bluuebunny/crossref_metadata_2025 huggingface dataset:
Swiftlink: a distributed, parallelised and scriptable linkage engine architecture for the 21st century
Authors: Farrow James
Published: September 2018

IntroductionMany currently extant generic tools for performing bulk linkage were developed last century and while they have accreted features to improve their utility they have failed to fully exploit modern computing architectures. Custom highly-parallelised and distributed solutions exist but there is a need for a modern generalised linkage toolkit.

Objectives and ApproachThe aim of this project has been to design, from the ground up, a modern flexible, i.e. scriptable, linkage suite which also takes advantage of contemporary computing architectures to address issues such as distribution of computation, parallelisation of computation and cloud-based computation. Rather than being a monolithic linkage tool or a programming suite, a domain specific language has been developed to specifically describe linkage tasks. Linkage tasks written in this language are then 'executed' or 'compiled and run' to perform pair-wise calculations on data elements. Since linkage tasks are generally bespoke, scriptability has been an important consideration.

ResultsDeveloping a domain specific linkage language has enabled problem specification to be more descriptive and flexible than a monolithic linkage application. The shift in focus from a programming language to a linkage language has enabled a corresponding shift in focus to linkage-related effort (such as blocking and comparison strategies) away from distracting 'glue' code which relates not to linkage tasks under consideration but rather to the distracting bookkeeping aspects of programme execution. The same linkage task may be compiled against different back ends and languages, e.g. FEBRL (python), swift, Amazon lambda (go). The architecture has enabled otherwise idle computing resource to be utilised as well as cloud-based computing facilities for increased throughput and performance. The architecture of the linkage system will be shown with examples.

Conclusion/ImplicationsContemporary advances in computing sciences can and must be leveraged in modern linkage tools. By providing a custom scriptable linkage language, tasks may be more clearly specified in a manner more flexible than monolithic linkage applications and by uncoupling linkage specification from execution, linkage may be performed optimally across multiple machines and resources.
Synthetic data generator for testing record linkage routines in Brazil.
Authors: Trentin Vitor , Bastos Valeria , Costa Myrian , Camargo Kenneth , Sobrino Rejane , Guillen Luis Carlos , Coeli Claudia
Published: September 2018

IntroductionRecord linkage has been increasingly used in Brazil. However, only a few studies report the quality of the linkage process. Synthetic test data can be used to evaluate the quality of data linkage.

Objectives and ApproachTo develop a synthetic data generator that creates test datasets with similar attributes and error characteristics found in the Brazilian databases.

We analyzed the 2013 mortality database from Rio de Janeiro State to know the characteristics and frequency distribution of the database attributes (name, mother’s name, sex, date of birth and address).

We used Python and C++ to customize and add routines to GeCo (http://dlrep.org/dataset/GeCo), a personal data generation tool developed by Tran et al. (DOI:10.1145/2505515.2508207).

ResultsBrazilian names have specific characteristics that distinguish them from other countries’ patterns: multiple family names are usual, as are composite first names, and, despite that, homonyms are frequent. Family names may include the full extension or only parts of either the father and mother’s respective family names, or both, so there is a wide variation in progeny family names and not necessarily a common family name for all family members.

Conclusion/ImplicationsDue to the specific national characteristics of name building in Brazil, modeling synthetic data is particularly challenging and needs to have more flexible rules in order to generate databases that will actually allow assessing the quality of data linkage processes.
Extracting Family Connections from Administrative Data
Authors: Vasquez Stacy Ann , Palfy Miro , Garcia Alexandre Franco
Published: September 2018

IntroductionRecord linkage has existed for decades, regarded as an activity where an entity is ascertained across different data sources. Initially medically driven to determine further outcomes. Developments in technology led to cheaper computing and storage, which ushered capture of information beyond the primary entities such as carer’s details.

Objectives and ApproachAdministrative datasets that capture an event involving a child would more often than not, capture information about their parents or carers including relationship to the primary entity within the record.

The main objective was to exploit the connections across the entities captured in a record and have that as an additional layer of information that may contribute to context. All entities extracted from one record were clearly defined as related. Information was considered true at the point of capture. Then record linkage for all the individual entities extracted were performed.

ResultsFamily structures evolve and a family structure during birth may change by the time the child attends school where carer information was recorded during enrolment.

Each data source inevitably becomes a “source of truth” and understanding the different aspects of a dataset affects its credibility. Authority for collection dictates if the recording of the event is mandatory or optional and directly influences the coverage. The process of data capture and storage is also important; manual vs digital input and if any validation was present.

Application of family connection to specific types of research will dictate the relevant “source of truth”. If one is to use family connections in the context of hereditary diseases, pregnancy outcomes would be more suitable than a birth registry.

Conclusion/ImplicationsFamily unit as defined in the traditional sense have changed and all variations are reflected in the data.

Assessing the impact of social connections to ones health may now be possible. Extracting these connections from administrative datasets can serve as additional intelligence that may inform if not shape longitudinal studies.
Exploring outcomes for children who have experienced out-of-home care in Western Australia.
Authors: Lima Fernando
Published: September 2018

IntroductionChildren who have been in out-of-home care have faced significant issues during their lives, and they are considered one of the most vulnerable groups in society. Given the limited evidence in Western Australia about outcomes for care-leavers, this study represents a base line for future studies of care-leavers outcomes.

Objectives and ApproachA retrospective cohort study exploring the outcomes for young people born between 1990-1995, who have reached at least 18 years of age and have had a period of care, compared to other similar children in WA. This project used administrative linked data from the Department of Communities Child Protection and Family Support Division, Departments of Health, Education, and Corrective Services. This study undertook a descriptive approach to compare outcomes for young people who have left out-of-home care, and logistic regression modelling to explore the odds of having poorer outcomes among those who had a period in care.

ResultsYoung people aged 18 years and over who had been in out-of-home care had worse outcomes compared to controls. Care-leavers had nearly twice the hospital admission rate of those who never had contact with the child protection system, almost three times more likely to have a mental health related contact, less likely to achieve a high school completion certificate and attend University, and more likely to have a juvenile community sentence or adult detention.

A group of young people who had a period in care were identified as more likely to have ‘poorer outcomes’ compared to the rest of the Care group if they: were Aboriginal; female; born in a more disadvantaged area; and first entered care after the age of 10.

Conclusion/ImplicationsYoung people who have been in care are at high risk of a range of poor outcomes, even compared to other children who have experienced similar disadvantage. Regardless of the causes, it is incumbent upon the State as acting ‘parents’ to provide the best possible support to improve their outcomes.
A Large Linked Study to Evaluate the Future Burden of Cancer in Australia Attributable to Current Modifiable Behaviours
Authors: Laaksonen Maarit A , Arriaga Maria E , Canfell Karen , MacInnis Robert , Hull Peter , Banks Emily , Giles Graham G , Mitchell Paul , Cumming Robert G , Byles Julie E , Magliano Dianna J , Shaw Jonathan , Taylor Anne , Gill Tiffany K , Hirani Vasant , Marker Julie , McCullough Susan , Velentzis Louiza S , Adelstein Barbara-Ann
Published: September 2018

IntroductionThe cancer burden preventable through modifications to risk factors can be quantified by calculating their population attributable fractions (PAFs). PAF estimates require large, prospective data to inform risk estimates and contemporary population-based prevalence data to inform the current exposure distributions, including among population subgroups.

Objectives and ApproachWe provide estimates of the preventable future cancer burden in Australia using large linked datasets. We pooled data from seven Australian cohort studies (N=367,058) and linked them to national registries to identify cancers and deaths. We estimated the strength of the associations between behaviours and cancer risk using a proportional hazards model, adjusting for age, sex, study and other behaviours. Exposure prevalence was estimated from contemporary National Health Surveys. We harmonised risk factor data across the data sources, and calculated PAFs and their 95% confidence intervals using a novel method accounting for competing risk of death and risk factor interdependence.

ResultsDuring the first 10-years follow-up, there were 3,471 incident colorectal cancers, 640 premenopausal and 2,632 postmenopausal breast cancers, 2,025 lung cancers and 22,078 deaths. The leading preventable causes were current smoking (53.7% of lung cancers), body fatness or BMI ≥ 25kg/m2 (11.1% of colorectal cancers, 10.9% of postmenopausal breast cancers), and regular alcohol consumption (12.2% of premenopausal breast cancers). Three in five lung cancers, but only one in four colorectal cancers and one in five breast cancers, were attributable to modifiable factors, when we also considered physical inactivity, dietary and hormonal factors. The burden attributable to modifiable factors was markedly higher in certain population subgroups, including men (colorectal, lung), people with risk factor clustering (colorectal, breast, lung), and individuals with low educational attainment (breast, lung).

Conclusion/ImplicationsEstimating PAFs for modifiable risk factors across cancers using contemporary exposure prevalence data can inform timely public health action to improve health and health equity. Testing PAF effect modification may identify population subgroups with the most to gain from programs that support behaviour change and early detection.
Needs and priority areas for building capacity for working with linked data in the Australian pharmacoepidemiology workforce
Authors: Lopez Derrick , Daniels Benjamin , Sanfilippo Frank , Pearson Sallie , Martini Angelita , Preen David B
Published: September 2018

IntroductionLinked data are increasingly used in pharmacoepidemiology studies to enhance value beyond that which can be achieve from stand-alone pharmaceutical data. The complexity of pharmaceutical data can make any linked data analysis challenging and it is imperative that this is matched by the human capacity to perform this work.

Objectives and ApproachResearch is needed to understand the state of the current pharmacoepidemiology workforce and to prioritise its capacity building needs. We aim to profile the Australian pharmacoepidemiology workforce to explore views, needs, priority areas and perspectives relevant to capacity building. Participants are the regular pharmacoepidemiology workforce (Group 1) and senior medicines stakeholders (Group 2). Following a literature review and consultation with a group of key informants, we developed survey and interview instruments for each group. We piloted the instruments in February 2018 and study data collection is planned for March 2018. We will use a mixed-methods approach to analyse the data.

ResultsWe conducted a review of existing literature and identified workforce views, needs and priorities at four levels: personal, team, organisation and wider community. During the consultative process, the informants highlighted the multidisciplinary nature of the pharmacoepidemiology workforce including many with non-health related backgrounds. They also raised concerns about attracting applicants with suitable skills and experience, job satisfaction, career progression and workforce retention. We developed instruments to (i) further explore these issues, (ii) ascertain their experience with linked health data, (iii) determine their training needs, and, (iv) learn about their future intentions. We will present findings on issues pertinent to the Australian pharmacoepidemiology landscape and suggest priorities for building workforce capacity.

Conclusion/ImplicationsThis study will provide empirical evidence to support and prioritise capacity building in the Australian pharmacoepidemiology workforce to improve their ability to work with linked data. The instruments that we developed and findings may be relevant to phamacoepidemiology workforce in other countries and other emerging fields that use linked data.
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.
Optimizing Impact in Fighting the Global HIV Epidemic
Authors: Ritz Derek , Ketani Violet
Published: September 2018

IntroductionLinking healthcare expenditure data with health outputs and outcomes data is challenging. This is especially true in low-resource (LMIC) settings. Where such linking can be accomlished, however, it supports the development of IMPACT-per-DOLLAR metrics that may be leveraged to support programme optimization.

Objectives and ApproachThis session will describe a new data-linking design for a global health programme targeted to low and middle-income countries (LMIC). The US Government's President's Emergency Plan for AIDS Relief (PEPFAR; www.pepfar.gov) is a \$7 billion/year programme spanning 58 LMICs. PEPFAR prepares annual budgets and plans. On a quarterly basis, Implementing Partners report programmatic district-level Monitoring, Evaluation and Reporting (MER) indicators to a central database. Starting in July, 2018, quarterly expenditure data will also be captured, broken down by major programme area and high level account code. The novel new data-linking approach triangulates the budget, indicator and expenditure datasets.

ResultsThe PEPFAR programme is managed by the US State Department's Office of the Global AIDS Coordinator (S/GAC). S/GAC undertook, in 2017, a significant re-design of its information system requirements and the software systems that will support these. A multi-year design and prototyping effort has yielded a novel new approach for financial monitoring intended to improve S/GAC's abilities to exert its mandates regarding both programmatic and financial oversight.

The new data-linking solutions are being implemented across all PEPFAR countries during the first half of 2018. Information model design choices (and trade-offs), solution architecture designs, and preliminary pilot results of this new implementation will be reported at the confernce. A discussion of implementation science lessons-learned specific to deploying in LMICs will also be presented.

Conclusion/ImplicationsPEPFAR's goal and mission is to operationalize HIV epidemic control and achieve an AIDS-free generation by 2030. Data analytics are proving essential to achieving this goal. S/GAC's re-design efforts will help better link its huge financial and programmatic (MER) datasets to support DALY-per-DOLLAR impact optimization on this important initiative.
Combining Artificial Neural Networks, Routine Health Records and Suicide Risk Estimation
Authors: Del Pozo Banos Marcos , Travieso Carlos M , Loxton Kate , Petkov Nicolai , Berridge Damon , Lloyd Keith , Jones Caroline , Spencer Sarah , John Ann
Published: September 2018

IntroductionEvery year ~800,000 people die by suicide worldwide. The pathway to suicide is mediated by highly complex processes, integrating a large number of risk factor variables which are extensively dependent on one another. Unfortunately, suicide risk prediction has been a challenging problem for epidemiological studies and their application to practice.

Objectives and ApproachWe aim at exploring the feasibility of using artificial neural networks (ANNs) based on routinely collected electronic health records (EHRs) to support the identification of those at high risk of suicide when in contact with health services. We used the Secure Anonymised Information Linkage Databank UK to extract those who died by suicide between 2001 and 2015 and identify controls. We extracted risk factors from primary and secondary care about mental health, injury, substance misuse, maltreatment, sleep disorders and prescriptions. We trained a simple ANN to differentiate between cases/controls to test the feasibility of this approach.

ResultsA total of 2,604 suicide cases and 20 paired controls per case were used during 10x10 K-Fold repeated cross-validation. We obtained an error rate of 26.78% ± 1.46 differentiating between cases and controls. The distribution of controls was concentrated around estimated risks"

Conclusion/ImplicationsThe proposed method performed similar to other works based on specialized questionnaire data. Prescription of psychotropics, depression and anxiety, and self-harm were strongly linked with higher risks, followed by hospital admission and substance misuse. This is in keeping with known literature, providing evidence that the proposed approach is feasible.
Getting animated about routine data: co-producing a video and toolkit with young families
Authors: Lugg-Widger Fiona , Segrott Jeremy , Gee Peter , Kenkre Joyce , Robling Mike
Published: September 2018

IntroductionWhile public engagement activity in healthcare may include a focus on uses of patient data, use of social care/ education data may be relatively overlooked. The public may be less aware about how their social care data may be used. Some concepts such as benefits and risks may also differ.

Objectives and ApproachOur objective is to co-produce with members of the public a package of materials that researchers may then use to promote public understanding of and engagement in research that uses routine data from health and social care settings.

We are running workshops with two groups of young families (teenage mothers, some of whom have received specialist home-visiting support) to explore key concepts and messages about routine data use, including sensitive data such as maltreatment and regulatory child protection interventions. The workshop includes completing a story board for an animated video. This will be co-produced by the same young people.

ResultsOur recent work with young families has shown a preference for visual based methods over text-based approaches to explain routine data linkage. This represents a key opportunity for innovation. We have explored how members of the public understand and make sense of routine data from various settings (health/social care) and its use in research. Working with a professional animator, the co-production of a prototype animation aims to engage members of the public about the nature, benefits and safeguards of using routine public records in health and social care research. The presentation will summarise the method of co-production, review the prototype animation and associated materials for researchers (‘toolkit’). Finally, we will recap key messages identified from the workshops that underpin the animation and other dissemination plans.

Conclusion/ImplicationsOur existing understanding of public views (e.g. from consumer panels) may not represent well perspectives from population groups, such as teenage parents. Working with young families who may otherwise rarely engage in such work provides their unique perspectives, adding richness to our understanding of public views on routine data usage.
Distributed team health data science in risk of non-Vitamin K Oral Anticoagulant after Intracranial Haemorrhage
Authors: Song Jiao , Elliot Elizabeth , Morris Andrew , Kerssens Joannes , Akbari Ashley , Thompson Simon , Lyons Ronan
Published: September 2018

IntroductionNew non-vitamin K Target Specific Oral Anticoagulants (TSOACs) have a favourable risk-benefit profile and debatable cost effectiveness. Large numbers and data from multiple countries in a European study are required to investigate safety issue of TSOACs in subgroups, e.g. people with an intracranial haemorrhage.

Objectives and ApproachWe developed an approach to rapidly replicate data and analyses to support cross-country distributed research within the UK/EU using Electronic Health Records (EHRs). This project was conceptualised and initialled by linking relevant datasets held in multiple data warehouses, in Scotland with the Scottish National Data Safe Haven, and in Wales through the Secure Anonymised Information Linkage (SAIL) databank. Analysts in Edinburgh and Swansea had remote access to each other’s datasets and worked collaboratively to harmonise variables and analysis scripts. . A common R code script has been produced to harmonise individual data as well as the outputs from the study.

ResultsThe study screened data on 8M people to develop a cohort that included pseudonymised information of 4,153 individuals in Scotland and 2,676 individuals in Wales, 6,829 individuals in total. Standardised risk analyses were completed in both settings with ongoing work in combining the analyses. In Wales, 39.5% of the patients in the cohort had been admitted to hospitals due to serious vascular events or died caused by these events, after intracranial haemorrhage. Incident rates for male and female are 0.63 and 0.7 respectively. Within the cohort, 0.5% were prescribed with TSOACs and 3% with Warfarin (included as reference). The project is also in the process of including other European jurisdictions.

Conclusion/ImplicationsThe adopted approach was the simplest, yet most efficient and cost-effective method to ensure consistency in analysis and coherence with currently available governance systems of both safe havens. It can also be considered as an initialisation of developing infrastructure to support research using EHRs across the UK and EU.