bluuebunny/crossref_metadata_2025_split huggingface dataset:
Engagement and Co-Design: Routes to Lawful Research?
Authors: Mourby Miranda , Smith Hannah 
Published: June 2018

BackgroundThe ‘deficit’ model of engagement, which educates the public about research, has been subject to increasing criticism, as if people’s attitudes arise from ignorance which should be corrected. Nevertheless, a number of attempts to understand public views on the use of Administrative Data for research have used informative models.
This can be problematic from a legal perspective, as the law is concerned with data subjects’ ‘reasonable expectations’, not their hypothetical expectations had they received more information. Recent controversies around reasonable expectations have included Google DeepMind and Royal Free, as well as Cambridge Analytica.
ObjectivesThis paper considers how public engagement can help administrative data controllers meet their legal obligations when data are processed for research, and how to avoid confusion by placing too much reliance on the views of informed participants as a means of gauging wider public opinion.
MethodsWe refer to the findings of an exploratory study of individual attitudes towards Administrative Data Research, which indicate that views and norms around ADR are incipient and ambivalent, especially when compared to perceptions of ‘conventional’ medical research. We consider the legal obligations administrative data controllers have to shape reasonable expectations in light of this uncertainty.
FindingsEngagement which informs the public about research does have value. It indicates what the attitudes of the public might be, were certain facts about research more commonly known, and thus underscores the importance of public information campaigns. However, this work cannot provide an accurate representation of public opinion as a whole in the absence of wider dissemination of information across society.
ConclusionsThere will inevitably be a number of facets to public engagement: information, representation and transparency. Each of these will correlate differently with data controllers’ legal obligations, and it is essential to understand these connections.
Welsh Government Flying Start Programme Evaluation Using Linked Data
Authors: Lowe Sarah , McGinn Laura , Quintela Marcos , Player Luke , Tingay Karen 
Published: June 2018

BackgroundFlying Start (FS) is the Welsh Government’s (WG) flagship Early Years programme for families with children aged less than 4 years of age. Running since 2006, the four entitlements are:

Free part-time childcare for 2-3 year olds
Enhanced Health Visiting
Parenting support
Speech, language, and communication support

ObjectivesCurrently, while we know which areas in Wales are receiving FS support, individual-level data on which child received what entitlements is not available. Area-level outcomes can be used as proxy indicators but the individual impact of receiving FS support cannot be examined.The project aims to evaluate FS by linking the FS cohort to a range of outcomes including health, education and social care.
MethodsA Dataflow Development Project (DDP) has been launched to install SAIL (Secure Anonymised Information Linkage) appliances into 6 pilot Local Authorities in Wales which will test acquiring and linking the individual level FS data from pilot Local Authorities with other datasets in SAIL.
FindingsThe project will report some emerging findings from the analysis of pilot data.
ImplicationsThere is a growing interest in using linked administrative data to evaluate government initiatives, and mounting enthusiasm in Local Government. If successful, this model is likely to be adopted by related WG programmes; improving the evidence base, facilitating effective evaluation, and adding to the data available for re-use in Wales.
Challenges in linking administrative data for monitoring bloodstream infection in neonatal units in England and Wales
Authors: Fraser Caroline , Gilbert Ruth , Blackburn Ruth , Muller-Pebody Berit , Harron Katie 
Published: June 2018

Monitoring risk-adjusted trends of neonatal bloodstream infection is vital and linkage of neonatal electronic health records to national infection surveillance enables this. We demonstrate why changes in data quality over time must be accounted for to minimise spurious findings.
First, we evaluated the impact of changes in identifier completeness over time in each database, and determined variation in infection rates according to linkage method (deterministic linkage on NHS number or probabilistic linkage). Second, we will use multiple imputation when link status cannot be determined due to missing identifiers.
Completeness of NHS number in infection surveillance increased from 69% (3,296/4,792) in 2010 to 92% (3,037/3,307) in 2017. We linked 12,003 neonatal admissions to 15,571 infection episodes (2% of 497,936 admissions and 41% of 37,660 infections). The proportion of links that were deterministic changed from 83% (1,089/1,307) in 2010 to 96% (968/1,008) in 2017. Link status could not be determined for 12,094 infections due to missing identifiers; multiple imputation will be used to determine if any are links.
Spurious infection incidence rates can arise from changes in data quality, impacting the quality of linkage to clinical data. Linkage and imputation of missing data minimises spurious findings due to data quality.
Daily exposure to the retail food environment and the association with child BMI
Authors: Mizen Amy , Rodgers Sarah , Fry Richard 
Published: June 2018

BackgroundThe dose-response relationship between exposure to food environments and obesity has not been widely investigated. This study examined whether increased retail food environment (RFE) exposure in children was associated with a larger body mass index (BMI).
ObjectivesGenerate household level daily exposure to the RFE for children aged 11-13 years and link these environmental exposure with health data in an anonymised data safe haven.
MethodsIndividually tailored environmental exposures were calculated in a GIS for home and school locations, and modelled walking routes to and from school. Local Authority food outlet data were used to generate the temporally accurate exposures. Exposures were linked to individual level health data in the SAIL databank for a cohort of individuals from south Wales aged 11-13 years, with BMI measurements. A fully adjusted multilevel regression model was fitted to investigate the association of RFE exposure with BMI.
FindingsHome exposure and exposure along the walk to school was significantly greater for children living in deprived catchments, compared with affluent school catchments (t = -5.25, p<0.05; t = -0.277, p<0.05, respectively). The RFE exposure along the walk home was the only environmental exposure positively associated with a higher BMI (0.22, p<0.05).
ConclusionsIncreased BMI was associated with greater REF exposure along the walk home from school. The findings suggest that the walk home from school may be important for developing interventions and policies to discourage unhealthy eating. Research should be undertaken to better understand child purchasing habits.
Are children who are treated for asthma and seasonal allergic rhinitis disadvantaged in their educational attainment when acutely exposed to air pollution and pollen? A feasibility study
Authors: Mizen Amy , Lyons Jane , Rodgers Sarah , Berridge Damon , Akbari Ashley , Wilkinson Paul , Milojevic Ai , Doherty Ruth , Dearden Lorraine , Lake Iain , Carruthers David , Strickland Sarah , Mavrogianni Anna , Davies Gwyneth 
Published: June 2018

BackgroundThere is a lack of evidence of the adverse effects which air quality has on cognition for people with air quality-related health conditions, these are not widely documented in the literature. Educational attainment, as a proxy for cognition, may increase with improved air quality.
ObjectivesPrepare individual and household level linked environmental and health data for analysis within an anonymised safe haven; analyse the linked dataset for our study investigating: Cognition, Respiratory Tract illness and Effects of eXposure (CORTEX).
MethodsAnonymised, routinely collected health and education data were linked with high spatial resolution pollution measurements and daily pollen measurements to provide repeated cross-sectional cohorts (2009-2015) on 18,241 pupils across the city of Cardiff, using the SAIL databank. A fully adjusted multilevel linear regression analysis examined associations between health status and/or air quality. Cohort, school and individual level confounders were controlled for. We hope that using individual-level multi-location daily exposure assessment will help to clarify the role of traffic and prevent potential community-level confounding. Combined effects of air quality on variation in educational attainment between those treated for asthma and/or Severe Allergic Rhinitis (SAR), and those not treated, was also investigated.
FindingsAsthma was not associated with exam performance (p=0.7). However, SAR was positively associated with exam performance (p<0.001). Exposure to air pollution was negatively associated with educational attainment regardless of health status.
ConclusionsIrrespective of health status, air quality was negatively associated with educational attainment. Treatment seeking behaviour may explain the positive association between SAR and educational attainment. For a more accurate reflection of health status, health outcomes not subject to treatment seeking behaviours, such as emergency hospital admission, should be investigated.
Making sense of change and stability in looked after children’s care placements using state sequence analysis
Authors: Matthews Ben , Playford Chris , McGhee Janice , Mitchell Fiona , Dibben Chris 
Published: June 2018

BackgroundLocal authorities in Scotland have a responsibility to provide support for vulnerable children and young people, known as ‘looked after children’. This support can include providing care for these children away from home, but there have been growing concerns that children with out of home placements may experience multiple placements before a stable home environment is established for them. As a result, policy changes have aimed to reduce the amount of instability which looked after children experience, but our current understanding of care placement stability in Scotland, and how this has changed over time, is limited.
ObjectivesThis project aims to develop our understanding of the patterns of instability experienced by children looked after away from home by applying state sequence analysis (SSA) administrative data regarding children’s care placements from 2008 to 2017 ( 70,000 children). Data are provided by Scottish Government.
Methods and FindingsSSA is a method commonly used to describe developmental processes which consist of changes between different states - in this case, types of care placement - and provides a number of tools to describe in/stability in these processes. In this paper we use SSA to analyse sequences of placements for multiple cohorts of children to give a detailed description of how placement stability for looked after children in Scotland has changed over the last decade, with results show differences in the typical placement sequence patterns for children of different ages. We and also discuss some methodological challenges which arise when implementing SSA using administrative data.
ConclusionsCombining SSA and administrative data provides a useful way to describe the typical patterns of looked after care placements, and how these patterns have changed over time.
Mining academic publications to automatically identify data sources
Authors: Anastasiou Athanasios , Tingay Karen 
Published: June 2018

BackgroundDiscovering suitable datasets is an important part of health research, particularly for projects working with cohort data, but with the proliferation of so many national and international initiatives, it is becoming increasingly difficult for research teams to locate real world datasets that are most relevant to their project objectives.
MethodsTo assist researchers in this, we developed bibInsight, a data analysis platform to identify potentially useful data sources and more generally enable large scale research over bibliographical datasets.
Data source names were identified from a broad, topic specific literature search. Context-specific terms like “annual”, “longitudinal”, and “prospective” were used to train a classifier that identified potential datasets.
ResultsThe classifier was able to identify 1588 of 1961 abstracts as containing cohort-relevant information: a precision of approximately 80%.
Further analysis such as topic analysis, geographical mapping, and collaboration networks can refine and prioritise the search results to determine the most relevant data source(s) for a research project.
ConclusionsA very large amount of information, including data source description and use, remains unexploited in unstructured bibliographical datasets. Here, we used a thematic search to provide a more manageable starting point towards locating disease specific datasets.
Maternal vulnerability and birth interval in England: a birth cohort study using hospital episode statistics from 2006 to 2016
Authors: Wijlaars Linda , Blackburn Ruth , Gilbert Ruth 
Published: June 2018

AimTo determine the association between birth interval for mothers with and without indicators of vulnerability.
MethodsWe included mothers who gave birth in 2011 recorded in Hospital Episode Statistics. Vulnerability indicators included diagnostic codes for mental health problems, adversity related injuries (ARI), material/social disadvantage (MSD) recorded in the previous five years, or age <20 years at index delivery.
We analyses birth interval using Cox proportional hazard models and adjusted for maternal and birth characteristics.
ResultsWe included 636,876 women, of whom 93,266 (14.6%) had indicators of vulnerability: 63,421 (10.0%) for mental health problems, 8,229 (1.2%) for ARI, 21,616 (3.4%) for MSD, and 32,622 (5.1%) were aged <20y.
Over a third of mothers (242,401; 38.1%) had another live birth. Median birth interval was 957 days (5-95%centiles:433- 1694), and was shortest for women with ARIs (904 days,391- 1697).
Vulnerable mothers were more likely than women with no vulnerability indicators to have another live birth (HRadj:1.13, 95%CI:1.12-1.14), particularly teenage mothers (HRadj:1.53,1.50-1.55).
ConclusionsVulnerable mothers had a shorter subsequent birth interval and were more likely to have another birth in the next five years. Maternity care may be an appropriate time to target interventions to vulnerable families to enable choice about timing of subsequent pregnancy.
EUROlinkCAT: Common Data Model
Authors: Loane Maria , Densem James , Morris Joan , Tan Joachim 
Published: June 2018

BackgroundOver 130,000 children are born in Europe every year with congenital anomalies which are a major cause of infant mortality, childhood morbidity and long-term disability. A European data linkage study (EUROlinkCAT) aims to investigate the health and educational outcomes of children up to 10 years of age with congenital anomalies, born between 1995 and 2014. While congenital anomaly data including information on potential risk factors are standardised across the EUROCAT network, information on mortality, morbidity and educational outcomes are not.
ObjectiveTo create a common data model that transforms key variables in local databases to standardised formats enabling data on health and educational outcomes to be pooled and analysed across multiple registries.
MethodTwenty-two EUROCAT registries in fourteen countries are participating in the study. Each registry records uniformly coded data on cases of congenital anomaly registered in their local population using the EUROCAT Data Management Program. The registries will link their congenital anomaly data to their local mortality, hospital discharge, prescriptions and educational data. The linked individual case data cannot leave the local institution or “safe haven” environment, therefore verification and validation of all derived variables, data transformations and proxy variables must be performed locally.
FindingsCreating a common data model is challenging as there are diverse coding classification systems, languages, healthcare and educational systems in Europe. As with many administrative datasets, the common data model is based on coded data rather than the often richer “free text” information.
ConclusionThe use of administrative datasets across Europe enables pooling of data on rare outcomes and allows hypotheses on the health and education of children to be investigated. However, a common data model must be applied to ensure that data from multiple sites conform to a standard format.
Health Inequalities in the British Nursing Workforce
Authors: Ball William , Kyle Richard , Atherton Iain , Dougall Nadine 
Published: June 2018

BackgroundAverage health in the UK is improving, yet geographical inequalities in health persist. The relative difference between the least and most deprived is also growing. Recent policy interventions to reduce these inequalities have not been effective.
MethodsThis work compares Self-Rated Health using the ONS LS and SLS linked to an adjusted UK-consistent small-area Deprivation measure.
This study aims to compare Nurses to the general population to assess whether they also exhibit a social gradient in health. Using a single occupational group adjusts for potential confounders and tests whether characteristics of Nurses, such a good health literacy, degree education and above average income, are protective against inequalities.
ResultsIn Scotland, Nurses are more likely to be older, female, homeowners who live in less deprived areas with better Self-Rated Health than Non-Nurses. We will test whether the social gradient in health is observed for this occupational group.Forthcoming results from cross-national analysis will be presented at conference following disclosure checks.
ConclusionThe relationship between area deprivation and health may remain even in relatively privileged groups.
Results from this study may inform recommendations to improve the effectiveness of policy aimed at improving population health and reducing socio-economic inequalities in health
Using administrative data to inform operational and policy developments relating to family homelessness in the Dublin Region
Authors: Morrin Holly , O'Donoghue Hynes Bernie 
Published: June 2018

BackgroundSince 2014 Ireland has seen an unprecedented growth in the number of families being accommodated in emergency accommodation, particularly in the Dublin Region. Indeed, family homelessness has become an issue of concern across Europe but there is also a widespread lack of data about the specific situation of homeless families (Baptista et al., 2017).
ObjectivesThis research uses administrative data to address this gap in knowledge and provide robust quantitative data on trends in family homelessness in the Dublin Region.
MethodsThe research was compiled using a combination of PASS data and data recorded from assessment forms completed with homeless clients by the housing sections in the four local authorities across the Dublin region. PASS, the national shared services database for all state funded NGO and local authority homeless services has uniquely allowed for analysis of data relating to the entire population of families newly experiencing homelessness in the Dublin Region since January 2016.
FindingsDedicated data collection and analysis of such administrative data has produced interesting findings covering not only profiles and reasons for family homelessness but also families’ rates of progression through homeless services. Of note was that one in six families departed homeless services without active engagement with support services, a figure previously unreported.
ConclusionsThe combined use of administrative and PASS data gives a more complete picture of patterns of service use and engagement with homeless services among families newly experiencing homelessness. Detailed findings were passed on to operational staff to both plan for services to support families experiencing homelessness and to assist in developing a regional and national response to the issue of housing supply.
Finding Maternal Siblings in Birth Registration Data to form a Pregnancy Spine – Data Linkage & Graph Based Methods for Unknown Cluster Sizes
Authors: Tomlin Charles , Gammon Shelley , Morris Charles , O'Brien Charlotte 
Published: June 2018

We have developed an innovative methodology to link maternal siblings within 2000-2005 England and Wales Birth Registration data, to form a Pregnancy Spine, a unification of all births to each unique mother. Key challenges were Blocking & Cluster resolution.
To optimise geographic blocking, Internal Migration data was incorporated to map likely geographic movement of mothers between births.
Following probabilistic linkage, sibling clusters were modelled as a graph and their structure optimised using community detection methods. Childhood statistics data relating to child DOB were incorporated to evaluate accuracy and remove false links.
Our development has resulted in a new blocking and cluster resolution method. We developed new ways to assess sibling group accuracy, beyond traditional classifier metrics, and infer error rates.We applied our method to Registration Data used in earlier studies for QA of our methods.
Using this, and other maternal sibling composition statistics, we present results showing that a high degree of accuracy was obtained for standard and new evaluation metrics.
These methods will improve other linkage projects linking unknown clusters sizes/multiple datasets, or longer time period longitudinal linkage. To this Spine, researchers can append and link other data sources to answer questions about maternal and child health outcomes.
Tackling health inequalities in access to Child and Adolescent Mental Health Services (CAMHS) using neighbourhood profiling of administrative data
Authors: Harris Rachel , Wilson Scott 
Published: June 2018

BackgroundReducing health inequalities includes addressing access to and use of services, including considering how deprivation may affect service engagement and referrals. NHS Greater Glasgow and Clyde (NHSGGC) are close to the national average for most CAMHS benchmarking indicators, including waiting times, and referrals received and accepted. When comparing UK rates of Did Not Attends (DNA), however, NHSGGC were fourth highest in 2016. More detailed analysis of service use was thus of interest. While some service and patient risk factors are known, this study used data from NHSGGC’s electronic children’s health record system to develop neighbourhood profiles of access and use.
Objectives

To analyse activity within communities so health resourcescan be best targeted.
To help clinical teams plan services and develop newmodels matched to local need.

MethodsPatient-level administrative data was linked to geographic and population data to identify outlying areas of demand. Data on referrals, open cases, appointments and DNAs were extracted and population-based analyses undertaken with 120,000 data points, linked to 273 neighbourhoods across NHSGGC.
FindingsGeomaps were used to highlight variation in activity, demand and population at neighbourhood and NHSGGC levels. Unexpectedly low levels of population reach (open cases/population under 18s) were found in some areas (Govanhill, 0.5%, relative to NHSGGC, 1.94%). Yet, the high level of DNAs in some East Glasgow areas is as one would predict given deprivation levels.
ConclusionsFindings have been used to identify potential improvements and to target communities who could benefit most. For example, West Dunbartonshire teams noticed high DNA rates in two neighbourhoods, both geographically distant from clinics. Satellite clinics have been introduced to improve attendance by reducing travel time and costs for families.
Neighbourhood profiles have proved valuable in highlighting population based trends and supporting comparisons locally and relative to NHSGGC wide benchmarks.