding time. While there initially appeared to be a large decrease in boarding time near the start of the pandemic, it was later determined to be a data quality issue corresponding to the rollout of the new EMR at one hospital, despite the data coming from a secondary standardized dataset that is used for provincial and national reporting and expected to be reliable.
This presentation will highlight how the issue was identified, what steps were taken to confirm the underlying cause, and how it was corrected to ensure accurate results in this research study.
New Zealanders living with a family member who has a long-term health condition: cross sectional analysis of integrated Census and administrative data
Authors: Underwood Lisa , Bowden Nick , Teng Andrea , Dewes Ofa , Marek Lukas , Milne Barry 
Published: September 2024

BackgroundLiving with a family member who has a long-term health condition (HC) is associated with poorer health and well-being outcomes, but the number and socio-demographics of people and families impacted by a family member who has an HC is unknown.
MethodsUsing the Integrated Data Infrastructure (IDI), a collection of linked administrative datasets for the full New Zealand (NZ) population, we identified n= 1,043,172 families using 2013 NZ Census data, and used health data over the previous 5-years to ascertain whether people in these families (n=3,137,517) had cancer, chronic obstructive pulmonary disease, coronary health disease, diabetes, dementia, gout, stroke, traumatic brain injury (TBI) or a mental health/behavioural condition (MHBC).
ResultsOver 60% of families included at least one person with a HC. The most common HCs were MHBCs (39.4% of families), diabetes (16.0%), and TBI (13.9%). A high proportion of multi-generation families (73.9%) included a member with a HC. Two-thirds (67.7%) of Pacific Peoples either had a HC themselves or were living with a family member who had a HC, compared with 62.3% of Europeans and 62.5% of Māori (the indigenous peoples of NZ). At the highest level of socioeconomic deprivation, 57.6% of children lived with a family member who had a HC.
ConclusionsThree in five NZ families were living with a HC, with differences in the proportion affected according to family composition, socio-economic status and an individuals’ ethnicity. This suggests that there are a substantial number of people at risk of the associated poor outcomes.