bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linking administrative data on children's social care to investigate the outcomes of provision
Authors: Hood Rick , Goldacre Allie 
Published: September 2023

ObjectivesThis paper will present findings from a study of linked administrative data on children’s social care (CSC) services in England. Its objectives were to identify and profile the underlying categories of demand for CSC services in England and compare the outcomes of provision between these categories, using a range of measures.
MethodsAn anonymised longitudinal dataset was assembled for children in need and children in care who received services in England between 2015-21. Latent Class Analysis (LCA) was used to identify mutually exclusive categories of demand for all children subject to a statutory social work assessment within that period, based on the factors identified at assessment. Descriptive profiles of these categories were constructed using demographic data as well as children’s intervention pathway following assessment. Survival analysis using adjusted Cox regression models was then undertaken to compare rates of re-referrals, repeat child protection plans, and re-entries to care, for children receiving services within each category.
ResultsThe latent class analysis found twelve distinct categories of demand for children’s social care services, which were consistent across local authorities. Conditional probabilities were used to interpret the demand represented by each category, in consultation with stakeholders. The most prevalent category was domestic abuse and violence (19.7%), followed by complexities around parental mental health (18.4%). Other categories included disability, child mental health, risks outside the home, complex domestic abuse, and concerns about another person in the family or household. The profile of children varied across these categories, including age, gender, ethnicity, the level of risk identified by services, and the type of provision received. Significant differences were also found in the outcomes of provision across categories, especially in re-referrals and re-entries to care.
ConclusionThe categories create an evidence-based typology of demand to supplement conventional performance measures. Local profiling of these categories can be support designing and planning services and aligning the textual information gathered in assessments with the statistical data used for audit and inspection.
An Introductory Synthetic Data Tool
Authors: Thomas Iori , Stuijfzand Bobby 
Published: September 2023

ObjectivesSynthetic data reproduces features of a dataset without disclosing sensitive information, allowing researchers to explore data structures and test code without requiring access to real, potentially sensitive, data. We produced a low-fidelity synthetic data generation tool, accompanied by extensive documentation, allowing novice and expert users to produce such data.
MethodsOur tool, consisting of a Python notebook and a user guide, takes a dataset as input, and produces ‘low-fidelity’ synthetic copy of this dataset, recreating the data fields (or columns) of a dataset, as well as the data types and statistical relationships within these fields, but not between them. It has been tested using real-world administrative data sets and with several users, looking at the quality of the data generated, inspecting whether the data is indeed low-fidelity (i.e. statistical relationships between fields are not recreated) and the usability of the tool.
ResultsOur tool successfully created synthetic datasets from administrative datasets. Users were positive about its usability and the generated data. Tests indicated that computational memory is a main constraint on the size of datatable that can be read in by the tool. We have since implemented improvements to the memory efficiency of the tool to partially address this and have also added procedures that allow for using subsets instead of complete datasets, allowing for the use of datasets which would have otherwise been too large to be used. Testing further indicated that, while the tool by design does not preserve any relationships between fields, they can be reproduced by coincidence, and a limited disclosure process may be required when correlations from the original data are reproduced.
ConclusionsThe tool is easy to use and therefore a useful introduction to synthetic data, providing users with a foundation before using more sophisticated synthetic data tools like Synthpop. Future work could include the development of a Python library and extension of the tool to handle linked datatables.
Data-driven research with historically excluded groups: Towards a model for co-production and democratisation
Authors: McKenna Sarah , Nelson Elizabeth , Maguire Aideen 
Published: September 2023

ObjectivesIt is widely recognised that involving “experts by experience” from historically excluded groups (e.g. those with experience of care) is necessary to maximise research impact, ensure mutual benefit, and is central to the democratisation of data for research. We aim to develop a model of co-production in data-driven research.
MethodPublic engagement in research generally, and data-driven research specifically, has increased significantly in the last decade. However, the co-production of data-driven research with the people behind the data is still rare. This pilot project was designed and delivered in partnership between Administrative Data Research Centre Northern Ireland (ADRC NI), Voice of Young People in Care (VOYPIC) and a group of care experienced young people. Through a range of methods (including meetings, a series of workshops and an internship for a care experienced young person at ADRC NI) we explored and tested opportunities for the co-production of research.
ResultsLed by the group of care experienced young people, we identified a range of entry points for the co-production of data-driven research. While some stages of the research process remained challenging for those with lived experience to penetrate, most notably the analysis of quantitative data in a secure environment, other stages of the research process were meaningfully co-produced. These include the prioritisation of research topics, the development of research questions, the interpretation of findings and the creation of lay language dissemination materials. Formal evaluation of the pilot is pending, but early benefits for all project partners include the development of new skills and knowledge, re-focused research priorities, and more accessible research outputs.
ConclusionCo-production of data-driven research with experts by experience is feasible and necessary for the democratisation of data. It delivers tangible benefits which amplify the positive impact of data-driven research. This session will share innovative practice, key learnings, and practical guidance for the co-production of data research with historically excluded groups.
The effect of on- and off-road cycle paths on cycling to work: A study using administrative data
Authors: Berrie Laurie , Feng Zhiqiang , Rice David , Clemens Tom , Williamson Lee , Dibben Chris 
Published: September 2023

ObjectivesPromoting cycling as a means of transport is now on multiple policy agendas, including health, transport and climate change. In this work, we use administrative data to explore whether cycle paths, particularly off road compared to on road, might increase the proportion of people choosing to cycle to work.
MethodsOur work used data from Edinburgh and Glasgow, Scotland in 2011 at the time of the population census. We limited our data to those aged 16–74 with a current job on the night of the census. Cycle route data were made computer readable from cycle route maps from the time of the census. We looked at the characteristics of those who cycled to work and how proximity to cycle path differed between groups. We used logistic regression to model cycle commuting against distance from home to the nearest cycle path adjusting for confounders identified using a Directed Acyclic Graph.
ResultsIn this paper we will show how the propensity to cycle commute differs across population characteristics such as sex, age and the National Statistics Socio-economic classification and how the interaction of these factors affects propensity to cycle commute. We also show that living closer to on- and off-road cycle paths increases the likelihood of cycle commuting.
ConclusionUnderstanding interventions, such as developing on- and off-road cycle paths, that could increase the number and diversity of people cycle commuting (especially if they move from ‘passive’ to ‘active’ modes of travel) has the potential to reduce carbon emissions, congestion and air pollution, increase physical activity and therefore improve health.
How is city living associated with psychosis? Findings from a novel data linkage of 612,988 people from an urban and ethnically diverse area
Authors: Hildersley Rosanna , Das-Munshi Jayati , Schofield Peter , Cybulski Lukasz , Wuerth Milena 
Published: September 2023

ObjectivesUK cities show higher incidence of psychotic disorders, but the reasons remain unclear. This case-control study uses data from one of the first and largest person-level data linkages between mental health records and the UK census to explore associations previously only assessed using ecological or smaller studies in England.
MethodsThe SocioEconomic Predictors of Mental Disorders (SEP-MD) project dataset comprises of data extracted from electronic health records (EHR) from the South London and Maudsley NHS Foundation trust (SLaM). These EHRs were linked to the 2011 UK census as a collaboration between SLaM, the ONS and King’s College London.
Cases with clinical diagnoses of non-affective (schizophrenia-spectrum) and affective psychoses (bipolar disorder, depression with psychosis) were identified. Population controls were sampled from the locality.
Logistic regression models were used to calculate weighted adjusted (age and sex) odds ratios (waOR) to assess associations. Robust standard errors were used to account for clustering.
Results16,863 linked cases with psychosis (affective n=5,694; non-affective n=11,169) were identified alongside 596,125 population controls.
Cases with psychosis were more likely to live in areas with the highest population density (waOR 1.17 (1.05, 1.30)) when comparing the lowest quintile to the highest. Non-affective disorders showed the highest association with population density.
Being born within the UK was associated with a higher risk of psychosis, and migrants living in the country for longer were at a significantly higher risk than those living in the UK for less time. Socioeconomic predictors, including education, occupation and tenure, were all associated with higher psychosis risk. Racialised minorities were at higher risk of specifically non-affective psychoses. Indicators of isolation (marital status and living alone) were highly associated with psychosis risk.
ConclusionsOur findings regarding urbanicity, ethnicity, migration socioeconomic position and social circumstances both confirm and provide further depth to previously identified associations. Novel findings relating to migration and interactions with ethnicity will require further investigation.
These insights will provide valuable information for future public health and policy development.
Making the GRADE (GRading and Admissions Data for England)
Authors: Walter Merlin , Zanini Nadir 
Published: September 2023

ObjectivesTo provide a data owner’s perspective into the journey to create GRADE, an Ofqual-DfE-UCAS linked administrative dataset, and to make it available for independent research. Moving from the ethical principles underpinning the sharing of data and through multiple strands of the project, we will draw lessons to inform best practice.
MethodsAreas of research interest were used as drivers to define the content of GRADE. To ensure that research questions of relevance to both policy makers and scholars could be addressed, we established an advisory committee including key representatives of the statistical, research, and educational policy communities. Thanks to the collaboration with technical staff from participating organisations, the data was linked to ensure that students’ anonymised data could be tracked across different data sources. We then focused on supporting external research by producing metadata and supplementary documentation, as well as establishing independent funding provision for projects using the GRADE data.
ResultsThe internal research conducted was helpful to inform policy development and to test the data. The GRADE data was created and made available to external researchers. A data sharing framework was published to describe, at high-level, the content of the data and its potential use, as well as to set out how to access this rich source of micro-data. Detailed data specifications have been made available, alongside a low-fidelity synthetic version of the data.
Numerous research projects based on the GRADE data are currently underway and preliminary findings are already being disseminated. More research is being planned, based on an upcoming refreshed version of the GRADE data. Significant funding has been made available for external researchers to conduct independent work.
ConclusionCreating, combining, and sharing administrative data is not an easy task. Engagement with researchers and the wider community is necessary from the beginning of the project. It is a considerable investment, with potentially high returns, but also significant costs, and the output produced must align with policy and research priorities.
The UK Longitudinal Linkage Collaboration: A trusted research environment for the longitudinal research community
Authors: Boyd Andy , Flaig Robin , Oakley Jacqui , Campbell Kirsteen , Evans Katharine , McLachlan Stela , Thomas Richard , Turner Emma 
Published: September 2023

ObjectivesOur Trusted Research Environment (TRE) provides a centralised infrastructure to pool Longitudinal Population Studies’ (LPS) data and systematically link participants’ routine health, administrative and environmental records. All data are held in a centralised research resource which is now certified by UK Statistics Authority as meeting the Digital Economy Act standard.
ApproachWe have created an unprecedented infrastructure integrating data from interdisciplinary and pan-UK LPS linked to participants’ NHS England records with delegated access responsibilities. Integrated and curated data are made available for pooled analysis within a functionally anonymous DEA and ISO 27001 accredited TRE. We developed a bespoke governance and data curation framework with LPS data managers and Public/participant contributors. New data pipelines are being built with partners at ADRUK and the Office of National Statistics to link non-health records. Our design supports long-term sustainability, linkage accuracy and the ability to link data at both an individual and household level.
ResultsThis organisation is a collaboration of >24 LPS with ~280,000 participants. Participants' data are linked to NHS records and geo-coded environmental exposures. This resource is now accessible for public benefit research for bona fide UK researchers. Administrative data including tax, work and pensions, and education are being added to the resource. This data flow is enabled by: (1) a model where TTP processes participant identifiers for many different data owners; (2) creation of a novel longitudinal data pipeline, enabling linkage, data extraction and update of records over time; (3) an access framework where Linked Data Access Panel considers applications on behalf of data owners (e.g., the NHS), with review by a Public Panel and distributing applications to LPS for approval of appropriate data use.
ConclusionOur organisation provides a strategic research-ready platform for longitudinal research. We are extending linkages of LPS participants to previously inaccessible datasets. The research resource is positioned to allow researchers to investigate cross-cutting themes such as understanding health and social inequalities, health-social-environmental interactions, and managing the COVID-19 recovery.
Renewable energy transition and the local economic development of lagging-behind regions: Evidence from the offshore wind energy industry in the UK
Authors: Vanino Enrico , Siddall Thomas , Rupp William 
Published: September 2023

ObjectivesIn this study we analyse the potential impact of the renewable energy transition on the local economic development of lagging-behind regions, focusing in particular on the causal impact of offshore wind farms on the economic development of local onshore coastal communities in the UK, which are usually deprived left-behind regions.
MethodsWe consider the current impact of this transition on the economic development of places, analysing employment and productivity growth in local industries directly affected, as well as the magnitude of potential externalities along the supply-chain. Linking publicly available data on offshore wind farms, offshore wind suitability, and local economic outcomes, we develop an instrumental variable approach to address endogeneity, modelling the probability of wind farms’ locations based on offshore natural characteristics, such as water depth, distance to shorelines, and wind speed factors.
ResultsThis is the first study providing empirically robust analysis at a granular level on this topic. After mapping the location of offshore wind farms and link these to onshore local communities through the location of ports and other infrastructures servicing these farms, we estimate a significant positive effect on local employment and productivity growth in the offshore wind energy sector, as well as the indirect effect along the supply chain.
ConclusionThe rapidly growing renewable energy sector could become a key player in the local economic development of peripheral and lagging-behind regions. This could allow many peripheral regions across developed countries, once dependent on mature declining industries, to regenerate their local economies by sustainably transitioning towards new higher added-value green industries.
Education pathways to the labour market for 16-year-olds who struggle to achieve maths and English in General Certificate of Secondary Education
Authors: Gorman Emma , Thomson Dave , Urwin Peter , Min Zhang 
Published: September 2023

We examine the post age-16 educational pathways taken by the 44% of young people who do not gain “good” grades in English and Maths at age 16 years. We then assess the causal effects of attending General Further Education (GFE) colleges on education and labour market outcomes for this group.
We use the Longitudinal Education Outcomes dataset, which comprises linked administrative education, employment and income records for the population of English school pupils aged 16 in 2011. To summarise complex post-16 education trajectories, we present Sankey charts stratified by indicators of disadvantage. We study the effects of attending GFE at age 17 on whether a pupil gains a Level 3 qualification by age 19, and their earnings and employment status at age 24. To estimate a causal impact, we use distance from home to the closest GFE college as an instrumental variable, controlling for a rich set of background characteristics.
Our graphical results highlight the complexity of post-16 educational pathways and transitions, which are differentiated by disadvantage. Over 50% have GFE as their first post-16 destination. Results from instrumental variable analyses show a positive association between attending GFE and gaining a Level 3 qualification by age 19, among pupils who do not gain a “good” pass in the General Certificate of Secondary Education (GCSE) in either English and/or Maths. Restricting analyses to the bottom of the distribution – those who gain an E, F or G grade in both English and Maths - we do not detect an impact of GFE on qualifications at age 19. Among both subgroups, we do not detect any impact of attending GFE on earnings and employment at age 24 years.
While the post-16 pathways taken by disadvantaged, lower-attaining pupils do increase qualification attainment for some, the value these have in the labour market appears limited. These results may indicate the importance of “soft-skills” and early employment experiences for this subgroup of lower-attainers.
Clinical coding of long Covid in Wales: A cohort study of 3.5 million people using linked health and demographic data
Authors: Abbasizanjani Hoda , Bedston Stuart , Robinson Lucy , Curds Matthew , Akbari Ashley 
Published: September 2023

Objectives‘Long COVID’ (LC) is broadly defined as signs and symptoms that continue or develop after the acute phase of COVID-19, and can affect cardiovascular, respiratory and other organ systems. Using electronic health records, we investigated clinical coding of LC in primary and secondary care for the population of Wales.
MethodsWe conducted a cohort study for the population of Wales, using anonymised individual-level linked data in the Secure Anonymised Information Linkage (SAIL) Databank. We used the Welsh COVID-19 e-cohort (doi:10.1136/bmjopen-2020-043010), which consists of all people (adults and children) alive and resident in Wales from 1st January 2020. To this e-cohort we linked primary and secondary care, COVID-19 testing, and ethnic group data. We then calculated the proportion of people with a LC diagnosis code (in primary and secondary care data) overall and stratified by demographic variables.
ResultsOf 3.5m residents, 7,696 (0.2%) had a LC clinical diagnosis. Compared with the general population, a higher proportion of people with LC were female, middle age, white, and hospitalised within 28 days of a confirmed COVID-19 infection. LC affected all socioeconomic groups, as assessed using the Welsh Index of Multiple Deprivation. When looking at LC diagnosis codes in primary care, 30.9% of practices in SAIL have not used these codes at all. And the number of recorded events was low until the end of January 2021, after which there was an increase in coding. These findings are likely a substantial underestimate of LC prevalence in Wales. Earlier estimates from self-reported surveys, such as the Office for National Statistics, are much higher, ranging anywhere between 3-5%.
ConclusionLow recording rates of LC and variation between practices could be due to a delay in introducing clinical coding and lack of presentation/recording. Understanding prevalence of LC is vital for addressing the scale of the problem. Therefore developing additional data-driven approaches is necessary to obtain an accurate prevalence estimate.