bluuebunny/crossref_metadata_2025_split huggingface dataset:
Childhood cognitive ability and the use of long-term care in later life
Authors: Iveson Matthew , Altschul Drew , Deary Ian 
Published: November 2019

Background As the population ages the demand for care is predicted to increase. Previous studies have reported that individuals with poorer post-morbid cognitive ability are at higher risk of entering long-term care, both institutionalised care and home-based care. Given that post-morbid cognitive ability is sensitive to the type and severity of morbidity, it remains unclear whether higher cognitive ability, as a trait-level measure of individual differences, contributes to care usage. Some success has been observed using non-cognitive early-life circumstances such as socioeconomic circumstances as pre-morbid predictors of care risk. However, the contribution of early-life cognitive ability has yet to be examined.
Main Aim We investigate the association between early-life circumstances, particularly cognitive ability, and the risk of entry into long-term care in later life (age 65+).
Methods Veterans were identified using the South London and Maudsley Biomedical Research Centre (SLaM) case register – a database holding secondary mental health care electronic records for the South London and Maudsley National Health Service Trust of 300,000 patients. We developed two methods. An NLP and machine learning tool were developed to automatically evaluate personal history statements written by clinicians.
Results This study uses a large sample of individuals born in Scotland in 1936 and who took part in the Scottish Mental Survey 1947. It links research data from childhood to routinely-collected administrative and healthcare records from across the life course.
Conclusion We demonstrate the importance of early-life factors for predicting care usage in later life and how this role differs between types of long-term care. The implications of the results for research and policy will be discussed.
The Emerging Crisis of Aged Homelessness in the US: Could Cost Avoidance in Health Care Fund Housing Solutions?
Authors: Culhane Dennis , Doran Kelly , Schretzman Maryann , Johns Eileen , Treglia Daniela , Byrne Thomas , Metraux Stephen , Kuhn Randall 
Published: November 2019

Background with rationale The US homeless population has had a fairly unique age structure for thirty years, with one-third concentrated among the 1955-1965 birth cohort. They are aging prematurely and are experiencing aging related morbidity and mortality in increasing numbers. This study uses demographic methods to project the growth in aged homelessness out to 2030. Using linked administrative data, historical patterns of health care, nursing home and shelter are used to estimate future costs. Potential offsets are estimated from anticipated reductions in excess services use associated with housing placement.
Main Aim To determine if housing placement among future aged homeless adults could be offset by reduced health and social service costs.
Methods/Approach Demographic methods are applied to historical shelter data in Los Angeles, New York and Boston to project the future growth in aged homelessness. Linked administrative data are used to estimate future age-related use of health and social services. Housing models are applied to meet population needs to estimate costs. Health and social service costs offsets are estimated from prior literature. The net cost is estimated based on the differential.
Results Elderly homelessness will triple in the US over the next ten years. Average annual health and shelter costs will be $22-28,000 per person. Housing costs are estimated at $7-11,000 annually. Cost offsets yield a positive ROI of 1.13 in New York and Los Angeles; Boston results are closer to break-even.
Conclusion Substantial public dollars will be spent on the aged homelessness problem as it triples over the next ten years. Some of those dollars could be shifted to housing solutions, reducing homelessness significantly, with little to no increase in public expenditures; positive returns on such investments are also possible.
Exploiting Administrative Data to Understand the Mental Health of Children Known to Social Services
Authors: McKenna Sarah , Maguire Aideen , O'Reilly Dermot 
Published: November 2019

Background Research has consistently found a high prevalence of mental ill-health among children in out-of-home care. However, results have varied significantly by study location, type of care intervention, sample population and mental health measurement, and concerns have been raised about appropriate reference populations. In addition, little is known about children known to social services who remain with their birth families.
Aim To examine mental ill-health amongst children known to social services based on care exposure including those who remain at home, those placed in foster care, kinship care or institutional care and the general population not known to social services.
Methods Northern Ireland is unique in that has an integrated health and social care system and holds data centrally on all children known to social services. Social services data (1995-2015) will be linked to hospital discharge data (2010-2015), prescribed medication data (2010-2015), self-harm data (2010-2015) and death records (2010-2015) to investigate mental health outcomes in terms of psychiatric hospital admissions, psychotropic medication uptake, self-harm and suicide.
Results Data cleaning has been completed and analysis is underway. Preliminary results will be available by December 2019. Descriptive statistics will provide a mental health profile of children in care compared not only to children in the general population but to those who are known to social services but remain in their own home. Regression models will determine which factors are most associated with poor mental health outcomes.
Conclusion This project is the UK’s first population-wide data linkage study examining the mental health of children in the social care system, including looked-after children and those known to social services who remain in their own home. Project partners in the Department of Health recognise the potential of these findings to inform future policy relating to targeting interventions for children in receipt of social care services.
A data driven approach to transforming population and migration statistics
Authors: Field Vicky 
Published: November 2019

The Office for National Statistics (ONS) is transforming the way we produce population and migration statistics, to a system that is led by administrative data.
Population and migration statistics underpin a wide variety of other statistics and inform a vast range of decisions, including pensions forecasting and provision of local services. These statistics are also used to help inform public debate, it is therefore essential that these statistics are accurate and timely. Until now the census has formed the cornerstone of these statistics, with updates from other surveys and some administrative data. Enabled by new data sharing powers in the Digital Economy Act (2017), we now have the opportunity to make use of more data: and this is essential to better understand our rapidly changing population at both national and local levels, and the wider policy impact and context.
We know that there is no single, comprehensive data source that tells us everything about the population. Therefore, we are developing ‘data-driven rules’ to use as part of a future system. This focuses on identifying the strengths of individual data sources and integrating them to provide richer understanding of how our population is changing.
This approach will also allow us to be flexible in implementing different definitions to understand sub-groups of the population. For example, the daytime population to understand the impact that the increasingly more mobile population has on different services.
We have undertaken research progressing administrative data-based population estimates, linked data to inform our understanding about migration patterns, reflecting the changing population and the impact that this has on society. The session will provide the latest on the research that ONS has carried out into the potential of administrative data in supporting and delivering a new system that can offer more responsive, frequent and timely insights into our society.
Variations in the use and availability of formal and informal care at the end of life over time and space
Authors: Schneider Anna , Atherton Iain 
Published: November 2019

Background In light of increasing death rates and a high need for palliative care at the end of life, it is important to plan formal care resources and understand in what ways informal care may alleviate the pressure on healthcare services. A piece to this information puzzle is understanding spatial variations in the use and availability of formal and informal care and how these develop over time.
Aim This paper explores spatial variations in place of death and living arrangements (as proxy for informal care availability) in the last year of life in Scotland, comparing two time points.
Data Analysis is based on population data from the Scottish Census 2001 and 2011 that was linked with NHS data and death records data for everyone who passed away within a year of census enumeration. Geographic data includes the Scottish urban-rural classification and healthboards.
Methods Data was linked via probability linkage. Descriptive analysis and regression models will be presented.
Results and Conclusions The study population comprises 49,356 cases from 2001/02 and 46,473 from 2011/12. Understanding how care use has changed over time and varies spatially is key to health and social care resource planning and social policy making.
Sexual crime against children with disabilities: a nationwide prospective birth cohort-study
Authors: Christoffersen Mogens 
Published: November 2019

Background The rate of violent and sexual victimization against children with disabilities is thought to be lower than the rate for children without disabilities but several studies shows otherwise.
Aims The study focuses on examining sexual crime against children with disabilities and explaining differences in victimization in order to elucidate to what extent types of disability, family disadvantages, gender, high-risk behavior, location influences adolescents’ risk of sexual victimization. Previous population studies lack scientifically sound research methodology and results are weak or inconclusive.
Method data is based on a national study of reported sexual crime against children in Denmark aged between 7 and 18 years of age using total birth cohorts (N=678,000). The statistical analysis is a discrete time Cox-model. An extended list of potential risk factors was included in the analysis in order to adjust for confounding. The potentially confounding risk-factors were collected independently from various population-based registers, e.g. employment statistics, housing statistics, education statistics, income compensation benefits, and population statistics (e.g. gender, age, location). Hospital records with information on types of disability based on the national inpatient register and national psychiatric register were collected independently of the collection of law enforcements records about reported sexual offences under the Danish Central Crime Register.
Results Children with disabilities are more likely to be victimized of a reported sexual crime than non-disabled children: ADHD odds ratio: 3.7 (3.5-3.9), mental retardation: 3.8 (3.6-4.0), autism 3.8 (3.6-4.0).
Conclusion The present study finds that family disadvantage e.g.parental substance abuse, parental violence, family separation, the child in care, and parental unemployment indicate an increased risk of being a victim of a sexual crime.
Assessment of risk factors may permit professionals to facilitate prevention and treatment interventions. The study underreports the size of the problem because adolescents with disabilities face barriers when reporting victimization.
Linked Administrative Data at Statistics Canada – new data resources for horizontal research
Authors: Xue Li 
Published: November 2019

There has been an increasing demand for analytics and research related to cross-cutting and horizontal issues in Canada, such as in the domains of housing, aging and immigration. Very often policy makers and stakeholders are posing a full spectrum of questions around a specific topic, requiring multidisciplinary evidence and data. Statistics Canada has a long history of record linkage. Over the past decade, the number of record linkage projects has increased exponentially. Several established platforms have been developed to facilitate linkage – Canadian Employer and Employer Database which brings together tax and employment records from both employees and employers; the Social Data Linkage Environment created to support linkages at the individuals level across a broad spectrum of social data (health, justice, education, socio-economic); and the Linkable File Environment for business data.
The breadth of our data holdings married with record linkage capabilities allows the creation of data sets that crosses disciplines and areas or research. This presentation will showcase the innovative data integration approaches that Statistics Canada has advanced to meet the inter-disciplinary data needs.
Statistics Canada are pioneering in some innovative linkages across various domains to help answer cross-cutting questions. For example, Longitudinal Administrative Databank linking longitudinal tax records to numerous other data files including tax records of spouses and children in the household, longitudinal Immigration Database linkage key and health records, is used to study economic impact of hospitalization, as well as better understand health outcomes of immigrants by various dimensions including socio-economic status. Other examples include the pilot projects linking Canadian Financial Capability Survey to tax records, to gauge the relationship between financial literacy and annual retirement savings behavior and Intergenerational Income Database being linked to Census to understand socio-economic factors affecting the intergenerational mobility.
Rapid growth in data availability for research also poses new challenges on IM/IT, governance, access, capacity building, etc. As Statistics Canada has moved on a path of modernization, data integration is key to the development of new data sources to fill information gaps as we move forward.
The use of Administrative Data to combat non-response
Authors: Moore Matthew , Rutherford Alasdair , Douglas Elaine , Bell David 
Published: November 2019

Background The issues arising due to participant non-response are problematic for many surveys. An international trend of falling survey response rates has led to more researchers questioning the extent to which non-response can cause issues with analysis, and what can be done to combat this non-response. Non-response weighting is a common method used by surveys, however the construction of these weights can rely upon untested assumptions and low quality and quantity of data. Administrative data can provide new opportunities in survey design, including improved construction of non-response weights, allowing better survey-based estimates.
Main Aim In this paper we explore the potential of using administrative data from the sampling frame of a large social survey to model non-response. The data used for this paper is part of the Healthy Aging in Scotland (HAGIS) Survey.
Methods The HAGIS pilot wave collected a sample of approximately 1000 individuals aged over 50 in the Scottish mainland. HAGIS utilised administrative data in the sampling with screening being provided to select households with at least one eligible respondent (over 50). Administrative data were also available for the full sample, and not just respondents to the HAGIS pilot wave. The administrative data consists of individual demographics, hospital admissions, and prescription data held by NHS Scotland.
Results Survey response is modelled as a function of explanatory variables from the administrative data in order to examine whether there is systematic demographic and health-based non-response in the survey. We further examine whether this non-response is causing significant biases and then how health data might predict non-response.
Conclusion Finally, we discuss how these models could be used in improving conventional weighting techniques to combat non-response in survey design.
Spatially Enabling The Master Linkage Map – Getting Straight To The Point
Authors: Stokes Brian , Albion Tim , Wiggins Nadine 
Published: November 2019

Background with rationaleIn recent years, rapid advancements in Geographic Information Systems (GIS) software has provided a range of users with an array of options to visually represent multiple digital data assets and for a range of purposes. The research community is increasingly able to represent complex data assets spatially to identify patterns in the population specific to burden of disease or for other reasons.
Main AimTo develop a methodology and software solution to effectively attribute spatial coordinates at a unit record level to data held in the Tasmanian Data Linkage Units (TDLU) Master Linkage Map (MLM). A further requirement was to support an integrated process of clerical review to enable incorrect or missing address information to be corrected.
Methods/Approach The Australian government makes available a geocoded address database for end users including Australian businesses, education and government. Known as the G-NAF (Geocoded National Address File), the database contains in excess of 30 tables for over 13 million principal Australian addresses. The TDLU developed software that extracts data held in its MLM and processes using the G-NAF to derive and store multiple spatial data elements.
ResultsThe TDLU has achieved in excess of 92% automated geocoding to address level for all records held in its MLM. Extensive metadata is produced, analysed and stored and used for many reasons including quality assurance. The release of spatial information, together with linkage keys, is strictly managed in accordance with Human Research Ethic Committee and custodian approvals.
ConclusionThe TDLU’s custom designed spatial information system has proven effective in both automatically allocating spatial coordinates for records held in its MLM, and for supporting review of missing or incorrect address information. Generating and storing spatial data at a unit record level enables the effective allocation of a range of geographic elements, population data and indices including measures of socio-economic disadvantage.
The Office for National Statistics Administrative Data Methods Research Programme
Authors: Shenton Claire , Tinklet Lucy , Finselbach Hannah 
Published: November 2019

The quantity of administrative data created, stored and processed in the world has grown exponentially over recent years, but the statistical theory to support its use in official statistics has not. That’s not to say that within the statistical community we are not using or investigating a wide variety of statistical sources, but the methods used are not based on well-established theory like we have for surveys. As a National Statistical Institute (NSI), we are transforming to put administrative and alternative data sources at the core of our statistics. Combining new sources with surveys will allow us to meet the ever-increasing user demand for improved and more detailed statistics. However, using administrative data involves addressing a range of statistical challenges as identified in “Statistical challenges of administrative and transaction data” (Hand, 2018).
The Administrative Data Methods Research Programme was set up to address these challenges by developing a statistical framework for using and integrating administrative and transactional data to produce official statistics and analysis. The key themes of the programme include quality, linkage, privacy, estimation, statistical design, analysis and innovative techniques.
The programme will ensure the rigor and assurance of methods developed, allowing where possible wide-reaching application, and to create and share best practice and guidance. A key part of this programme will be engaging and working collaboratively with the statistical community in academia, government and private sector to help prioritise the challenges, shape the work programme and drive the work forward.
Common Mental Disorder across Standard Occupational Classifications in Northern Ireland: an administrative data study
Authors: Ferry Finola , Roasto Michael , Curran Emma , Leavey Gerard 
Published: November 2019

Background While employment in general promotes positive health and wellbeing, a number of studies show disparities in mental health across occupational groupings (Bell et al., 2014; Stansfield et al. 2011). However, definitive studies to allow estimation of prevalence and risk across a range of occupation types require large population sub-groups and common measures of mental health.
AimWe hypothesise: (1) we will detect differences in the prevalence of Common Mental Disorder (CMD) across occupational groupings; and (2) risk of CMD across occupation types is moderated by family demands.
MethodsUsing linked administrative data we examine CMD among the Northern Ireland (NI) adult population across Standard Occupational Classifications (SOCs). We also examine the influence of socio-demographic, socio-economic and health-related variables. Data is accessed through the Administrative Data Research Centre NI (ADRC-NI) and comprises the individual and household 2011 Census; Land and Properties Services data; Enhanced Prescribing Database and Multiple Deprivation Measures.
ResultsExamination of nine major SOC groups shows the prevalence of any self-reported (SR) mental health condition was highest among ‘elementary trades and related occupations’ (24.63%) followed by ‘process, plant and machine operatives’ (23.65%). The risk of mental ill health among these groups remained elevated for both men and women after controlling for age, sex, marital status and educational attainment. Analyses based on the 90 sub-minor SOC groups shows that ‘textiles and garments trades’ workers had the highest prevalence of a SR mental health condition (36.15%). Logistic regression analyses shows that individuals with dependent children and informal caregiving responsibilities had lower risk of a SR mental health condition (OR=0.75, CI=0.73, 0.76 and OR=0.81, CI=0.79, 0.82 respectively).
ConclusionResults presented in this abstract are based on preliminary analyses using self-reported mental health. Further analyses based on mental health prescription data will subsequently be undertaken and presented at the ADR conference.