bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrating Ontario Health and Social Services Data to for Research and Policy Development
Authors: Flora Mandeep , Ratnasingham Sujitha , Tefera Aki , Victor J. Charles , Schull Michael
Published: September 2018

IntroductionIntegrating health and social services data is critical to understanding social determinants of health and responding to public expectations for evidence-based policies amidst changing demographics and fiscal constraint.

While academia has long understood the importance of social determinants of health, real and perceived obstacles have slowed their evaluation in Ontario.

Objectives and ApproachThis report describes how the Institute for Clinical Evaluative Sciences (ICES) and the Ministry and Community and Social Services (MCSS) have partnered to bring social services data and health data together to better understand the Ontario population and better support decision makers across various sectors.

We present how ICES and MCSS tackled barriers to data access and cultural challenges to data sharing in the Ontario context, provide an overview of their unique data and research partnership - including the new collaboration research and data access platforms created, highlight research findings to date, and identify key topics of interest moving forward.

ResultsOver the last decade, ICES and MCSS have led the way in Ontario linking health administrative and social services data. An initial single year linkage enabled the success of the Health Care Access Research and Developmental Disabilities project. This cross-sectoral initiative provided a clearer sense of how people with developmental disabilities experienced health care in Ontario.

Building on this work, ICES and MCSS recently expanded their partnership bringing together 15 years of social services and health data through a broader data sharing agreement. This agreement allows greater data access to researchers. In addition, ICES and MCSS have been successful in creating a new integrated research platform that will increase the depth and quality of health and social services research and policy evaluation in Ontario.

Conclusion/ImplicationsA broader collaborative research community will now be able to answer questions of interest, do self-directed integrated data analytics and leverage respective program data expertise to tackle joint research projects. Importantly, MCSS analytics teams will now also have access to linked data on this platform to conduct their own research.
Challenges of identifying children with Cystic Fibrosis to explore inequalities in social and health outcomes, using multiply linked data sources.
Authors: Griffiths Rowena , Akbari Ashley , Schlueter Daniela , Taylor-Robinson David , Tucker David
Published: September 2018

IntroductionCystic fibrosis (CF) is the most common life-limiting inherited disease in white populations, with most patients dying prematurely from respiratory failure. As it is rare, it is important to reduce misclassification. We therefore aimed to assess how well CF children could be identified across routine data in Wales.

Objectives and ApproachData from the Secure Anonymised Information Linkage (SAIL) databank, identified children with CF from 1998 to 2016, within hospital, General Practice (GP), and the Welsh Congenital Abnormality Register (CARIS), which uses new born screening to identify congenital abnormalities, including CF. The International Classification of Diseases (ICD10) E84 was used to identify CF children in both hospital and CARIS data, with READ codes used in the GP data (approximately 80% coverage of Wales). The data was linked using anonymised linking fields and matching rates analysed, as unmatched records in linked data can reduce the utility of the data for epidemiological studies.

Results352 cases were identified in total, with 158 matched across all three datasets over an 18 year period (9-19 cases per year). The Welsh rate from the disease registry is 12–14. Since CF is a severely debilitating condition, a greater match was expected. This prompted further investigation of cases which appeared in only one dataset, as these seemed least likely to be true cases. In the ‘CARIS only’ data, 79% of the admissions, were found coded for respiratory, digestive and health complications not for an E84 CF condition. In the 43 cases in ‘GP only’ data, and the 19 in ‘hospital only’ data the events indicated the possible late presentation of CF by older children or children with very mild CF phenotypes.

Conclusion/ImplicationsCases of rare diseases like CF can be identified in routine data. Linking across multiple datasets, particularly with specialist datasets like CARIS, help identify potentially misclassified cases. This increases confidence in the data. Future work will include the CF registry, permitting checks against a gold standard data resource.
Dementias Platform UK (DPUK) Data Portal - World-leading infrastructure facilitating innovative multi-modal research
Authors: Orton Christopher , Gallacher John , Lyons Ronan , Ford David , Thompson Simon , Mackay Clare , Williams Julie , Bauermeister Sarah , Menzies Georgina , South Matthew , Biddle Justin , Bale Michael , North Laura
Published: September 2018

IntroductionModern team science requires effective sharing of data and skills. The DPUK Data Portal is a collection of tools, datasets and networks that allows for epidemiologists and specialist researchers alike to access, analyse and investigate cohort and different modalities of routine data across UK and international sources.

Objectives and ApproachThe Portal is housed on an instance of UKSeRP (UK Secure eResearch Platform), that allows customisable infrastructure to be used for multi-modal research (thus far live in genetics, imaging and clinical data) for researchers across the world using remote access technology whilst allowing governance to remain with the data provider. A central team at Swansea University is responsible for data curation and processing, and runs an access procedure for researchers to apply to use data from multiple sources to be analysed in a central analysis environment. Other modalities are similarly hosted, with input from partner sites in Cardiff and Oxford.

ResultsDPUK facilitates data access and research on 49 cohorts, 40 UK-based and 9 international. The centralised repository model including remote access and ability to store and make available different modalities of data, from phenotypic data, to genetic and imaging data, has allowed DPUK to begin to support research of varying topics, from those studying cognitive decline and Dementia as a disease, to those maturing analytical models. By providing access to data platforms specialising in genetics, imaging and routine clinical data, as well as to specialists in disease and biology to aid with its understanding, DPUK has realised a large-scale research exercise combining major data modalities on a central platform, and allow access to such rich data across the world under an umbrella of robust governance.

Conclusion/ImplicationsGlobally, cohorts are pooling data, expertise and desire to enrich their own aims in partnership with a federated research community to enable in-depth scrutiny of the biological origins of dementia and the development and evaluation of novel approach to disease prevention and cure.
The Effect of Surgical Site Infection on Cost and Utilization Following Primary Knee Replacement in Nova Scotia 2005-2014
Authors: Lethbridge Lynn , Dunbar Michael
Published: September 2018

IntroductionSurgical site infection (SSI) following joint replacement results in considerable disease burden. Costs include longer wait times for initial joint replacements which, in Nova Scotia (NS), are the longest in the country. Despite the widespread consequences, costs and utilization following knee replacement have not been comprehensively measured in Canada previously.

Objectives and ApproachThe objective was to measure costs and health care utilization of SSI following knee replacement through linked administrative data sources. The study cohort was constructed using procedure codes from hospital discharge data. Diagnostic variables were examined to determine the occurrence of infection within one year of discharge. A non-infected control group matched on age, sex and comorbidities was also selected. Costs and utilization from inpatient, day surgery, clinic and physician claims data were totaled over two years following discharge. Resource weights multiplied by standard cost was used to measure hospital costs while outpatient costs were government approved payments to physicians.

ResultsOver the 2005-2014 period, there were 204 infected cases for an overall 1-year rate of 1.8%. Non-infected controls visited a physician or were admitted to the hospital 21 times in the two year period following surgery compared to infected cases who averaged 40 (p-value

Conclusion/ImplicationsCosts attributable to infection following primary knee replacement are substantial in NS affecting both inpatient and outpatient services. With an increased focus on program evaluation by policy-makers, infection control administrators should include regular monitoring of the direct and indirect costs of SSI on a system-wide basis using linked administrative data.
Breast cancer care in Alberta: a Patients perspective
Authors: Crocker Alysha , Anderes Susan , Verbeek Linda , Chobanuk Janice , Olson David W , Kortbeek John , Elwi Adam , Dean Stafford , Estey Angela , Quan May Lynn
Published: September 2018

IntroductionEach year in Alberta, over 2,300 women are affected by breast cancer. In Alberta, a multi-year Breast Health Initiative is underway to improve breast cancer care; reduce wait times, coordinate care, and enhance patient experience. Patient reported experience measurements are important to inform and advance patient and family-centred care.

Objectives and ApproachThe aim is to assess breast cancer patients’ experiences at two survey points; after surgeon consult and after breast surgery. Patients meeting inclusion criteria; highly suspicious of cancer on imaging result (i.e. BI-RADS 5), referral to Calgary or Edmonton breast program, English speaking, and having an email address are recruited by RN coordinators or nurse navigators. Automated survey invitations from REDCap are used. Seven days after the surgeon consult the first survey is sent and seven days after breast surgery the second survey is sent.

ResultsPatient recruitment began November 27, 2017 and January 2, 2018 for Edmonton and Calgary, respectively. As of February, 2018, 45 patients had been recruited. Of these, the first survey was sent to 34 (i.e. seven days post surgeon consult) and 19 (56%) had completed the survey. All those eligible (18) agreed to participate in the upcoming second survey. Of those, six had provided their surgery date and the second survey which both were completed. Recruitment is ongoing until the conference, at that time there will be sufficient numbers to report findings.

Conclusion/ImplicationsPatient and family-centred care is an element of high-quality healthcare which AHS has identified as a priority. These results will report on the breast cancer patients’ perspectives and generate important information for clinicians and administrators to use for decision making and quality improvement of health services.
The Canadian Urban Environmental Health Research Consortium (CANUE): a national data linkage initiative
Authors: Doiron Dany , Setton Eleanor , Seed Evan , Shooshtari Mahdi , Brook Jeffrey
Published: September 2018

IntroductionHealth and environmental exposure databases are generally siloed in different research institutions across Canada and integrating them for environmental health research is a considerable challenge. Facilitating the linkage of these databases is essential to provide new analytical opportunities and help create efficiencies for research on environmental determinants of health.

Objectives and ApproachCANUE is a Canadian Institutes of Health Research-funded platform for supporting environmental health research. CANUE collates and generates standardized environmental data on air and noise pollution, land use, green/natural spaces, climate change/extreme weather, and socioeconomic conditions for every postal code in Canada and makes them freely available to researchers. Systems and procedures are being developed by CANUE to facilitate the sharing and integration of these extensive geospatial exposures with existing observational cohorts and administrative health databases across Canada. This linkage will enable investigators to test hypotheses on the interdependent associations of environmental features with health impacts or benefits.

ResultsCANUE now hosts a dozen national exposure databases and related metadata files, and actively adds new regional and national datasets. Streamlined processes for data sharing have been developed to facilitate easy merging with health data. Substantial consultation has also taken place with a wide range of health data holders to establish appropriate processes for receiving and managing environmental data, with particular focus on addressing challenges presented by differing ethics, consent and confidentiality requirements. These processes help accelerate the research process by making analysis-ready data available to investigators, create opportunities to study how multiple environmental factors are linked to a wide range of health outcomes, and generally increase the use of health and population databases for environmental health research.

Conclusion/ImplicationsThe CANUE collaborative model illustrates how the production of policy-relevant evidence can be advanced through better coordination among environmental health researchers and linkage with health databases. CANUE is improving the scientific potential and cost-effectiveness of research in environmental epidemiology through streamlining linkage and access to standardized exposure datasets.
A comparison of methodologies: sibling identification using a relational versus a graph-based approach
Authors: Farrow James , Adams Suzi
Published: September 2018

IntroductionThe detection of siblings is an important pre-requisite for many research problems, yet is traditionally difficult or time-consuming owing to the way linked data is conventionally stored. We compare the methodology and results for an identification of siblings by SANT DataLink using a legacy relational approach and a graph-based approach.

Objectives and ApproachAn existing project involving the identification of sibling clusters using relational techniques was replicated using the same core data in SANT DataLink’s Next Generation Linkage Managment System (NGLMS) which is graph-based. Data is stored in the NGLMS using richer relationships than just 'is W% similar to' or 'is part of group N'. Birth data was separated into children and parents and explicit child/parent relationships recorded. When coupled with electoral roll data using probabilistic and deterministic linkage, sibling structure can be identified by performing network traversal via parents, e.g.

crecord—[MOTHER]⟶mrecord—[IS_SIMILAR_TO]⟶mrecord*⟵[MOTHER]—crecord*

i.e. find the mother, find that mother’s cluster, find all children related to that cluster. The resulting records are siblings.

ResultsA graph-based approach enabled the methology of 'finding siblings' to be more clearly described and communicated by mirroring genealogical structures natively within in the data. Comparable results were achieved in a shorter time with less manual effort using the same underlying data. Generating sibling clusters from the graph-based data required less manual intervention and review as explicit PARENT/CHILD relationships were stored in the data and able to be quickly traversed to assemble familial units and thus siblings. A focus on automated linkage quality rather than manual review was facilitated by the approach. Anomalous structures requiring detailed review, such as multiple fathers and mothers of a single child, were trivially identifiable and so manual effort was focussed on actions where a higher return in terms of end-product quality could be achieved.

Conclusion/ImplicationsStoring data in a representation which more closely resembles the the underlying real-world situation allows greater fidelity with respect to data modeling. This in turn, enables the asking of richer questions and makes the answering of such questions much easier.
Evaluating hardening techniques against cryptanalysis attacks on Bloom filter encodings for record linkage
Authors: Ranbaduge Thilina , Vidanage Anushka , Vaiwsri Sirintra , Schnell Rainer , Christen Peter , Christen Peter
Published: September 2018

IntroductionDue to privacy concerns personal identifiers used for linking data often have to be encoded (masked) before being linked across organisations. Bloom filter (BF) encoding is a popular privacy technique that is now employed in real-world linkage applications. Recent research has however shown that BFs are vulnerable to cryptanalysis attacks.

Objectives and ApproachAttacks on BFs either exploit that encoding frequent plain-text values (such as common names) results in corresponding frequent BFs, or they apply pattern mining to identify co-occurring BF bit positions that correspond to frequent encoded q-grams (sub-strings). In this study we empirically evaluated the privacy of individuals encoded in BFs against two recent cryptanalysis attack methods by Christen et al. (2017/2018). We used two snapshots of the North Carolina Voter Registration database for our evaluation, where pairs of records corresponding to the same voter (with name or address variations) resulted in files with 222,251 BFs and 224,061 plain-text records, respectively.

ResultsWe encoded between two and four of the fields first and last name, street, and city into one BF per record. For combinations of three and four fields all plain-text values and BFs were unique, challenging any frequency-based attack. For hardening BFs, different suggested methods (balancing, random hashing, XOR, BLIP, and salting) were applied.

Without any hardening applied up to 20.7% and 5% of plain-text values were correctly re-identified as 1-to-1 matches by both the pattern-mining and frequency-based attack methods, respectively. No more than 5\% correct 1-to-1 re-identification matches were achieved with the frequency-based attack on BFs encoding two fields when either balancing, random hashing, or XOR folding was applied; while the pattern-mining based attack was not successful in any correct re-identifications for any hardening technique.

Conclusion/ImplicationsGiven that BF encoding is now being employed in real-world linkage applications, it is important to study the limits of this privacy technique. Our experimental evaluation shows that although basic BFs without hardening technique are susceptible to cryptanalysis attacks, some hardening techniques are able to protect BFs against these attacks.
Proof of Concept for a Privacy Preserving National Mortality Register
Authors: Schnell Rainer , Borgs Christian
Published: September 2018

IntroductionNational mortality registers are essential for medical research. Therefore, most nations operate such registers. Due to the administrative structure and data protection legislation, there is no such registry in Germany. We demonstrate that a national mortality registry is technically feasible under the given constraints with privacy preserving record linkage (PPRL).

Objectives and ApproachGetting the legal permission to operate a national mortality registry for research will be easier if the linkage can be done without revealing personal identifiers by using PPRL. To estimate precision and recall of different encodings, we used two settings: (1) matching a local mortality registry (n = 14,003) with mortality data of a university hospital (n = 2,466); (2) matching 1 million simulated records from a national database of names with a corrupted subset. This corresponds to a match of all deceased persons with the deceased persons in the largest federal state (n = 205,000).

ResultsLinkage results for clear-text identifiers show very high recall and precision. Bloom-Filter based encryptions yield comparable results. Neither precision nor recall declines more than 2%. Phonetic codes yield high precision but low recall. Some variants of Bloom Filter-based encodings yield better results than probabilistic linkage on clear-text identifiers. This is mainly due to the rarely mentioned detail of using different passwords for different identifiers in the same Bloom Filter. Therefore, implementation details of Bloom Filters are more important than commonly thought. Overall, we recommend the use of salted Bloom Filter-based methods with different passwords for different identifiers to increase security and to prevent all known attacks on identifier encryptions.

Conclusion/ImplicationsAlthough most PPRL techniques would yield acceptable results in the given setting of a national register, salted Bloom filter encodings are more secure against attacks while still showing high precision and recall. Therefore, we consider a national mortality register using only encrypted identifiers of deceased persons as feasible.
Creating a Community of Practice to Support Data Linkage Across Government Agencies
Authors: Nelson Amy Hawn
Published: September 2018

IntroductionSince 2008, our team has studied sites with strong track records of integrating cross-sector data to address complex social problems. We have identified best practices and are supporting 16 sites with individualized technical assistance and participation in a learning community to facilitate data sharing that leads to actionable intelligence.

Objectives and ApproachThis presentation will provide an overview of our findings in studying established integrated data systems from across the United States, and a summary of our efforts in supporting the development of new integrated data systems (IDS). Specifically, we will answer:

What are the core features of integrated data systems (IDS) needed to support data linkage across government agencies?

How can sites develop these core features to facilitate data sharing, and ultimately, actionable intelligence?

What are the common roadblocks to successfully sharing data across agency siloes?

How can these roadblocks be addressed to create a sustainable data sharing ecosystem?

ResultsAgencies collect vast amounts of data, yet struggle to assess program impacts due to data siloes. The ability to link data across agencies to develop potential policy and service improvements is difficult, and at times, impossible. This technical assistance supports a different approach.

Our Integrated Data System (IDS) Learning Community initiative is an 18-month engagement for sites selected through a competitive process. As of September 2018, sites will be 11 months into the IDS development process. This presentation will discuss site-based approaches to facilitate data sharing, with an emphasis on governance, foundational agreements, data management and security, and stakeholder driven processes that ensure ethical use of data. This discussion will include progress to date, common challenges and solutions to developing an IDS.

Conclusion/ImplicationsWhile there is broad agreement in the value of integrating data across domains, developing the capacity and skills necessary to link administrative data for policy evaluation and research remains an elusive goal. Initial results indicate that an individualized yet collaborative technical assistance approach is successful in developing data integration capacity.
Utilization of Radiotherapy for Bladder Cancer: Evolving referral and practice patterns in the general population
Authors: Wei Shelly , Booth Christopher , Siemens Robert
Published: September 2018

IntroductionDefinitive treatment for muscle invasive bladder cancer (MIBC) includes either cystectomy or radical radiotherapy (RT). Despite the growing evidence suggesting that cystectomy and RT may offer comparable survival benefit, utilization of RT and referral rate to radiation oncology (RO) have historically been low in routine practice.

Objectives and ApproachThe aims were to describe the use of RT and RO referral patterns in the contemporary era. A retrospective cohort study was conducted by linking administrative treatment records with the Ontario Cancer Registry to identify all patients who received treatment with curative intent for bladder cancer in Ontario from 1994-2013. Physician billing records were linked to identify RO consultation before radical treatment. Practice patterns in the contemporary era (2009-2013) were compared with data from 1994 to 2008. Multilevel (patient-, surgeon- and hospital-level) logistic regression models were used to examine sociodemographic and organizational variation in referral to RO.

ResultsIn total, 7461 patients underwent cystectomy or RT for bladder cancer in Ontario from 1994-2013; 5574 (75%) had primary cystectomy and 1887 (25%) primary had RT. Use of RT decreased from 43% in 1994 to 23% in 2008 and remained stable during 2009-2013 (23%). RO referral rate among all cases decreased from 46% in 1994 to 30% in 2008; however, the rates began to rise in the contemporary era from 31% in 2009 to 37% in 2013. A similar trend was seen in preoperative referral rate among patients treated with cystectomy (11% in 1994-1998, 9% in 1999-2008, and 14% in 2009-2013). Patient, surgeon, and hospital-level factors associated with preoperative referral to RO include older age, year of cystectomy, higher surgeon case volume, and teaching hospital.

Conclusion/ImplicationsA minority of patients underwent curative intent therapy for bladder cancer received RT. Recent data suggest that referral rates to RO are increasing; future data will identify the extent to which this has altered practice. Collaborative efforts promoting multidisciplinary care and RO consultation at the urologist and hospital-level are warranted.
Linkage of whole genome sequencing with administrative health, and electronic medical record data for the study of autism spectrum disorder: Feasibility, Opportunities and Challenges
Authors: Brooks Jennifer , Anagnostou Evdokia , Rahman Farah , Tu Karen , Uruthiramoorthy Lavnaya , Nylen Kirk , McLaughlin John , Schull Michael , Bronskill Susan
Published: September 2018

IntroductionAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) that presents with a high degree of heterogeneity (e.g., co-occurrence of other NDDs and other co-morbid conditions), contributing to differential health system needs. Genetics are known to play an important role in ASD and may be associated with different disease trajectories.

Objectives and ApproachIn this proof of principle project, our objective is to link >2,200 children with a confirmed diagnosis of a NDD from the Province of Ontario Neurodevelopmental (POND) Study to administrative health data and electronic medical record (EMR) data in order to identify subgroups of ASD with unique health system trajectories. POND includes detailed phenotype and whole genome sequencing (WGS) data. Identified subgroups will be characterized based on clinical phenotype and genetics. To meet this goal, consideration of WGS-specific privacy and data issues is needed to implement processes which are above and beyond traditional requirements for analyzing individual-level administrative health data.

ResultsLinkage of WGS data with administrative health data is an emerging area of research. As such it has presented a number of initial challenges for our study of ASD. Privacy concerns surrounding the use of WGS data and rare-variant analysis are of particular importance. Practical issues required the need for analysts with expertise in administrative data, EMR data and genetic analyses, and specialized software and sufficient processing power to analyze WGS data. Transdisciplinary discussions of the scope and significance of research questions addressed through this linkage were crucial. The identification of genetic determinants of phenotypes and trajectories in ASD could support targeted early interventions; EMR linkage may inform algorithms to identify ASD in broader populations. These approaches could improve both patient outcome and family experience.

Conclusion/ImplicationsAs the cost of genetic sequencing decreases, WGS data will become part of the routine clinical management of patients. Linkage of WGS, EMR and administrative data has tremendous potential that has largely not been realized; including population-level ASD research to improve our ability to predict long-term outcomes associated with ASD.
Data Linkages to Study Pharmaceutical Health Services Delivery: Three Applied Examples
Authors: Johnson Karin , Xu Lanlan , Kiptanui Zippora , Gill Christine , Triebwasser Corey , Emery Danielle , Perlman Evan , Harris Ilene
Published: September 2018

IntroductionThe safe, effective, and efficient use of pharmaceutical health services is a critical area of public health and social policy. Implementation and monitoring studies often use single data sources but require diverse data elements pertaining to patients and health services. Linking multiple data sources may enable more comprehensive studies.

Objectives and ApproachThe objective of this presentation is to describe three applied pharmaceutical health services projects in the United States that use data linkage to support program monitoring. A conceptual model was defined including the following key domains: (1) contextual determinants; (2) pharmacy service availability and coverage by payers; (3) receipt/use of services by patients; and (4) outcomes such as clinical outcomes, patient satisfaction and healthcare costs. Applied studies were selected to illustrate data linkage across different domains. For each study, we present the data sources used and the domains addressed by each data source. We also describe the linkage process.

ResultsStudy 1 assesses distance between patients and pharmacies in-network for health plans. It determined pharmacy address and patient ZIP code using several Medicare datasets and calculated driving distance using geocoding software.

Study 2 measures if patients targeted for services receive an intervention designed to prevent adverse drug events and improve patient health outcomes while reducing healthcare costs. Targeted beneficiaries are determined using Medicare administrative data and delivery of services is assessed based on a custom-developed encounter data set, linked by beneficiary.

Study 3 examines the association between prescription drug formulary design, medication use, and cost and health outcomes. For this, formulary data are obtained from a commercial source and utilization/outcomes data from commercial claims data. These datasets are linked by payer.

Conclusion/ImplicationsMany data sources are available for pharmaceutical health services research studies and linkage can be made at the patient, region, or payer level to support program monitoring and evaluation. Data linkage enables the inclusion of multiple domains, although multiple linkages and/or custom data may be needed for more complex studies.
The Effect of Medication Adherence on the Disease Course in Pregnant Women with Inflammatory Bowel Disease
Authors: Lee Sangmin (Sarah) , Seow Cynthia , Dahal Kamala Adhikari , Metcalfe Amy
Published: September 2018

IntroductionMedical therapy to maintain disease remission is important in pregnant women with inflammatory bowel disease (IBD), as disease flares can predispose adverse materno-fetal outcomes. However, women with IBD are more concerned about medication exposure on their newborn during pregnancy, and often discontinue their medications.

Objectives and ApproachWe assessed the rate of disease flare on medication adherence pattern during pregnancy for women with IBD. Validated case definition was used to identify women with IBD from the Albertan’s hospitalization, emergency room, and physician claims databases (2010-2016). Pharmaceutical Information Network provided the dispensed medications. Adherence to medication was defined by medical possession ratio (MPR)≥0.8. Women with two consecutive prescriptions and MPR≥0.8 during pre-conception were included. Disease flare was defined by ≥1 hospitalization or emergency visit for IBD, or ≥1 prescription for steroids/rectal therapy. Chi-square tests and log binomial regression were used; covariates included age, drug class, and IBD subtypes.

ResultsOf the 370 women identified with IBD, 170 (45.9%) women were adherent to maintenance IBD medications in the one year prior to pregnancy. During pregnancy, 47 (27.6%; 95% CI: 21.4% to 34.9%) women, who demonstrated adherence in the pre-conception period, discontinued or were not adherent to their IBD medications, and 67 (39.4%; 95% CI: 32.3% to 47.0%) women had a disease flare during pregnancy. There was no significant difference between adherence to medication during pregnancy and a disease flare during pregnancy (p=0.38). In comparing women who were not adherent or discontinued their medication to those that were adherent, the adjusted relative risk ratio for a disease flare during pregnancy was 1.22 (95% CI: 0.81 to 2.04).

Conclusion/ImplicationsThe rate of disease flare during pregnancy was not significantly different for women with IBD that were adherent or not-adherent to their IBD medications during pregnancy. Future analysis will assess the rate of disease flare on medication adherence pattern prior to pregnancy.
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.