bluuebunny/crossref_metadata_2025_split huggingface dataset:
Special educational needs provision in primary education and its impact on unauthorised absence in children born with Cleft Lip and Palate: a population level study using linked health and education data
Authors: Nguyen Vincent , Lewis Kate , Dearden Lorraine , De Stavola Bianca 
Published: September 2024

IntroductionWithout accounting for the underlying need for Special Educational Needs (SEN) provision, pupils with SEN in England have been shown to have higher rates of unauthorised absences.
Objectives and ApproachOur objective was to understand the impact of SEN provision on unauthorised absences in children with an expected need for SEN, those born with Cleft Lip and/or Palate. We used linked health and educational records to investigate the relationship between three categories of SEN provision (No Provision versus SEN Support versus Education and Healthcare Plan (EHCP)) and unauthorised absences. We aimed to causally estimate the incident rate ratio (IRR) of SEN Support vs No Provision and EHCP vs SEN support on unauthorised absences between years one and six of primary education. Combining sociodemographic, educational and clinical variables from linked data, we used propensity-score based methods to adjust for non-randomised assignment to these categories of treatment.
ConclusionsBetween academic years 2009 to 2019, we found common support between comparison groups No provision (n = 4350) and SEN support (n = 2009) only for children with Cleft lip and palate and only reported on these comparisons. Unadjusted estimates demonstrate a 22% increase in absences in children with SEN support over No Support (IRR: 1.22 95%CI: 1.10-1.34). Reweighting for need using data from linked health and education records demonstrated a 14% reduction in unauthorised absences (IRR: 0.86 95%CI: 0.76-0.97).
ImplicationContrary to prior findings, by carefully matching on detailed linked health and education characteristics, we find SEN provision reduces unauthorised absences
Federated Data Linkage in Practice
Authors: Edwards Michael , Lee Alexandra , Thompson Simon 
Published: September 2024

In recent years, great strides have been made towards the deployment of federated systems for data research, including exploring federated trusted research environments (TREs). These federated TREs allow data analysts to securely use data within multiple institutions with minimal overheads while preserving information governance. However, due to the sensitive nature of data utilised in data linkage pipelines, especially within privacy-preserving applications, the real-world deployment of federated data linkage is in its early stages.
There are several challenges to overcome with privacy-preserving federated linkage, including securely and efficiently sharing information that enables the identification of record similarities. Another key consideration is the resilience of the federated linkage ecosystem to information being added or removed as data providers move in and out of the initiative, as changes in the record set can cause intermediary links to be destroyed or created. Within non-federated linkage, a spine dataset such as a national census can provide an anchor on which incoming datasets can be linked. In federated linkage this may not explicitly be the case, especially if providers are globally dispersed or such a broad spine does not exist for the use case, and so there may be critical datasets within the overall system.
We present a discussion over the real-world challenges to federated linkage on population-scale applications, including approaches which allow privacy-preserving linkage across multiple TREs and handling information governance requirements. We also explore the impact of the collaborative nature of federated linkage, looking at how quality and record links are affected in practice.
Improving and re-developing the Clerical Resolution Online Widget (CROW): open-source software for clerical review
Authors: O'Dair Hannah , Cummins Sarah , Ashia Ira , Huck Rachel 
Published: September 2024

ObjectivesClerical review is an integral part of linkage and is fundamental to quality assurance. CROW is a low dependency, adaptable, clerical matching tool that has been deployed across 40 linkage projects. Due to limitations of the interface, with the initial version (including the limited ability to view large clusters); this project sought to re-develop the tool as a flask application.
ApproachOur approach was to transform the existing CROW desktop application into a Flask (web based) application. Flask was chosen because it is a flexible web framework that enabled the development of a more dynamic interface, as well as better integration with the infrastructure of secure environments. We also engaged with accessibility experts to ensure the inclusivity of our product. Once the development was completed, testing was carried out by a range of users and stakeholders, to identify further issues and bugs for resolution.
ResultsThe successful redevelopment of CROW into a Flask application improved functionality, usability, parametrisation, and accessibility of the tool. Feedback from users indicates that because of using CROW, the clerical matching experience has improved vastly. Users reported that CROW increased accessibility, reducing eye strain and fatigue. CROW also increases efficiency and leads to better decision making.
ConclusionThe successful redevelopment of CROW is a significant improvement to the clerical review process for data linkage projects.
ImplicationsCROW is open-source and low dependency so could be adapted to work in most environments. Therefore, it could be adapted and used by the wider data linkage community.
Looking beyond the neonatal: what does administrative data tell us about the preschool health and developmental outcomes of children exposed to opioids in utero?
Authors: Marryat Louise , Robertson-Albertyn Senga , Boardman James P , McFadden Alison , Whittaker Anne 
Published: September 2024

BackgroundThe world is facing an opioid epidemic. Children of women who use opioids in pregnancy are difficult to follow-up over long periods using traditional research methods due to the complexity of their lives. Resultingly, we have little robust evidence on their longer-term outcomes.
ObjectiveThis study aimed to explore the impact of illicit and prescription opioid exposure in pregnancy on preschool health and developmental outcomes.
ApproachData identified 6,408 children (born 2009-2019 in Scotland) exposed to opioids through illicit use and/or medication assisted treatment (MAT), alongside a matched control group (n.19,089). Regression models will examine associations between opioid exposure and key outcomes up to age 5, including the Ages and Stages Questionnaire, accidents and injuries, and chronic health conditions such as asthma, controlling for other risk factors (e.g. alcohol use in pregnancy, gestation).
ResultsEarly results indicated differences in neonatal outcomes, with poorer outcomes for the illicit opioid/MAT cohort in terms of being born early, having lower birthweight, length and head circumference, and being more likely to be removed from their mother prior hospital discharge. Modelling of preschool outcomes is underway and will be finished in June 2024.
ConclusionsHaving proven that we can identify these children in administrative data, this paper will present cutting-edge data on the impact of exposure to illicit and prescription opioids on preschool outcomes. This will provide robust evidence on these impacts, and highlight where additional support might be required for these children from birth to starting school.
Associations between household overcrowding and adult mental illness in an ethnically diverse urban population: cross-sectional study using linked primary care and housing records.
Authors: Wilk Marta , Firman Nicola , Harper Gill , Fry Rich , Griffiths Lucy , Mathur Rohini , Dezateux Carol 
Published: September 2024

ObjectiveWe hypothesised that adults living in overcrowded households were more likely to be diagnosed with Serious Mental Illness (SMI) or depression after accounting for individual, household and area-level factors.
ApproachWe linked primary care records of adults currently registered with all general practices in the north-east London region on 21/03/2021 to housing data using pseudonymised unique property reference numbers (pUPRNs), giving a sample of 762,718 adults in 329,948 households. Primary outcomes were a primary care diagnosis of SMI or depression. Overcrowding was defined as <32.5m2 per person. We estimated the adjusted odds (aOR) and 95% confidence intervals (CI) of SMI or depression when living in an overcrowded household, adjusting for individual (age, sex, ethnic group), household (size, composition), and area (Index of Multiple Deprivation) characteristics. We conducted logistic regression models with robust standard errors in RStudio.
ResultsSMI and depression were diagnosed in 6,492 (0.9%) and 45,053 (5.9%) adults respectively; 584,515 (76.6%) adults lived in overcrowded households. In univariable analyses, household overcrowding was inversely related to risk of SMI or depression (aOR[CI]: 0.42 [0.39,0.44]; 0.62 [0.61,0.64]). The direction of effect reversed on adjustment for household size, composition, and area-level deprivation: 1.22 [1.12,1.33]; 1.07 [1.04,1.10] for SMI and depression respectively.
ConclusionsWe found significant associations between SMI and depression and household overcrowding, using a novel method of record linkage to estimate household overcrowding but are not able to establish causality of observed associations in this cross-sectional study.
ImplicationsMore research is needed to understand how housing conditions affect mental health.
Self-reported Ethnicity Changes over Time: A Population Level Study using Public Voter Databases
Authors: Ziyad Sumayya , Lam Joseph , Christen Peter , Schnell Rainer , Nanayakkara Charini 
Published: September 2024

ObjectivesAdministrative databases such as voter registries contain a wealth of information about a large proportion of a population. This is especially true when longitudinal information about individuals is available, such as changes in their names, addresses, or self-reported ethnic attributes. Apparent changes in ethnic attributes might be due to perceptions of majority evaluations of ethnic groups. In this project, we analysed snapshots of a publicly available US voter database from 2015 to 2022, exploring if and how recent presidential elections impacted changes in voters' self-reported ethnic attributes.
ApproachUsing bi-monthly snapshots of a publicly available US voter database from 2015 to 2022, we generated cohorts by linking active voters during seven twelve-month periods (2015/16 to 2021/22). These seven time periods included one mid-term and two presidential elections, and four control time spans before, between, and after these elections. We compared changes in self-reported ethnic attributes of voters during the times of elections versus the control time periods.
ResultsPreliminary results reveal a substantial change to self-reported ethnic attributes during the periods leading to an election. A majority of such changes are by individuals from a specific ethnic group choosing not to report an ethnicity, an indication of their preference to not disclose ethnicity-related sensitive information.
ConclusionsWe have shown how a publicly available voter database can be used to explore how self-reported ethnic attributes are modified by individual voters over time, and how the prevalence of such changes is influenced by political events such as presidential elections.
A discovery platform to improve visibility of linked data across Australia.
Authors: Miller Kate , Flack Felicity , Bennett Vicki , Smith Merran 
Published: September 2024

BackgroundMetadata plays a crucial role in the health research infrastructure ecosystem. Despite the abundance of metadata for data collections in Australia, there is no simple way for researchers to search for standardised metadata about linked and linkable data collections across sectors or jurisdictions, making it an arduous and time-intensive task.
MethodsThe project comprised three phases: an initial scoping exercise to understand the current state of metadata and best practice; a national consultation involving researchers data linkage staff and data custodians to develop a high-fidelity prototype; and a final build and implementation phase. The platform underwent several prototyping and testing cycles to refine the digital experience.
ResultsKey expert interviews confirmed that there is a wealth of metadata available in Australia, but it is difficult for researchers to access and evaluate. Consultations with researchers identified opportunities to standardise metadata and provide a centralised platform to enhance the discoverability of linkable data collections for research. High value platform features included searching, browsing and filtering capabilities, variable list metadata, standardised formats, sample data, and frequently asked questions. The final design and functionality reflected user consultations and data custodian input on feasibility and resulted in the standardisation of metadata for linked data collections across sectors and jurisdictions.
ConclusionThe Population Health Research Network developed and implemented a metadata platform to enable researchers to assess and evaluate the suitability of data collections for data linkage projects more readily. Improved metadata accessibility will save time and enhance the quality of linked data applications.
Methodological Challenges when Using Routinely Collected Health Data for Research: A scoping review.
Authors: Wei Lili , Kuenzig Ellen , Im James , Zheng Yan , McLinden Taylor , Emerson Scott , Eissa Azza , Halder Henry , Shaw Richard , Chan An-Wen , Dixon William , Ehrenstein Vera , Guttmann Astrid , Harron Katie , Hemkens Lars G. , Hróbjartsson Asbjørn , Lyons Ronan , MacDonald Shannon E. , Maniate Jerry , Moher David , Petersen Irene , Quan Hude , Schmidt Sigrún , Sørensen Henrik , Wang Shirley , McAllister David A. , Langan Sinéad M. , Benchimol Eric I. 
Published: September 2024

Routinely collected health data (RCD) including electronic health records, disease registries, health administrative data and wearables data are not specifically collected for research purposes. Analysis of these data poses unique methodological challenges that must be addressed when conducting research, particularly as availability and use increase.
This scoping review aimed to identify methodological challenges in research using RCD from existing literature (registered protocol: https://doi.org/10.17605/OSF.IO/EBM4D). We searched 6 electronic databases, including medical, health economics, nursing and psychology research databases, between Jan 2015 and Jan 2023, combining multiple “RCD” and “research” search terms (e.g., epidemiologic, informatics, pharmaceutical research). After screening abstracts and full-texts, we doubly extracted methodological themes, categorizing them into different study stages.
We screened more than 23,000 records and included 430 papers. Bias and confounding were the most common methodological issues identified, discussed in relation to both study design and data analysis. Data quality, including data accuracy, validation, completeness, timeliness and cleaning, also posed substantial challenges, particularly during data processing stage. Record linkage and conducting analyses using distributed health networks also pose unique methodological challenges. Heterogeneity, incorporating social determinants of health and statistical models that address methodological challenges are also described in the literature. External validity and reporting are important considerations for RCD research.
Our review identified several methodological challenges facing researchers using RCD. These issues should be addressed to ensure methodologically sound research. These findings will inform the development of a standardized protocol template and accompanying educational platform aimed at enhancing methodological quality and transparency when conducting research using RCD.
Identifying patterns of Long-COVID diagnosis pathways: a Latent Class Analysis of Welsh clinical data
Authors: Abbasizanjani Hoda , Bedston Stuart , Robinson Lucy , Curds Matthew , Akbari Ashley 
Published: September 2024

ObjectivesLong-COVID (LC) diagnosis remains challenging due to its variable, multisystem, episodic symptoms, and lack of uniformity in the critical time points. We aim to classify diagnosis pathways and associated demographic and health determinants for clinically diagnosed LC patients in Wales.
ApproachWe created a cohort of LC-diagnosed adults in Wales from 2020 to 2023 using population-scale data within the SAIL Databank. To classify diagnosis pathways, we extracted a set of indicators suggestive of the general diagnosis process within 6-months before the diagnosis, including LC symptoms, clinical investigations, and management. We conducted Latent Class Analysis (LCA) to examine how these indicators cluster, adjusting the best-fit model for various covariates such as COVID-19 infection history and sociodemographic characteristics.
ResultsOf the 7,973 identified LC patients, 59% had clinical investigations, 59% had a clinical management record, and 39% had at least one related symptom. Using LCA, we identified 7 distinct patterns of diagnosis pathways, and how background characteristics associated with each.
ConclusionDespite existing guidelines, LC diagnosis is complex due to LC’s diverse manifestations and the absence of exclusive symptoms/tests. Late or missed LC diagnosis can have a detrimental effect at individual, community, and national levels. Previous UK studies have indicated low recording rates of LC compared with self-reported surveys. Understanding current diagnosis pathways can shed light on this discrepancy and identify potential confounding factors that delay/impact LC diagnosis. This insight is crucial for standardising diagnosis pathways, enabling earlier and more accurate detection of LC and thereby mitigating its impact.
From Probabilistic to Fuzzy Matching Record Linkage: A promising Transition
Authors: Azimaee Mahmoud , Kalappa Gangamma , Ma Charlotte , Wang Nan , Shen Winnie 
Published: September 2024

ObjectiveProbabilistic Record Linkage (PRL) heavily relies on manual intervention for gray area resolutions. This makes PRL extremely time and resource intensive. No matter how scientifically sound and robust PRL method is, it didn’t meet close-to-real-time data availability requirements at ICES.
ApproachDuring a thorough evaluation and comparison process of three software and two methods of Record Linkage, ICES initiated a semi-design of experiment to select an optimal record linkage approach. For this experiment, a large Ontario data on 12 million individuals with required linkage variables plus valid Ontario health card numbers was selected. While the availability of the health card number enabled assessment of the accuracy of different approaches, the analysts were blinded to the correct health card numbers during the process. If manual intervention was required, it was repeated by two analysts to allow capturing human error.
ResultsPRL-based software needed the most personnel time to complete the process. Human errors were identified during the manual intervention due to subjective decisions by the analysts. Fuzzy Matching approach eliminated manual intervention but achieved comparable linkage rate to PRL while maintaining the same accuracy. The Fuzzy Matching software costs were higher; however, the data timeliness was significantly improved, and the clerical review costs and human error were eliminated.
ConclusionsThe Modernization of Record Linkage (MORL) project was a successful demonstration of the advantages of Fuzzy Matching over PRL method. However, the implementation of new approach at the organization level was a challenging change management.
A population-based repeated cross-sectional study using administrative health data to examine the impact of the COVID-19 pandemic on mental wellness in citizens of the Métis Nation of Ontario
Authors: Simms Abigail , Beaulieu Tera , Michaud Benny , Tsui Noel , Zagorski Brandon , Edwards Sarah 
Published: September 2024

Objective and ApproachThe COVID-19 pandemic led to unprecedented levels of mental unwellness and yet there are no reports to date that share Métis-specific data. Our study examined changes in patterns of mental and addictions-related (MHA) outpatient health service utilization using population-based data on Métis Nation of Ontario (MNO) citizens before and during the COVID-19 pandemic. Administrative health data in Ontario, Canada between 2017 and 2022 was linked with the MNO's Citizenship Registry (2022) under an existing Data Governance and Sharing Agreement. Monthly rates of MHA outpatient visits were compared between the pre-COVID-19 period (March 2017 to February 2020) and post-COVID-19 onset (March 2020 to December 2022), and rate ratios comparing observed and expected rates were derived using Poisson generalized estimating equations. Stratifications by age and sex were examined.
Results and ConclusionAmong 28,400 MNO citizens (50.3% male; median age 44 years; 28.3% living rurally), the average monthly rate of MHA outpatient visits was 47.4 per 1000 population pre-COVID-19. During the COVID-19 pandemic, the observed MHA outpatient visit rates were 13% higher than expected (RR=1.13; 95% CI: 1.02-1.26). MHA outpatient visits were higher in females compared to males and MNO citizens aged 30 to 64 years versus older or younger citizens.
Implications This study is the first to use population-level study to examine MHA health service use in Métis people. High-quality, contemporary data on the mental health outcomes of MNO citizens is crucial to inform MNO’s Mental Health and Addictions programming, resource allocation and emergency preparedness.
Enhancing Disease Detection in Electronic Medical Records: Integrating Human Expertise and Large Language Models with Application to Diabetes, Hypertension, and Acute Myocardial Infarction
Authors: Pan Jie , Lee Seungwon , Cheligeer Cheligeer , Martin Elliot , Riazi Kiarash , Quan Hude , Li Na 
Published: September 2024

ObjectiveElectronic medical records (EMR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EMR is labour-intensive, requiring advanced medical informatics knowledge, and is challenging without effective data extraction tools. This study developed a high-throughput pipeline to detect diseases in EMRs.
MethodsWe developed a pipeline that leverages a generative large language model (LLM) to analyze, understand, and interpret EMR notes by following clinical experts’ designed prompts. The pipeline was applied to detect diabetes, hypertension, and acute myocardial infarction (AMI) from the EMRs for a cardiac patient cohort in Calgary, Canada. The performance was compared against clinician-validated diagnoses as the reference standard.
ResultsThe cohort consisted of 3,413 patients with 551,095 clinical notes. The prevalence was 27.8%, 66.3%, and 54.3% for diabetes, hypertension, and AMI, respectively. The performance for detecting conditions varied: diabetes had 90.5% sensitivity, 83% specificity, and 67% positive predictive value (PPV); hypertension had 94.2% sensitivity, 30.2% specificity, and 73.8% PPV; and AMI had 86.4% sensitivity, 61% specificity, and 75.3% PPV. The monthly prevalence trends between the detected cases and reference standard showed similar patterns.
ConclusionThe proposed pipeline demonstrated reasonable accuracy and high efficiency in disease detection without manually curated labels, indicating the potential for automated real-time disease surveillance using EMRs.
ImplicationVariations of documentation practices in clinical note can impact the detection performance of different diseases. Hence, an automated pipeline integrating LLMs with expert knowledge may improve detection accuracy with reduced labour costs while indicating documentation quality.