bluuebunny/crossref_metadata_2025_split huggingface dataset:
Supporting Transition from Out of Home Care to Adulthood: Linked Data Is Central to The Compass Social Impact Bond
Authors: Williams Sharon , Sipthorp Mark , Ivkovic Nicholas , Inglis Alexander 
Published: December 2020

The COMPASS program supports young people to successfully transition from Out of Home Care to adulthood. COMPASS is a social impact bond partnership between Victorian government, not-for-profit organisations and investors. COMPASS uses linked data in every phase from design, to implementation, to outcome measurement.
IntroductionStudies demonstrates that young care leavers experience significantly poorer outcomes than their peers. COMPASS is a preventative program progressivelyproviding 200 care leavers with access to housing and individualised support. The Centre for Victorian Data Linkage (CVDL) developed the Victorian Linkage Map (VLM) in 2016, linking 20 plus health and human services datasets with births and deaths data. Linked data provides a critical evidence base for COMPASS.
Objectives and ApproachThe presentation describes application of linked data for COMPASS design, implementation and measurement. Linked data cohort analysis of post-care service use of 6000 young people informed the program design. Linked data also provides the basis for a stratification tool to measure the complexity profile of referrals. Health, housing and justice payable outcomes are measured by comparing relative performance of participants with a matched control group using linked data.
ResultsImplementation of COMPASS has highlighted the value of linked data in service design and measurement of payable outcomes. It has also highlighted the challenges of using linked data in a real-world environment, including the need for thorough documentation and testing of specifications, calculations and processes.
Conclusion / ImplicationsThe use of linked data for COMPASS provides a model for evidence-based service design and tests the use of linked data for robust and sustainable outcome measurement. The lessons from COMPASS are applicable to other social impact bonds and service implementation and outcome measurement more broadly.
Using Multiple Administrative Health Datasets to Identify A Mental Illness Cohort
Authors: Barr Margo , Welberry Heidi , Finch Julie , Blunden Lou Anne 
Published: December 2020

IntroductionIt is well established that mental illness is associated with poor physical health and chronic diseases possibly related to high levels of risk factors such as smoking, physical inactivity, poor diet and alcohol consumption. There is also evidence to suggest that having a mental illness may be related to poorer management of chronic diseases. Health service providers wanted to investigate this in Central and Eastern Sydney (CES).
Objectives and ApproachThe purpose of this study was to scope the feasibility of identifying a mental illness cohort using CES residents from the 45 and Up Study (n=30,049) linked to administrative health datasets. These included Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) provided by Services Australia and hospital admissions provided by the NSW Centre for Health Record Linkage. We then compared differences in the identified cohort size, characteristics and the 8-year mortality rates.
ResultsUsing only hospitalisation data, 6% of the CES cohort were identified as having a mental illness, compared to 17% using MBS data only, 26% PBS data only, 35%using both the MBS and PBS data and 36% using all of the data sources. Crude mortality was 58% in those identified in the hospitalisation data, 27% based on the PBS data,10% using MBS data, 21% using the MBS and PBS data and 23% based on all combined sources.
Conclusion / ImplicationsWe decided that the most appropriate option was to include the MBS, PBS and hospitalisation data to identify the mental illness cohort. This cohort will now be used to examine difference in the management of chronic disease, such as care plans and cycles of care, between those who do and do not have a mental illness.
Guidelines for Data Linkage in Pharmacoepidemiology: Assessing Feasibility, Evaluating Quality and Transparent Reporting
Authors: Pratt Nicole , Chun Danielle , Davis Kourtney , Hammill Brad , Hampp Christian , Mack Christina , Meyer Anne-Marie , Raman Sudha , Rivera Donna , Setoguchi Soko , Stürmer Til , Lund Jennifer 
Published: December 2020

IntroductionIncreasingly in pharmacoepidemiology, linking is required to enrich analytic data to more accurately define study populations, enable adjustment for confounding, and improve capture of health outcomes. When creating such novel linked datasets, researchers should consider their suitability to meet research objectives, assess source data completeness and population coverage, and ensure well-defined data governance standards and protections exist. Additionally, while the RECORD-PE guidelines assist in the reporting of studies using observational health data specific to pharmacoepidemiology, they do not address the unique requirements for transparent evaluation and reporting of the data linkage process.
Objectives and ApproachWe aimed to 1) provide guidance on data linkage appropriateness and feasibility to plan purposeful and sustainable new linkages that advance pharmacoepidemiological research and 2) generate a checklist with specific recommendations to assist researchers in providing clear and transparent assessment of the linkage process. To develop these guidelines, a working group comprised of members of the International Society of harmacoepidemiology was formed. Recommendations were open for comment by Society members and endorsed by the Society.
ResultsGuidance for feasibility assessment was categorized into five domains: (1) research objectives and justification; (2) data quality and completeness; (3) the linkage process; (4) data ownership and governance; and (5) overall value added by linkage. A checklist for evaluation and reporting of data-linkage processes covered five domains including; (1) data sources; (2) linkage variables; (3) linkage methods; (4) linkage results; and (5) linkage evaluation, including validation and verification of the resulting linked data.
Conclusion/ImplicationsOur guidelines for data linkage feasibility assessment and reporting can be used to inform the design of sustainable linked data resources and for transparent communication of linkage processes. Together, these guidelines will help various stakeholders to critically assess the potential for bias in research based on linked data and help generate actionable evidence.
Enhancing Joint Replacement Outcomes Through Registry Linkage with National Health Administrative Data in Australia
Authors: Duszynski Katherine , Graves Stephen E , Pratt Nicole , Inacio Maria , De Steiger Richard , Harris Ian , Ackerman Ilana , Jorm Louisa , Lorimer Michelle , Gulyani Aarti 
Published: December 2020

IntroductionMonitoring of joint replacement (JR) data from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) has reduced revision rates and improved surgical practice. Outcome assessment post-arthroplasty is limited however, to revision (reoperation) surgery and mortality outcomes. The AOANJRR National Data Linkage project seeks to broaden the scope of outcomes investigation in Australia by linking registry and health administrative datasets.
Objectives and ApproachUsing linked registry and administrative data, the project seeks to describe and quantify national/regional trends and variation in major complications (infection, dislocation, arthrofibrosis, chronic pain, venous thromboembolism, cardiac events), malignancy and health service utilisation (readmissions, emergency encounters and inpatient rehabilitation) following hip, knee and shoulder joint replacement surgery. Evidence will be generated on how these outcomes are associated with and vary according to patient, surgical, implant, hospital and pharmacological factors.
As Australia lacks a national identifier, seven linkage agencies are probabilistically linking AOANJRR hip, knee and shoulder replacement data (1999-2017) with 20 datasets. Datasets include government-subsidised health services, procedural and prescription data. Hospital separations and emergency attendance data from Australia’s eight jurisdictions together with national cancer registry and rehabilitation service data are also planned for linkage. Linked data are maintained in a secure remote access computing environment.
ResultsTo date, national Medicare Benefits Schedule, Pharmaceutical Benefits Scheme and the Australian Cancer Database data have been linked with >900,000 AOANJRR patients, representing 607.6 million health service records (1999-2018), 467.7 million prescriptions (2002-2018) and 184,000 cancer records, respectively. Remaining linked data will be available in mid-2020. Some initial summary results across a selected range of studies will be presented.
Conclusion / ImplicationsThis national data-linkage program will identify areas for improvement in joint replacement surgery and modifiable risk factors contributing to poor patient outcomes.
Sex Differences in Infant Mortality: A Population Data Linkage Study of Brazilian Male-Female Twin Pairs
Authors: Calais-Ferreira Lucas , Mendonça Everton , Barreto Marcos E , Dite Gillian M , Hickey Martha , Ferreira Paulo H , Scurrah Katrina J , Hopper John L 
Published: December 2020

IntroductionEpidemiological studies of twin pairs provide researchers with the opportunity to better understand the roles of genetics and the environment on human traits and health conditions. Twin births are also of interest for public health, given they are five times more likely to be of low birth weight and preterm compared to singletons. Male twin newborns are at high risk of mortality, although the causes of such disadvantage are still largely unknown.
Objectives and ApproachWe deterministically linked population birth to death records of twins born in Brazil from 2012–2016, and probabilistically linked twins within pairs through a bespoke algorithm. We studied male-female twin pairs to investigate sex differences in infant mortality stratified by early neonatal (0–6 days), late neonatal (7–27 days) and late infant (28–365 days) deaths. We used conditional logistic regression to estimate odds ratios (OR) for male sex, adjusting for birth weight and matching for familial factors by design.
ResultsOur algorithm successfully matched 101,382 twin pairs, 28,558 were male-female pairs included in the study. Average birthweight was 100g greater for males compared with females. We found that males were at higher risk of infant mortality than their female co-twins even after adjusting for birth weight and familial factors (aOR = 1.42, 95% CI: 1.22–1.66). The aORs for neonatal death increased from those born at less than 28 weeks to those born at 28–31, 32–26 and 37+ weeks: 1.47 (1.02–2.13), 1.73 (1.17–2.57), 1.99 (1.17–3.38), and 3.35 (1.29–8.73), respectively.
Conclusion / ImplicationsMale twins have greater risk of infant and neonatal mortality compared with female co-twins, more so the higher the gestational age. Unmeasured familial and maternal factors may influence the role of birth weight in the association between sex and poor early life outcomes.
The Lumos Pilot Project: Feasibility of Linking General Practice Data to Other Health-Related Data
Authors: Correll Patricia , Power Adrian , Moubarak Samantha , Irvine Katie , Feyer Anne-Marie 
Published: December 2020

IntroductionDelivering integrated care is an aspiration of health systems around the world. General Practices (GPs) operate between other types of health care and are therefore key to effective integrated care. However, information to understand the patient journey across the health system, such as between primary and acute care, is currently impeded in Australia by lack of interoperable clinical software, privacy concerns, and cross-jurisdictional barriers. This presents a challenge to the formation of an evidence base for the design and implementation of integrated health services.
Objectives and ApproachThe Lumos Pilot Project aims to address these challenges. Following ethics approval in February 2016, the pilot was rolled out over five stages to develop a proof of concept for extracting and linking GP data to other health-related data. The objectives were to:I. Build cross-jurisdictional collaborations between primary care and the state health system in New South Wales (NSW), Australia.II. Develop methodologies to address confidentiality requirements.III. Develop technological solutions that will allow for a large scale program.
ResultsData were extracted from 102 GPs, covering nearly 1 million patients, and linked to emergency department, hospital and mortality records. GPs were recruited to participate through collaborations with all NSW Primary Health regions. Privacy preserving record linkage was successfully implemented in the fifth stage of the project. This process encoded patient identifiers prior to extraction from the GP and negated the need for identified information during data linkage. Software was developed and tested across all stages of the project so that once enrolled, GP data extraction and transfer were fully automated.
Conclusion / ImplicationsThe resultant dataset has provided a new picture of patients’ healthcare journeys while preserving patient privacy. The technological solutions allow for a large scale up of GP recruitment in future.
Natural History of Cardiac and Peripheral Vascular Death In ESKD: An Australian And New Zealand Data Linkage Study
Authors: Khou Victor , De La Mat VictorNicole L , Kelly Patrick J , Webster Angela C 
Published: December 2020

IntroductionCardiovascular disease is a leading cause of death in patients with end-stage kidney disease (ESKD). However, ascertaining the impact of cardiovascular deaths has not been well characterised over long periods of follow-up and across different treatment states. Further insights into the lifetime risk of cardiovascular death are required to better inform clinical practice and economic planning.
Objectives and ApproachWe performed a population-based cohort study on incident patients receiving ESKD treatment from the Australian and New Zealand Dialysis and Transplant registry (ANZDATA). Cardiac/vascular deaths were determined from ICD-10-AM codes listed in the underlying cause of death obtained via data linkage with the Australian National Death Index and New Zealand Mortality Collection database. We estimated mortality rates from cardiac/vascular death across time from ESKD treatment, and calculated probability of death and transplant status over time using multistate models.
ResultsAcross 60,823 incident ESKD patients and 381,874 person-years of follow-up, 22% (7,551) of deaths were from cardiac/vascular disease. At 15 years from treatment, 15.6% of patients had died from cardiac/vascular causes, most of whom never received a transplant (13.6% vs 2.0% of cohort). Within the first year of dialysis, cardiac/vascular mortality was highest in the second month, at 3,632/100,000pys. Improvements in cardiac/vascular mortality with calendar year were only seen after 9 months of dialysis. Transplant recipients had consistently lower cardiac/vascular mortality rates (598/100,000 pys) compared to dialysis patients. However, comorbid cardiovascular disease was a risk factor for graft failure and death in transplant recipients (HR:1.52, 95% CI:1.42-1.62).
Conclusion / ImplicationsDespite improvements in cardiac/vascular outcomes over time, cardiovascular death remains common in ESKD, particularly in the first few months of treatment. A greater focus on secondary prevention in earlier stages of chronic kidney disease may improve outcomes in new ESKD patients.
Linking Registry Data with Australian Medicare And Medication Dispensing Claims Data: Feasibility, Benefits and Limitations
Authors: Andrew Nadine E , Cadilhac Dominique A , Sundararajan Vijaya , Thrift Amanda G , Anderson Phil , Lannin Natasha A , Shehata Sam , Kilkenny Monique F 
Published: December 2020

IntroductionRecent advances in Australia mean that it is possible to link national clinical registries with government held administrative datasets. However, formal evaluations of such activities and the potential impact for research are lacking.
Objectives and ApproachWe aimed to assess the feasibility and accuracy of linking registrants from the Australian Stroke Clinical Registry (AuSCR) with the Medicare enrolment file. Following data custodian and ethics approvals, personal linkage identifiers were submitted to the Australian Institute of Health and Welfare (AIHW). De-identified data from AuSCR and the AIHW were submitted into the Secure Unified Research Environment and merged using project specific person-based IDs. We calculated the proportion of patients linked with the Medicare enrolment file that were present in the associated Medicare and medication dispensing datasets and the proportion with claims after their date of death. Logistic regression was used to identify factors associated with a non-merged patient.
Results17,980 AuSCR registrants (January 2010-July 2014) were submitted for linkage (median age 76 years; 46% female; 67% ischaemic stroke; 16% TIA). Of these, 93% were merged with Medicare (N=16,648) and 95% with subsidised medication dispensing claims data (N=17,079). In those who died, 127 (0.8%) had one or more Medicare claim and 411 (2.4%) had one or more medications dispensed after their death date. Asian born registrants were less likely to be merged with Medicare (adjusted Odds Ratio [aOR]: 0.54; 95% Confidence Interval [CI]: 0.40, 0.72) than Australian born registrants. Those aged ≥85 years were less likely to be merged with Medicare data than those aged <65 years (aOR 0.24; 95% CI: 0.19, 0.29) but were more likely to be merged with dispensing data (aOR: 2.22 (95% CI: 1.73, 2.84).
Conclusion/ImplicationsLinkage between a national clinical quality registry and the Medicare spine is feasible. These linkages will provide novel insights into post-stroke care.
Secure Automated File Exchange (SAFE) – Enabling More Efficient Transfers of Sensitive Data
Authors: Schneider Matthias 
Published: December 2020

IntroductionWith the explosion in data being collected and made available for research, linkage units receive an increasing amount of data. At the same time, researchers also expect access to more current data. This increase in the influx of data can create resource constraints for linkage units, which need to mobilise more staff time for data processing, as well as data custodians, who are required to provide data updates more frequently.
Objectives and ApproachSA NT DataLink has designed the Secure Automated File Exchange (SAFE), in collaboration with the University of South Australia. SAFE provides a framework to safely transfer encrypted data from custodians into SA NT DataLink’s systems. A given custodian uses one private key to send personally identifying data via Secure File Transfer Protocol (SFTP). This data flows via the university’s IT infrastructure, where it is checked for encryption, directly into a Demilitarised Zone (DMZ) within SA NT DataLink’s Data Linkage Unit’s (DLU) highly protected environment. The same custodian then uses a separate private key to provide the corresponding encrypted anonymised content data, again via SFTP. Given the less sensitive nature of this data type, it is deposited on secure university on-site storage, from where it is manually transferred by Data Integration Unit (DIU) staff to SA NT DataLink’s Custodian Controlled Data repository (CCDR).
ResultsSA NT DataLink considers implementing SAFE with one data provider as a trial project. After successful testing, a rollout to other data custodians is possible. In parallel, alternative technical solutions for automated data transfers are being evaluated.
Conclusion / ImplicationsAutomated data transfer solutions will reduce effort by data custodians to send data and for linkage units to receive and process data updates. Moreover, by limiting manual intervention, they will limit vulnerability to data privacy breaches and the risk of introducing errors into the data. However, data workflow automation is dependent on data provider requirements and the availability of resources to process received data.
Do You Need My Health Data - Just Ask: Using Blockchain Technology for Collaborative Patient-Centric Health Care
Authors: Rupasinghe Tharuka 
Published: December 2020

IntroductionAcquiring healthcare data for secondary use should benefit from a transparent and highly auditable process when handling patient consent. In the current healthcare infrastructure, the healthcare providers hold the stewardship for the patient data, which includes an authority to determine the data access often without involving the respective patients. The current approach to obtaining patient consent as a one-off task is inadequate to facilitate continuous communication among the patients, healthcare providers and data requestors to manage more personalised data access.
Objectives and ApproachThis study explores a novel dynamic patient consent mechanism based on blockchain technology and smart contracts. The aim is to enable patients to actively participate in this process by dynamically managing data consent preferences. Furthermore, it also explores the feasibility of implementing a transparent and auditable collaborative access control infrastructure for clinical data analytics, where all the stakeholders can be involved in determining access to health data.
ResultsThe solution has been designed leveraging the blockchain and smart contracts to ensure auditability and transparency of the consent management process where a set of smart contracts controls access to data with minimum human-interferences. In addition, multiple design goals such as data security, privacy, interoperability, legal compliance and accessibility have also been considered when designing a prototype solution. An empirical evaluation is planned to obtain feedback from stakeholders involved in health data sharing for secondary use.
Conclusion / ImplicationsThe contribution of this research study is to augment the existing data acquisition procedures for clinical data analytics using blockchain and smart contracts. The proposed novel approach aims to empower and enable patients to play a more active role in controlling access to their data for secondary use. The study will also illuminate the opportunities and challenges which blockchain-based technologies can address related to creating collaborative patient-centric healthcare.