bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long-term trends in co-occurring medical and psychiatric hospitalizations among children and adolescents in Ontario, Canada.
Authors: Saunders Natasha , Guttmann Astrid , Chiu Maria , Gandhi Sima , Vigod Simone , Kurdyak Paul , Fung Kinwah , Sharpe Isobel , Emerson Scott , Toulany Alene 
Published: September 2024

BackgroundPsychiatric conditions are common amongst hospitalized children. Co-occurring psychiatric conditions for medical hospitalizations contribute to length of stay, costs, and readmissions. We sought to measure trends over 20 years in pediatric hospitalizations for co-occurring medical and psychiatric conditions and compare with those without psychiatric comorbidity, overall and in free-standing children’s hospitals.
MethodsWe identified all 3- to 17-year-olds hospitalized in Ontario, Canada between April 1, 2003 and March 31, 2022. Using health record discharge diagnoses, hospitalizations were assigned to 1 of 4 groups: 1) medical-diagnosis-only, 2) psychiatric-diagnosis-only, 3) primary medical diagnosis with psychiatric comorbidity, and 4) primary psychiatric diagnosis with medical comorbidity. Hospitalization trends for 1) all hospitals, and 2) free-standing children’s hospitals were described and compared.
ResultsFrom 2003 to 2022, medical-diagnosis-only hospitalizations declined 39% (41,909 to 25,486 hospitalizations), psychiatric-diagnosis-only hospitalizations increased 96% (3227 to 6337), medical hospitalizations with psychiatric comorbidity increased 127% (977 to 2221) and psychiatric hospitalizations with medical comorbidity increased 100% (2051 to 4096). Among pediatric hospitals, medical-diagnosis-only hospitalizations increased 23% (12,430 to 15,318), psychiatric-diagnosis-only hospitalizations increased 420% (271 to 1408), psychiatric hospitalizations with medical comorbidity increased 172% (539 to 1468) and medical hospitalizations with psychiatric comorbidity increased 235% (478 to 1599).
ConclusionsHospitals have experienced large absolute and relative increases in volumes for psychiatric conditions both with and without co-occurring medical conditions, particularly among free-standing children’s hospitals. Healthcare provider training, hospital resourcing, and health system planning must consider how best to accommodate the increasing acute psychiatric care needs of hospitalized children and adolescents.
Research to understand and provide evidence for a generalised approach to data linkage
Authors: Huck Rachel , Cummins Sarah 
Published: September 2024

ObjectiveOrdinarily data linkage is approached on a project-by-project basis, tailoring the method to the research need. An increase in demand for data linkage has led to the development of a more efficient strategy, i.e. a generalised approach.
Through data linkage, a series of indexes representing people, businesses, geography and classifications are created using administrative data. Additional data are linked to the indexes via a general method to collect a common identifier.
Adopting a generalised approach requires an evidence base to ensure research aims can be met by the methods. This research will provide the evidence needed to underpin the strategy as well as guidance for implementing a generalised approach using an index.
ApproachThe existing data linkage portfolio provides an opportunity to compare the application of a bespoke to a generalised methodology. Comparisons will collate information on:

Index coverage;
Linkage results and quality including precision and recall estimates;
Analysis of the bias observed within the linked data.

Of particular interest is the linkage of different population sub-groups, particularly those classified as vulnerable or disadvantaged, for example, data for prisoners, refugees, the homeless and third-sector organisations.
ResultsWe will present the findings to date on recommendations for the use of a generalisable method for indexing, by exploring:

Data linkage quality by bespoke methods versus a generalised approach
The coverage strengths/limitations of the indexes
Sub-groups representation in the linked data

Conclusions/Implications Findings will provide evidence for adopting a new data linkage strategy, ensuring the most appropriate data linkage methods and quality assurance have been applied. Therefore, producing accurate and unbiased analysis to form the evidence base for policy decisions.
The hidden impact of alcohol on young victims: an analysis of police-related alcohol offences resulting in hospitalisation
Authors: Sims Scott 
Published: September 2024

BackgroundAlcohol-related harm (ARH) remains a significant public health concern, particularly affecting young individuals who are disproportionately impacted. This study focuses on the often-overlooked impact of alcohol on young victims involved in alcohol-related police incidents resulting in hospitalisation. While existing research often centres on the drinker, this study explores the characteristics of both offenders and victims, analysing offence types and investigating the under-ascertainment of ARH in hospital records.
MethodsOur research, conducted as a retrospective longitudinal study in Western Australia, utilises linked data from hospital admissions, emergency department (ED) presentations, and police incident records. The study includes 22,747 individuals aged 12–24 involved in alcohol-related police incidents between 2004 and 2015.
ResultsFemale victims were 10 times more likely than males to be involved in alcohol-related police incidents, while women had a 2 times higher likelihood of undiagnosed alcohol-related hospital admissions than men. Aboriginal people were overrepresented as both offenders and victims. ED presentations and hospital admissions were prevalent, with head injuries being the most common outcome. Notably, 71% of victims hospitalised did not receive an ARH diagnosis.
Conclusions A noticeable gap exists between hospital admissions associated with alcohol-related incidents and cases officially classified as ARH in the hospital system. This difference points to a more significant issue of substantial under-ascertainment or insufficient identification of ARH than previously acknowledged. Our study emphasises the need for targeted interventions, collaborative efforts between health and justice agencies, and ongoing research to address the hidden impact of alcohol on young victims.
Linking for Impact: Enhancing Linked Data Infrastructure to Meet Clinical Trials Sector Needs
Authors: Miller Kate , Flack Felicity 
Published: September 2024

BackgroundAustralia possesses valuable population health data, yet its potential for advancing medical product development remains largely untapped. Transformative growth in the use of linked data in clinical trials will support Australia’s therapeutic development sector to be more responsive in the development of new therapeutics and the monitoring and surveillance of their safety and effectiveness.
MethodsWe conducted interviews with clinical trialists and data linkage experts to understand the current state and awareness of linked data within the clinical trials sector, identify existing pain points in the linkage process, and explore opportunities for improving visibility and access for the clinical trials sector. Researchers represented all stages of the medical product development pipeline and had a diverse range of experience with linked data.
ResultsInterviews revealed numerous barriers hindering the clinical trials sector from effectively using linked data, limiting its impact. Barriers included a lack of awareness, the complexity of the application and approval process, and lengthy delays in accessing data.
Participants readily recognised several ways that linkage of real-world data could be applied to their work in clinical trials, with pre-recruitment data, measurements of primary and secondary endpoints, and health economic analysis being the most consistently noted use cases.
ConclusionTo optimise the use of linked data to support clinical trials and medical product development in Australia, increased awareness among the therapeutic development sector regarding available real-world data and its potential is needed urgently. Lowering the barriers to accessing linked data will enhance health services and patient outcomes across Australia.
Measuring error in the Demographic Index – steps towards a future population statistics system in England and Wales
Authors: Archer Rosalind , Diyasena Peshali , Grant Emma 
Published: September 2024

ObjectiveWe are developing methods to measure the quality of the Demographic Index at a National Statistics Institute. The Demographic Index is the foundational dataset for a future, transformed, population statistics system for England and Wales. It clusters records for individuals across many years of health, education, and tax data, and assigns them a unique ID.
Previous research has helped us to conceptualise three types of error in the Demographic Index: clustering error, coverage error, and data measurement error. We are currently working on estimating one type of clustering error, whereby records for two individuals are mistakenly assigned the same ID (“false positive clusters”).
ApproachPrevious work has allowed us to identify variables associated with false positive clusters. We are now working on a stratification method, whereby each ID in the Demographic Index is scored according to how likely it is to contain this error. We plan to measure uncertainty using bootstrapping.
Results and ConclusionsThis work is ongoing, and we hope to obtain results by summer 2024.
ImplicationsIt is vital for us to understand and measure error in the Demographic Index because this is necessary for measuring error in any statistics derived from the Index. And it is crucial that population statistics made by any future system should have measures of error. Assuming that our work shows promise, we will continue by measuring other types of error, and by creating use cases to test how these measures can be fed forward into secondary analyses (e.g. population estimation).
Disability Benefit Reform and Benefit Outcomes: A Re-examination of the Introduction of the UK Personal Independence Payment Using Linked Population Data
Authors: Miller Ana-Corina , McVicar Duncan , Rowland Neil , Jahanshahi Babak 
Published: September 2024

ObjectivesMany countries face the problem of how to provide support for additional costs faced by disabled people while restricting disability benefit program growth given tight public finances. In 2013, the UK reformed its main additional costs disability benefit with this objective, changing both medical screening and payment rates with the introduction of the Personal Independence Payment (PIP). This study uses newly available linked administrative population data to examine the impact of this major reform on disability benefit receipt.
ApproachWe use the newly available 2011CBI dataset, which links Census records to microdata on benefit receipt for the population of England and Wales from 2011-2015. Difference-in-difference and synthetic control methods are used to estimate the reform impact on the probability of flowing onto disability benefits, exploiting the structure of the roll-out of PIP’s introduction across English regions.
ResultsIntroducing PIP led to a sharp fall in the probability of disability benefit onflow, with tentative evidence of a partial reversal in the following year. Further analysis is required to quantify the extent to which this reflects well-known administrative delays in processing applications as opposed to a step change in the probability of claims being successful.
ConclusionsThis study is the first to present linked administrative population data estimates of the benefit recipiency effects of the introduction of PIP. In doing so it both contributes to the wider international literature on disability benefit reform and provides an early research application of a significant new administrative dataset for England and Wales.
Mapping UK Pain Datasets to the OMOP Common Data Model: Lessons Learned
Authors: Milligan Gordon , Masood Erum , Quinlan Phil , Cox Sam , Giles Tom , Mendez Villalon Armando , Cole Christian 
Published: September 2024

ObjectiveChronic Pain is defined as pain lasting for three months or longer and a report by the British Pain Society in 2016 found that 28 million adults live with this condition. Chronic pain research is problematic as it is poorly represented in health data and often in silos. Alleviate is the Advanced Pain Discovery Platform (APDP) Data Hub which is removing barriers to access to data.
ApproachThe aim for Alleviate is to transform pain data sets within the UK to be Findable, Accessible, Interoperable and Reusable (FAIR) and be more discoverable for researchers via Health Data Research UK’s Cohort Discovery Tool. The tool enables identification of potential cohorts in real-time from datasets Alleviate provides. The onboarding requires datasets to be transformed to the open and widely used Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
ResultsWe collaboratively developed CaRROT tools and used our mapping expertise to transform datasets to OMOP-CDM. The tools have improved efficient mapping of clinical and research data, covering ~10 million individuals’ records. Subsequently we learned valuable lessons on how to accurately and consistently map across data sets to support federated discovery. We also identified a lack of standard vocabulary representations for pain specific data.
ConclusionsMapping data to OMOP is not straightforward. Attention to detail is required to ensure that consistent mapping decisions are made to maximise data reusability and interoperability.
ImplicationsIn sharing the lessons learned of mapping datasets to OMOP we aim to help improve interoperability of disparate data sources.
The role of emergency department crowding on the implementation of efficiency strategies to improve care for adult patients presenting with chest pain.
Authors: Rowe Brian , Yang Esther , Doran Shandra , Graham Michelle , Van Diepen Sean , Raizman Josh , Tsui Albert 
Published: September 2024

IntroductionThis study evaluated the operational and clinical outcomes for patients presenting to an emergency department (ED) with chest pain using a 2-hour high-sensitivity troponin T (hs-TnT) protocol compared to an existing 3-hour hs-TnI protocol.
MethodsThis retrospective cohort study used eight population-based linked health administrative datasets for adult patients presenting to a Canadian urban ED with chest pain over a two-year period. The primary outcome was ED length of stay (LOS). Secondary outcomes included health outcomes within 30 days.
ResultsOverall, 5426 patients were included; 2879 and 2547 in the hs-TnI group and hs-TnT groups, respectively. The cohorts were similar: the median age was 58 years, 55% male, and comorbidities matched. Delays in physician initial assessment (PIA; +54 min; 95% CI: 42.2-65.8) and patients leaving without being seen increased (7% vs 12.5%; P < 0.0001) due to worsening overcrowding. The median ED LOS increased from 448 to 481 minutes after hs-TnT implementation (median difference = 33.0; 95% CI: 19.9 to 46.1), although time to second tests decreased by 54 minutes. At 30 days, there were no differences in readmission or death before and after the intervention.
ConclusionImplementation of a 2-hour hs-TnT protocol resulted in mixed outcomes: increased PIA and ED LOS, yet faster time to second test. The journey through the ED for patients with chest pain is complex and affected by issues beyond the assay type and the timing of repeat Tn measurements. ED overcrowding needs to be addressed for efficiency to be realized in this setting.
Enhancing population-level research among people who inject drugs: a validation and retrospective cohort study using health administrative data in Ontario, Canada
Authors: Greenwald Zoë R , Feld Jordan J , Werb Dan , Austin Peter C , Drover Samantha , Fridman Daniel , Bayoumi Ahmed , Gomes Tara , Kendall Claire E , Lapointe-Shaw Lauren , Scheim Ayden I , Bartlett Sofia R , Benchimol Eric I , Bouck Zachary , Greenaway Christina , Janjua Naveed Z , Leece Pamela , Wong William WL , Sander Beate , Kwong Jeffrey C 
Published: September 2024

ObjectiveHealth administrative data can support population-level research among people who inject drugs (PWID), however these data sources remain underutilized due to the difficulty in identifying drug use in routinely collected data. We validated case-ascertainment algorithms to identify PWID and tested their application in a hepatitis C (HCV) cohort in Ontario, Canada.
ApproachWe conducted a validation study using reference standard cohorts of PWID recruited via community-based studies and population controls linked to health administrative data in Ontario, Canada (1992-2020). Tested case-ascertainment algorithms included combinations of hospitalizations/emergency department (ED) visits for drug use/poisoning, physician visits for drug use, opioid agonist treatment (OAT), or injecting-related infections. Sensitivity and specificity were estimated for lifetime history and recent injecting (past 1-5 years). We applied a high-performing algorithm among all Ontarians with laboratory-confirmed HCV between 1999-2018, to identify a sub-cohort of PWID with HCV.
ResultsAn algorithm including ≥1 hospitalization/ED visit or ≥1 physician visit for drug use or ≥1 OAT record had high accuracy for identifying IDU history (91.6% sensitivity, 94.2% specificity) and recent IDU (using 3 years lookback: 80.4% sensitivity, 99% specificity). When applied to a provincial cohort of 112,947 Ontarians diagnosed with HCV, this algorithm estimated 46% (N=52,248) had a history of IDU, of whom 52% (N=27,246) had an indication of recent IDU (within the past 3 years).
ConclusionThe methods developed in this study can enhance the capacity of population-level health research among people who inject drugs and support applied public health interventions towards hepatitis C elimination.
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: Lyons B. Casey , Stewart Andrea , Obiekwe Rochelle 
Published: September 2024

ObjectiveLinking data on people who experience drug overdoses can provide new insights and opportunities for intervention. However, because public health data infrastructure in the United States (US) is often siloed, linkage activities can require incentivization and support. In 2023, we began funding 20 states to conduct overdose-related data linkage. We sought to understand baseline capacity in funded states.
ApproachData linkages in this project focus on two areas: 1) linking fatal and nonfatal overdose data and 2) linking fatal or nonfatal overdose data to prescription drug monitoring program (PDMP), criminal justice, or social determinants of health (SDOH) data. We report opportunities identified during implementation regarding states’ capacity to perform the required linkages as well as needs for additional training and technical assistance.
ResultsOf 20 funded states, 11 (55%) reported some previous experience linking fatal and nonfatal overdose data; 10 (50%) have linked PDMP data, while only 3 (15%) have linked SDOH data and 2 (10%) criminal justice data. Jurisdictional needs included: training on data linkage methods, including developing and validating linkage algorithms; overcoming administrative barriers to data sharing, such as data use agreements; and developing standardized approaches to characterizing events of interest in diverse data sources. States valued sharing strategies and experiences with each other.
Conclusions/ImplicationsAdditional capacity building is needed for states to successfully link and utilize overdose-related data. We are actively collaborating with state partners to facilitate peer-to-peer knowledge exchange to develop successful linkage programs.
Linkage for risk-sensitive environments: Australian examples of privacy-preserving record linkage using Bloom filters
Authors: Ferrante Anna , Brown Adrian , Randall Sean , Boyd James 
Published: September 2024

Introduction Organisations are increasingly aware of the risks and responsibilities of handling personally identifying information (PII). These factors not only influence internal data management practices but also impact on data linkage arrangements with other parties. Some linkage environments are particularly sensitive to the release or use of PII. Advances in privacy-preserving record linkage methods such as PPRL-using-Bloom make it possible to undertake highly accurate data linkage without release or disclosure of PII. Such methods play a role in enabling data linkage in risk-sensitive environments.
Objectives and ApproachWe present and describe several Australian case studies where the PPRL-using-Bloom method has been used to enable data linkage between organisations. We report on the defining elements of each case, the associated risks and solutions, as well as quality and performance issues. We also reflect on challenges and opportunities for future improvement.
ResultsAustralian use cases utilising privacy preserving linkage (PPRL-using-Bloom) include projects linking state-based datasets to Commonwealth datasets, some linking primary care data to state-based secondary healthcare data, and others linking healthcare data to non-health datasets such as police and criminal justice datasets.
Conclusion / ImplicationsMethods such as PPRL-using-Bloom play a critical role in enabling data linkage in highly risk-sensitive environments. However, in an ever evolving world where risks and requirements are constantly changing, linkage methodologies and technologies must remain adaptable to meet evolving demands.
Designing a Data Dashboard for Health Departments and Overdose Fatality Review Teams
Authors: Pisani Marie , Oguss Madeline , Manneh Claire , Mayampurath Anoop , Afshar Majid 
Published: September 2024

IntroductionOverdose Fatality Review (OFR) teams within health departments rely on data from multiple isolated sources to study the opioid crisis. Recently, we linked electronic health record data with state agency data to form a regional Substance Misuse Data Commons (SMDC) using privacy-preserving record linkage. Our goal in this study was to use human factors design principles to design a data dashboard for OFR teams using the linked datasets that will overcome current barriers within OFR workflows.
MethodsWe utilized NASA task load surveys, semi-structured interviews, and design sessions with end users to identify data needs for an optimal dashboard design. We assessed current workloads, data collection processes, and desired future state. We subsequently performed iterative design sessions for the generation and evaluation of low-fidelity prototypes. To overcome issues with privacy and security, we used synthetic data in a cloud-based platform to represent the SMDC for simulation.
ResultsEleven OFR organizers participated. Pre-dashboard surveys on existing workflow showed high mental workload associated with data aggregation and case review, identifying a need for more accessible, comprehensive data. In our low-fidelity dashboard demo with synthetic data, iterative design adjustments were made in data visualization, storyline organizations, and theme-based data aggregation across pre-hospital and hospital data.
ConclusionsWe refined the data dashboard prototype into a high-fidelity version, set for further usability and human factors evaluation. We addressed privacy and security concerns through synthetic data use while the real-world data is maintained in a HIPAA-secure Azure cloud environment with access for approved users.