bluuebunny/crossref_metadata_2025 huggingface dataset:
Balancing constraints and requirements in an application process
Authors: Menting Stein , McDowell Ronald , Redican Enya , Rosato Michael , Murphy Jamie , Leavey Gerard
Published: September 2024

ObjectiveAdolescence is a vulnerable period for onset of depression. Socioeconomic deprivation may elevate the risk of depression. We explored the risk of depression in early adulthood in relation to deprivation during adolescence.
Approach97,561 individuals aged 10-14 years were grouped at baseline (2010) using small-area indicator of area-level deprivation (measured in quintiles) and linked with yearly antidepressant prescriptions (2010–2021). A logistic random-intercept multilevel model estimated the longitudinal trajectory of yearly antidepressant prescriptions (2010-2021) between deprivation quintiles in 2010, adjusting for sex and age at baseline.
ResultsIn 2010, the marginal probability of receiving antidepressants for an average young person differed little between those living in the most (0.0028: 95%CI=0.0025,0.0032) and least (0.0027: 0.0023,0.0032) deprived areas (p=0.75). By 2021, the marginal probability had increased more for those who lived in the most deprived areas in 2010 (0.191: 0.187,0.195) compared to those who lived in the least deprived areas in 2010 (0.140: 0.135,0.145)(p<0.001). Estimated antidepressant prescriptions were higher in females compared to males (Odds Ratio[OR]=3.15: 3.00,3.29) and increased per year of age at baseline (OR=1.26: 1.24,1.28).
ConclusionsGrowing up in more socially deprived areas is associated with an increase in antidepressant prescription risk during early adulthood. Improvements in administrative data access and linkage are needed to further explore these findings.
ImplicationsPolicy makers and data custodians should recognise the potential for administrative data in identifying social disadvantage and associated vulnerabilities in the general population, and take steps to make this data accessible to the research community.
Balancing constraints and requirements in an application process
Authors: McLachlan Stela , Evans Katharine , Whitehorn Rebecca , Turner Emma , Oakley Jacqueline , Boyd Andy , Flaig Robin
Published: September 2024

ObjectivesDeveloping a rigorous application process for linked data within the UK Longitudinal Linkage Collaboration’s Trusted Research Environment (TRE) included balancing the requirements of >20 longitudinal studies and several data providers with the need to provision data to researchers in a safe, ethical and efficient manner.  
ApproachUK LLC was commissioned as a resource to enable the investigation of COVID-19 questions: a secure setting where longitudinal population studies (LPS) can be linked efficiently to regularly refreshed NHS electronic health records, and then accessed by researchers. UK LLC received funding to continue as a resource for any research in public good and to link to the administrative records (education, occupation, tax and benefits). To accommodate the constraints imposed by a new set of data controllers and to account for wide variety of proposals expected, we expanded our data access process.  
ResultsUK LLC has a three-stage application and review process. The addition of administrative data meant that there is additional review for those applications requesting that data (Stage 4) and need to align processes with the UK Statistics Authority. There is also need to review complexity of the data requests and ethical implications of any application to access the data. We have developed an ethics decision tree to help guide the decision making around ethics requirements.  
ConclusionOur process is evolving as we are trying to meet the challenge of balancing the needs of >20 studies, stakeholders, applicants, and participant/public contributors, to create a process that can handle complex needs and requirements.
Leveraging Fuzzy Match: Building an Automated Record Linkage Template in SAS Dataflux
Authors: Ma Charlotte , Kalappa Gangamma , Wang Nan , Azimaee Mahmoud , Shen Winnie
Published: September 2024

Background ICES has conducted Probabilistic Record Linkage (PRL) for heath and non-health data for over 15 years. The PRL process has made data linkable to many ICES data holdings. However, due to the manual gray area resolution, record linkages for large datasets are time consuming with inconsistent results.
ApproachAdapting from many years of experience with PRL methodology, record linkage templates were built in SAS Dataflux. By using combination of deterministic linkage and fuzzy match algorithms, the multi-pass linkage strategy is maintained by passing unlinked records in subsequent linkage comparison cycles. The new template uses personal identifiers of multiple given names, surnames, date of birth, death date (if applicable) and sex. In each pass, the pool of possible matched pairs is created by merging on low sensitive matchcodes with different conditions. Then, using a rule-based approach, possible pairs are examined, flags are assigned, and survival rules are applied to select the best matched records.
ResultsBy feeding a pre-prepared linkage file along with updating the input and output file names, the record linkage job will be conducted automatically in SAS Dataflux through submitting a “run” command. It significantly reduces the turnaround time with no clerical review required, achieves a similar linkage rate as PRL consistently, and is utilized by various research projects.
ConclusionThe automated SAS Dataflux record linkage template is more efficient than the traditional PRL process. It eliminates the human intervention of gray area resolution, while reducing project turnaround times, and maintaining a comparable accuracy and linkage rate.
Towards standardised cross-sectoral agreements for data depositing and data access in Trusted Research Environments: streamlined contracting to advance public benefit research.
Authors: Brophy Rachel , Smith Cassie , Boyd Andy , Pan UK Data Governance Steering Group
Published: September 2024

Objective Trusted Research Environments (TREs) rely on Data Contributor Agreements (DCAs) and Data Access Agreements (DAAs) to bind organisations to terms and conditions in place to protect data. Our objective is to develop and drive adoption of DCA and DAA standardised templates which are optimised for data science in TREs. This is intended to reduce delays arising from the legal review process yet must not compromise on the safeguards needed for trustworthy data science.
ApproachA template DAA and DCA have been developed by the Pan-UK Data Governance Steering Group of the UK Health Data Research Alliance, underpinned by a core set of principles, and informed by a benchmarking exercise of existing DCAs/DAAs and meaningful involvement of public members and stakeholders.
ResultsPrinciples for TRE data contribution and data access in TREs have been mapped to core DCA and DAA terms for UK-based TREs and assigned to Five Safes’ categories. Flexibility will be ensured by including terms specific to TREs or specific data/data owners in customisable annexes to the agreements. Public views obtained through public involvement and engagement (PIE) activities have been formative in the development of the outputs.
Conclusions By providing a familiar structure and terms, the templates should build trust among data owners and the UK public and provide clarity to all parties on their data protection obligations.
ImplicationsThis familiar structure and terms are intended to accelerate   data research by enabling faster ingress of data into TREs and approval of projects, enabling more timely research.
Navigating the barriers to multi-regional research administrative data access: Forward thinking solutions
Authors: Curtis Maillet Donna , Yada Nicole
Published: September 2024

In 2021, aware of both the importance of pan-Canadian data access for advancing health research and the conditions and requirements that make administrative data access onerous, HDRN Canada worked with CATCO the Canadian arm of an international COVID-19 clinical trial to create a roadmap for navigating the barriers to multi-regional administrative data access in Canada.
While developing the roadmap, data access request processes were studied in real time for a multi-region study seeking access to link administrative data to clinical trial data, at the individual level. The intent was to move beyond anecdotal evidence and document the specific nature of policy and processes that hamper data access. The case study allowed the study team to define the barriers permitting application of tangible mitigating measures. 
In 2022, barriers such as the lack of ability to share data across provincial borders became too great to continue the study. Although the roadmap was not completed, insights were gained for waypoints along the path enabling HDRN Canada to make important process improvement to harmonize data access across regions. Solutions to challenges with data access request forms, regional data flows, data sharing partner relationships, data sharing agreements, and REB and project reviews were identified.  
Recently pan-Canadian initiatives have released important guidance for addressing data access challenges. The adoption of this guidance is paramount to ensure Canada continues to meaningfully contribute to health research. 
Forward thinking solutions to data access challenges today will establish the necessary infrastructure for a future with minimal data access barriers.
Improving Detection of Hospital Adverse Events Using Machine Learning on Real-World Narrative EMR Data
Authors: Cheligeer Cheligeer , Wu Guosong , Lee Seungwon , Pan Jie , Sapiro Natalie , Southern Danielle A. , Eastwood Cathy A. , Quan Hude , Xu Yuan
Published: September 2024

ObjectiveAdministrative data often underrepresents hospital adverse events (AEs) due to limitations in International Classification of Diseases (ICD) coding. By leveraging electronic medical records (EMRs), we aim to mitigate these discrepancies and enhance the precision of healthcare surveillance and performance evaluations. To this end, we have developed a machine learning (ML)-based approach that utilizes EMR text data to detect common AEs.
MethodsWe sampled adult admissions from four Calgary hospitals (2017 - 2022). Registered nurses assessed charts for 17 AEs, and the results were used as reference standard. We compared two AE detection methods: the standard ICD-based method following Canadian guidelines, and our ML algorithm applied to EMR narratives. Sensitivity, positive predictive value (PPV), negative predictive value (NPV), and specificity for both methods were calculated and compared against the reference standard.
ResultsWe analyzed 9,566 patients, of whom 1,506 were identified with AEs. Of the 17 AEs, the sensitivity in ICD-coded data ranged from 0-37%, and in EMRs, it was between 75-100%. Both showed low PPV (0-50% ICD vs.1-34% EMR). ICD data had high specificity ranging from 99-100% and NPV (99%-100%), while EMRs had specificity between 68-94% and an NPV of 100%.
Conclusion and ImplicationsML significantly enhances sensitivity for AE detection compared to ICD-10-CA coding, despite both methods experiencing low PPV due to imbalances in EMR data. This marked improvement in sensitivity highlights ML's potential to transform AE surveillance and reporting, promising significant advancements in patient safety and healthcare quality by enabling more accurate and comprehensive identification of AEs.
Administrative health data validity: Changes over 19 years
Authors: Pan Jie , Lee Seungwon , Cheligeer Cheligeer , Sapiro Natalie , Li Bing , Wu Guosong , Eastwood Catherine , Quan Hude , Xu Yuan
Published: September 2024

ObjectiveTo evaluate validity of hospital-discharged abstract administrative health data (DAD) over years through chart reviews.
MethodsWe analyzed three chart review cohorts (4,008 patients in 2003, 3,045 in 2015, and 9,024 in 2022) in Calgary, Canada. Nurse reviewers determined the presence or absence of 17 clinical conditions employing similar protocols. The reviews were linked with DAD using a unique lifetime identifier, chart number, and admission date. We evaluated the validity of DAD, coded in ICD-10-Canada version, in recording conditions by comparing against chart reviews. The C-statistics was calculated in predicting in-hospital mortality.
ResultsThe mean difference in prevalence between chart reviews and DAD for these 17 conditions was 2.1% in 2003, 7.6% in 2015, and 6.3% in 2022. However, some conditions were relatively stable, such as diabetes (1.9%, 2.1%, and 1.1%) and metastatic cancer (0.3%, 1.1%, and 0.4%). For 17 conditions, the sensitivity ranged 39.6-85.1% in 2003, 1.3-85.2% in 2015, and 3.0-89.7% in 2022. The C-statistics for in-hospital mortality based on DAD was 0.84 in 2003, 0.81 in 2015, and 0.78 in 2022.
ConclusionDAD increasingly under-coded conditions over 19 years. The validity of DAD decreased but remained relatively stable for certain conditions mandated for coding. The under-coding exerted minimal impact on in-hospital mortality prediction.
ImplicationsThe under-coding could be primarily due to the increase of hospital patient volumes and the limited time allocated to coders. Consequently, there is a need to develop artificial intelligence methods based on electronic medical records to support coding practices and improve coding quality.
Enabling Advanced Multi-Modal Neuroimaging Analysis within a Trusted Research Environment
Authors: Hotchkiss Lewis , Squires Emma , Gallacher John , Newbury Mark , Morris Catrin , Lyons Ronan , Thompson Simon
Published: September 2024

IntroductionGlobally, 55 million individuals have dementia, with an increasing annual incident of 10 million. Enabling the development of new multi-modal models can improve the current diagnostic pathways and potentially contribute to early diagnosis and treatment of dementia. Here, we report how multi-modal resources is achieved within the successful Trusted Research Environment (TRE).
ObjectivesWe aimed to identify the challenges of the storage, distribution and analysis of neuroimaging data and how we could implement a comprehensive infrastructure to deal with these. The problems we specifically aimed to address were how to: anonymise scans, store large amounts of data, standardise datasets to a common format, extract metadata, provision the data, and allow for analysis.
MethodsWe document various stages and capacities required for multi-modal neuroimaging analysis for dementia and conclude that achieving research ready assets to enable neuroimaging analysis for dementia from existing resources requires an engineered process to facilitate multiple aspects of curation, provisioning and large-scale analysis.
ResultsWe developed an ingest pipeline for neuroimaging data to meet the requirements set out in the objectives. This involved standardising all datasets to the Brain Imaging Data Structure, defacing scans and anonymising data, using MinIO for data storage and extracting metadata from header information for data discovery and provisioning.
ConclusionThe neuroimaging ingest pipeline developed has allowed for the distribution of imaging datasets within DPUK which has facilitated multi-modal research on anonymised and standardised data and enabled linkage with phenotypic and genomic data.
Identifying homelessness using health administrative data in Ontario, Canada: how coding policies impact case validity
Authors: Richard Lucie , Carter Brooke , Nisenbaum Rosane , Liu Michael , Hwang Stephen
Published: September 2024

Conducting longitudinal research about the health of people experiencing homelessness poses unique challenges. In Canada, we previously demonstrated that identification through health administrative databases permits population-level studies, despite relatively low sensitivity. Since then, coding of homelessness became mandatory in hospitals nationally. As a result, case validity since 2018 is unknown.
We re-validated case definitions for identifying homelessness in health administrative databases between 2018 and 2022 against the longitudinally collected housing history of a representative sample of people experiencing homelessness (n=640) and randomly selected, housed people (n=128,000) in Toronto, Canada. We calculated sensitivity, specificity, positive and negative predictive values, and positive likelihood ratios for 42 unique case definitions. We compared the resulting true positives against false positives and false negatives to identify potential causes of misclassification.
The optimal case (best sensitivity/scalability beyond Ontario) definition included any indicator during a hospital-based encounter within 180 days of a period of homelessness (sensitivity=52.9%; specificity=99.5%). Among periods of homelessness with ≥1 hospital-based encounter, the optimal case definition had good sensitivity (75.1%) with minimal reduction in specificity (98.5%). Review of false positives suggests homeless status is sometimes improperly carried forward in healthcare encounters occurring after transitioning out of homelessness.
Case definitions to identify homelessness using health administrative data exhibit moderate sensitivity and excellent specificity, with two-fold increases in sensitivity since the implementation of mandatory coding in Canada. Mandatory collection of social determinants of health information within administrative data present invaluable opportunities for advancing research on the health and healthcare needs of people experiencing homelessness.
Persisting social disadvantage and intercensal social mobility in Northern Ireland: an examination of morbidity and all-cause mortality using three waves of the Northern Ireland Longitudinal Study (NILS).
Authors: McCarter Rachel , Rosato Michael , Leavey Gerard
Published: September 2024

Objective and ApproachUsing census returns from three waves of NILS (1991, 2001, 2011) linked with mortality data, we examined morbidity/mortality outcomes by socio-economic disadvantage over time (using an individual-level deprivation index derived from educational attainment, social class, housing tenure and household car availability at each census). These were combined to generate two indicators of Intercensal social mobility: measuring no change, and upward/downward mobility between censuses. The relationship between mobility (2001-2011) and (separately) self-reported mental ill-health (MIH) at the 2011 Census and all-cause mortality (2011-2015) were examined using logistic regression and Cox PH modelling respectively.
Results  Population comprised 288,262 individuals aged 25-74 in 2011, recording 19,318 deaths (2011-2015) and 23,959 (8.3%) reporting MIH. MIH: those with no intercensal mobility followed the standard pathway associated with persisting disadvantage (comparing with the most advantaged the least advantaged recorded OR=16.13:95%CI=13.50,19.28); and while both the upwardly and downwardly mobile generally recorded higher ORs than those consistently most advantaged, the magnitude of ORs increased with social distance traversed; and ORs for downward mobility were consistently higher than with upward mobility. Patterns associated with all-cause mortality were similar.
ConclusionsThis suggests: upward mobility retains something of the social patterning pertaining in social disadvantage levels left behind; downward mobility may be connected to ongoing health issues. Poor health outcomes remain strongly associated with socio-economic circumstance. 
Implications  Research focusing on relationship between social mobility, disadvantage and health outcomes in NI is limited: access to more precise administrative data will enhance possibilities associated with evidence-based policy formation.
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.
Improving risk models for patients having emergency bowel cancer surgery using linked electronic health records: a national cohort study
Authors: Blake Helen A , Sharples Linda D , Boyle Jemma M , Kuryba Angela , Moonesinghe Suneetha R , Murray Dave , Hill James , Fearnhead Nicola S , Van der Meulen Jan H , Walker Kate
Published: September 2024

ObjectiveTo investigate whether accuracy of a risk model for colorectal cancer (CRC) patients undergoing emergency surgery, including patient and tumour characteristics from disease-specific data, was improved by inclusion of physiological and surgical measures from linked treatment-specific data.
ApproachLinked, routinely-collected data on patients undergoing emergency CRC surgery in England between December 2016 and November 2019 were used to develop a risk model for 90-day mortality. Backwards selection identified a 'selected model' of physiological and surgical measures in addition to patient and tumour characteristics. Model performance was assessed compared to a 'basic model' including only patient and tumour characteristics. Missing data was multiply imputed.
Results846 of 10,578 (8.0%) patients died within 90 days of surgery. The selected model included seven pre-operative physiological and surgical measures (pulse rate, systolic blood pressure, breathlessness, sodium, urea, albumin, and predicted peritoneal soiling), in addition to the ten patient and tumour characteristics in the basic model (year of surgery, age, sex, ASA grade, cancer site, number of comorbidities, emergency admission, TNM T stage, N stage and M stage). The selected model had considerably better discrimination than the basic model (C-statistic: 0.824 versus 0.783, respectively).
ConclusionLinkage of disease-specific and treatment-specific datasets allowed the inclusion of physiological and surgical measures in a risk model alongside patient and tumour characteristics, which improved the accuracy of predictions.
ImplicationsOur new accurate and relatively simple risk prediction model for patients undergoing emergency CRC surgery will allow more accurate performance monitoring of healthcare providers and enhance clinical care planning.
Neighborhood-level sociodemographics and kindergarten children’s developmental vulnerability, pre- and post-COVID-19 in Canada
Authors: Reid-Westoby Caroline , Gaskin Ashley , Offord Amanda , Duku Eric , Forer Barry , Jambon Marc , Janus Magdalena
Published: September 2024

A child’s environment and early life experiences play an important role in shaping their development. The COVID-19 pandemic altered many aspects of everyday life for young children in Canada, and it has been argued that these changes did not impact all children equally. The current study explored the association between early child development and neighborhood sociodemographic indices (e.g., income, deprivation) before and after the onset of the COVID-19 pandemic. The Early Development Instrument (EDI) data, measuring child development and school readiness in kindergarten, were collected in 7 Canadian provinces and 1 territory from 2017-2020 (pre-COVID) and 2020-2023 (post-COVID) and were linked with neighborhood-level socioeconomic indices from 2016. We compared the gradients in development in a pre- (n=293,700) and a post-COVID (n=246,305) cohort of Canadian children. Rates of developmental vulnerability, as measured by the EDI, were examined for the sociodemographic variables in both the pre- and post-COVID cohorts. The overall vulnerability increased nationally from 27.3% in the pre- to 28.0% in the post-COVID cohort. A gradient in vulnerability rates for all the socioeconomic indices was found, with children in the highest quintiles, representing greater deprivation, having greater odds of being vulnerable than their peers in the lowest, least deprived quintile. Contrary to expectation, the magnitude of the gradient was very similar in both cohorts, with some jurisdictional differences. The largest gradient was found for average neighborhood income. These findings add the context of social determinants of health to the understanding of the impact of the COVID-19 pandemic on young children.
The ICES Indigenous Health Data Training Program: Building knowledge and strengthening capacity within Indigenous communities and organizations
Authors: Mecredy Graham , Jordao Jessica
Published: September 2024

Objective and ApproachThe responsible use of population-level health data requires equitable access, and considerations of Indigenous data sovereignty. There is often a baseline level of knowledge and understanding required to access data. To facilitate data access and strengthen capacity within Indigenous communities and organizations, we created the ICES Indigenous Health Data Training Program (IHDTP).
ResultsThe IHDTP provides learning opportunities for Indigenous communities and organizational members to gain a better understanding of ICES data with a focus on Indigenous health. It provides an opportunity to gain practical knowledge on the research process, including how to develop a research question, understanding and interpreting research outputs, and knowledge translation. The program offers ongoing one-on-one technical support to participants, as well as the opportunity to submit a research project request for participating communities and organizations.
ConclusionsThe IHDPT has been run several times, including both virtually and in-person, with a range of Indigenous partners. The materials presented in each administration of the program were tailored to the partner. Recorded sessions have also been created to allow a greater number of additional Indigenous partners to take the course flexibly. Program participants have indicated that the information learned would be used to advance issues that are important to their communities via policy, advocacy, and planning.
ImplicationsThe IHDTP has enabled a variety of Indigenous partners to strengthen their understanding of health data and research to ultimately facilitate equitable and inclusive public health data access.
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.
Congenital Anomalies among Offspring of Women Living in Low Socioeconomic Status Neighborhoods or Hispanic/Latino Enclaves, Texas, 1999-2018
Authors: Schraw Jeremy , Jaime Elwin , Rathod Rutu , Shumate Charles , Hughes Amy , Pruitt Sandi , Shaw Gary , Lupo Philip
Published: September 2024

Objectives and ApproachNeighborhood influences pregnancy outcomes through effects on maternal health. We evaluated prevalence of >140 congenital anomalies monitored by the population-based Texas Birth Defects Registry among residents of Hispanic/Latino enclaves (census tracts with high proportions of Hispanic/Latino residents, immigrants, and Spanish-speaking households) and tracts with low socioeconomic status (low nSES). We included all cases regardless of pregnancy outcome (1999-2018) and a reference population of all livebirths, identified by linkage to Texas vital records. We calculated Yost socioeconomic index and Hispanic/Latino enclave index scores using linked U.S. Census Bureau data. We used Poisson regression to estimate the prevalence ratio (PR) and 95% confidence interval of anomalies among residents of: low nSES/enclave (34.5%); low nSES/non-enclave (25.6%); high nSES/enclave (4.6%); and high nSES/non-enclave (referent; 35.3%) tracts. We adjusted for maternal age, education, race/ethnicity, parity, and pre-pregnancy body mass index.
ResultsLow nSES was associated with microcephaly, pulmonary artery anomalies, atrial septal defect, and Down syndrome (PRs 1.1-1.6), and inversely associated with hypospadias and other male genital anomalies (PRs 0.8-0.9). Offspring of women in low nSES enclaves were at increased risk of several congenital heart defects relative to those in other neighborhoods. Conversely, prevalence of hypospadias was lowest among offspring of women in low nSES enclaves.
ConclusionsLinkage between a population-based registry, vital records, and Census Bureau data revealed novel associations between neighborhood characteristics and congenital anomalies.
ImplicationsFurther research is warranted to understand mechanisms linking neighborhood factors to congenital anomalies.
Outcomes of the Health Data Research Network (HDRN) Canada and Canadian Longitudinal Study on Aging (CLSA) Partnership
Authors: Hayes Anne , Hogeveen Sophie , Gatley Jodi , Costa Andrew , Griffith Lauren , Jones Aaron , Youssef Sarah , Raina Parminder , Gruneir Andrea , Whyte Carrie Anne , Hovey Carla , Ethier Marie-Chantal , Curtis Maillet Donna , Azimaee Mahmoud , Youngson Erik , Choi Tim , Gilbert Lindsey , Nunes De Melo Magda , Stewart Lindsay , Lester Kendra , Frizzell Katelyn , Kydd Robyn , Standing Mary-Ann , Burchill Charles , Towns Dave , Cantin Jean-Francois
Published: September 2024

Objective and ApproachThe Canadian Longitudinal Study on Aging (CLSA) is the largest national longitudinal cohort study, following ~50,000 adults across 10 provinces for at least 20 years. Health Data Research Network (HDRN) Canada is a distributed pan-Canadian network including members in all 13 provinces and territories. In February 2021, the organizations partnered with the objective to provide researchers with efficient access to multi-regional CLSA data linked to other health/health-related data through HDRN Canada’s data centres. A Steering Committee was convened including representatives from all parties to oversee the process from data sharing agreements (DSAs), negotiating terms of access, through to data transfer and linkage.
ResultsIn Spring 2024, linkage was complete at four data centres (Ontario, New Brunswick, British Columbia, and Nova Scotia), with a DSA also in place for Newfoundland and Labrador. DSAs are nearing approval at three others (Manitoba, Prince Edward Island, and Quebec), and in progress in Alberta. Saskatchewan is working towards participation.
ConclusionsThe HDRN Canada-CLSA partnership is on the way to making linked provincial CLSA cohort data available in all participating jurisdictions, facilitated by streamlined agreements and processes.
ImplicationsCollaborative efforts of organizations such as HDRN Canada and CLSA to foster data linkages and streamline data access at a multi-regional level offers an unparalleled opportunity to explore the intersection between aging and health care utilization. Research enabled by this initiative will inform health care decision making and evidence-based policy development at multiple levels, from the individual to multiple levels of government.