bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding recurrent care proceedings: Competing risks of how mothers and fathers enter subsequent care proceedings in England
Authors: Bedston Stuart , Hu Yang , Philip Georgia , Youansamouth Lindsay , Brandon Marian , Broadhurst Karen , Clifton John 
Published: November 2019

BackgroundDespite progress in understanding mothers' (re)appearances within family justice, fathers have not yet received due attention in research on recurrent care proceedings.
Aims

Compare parents' gendered risks of entering subsequent care proceedings;
Map family relations underpinning recurrent care proceedings;
Investigate the role of family members' life course characteristics (e.g. age, number of children) in shaping the risk of returning to court.

MethodsAnalysis drew on 2007/08-2017/18 administrative records from the Child and Family Court Advisory and Support Services (Cafcass) in England. From a sample of recurrent parents (N = 24,460), a latent class analysis established profiles of who they returned with. A competing risks analysis of all parents (N = 165,550) modelled the risk of returning into each profile given index characteristics.
ResultsOverall rate of return for mothers was 1.7 times that of fathers: 22% after 5 years, compared to 13% for fathers, and 29% and 17% after 10 years, respectively.
Five distinct profiles of recurrent parents were established: 'recurrent family', 'recurrent couple', 're-partnered couple', 'complex recurrence', and 'lone parent'. The vast majority of fathers who entered subsequent proceedings did so as either a part of a recurrent family (41%) or recurrent couple (36%). However, these two profiles represented a much smaller proportion (25% and 19%, respectively) of all recurrent mothers, while the remainder returned with either a new partner or as a lone parent (49%), both with a new child. Complex recurrence represented a small proportion for both mothers and fathers (7% and 11%, respectively). The risk of each of these profiles is characterised by the distinct life course positions of the parent.
ConclusionThe results underscore the value of a relational approach and understanding a parent's position within the life course in social work research towards building a fuller picture of recurrence.
Regulating Statistics in the Age of Data Abundance
Authors: Bromley Catherine 
Published: November 2019

Background with rationaleThe Office for Statistics Regulation is the UK’s independent regulator of official statistics produced by public sector bodies. The Code of Practice for Statistics sets out our expectations for statistics to be produced in a trustworthy way, be of high quality, and to serve the public good by informing answers to society’s important questions. We now live in a world of increasingly abundant data. Statistics producers need to adapt to this environment, and so do we as regulators.
ApproachThe Code of Practice was updated in 2018 with new provisions to maximise the potential use of data for both citizens and organisations, and to make data available for wider reuse with appropriate safeguards. We have supplemented our commitment to these provisions with a review of data sharing and linking in government, new regulatory guidance on data governance, an increased focus on data access challenges (particularly users of English health data), and by putting data at the heart of our regulatory vision (published in summer 2019). These steps build on our existing work around admin data quality.
OverviewThe National Statistician’s response to our data sharing and linkage review included many welcome commitments and a major review of data linkage methodology is now underway. A data linkage community is developing across government. However, we have raised concerns about ongoing difficulties with admin data sharing between departments, resource constraints, and the limited extent of public engagement about data sharing and use.
ConclusionsOur regulatory approach to data is evolving and we are building new relationships with organisations with an interest in data beyond the statistics world. Our work to support users to access admin data may yet require more direct interventions to bring about the outcomes we desire. We are keen to share our experiences with admin data users.
Better data, better knowledge, better society: Developing an ideal homelessness data system drawing on lessons from global practice
Authors: Thomas Ian , Mackie Peter 
Published: November 2019

The aim of this paper is to set out the principles of an ideal data system. Good data is crucial to effective policy and practice development in all social policy spheres and this is a particular challenge in the context of homelessness policy. Policy makers, practitioners and researchers have been highly critical of the current state of homelessness data across the globe, with concerns largely focused on the incompleteness of the data. Most research has narrowly focused on the strengths and weaknesses of different data collection techniques, such as Point-In-Time counts. However, good data does not only derive from the data collection method - consideration must also be given to the wider data system, including how data are generated, reported, analysed, and crucially, how they are made accessible and to who.
The evidence base for the paper is a desk-based review of 49 data collection systems from 8 countries, including systems in health and social care settings—where data are being increasingly used to drive more effective care. The different systems are synthesised to generate 8 areas of design, being: data architecture, governance, data quality, ethical and legal, privacy/security, data access, and importantly, purpose. Drawing these elements together, the paper concludes that data collection should adopt a common data standard shared across the sector, enabling inter-organisational information sharing and improving collaboration; reporting to local and central government must not be one-sided, instead data providers should receive some tangible benefit for their engagement; the focus of analysis needs to shift from statistics toward evaluation into the effectiveness of interventions; and access must be available to a range of sector actors, including service providers and academia. Importantly, the paper also concludes that in delivering the ideal system, care must be taken not to interrupt the delivery of effective homelessness interventions.
Alberta's Data and Analytic Strategy: Leveraging Linked Data to Drive Innovation
Authors: Svenson Larry 
Published: November 2019

BackgroundThe Province of Alberta, Canada, maintains a mature data environment with linkable administrative and clinical data dating back up to 30 years. Alberta has a single payer, publicly funded and administered, universal health system, which maintains multiple administrative data sets.
Main AimThe main aim of the strategy is to fully maximize the data assets in the province to drive health system health system innovation, with a focus on improving health outcomes and quality of life.
Methods/ApproachThe Alberta Ministry of Health has created the Secondary Use Data Access (SUDA) initiative to leverage its administrative health data. SUDA envisions strengthening partnerships between the public and private sectors through two main data access approaches. The first is direct access to de-identified data held within the Alberta Health data warehouse by key health system stakeholders (e.g. academic institutions, professional associations, regulatory colleges). The second is indirect access to private and not-for-profit organizations, using a data access safe haven (DASH) approach. Indirect access is achieved through private sector investments to a trusted third party that hires analysts placed within the Ministry of Health offices.
ResultsStaffing agreements and privacy impact assessments are in place. Indirect access includes a multiple stakeholder steering committee to vet and prioritize projects. Private and not-for-profit stakeholders do not have access to raw data, but rather receive access to aggregated data and statistical models. All data disclosures are done by Ministry staff to ensure compliance with Alberta's Health Information Act. Direct access has been established for one professional organization and one academic institution, with access restricted to de-identified data.
ConclusionThe Secondary Use Data Access initiative uses a safe haven approach to leveraging data to provide a more secure approach to data access. It reduces the need to provision data outside of the data warehouse while improving timely access to data. The approach provides assurances that people's health information is held secure, while also being used to create health system improvements.
Administrative Data Censuses in US States
Authors: O'Hara Amy 
Published: November 2019

This paper describes work underway to conduct administrative data censuses in several US states. We are helping states identify sources that, when combined, reflect their entire population, and introduce approaches to efficiently and routinely integrate their data.
The national 2020 Census of Population and Housing faces numerous challenges. Operationally, there are concerns about self-response, inadequate technology integration and testing, hacking and data manipulation of this first online census, and the application of differential privacy to published results. This project’s main aim is to support population benchmarking in states to ensure correct counts affecting federal funding and political representation. This capacity-building has spillover effects for states: it can support their program administration, emergency preparedness, and policymaking.
We describe our plan and results to date. We have developed governance approaches and templates and proposed schema to de-duplicate individuals across data sources. We established a secure project workspace designed for cross-institution collaboration to design approaches for states to clean and standardize identifiers, to link across and de-duplicate files, to determine or impute relationships, and to resolve discrepancies observed between sources on residence and demographic characteristics.
We have assessed operational readiness in the states and gathered the concerns and recommendations of state integrated data system owners, demographers, advocacy groups, and program administrators. This stakeholder engagement was critical to design workflows and planned products. We need to report on the coverage, completeness, and overlap of data sources, and of the linked data. Next we will work with states to determine what population measures they need from this new capacity: what demographic characteristics and levels of geography are desired for tables and maps.
The project aims to build trust in the data and process from the ground up. We will develop training materials and offer technical assistance to help other states adopt this population benchmarking capacity.
ATC-ICD: enabling domain experts to explore and evaluate machine learning models estimating diagnoses from filled predictions
Authors: Endel Florian , Weibrecht Nadine 
Published: November 2019

IntroductionAdministrative and reimbursement data from the Austrian health care system is linked and utilized for research and to support policy makers. Lacking standardized, reliable and systematic coding of diagnoses in the outpatient sector, statistical and machine learning models are developed to estimate individual diagnoses coded as ICD-10 based on filled prescriptions (ATC codes), hence called “ATC->ICD models”.
Evaluating the performance of such models, presenting predictions on a global as well as individual level, comparing different technological approaches and establishing trust by providing an intuitive insight into results for non-technical users are the aim of this project.
MethodATC->ICD models are presented utilizing interactive web interfaces based on the R shiny package. As one size does not fit all, customized applications are required for different models and points of view. Applying modularization of reoccurring functionality and retaining design principles like a common dashboard layout facilitates the development and training of users. Software containers and centralized infrastructure providing e.g. backup, encryption and authentication enables efficient deployment of new application and their maintenance.
ResultsWe developed interactive web-based dashboards enabling experts to explore the prediction of single ATC->ICD models and compare the output of different approaches. The possibility to export and annotate results allows us to collect expert opinions, enhance understanding and gain acceptance conveniently. The combination of various dynamic controls, e.g. to filter, search, sort and cluster results, provides flexible access to complex models and large datasets. Linked and interactive graphs and tables help to understand valid and identify erroneous results much faster than with raw output and printed reports.
ConclusionPresenting ATC->ICD models renders them accessible to data scientists and domain experts. It allows us to collect valuable feedback and gain trust in complex, hard to understand methodologies and results.
Leveraging Electronic Health Records and Administrative Datasets to Understand Social Determinants of Health: Opportunities and Challenges
Authors: Tan Jonathan , Wasey Jack , Nelson Olivia , Tam Vicky , Simpao Allan , Galvez Jorge 
Published: November 2019

BackgroundElectronic health records (EHRs) are ubiquitous in modern health care systems and contain granular detail about an individual’s medical care. EHRs, however can be extremely limited in the amount of information about the patient’s socioeconomic status and other non-clinical factors that relate to social determinants of health (SDOH). As increasing evidence supports the role of socioeconomic and environmental factors having a greater impact on overall health than health care services, it is important to create novel data analysis methods that can improve our understanding of SDOH factors for each patient and the greater population a health system cares for.
Main AimTo demonstrate the feasibility and value of linking EHRs with administrative datasets to produce SDOH insights and better support research, quality improvement initiatives and operational decision making.
Methods/ApproachWe linked EHR’s at the Children’s Hospital of Philadelphia, between 2015-2019, with administrative datasets including the US Census and the American Community Survey Datasets for over 40,000 pediatric patients. This required the validation of geocoding addresses and spatial join techniques to provide clinical insight into the individual patient along with SDOH insights into their neighborhood environment. We then conducted several studies that combined individual clinical factors and neighborhood socioeconomic risk factors to improve our understanding and care pathways for patients.
ResultsLinking EHR data and administrative datasets required novel methods with unique challenges. Challenges included conducting such analysis with concern for the protection of patient privacy, overcoming technical data linkage challenges, and understanding the limitations of administrative dataset granularity when compared to detailed granularity of individual medical records. Presentation of results to further ensure patient confidentiality and privacy was also critical to our analysis.
ConclusionEHRs and administrative datasets can be successfully linked together to leverage the strength of both traditional clinical data and SDOH factors. While there can be technical and privacy related challenges, there are many benefits of using administrative datasets to provide unique multidimensional insights into socioeconomic and environmental impacts of health.
High-dimensional propensity score adjustment in HIV research using linked administrative health data
Authors: McLinden Taylor , Barrios Rolando , Hogg Robert 
Published: November 2019

BackgroundDespite not being collected for research purposes, linked administrative health data are increasingly being used to conduct observational epidemiologic analyses. In the field of HIV research in British Columbia (BC), Canada, the Comparative Outcomes And Service Utilization Trends (COAST) Study is based on a linkage between HIV-related clinical data and several provincial administrative health datasets. Specifically, the BC Centre for Excellence in HIV/AIDS Drug Treatment Program, which manages antiretroviral therapy (ART) dispensation for all known people living with HIV (PLWH) in BC, is linked with several Population Data BC data holdings. Population Data BC is a repository that houses longitudinal administrative data for all BC residents.
RationaleWhile the use of administrative data for research poses several challenges, bias due to confounding remains to be a key issue in this context. While randomized controlled trials of ART are common, an objective of COAST is to further examine the "real-world" impact of ART on health and clinical outcomes in a population-based sample of 13,907 PLWH in BC. Therefore, while longitudinal administrative data provide a unique opportunity to estimate the effect of ART on outcomes that are infrequently assessed in trials (e.g., chronic conditions), such data often lacks information on sociodemographic, socioeconomic, and behavioural confounders.
ApproachIt has been shown that adjustment for large numbers of covariates, in the form of administrative codes (e.g., diagnostic ICD codes, procedure codes, drug identification numbers), allows for better control of confounding bias. Therefore, relying on an established methodology in pharmacoepidemiology, we will use the high-dimensional propensity score algorithm to select and prioritize covariates (codes) that collectively act as proxies for unmeasured confounders. The use of this causal inference methodology in COAST will enhance our ability to generate stronger evidence to inform strategies that may improve the health and wellbeing of PLWH in this setting.
Education and health outcomes of children treated for chronic conditions
Authors: Fleming Michael , Fitton Catherine A , Steiner Markus FC , McLay James S , Clark David , King Albert , MacKay Daniel F , Pell Jill P 
Published: November 2019

Background with rationaleThis retrospective cohort study linked Scotland-wide education data to national health data to explore associations between childhood chronic conditions and subsequent educational and health outcomes.
Main AimConditions studied were diabetes, asthma, epilepsy, attention deficit hyperactivity disorder (ADHD) and depression. We also explored neurodevelopmental multimorbidity (comorbid autism, learning disability, ADHD or depression). Educational outcomes were school absenteeism and exclusion, special educational need, academic attainment and subsequent unemployment. Health outcomes were hospital admissions and all-cause mortality.
Methods/ApproachPupil census data and associated education records for all children attending primary and secondary schools in Scotland between 2009 and 2013 were linked to national prescribing data, acute and psychiatric hospital admissions, death records and retrospective maternity records enabling conditions to be studied whilst adjusting for sociodemographic and maternity factors and comorbid conditions. Conditions were ascertained from prescribing data and school records.
ResultsAll conditions were associated with increased school absenteeism, special educational need, and hospitalisation. All, excluding diabetes, were associated with poorer academic attainment and all, excluding ADHD were associated with increased mortality. ADHD and depression were associated with increased exclusion from school whilst epilepsy, ADHD and depression were associated with subsequent unemployment. Children experiencing neurodevelopmental multimorbidity had poorer outcomes across all educational domains. Depression was the biggest driver of absenteeism and ADHD was the biggest driver of exclusion.
ConclusionIn addition to poorer health outcomes, schoolchildren with these chronic conditions appear to experience significant educational disadvantage compared to their peers. Therefore interventions and further understanding of the intricate relationships between health and education among children with these conditions is required.
An Administrative Data Maturity Model - Building national administrative data capacity to produce results for children.
Authors: Carter Karen 
Published: November 2019

Administrative data is widely recognized as essential for improving service provision, identifying and monitoring development outcomes. However, despite significant investments, many countries remain unable to report against key development indicators and data quality significantly limits the utility of much of the data that are available. At the same time, the data landscape is changing rapidly with new technologies and the expansion of national identity systems.
Many of the requirements for effective national administrative data systems are not sectoral, but rather the “foundational” elements of government data policy, use and supporting infrastructure. UNICEF is working closely with partners to develop a maturity model for administrative data systems to prioritize investments and needs across sectors, help donors assess the capacity to absorb proposed investments, and ensure impact for children. The model provides a framework for best-practice examples, and to highlight areas where additional guidance is needed.
The concept draws on the wealth of existing assessment tools and quality indicators for administrative data systems across sectors and organisations and is built on three main elements:

It is child focused: putting the best interests of children at the centre of system design and operation.
It is built from the community up: recognizing the importance of local engagement in supporting development outcomes for children and the collection of high quality data.
It recognizes the need for strong cross
sectoral foundations at the national level to support the effective and sustainable functioning of administrative data systems.

Under each element, the outcomes that a mature administrative data landscape should deliver are defined, along with the system and landscape characteristics required to achieve these. The approach also places administrative data as part of a broader national data landscape – recognizing the importance of integrating multiple data sources to validate data quality, address data gaps, and meet national data requirements.