bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Ontario Data Safe Haven: Bringing High Performance Computing to Population-wide Data Assets
Authors: Victor J. Charles , Paprica P. Alison , Brudno Michael , Virtanen Carl , Wodchis Walter , Goldenberg Anna , Schull Michael 
Published: September 2018

IntroductionCanadian provincial health systems have a data advantage – longitudinal population-wide data for publicly funded health services, in many cases going back 20 years or more. With the addition of high performance computing (HPC), these data can serve as the foundation for leading-edge research using machine learning and artificial intelligence.
Objectives and ApproachThe Institute for Clinical Evaluative Sciences (ICES) and HPC4Health are creating the Ontario Data Safe Haven (ODSH) – a secure HPC cloud located within the HPC4Health physical environment at the Hospital for Sick Children in Toronto. The ODSH will allow research teams to post, access and analyze individual datasets over which they have authority, and enable linkage to Ontario administrative and other data. To start, the ODSH is focused on creating a private cloud meeting ICES’ legislated privacy and security requirements to support HPC-intensive analyses of ICES data. The first ODSH projects are partnerships between ICES scientists and machine learning.
ResultsAs of March 2018, the technological build of the ODSH was tested and completed and the privacy and security policy framework and documentation were completed. We will present the structure of the ODSH, including the architectural choices made when designing the environment, and planned functionality in the future. We will describe the experience to-date for the very first analysis done using the ODSH: the automatic mining of clinical terminology in primary care electronic medical records using deep neural networks. We will also present the plans for a high-cost user Risk Dashboard program of research, co-designed by ICES scientists and health faculty from the Vector Institute for artificial intelligence, that will make use of the ODSH beginning May 2018.
Conclusion/ImplicationsThrough a partnership of ICES, HPC4Health and the Vector Institute, a secure private cloud ODSH has been created as is starting to be used in leading edge machine learning research studies that make use of Ontario’s population-wide data assets.
Using Biomedical Text as Data and Representation Learning for Identifying Patients with an Osteoarthritis Phenotype in the Electronic Medical Record
Authors: Meaney Christopher , Widdifield Jessica , Jaakkimainen Liisa , Escobar Michael , Rudzicz Frank , Tu Karen 
Published: September 2018

IntroductionElectronic medical records (EMRs) are increasingly used in health services research. Accurate/efficient identification of a target population with a specific disease phenotype is a necessary precursor to studying the health of these individuals.
Objectives and ApproachWe explored the use of biomedical text as inputs to supervised phenotype identification algorithms. We employed a two-stage classification approach to map the discrete, sparse high-dimensional biomedical text data to a dense low dimensional vector space using methods from unsupervised machine learning. Next we used these learned vectors as inputs to supervised machine learning algorithms for phenotype identification.
We were able to demonstrate the applicability of the approach to identifying patients with an osteoarthritis (OA) phenotype using primary care data from the Electronic Medical Record Administrative data Linked Database (EMRALD) held at ICES.
ResultsEMRALD contains approximately 20Gb of biomedical text data on approximately 500,000 patients. The unit of analysis for this study is the patient. We were interested in identifying OA patients using solely text data as features.
Labelled outcome information wass available from a random sample of 7,500 patients. We divided patients into training (N=6000), validation (N=750) and test (N=750) cohorts. We learned low dimensional representations of the input text data on the entire EMRALD corpus (N=500,000). We used learned numeric vectors as inputs to supervised machine learning models for OA classification (N=6,000 training set patients).
We compared models in terms of accuracy, sensitivity, specificity, PPV and NPV. The best learned models achieved approximately 90\% sensitivity and 80\% specificity. Classification accuracy varied as a function of learned inputs.
Conclusion/ImplicationsWe developed an approach to phenotype identification using solely biomedical text as an input. Preliminary results suggest our two-stage ML approach has improved operating characteristics compared to existing clinically derived decision rules for OA classification. Future work will explore the generalizability of this methodology to other disease phenotypes.
Linking population-based survey and cancer registry data to examine the association between behaviours consistent with cancer prevention recommendations and cancer risk in Ontario
Authors: Young Stephanie , Wang Ying , Haque Mohammad , Klein-Geltink Julie , Candido Elisa , Boucher Beatrice , Harris Shelley , Peter Alice , Cotterchio Michelle 
Published: September 2018

IntroductionCertain subject behaviours and characteristics increase the risk of some cancer types (e.g., obesity, alcohol intake) while others reduce cancer risk (e.g., physical activity). In 2007, the World Cancer Research Fund (WCRF) and American Institute for Cancer Research (AICR) published recommendations to reduce cancer risk related to these behaviours.
Objectives and ApproachThe objective is to examine the association between self-reported behaviour consistent with WCRF/AICR recommendations for body fatness, physical activity, vegetable/fruit consumption, and alcohol intake and the risk of all cancers combined and specific cancer types. The study cohort, comprised of the Canadian Community Health Survey (CCHS) Ontario sample, will be linked with health administrative databases, including the Ontario Cancer Registry to determine cancer outcomes. Individuals will be assessed for behaviours consistent with WCRF/AICR recommendations based on their responses to CCHS questions and the association of these behaviours with cancer risk will be explored using multivariable Cox proportional hazard regression models.
ResultsTo detect a log hazard ratio of 1.10 (where a=0.05, power=0.80, proportion of the sample assigned to the exposure group=0.25 and R2=0.20), a sample size of 4,538 is required. Based on the number of records in the CCHS data frame (159,474) and an assumption that the CCHS sample experiences cancer incidence at a similar rate to the rest of the Ontario population, we expect to have 5,000 cancer cases for these analyses. Upon completion of the analysis, we will report hazard ratios that estimate the difference in cancer risk between individuals reporting behaviour consistent with the WCRF/AICR recommendations and those reporting behaviour not consistent with the recommendations.
Conclusion/ImplicationsWCRF/AICR recommendations were developed as the basis for primary cancer prevention, both for individuals and population-wide policies and programs. The current study will quantify the difference in overall cancer risk between individuals who do and do not adhere to selected WCRF/AICR recommendations for the first time in a Canadian population.
Diagnosis incidence of autism spectrum disorders is underestimated in Australian children, and there are inequalities in access to diagnosis and treatment services: a data linkage study of health service usage
Authors: Mallitt Kylie-Ann , Jorm Louisa R 
Published: September 2018

IntroductionThe prevalence and diagnosis incidence of autism spectrum disorders (ASD) are difficult to determine. Estimates of ASD burden in Australia are produced from sample surveys of disability, and government records of welfare disability payments. While disability does affect many people with ASD, ASD itself is not a disability.
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/ImplicationsThe diagnosis incidence of ASD in Australian children is higher than previously estimated. The prevalence of ASD is therefore also underestimated. Multidisciplinary ASD assessment and treatment services are underutilised, likely due to out-of-pocket co-payments reducing affordability. These findings have significant implications for government health service planning for ASD.
Maternal vulnerability and birth interval in England: a retrospective analysis of hospital episode statistics
Authors: Wijlaars Linda , Blackburn Ruth , Gilbert Ruth 
Published: September 2018

IntroductionSeveral studies have shown that a short birth interval is associated with increased risks of adverse perinatal outcomes (e.g.: preterm birth, stillbirth) and neonatal and maternal death. It is unclear whether this relationship is likely to be causal or to reflect confounding due to psychosocial vulnerability.
Objectives and ApproachTo determine the association between birth interval for mothers with and without indicators of vulnerability.
Using Hospital Episode Statistics for England, we included mothers with a live birth in 2011. Vulnerability indicators were codes for mental health problems, adversity related injury (ARI), material or social disadvantage (M/SD) recorded in admissions in the previous five years, or age
Time (in days) to next delivery within five years was analysed using Cox proportional hazard regression models and adjusted for maternal age, parity, multiple and preterm birth.
ResultsOf 636,876 women delivering in 2011, 93,266 (14.6\%) had indicators of vulnerability: 63,421 (10.0%) for mental health problems, 8,229 (1.2%) for an ARI, 21,616 (3.4%) for M/SD, and 32,622 (5.1%) were teenage mothers.
Over a third of mothers (242,401; 38.1%) had another live birth within five years. Median time to next delivery was 957 days (5-95% centiles: 433-1694), and was longest for teenage mothers (979 days, 391-1726), and shortest for women with ARIs (904 days, 391-1697).
Vulnerable mothers were more likely than women with no vulnerability indicators to have another live birth (HRadj: 1.13, 95%CI: 1.12-1.14): risks were independently increased for teenage mothers (HRadj: 1.53, 1.50-1.55), women with an ARI (HRadj: 1.15, 1.11-1.19) or mental health problems (HRadj: 1.05, 1.03-1.06).
Conclusion/ImplicationsVulnerable mothers had a shorter subsequent birth interval and an increased risk of birth in the next five years. Maternity care may be an appropriate time to target interventions to vulnerable families to enable choice about timing of subsequent pregnancy.
Stroke prevention: Evaluation of the use of anticoagulation in the population with known Atrial Fibrillation
Authors: Torabi Fatemeh , Akbari Ashley , Harris Dan , Halcox Julian , Haxton Andrew , Kalra Navjot , Lyons Ronan 
Published: September 2018

IntroductionAtrial Fibrillation (AF) is a common abnormal heart rhythm that is associated with five times the risk of stroke and twice the risk of death. However, this risk can be reduced by approximately two thirds through the appropriate use of anticoagulation (AC).
Objectives and ApproachReducing the incidence of stroke through effective management of AF is a priority recognised by Abertawe Bro Morgannwg University Health Board (ABMUHB), in Wales, UK. An understanding of how closely services follow appropriate clinical guidelines for stroke prevention allows identification of opportunities to improve stroke outcomes and service efficiency. This study was commissioned to describe the nature of antithrombotic drug prescribing in ABMUHB patients with AF according to thromboembolic and bleeding risk status, and the numbers of non-anticoagulated patients with AF presenting with stroke. This study was completed using linked data held in the Secure Anonymised Information Linkage (SAIL) databank.
ResultsAF was identified in 12,778 ABMUHB patients (approximately 3% of the population), with 97% providing linked primary care records for our required period of follow-up. Of the AF patients with linked prescribing data, 60.5% were prescribed anticoagulants, 15.8% were prescribed antiplatelet agents and 23.7% received no antithrombotic medication. Notably, the thromboembolic risk and bleeding risk profiles (characterised by modified CHA2DS2-VASc and HAS-BLED scores respectively, implemented within the SAIL databank data) were remarkably similar in those receiving and those not receiving AC. 965 patients were admitted to ABMUHB hospitals with a stroke during 2015. AF was previously diagnosed in 18% of these patients, of whom just over half (50.3%) were not being prescribed AC during the 3 months prior to their stroke.
Conclusion/ImplicationsThis study demonstrates under prescribing of AC in patients with AF, which is not explained by stroke risk or bleeding scores. ABMUHB colleagues are developing strategies for service improvements, with plans for further sequential analysis to evaluate the effectiveness of implemented measures for outcome monitoring and reporting purposes.
Population-based analysis of the effect of a comprehensive, systematic change in an emergency medical service resource allocation plan on 24 hour mortality
Authors: Tallon John , Wei Julie , Zheng Lu , Djurdjev Ognjenka , Papadopoulos George , Slemko Randy , Dick William 
Published: September 2018

IntroductionThe British Columbia Emergency Health Services developed a new resource allocation plan (RAP) using an evidenced informed methodology and with further clinical input from EMS physicians, paramedics and allied EMS providers. Population-based analysis was used to determine the effect by comparing 24-hour mortality before and after province-wide implementation of the revised RAP.
Objectives and ApproachThe primary outcome, 24-hour mortality, was obtained through linked provincial health administrative data. All adult cases with evaluable outcome data were included in the analysis. A pre and post methodology was used to evaluate the effect of post-RAP revision (post-RAP-revision) on 24-hour mortality compared to pre-RAP revision (pre-RAP-revision). Multivariable logistic regression was used to adjust for variations in other significant factors associated with 24-hour mortality. The interrupted time series (ITS) estimated any immediate changes in the level or trend of outcome after the start of the revised RAP implementation (fall of 2013), while simultaneously controlling for pre-existing trends.
ResultsThe linked data set included some 1500–2000 children per school census classified as having a CP, representing a prevalence of some 0.3%. Provisionally, results show: prevalence of CP is higher amongst children living in relatively deprived areas; around 60% of CP children have a statement of SEN; the SEN type most commonly recorded for CP children with SEN is ‘Physical and medical difficulties’ and relatively high proportions have profound, multiple or severe learning difficulties; around 30% of CP children are educated in special schools; CP children in main stream (primary, middle and secondary) schools tended to miss more school sessions (~50% more) than other children and lower percentages achieved the expected levels at key stages 2 and 3 and the Level 2 GCSE threshold.
Conclusion/ImplicationsOur results demonstrate that a comprehensive, evidence informed reconstruction of a provincial EMS RAP is feasible. Despite considerable change in crew level response and resource allocation, there was significant decrease in 24 hour mortality in a large pan-provincial population based patient cohort.
Using linked administrative, clinical and primary data to explore the impact of and factors associated with non-adherence to in-hospital medication changes in 30-days post hospital discharge
Authors: Weir Daniala , Motulsky Aude , Lee Todd , Abrahamowicz Michal , Morgan Steve , Buckeridge David , Tamblyn Robyn 
Published: September 2018

IntroductionIdentifying strategies to prevent hospital readmissions remains elusive since the reasons for returning to hospital can include a number of interlinked patient, health provider and system level factors. The impact of patient medications are of significant interest since a large proportion of re-admissions are related to adverse drug events.
Objectives and ApproachThe objective was to determine which factors are associated with non-adherence to in-hospital medications and the impact of non-adherence on re-hospitalization, emergency department visits and death in the 30-days post discharge for patients admitted at two tertiary care academic hospitals in Montreal, Quebec between October 2014 and May 2016. Non-adherence to in-hospital changes was measured by comparing patient discharge prescriptions (patient chart) to medications filled in community 30-days post-discharge (dispensing data) and included i) community medications stopped in-hospital and filled post-discharge, ii) community medications modified in-hospital but not filled at the modified daily-dose, and iii) new medications not filled post-discharge.
ResultsAmong 2,895 included patients, mean age was 70 (SD 15) and 58% were males. A median of 4 in-hospital medication changes were made (IQR:3-6) and 54% of patients were non-adherent to at least one change. Multivariable Poisson models suggested that the most important factor associated with the number of new medications not filled post discharge was out of pocket cost; for each additional $10 increase in costs there was a 20% increase in the number of new medications not filled. Multivariable time-varying Cox models suggested that in patients who filled medications post-discharge, selective non-adherence to new and discontinued medications reduced the risk adverse health outcomes in 30-days, while not filling any medications post discharge more than doubled the risk of an adverse event in 30-days.
Conclusion/ImplicationsNot only did the majority of patients not follow all medication changes that were made during hospitalization, the extent to which this occurred significantly impacted the risk of hospital re-admissions and ED visits. Policy and patient level interventions should be developed specifically targeting barriers for adherence to medication changes.
Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living With Frailty?
Authors: Wong Sabrina , Katz Alan , Williamson Tyler , Peterson Sandra , Taylor Carole , McGrail Kim 
Published: September 2018

IntroductionFrailty is a combination of factors that increase vulnerability to functional decline, dependence and/or death. Frailty cannot easily be defined by comorbidities or medical treatment alone. Accurate detection of frailty in practice and at a population level is needed. This may be achieved using a combination of data sources.
Objectives and ApproachWe construct algorithms that can identify frailty using electronic medical record (EMR) and administrative data. We linked EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) nodes and the administrative (e.g. billings, hospitalizations) from Population Data BC and the Manitoba Health Policy Centre. Frailty was defined as individuals 65+ who were receiving home services, had specific chronic conditions, received specific diagnoses, and/or had specific lab or other clinical indicators. We describe sociodemographic characteristics, risk factors, prescribed medications, use and costs of healthcare for those identified as frail.
ResultsPeople were identified as frail in 2014 and all analysis was completed with 2015 data. Among those who were > 65 years, who had a record in both EMR and administrative data, 5\%-8\% (n=191 of 3,553, BC; n=2,396 of 29,382, MB) were identified as frail. There was a higher likelihood of being frail with increasing age and being a woman. In BC, those identified as frail have higher contacts with primary care (n=20 vs. n=10) and more days in hospital (n=7.4 vs. n=2.0) compared to those who are not frail. Twenty two percent of those identified as frail in 2014 died in 2015, compared to a mortality rate of 2\% among those who are not frail.
Conclusion/ImplicationsIdentifying and reporting on those who are frail in primary care as well as in communities could enable targeted communications with patients and families and community based resources in order to improve patient care, patients’ and caregivers’ quality of life and better use of the healthcare system.