bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ligo: An Open Source Application for the Management and Execution of Administrative Data Linkage
Authors: Lawrance Greg , Hernandez Raphael Parra , Mamakani Khalegh , Khan Suraiya , Hills Brent , Yip Harold , Marrville Caelan 
Published: September 2018

IntroductionLigo is an open source application that provides a framework for managing and executing administrative data linking projects. Ligo provides an easy-to-use web interface that lets analysts select among data linking methods including deterministic, probabilistic and machine learning approaches and use these in a documented, repeatable, tested, step-by-step process.
Objectives and ApproachThe linking application has two primary functions: identifying common entities in datasets [de-duplication] and identifying common entities between datasets [linking]. The application is being built from the ground up in a partnership between the Province of British Columbia’s Data Innovation (DI) Program and Population Data BC, and with input from data scientists. The simple web interface allows analysts to streamline the processing of multiple datasets in a straight-forward and reproducible manner.
ResultsBuilt in Python and implemented as a desktop-capable and cloud-deployable containerized application, Ligo includes many of the latest data-linking comparison algorithms with a plugin architecture that supports the simple addition of new formulae. Currently, deterministic approaches to linking have been implemented and probabilistic methods are in alpha testing. A fully functional alpha, including deterministic and probabilistic methods is expected to be ready in September, with a machine learning extension expected soon after.
Conclusion/ImplicationsLigo has been designed with enterprise users in mind. The application is intended to make the processes of data de-duplication and linking simple, fast and reproducible. By making the application open source, we encourage feedback and collaboration from across the population research and data science community.
Policy advocacy to enable administrative data linking: building a civil society coalition
Authors: Lenczner Michael , McPhedran Waitzer Jonathan 
Published: September 2018

IntroductionAdministrative data linking holds tremendous promise for improving understanding of social problems, enhancing service delivery, and revolutionizing impact evaluation in Canada. Embracing this opportunity at scale requires navigation of significant technical and policy challenges. The greatest challenge, though, may be a lack of political will.
Objectives and ApproachThe nonprofit sector is uniquely positioned to advocate for a strong political commitment to linked administrative data. As a sector, it could directly benefit from that data for impact evaluation and for advocacy. It is also closest to the people who are most likely to be negatively impacted by the resulting surveillance and stigmatization.
We are building a network of social service organizations, foundations, and advocacy groups to explore the possibility of creating a shared policy agenda. We’ve developed a coalition model that engages these unequally resourced stakeholders on equal footing - with the goal of enabling fully-informed and equitable participation.
ResultsThis coalition is working to develop a set of conditions for increased administrative data linking that reflect the shared interests of funders, service providers, advocacy groups, and beneficiary communities. We are also researching the legislative and policy changes required to enable that desired outcome.
In developing this agenda, and bringing it to government, we hope to provide the social license (and public pressure) required to create an enabling policy environment for increased data linking in Canada.
Beyond developing a shared agenda, this initiative also aims to deliver long-term outcomes involving increased data policy literacy among Canadian nonprofits. This coalition represents collaborative infrastructure to enable ongoing, coordinated input from the nonprofit sector on key questions of data governance and policy.
Conclusion/ImplicationsThis equity-focused, multi-stakeholder coalition approach to digital policy development represents a significant innovation in public engagement. We’re excited to share our process and key learnings with conference participants with hopes of receiving expert feedback while inviting key allies to engage in this emerging initiative.
Predicting health care utilization using CIHI's Population Grouping Methodology
Authors: Rosehart Yvonne 
Published: September 2018

IntroductionCIHI’s Population Grouping Methodology uses data from multiple sectors to create clinical profiles and to predict the entire population’s current and future morbidity burden and healthcare utilization. Outputs from the grouper can be applied to healthcare decision making and planning processes.
Objectives and ApproachThe population grouping methodology starts with everyone who is eligible for healthcare, including those who haven’t interacted with the healthcare system, providing a true picture of the entire population. The grouper uses diagnosis information over a 2-year period to create health profiles and predict individuals’ future morbidity and expected use of primary care, emergency department and long-term care services.
Predictive models were developed using age, sex, health conditions and the most influential health condition interactions as the predictors. These models produce predictive indicators for the concurrent period as well as one year into the future.
ResultsThe power of the model lies in the user’s ability to aggregate the data by population segments and compare healthcare resource utilization by different geographic regions, health sectors and health status.
The presentation will focus on how CIHI’s population grouping methodology helps client’s monitor population health and conduct disease surveillance. It assists clients with population segmentation, health profiling, predicting health care utilization patterns and explaining variation in health care resource use. It can be used for risk adjustment of populations for inter-jurisdictional analysis, for capacity planning and it can also be used as a component in funding models.
Conclusion/ImplicationsCIHI’s population grouping methodology is a useful tool for profiling and predicting healthcare utilization, with key applications for health policy makers, planners and funders. The presentation will focus on how stakeholders can apply the outputs to aid in their decision-making and planning processes.
Adherence to Follow-up Care Guidelines for Breast Cancer Survivors in four Canadian provinces: a CanIMPACT study
Authors: McBride Mary , Groome Patti , Jiang Li , Whitehead Marlo , Li Dongdong , Decker Kathleen , Kendell Cynthia , Winget Marcy , Turner Donna , Urquhart Robin , Grunfeld Eva 
Published: September 2018

IntroductionBreast cancer survivors are at risk for late and ongoing problems including cancer recurrence and late effects of treatment. Lack of access to quality follow-up care may affect later mortality, morbidity, and quality of life. This study examines variation in utilization of guideline-based follow-up care separately for four Canadian provinces.
Objectives and ApproachFor our retrospective population-based cohort study of breast cancer survivors diagnosed from 2007 to 2010 in British Columbia (BC), 2007-2011 in Manitoba (MB), 2007-2010 in Ontario (ON), and 2007-2012 in Nova Scotia (NS), we linked provincial cancer registries, clinical and health administrative databases, and followed cases alive at 30 months post-diagnosis to five years from diagnosis. For each province, we calculated percent adherence, overuse, and underuse of recommended follow-up care, including surveillance for recurrent and new cancer, surveillance for late effects, and general preventive care. We also examined variation among provinces and over time.
ResultsSurvivor numbers were 23,700 (ON), 9493 (BC), 2688 (MB), and 2735 (NS). Annual oncologist visit guideline compliance varied provincially (e.g. Year 2 ON=32.7%, BC=15.0%). For most provinces and follow-up years, the majority of survivors had fewer oncologist visits than recommended. However, survivors had additional annual breast cancer-related visits to a primary care provider. Surveillance breast imaging guideline compliance was high (e.g. Year 2, ON=81.1%, MB=72.0%, NS=52.8%, BC =49.7%), with rates declining in ON and MB (to approximately 64%), but increasing in NS and BC (to approximately 58%) by Year 5. Overuse of breast imaging was identified in NS (9.1%-20.7% overuse in follow-up years 2-5). As per the guideline, 72.9%-79.7% (Years 2-5) of BC survivors had no imaging for metastastic disease, highest among all provinces.
Conclusion/ImplicationsProvincial and temporal variations in guideline adherence were identified. Patterns differed by guideline, and both overuse and underuse were observed. These results point to opportunities to improve survivor care and efficiencies in care delivery. In particular, regular care with a primary care physician has been shown to improve follow-up care.
Maternal assault admissions are associated with increased risk of child protection involvement
Authors: Orr Carol , Preen David B , Glauert Rebecca , O'Donnell Melissa , Fisher Colleen , Sims Scott 
Published: September 2018

IntroductionAlmost half of Australian women assaulted by their partner have children in their care. Evidence suggests a link between children’s exposure to violence, and subsequent maltreatment allegations. However, this evidence is limited by small sample sizes. Linked administrative data present an opportunity to further investigate this sensitive topic.
Objectives and ApproachThis study investigated the relationship between assaults on mothers and subsequent child maltreatment allegations. The sample included all live births in Western Australia from 1990 to 2009 (N=524,534) and their parents, with follow up to 2013. Linked administrative data on child maltreatment allegations and mothers’ assault-related hospital admissions were obtained. Multivariate Cox regression estimated the risk of maltreatment allegation following maternal assault admission. Adjusted and unadjusted hazard ratios (HR) and 95\% confidence intervals (CI) were calculated for the risk of maltreatment allegation, and time (in months) between assault admission and the first maltreatment allegation.
ResultsOne in five children whose mother had an assault admission had a subsequent maltreatment allegation, increasing to more than one in three children when restricted to assault admissions in the prenatal period. More than half of the children who had a maltreatment allegation after their mother was admitted for assault were Aboriginal. After adjusting for covariates, children whose mother had an assault admission had two-fold increased risk of having a maltreatment allegation. The risk of maltreatment allegation was greatest in young children, 5.5-year-old (SD=4.6), when restricted to maternal assault admissions in the prenatal period the children were younger at 4-year-old (SD=4.1). The time from maternal assault admission to maltreatment allegation was around 12 months longer for Aboriginal children than for non-Aboriginal children.
Conclusion/ImplicationsChildren of mothers who have been assaulted are at higher risk of child maltreatment allegation. Targeted early intervention is required for families with young children, and pregnant women experiencing violence. The time to maltreatment allegation for Aboriginal children warrants community developed culturally-safe partnerships between Aboriginal communities and government services.
What is an admission? A standardised approach to classifying inpatient episode data from multiple jurisdictions
Authors: Rees Sarah , Akbari Ashley , Collins Huw , Marchant Amanda , Thayer Daniel , Wang Ting , Wood Sophie , John Ann 
Published: September 2018

IntroductionInpatient datasets in the UK are primarily organised by episodes (periods of care under an individual consultant), while researchers often want to measure admissions (periods of stay in hospital). We developed a standardized method for identifying admissions in inpatient data, while accounting for differences between the four UK nations.
Objectives and ApproachAll UK inpatient datasets include date variables, permitting chronological sequencing of episodes. They include flags describing whether an episode is a transfer of care, although structures and definitions differ. Data quality is variable leading to concurrent and overlapping episodes, duplication and “orphan” or “childless” transfer episodes, where no originating or destination episode can be identified.
Our objective was to define a method for classifying individual episodes into a continuous period of stay in hospital, which would be consistently applicable to the analysis of inpatient datasets of all four UK nations, while prioritising clinical meaningfulness. Three permutations were considered.
ResultsFor each permutation, episodes for an individual were linked when they related to the same individual and met the following criteria:

Zero or one day gap between episode end and subsequent episode start
Evidence of transfer according to admission method or discharge destination variables
Episode overlapping or completely nested within another episode

Permutations:

a and b
a only
a and c

Permutation three was adopted, as it was felt to be the most clinically meaningful approach, was not dependent on accurate recording of transfers and captured nested or overlapping episodes, which may occur for example when a patient is in a long-stay psychiatric or elderly care ward but requires care from a different specialty. Importantly it permitted consistent analysis of episodes across all UK nations.
Conclusion/ImplicationsThe output of this work provides a useful guide for the classification of inpatient episodes into more clinically meaningful periods of care, and is applicable to the inpatient datasets of all four UK nations. It describes important issues to consider when classifying episodes of care, particularly relating to data quality.
Evaluating the implementation of the Inverse Care Law Programme in Wales, a population based intervention in primary care
Authors: Akbari Ashley , Song Jiao , Cox Caryn , May Leon , Watkins Williams , Thomas Sarah , Aitken Sarah , King William 
Published: September 2018

IntroductionThe Inverse Care Law (ICL) programme in Wales was setup to identify people in deprived communities at risk of cardiovascular disease (CVD) through a health check; offering lifestyle and medical intervention as appropriate. Evaluation of this programme to tackle health inequalities ensuring services are available is vital.
Objectives and ApproachTo evaluate the uptake and long term outcomes of the programme, using longitudinal evidence-based results, it was necessary to develop an efficient and cost effective approach with a readily available source of data. To achieve this, the Welsh Longitudinal General Practice (WLGP) data held in Secure Anonymised Information Linkage (SAIL) databank was utilised, with programme-specific code deployed within primary care at the point of the health check, which identified the intervention, potential CVD risks, referrals and any follow-up. Lifestyle risk factors could be evaluated such as poor diet, physical inactivity, smoking and high alcohol intake.
ResultsUtilising routine data sources and reproducible SQL (Standard Query Language), we evaluated the programme initialisation between February 2015 and November 2016, and found of 55 General Practices who participated, 31 of 35 in Aneurin Bevan (AB) and 17 of 20 in Cwm Taf (CT) University Health Boards, providing data which allowed identification of the health check and associated outcomes of interest in the routine data, with 3 (2 AB, 1 CT) since delivering. There are ongoing evaluations on the various risk factors longitudinally as well as the overall implementation of the programme itself, with this collaborative approach succeeding in utilising existing powerful data linkage within the SAIL databank to identify our intervention and facilitate long-term follow-up at an individual level using robust information governance mechanisms.
Conclusion/ImplicationsLessons learned and challenges encountered are being fed back as part of our evaluation, with further work assessing the long term population level outcomes and impact of the health check and services provided across these deprivation groups, informing and refining programme delivery of similar work across Wales in the future.
Further Development and Validation of the electronic Frailty Index using the Secure Anonymised Information Linkage Databank
Authors: Hollinghurst Joe , Akbari Ashley , Rodgers Sarah , Clegg Andrew 
Published: September 2018

IntroductionAging populations with increasing frailty have major implications for health services internationally, and evidence-based treatment becomes increasingly important. The development of an electronic Frailty Index (eFI) using routine primary care data facilitates implementation of evidence-based interventions. However, the eFI does not account for time restrictions regarding when information was recorded.
Objectives and ApproachOur aim is to implement and further validate the eFI using the Secure Anonymised Information Linkage (SAIL) databank, introducing refinements based on time restrictions.
Our implementation of the eFI identifies frailty based on 1574 Read codes, which are mapped amongst 36 categories known as deficits. The eFI is based on the internationally established cumulative deficit model, and each deficit contributes equally to the eFI value.
However, although each deficit is equally weighted, only one of them is currently time dependent. We therefore analyse the time at which each deficit is identified, and propose time dependent cut-points based on our findings.
ResultsWe were able to successfully implement the eFI using data from over 400,000 individuals from the Welsh population using data held in the SAIL databank. Our results agree with the baseline characteristics and distributions of frailty found in the original development of the eFI.
We also found that the percentage of individuals identified as frail increased as the number of years of records included was increased. Furthermore, the increase in percentage year by year was almost linear for a number of the deficits. This led to the identification of time bounds for particular deficits, which could help to refine future implementations of the eFI.
Conclusion/ImplicationsOur work validates and refines the eFI, which is a particularly useful resource as it uses existing primary care data to identify frailty, meaning no additional resources are required. Furthermore, our implementation is readily available, meaning that future research related to frailty is easily reproducible and achievable by others.
Characteristics of Opioid-Related Deaths in Ontario, Canada: Leveraging the Drug and Drug/Alcohol Related Death (DDARD) Database
Authors: Singh Samantha , Martins Diana , Khuu Wayne , Tadrous Mina , Gomes Tara , Juurlink David 
Published: September 2018

IntroductionReview of post-mortem toxicological results is the gold standard for identifying whether a death is opioid-related. The Drug and Drug/Alcohol Related Death (DDARD) database contains abstracted information from the Office of the Chief Coroner of Ontario, for all opioid-related deaths that occurred in Ontario, Canada between 1991 and 2016.
Objectives and ApproachThe DDARD, which contains manner of death and drug concentrations from post-mortem toxicology results for opioids-related deaths in Ontario, was linked to the data repository housed at ICES. The objective of this project was to examine demographic characteristics and the type of opioid contributing to opioid-related deaths in FY2015/16. Individuals identified within DDARD who died from an opioid-related cause were linked to demographic, hospitalization and prescription drug databases to report on age, gender, neighbourhood income quintile, past health services utilization for opioid-toxicity, alcohol use disorders (AUD), mental health emergency department (ED) visits, and opioid(s) present at time of death.
ResultsWe identified 737 opioid-related deaths in FY2015/16, the majority of which involved men (n=497; 67.4%), those living in lower socioeconomic status areas (n=395; 53.6%), and those residing in urban regions (n=655; 88.9%). Nearly half (n=325; 44.1%) of opioid-related deaths occurred among those aged 45 to 65 years. We found 9.5% (n=70) of individuals had a previous hospital visit for opioid toxicity, 25.4% (n=187) had previously diagnosed AUD, and 42.5% (n=313) had a previous mental health ED visit. Overall, 250 (33.9%) individuals had an active opioid prescription at time of death with oxycodone (n=92; 36.8%) the most commonly dispensed. Among those who didn’t have an active opioid prescription at time of death (n=484; 66%), fentanyl (n=184; 37.8%) was the most commonly found opioid on post-mortem toxicology.
Conclusion/ImplicationsThis project demonstrates how data obtained through chart abstractions can be used to enhance existing administrative health datasets. Given the concern around the safety of opioids, it is important to examine the characteristics and type of opioid(s) involved at time of opioid-related death in order to develop targeted preventative strategies.
Identifying all persons in Wales with type 1 diabetes mellitus using routinely collected linked data
Authors: Rafferty James , Akbari Ashley , Atkinson Mark D , Bain Stephen , Luzio Stephen , Stephens Jeffery , Owens David , Thomas Rebecca 
Published: September 2018

IntroductionType 1 diabetes mellitus (T1DM) is an autoimmune condition characterised by hyperglycaemia, caused by the destruction of insulin producing β-cells in the pancreas. Previous epidemiological population level studies of T1DM and its complications have typically used recorded T1DM diagnoses to determine diabetes status and define cohorts.
Objectives and ApproachThe objective was to identify all persons with T1DM in Wales from Primary (~70\% population coverage) and Secondary Care (100% coverage) data held in the Secure Anonymised Information Linkage (SAIL) databank. People with a coded T1DM diagnosis (using Read codes in Primary Care data and International Classification of Disease (ICD10) codes in Secondary Care data), plus either insulin prescribed shortly after diagnosis or a hospital admission for diabetic ketoacidosis were identified as having T1DM. A sub-group of this SAIL e-cohort were validated using a register of persons diagnosed with T1DM in Wales under 15 years old (Brecon cohort).
Results18,285 people had a T1DM diagnosis and 10,539 had more T1DM than type 2 diabetes mellitus (T2DM) diagnoses. 6,375 persons were identified with T1DM in Primary Care data using our criteria, with a median diagnosis age of 19.2 years (interquartile range 11.0, 35.5). 47.5\% were diagnosed under 18 years of age. 39.6% of people with a T1DM diagnosis did not have T1DM using our criteria. False positive and negative rates of 4.8% and 4.5% respectively were achieved by comparing persons in the SAIL e-cohort against the Brecon cohort. Clinician estimated false positive and negative rates were 1.4% and 3.9% respectively. The prevalence of T1DM in Wales in 2016 was 0.37% or 11,049 people.
Conclusion/ImplicationsOur criteria for identifying people with T1DM was more reliable than using diagnosis codes alone, allowing for a more accurate, efficient and reproducible means of identifying individuals with T1DM for researchers utilising the SAIL databank, and other national health repositories.