bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of a framework to facilitate a data assembly plan for multi-regional research
Authors: Anis Ali , Whyte Carrie-Anne , La Carmen , Ethier Jean-François , McGilchrist Mark
Published: September 2024

A network of organizations works together to facilitate multi-regional research across Canada. This network is streamlining the traditionally burdensome data access process, a major part of which is a project’s data assembly plan (DAP). A framework is proposed for the development of a DAP usable by researchers and multiple data centres across Canada. 
The network of 13 provincial/territorial and pan-Canadian data centres collaborated to understand variations in data request processes and local requirements. During this collaboration, partners used an iterative approach to review local forms, processes, and undertake consultations with the research community, and to identify critical components of the DAP. 
In April 2022, the network launched the centrally provisioned, standardized DAP form, which to date has been deployed by approximately five projects. Users have found the DAP’s unique aggregation and documentation capabilities particularly helpful. Overall, the preliminary feedback from researchers has been positive. The DAP has allowed aggregation of specific details about a project’s data requirements: cohort definition(s), data extraction(s) and analytical plans, in a single unified form.
The DAP is an important component in streamlining the process for requesting data from multiple provinces/territories, organizations, and data sources. The DAP has ensured consistency across the network’s data centres that are providing data, supporting data linkage, and helping safeguard the quality of analytical results. Further process improvements are anticipated to address user experience feedback and to promote quality research.
Mental Health Crisis and Policing Demand
Authors: Kurpiel Allison , McVie Susan
Published: September 2024

ObjectivesPolice are frequently called to assist in situations where mental ill health, rather than crime, is the primary concern. There is an urgent need to reduce this demand on police by improving appropriate health service delivery. This research examines police interactions in which an individual is identified as being vulnerable due to a mental health concern. We aim to determine the demographic, health, and social factors that are associated with being identified by police as vulnerable due to a mental health concern and assess patterns of health system service use preceding police contact to identify potential points for intervention.
MethodsA dataset of police records for individuals identified as vulnerable due to a mental health concern is linked to health and social administrative data including: information on mental ill health, alcohol and drug related problems, disabilities and long-term illnesses, Accident and Emergency calls, hospital visits, and demographic characteristics. We describe a cohort of individuals identified by police as vulnerable due to a mental health concern and assess their traits and patterns of healthcare service use compared to the general population.
ResultsThe results allow us to better understand who comes into contact with the police while experiencing mental health difficulties and point to the risk factors indicating where to concentrate resources for improved health service delivery.
Conclusions/ImplicationsThis research will improve partnership working between the police and healthcare services, reduce unnecessary police demand, and improve outcomes for people suffering from mental health crisis.
Population health value of being in target: Results from the Canadian Multi-Morbidity Model for Type 2 Diabetes
Authors: Sobotka Ian , Cipriano Lauren , Coyle Doug , Thiruchelvam Deva , Shah Baiju
Published: September 2024

ObjectiveTo develop a microsimulation model for type 2 diabetes using population-level real-world data. Such a model allows for the synthesis of multiple data sources for comparative effectiveness analysis related to a variety of correlated outcomes.
ApproachThe model was built using health state features including sex, age, diabetes duration, laboratory test results, and a history of major acute events. Features update using a cycle length of one month. Events modelled include myocardial infarction, stroke, heart failure, amputation and death. Model outputs include counts of events, lifetime healthcare costs and quality-adjusted life-years (QALYs). We then used the model to calculate the number of events that could be averted if a population with type 2 diabetes achieved treatment targets for HbA1c, blood pressure and LDL-cholesterol for 10 years.
ResultsBringing all 60-year-olds with type 2 diabetes into target for 10 years would result in annual event reductions of 87.0 per 100,000 person-years for myocardial infarction, 49.4 for stroke, and 166.8 for heart failure. QALYs would improve by 1,155 per 100,000 patients. For 75-year-olds, annual event reductions would be 178.2, 56.8 and 261.6 per 100,000 person-years, respectively, and QALYs would improve by 2,121 per 100,000 patients.
ConclusionsPopulation-level real-world data can be used to develop microsimulation models for type 2 diabetes that estimate long-term event risks, mortality and healthcare costs. This model is capable of comparative effectiveness and cost-effectiveness analysis of novel therapies in diabetes.
Spatial variation of healthcare resource use in Wales following the COVID-19 pandemic
Authors: Osborne Timothy , Bailey Rowena , Mizen Amy , Fry Richard
Published: September 2024

BackgroundThe COVID-19 pandemic caused significant disruption to healthcare services and changed patterns of healthcare resource utilisation in Wales, but the geographic variation of this disruption is not known. 
Objective/Approach We aimed to examine geo-spatial variation in the impact of the pandemic on healthcare service use, and identify socio-demographic and environmental factors associated with the geographical differences. 
We accessed and linked individual electronic healthcare records within the Secure Anonymised Information Linkage (SAIL) Databank. 
We calculated age-sex standardised rates of interactions across 1909 small areas in Wales, from January 2017 to December 2022. We compared rates in the 3 years pre-COVID (2017-2019) to the following 3 years (2020-2022) and investigated if spatial autocorrelation of results existed, using the Moran’s I test. 
We linked area-level characteristics and carried out Geographically Weighted Regression (GWR) analysis to understand the socio-demographic and environmental characteristics associated with ‘hot’ and ‘cold’ spots of healthcare resource use. 
Results We included 1,277,532 individuals with 7,502,485 healthcare service interactions related to a long-term condition.  
Across the 1909 small areas in Wales, we observed large variations in age-sex standardised rates of healthcare interactions. Area-level general practice and hospital admission rates decreased by a median of 22%, and 13.1% respectively, from 2017-2019 to 2020-2022. The change in general practice rates were more significantly spatially autocorrelated than the change in hospital admission rates.  
Spatially varying relationships between healthcare resource use and area-level characteristics such as income deprivation, travel time to general practice/hospital, have been analysed using GWR (results to be finalised). 
Conclusions/Implications We have found that the COVID-19 pandemic impacted population level healthcare resource use in some areas of Wales more than others. Identifying the underlying characteristics causing these differences will help inform policy to provide targeted services to areas and subgroups of the population.
Democratising data within a data-driven research centre: Examining the role of public engagement and involvement
Authors: Nelson Elizabeth
Published: September 2024

Over the last decade there has been an increasing focus on utilising government-held data for large-scale data linkage research projects, with an emphasis on public benefit.
Most, if not all, administrative data research initiatives will recognise public involvement and engagement (PI&E) as a cornerstone of research and emphasise that administrative data is essentially public data, and, therefore, that publics must have a say in how it is used. Much of this focuses on engaging with communities and their representative organisations.
What is less explicitly discussed is the role that data-driven research plays within a broader public realm that is increasingly driven by data, or datafied.
This paper will examine the ways in which PI&E both challenges and contributes to the datafication of society, through the work of the Administrative Data Research Centre Northern Ireland (ADRC NI), part of ADR UK. It will explore if – and how  – PI&E in administrative data research can play a role in democratising this datafied society.
The paper will use the Northern Ireland Public Data Panel (NIPDP) and the Voices of Young People in Care (VOYPIC) initiative as case studies to explore the ways in which ADRC NI successfully or unsuccessfully create sites of democratisation within the data ecosystem.
Finally, the paper will consider how to amplify the democratic benefits of PI&E in data-driven research while minimising any potential harms, in the form of a potential framework for data justice within PI&E in data-driven research.
Leveraging Machine Learning to Combat Missingness and Error in Data
Authors: Phillips Beverley , Witowski Philip , Sulaiman Windra , Ismail Adam , Sipthorp Mark , Williams Sharon
Published: September 2024

ObjectivePoor quality data confounds efforts to link clients across datasets. To combat this, we have trialed an approach which seeks to identify linkage candidates using associations with related services.  We utilised a machine learning (ML) approach to query linkage candidates’ dataset associations and make predictions about whether candidates are likely to be a client of the service being integrated for linkage. 
ApproachUtilizing a K-nearest neighbors algorithm, we trained a model using 15 variables to predict whether a linkage candidate is a client of a family service dataset. We then evaluated the model's success, and modified its native performance to minimise false positive matches. Subsequently, we tested the validity of these predictions as linkage criteria by employing blocking strategies and linking the service dataset to our linkage spine. The trained model was then used to identify correct links against the spine. 
ResultsThe evaluation of the machine learning model yielded promising results, with high accuracy (88.5%) and precision (95.5%). Testing the predictions as linkage criteria resulted in highly accurate links ranging from 96.0% to 98.7% across different blocking strategies. Despite some records failing to establish any links to the spine, rates of false positive matches remained low (0.7% to 3.1%). 
ConclusionMissingness and inaccuracy in data remains a key problem for data linkage, and a robust approach is required to resolve complex linkage cases. However, these findings suggest that machine learning can present novel options for a toolbox of many approaches to link problem records to a linkage spine.
Creating a population-wide Desirable Health Service Utilisation Indicator using linked health and administrative data
Authors: Doel Harri , Griffiths Lucy , Fry Richard , Johnson Rhodri , Turner Samantha , Lyons Ronan , Lyons Jane
Published: September 2024

Background and ObjectiveHealthcare research faces challenges in developing metrics that resonate with the general public or policymakers. We created a Desirable Health Service Utilisation Indicator (DHSUI) to address this gap, centred around New Year’s wishes for survival and non-occurrence of undesired events in the following year, for the population of Wales, UK, following discussions with policy-makers. 
MethodsWe accessed and linked individual electronic healthcare records from primary and secondary care sources within the Secure Anonymised Information Linkage (SAIL) Databank. The study used data for all Welsh General Practice registered individuals on the 1st January 2015 with follow-up until residency break, death, or 31st December 2022. The DHSUI was calculated per person per year and quantified the distribution of the population who survive calendar years and do not use selected health services (not admitted to hospital; no emergency department attendance; and not prescribed antibiotics, analgesics, or mental health drugs). We linked area level demographic data to measure socio-economic differences in DHSUI distribution. 
ResultsFocussing on 2022, a population of 2,668,977 people were included, with 1,150,347 (43%) identified as meeting the indicator. The proportion of desirable health decreased significantly with age and with increasing socio-economic deprivation. A higher proportion of males (50%) had desirable health compared to females (37%). The most prevalent sub-indicator among those with undesirable health was antibiotic prescriptions accounting for 52% of cases. 
ConclusionThis DHSUI indicator is easy to interpret and will assist policymakers and the public in understanding the magnitude and distribution of desirable healthcare utilisation.
Exploring care-experienced young people’s perceptions of the use of administrative data in research
Authors: Cummings Aimee
Published: September 2024

Objective and ApproachData about people is generated every day and research based on administrative data, especially in the social care sector, is becoming more prevalent. This piece of work involves running a public engagement workshop alongside care-experienced young people with the aim to learn what young people understand when researchers say they will be using administrative data in their research. 
Results The following questions will be explored:

What is understood by the terms ‘administrative data’ and ‘data linkage’?
What are young people’s thoughts on how their data is used?
What would they like to know about how their data is being used?
Can they think of reasons why you would not want their data to be used?
Do they think that the data that is collected (from a social worker, GP, etc.) accurately reflects their experiences?

As well as this, important aspects of the collection of administrative data will be discussed.
ConclusionsThe output of this piece of work will be an infographic. The aim is that this infographic will contain two facets. First, it will illustrate how administrative data is collected and used (including a life cycle of the data). Second, it will contain information that young people regard as pertinent to this topic and will contain quotes ‘for young people, for young people’. 
ImplicationsThe infographic from this work can be used by other researchers as part of their own public engagement work with young people.
Up-skilling the next generation of researchers to analyse complex linked population-level datasets: ADR UK PhD studentships case study
Authors: Mir Saba , Oliver Emily , Gordon Emma
Published: September 2024

ObjectiveAdministrative Data Research UK (ADR UK) has invested £1.9M in its first pilot PhD scheme, as part of a larger Training and Capacity Building Strategy to increase the capability of researchers trained to analyse complex linked population-level datasets. This session will highlight the successes and lessons learned in the first year of implementing this pilot in the context of a wider strategic drive to increase the number of data skills-focused PhD students in the UK.
Approach22 PhD projects from across the UK were selected for funding by an independent panel of experts. Students and supervisors joined a unique cohort receiving targeted support from ADR UK. Activities, events and resources were devised with advice from existing users of the data, including regular events and check-ins, training opportunities, an online Learning Hub and noticeboard, and establishment of themed networks and communities. 
ResultsSupporting cross-disciplinary cohorts of students and supervisors who span the UK has come with its own challenges. These include accessing data through different models and gateways, addressing a variety of skills gaps, and developing community building approaches that address multiple needs and which sustain the momentum beyond formal in-person events. 
Conclusions & Implications ADR UK's targeted support model will continue to evolve to ensure the students in this PhD cohort make the most of their experiences. We welcome the opportunity to connect with international colleagues about their experiences of programmes designed to up-skill the next generation of researchers to analyse complex linked population-level datasets.
Is Medium-term Exposure to Ambient Air Pollution a Risk Factor for Parkinson’s Disease?
Authors: Jahanshahi Babak , McVicar Duncan , Rowland Neil
Published: September 2024

ObjectivesThis study used Census data from Northern Ireland linked to administrative data on prescriptions between 2010 and 2016, to examine the association between ambient air pollution exposure and the risk of Parkinson’s Disease (PD) onset. It contributes to this literature by providing new evidence in a comparatively low pollution context, using rich and nationally representative cohort data with comprehensive information on Parkinson’s Disease medications dispensed over an extended period. 
ApproachWe estimated Cox Proportional Hazards models for the association between pollution exposure and PD onset as proxied by first receipt of PD medication, controlling extensively for potentially confounding factors at the neighbourhood, household, and individual levels. Estimates are presented in the form of hazard ratios for the effect of medium-term PM2.5 exposure on the risk of PD onset.  
ResultsThere was a clear unadjusted association between medium-term exposure to ambient pollution and the risk of developing PD, with those experiencing higher exposures being at greater risk. Once individual, household and neighbourhood confounders were adjusted for, however, we found no evidence supporting such associations. However, there was evidence of a positive association with both PM2.5 and NO2 exposure for sub-samples by age and education.
ConclusionsThis study contributes to an emerging literature examining the association between ambient PM2.5 pollution and onset of PD. Despite finding an unconditional association, we find no evidence for an association once individual, family and contextual characteristics are controlled for, at least in the relatively low-pollution context of Northern Ireland.
Using linked cohort data to help address residual confounding in analyses of population administrative data
Authors: Silverwood Richard , Baranyi Gergo , Calderwood Lisa , De Stavola Bianca , Ploubidis George , Harron Katie
Published: September 2024

ObjectivesAnalyses of population administrative data can often only be minimally adjusted due to the unavailability of a full set of control variables, leading to bias due to residual confounding. Cohort studies will often contain rich information on potential confounders but may not be sufficiently powered to meaningfully address the research question of interest. We aimed to use linked cohort data to help address residual confounding in analyses of population administrative data.
ApproachWe propose a multiple imputation-based approach, introduced through application to simulated data. We then apply this approach to a real-world problem – examining the association between pupil mobility (changing schools at non-standard times) and Key Stage 2 attainment using data from the UK National Pupil Database (NPD). The limited control variables available in the NPD are supplemented by multiple measures of socioeconomic deprivation captured in linked Millennium Cohort Study (MCS) data.
ResultsThe proposed approach is observed to perform well when using simulated data. The association between pupil mobility and Key Stage 2 attainment was attenuated after supplementing the NPD analysis with information from linked MCS data, though with a decrease in precision.
ConclusionWe have demonstrated the potential of the proposed approach, but more work is required.
ImplicationsThe principles underlying this innovative approach are widely applicable: any analysis of administrative data where confounder control is limited by the availability of information could potentially be strengthened by linking a subset of individuals into richer cohort data and leveraging the additional information to inform population-level analyses.
A Bayesian Record Linkage Approach that Adjusts for Variables in One File
Authors: Kamat Gauri , Shan Mingyang , Gutman Roee
Published: September 2024

In many healthcare and social science applications, information about units is dispersed across multiple data files. Linking records across files is necessary to estimate associations between variables exclusive to each of the files. Common record linkage algorithms only rely on similarities between linking variables common to all the files. Moreover, analysis of linked files often ignores errors that may arise from incorrect or missed links. Bayesian record linking methods allow for natural propagation of linkage errors, by jointly sampling the linkage structure and the model parameters. We extend an existing Bayesian record linkage approach to integrate associations between variables exclusive to each file being linked. We show analytically, and using simulations, that the proposed method improves the linkage process, and results in accurate statistical inferences. We apply the proposed method to link Meals on Wheels (MOW) recipients to Medicare Enrollment records, and examine the relationship between activities of daily living and healthcare utilization among MOW recipients.
Identifying “problematic” clusters in record linkage using graph measures
Authors: Stone Tony
Published: September 2024

Manual review of outputs from record linkage processes remains a frequently necessary but time-consuming operation. This work outlines a potential means to reduce the manual review workload through the extraction of graph measures from the outputs of record linkage processes combined with machine learning classification methods.
The outputs of a pairwise record linkage process can naturally be represented by undirected simple graphs with a vertex representing each record and an edge representing each comparison of records. One representation may be a graph which only includes edges representing pairwise comparisons that have a match score greater than a given threshold. A second representation may include match scores (suitably transformed) as the weight of the edges (pairwise record comparisons) connecting vertices (records). A third representation may include not just the overall match score as a weight on each of the edges (pairwise record comparisons) but also, for each compared attribute (e.g. names, date of birth, address, etc.), the contributing component to the overall match score as additional edge attributes.
In this work, a "problematic" cluster is a set formed of three or more records which, in totality, belong to two or more true entities. Equivalently, a connected graph of three or more vertices (representing records) with at least one edge that represents a false positive link.
Finally, this work evaluates the performance and generalisability of various combinations of graph measures for identifying problematic clusters across linkage processes featuring varying degrees of corruption and duplication.
Evaluation of the Adult Migrant English Program for the Australian Government Department of Home Affairs
Authors: Mitrou Francis , Nguyen Ha Trong
Published: September 2024

Objective and ApproachThe Adult Migrant English Program (AMEP) is a free service to help eligible migrants and humanitarian entrants with low English proficiency to improve their English skills and elevate their participation in Australian society. In partnership with AMEP funder, the Australian Government Department of Home Affairs, we have completed a longitudinal research study analysing the effectiveness of AMEP by linking participant information for over 400,000 AMEP clients with administrative data from social services, taxation, and the national census, from 2003 – 2019.  This is the first time AMEP participant data has been linked with other data assets and represents the most comprehensive evaluation of AMEP since program inception in 1948.
ResultsAMEP participation was associated with improved English proficiency of clients, especially when clients studied for longer periods. Migrants with higher levels of English on-entry had better labour market outcomes, higher income levels (male and female), lower rates of public housing tenancy, and were less likely to receive income support in the years following program exit.
ConclusionsWhile the design does not support true causal inference, this evaluation provides good evidence that AMEP is effective at improving English proficiency and suggests AMEP participation can improve a range of economic and social outcomes for eligible migrants with low English proficiency.
ImplicationsThe launch of these results coincided with new investment in AMEP and a broadening of eligibility criteria to make AMEP available to a wider range of migrants with low English proficiency.
Modelling The Effects of Multi-Indication Treatment for Multiple Long-Term Conditions Using Population-Scale Linked Electronic Health Records To Inform Healthcare Policy
Authors: Owen Rhiannon , Rafferty James , Lyons Jane , Lyons Ronan , Abrams Keith
Published: September 2024

Background and ObjectivesHealthcare decision-making has previously focused on developing recommendations for single conditions. This research aimed to develop a modeling framework to estimate the effects of multi-indication treatment in people living with multiple long-term conditions.
ApproachUsing a case study in type 2 diabetes mellitus [T2DM], chronic kidney disease [CKD], and heart failure [HF], age-adjusted multistate models were used to estimate the effect of sodium-glucose cotransporter-2 (SGLT2) inhibitors as a multi-indication treatment. Baseline hazards were estimated using population-scale, individual-level, linked anonymized electronic health record (EHR) data for 613,195 individuals aged 55 to 85 years in Wales over a 20-year period. Hazard ratios for treatment effects compared to standard of care were obtained from randomized controlled trials for combinations of multiple-long term conditions and incorporated in a patient-level simulation.
ResultsSGLT2 inhibitors increased the estimated mean life expectancy from 0.02 (95% Confidence Interval (CI): -0.02, 0.05) to 1.44 (95% CI: 1.12, 1.74) years in people living with coexisting T2DM, CKD, and HF. The estimated gain in life expectancy was less than 0.5 years for individuals with T2DM, CKD, and HF in different temporally ordered sequences. The estimated mean time to develop HF increased by 3.1 (95% CI: 3.00, 3.24) and 1.34 (95% CI: 1.12, 1.56) years in individuals with CKD, and in those with CKD followed by T2DM.
Conclusions and ImplicationsThere is an ever increasing need to appraise interventions in people living with multiple long-term conditions to identify optimal treatment strategies and reduce polypharmacy. Multistate models applied to linked EHRs allow for a more rigorous assessment of treatment effects to inform healthcare policy.
The employment, retention and exit of public school teachers in New Brunswick, Canada: an analysis using linked administrative data
Authors: McDonald Ted , Miah Pablo
Published: September 2024

ObjectiveAs in many jurisdictions, New Brunswick (Canada) is facing increasing shortages of K-12 school teachers as retirements loom at the same time that the school age population of NB continues to exceed long term trends. The purpose of this retrospective study is to analyze the recruitment, retention and exit decisions of teachers in the NB public education system in order to support ongoing planning around teacher staffing.
ApproachThe analysis uses a unique linked administrative data combining province-wide individual-level teacher employment data, immigration records, university graduation data and public health insurance registration on NB teachers and individuals who obtained a B.Ed. degree in NB. Data are accessed through the secure facilities of the NB Institute for Research, Data and Training. The analysis includes both descriptive statistics and econometric methods appropriate to the particular outcomes of interest. 
ResultsThe analysis will present results on three types of labour market transitions: 1) from NB university education to employment as a NB teacher. 2) exits from employment as a teacher, including both retirement and pre-retirement exits, and 3) decisions of ex-teachers about remaining in NB. The potential effects of a range of demographic, geographic and system-level factors on these outcomes are considered. 
Conclusions and ImplicationResults from the analysis of entry to and exit from K-12 teaching in NB will be vital to human resource planning for a province dealing with looming retirements, growing labour shortages and an increasing population. Future work will consider subject-specific teacher shortages in critical fields like STEM.
How ethnic name variations influence data linkage results: A population level study using public voter databases
Authors: Lam Joseph , Ziyad Sumayya , Christen Peter , Schnell Rainer
Published: September 2024

ObjectivesIt has been shown that record linkage methods can induce bias in resulting datasets since the linkage quality may differ between ethno-racial groups. In this project, we used publicly available voter databases to investigate whether ethno-racial attributes can influence the similarities calculated using approximate string comparison functions on voters' names.
ApproachWe used 11 annual snapshots of a publicly available US voter database between 2011 and 2021 with uniquely identifiable voter data. We extracted pairs of the same voter’s first and last name values from two consecutive snapshots where these values differ. We calculated string similarities and created similarity histograms for each of the available ethno-racial categories, and other sociodemographics variables such as gender and age. We characterized common patterns of name disagreements by ethno-racial groups and compared the shapes of these histograms using cumulative density distributions.
Preliminary ResultsAcross the 10 snapshot pairs, eligible voters with non-missing first and last names are included (N2011/2012 = 6,193,001, N2020/2021 = 7,852,763), describing 124,009 first name changes and 485,807 last name changes. Results will be presented as a series of density plots. Using Jaro-Winkler similarity scores of 0.7, 0.8 and 0.9 as threshold, we examined whether match rates differ by ethno-racial categories.
Conclusions and ImplicationsUsing the same string similarity function on names of individuals of different ethno-racial groups may lead to different distributions of the resulting similarity values. Understanding the patterns of ethno-racial-based name changes in your particular dataset is crucial on selecting linkage parameters that would minimise ethno-racial bias.
Transitions of care for people living with dementia – insights using linked data
Authors: Gearon Emma , Wyatt Bronwyn , Evans Ingrid , Fraser Megan , Raymer Ann-Kristin , De Crespigny Fleur , Dunford Melanie , Sparke Claire
Published: September 2024

Objective People living with dementia have increasing care needs as their condition progresses, and tend to be high users of health and aged care. Linked data provide an opportunity to better understand transitions between services, to enable quality and timely care.
Approach The pilot study used a new multi-source enduring linked dataset to analyse the use of health and residential aged care by Australians living with younger onset dementia between 2011–2016. The subsequent study focused on movements between hospital and residential aged care for older Australians who were hospitalised in 2017.
ResultsThe younger onset dementia cohort comprised 5,400 Australians aged 30–69. More than half (58%) the cohort had moved from the community into residential aged care by the end of the study period. Of these, 21% had a hospital stay while living in the community that ended with entry to residential aged care. This important transition point was analysed further using a dementia cohort of 79,000 people aged 65 and over. One in 4 people (23%) with dementia who were living in the community moved into residential aged care within 7 days of a hospital stay. This group spent 20 days longer in hospital than other older people. People living in aged care were less likely than those in the community to return to hospital during the following year.
ConclusionsThese results can help inform planning of community-based dementia support services, improve coordination of care, and investigate sources of delay for people awaiting entry to aged care.
A UK Specification for Trusted Research Environments
Authors: Sood Hari , Li Simon , Machin Tim , Oldfield Katie , Chuter Antony , Beggs Jillian , Coleman Sonya , Kerr Dermot , Chalstrey Ed , Craddock Matthew , Madge Jim , Sarmiento-Perez David , Robinson James , O'Donovan Cian , O'Reilly Martin , Cole Christian
Published: September 2024

ObjectivesThe need for Trusted Research Environments (TREs) is clear. Several influential reports have highlighted that personal or sensitive data which have been collected for operational, commercial or governmental reasons need to be managed securely in an environment that encourages best practices.
TREs are designed to enable only authorised projects and researchers access to sensitive data whilst minimising risk of data exposure. Yet the TRE landscape has grown organically over at least the last decade resulting in heterogeneous environments, making  it harder for data to be discovered, shared and used for public benefit.
A baseline specification for TREs is required.
ApproachWe engaged with around 60 organisations covering government, industry, health and academia through regular, online, shared spaces or “Collaboration Cafés”. We also embedded the public voice within the project by having members of the public within the research team and developed a series of workshops to include and reflect their input to the architecture. The whole project was developed in the open to instil trustworthiness.
ResultsThe SATRE project has produced a reference TRE architecture and implementation through significant engagement with the UK TRE community. This baseline specification for UK TREs includes a set of four pillars, 28 capabilities and 160 statements.
Conclusions and ImplicationsAn open and transparent TRE specification and architecture for a wide range of stakeholders has been developed. TREs can now use the SATRE specification to evaluate their environments against a common baseline.
Poverty and Intellectual Development in Childhood.
Authors: Roos Leslie , Detillieux Gilles
Published: September 2024

Objectives and ApproachThe effects of the timing and duration of economic deprivation and maternal mental health on intellectual development is researched using unique linkable databases following almost 90 thousand children born in the Canadian province of Manitoba. 
This paper studies such questions as: 

How important are differences in measurement of poverty in assessing intellectual development?
How do various differences change over childhood?

ResultsConcentrating on those born in 2000-2002 generated a total of 7,424 children having scores on all six measures of intellectual development. Such person-specific information controlled for many individual factors and extended from ages 5 through 17.
Major differences were found in the scores associated with exposure to the two kinds of poverty and to poor maternal mental health. 
In summary, differences emerge by age 5, with administration of the Early Development Index. These differences are largely the same at age 8, even though another measure (the Grade 3 Competencies Index) is used.
ConclusionsIf household poverty is taken as a definition of poverty, poverty seems much more important than maternal mental health in affecting a child’s intellectual development. However, if neighborhood poverty is used to define poverty, maternal mental health and poverty have effects on childhood intellectual development which are more similar. Definition plays a critical role in interpretation.
ImplicationsThese population-based data, with multiple measures and multiple time points, suggest many analytical possibilities. If childhood conditions are included, the list of medical conditions which might correlate with intellectual development multiplies.
Adverse childhood experiences and multimorbidity of internalising and cardiometabolic conditions in an older-age population.
Authors: Benger Lauren , Benger LINC Consortium , Holmans Peter , Owen Michael , Mon-Williams Mark , Katzourou Ioanna , Van den Bree Marianne
Published: September 2024

BackgroundTackling multimorbidity (ICM-MM) of internalising conditions (IC) and cardiometabolic conditions (CM) is key to improving the future of healthcare. Early life factors such as adverse childhood experiences (ACEs) have been linked to long term, poor mental and physical health conditions.
AimsThis study investigated whether: 1) ACEs increase the risk of IC, CM and ICM-MM; 2) the effect of ACEs on risk of developing CM depends on the presence of IC and vice versa; 3) how risk differs by gender, socioeconomic status and ethnicity.
MethodsIn UK Biobank, the Childhood Trauma Screener was used to identify 157,256 participants with ACE data. Electronic healthcare records (EHR) and interview data identified those with anxiety or depression (IC) and those with hypertension, obesity, type 2 diabetes, dyslipidaemia or chronic kidney disease (CM). Multivariate logistic regression models were employed to find associations between ACEs and IC, CM and ICM-MM. Covariates included age, gender, ethnicity, socioeconomic status, diet, alcohol intake, smoking behaviour and physical activity.
ResultsAll ACEs significantly increase risk for IC (all ACEs, p = <2e-16). Emotional neglect, physical neglect and emotional abuse increase risk of CM (e.g. physical neglect, OR = 1.43, CF = 1.30-1.58, p = 1.88e-13). ACEs appear to increase IC and CM risk independently.
ConclusionsThe effects of ACEs last well into adulthood and confer risk for IC and, sometimes, CM. ACE history should be included in routine primary care and the development of therapeutic interventions tailored to older adults with ACEs is key to reducing poor health.
Hospital admissions among adolescents in out-of-home care or special educational needs in England: insights from the ECHILD database of linked health, education and social care records
Authors: Blackburn Ruth , Zylbersztejn Ania , Kokosi Dora , Heys Michelle , Gilbert Ruth
Published: September 2024

ObjectiveWe quantified planned and unplanned hospital admissions during adolescence for children with special educational needs (SEN) or placed into the care of the local authority (child looked after in out-of-home care; CLA).
ApproachWe analysed linked hospital, education and social care records with whole nation coverage of England (ECHILD). Our cohort comprised pupils who started secondary school between 2007/8 and 2011/12 (aged 11), with follow-up until 2018/19 (up to 17-22 years old). Hospital admission rates were examined by sex across distinct groups with varying levels of support: i) no support, ii) school-based SEN only, iii) higher SEN (an Education and Health Care Plan) only, iv) CLA only, v) SEN and CLA, and vi) higher SEN and CLA. Negative binomial regression models were used to estimate rate ratios (RR) for each group relative to no support.
Results The study population totalled 2,827,464 children; 65% (1,835,404) had no support, 30% (848,620) SEN only, 3% (93,620) higher SEN only, 1% (30,880) SEN and CLA, with the remaining groups accounting for <1%. Planned and unplanned hospital admission rates were higher for girls than boys. Planned admission rates were highest among younger children in both higher SEN groups. CLA groups had the highest rates of unplanned hospital admissions, especially girls aged 13-15 years with higher SEN (RR: 7.69, 95% CI: 6.86-8.65). A significant proportion of all unplanned admissions in adolescence were mental health-related.
ImplicationsOur findings highlight significant unmet health needs, particularly in mental health, among care-experienced adolescent girls with SEN in their mid-teens.
The Data Linkage Hub: a Community of Practice
Authors: Lewis Esther , Huck Rachel , Cummins Sarah
Published: September 2024

Objective and ApproachThis paper discusses the Data Linkage Hub (DLH) which is a centre for data linkage excellence and aims to produce and support the delivery of high quality, linked data assets for researchers in the UK. Here, we will give an overview of the DLH’s activity as a Community of Practice for Data Linkage. 
ResultsWe will discuss the different project types conducted in the DLH; the methods used ranging from bespoke linkage development to the adoption of generalised methodologies; and the customers of the DLH.
We will also discuss the tools the DLH have developed, and make available to others, to standardise, streamline and improve quality assessment of linkage. This will include clerical matching tools known as CROW and the CMS, BAT, a bias analysis tool, and a package of PySpark code specifically developed for data linkage.
In addition, we will explore the data linkage training materials we offer for different audiences and our involvement in a Data Linkage Champions Network, both increasing the awareness of data linkage and promoting best practise.
Conclusion and ImplicationsThe impact of the DLH’s work is far reaching, underpinning statistical outputs, research and public policy. This success is down to the creation of a community of practice bringing together experts to regularly update and discuss linkage.
Establishment of an Australian National Master Linkage Key (NMLK) between AIHW Health Linkage spine (HLS) and Jurisdictional Master Linkage Keys (MLK) - NSW example
Authors: Ougrinovski Elena , Paine Clinton , Cooper Natalie , Nair Pooja , Smith Michael , Voruganti Rama Devi , Smith Merran , Flack Felicity
Published: September 2024

ObjectivesThis paper describes a Population Health Research Network project establishing the NMLK as a national linkage system utilising an enduring linkage between national and jurisdictional linkage spines. It used keys from existing enduring linkages within each participating jurisdictional Data Linkage Unit (DLU). The process is demonstrated with reference to an enduring map between AIHW Health Linkage Spine (HLS) and Centre for Health Record Linkage (CHeReL) MLK.
ApproachThe NMLK has jurisdictional sections. The NSW section was established in four steps.

CHeReL provided data from its regularly updated MLK for the NMLK project authorised by existing project ethics approval.
AIHW linked NSW MLK subset to HLS and the de-identified linkage map and report were sent to CHeReL for review.
CHeReL reviewed the results, particularly the additional duplicates identified through the linkage process. The agreed links form the AIHW-NSW section of NMLK quarantined for a year.
The annual update, scheduled for later in 2024, will focus only on linking the new and unlinked individuals in both datasets.

ResultsThe NSW cohort was supplied with almost 50 million records, comprising over 14 million unique person ids. Of these, 12 million were matched with reasonable confidence to HLS, or 84.4% of the total (or around 97% if we exclude NSW records with missing personal information and around 140,000 individuals out of scope of HLS.)
Conclusion  Establishment of the NMLK enabled effective production of high-quality enduring linkage maps used across multiple cross-jurisdictional projects while minimising the privacy risks.
Kids’ Environment and Health Cohort
Authors: Adedire Tolu , Lewis Esther , Stephenson Dilly , O'Connor Nathan , Quinn Leah , Goode Hannah
Published: September 2024

This presentation discusses Kids’ Environment and Health Cohort (KEHC), a bespoke Administrative Data Research UK (ADR UK) funded linkage project. KEHC will be a comprehensive and yearly updated dataset for children born in England from 2006 onwards, integrating health, education, pregnancy, and environmental data. This integrates data from different administrative systems including the Office for National Statistics, the National Health Service, and the Department for Education as well as open environmental data.
This presentation will outline the deterministic and probabilistic linkage methods utilised and the quality assurance undertaken through clerical review and bias analysis. Additionally, we will delve into the challenges encountered in the process of data ingest, linkage, and delivery due to the diverse origins and purposes of the collected data, as well as temporal disparities. In navigating the intricacies of multiple linkages, we will discuss our unique methods, including plans to use an indexing first approach.
The first cohort of approximately 10 million children has been created to form the spine and further linkages to the 2011 and 2021 UK census data have been concluded. Linkages between address histories and open environmental data is ongoing.  Linkage results have been promising, with high match rate and excellent quality.
KEHC is a powerful example of how integrated data can be used for research for the public good.
KEHC will provide a unique opportunity for academic researchers investigating the longitudinal effects of local environments on children’s health and educational outcomes and will become one of ADR UK’s flagship Research Ready Datasets.
Developing a generalisable stratification approach for clerical review of linked data
Authors: Maizey Leah , Platcha Josie , Gammon Tim , Wray Matt , Thompson Gavin , Antal Laszlo , Archer Rosaland
Published: September 2024

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

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

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