bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Influence of Non-Clinical Patient Factors on Clinical Decision Making: Uncovering the Impact on Mental Health
Authors: Burns Lauren , Sergio da Silva Ana , John Ann 
Published: November 2019

BackgroundUnderstanding how non-clinical patient factors (NCpF) such as gender, educational level and socioeconomic status impact clinical decisions regarding one’s mental health is important for appropriate and equitable care.
Main AimThis research aims to i) investigate the feasibility of using administrative health data to investigate clinical decision making in mental health; ii) understand the impact of NCpF on mental health-related diagnosis, treatment, and referral decisions.
Methods/ApproachThree waves of the Welsh Health Survey, containing a five-item Mental Health Inventory (Short- Form SF36), and NCpF information were used to create our interest cohort.
The records with a low SF36, a ‘gold standard’ identifier of common mental health conditions, were then linked to the healthcare records datasets (Primary Care GP Dataset, Patient Episode Database for Wales, Emergency Department Dataset, Outpatient Referral Dataset, Annual District Death Extract) securely stored on the Secure Anonymised Information Linkage Databank.
ResultsWe will present the methodological challenges and benefits of using administrative data the study of decision making in mental health.
The differences in NCpF between those with a low SF36 and a mental health diagnosis, symptoms and treatment as well as those with a similar SF36 score but no diagnosis, symptoms or treatment recorded will be presented and discussed.
ConclusionAdministrative data can provide a unique opportunity to investigate issues related with clinical decision-making in mental health and improve health equity. Having a better understanding of the influence of NCpF on mental health decisions is necessary to prevent inequity in mental health care.
Measuring the Dynamic Risk of Further Offending: A Feasibility Study
Authors: Hodges Helen , Fahey Kevin 
Published: November 2019

BackgroundYoung people who have offended were, until recently, assessed using the Core ASSET Profile – a tool which determined their likelihood of reoffending based on their criminal history and practitioner scores across 12 domains. The repeated assessments provide a set of data for each individual which can be used to model how their risk of further offending varies over time. Having conducted an initial proof of concept study, this work explores the potential of extending the range of ‘risk’ and ‘protective’ factors using anonymised linked data held within the SAIL Databank.
Main AimThe feasibility study is designed to establish the potential for using administrative data to develop a more sensitive assessment tool for use in the youth justice system. Specifically, the study explores the impact of being care experienced and of subsequent system contact in elevating the risk of further offending.
ApproachA series of Bayesian hierarchical models will be generated which mimic the features of the Core ASSET Profile under the Scaled Approach. These include a range of time-varying and non-time varying variables matched to the individual, drawn from education, health and social services datasets as well as their court and offending records.
ResultsThe anticipated findings will advance our understanding of how the likelihood of further offending varies over time for different groups, and how further system contact increases the risk. This will enable the complexity of young people’s real lives to be explored, and hence appropriate and timely interventions to be developed.
ConclusionModelling under a Bayesian framework affords the opportunity to generate robust analysis based on smaller datasets. Findings have significant implications for policy and practice, particularly in the context of assessment processes across the justice system and social welfare.
Using linked English cancer registration data to assess variation in diagnostic pathway length
Authors: Fraser Jess , Pearson Clare , Shelton Jon , Moffat Jodie 
Published: November 2019

BackgroundFor the majority of cancer patients, the length of the diagnostic pathway is unknown. Only those on a specific pathway are monitored. Quantifying pathway length is important in understating which patients may benefit most from an expedited pathway.
AimThis study aimed to develop a novel methodology using linked cancer registration data to calculate the Secondary Care Diagnostic Interval (SDCI): the period from first interaction with secondary care to receiving a cancer diagnosis. Subsequently variation in SCDIs across twenty-five cancer sites were analysed.
MethodsEnglish cancer registrations (2014-15) from Public Health England’s National Cancer Registration and Analysis Service were linked to three routine health datasets: Hospital Episode Statistics, Diagnostic Imaging Dataset and Cancer Waiting Times.
The SCDI was calculated as the difference between the earliest event relating to the secondary care diagnostic process (comprised of referral into/secondary care appointment or diagnostic procedure in the 6 months before diagnosis) and the diagnosis date.
SCDI length was stratified by stage at diagnosis, route to diagnosis (e.g. emergency presentation, GP referral) and patient characteristics.
ResultsThe median SCDI (days) was shortest for acute lymphoblastic leukaemia (ALL) (2) and longest for kidney (45). In general, SCDIs decreased with later stage (for example stage 1-4 colorectal: 35 to 20, lung: 75 to 25). Patients diagnosed after routine GP referrals had longest SCDIs compared to urgent referrals, for all sites except ALL. Geographical variation in SCDIs exists by Cancer Alliance, with the greatest differences compared to the England average observed for prostate cancer (median national SCDI: 28, Cancer Alliance range: 21 - 42).
ConclusionSubstantial variation exists in SCDIs by cancer site, stage and presentation route. Many patients are experiencing SCDIs over 28 days, even those diagnosed via urgent GP referrals. Understanding variation can support creation of targeted initiatives to expedite diagnostic pathways where appropriate.
Admin vs. questionnaire data: Can we replace ‘highest qualification’ questions with admin data?
Authors: Tietz Stephan , Haines Nicola , Taylor Brogan 
Published: November 2019

Information on qualifications is used widely across central and local government to inform service delivery and policy development; main user requirements are for highest level of qualifications and, no qualifications. We explored the feasibility of using administrative data to derive high quality information on educational qualifications held.
For this feasibility research, we used data supplied by the Department for Education. This covered 14-25y/o from the three funding streams in England: primary and secondary education, further education and higher education. We compared our results at national level with the 2011 Census and Labour Force Survey/Annual Population Surveys (LFS/APS). We also undertook linkage to the Census to compare our results at case-level.
We were able to derive a highest level of qualification for more than 96% of individuals in the data. There is a high level of agreement at national level when compared to the Census and LFS/APS. Differences are likely due to mode of data collection and the accuracy of differentiating between full and partial attainment as limited information was available in the feasibility dataset.
Moreover, we successfully linked 84% of 14-25y/o on the English Census. We found that highest qualification level as derived from admin data agreed with 57% of Census records and either agreed or was within one level for 84% of records. Disagreement patterns were similar to the ones observed by the Census Quality Survey, which suggest that they are driven by mode effects.
We demonstrated that we can produce high quality information on highest level of qualification for a large proportion of first-time entrants to the labour market. We also opened the door to providing more accurate information on highest level of qualification achieved by individuals than self-reported data since it does not rely on respondents recall ability or proxy responses.
The looked-after child in time: Creating and analysing longitudinal data on placement history and educational outcomes
Authors: Raab Gillian , MacIntyre Cecilia 
Published: November 2019

Background with rationaleThe Scottish government collects data on Looked-after-children (LAC) from the 32 local authorities (LAs) in Scotland. Since 2008, the LAs have provided individual data on a yearly basis that covers every child who was looked after at any time during the year up to the Census date. These data have been linked to data on educational outcomes for these children. Cross-sectional analysis by the Scottish Government show that the educational outcomes for these children are much poorer than for other children in Scotland. This presentation will discuss methods to create a longitudinal data set from these data and thus infer how a child’s lifetime history of care relates to their educational outcomes.
Main AimTo relate the children’s educational outcomes (school attendance and exclusion) to their history of being in care. Features of their care history include, age a start of care, number and type of care episodes (e.g. at home, with relatives, in foster care or residential care) as well as their legal reasons for being in care.
Methods/ApproachThese data present a number of challenges to achieving the aim. The process of creating longitudinal records from nine cross-sectional samples revealed many data problems. A child’s history in care can potentially last from birth to aged 16, or even older, but the data available was for a nine-year window (2008 to 2017) along with some details of episodes that started before 2008 for those in care later. Individual histories were either right-censored or left-truncated. The latter posed a bigger problem for our analyses since most of the early history is missing for a large proportion of the children. The methods used involved using multiple imputation methods to infer the age at start of care and the number of previous episodes for these children.
ResultsWe will compare the results from the longitudinal analyses with those obtained from cross-sectional results.
ConclusionLongitudinal data provides the opportunity to understand which patterns of care are associated with the poorest educational outcomes.
The prevention priority: linking education and homelessness data to inform policy and practice
Authors: Mackie Peter , Thomas Ian 
Published: November 2019

Childhood and youth homelessness is a seemingly intractable problem across the globe. Yet, the paradigm shift in homelessness policy towards prevention is raising new questions and new opportunities for researchers, policy makers, and practitioners; specifically, schools as sites for early intervention. The aim of this paper is to shed new light on the potential power of data linkage in both understanding the relationship between homelessness and education (particularly attainment) and in guiding prevention policy and practice. Significantly, the paper draws on data from two separate projects exploring the links between youth homelessness and education – each using a different methodology for data linkage, hence the paper offers both methodological and practical insight.
The first project has sought to understand educational achievement of homeless children by linking local authority administrative homelessness data with national education data in Wales. The key methodological challenge here has been that the homelessness data only relate to the main applicant for homelessness assistance, so other administrative data sources have had to be drawn on to identify young people living in homeless households. Early findings suggest young people experiencing homelessness attain below average in school, but the key lesson here is largely methodological. The second part of the paper focuses on identifying which schools homeless young people have attended – with the aim of informing future interventions in these schools and providing a baseline for monitoring their impacts. The methodology differs markedly from the first project and is based upon local authority homelessness and education departments sharing data and undertaking linkage within the authority. This method proves to be highly effective in matching nearly all young people and not only identifying schools but also other key experiences of the young people (e.g. having been in care). The conclusion of the paper is that data linkage, at different scales, i.e. national infrastructure vs local linkage, offer different pathways through which knowledge can influence practice.
The Unmet Need for Psychotropic Medication within the Migrant Population of Northern Ireland - A Record Linkage Study
Authors: Patel Kishan 
Published: November 2019

BackgroundA large proportion of people with mental ill-health do not receive the care that they require - constituting unmet need. Migrant populations are particularly at risk of not receiving necessary care for a range of reasons including perceptual differences and multiple barriers to health service.
Main AimThis record linkage study compares the likelihood of people with poor mental health receiving psychotropic medication between migrant and settled communities and amongst different migrant groups in Northern Ireland.
MethodsA cohort of 78,267 people aged 16-64 who reported chronic poor mental health in the 2011 Census records was generated and followed for the next 12 months by linkage to a centralised prescribing dataset to determine rates of treatment. All individual and household attributes were derived from Census forms. Absence of a psychotropic medication prescription from the beginning of 2011 represented unmet need. Logistic regression analyses quantified the relationship between psychotropic medication and migrant status, while accounting for relevant demographic and socio-economic factors.
ResultsOverall, 86% of the settled population with chronic poor mental health received at least one psychotropic medication during the study period, compared to 67% of the equivalent population of 1,736 migrants. Although migrants were 61% less likely than the settled community to report poor mental health (OR 0.39 (95% CI 0.37 - 0.41)), those reporting poor mental health were less than one-third as likely as to be on psychotropic medication in fully adjusted models. There also existed considerable variation between individual migrant groups, with uptake of medication lowest amongst the Lithuanian migrant group (OR 0.09 (95% CI 0.05 – 0.14)), and highest amongst German migrant group (OR 1.30 (95% CI 0.87 – 1.94)).
ConclusionAlthough this study suggests substantial unmet need for treatment of poor mental health amongst the migrant population of Northern Ireland this may not represent the whole story. Differences between individual migrant groups suggest that other factors may also be at play including differing approaches to treatment, or alternative routes to treatment. The implications of the study will be discussed in the paper.
The Differential Privacy Corner: What has the US Backed Itself Into?
Authors: O'Hara Amy , Brummet Quentin 
Published: November 2019

An expanding body of data privacy research reveals that computational advances and ever-growing amounts of publicly retrievable data increase re-identification risks. Because of this, data publishers are realizing that traditional statistical disclosure limitation methods may not protect privacy.
This paper discusses the use of differential privacy at the US Census Bureau to protect the published results of the 2020 census. We first discuss the legal framework under which the Census Bureau intends to use differential privacy. The Census Act in the US states that the agency must keep information confidential, avoiding “any publication whereby the data furnished by any particular establishment or individual under this title can be identified.” The fact that Census may release fewer statistics in 2020 than in 2010 is leading scholars to parse the meaning of identification and reevaluate the agency’s responsibility to balance data utility with privacy protection.
We then describe technical aspects of the application of differential privacy in the U.S. Census. This data collection is enormously complex and serves a wide variety of users and uses -- 7.8 billion statistics were released using the 2010 US Census. This complexity strains the application of differential privacy to ensure appropriate geographic relationships, respect legal requirements for certain statistics to be free of noise infusion, and provide information for detailed demographic groups.
We end by discussing the prospects of applying formal mathematical privacy to other information products at the Census Bureau. At present, techniques exist for applying differential privacy to descriptive statistics, histograms, and counts, but are less developed for more complex data releases including panel data, linked data, and vast person-level datasets. We expect the continued development of formally private methods to occur alongside discussions of what privacy means and the policy issues involved in trading off protection for accuracy.
Harnessing administrative data for humanitarian responses
Authors: Carter Karen , Alley Kate , Han Hye Jung 
Published: November 2019

The need to improve data to support effective responses in humanitarian settings and emergencies is widely recognized; and there is a growing understanding that administrative data systems can be an important source of data in these contexts. Government data systems however are often overlooked, particularly in developing countries, due to access or quality concerns. New systems are frequently established to respond to humanitarian data needs – particularly in protracted emergency settings. This may be necessary in some circumstances due to a collapse or absence of existing systems, but often duplicates government information structures. In the long term, parallel systems risk undermining national systems which may take a significant time to recover or re-build, and can divert highly limited resources.
Routine government administrative data systems need to be both more responsive to changes in the environments and communities in which they are operating, and resilient enough to remain operational and secure in those changed circumstances. This work aims to explore what this means in system design, operation, governance, and preparedness for key sectors; to provide practical guidance for national governments and supporting agencies.
Findings will be drawn from literature review, expert opinion, and field examples. Case studies will be undertaken in two countries that are either considered “fragile” or have recently responded to an emergency. Work will focus on areas in which UNICEF has a formal role, however it is anticipated to highlight issues relevant to a broad spectrum of humanitarian data needs.
Preliminary findings are anticipated to be published towards the end of 2019; and will compliment work programs on developing an understanding of administrative data maturity, and the global standards and norms for upholding the rights of children affected by humanitarian crises, such as UNICEF's Core Commitments for Children in Humanitarian Action.
Utilisation of Personal Care Services in Scotland: the Influence of Unpaid Carers
Authors: Lemmon Elizabeth 
Published: November 2019

Scotland is unique in its collection of routine data for all individuals in receipt of social care services. This care encompasses home and personal care services, down to telecare and meals services. As the Scottish population continues to age and local authorities stretch shrinking budgets over an increasing number of people, there is a pressing need to understand how older people use these services to ensure they are delivered in an efficient and effective way. The availability of administrative data in Scotland provides an opportunity to explore how it might be used in a research setting to enhance this understanding.
One area of interest concerns the relationship between unpaid care and formal care services. In particular, how unpaid carers might influence older peoples use of formal care services. Whether this influence is positive or negative will have important implications for the costs of care provision. The existing evidence on the impact of unpaid care on social care utilisation is extremely mixed.
Scotland provides an interesting context in which to study this relationship because unlike many other jurisdictions, personal care in Scotland is provided free to all individuals aged 65+ who are assessed as needing it. This may affect the incentives faced by unpaid carers, leading to different conclusions about the relationship between unpaid and paid care, compared to previous literature.
This paper uses Scotland's unique administrative Social Care Survey (SCS) for the years 2014-2016 to investigate how the presence of an unpaid carer influences personal care use by those aged 65+ in Scotland.
The results suggest that unpaid care complements personal care services. Complementarity between unpaid and paid care may imply that incentivising unpaid care could increase personal care costs, and at the same time it points to the potential for unmet need of those who do not have an unpaid carer. The paper highlights some of the limitations of the administrative SCS but also demonstrates how it can be used in an effective way to enhance our understanding in an important, policy relevant area.
Maternal adversity and variation in the rate of children entering local authority care during infancy in England: a longitudinal ecological study
Authors: Pearson Rachel , Jay Matthew , Wijlaars Linda , De Stavola Bianca , Bedston Stuart , Gilbert Ruth 
Published: November 2019

BackgroundIn England, the rate at which infants enter care varies considerably by local authority, with little evidence on what drives these differences. Decisions instigating infant entry into care may be triggered by child protection concerns arising from parental ill-health or risky behaviour from pregnancy onwards.
Main AimWe explored the extent to which adversity indicated within women’s hospitalisation history, pre-delivery, explained differences in rate of infant entry into care between local authorities.
Methods/ApproachCombining data from several sources (Office for National Statistics Population Estimates, Public Health England Fingertips, 2011 Census, Children Looked After Return, and Hospital Episode Statistics), we derived population-level predictors for entry into care (i.e. local case-mix) for 131 English local authorities from 2006/07 to 2013/14. Our primary outcome was rate of infant entry into care. We used linear mixed-effects models to analyse the relationship between the outcome and percentage of live births with maternal history of adversity-related hospital admissions (i.e. any admission indicating substance misuse, mental ill-health, or violence, up to 36 months pre-delivery), adjusting for other case-mix measures.
ResultsRate of infant entry into care (mean: 85.16 per 10,000 [min-max: 0.00-318.51]) and percentage of live births with maternal history of adversity-related hospital admissions (4.62%, [0.52-16.19%]) varied greatly by local authority. Prevalence of maternal adversity accounted for 24% of variation in rate of entry (95% CI: 14-35%). After adjustment, there was evidence that a percentage point increase in prevalence of maternal adversity - both over time and between local authorities - is associated with an extra 2.56 infants, per 10,000, entering care (1.31-3.82).
Conclusion Prevalence of maternal adversity prior to birth helps to explain differing rates of infant entry into care among local authorities. Further research using individual-level linked parent-child data is required to ascertain whether interventions to reduce maternal adversity before birth would decrease rate of infant entry into care.