bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring local variation in the use of out-of-home care in England using linked administrative data
Authors: Mc Grath-Lone Louise , Harron Katie , Dearden Lorraine , Nasim Bilal , Gilbert Ruth 
Published: April 2017

ABSTRACT
ObjectivesIn England, approximately one in thirty children spend time in out-of-home care (OHC) by age 18.Use of OHC is known to vary across the country, which may be explained in part by ethnic group differences in the risk of being placed in OHC. This study uses linked administrative data to determine whether local variation in the cumulative proportion of children placed in OHC is explained by differences in the ethnic distribution of the local populations.
Approach Administrative social care records from January 1, 1992 to December 31, 2012 for a one-third sample of children were linked to publicly-available local authority population estimates. Two artificial birth cohorts were selected for analysis; 1992-94 and 2009-11. The cumulative proportion of children who entered OHC was calculated for each of the current 150 local authorities (the local government bodies who deliver children’s social care services). Funnel plots were used to identify local authorities with higher or lower than expected rates of entry to OHC, taking into account the size of their child population and its ethnic distribution.
ResultsOverall 3.3% of children in England born 1992-94 had entered OHC by age 18. Rates varied considerably by local authority from this national average, ranging from just 1.0% in Rutland to 6.9% in Manchester (both p<0.001). Local authority variation in rates of entry to OHC was also evident among the most recent birth cohort: while 0.8% of children born 2009-11 had entered OHC by age one, this figure varied from 0.2% in Rutland to 2.7% in Blackpool (p<0.05 and p<0.001 respectively). Indirect standardisation of rates for ethnicity did not appreciably change the cumulative percentage of children entering care in each local authority or the relative rankings of local authorities.
Conclusion There is considerable, persistent variation between local authorities in England in the rate of entry into OHC during childhood which cannot be attributed to differences in ethnic composition. Further linkage of administrative social care data to area-level data (for example, related to social care spending or indicators of social deprivation) would be useful for understanding local variation in thresholds for placing a child in OHC.
Causes of death in the decade after hospitalisation for injury during adolescence: a study using linked hospital admissions and death registrations data
Authors: Herbert Annie , Gilbert Ruth , Cottrell David J , Gonzalez-Izquierdo Arturo , Li Leah 
Published: April 2017

ABSTRACT
ObjectivesTo quantify risks of cause-specific death up to ten years after discharge from an emergency admission to hospital for violent, self-inflicted, or drug/alcohol-related injury, during adolescence. To compare these risks by type of original injury, and with risks after accident-related injury.
ApproachWe used admissions data for England linked to death registrations from 1997 to 2012. We identified emergency admissions for injury in 10-19y olds and categorised type of injury as either violent, self-inflicted, drug/alcohol-related, or accident-related (no record of violent, self-inflicted, or drug/alcohol-related injury, but record of an accident), using ICD-10 codes in admission records. We categorised causes of death as homicide, suicide, drug/alcohol-related, accidental (excluding drug/alcohol-related accidents), or ‘other’ (remaining causes), using ICD-9 and ICD-10 codes from death registration records. We estimated cumulative risks of cause-specific death in the ten years after discharge, by sex and type of original injury. We used time-to-event regression models to estimate risks of cause-specific death, after violent, self-inflicted or drug/alcohol-related injury (relative to those after accident-related injury), adjusted for age-group (10-15, 16-17, 18-19y) and chronic condition status (yes/no; indicated by ICD-10 codes in past year admission records), and stratified by sex.
ResultsThere were 333,009 adolescents admitted for violent, self-inflicted, or drug/alcohol-related injury (girls 181,926, boys 181,053), and 649,818 for accident-related injury (girls 166,462, boys 483,356). There were 4,782 deaths in the ten years after discharge: 2,415 after violent, self-inflicted or drug/alcohol-related injury (girls 873, boys 1,542) and 2,367 after accident-related injury (girls 439, boys 1,928). Deaths after violent, self-inflicted or drug/alcohol-related injury injury were mostly accounted for by suicide (girls 35.8% of all deaths, boys 34.2%) or drug/alcohol-related death (girls 31.7%, boys 35.6%). Risks of suicide were similar to those for drug/alcohol-related death, regardless of the type of original injury. Adjusted risks of death were 1.4 to 6.8 times greater than after accident-related injury (by cause and sex).
ConclusionAdolescent girls and boys discharged after violent, self-inflicted, or drug/alcohol-related injury had similar risks of suicide and drug/alcohol related death, regardless of the category of the original injury. These adolescents also had increased risks of cause-specific deaths compared to those discharged after accident-related injury. Current practice to assess and reduce risks of future harm after self-inflicted injury should be extended to adolescents discharged after violent or drug/alcohol-related injury. Preventive strategies should address risks of drug/alcohol-related death as well as risks of suicide.
Investigating the incidence of youth mental health problem by merging person register and school level class room survey data
Authors: Merikukka Marko , Ristikari Tiina , Paananen Reija , Gissler Mika 
Published: April 2017

ABSTRACT
ObjectivesAim of the study is to investigate the incidence of youth mental health problems by merging two different types of datasets; a person register, the 1987 Finnish Birth Cohort and school level class room survey data, Finnish School Health Promotion study.
ApproachThe 1987 Finnish Birth Cohort is a longitudinal nationwide follow-up data including a complete census of children born in a single year 1987. Children have been followed subsequently over time from the prenatal period through the year 2012, using official registers collected by Finnish authorities. Register data includes forms of documentation of children’s own and their parents’ health status and social circumstances from the perinatal period into early adulthood.
Finnish School Health Promotion study is a cross-sectional classroom survey that has been carried out in the Finnish lower secondary schools nationally every other year since 1996. Depending on the geographical location of the school, half of the age group (born in 1987) voluntarily responded to the survey on their health and lifestyle habits in 8th grade (2002) and other half in 9th grade (2003).
The 1987 Birth Cohort data includes a unique numeric code regarding the school the cohort members attended in 2003. The Finnish School Health Promotion study dataset also includes the same unique school code from the year 2003 through which it’s possible to merge datasets. The linked data can be analyzed both at school and municipality level.
ResultsThere were 59 476 children in the 1987 Finnish Birth Cohort, of whom 57 620 (96.9 %) had school code. School health survey was filled by 48 146 of the 1987 age group. There was positive correlation between self-reported help from home and F-diagnoses F40-F49 (correlation: 0.137, p-value: 0.011). Also self-reported depression score correlated with F-diagnoses F10-F19 (correlation: 0.152, p-value: 0.005) in municipality level.
ConclusionIt is possible to merge datasets from school level to an individual level register data. The merged big data offers new possibilities to study questions related to the prevalence of mental health problems. The new linked data can be further analyzed to hierarchical model.
Data linkage in social care: a pilot project
Authors: Orrell Alison , Heaven Martin , Seddon Diane , Robinson Catherine 
Published: April 2017

ABSTRACT
BackgroundTo build the complete picture of service provision there is a need to broaden the linked data available to include health, social service provision by Local Authorities, and provision of support by third sector organisations. Data Linkage in Social Care is a pilot project to test the feasibility of linking datasets from a local authority, the NHS and third sector organisations. The focus of this work is individual level data from adults who are referred to social services in order to avoid admission to hospital or to facilitate their discharge from hospital. The data linkage will include data from statutory and third sector organisations and services which provide interventions and support in community settings.
ObjectivesThe main aims of this research are to:1. Test the feasibility of linking datasets from Local Authority, the NHS and third sector organisations.2. Build a more complete picture of service provision using adults who have been referred to social services in order to avoid admission to hospital or to facilitate their discharge from hospital.3. Assess the range and quality of data available in each of the relevant organisations providing services to those individuals.
The research outcome is a better understanding of the utility of data linkage across statutory and third party organisations.
ApproachA Bangor University led research team partnered with the Gwynedd Local authority to explore the Governance Issues and practicalities of providing an anonymised dataset to the SAIL databank at Swansea. Two third sector agencies were also approached. With the various required Service Level Agreements in place, data were put through the tried and trusted SAIL process for analysis.
Results Data relating to well over 20,000 referrals generated by 17,000+ social services clients in Gwynedd Local Authority from the period 2008 to 2015 were anonymised into the SAIL databank in Swansea, and linked to records from primary and secondary care. We will present results on the success of this process and on the emerging findings from the linked datasets.
Understanding NHS hospital admissions in England, Scotland and Wales: data linkage to the King’s College Military Cohort Study
Authors: Leightley Daniel , Chui Zoe , Goodwin Laura 
Published: April 2017

ABSTRACT
ObjectiveSecondary health systems in the United Kingdom (UK) are unique for recording Outpatient, Inpatient and Accident & Emergency (A&E) visits in the form of electronic health (eHealth) records. Linking regional healthcare datasets is a problematic, further challenging when linking externally, such as to the King’s College Military Cohort Study (KCMCS). We introduce our methodology used for eRecord linkage.
ApproacheHealth records from England, Scotland and Wales offer a variety of parameters such as admission/discharge date, diagnosis, treatment/procedure undertaken and the cost of treatment. To acquire eHealth records, unique patient identifiers: NHS number, forename, surname, sex and date of birth extracted from KCMCS were provided to each region. The KCMCS contains self-reported questionnaire results for 9,990 serving/ex-serving military personal, 8,602 participants consented to linkage. eHealth records prepared for linkage in two stages. First, admission and discharge date were checked to ensure a valid date. Second, episodes were checked for consistency, ensuring that no records for individual participants were duplicated. Data available varied based on the region, this disparity between regions can result in data type variation. Hence, linkage was performed on mutual variables to ensure a uniform admission history. Creation of the linked dataset was as follows. First, records and episodes relating to an individual were brought together, to create a personal admission history. Secondly, personal admission history were linked to the KCMCS.
ResultsLinking to regional health datasets is not without its challenges. England, Scotland and Wales obtain, store and process eHealth records using different methodologies. A total of 6,336 (76.66%) participants were matched by regional health providers, with a total of 61,558 eHealth records. A total of 187 eHealth records were identified and discounted from linkage due to failure to meet criteria listed above. Verifying diagnoses completeness, Inpatient admissions were consistently code, with full completeness. Conversely, Outpatient admissions were poorly coded with 98% lacking any type of diagnosis. In addition, A&E records were sparsely coded; we identified four different regional and local coding systems to identify reason for admission. The eHealth records show promise for identifying health traits of the military. However, further work is required to identify synergy and overcome regional variations.
ConclusionLinkage techniques provide new opportunities for exploring the health of serving and veteran population. However, quality of identifier and linkage error are still of major concern. Further, record completeness, diagnoses accuracy and data cleaning impact the data quality.
Treating heterogeneity and uncertainty in data integration: study on Brazilian healthcare databases.
Authors: Barreto Marcos , Denaxas Spiros 
Published: April 2017

ABSTRACTBackgroundData integration comprises methods and tools to aggregate data from disparate sources to various purposes. Heterogeneity and uncertainty are technical challenges in this field. The first involves different data representation or meaning, while the second refers to incomplete data or the expectancy that a data item exists in a data source. Our goal is to design and validate a data integration model and computing tools able to address both problems. Such model and tools will support the setup of a population-based cohort comprised by 100 million individuals and the generation of data marts (domain-specific data) to be used in epidemiological studies within an ongoing cooperation Brazil-UK. Such studies will assess the impact of a conditional cash transfer programme (PBF – Bolsa Família) on the occurrence, severity and mortality of several diseases and health problems (hospitalization, mortality, child health etc) over this cohort.
ApproachWe propose a three-dimensional data model to aggregate information on the cohort, exposition (payments received during the observed period) and health outcomes. We treat heterogeneity based on our existing probabilistic linkage pipeline that provides data quality assessment, data conditioning (standardization, cleansing, blocking, and anonymization), two methods for probabilistic record matching, and accuracy assessment. Through this pipeline, we are able to probabilistically link records from PBF, CadastroÚnico (CADU - socioeconomic information) and healthcare databases from the Unified Health System (SUS). Uncertainty is modeled through “possible worlds”, which represent a data instance (record) with a corresponding probability. There exist 2^records possible worlds with the probability distribution being the product of record probabilities. We map the most probable relationships between all the databases involved and create some simulation scenarios in order to validate them. We are seeking for a good balance between the set of possible worlds, not overextending the possibilities, and their proximity to a real scenario.
ResultsThe current implementation comprises the linkage of the 2011 extraction of CADU and healthcare databases to populate the proposed model. Such linkage provides timely execution (up to 9 hours depending on the databases) with high accurate data marts (over 95% of true positive matched pairs) for samples with increasing size (from 1,447,512 to 12,036,010 records).
ConclusionOur model is able to treat heterogeneity aspects present in huge databases. Our tools provide timely execution of probabilistic linkage with high accuracy. We started to model uncertainty in order to perform simulations and decide how to incorporate it in our model.
Trajectories through child protection and education: Patterns, timing and causality
Authors: Maclean Miriam , Taylor Catherine , O'Donnell Melissa 
Published: April 2017

ABSTRACTObjectives1) To examine the reading trajectories of children with different levels of child protection involvement from Year 3-9 of school 2) To estimate the ‘effects’ of entering care on Year 9 reading achievement, attendance and suspensions among children with substantiated maltreatment
ApproachRecord-linkage of population data was used to investigate educational outcomes for children born in Western Australia from 1990-2010. Data from the Departments of Health, Child Protection and Family Support, Education, and Disability Services were used. Multi-level modelling was used to assess children’s reading trajectories from Year 3-9, and identify child, family, neighbourhood and child protection factors associated with reading achievement over time. Educational outcomes for maltreated children placed in out-of-home care were compared to a propensity-matched comparison group of children that remained at home using regression analyses.
ResultsChild protection involved children most often showed a stable pattern of low achievement from Year 3-9. Of those with mid-level Year 3 scores, 45%-50% showed declining achievement with scores in the lowest third of their Year 9 cohort. Particularly low achievement in Year 3 and 9 was found among children with early unsubstantiated maltreatment followed by older-aged entry to care. Propensity matched analysis showed that after controlling for maltreatment, child, family, and neighbourhood characteristics, maltreated children did not significantly differ by placement status on reading or suspensions. Absences were significantly lower among children that entered care versus those remaining at home (OR=0.36,95%CI[0.15, 0.91).
ConclusionFindings suggest that poor educational outcomes for children that have entered care are not primarily caused by out-of-home care, but reflect prior disadvantage and maltreatment. Child protection involved children are more likely to show stable low and declining patterns of achievement than other children, highlighting a need not only for early intervention, but also for interventions to address academic problems that arise later in childhood.
Integrating text analytics and statistical modelling to analyse kidney transplant immune suppression medication in registry data
Authors: Alfattni Ghada , Peek Niels , Nenadic Goran , Caskey Fergus 
Published: April 2017

ABSTRACT
ObjectivesElectronic Health Records (EHRs) contain a wealth of routinely-collected data that could potentially be used to inform clinical decisions such as the choice between competing treatment regimens. Apart from structured data about diagnoses and biomarkers, these records often include unstructured data such as free-text medication prescriptions. Disjoint toolsets exist for structured and unstructured data, making it difficult to analyse datasets that comprise both structured and unstructured data. Representing free-text items in a structured and standardised format would enable their statistical analysis. The aim of this study was to develop a generic, analytical pipeline that integrates different tools for text analytics and statistical modelling, and to apply it to data from the UK Renal Registry (UKRR) to answer specific clinical and epidemiological questions on kidney transplant immune suppression.
ApproachThe UKRR database comprises data from all renal units in England, Wales and Northern Ireland and consists of both structured and semi-structured data. Our workflow starts by using rules to extract medication regimen from free-text prescriptions, which is then automatically combined with structured patient's record. The data is then analysed for patterns of transplant immune suppression prescribing by specific centres across the UK.
ResultsWe have developed an analytical pipeline for improving concordance between unstructured and structured medical records, combining new and established text analysis and subsequence analysis tools. Results of the study are underway and will be presented at the conference.
ConclusionsWe have developed new framework that integrates tools for text analytics and statistical modelling, to facilitate the analysis of mixed structured and unstructured data. Our analysis of the UKRR data will help to compare immune suppressive treatment regimens, identify best practice, and explore the associations between transplant medication and transplant outcomes.
Balancing Privacy and Utility in Secondary Data Use to Inform Policy
Authors: Cui Xinjie , Blackadar Robyn 
Published: April 2017

ABSTRACT
ObjectivesUsing existing data for research can generate new knowledge and evidence for policy with relatively little cost. Privacy concerns are paramount in such secondary usage of data collected on human subjects. Information privacy protection and data security are critical considerations in reuse and repurposing of data especially linked data, longitudinal data, and large amounts of data. Data sharing and privacy protection are both in the public interest and we need to assess the risk of “doing” (sharing) as well as the risk of “not doing” (not sharing or not protecting).

ApproachThe Alberta Centre for Child Family and Community Research (the Centre) establishes the Child Youth Data Lab that links and analyzes administrative data from multiple provincial ministries and the Child Data Centre of Alberta that repurposes research data and manages its access for reuse. The Centre partners with provincial Office of the Information Privacy Commissioner, Research Ethics Boards and leaders in the research communities and technology industry to design and develop measures to enable secondary use while safeguarding the data, and to explore and adopt best practices on data sharing processes, governance, and technologies.
ResultsIn principle current privacy laws and regulations provide good guidance in collection, use, and disclosure of data, however there is a lack of consistency in the interpretation of these laws at the operational level with regard to secondary data use. The experiences of establishing different data sharing models at the Centre through multiple initiatives are discussed. Cross-sectoral broad partnership brings understanding and builds trusting relationships, which are crucial to establishing data sharing processes. The recognition of the significance of secondary data use to provide direction for policy and program development at the executive level provides commitment for data sharing initiatives. Strong governance structure consists multi-level ministry and multiple stakeholder involvement ensures ongoing support and engagement. The highest data security standards and anonymous solution for data linkage enables the sharing of data with good privacy protection.
ConclusionSecondary use of data to improve system performance and contributing to scientific discovery has been broadly recognized. A balance between utility and privacy can be realized through broad partnership in building proper governance, technology, processes and policies.