bluuebunny/crossref_metadata_2025_split huggingface dataset:
Depression and changes in educational attainment using administrative data: The role of socio-demographic characteristics
Authors: Wickersham Alice , Dickson Hannah , Stewart Robert , Ford Tamsin , Downs Johnny 
Published: November 2019

Background Research has shown depression to result in poorer educational attainment among children. However, comparatively little is known about the relationship between depression and attainment over time, and the role that children’s socio-demographic characteristics might play.
Aim To describe the educational attainment trajectory profiles of children with a clinical diagnosis of depression, and to explore whether these trajectory profiles vary by gender, ethnicity, area-level deprivation and age at diagnosis.
Methods This is a historical, longitudinal cohort study, making use of two linked administrative datasets. First, a cohort of children (<18 years) were identified who had received a first diagnosis of depression from the South London and Maudsley NHS Foundation Trust between 2007 and 2013 (n=1490). Their pseudonymised secondary mental healthcare records were extracted via the Clinical Record Interactive Search application at the Trust’s Biomedical Research Centre. These records were then linked to England’s National Pupil Database, which contains attainment data for these children at three timepoints of interest: Key Stage 1 (ages 5 to 7 years), Key Stage 2 (7 to 11 years) and Key Stage 4 (14 to 16 years). Longitudinal mixed models for repeated measures will be conducted to model changes in attainment over these three timepoints according to gender, ethnicity, area-level deprivation and age at diagnosis.
Results Preliminary results are suggestive of attainment trajectories in this cohort varying according to their socio-demographic characteristics. Analysis will be completed August 2019.
Conclusion The findings from this study will identify whether some groups of depressed children experience better or worse patterns of educational attainment over time. This could highlight priorities for intervention in England’s ongoing efforts to improve child mental health provision in schools.
The Office for National Statistics Longitudinal Study
Authors: Sizer Alison , Duke-Williams Oliver 
Published: November 2019

Background and Rationale The ONS Longitudinal Study (‘the LS’) covers England and Wales and includes individual data from the 1971 – 2011 decennial censuses and linked information on births, deaths and cancer registrations. It is representative of the population of England and Wales.
Aim This presentation describes the LS and the linked administrative data, and showcases recent/ prominent examples of research.
Methods and Approach The LS is built around samples drawn from decennial censuses, with its initial sample drawn from the 1971 Census. It also contains information about other people living in a sample-member’s household. Substantial emphasis is placed on security of access to the data and its responsible use. All research outputs are checked and are only released to users once disclosure control requirements are met. Linkage of study members from one census to another and vital events is carried out by ONS.
Results The LS has been used for a variety of research. Using linked census and death records occupational differences in mortality rates have been researched. Individual records from all five censuses have been used to contribute to research social mobility, and research has also investigated the effects of long-term exposure to air pollution. Research has provided evidence of impact for social policy issues, e.g. health inequalities and the State Pension Age Review.
Discussion The main strength of the LS is its large sample size (>1 million), making it the largest nationally representative longitudinal dataset in the UK. This allows analysis of small areas and specific population groups. Sampling bias is almost nil, and response rates are very high relative to other cohort and panel studies.
Conclusion The ONS Longitudinal Study is a vital UK research asset, providing access to a large sample of census data linked across five censuses. It is strengthened through linkage to events data.
Data linkage for public health research – the Fforestfach tyre fire
Authors: May Leon , Evans Lloyd 
Published: November 2019

Background The Fforestfach tyre fire started on the 16th of June 2011 and continued to burn for 22 days.
Smoke from tyre fires contain a number of chemicals that might cause health problems, especially for people who already have long-term health conditions.
This research investigated whether people living close to the Fforestfach fire contacted their General Practice (GP) more often during the fire than they might have done otherwise. This is important both for the people living in the Fforestfach area and also for those living near similar fires in the future.
Aim To use advances in mapping and data linkage techniques to assess associations between the Fforestfach fire incident and respiratory and cardiovascular health outcomes. The report focusses on the occurrence of respiratory and cardiovascular Read codes in patient’s GP records.
Methods Using data linkage, information provided by the Met office was used to identify households likely to have been exposed to above threshold levels of pollution. Residents from these households were linked to their GP records via the Secure Anonymised Information Linkage (SAIL) databank. Logistic regression models tested associations between above-threshold exposure to a specific type of pollution (PM10) and an increase in GP contact.
Results Regression modelling demonstrated a small but significant increase in GP contact for respiratory conditions in patients with pre-existing asthma. The models did not demonstrate any affect in the general population.
Conclusion The study demonstrated the value of linking health and environmental data using advanced data linkage techniques.
Findings support current health advice used in environmental incidents such as this, that individuals with certain chronic conditions may be more likely to experience symptoms when exposed to 24-hour mean concentrations of PM10 exceeding 50µg/m3; but the risk of significant symptoms as a result of such exposure in the general population is likely to be minimal.
Building a research partnership between computer scientists and health service researchers for access and analysis of population-level health datasets: what are we learning?
Authors: Schull Michael , Brudno Michael , Ghassemi Marzyeh , Gibson Garth , Goldenbrg Anna , Paprica Alison , Rosella Laura , Stukel Therese , Victor Charles , Virtanen Carl 
Published: November 2019

Background and rationale There is widespread enthusiasm to improve health through the application of artificial intelligence and machine learning (AI/ML) methods to large population-level health datasets. Achieving this may require successful collaboration between institutions as well as between computer scientists (CS), machine learning researchers (MLR) and health service researchers (HSR).
Main Aim Describe lessons learned in creating the Health Artificial Data and Analysis Platform (HAIDAP) in Ontario, Canada.
Methods/Approach A partnership between a HSR institute (ICES), an AI/ML institute (Vector) and a high-performance computing center (HPC4H) was initiated in 2017 to enable the application of AI/ML methods to population-level health data for the province of Ontario (population 14M). We describe lessons learned (and being learned) following the HAIDAP’s launch.
Results The HAIDAP was launched in 2019. Major learnings include: 1) importance of institutional partnerships and alignment with institutional strategies; 2) potential of joint institutional risk-sharing models; 3) need for scientific collaborations bridging disciplines around joint research projects; 4) sensitivity to different scientific cultures (e.g., academic prestige of conference proceedings for MLR vs journal publications for HSR; traditional statistical vs. ML model assumptions); 5) differences in research timeline expectations; 6) different experience with and expectations for access to de-identified routinely collected data (e.g., need for research ethics committee project approvals and privacy impact assessments); 7) developing data access models that enable greater flexibility (e.g., importing code or using open source tools); 8) broadening data access models to allow modern high-dimensional exploratory data analysis; 9) obtaining support of information/privacy regulator; 10) the hardware is the (relatively) easy part compared to other success factors.
Conclusion The HAIDAP has enabled multi-disciplinary collaborations and novel AI/ML research of Ontario’s population-level health data. Collectively we have learned that additional effort is required to develop systems and processes enabling more efficient access to data and analytic tools for the analysis of administrative health data.
Thriving, catching up or falling behind: Immigrant and refugee children’s kindergarten competencies and later academic achievement
Authors: Gagné Monique , Guhn Martin , Emerson Scott , Magee Carly , Milbrath Constance , Gadermann Anne 
Published: November 2019

Background with rationale Immigrant and refugee children and adolescents form a growing socially, culturally, and economically diverse group with varying adaptation outcomes.
Main Aim In this Canadian, population-based study, we wanted to identify the varying academic achievement trajectories that immigrant and refugee children followed from childhood to adolescence (e.g., thriving, catching up, or falling behind) and whether these differences could be predicted at school entry, based upon select social/migration factors and teacher-assessed literacy, numeracy, and social-emotional competencies in kindergarten.
Methods The study used a retrospective, longitudinal, population-based design and leveraged linked, individual-level administrative data from four sources (Immigration, Refugees, and Citizenship Canada, Ministry of Education, Ministry of Health, Human Early Learning Partnership) to identify a cohort of immigrant and refugee children in British Columbia, Canada (N = 9,216). We utilized a novel analytical approach (Group-based Trajectory Modeling) that allowed us to capture heterogeneity in the Grade 4 to Grade 10 academic (literacy and numeracy) trajectories.
Results We found that immigrant and refugee children followed a range of academic achievement trajectories from Grade 4 to Grade 10 – some children thriving, some catching up, and others falling behind over time. A number of social/migration factors (e.g., sex and refugee status) as well as literacy, numeracy, and social-emotional competencies in kindergarten predicted these later academic trajectories in unique and sometimes powerful ways.
Conclusion In all, we found that not all immigrant and refugee children start school on equal footing and this was associated with long-term outcomes. The implications for the importance of early, tailored interventions to set immigrant and refugee children onto paths of positive adaptation will be discussed.
Ambient Air Pollution and Health in Northern Ireland
Authors: Rowland Neil , McGovern Mark , McVicar Duncan , O'Reilly Dermot 
Published: November 2019

The breathing of polluted air is associated with the development and exacerbation of various health conditions, and the cost of treating such conditions is substantial. However, while the correlation with respiratory-related health conditions is well documented, less is known about this relationship from a longitudinal perspective and how pollution affects other health problems, such as neurodegenerative diseases (e.g. dementia) or diseases of the endocrine system (e.g. diabetes). More research is therefore needed to better identify the health risks and costs of pollution.
The aim of this study is to investigate the relationship between exposure to ambient air pollution and health outcomes at the individual level. The analysis is based on data from the Northern Ireland Longitudinal Study (NILS) - a representative 28% sample of the Northern Ireland population drawn from an administrative health database and linked to Census returns and other administrative databases - linked with routinely collected prescriptions data. Exposure to air pollution is assessed by linking modelled background pollution data at a 1x1 km grid square resolution to residential address. Annual measures of eight separate pollutants will allow us to examine cumulative exposure between 2001 and 2016.
Regression analysis will be used to estimate the health effects of exposure to air pollution. Preliminary analysis currently underway will focus on outcomes such as self-reported health at the 2011 Census (e.g. the presence of breathing difficulties and memory loss) and all-cause and respiratory-related mortality since 2001. Stage two of the analysis will utilise the prescriptions linkage to define illness-specific health outcomes using receipt of medicines prescribed by General Practitioners (GPs) to treat cardiovascular- and respiratory-related health conditions as well as diabetes, dementia and Parkinsonism. Longitudinal analysis will enable us to assess whether these relationships are affected by unobserved confounders. Preliminary results for both stages of the analysis are expected by autumn 2019.
InFORM: Improving care for people who Frequently call 999: co-production of guidance through an Observational study using Routine linked data and Mixed methods
Authors: Khanom Ashrafunessa , Edwards Adrian , Edwards Bethan , Hughes Heather , John Ann , Petterson Robin , Phillips Ceri , Porter Alison , Rees Nigel , Scott Jason , Watkins Alan , Snooks Helen 
Published: November 2019

BackgroundPeople who frequently call the 999 ambulance service present an operational challenge to providers and their needs are inadequately met by current service provision.
Aim of researchTo understand patterns and health outcomes of frequent calling and to work with stakeholders to co-produce guidance for formal testing in a future evaluation.
MethodsThis mixed methods study will include a scoping review of national and international literature followed by an epidemiological study of callers at the all Wales level exploring health outcomes through anonymised linked data. We will also explore the views of patients using qualitative Bio-photographic interview method with a follow up interview at six months and use focus groups with care providers from across primary and emergency care and the third sector. We will use generalised linear model to analyse quantitative data and qualitative data will be analysed thematically.
ResultsFindings will include follow up of eligible patients; successful data matching and data linkage; retrieval of outcomes within 12 months. Outcomes will include: adverse events, deaths, emergency admissions; 999 attendances. Qualitative results will include Bio-photographic interviews with completed scrap books and interviews based on the books (n=34). Care provider focus groups (n= 22).
Output Co-produced guidance developed with stakeholders.
ConclusionHigh users of the 999-ambulance represent a significant policy challenge to emergency ambulance services and often present with complex needs. This study will inform on the characteristics of callers and how to address their care supported with a co-produced guidance for care providers.
National Therapeutic Indicators in Scotland and Financial Incentives
Authors: MacBride-Stewart Sean , Guthrie Bruce , Marwick Charis , Hurding Simon 
Published: November 2019

Background with rationale National Therapeutic Indicators (NTI) in Scotland use national administrative data to identify variation and drive quality improvement in GP practice prescribing. Twelve indicators were developed in 2011 and in 2012 practices were offered financial incentives to review their prescribing in two where there was greatest potential for improvement.
Main AimTo quantify the impact of 2012 NTI alone and in combination with financial incentives.
Methods/ApproachSix NTI were selected for analysis, aiming for heterogeneity in terms of whether the focus was quality of care (clinical) or cost effectiveness (cost). Impact was evaluated using segmented linear regression of time-series prescribing data, and changes in prescribing at 12 and 24 months after NTI introduction were estimated. Other national interventions expected to influence GP prescribing were also modelled.
ResultsOne selected NTI could not be modelled because of non-linear trends in the baseline period. In practices not financially incentivised to review prescribing, improvements were variable. At 12 months relative changes were dipyridamole −31.9% (95%CI; −43.1 to −20.7), quinine −3.6% (95%CI; −7.2 to −0.1), non-preferred statins 6.2% (95%CI; 1.0 to 11.5), hypnotics and anxiolytics −2.9% (95%CI; −7.3 to 1.6) and high strength inhaled corticosteroids −1.0% (95%CI; −1.7 to −0.3).
In comparison financially incentivising practices resulted in larger improvements and improvements in all NTIs modelled. At 12 months relative changes were dipyridamole −65.3% (95%CI; −68.4 to −62.2), quinine −52.3% (95%CI; −54.7 to −49.9), non-preferred statins −17.8% (95%CI; −21.9 to −13.6), hypnotics and anxiolytics −9.6% (95%CI; −12.6 to −6.7) and high strength inhaled corticosteroids −6.0% (95%CI; −7.3 to −4.8).
ConclusionGP prescribing can be improved through feedback of national administrative data prescribing indicators. Impact on prescribing is larger when financial incentives to focus on particular areas of prescribing are additionally used.
Linking Pathology Datasets – Trials and Triumphs
Authors: Stokes Brian , Jose Matthew , Wiggins Nadine , Albion Tim 
Published: November 2019

Background with rationaleThe burden of chronic kidney disease (CKD) has increased rapidly in Australia over recent years. The financial cost of treating people with end-stage kidney failure by conventional models of dialysis or transplantation represents a substantial healthcare cost and is likely to continue to increase in coming years. Tasmania, the smallest state of Australia and its only island state, has the highest burden of chronic disease nationally, including kidney disease. The aim of this study was to use data-linkage to develop a state-wide dataset to quantify the burden and distribution of CKD, including identifying barriers to dialysis treatment services.
Methods/ApproachThe Tasmanian Data Linkage Unit (TDLU) used a complex data linkage design comprising seven disparate datasets representing public and private pathology, public hospital admitted patient and emergency department data, cancer records, dialysis and transplant records and death notifications. A cohort was selected from public and private providers of pathology services in the state to support the establishment of a comprehensive researchable dataset. The datasets spanned the period 2004-2017 and included linkage of both state and national data.
ResultsThe study cohort comprised just under 490,000 individuals in the Tasmanian population from the two pathology datasets, with a combined total of 1,347,00 total links made across all datasets. Individual unit records were geocoded according to the Australian Statistical Geographic Standard (2011) with over 92% of the 374,000 unique addresses identified in the public pathology dataset geocoded to address level.
ConclusionThe final researchable dataset compiled by the research team following linkage is providing an enormously powerful asset to help answer questions specific to CKD in Tasmania, and that in turn is anticipated to result in greater access to services, improved care and better patient outcomes.
Unleashing The Power of Your Master Linkage Map – Is There A Role For Business Intelligence Tools In Supporting Data Linkage?
Authors: Stokes Brian 
Published: November 2019

Background with rationaleBusiness Intelligence (BI) software applications collect and process large amounts of data from one or more sources, and for a variety of purposes. These can include generating operational or sales reports, developing dashboards and data visualisations, and for ad-hoc analysis and querying of enterprise databases.
Main AimBusiness Intelligence (BI) software applications collect and process large amounts of data from one or more sources, and for a variety of purposes. These can include generating operational or sales reports, developing dashboards and data visualisations, and for ad-hoc analysis and querying of enterprise databases.
Methods/ApproachIn deciding to develop a series of dashboards to visually represent data stored in its MLM, the TDLU identified routine requests for these data and critically examined existing techniques for extracting data from its MLM. Traditionally Structured Query Language (SQL) queries were developed and used for a single purpose. By critically analysing limitations with this approach, the TDLU identified the power of BI tools and ease of use for both technical and non-technical staff.
ResultsImplementing a BI tool is enabling quick and accurate production of a comprehensive array of information. Such information assists with cohort size estimation, producing data for routine and ad-hoc reporting, identifying data quality issues, and to answer questions from prospective users of linked data services including instantly producing estimates of links stored across disparate datasets.
Conclusion BI tools are not traditionally considered integral to the operations of data linkage units. However, the TDLU has successfully applied the use of a BI tool to enable a rich set of data locked in its MLM to be quickly made available in multiple, easy to use formats and by technical and non-technical staff.
Linking two administrative datasets about looked after children: testing feasibility and enhancing understanding
Authors: Hooper Jade , Cusworth Linda , Whincup Helen 
Published: November 2019

Background with rationaleEvery year all 32 local authorities in Scotland provide information on looked after children in their area to the Scottish Government. This forms the basis for the annual Children Looked After Statistics (CLAS). Information is also collected by Scottish Children’s Reporter Administration (SCRA) on all children who are involved in the Children’s Hearings System. Until now these two data sets had never been linked.
Main Aim To test the feasibility and success of the linkage on the basis that these datasets had not previously been linked, and if linkage was possible, use this data to enhance our understanding of the child and process factors associated with pathways to permanence or lack of permanence.
Methods/ApproachVeterans were identified using the South London and Maudsley Biomedical Research Centre (SLaM) case register – a database holding secondary mental health care electronic records for the South London and Maudsley National Health Service Trust of 300,000 patients. We developed two methods. An NLP and machine learning tool were developed to automatically evaluate personal history statements written by clinicians.
ResultsFor the first time, as part of the Permanently Progressing? Building secure futures for children in Scotland study, these two data sets were linked safely and successfully for 1,000 children who became looked after in 2012-13 when they were aged five and under.
The linkage provided important new information for practitioners and policymakers. In this presentation we will focus on the key findings, such as what it told us about previous referrals and methodological insights regarding these data sets and their linkage.
ConclusionThe data linkage process was complex and time-consuming but possible. The data we were able to link provided valuable information that enhanced our understanding of child and process factors.