bluuebunny/crossref_metadata_2025_split huggingface dataset:
Self-Rated Health Inequalities in British Nurses
Authors: Ball William , Atherton Iain , Kyle Richard 
Published: December 2020

IntroductionImprovements in health in the UK are beginning to stall. Differences between the health of people living in the most and least deprived areas continue to grow. An excess in mortality, not explained by deprivation, has been observed in Scotland. Some of this difference likely results from limitations in deprivation measures.
Objectives and ApproachWe seek to test whether Nurses experience health inequalities in Self-Rated Health comparable with the general population. We also aim to explore cross-national differences within the Nursing occupational group.
We utilise data from Census-derived Longitudinal Studies in Scotland and England & Wales which are linked to an adjusted UK-consistent Multiple Deprivation measure. The databases can only be accessed securely, so an innovative method (eDatashield) has been used to conduct analysis as if the two were combined.
Nurses are of interest as they are a large occupational group with potentially protective characteristics against inequalities including high health literacy and level of education. Socioeconomic homogeneity in this group may reduce the effect of confounding when exploring area-based deprivation measures.
ResultsComparing Nurses to Non-Nurses we found they have systematically different and more homogenous characteristics. Nurses are; older, have a higher level of education, are more likely to be female, own their home, are less likely to live in deprived areas and they report better Self-Rated Health. However, inequalities persist.
Comparing Self-Rated Health of Scottish with English & Welsh Nurses will determine whether an ‘excess’ in worse health outcomes exists and if so, whether the UK- consistent Deprivation Measure can account for this. Full results will be cleared for dissemination through disclosure control, prior to the conference.
Conclusion / ImplicationsEven in a privileged group with characteristics which protect against poor health, inequalities remain. The methods applied here present an opportunity for improved cross-national comparison and address limitations in confounding when exploring inequalities based on area deprivation.
Linked Administrative Data’s Role in Victoria’s First Social Impact Investment – Journey to Social Inclusion – Working to End Chronic Homelessness
Authors: Williams Sharon , Sidthorp Mark , Flatau Paul , Zaretsky Kaylene , Callis Zoe , Findlay Suzanne 
Published: December 2020

The Journey to Social Inclusion (J2SI) Social Impact Investment (SII) is Victoria’s first social impact investment. It has two outcome payments, for stable housing and reduction in hospital bed days. Linked administrative data is being used to calculate the performance levels and outcome payments from the Victorian Government to J2SI. This paper discusses the methodologies and challenges encountered in the use of the linked administrative data for the calculation of the outcome payments for the J2SI SII.
IntroductionJ2SI will receive half of the program costs for service delivery based on the level of performance of two outcome payments. The outcome payments are the difference between the results for the Intervention Group and the Control Group. The results are being calculated using linked administrative datasets for health, housing and justice, by the Centre for Victorian Data Linkage (CVDL) and the Centre for Social Impact, University of Western Australia (CSI UWA) respectively.
Objectives and ApproachThe paper will outline the approach taken to calculate the results for the Intervention Group and the Control Group, explore the benefits and challenges of arising from different parties being involved in the calculations and present the lessons learned for future social impact bonds in Victoria and other States/Territories.
ResultsThe work has highlighted the importance of clearly understanding and documenting all of the datasets and how they are to be used prior to the commencement of the calculations. High level descriptions are not adequate preparation for the precision required for the actual calculations. The work has also highlighted the differences is using linked data for research and service implementation.
Conclusion / ImplicationsLinked administrative data is a robust source of data for the calculation of outcome payments for social impact investments. However, with the timeliness of payments important for service delivery and financing, thorough preparation for the calculation of the results and outcome payments is essential.
Creating A Research Ready Data Asset and Empowering Dynamic and Efficient Research: The Sail Dementia E-Cohort (SDEC)
Authors: Akbari Ashley , North Laura , Orton Chris , Thompson Simon , Schnier Christian , Wilkinson Tim , Gallacher John , Lyons Ronan 
Published: December 2020

IntroductionResearch can often be slow to start and require duplication of effort which in lots of cases has previously been completed to generate research-ready-data-assets (RRDA). Within the UK, two programmes: Dementias Platform UK (DPUK) that brings together over 50 different dementia-related cohorts and Secure Anonymised Information Linkage (SAIL) Databank, which provides access to longitudinal population-scale person-level data for every person in Wales have tried to tackle this challenge of creation, use and management of RRDA’s.
Objectives and ApproachCombining clinical, data and management expertise from DPUK and SAIL, we hoped to construct a RRDA that was easily accessible and well described for a dementia e-cohort. Welsh residents with available primary care records were included, with clinical and demographic information including follow-up times, several dementia indicators using validated diagnostic code lists, information on every dementia-related diagnostic event and several covariates and co-morbidities. SDeC was made available to researchers and can be modified according to appropriate study designs, with learning from projects used to update the SDeC to improve future uses. Interactive visualisations effectively summarise cohort characteristics, aiding researchers to quickly determine cohort eligibility for dementia studies.
ResultsSDeC contains data from 4.6 million participants in SAIL, with 1.5 million meeting cohort inclusion criteria, resulting in 24.3 million person-years of follow-up. Of these, 146,323 (10%) developed all-cause dementia during follow-up, with 90,150 (60%) having dementia subtype codes. We made this resource available to researchers who had never used SAIL before, with limited experience of population-scale routine-data, and projects have proceeded with one managing to proceed from point of initial access to submission of publication in less than 6-months.
Conclusion / ImplicationsSDeC provides a reproducible dynamic method for completing dementia research, and expediting learning and understanding of the use of these data, with further developments and maintenance planned to increase the complexity and detail available to researchers over time.
Association Between Active Living Environments and Hospitalization for All-Causes and Cardiometabolic Disease
Authors: Mah Sarah M , Sanmartin Claudia , Riva Mylene , Dasgupta Kaberi , Ross Nancy 
Published: December 2020

IntroductionNeighbourhoods have the potential to influence population-wide modifiable risk factors such as physical inactivity and obesity. Built environments that encourage active living hold promise as a policy lever for reducing health care burden, particularly that related to cardiometabolic disease.
Objectives and ApproachWe examined the role of active living environments on hospitalization risk, frequency, and cumulative length of stay for all-causes and cardiometabolic diseases. The linked dataset is a combination of survey data from Canadian respondents aged 45+, records from a national census of acute hospitalizations, and the Canadian Active Living Environment (Can-ALE) - a 5-class measure of how conducive one’s neighbourhood is to active living based on street connectivity, points of interest, and population density. We modelled the risk of all-cause and cardiometabolic hospitalizations for respondents living in more and less favourable environments using logistic regression. Frequency and cumulative length of stay were modelled using truncated negative binomial regression. Models were adjusted for individual-level factors and proximity to a hospital. An offset variable was included to account for different follow-up times.
Results232,000 respondents were included with a mean follow-up time of 5.37 years. Those living in progressively more favourable active living environments (classes 2, 3, 4, and 5) exhibited incrementally lower risk of hospitalization compared to those living in the least favourable (class 1). Relative to respondents living in the least favourable environments (class 1), odds ratios were 0.84 (95% CI 0.76-0.93) for all-cause hospitalization and 0.80 (95% CI 0.68-0.93) for cardiometabolic hospitalization for respondents living in the most favourable environments (class 5). There was little evidence of similar associations for hospitalization frequency and cumulative length of stay.
Conclusion / ImplicationsLiving in neighbourhoods that are more conducive to active living are associated with lower risk of all-cause and cardiometabolic hospitalization.
Using Linked Health Service Data in The Evaluation of Innovative Models of Healthcare
Authors: Pang Rebecca Kwok-yee , Srikanth Velandai , Braun Gary , McGee Fergus , Berry Belinda , Edwards Iain , Azzopardi Ruth , Andrew Nadine 
Published: December 2020

IntroductionAgeing populations place considerable burden on healthcare systems. Innovative methods to implement and evaluate new care models to reduce unwarranted hospital presentations, are needed.
Objectives and ApproachTo use linked Electronic Medical Record (EMR) data to evaluate a pilot community-based model of care to reduce hospital presentations in a cohort of patients at-risk of hospital representations following an acute admission. Patients admitted to a metropolitan hospital with a non-surgical condition andidentified as being at-risk of readmission using a state-wide risk algorithm, were eligible to receive a 30-day care navigation model of care. The intervention group were matched to similar eligible patients, who received usual care using propensity score matching. Linked data were obtained from the EMR based Data Warehouse to provide information on subsequent readmissions and community-based health service contacts. Readmission rates were compared at 30, 60, and 90-days post-discharge using Cox Proportional Hazards Regression, adjusted for confounders and weighted by the propensity score. Descriptive analyses were used to compare demographics and healthcare utilisations.
ResultsData from 412,971 potentially eligible patients were extracted. Sixty-five received the intervention of which 63 were matched to 280 controls (aged 51-91 years, 54% female, 63% lived alone). At 30-days post-discharge (during intervention delivery) an 80% reduction in readmission rate in the intervention group was observed (Hazard Ratio [HR] 0.20; 95%Confidence Interval (CI) [0.01, 4.20]). At 60-days (HR 0.48; 95%CI [0.03, 8.05]) and 90-days (HR 0.83; 95%CI [0.06, 11.36]), readmission rate reductions decreased to 52% and 17% respectively. In the intervention group, 68.9% received an out-patient appointment compared to 40% of controls (P<0.001) and 7.7% received allied health compared to none of the controls (p=0.003) within 90-days post-discharge.
Conclusion/ImplicationsResults have informed hospital-wide service implementation thereby demonstrating the value of linked EMR data and advanced statistical methods in the evaluation of real-world healthcare.
Building A Research Partnership Between Computer Scientists and Health Service Researchers for Access and Analysis of Population-Level Health Datasets
Authors: Schull Michael , Brudno Michael , Ghassemi Marzyeh , Gibson Garth , Goldenberg Anna , Paprica P. Alison , Rosella Laura , Stukel Thérèse , Victor J. Charles , Virtanen Carl 
Published: December 2020

IntroductionThere is widespread enthusiasm to improve health through the application of artificial intelligence and machine learning (AI/ML) methods to large population-level health datasets. Achieving this may require successful collaboration between institutions as well as between computer scientists (CS), machine learning researchers (MLR) and health service researchers (HSR). The objective is to describe lessons learned in creating the Health Artificial Data and Analysis Platform (HAIDAP) in Ontario, Canada.
Objectives and ApproachA partnership between a HSR institute (ICES), an AI/ML institute (Vector) and a high-performance computing center (HPC4H) was initiated in 2017 to enable the application of AI/ML methods to population-level health data for the province of Ontario (population 14M). The HAIDAP was launched in 2019. We describe lessons learned (and being learned) following the HAIDAP’s launch.
ResultsMajor learnings include: 1)importance of institutional partnerships and alignment with institutional strategies; 2)potential of joint institutional risk-sharing models; 3)need for scientific collaborations bridging disciplines around joint research projects; 4)sensitivity to different scientific cultures (e.g., academic prestige of conference proceedings for MLR vs journal publications for HSR; traditional statistical vs. ML model assumptions); 5)differences in research timeline expectations; 6)different experience with and expectations for access to de-identified routinely collected data (e.g., need for research ethics committee project approvals and privacy impact assessments); 7)developing data access models that enable greater flexibility (e.g. importing code or using open source tools); 8) broadening data access models to allow modern high-dimensional exploratory data analysis; 9)obtaining support of information/privacy regulator; 10)hardware is (relatively) easy part compared to other success factors.
Conclusion / ImplicationsThe HAIDAP has enabled multi-disciplinary collaborations and novel AI/ML research of Ontario’s population-level health data. Collectively we have learned that additional effort is required to develop systems and processes enabling more efficient access to data and analytic tools for the analysis of administrative health data.
A National Level Case-Control Study for Determining Risk of Major Cardiovascular Events in People with Epilepsy
Authors: Torabi Fatemeh , Lee-Lane Ella , Lacey Arron , Fonferko-Shadrach Beata , Harris Daniel , Akbari Ashley , Lyons Ronan A , Rees Mark , Sawhney Inder , Halcox Julian , Powell Rob , Pickrell W Owen 
Published: December 2020

IntroductionThe risk of cardiovascular events amongst people with epilepsy who are receiving enzyme-inducing anti-epileptic drugs (EIAEDs) seems to be higher than those on other medications and the general population. National-level record linkage enables development of case-control studies at a wider scope accounting for multiple factors.
Objectives and ApproachPeople with epilepsy were identified between 2003-01-01 and 2017-12-31 and were matched to a control group on: age, gender, deprivation quintile and year of diagnosis, accounting for any changes in clinical therapeutic guidelines. Primary and secondary care population records were linked to capture relevant comorbidities and major cardiovascular events. Annual district birth and death extract were used in combination with the Welsh Demographic Service (WDS) dataset to capture demographic and cardiovascular related death records. The WDS dataset was used to identify eligible control groups for each case and a linkage approach between the control and case database was developed for matching cases and controls with replacement and randomization. Survival analysis was conducted to evaluate the difference in time to first major cardiovascular event in patients receiving EIAED versus Non-EIAEDs and controls.
Results10,241 cases (mean age 49.6 years, 52.2% male) with diagnosis of epilepsy were matched to 35,145 controls. 3,180 (31.1%) cases received EIAEDs and 7,061 (68.9%) received non-EIAEDs. The risk of experiencing a major cardiovascular event was higher in cases compared to controls (adjusted hazard ratio 1.52,95%CI[1.50–1.55];p<0.001). There was no significant difference in cardiovascular events between those treated with non-EIAEDs and EIAEDs (adjusted hazard ratio 1.04,95%CI[0.95-1.12];p=0.407).
Conclusion / ImplicationsData linkage provides a unique opportunity and insight into studying disease risk factors. We have shown that individuals with epilepsy prescribed antiepileptic drugs, re at an increased risk of a major cardiovascular events regardless of treatment type (EIAED,NEIAED) compared with a matched control population.
Using the Canadian Institute for Health Information’s Information Quality Framework to Support Integration and Utilization of Complex, Multi-Jurisdictional Data
Authors: Willemse Chrissy 
Published: December 2020

The Canadian Institute for Health Information (CIHI) provides essential information on Canada’s health systems and the health of Canadians. This presentation discusses information quality’s role in the integration and utilization of CIHI’s complex, multi-sector and multi-jurisdictional data.
IntroductionCIHI’s Data and Information Quality Program is recognized internationally for its comprehensiveness and high standards. As the need for linked data research increases, the requirements on quality continue to grow. CIHI’s multi-sector, multi-jurisdictional healthcare system and the varying health policies, care delivery models, and data collection practices that go with it pose challenges for researchers as they try to pull the data together in a comprehensive way. CIHI’s Information Quality Framework forms the foundation for addressing these challenges and ensuring data are fit for integration and are properly utilized.
Objectives and ApproachIn 2019, a connected data quality project was initiated to improve the usability of CIHI’s analytical data. Information quality framework concepts were applied across CIHI data sources to better understand data linkage challenges, measure inconsistencies across data sources, identify opportunities to improve data and standards, and develop resources to support users.
ResultsFindings from the project identified key connected data quality activities for the organization to operationalize. These focus on quality assessment and reporting; harmonization of data standards; expanded documentation and analytical resources; data classification and profiling tools to support descriptive analysis; and new source of truth and pre-linked datasets. Quality activities were prioritized based on need and complexity, and “connected data teams” were established to carry out the work.
Conclusion / ImplicationsExpansion of CIHI’s quality framework across data sources facilitates its data linkage capabilities and “connected data” use. It enables the evolution of CIHI’s analytical environments and information products from being database specific to integrated-data driven, and facilitates the use of CIHI’s analytical data for research.
Learnings from Multi-Source Enduring Linked Data Assets (MELDAS)
Authors: Neideck Geoff 
Published: December 2020

IntroductionDemand continues to grow for accessible and large scale linked data assets to answer complex cross-sector, and cross-jurisdiction research questions. To meet this demand, a number of Multi-source, Enduring Linked Data Assets (MELDAs) have emerged including the National Integrated Health Service Infrastructure (NIHSI), National Disability Data Asset (NDDA) and Multi-Agency Data Integration Project (MADIP). Using these MELDAs has proven much more efficient than project-specific linkages, and provides consistent national data assets for multiple uses. However, the development of these assets raises new challenges, including complex data models, governance, and access arrangements, and new approaches to analysis.
Objectives and ApproachThrough developing the NIHSI Analytical Asset in collaboration with state/territory and Federal Government partners, the AIHW has identified challenges in traditional linkage approaches, which require innovative approaches to ensure high quality linkage. As AIHW commences scoping on new MELDAs, we are taking lessons from building the NIHSI and applying them to future design.
ResultsAIHW’s development of MELDAs across jurisdictions and portfolios provides new learnings on how to address advanced real world data integration issues. This review will focus on lessons learnt at the Australian Institute of Health and Welfare (AIHW) working with new data sharing arrangements, applications of technologies and innovative approaches to streamline MELDA processes.
Conclusion / ImplicationsThe learnings from the AIHW development of MELDAs will assist others developing enduring assets to establish effective sharing arrangements, governance and technical solutions to ensure efficient management. These learnings will save time and resources, and prompt further discussion on a gold standard for building MELDAs moving forwards.
Drug-Related Deaths Among Young People Involved in The Criminal Justice System: A Whole-Population Multi-Sectoral Data Linkage Study
Authors: Young Jesse T , Tibble Holly , Borschmann Rohan , Kinner Stuart A 
Published: December 2020

IntroductionDrug-related death is substantially higher in formerly incarcerated adults compared to the general population. Despite this, remarkably little is known about the epidemiology of drug-related death among justice-involved adolescents. A richer understanding of drug-related mortality in justice-involved young people is essential for the development of effective, evidence-based interventions for this vulnerable group.
Objectives and ApproachIn a whole-population cohort of justice-involved young people in Queensland, Australia, we aimed to: 1) calculate the rate of drug-related death overall, and separately by intent and drug category; and 2) estimate the probability, and identify the predictors of, drug-related death. We probabilistically linked Youth Justice Queensland and National Death Index (NDI) records for every young person who came into contact with the youth justice system in Queensland between 1 January 1993 and 31 December 2014 (N=48,963). The NDI provided death data until 31 December 2017. Crude mortality rates (CMR) were calculatedoverall, separately by intent, and by prescribed versus illicit drug cause. Competing risk survival analysis will be conducted.
ResultsOf the 48,963 individuals, 1452 (3%) died by 31 December 2017. Of these, 204 (14%) deaths were due to drugs, yielding a CMR of 31 (95%CI:27-36) per 100,000 person-years. Most drug-related deaths were recorded as intentional (CMR=28; 95%CI:24-33 per 100,000 person-years) and deaths from prescribed medications were more common than illicit drugs (CMR=17; 95%CI:14-20 and CMR=11; 95%CI:9-14 per 100,000 person-years, respectively).
Conclusion / ImplicationsRates of drug-related deaths following contact with the youth justice system are disproportionately high and represent a major public health concern. Our findings highlight the importance of initiation of alcohol and other drug (AOD) treatment upon contact with the youth justice system and continuous engagement with community treatment after discharge from the youth justice system. Evidenced-base prevention strategies and efforts to improve the continuity of care are urgently needed.