bluuebunny/crossref_metadata_2025_split huggingface dataset:
International Comparison of Approaches to Common Data Models for Comparative Effectiveness Research
Authors: Levy Adrian , Platt Robert , Setoguchi Soko , Brown Jeffrey , Paterson Michael 
Published: September 2018

Over the past decade, characterizing the safety and effectiveness of drugs has advanced through distributed networks of data repositories where investigators implement the same procedures to address the same topic using a common data model. Distributed networks for pharmacoepidemiology have now been established in the United States (US), Globally/Europe Canada, and Asian countries. Sentinel in the US was developed in response to legislation and is funded by the US Food and Drug Administration to address their safety queries. The Observational Medical Outcomes Partnership (OMOP) is an international collaborative with a growing European data network that developed a common data model through a public-private partnership. The Canadian Network of Observational Drug Effect Studies (CNODES) receives funding and study queries from Health Canada and dissemination is directly back to the regulator as well as through the peer-reviewed literature. The Asian Pharmacoepidemiology Network (AsPEN) is an investigator-initiated multi-national research network formed to support the safety and effectiveness assessment of medications and other therapeutics and to facilitate the prompt identification and validation of emerging safety issues among the countries in Asia and Pacific regions. While these networks have implemented two different common data models (CNODES with Sentinel, ASPEN with OMOP), each network differs from the others in the aims, stage and implementation, operational approach, data quality assurance mechanisms, funding, and dissemination.
The objectives of this session are to compare and contrast the role and goals, design principles, implementation approaches, and analytic conventions and procedures between common data models implemented by SENTINEL, OMOP, CNODES, ands AsPEN.
Divided into seven 15-minute segments the session begins with an overview of distributed networks of common data models for pharmacoepidemiology. In four slides, each presenter then characterizes their network by describing the following:

number of data holders, lives covered, and records, data holdings, data access model, network governance.
process for transforming a repository’s data into the common data model
target audience(s), process of identifying queries and knowledge dissemination plan
two key challenges faced by the network and the lessons learned

In identifying similarities and meaningful differences between the networks, in the next segment the discussant will articulate the relative strengths of the different approaches taken. This will lead into the last segment in which the floor will be opened for questions and comments from the audience. The session would be of benefit to researchers seeking to better understand or join an existing distributed network as well as researchers interested in broadening their understanding of global comparative effectiveness research.
Where it’s at - linking data geographically.
Authors: Gozdyra Peter 
Published: September 2018

IntroductionMost public health-related concepts and outcomes can be defined as to their geographic location. The surroundings often have a strong influence or interactions with studied phenomena. For this reason a good understand and accurate geographic placement, linking, and aggregation of studied concepts is a critical yet often underestimated procedure.
Objectives and ApproachThe main objectives of this presentation are: 1) an easy to understand review and explanation of geographic delineation markers in common healthcare databases, and 2) ways and pitfalls of geographic data linkages. Common point- and area-defined databases will be described. Nuances of ‘point-to-area’, ‘area-to-area’ linkages will be discussed, with additional explanations of scale and zone effects. Examples of common linkages between the following common spatial delineators will be explained: Postal Code Conversion File (PCCF), small area Canada Census units, common health system geographies (e.g. sub-regions, LHINs). Frequently committed errors and best practices in geographic data linkages will be discussed.
ResultsExamples of the influence of various methods of geographic data linkages on study simulated outcomes will be shown.
Conclusion/ImplicationsImproper geographic linkage procedures can lead to incorrect study results. Enhancing the knowledge of geographic concepts in public health research and promotion of correct procedures in spatial placements, linkages and aggregation are the main take home messages of this presentation.
Parental mental health and risk of poor mental health and death by suicide in offspring: a population-wide data-linkage study
Authors: O'Reilly Dermot , Maguire Aideen 
Published: September 2018

IntroductionSuicide is a major public health concern and identifying those most at risk is vital to ensure the implementation of effective interventions. There are known associations between parental and sibling mental health but little is known on the effect of parental mental on a child’s risk of death by suicide.
Objectives and ApproachThis population-wide data linkage study utilised data from the 2011 Northern Ireland Census linked to 5 years’ death records (2011-2015) to construct multi-level regression models to determine if children living with parents with poor self-rated mental health are at an increased risk of poor mental health themselves and whether they are at an increased risk of death by suicide.
ResultsOf the 618,970 individuals who live with their parents, 13.7% live with parents with poor mental health, 11.6% have poor mental health themselves and 0.1% (n=225) died by suicide. Living with a parent with poor mental health was associated with likelihood of poor mental health in children. After adjustment for age, gender, physical illness, socio-economic status and own mental health status, children with 1 parent with poor mental health were 67% more likely to die by suicide compared to children of parents with good mental health (OR=1.67, 95%CI 1.19, 2.33). The effect size increases for children living with 2 parents with poor mental health.
Conclusion/ImplicationsParental mental health is associated with a child’s suicide risk even after adjustment for their own mental health status. This is an at-risk group.
Navigating Legal and Ethical Requirements for Multi-Stakeholder Data Access Requests Through Harmonized Documentation
Authors: Galo Jessica , Choi Tim , Kim-Bautista Maria 
Published: September 2018

IntroductionResearch increasingly involves linking data from multiple sources, including data collected by researchers. This creates complexity because data providers often have differing policies and requirements for data access. Harmonization of processes requires resources, especially as new data providers are added, and needs to be prioritized appropriately.
ObjectivesOur objectives were to: 1. understand the challenges encountered by researchers interested in collecting data and/or linking multiple data sets; and 2. outline and evaluate Population Data (PopData) BC’s efforts into harmonizing documentation and processes to address these challenges. With this information, we aim to better support research and streamline the data access request process.
ApproachWe compared data access timelines of projects that did and did not utilize harmonized templates, including consent forms, data access request forms, and research agreements. We then identified the challenges arising from non-harmonized requirements including their number and complexity, and developed priorities for action.
ResultsWhile existing consent form templates provided the ethics board-required language to support the collection of researcher-collected data, they lacked the text requirements of the administrative data stewards/providers. These text deficiencies slow down the data access request process, affect data provider workflow, and can be associated with researcher costs to re-consent. To address these gaps, harmonized consent templates were developed and finalized in November 2017. These templates included the data steward text requirements on governance, data sets, data transfer, data storage, and withdrawal.
Non-harmonized data access request forms and research agreements varied in format and detail and resulted in coordination challenges and delays. A harmonized form was developed to capture key information required by all stakeholders. Research agreement harmonization discussions are underway. Impact evaluation is ongoing.
Conclusion/ImplicationsThe complexity multi-stakeholder dataset research need not extend to the data access process. Coordinated requirements and harmonized documentation reduce the burden on all stakeholders including researchers, ethics boards, and data stewards and improve the project timelines.
Harmonising data from different sources to conduct research using linked survey and routine datasets
Authors: Bandyopadhyay Amrita , Tingay Karen , Borja Mario Cortina , Griffiths Lucy , Akbari Ashley , Bedford Helen , Brophy Sinead , Walton Suzanne , Dezateux Carol , Lyons Ronan 
Published: September 2018

IntroductionHarmonization of different data sources from various electronic health records across systems enhances the potential scope and granularity of data available to health data research, providing more opportunities for research by improving the generalizability and effective sample size of a range of outcome metrics.
Objectives and ApproachThis study describes data harmonisation for a UK longitudinal birth cohort, the Millennium Cohort Study (MCS) which was linked to routine inpatient and emergency department, and, where available, general practice and child health records for 1838 Welsh and 1431 Scottish consenting MCS participants. Datasets requiring harmonisation were: from Wales, Patient Episode Dataset for Wales (PEDW) and Emergency Department Data Set (EDDS) data and from Scotland, Scottish Medical Record 01 (SMR01) and Accident and Emergency dataset (A&E2). Heterogeneous variables were created by transforming variable names, concepts, codes to improve scope for analysis.
ResultsA harmonized dataset of 2166 participants and 5747 hospital admissions were derived of cohort members who had at least 1 hospital inpatient or A&E event before their 14th birthday. Harmonisation included: dealing with date granularity by generating random dates of birth; standardising periods of data collection; identifying inconsistencies and then mapping and bridging differences in definitions of periods of care and levels of diagnostic and operational coding across countries and datasets.
Conclusion/ImplicationsHeterogeneous variables from different data sources were pooled and converted into standardised data for research, extending existing harmonisation work, including curation of a population based anonymously linkable longitudinal cohort. [AA1] These methods are reproducible and can be utilised by other researchers and projects applying to use these routine data sources.
Provincial Data-linkage to Address Complex Policy Challenges
Authors: Coad Jeremy , Marrville Caelan , MacKenzie Dan , Henderson Lori , Pope Naomi , Wilmer Brett , Monkman Martin , Bisschop Lindsay , Black Gordon , Boozarjomehri Mahi , Chalifour Chelsea , Fraser Sarah , Hawkins Julie , McLay Cheryl , Hernandez Raphael Parra , Wongkanlayanush Amy , Zakopcan Dennis 
Published: September 2018

IntroductionThe Province of British Columbia, Canada has established a Data Innovation Program (DI Program) and a Data Science Partnerships Program (DSP Program) to use integrated public-sector data to drive insights into complex policy challenges and support the public good. These programs are a part of the province's new Integrated Data.
Objectives and ApproachThe DI Program was built to enable policy decisions based on a more complete picture of the citizen journey across and throughout government programs. It provides a privacy and security framework for corporate data analytics and a cross-government secure research environment. The DSP Program provides analytics and/or project support for high-priority cross-government projects. The opportunity afforded by this approach to policy decision-making is that valuable data and evidence from multiple sectors can be utilized to make positive changes in the lives of citizens.
ResultsThe IDO has partnered with cross government experts on a series of pilot projects that used linked data spanning social services, families and households, education, and health and clinical records. Research topics ranged from the prediction of risk of long-term unemployment, to the impact of the foreign home buyers tax, to the effectiveness of labour market programs. Throughout our presentation we will use these projects as case examples to address the benefits and opportunities provided through our citizen-centred, integrated approach.
Conclusion/ImplicationsThe future of policy decision-making in terms of service delivery relies on mutually beneficial collaboration and the evidence-based insight available through integrated data. Moving forward, it is essential that researchers across government make the most out of integrated population-level data to solve pressing issues affecting the lives of citizens.
Intergenerational teen pregnancy: a population based cohort study
Authors: Liu Ning , Vigod Simone , Farrugia Michèle , Urquia Marcelo , Ray Joel 
Published: September 2018

IntroductionMothers can influence on a teen’s knowledge and attitude about sexual and reproductive health practices. Prior studies on intergenerational teen pregnancy between mother and daughter were largely limited to livebirths.
Objectives and ApproachTo estimate the odds of intergenerational teenage pregnancy, and whether there is a coupling tendency between mother and daughter in how their teen pregnancies end, such as induced abortion (IA) vs. livebirth. A cohort study was conducted in Ontario, Canada among 15,097 mothers and their 16,177 daughters. The mother and her daughter were each assessed, between ages 15-19 years, for the number and nature of teenage pregnancy. The nature of teen pregnancy was categorized as i) no teen pregnancy, ii) ≥1 teen pregnancy, all exclusively ending with livebirth, and iii) ≥1 teen pregnancy, with at least one ending with IA.
Results13,036 daughters’ mothers had a teen pregnancy and 3,141 daughters’ mothers did not. Teen pregnancy occurred among 3,777(29.0%) and 513(16.2%) daughters, respectively. The adjusted odds ratio (aOR) of a daughter having a teen pregnancy was 1.42 (95% CI 1.25-1.61) if her mother had 1, 1.97 (95% CI 1.71-2.26) if her mother had 2, and 2.17 (95% CI 1.84-2.56) if her mother had ≥3 teen pregnancies, relative to none. If a mother had ≥1 teen pregnancy ending with IA, then her daughter had an aOR of 2.12 (95% CI 1.76-2.56) for having a teen pregnancy also ending with IA; whereas, if a mother had ≥1 teen pregnancy, all ending with livebirth, then her daughter had an aOR of 1.73 (95% CI 1.46-2.05) for that same outcome.
Conclusion/ImplicationsThere is a strong intergenerational occurrence of teenage pregnancy between a mother and daughter, including a coupling tendency in how the pregnancy ends. Such evidence supports the use of family-centered interventions that engage parents to reduce unprotected sex and unintended pregnancies among teenagers.
What makes great data documentation?
Authors: Smith Mark , Azimaee Mahmoud 
Published: September 2018

Session topic: What makes great data documentation?Documentation is the tool that describes how and why a database was created, what its strengths and limitations are, and how all of the various components fit together. As such, it is an invaluable resource for helping others understand what they can do with the data. Please join us for a discussion of what makes great data documentation.
This session will begin with a 40-minute integrated presentation by the Manitoba Centre for Health Policy (MCHP) and the Institute for Clinical Evaluative Sciences (ICES), two of the leading population data research centres in Canada, covering the following topics:
1 – Structured Overviews
2 – Data Models
3 – Data Dictionaries
4 – Other Documentation and Published Reports
5 – Integrating Blog or Analyst Notes
6 – Data Quality Reporting

VIMO tables
Heat maps
Trend analysis
Relevancy

Session Facilitators:Mahmoud Azimaee, Institute for Clinical Evaluative Sciences (ICES)
Mark Smith, Manitoba Centre for Health Policy (MCHP)
The Intended Outcome:A research paper based on the discussion for publication in the International Journal of Population Data Science (IJPDS). All participants are invited to join us as co-authors in drafting and revising the paper.
Learning Unsupervised Representations from Biomedical Text
Authors: Meaney Christopher , Tu Karen , Jaakkimainen Liisa , Escobar Michael , Rudzicz Frank , Widdifield Jessica 
Published: September 2018

IntroductionHealthcare settings are becoming increasingly technological. Interactions/events involving healthcare providers and the patients they service are captured as digital text. Healthcare organizations are amassing increasingly large/complex collections of biomedical text data. Researchers and policy makers are beginning to explore these text data holdings for structure, patterns, and meaning.
Objectives and ApproachEMRALD is a primary care electronic medical record (EMR) database, comprised of over 40 family medicine clinics, nearly 400 primary care physicians and over 500,000 patients. EMRALD includes full-chart extractions, including all clinical narrative information/data in a variety of fields.
The input data (raw text strings) are discrete, sparse and high dimensional. We assessed scalable statistical models for high dimensional discrete data, including fitting, assessing and exploring models from three broad statistical areas: i) matrix factorization/decomposition models ii) probabilistic topic models and iii) word-vector embedding models.
ResultsEMRALD is comprised of 12 text data streams. EMRALD text data is structured into 84 million clinical notes (3.5 billion word/language tokens) and is approximately 18Gb in storage size. We employ a “text as data” pipeline, i) mapping raw strings to sequences of word/language tokens, ii) mapping token sequences to numeric arrays, and finally iii) using numeric arrays as inputs to statistical models.
Fitted topic models yield useful thematic summaries of the EMRALD corpora. Topics discovered reflect core responsibilities of primary care physicians (e.g. women’s health, pain management, nutrition/diet, etc.).
Fitted vector embedding models capture structure of discourse/syntax. Related words are mapped to similar locations of vector spaces. Analogical reasoning is possible in the embedding space.
Conclusion/Implications“Text as data” requires an understanding of statistical models for discrete, sparse, high dimensional data. We fit a variety of unsupervised statistical models to biomedical text data. Preliminary results suggest that the learned low dimensional representations of the biomedical text data are effective at uncovering meaningful patterns/structure.
Methodological issues for measuring pharmacotherapy treatment and its calibration with patient outcomes using real-world data
Authors: Chen Guanmin , Farris Megan , Cowling Tara 
Published: September 2018

IntroductionHealth system and administrative data sources, although collected for process/procedural healthcare use, have recently been leveraged for research purposes. Complex methodologies such as data linkages, defined algorithms, and proxy measures are needed to validate the use of these data. Alberta has exceptionally rich health system data to be explored.
Objectives and ApproachOur objectives were to explore data sources in Alberta regarding pharmacotherapy treatments, patient outcomes, and develop methods to calibrate associations. We scoped data sources for pharmacotherapy treatment and patient outcomes in Alberta, Canada. Using a cohort of patients with multiple sclerosis (MS) as an example, we developed algorithms to measure medication possession ratio (MPR), proportion of days covered (PDC), and treatment discontinuation (TD). Patient outcomes included: ambulatory care visits, physician claims and hospitalizations. We explored different algorithms for statistical modeling of patient treatments and outcomes. Optimal cut-points and statistical model performance of MPR/PDC and TD with patient outcomes were evaluated.
ResultsWe utilized six Albertan data sources to examine treatment patterns and patient outcomes. From the year 2000, the pharmaceutical information network (PIN) and Alberta Blue Cross drug datasets collected: service dates, dosages, drug identification numbers, anatomical therapeutic chemical classification codes, and days of supplies; which were used to calculate MPR, PDC and TD. MPR and PDC were calculated using three different methods: (1) fixed follow-up period; (2) with and without last fill; and (3) adjustment for hospitalization periods. No significant differences between methods were found. In addition, TD using 60- and 90-day gaps, while considering the medication day supplies, were calculated. Adherence with optimal cut-point 0.8 of MPR/PDC and TD were significantly associated with ambulatory care visits, physician claims, and hospitalizations (p=0.000; C-statistic range: 0.78-0.89).
Conclusion/ImplicationsThe PIN data can be utilized for measuring pharmacotherapy treatment in patient outcomes research. To understand the impact of therapies on outcomes in a real-world setting, however, comprehensive methods for measuring treatment indicators as well as patient outcomes should be developed to control for inherent biases in observational data.
Record Linking Techniques in the Utah Population Database to Improve Linking Rates of Hispanics
Authors: Fraser Alison , Kinnear Stevie , Smith Ken 
Published: September 2018

IntroductionHispanic naming conventions frequently follow historical traditions. A person’s name consists of a given name or names followed by the father’s first surname and the mother’s first surname or reversed if the parents wish. The challenge occurs in keying and linking these non-standard names resulting in a potential linking bias.
Objectives and ApproachHistorically the Utah Population Database (UPDB) has combined multiple surnames into a single surname to standardize names such as VAN WINKLE, however this resulted in Hispanic surnames combined into nonsensical names, for example ‘MARTINEZCRUZ’ that were difficult to match to surnames stored in separate fields in assorted combinations. The objective of this study was to see if name specific frequencies and name arrays created with the second and third given name, maiden name and surname allowed for ultimate flexibility in matching to records which did not adhere to any standardized keying convention and resulted in better linking results.
ResultsA “Gold Standard” set of Hispanic individuals with multiple record sources in UPDB and the presence of two names in the surname field were evaluated. Two Linking approaches, one using the UPDB standard methodology and the other using name arrays were compared. Both methodologies resulted in high linking rates into complete or partial sets of records per individual. Overall, the array methodology linked more records into complete sets than the standard (94.7% cf. 82.6%). Using arrays, males linked at a higher rate than females and persons from Spanish speaking countries linked at the highest rate compared with USA born or other countries. However, there was an increase in incorrect links using arrays. Name frequency distributions specific to Hispanics also proved important.
Conclusion/ImplicationsThis study found weights based on frequencies specific to the population being linked is critical to complete linking. Using name arrays for Hispanics was most effective in males with indicators of strong ethnic ties. However, the cost of using arrays was an increase in incorrect links and further refinement is needed.