bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Longitudinal Analysis of the Families First Screening Program in Manitoba, Canada: Cleaning, Validating and Linking via Health Registry Data
Authors: Au Wendy , Brownell Marni , Chartier Mariette , Santos Rob 
Published: September 2018

IntroductionManitoba Public Health Nurses (PHNs) attempt to visit all families with newborns shortly after discharge from birth hospitalizations. Since 2000, PHNs have completed the Families First Screen (FFS) at these visits, to identify families at risk for child maltreatment. The information captured in FFS is a valuable tool for research.
Objectives and ApproachOur objective was to clean and validate FFS data and link to health data in the Manitoba repository in order to determine the percent of births in Manitoba hospitals that had FFS. We identified all babies born in Manitoba hospitals 2000-2015 using ICD-9-CM /ICD-10-CA codes. Mothers were identified through the Health Registry (Mom_Baby Link File) using scrambled Personal Health Identification Numbers (sPHINs). FFS data were linked to births via baby’s sPHIN. Determining which FFS records linked to babies required several steps of cleaning and validating the data to account for differences in birthdates between files, missing sPHINs, and multiple records.
ResultsFor example, in 2014 there were 16,079 births and 14,002 FFS records; 13,524 FFS had mother and/or baby sPHIN. For those missing baby sPHIN (9,295), 99.8% were retrieved via the Mom_Baby Link File. Linking the FFS to the hospital births we found: 3,043 births didn’t have an FFS; 12,762 had a single FFS, and 274 births had multiple FFS (i.e., baby associated with more than one mother, FFS and/or form date). To ensure that the baby was only associated with one mother and one FFS the most current FFS was kept. We found that in 2014, 81.07% (13,036/16,079) of the births had an FFS. In the longitudinal analysis, the percent of births with an FFS ranged from 74.6% in 2000 to 81.1% in 2014.
Conclusion/ImplicationsWe were able to achieve good linkage between FFS and health registry data, allowing this rich data source to be used for research on maternal and child health. Information on percent of births with FFS has been shared with policy-makers over the years and changes to screening practices implemented.
A Data Science Approach to Predictive Analytic Research and Knowledge Translation
Authors: Fisher Stacey , Talarico Robert , Sequeira Yulric , Bennett Carol , Spruin Sarah , Hsu Amy , Tanuseputro Peter , Manuel Doug 
Published: September 2018

IntroductionCurrent approaches to the development and application of predictive studies is inefficient and difficult to reproduce. Thousands of predictive health algorithms have been developed; however, less than 2\% have been assessed outside their original setting and even fewer have been applied and evaluated in practice.
Objectives and ApproachObjective: To develop a standardized workflow for algorithm development, dissemination and implementation.
Existing predictive analytics workflow and open standards were adapted and expanded for health research and health care settings. The approach was designed to work within multidisciplinary teams and to improve research transparency, reproducibility, quality, efficiency and application. Key components include standardized algorithm description files, documentation and code libraries. All libraries and programming packages, which were created for/with open-source software, can be used for a wide range of statistical and machine learning models. Publicly-available repositories contain the algorithms, validation data, R code and other supporting infrastructure.
ResultsAlgorithm development involves variable pre-specification and documentation of model variables, followed by creation of data preprocessing code to generate model variables from the study dataset. Preprocessing uses algorithm specification documentation and a function library, building upon and integrating with existing algorithms when possible to preventing code duplication. Models are output as a Predictive Modelling Markup Language (PMML) file, a portable industry standard for describing and scoring predictive models. A separate scoring "engine" is used to implement PMML-described algorithms in a range of settings, including algorithm validation at other research institutions. Algorithm applications currently include the Project Big Life (www.projectbiglife.ca) online calculators, population, health services and public health planning uses and an algorithm visualization tool. An API permits use of the calculator engine by other organizations.
Conclusion/ImplicationsBarriers to the implementation of predictive analytics in real-world settings—such as within electronic medical records or decision aid applications—can be mitigated with well described algorithms that are easy to replicate and implement, especially as access to big health data increases and algorithms become increasingly complex.
International Comparison in Opiate Prescribing for New Users in Primary Care using Electronic Medical Record Data
Authors: Tamblyn Robyn , Girard Nadyne , Habib Bettina , Dixon William , Jani Meghna , Bates David , Haas Jennifer 
Published: September 2018

IntroductionThe opioid epidemic in North America has, in part, been attributed to an increase in opiate use for non-cancer pain and the prescription of more potent molecules. In contrast, the United Kingdom appears unaffected by this crisis, possibly because of differences in primary care prescribing, or health system policies.
ObjectiveTo determine if there are differences in opiate prescribing for new users in primary care in the United Kingdom, United States, and Canada.
ApproachElectronic health record data from Quebec, Canada (MOXXI), the United States (Partners Health Care, Boston MA), and the United Kingdom (CPRD random sample of 600,000) were used to identify new users of opiates (no prior prescription in 2 years), at least 18 years old between 2006-2016. Cancer patients were excluded after harmonizing equivalent READ and ICD9/10 codes. Generic drug names in each jurisdiction were mapped to the WHO ATC classification, and characterized using morphine milligram equivalents (MME).
ResultsOverall 655,877 new users were identified, of whom 78% of 58,286 (U.S.), 88% of 6,251 (Canada), and 96% of 600,000 (UK) were non-cancer patients. Mean age of new users was 49 (SD 16) in the US, 57 (SD 16) in Canada, and 52 (SD 19) in the UK. 57.6% (UK) to 67.3% (US) of new users were women. In the UK, 86.5% of patients were started on codeine (MME:0.15), compared to 43.9% in Canada and 8.5% in the U.S. In the U.S 65.0\% were started on oxycodone (MME:1.5), and 10.9% on hydrocodone (MME:1). In Canada, tramadol (18.2%; MME: 0.1) followed by oxycodone (13.2%) were the next most commonly prescribed drugs.
Conclusion/ImplicationsSubstantial differences in opioid prescribing practices for non-cancer pain were observed between the UK and Canadian and United States sites. The predilection to start patients on more potent opiates in North America may be a contributing cause to the opiate epidemic.
Defining a GP consultation
Authors: Lugg-Widger Fiona , Jones Hywel , Angel Lianna , Cannings-John Rebecca , Lau Mandy , Hughes Kathryn 
Published: September 2018

IntroductionThe method used to define a GP-consultation using routine data will vary depending on the data source and research question. However, without consensus the approach will be tackled by numerous researchers and invariably result in different definitions. The LUCI study is assessing outcomes of childhood UTI using routine data sources.
Objectives and ApproachWe are interested in GP urine sampling rate and re-consultation rate. The GP dataset includes multiple clinical (acute, routine monitoring, prescribing, results) and administrative codes. A GP consultation therefore needed to be defined. GP read code frequencies for our study cohort of children were produced. One researcher reviewed the codes that represented 80% of the data and classified them as a consultation or not. Where there was uncertainty this was independently reviewed by two clinicians and discrepancies agreed by the management team.
ResultsWe will present the decisions and assumptions made for this study in defining a GP consultation. 25,623 Read Codes represented the consultations for our study cohort of which 460 codes represented 80% of the consultations. The remaining 20% of codes represented <0.032% of records each. The 460 Read Codes were reviewed and following independent review and agreement from the management group 149 Read Codes were excluded. These were grouped into the following categories Administrative (e.g. Incoming Mail Processing, Ethnicity codes), Vaccinations, Child Health (e.g. First Smiled), Asthma and Medication Review. The GP consultations to be included in the analysis were then restricted to exclude these codes. This has been an important step in cleaning and understanding the data prior to analysis.
Conclusion/ImplicationsA consultation was defined for this study however there will be pros and cons to this method. In lieu of a consensus approach to the method this work is the first step to sharing approaches to help researchers with the same challenge.
Cognitive development Respiratory Tract Illness and Effects of eXposure (CORTEX) project: Combining high spatial resolution pollution measurements with individual level data, a methodological approach.
Authors: Rodgers Sarah , Lyons Jane , Mizen Amy , Berridge Damon , Akbari Ashley , Carruthers David , Davies Gwyneth , Dearden Lorraine , Doherty Ruth , Lake Iain , Mavrogianni Anna , Milojevic Ai , Strickland Sarah , Wilkinson Paul 
Published: September 2018

IntroductionThe Secure Anonymised Information Linkage (SAIL) databank facilitated linkage of routinely collected health and education data, high spatial resolution pollution modelling and daily pollen measurements for 18,241 pupils in 7 cross-sectional cohorts across Cardiff city, UK, to investigate effects of air quality and respiratory health conditions on education attainment.
Objectives and ApproachAn urban atmospheric dispersion and chemistry modelling system (ADMS-Urban) simulated modelled hourly concentrations of air pollutants: PM2.5, PM10, NO2 and ozone levels. These were summarised into minimum, average and maximum daily readings for 4 time periods (e.g. school hours 9am-3pm) for all home and school locations across Cardiff between 2009 and 2015. The combination of different pollutants, measurements and time-periods created a comprehensive multi-row dataset per location. We transformed the dimensionality of this high-resolution data to create one row of summarised data per pupil per cohort, in preparation for statistical analysis.
Results157,361 school and home locations across Cardiff were anonymised and household linkage fields were appended to combine pollution estimates at the household/school to individual health data. The pollution dataset contained 369 columns, 472,083 rows per year with one column per location, pollutant type, pollutant measurement, daily time-period, and day of year. Dataset transformation reduced algorithm computation by creating a single date column, producing a five column, 3,446,205,900-row matrix per year dataset. The algorithm adjusted for weekends, school/bank holidays and allowed location to vary 3pm-5pm on school days when pupil location was uncertain. The algorithm calculated tailored pollution exposures per pupil for revision and examination periods, creating one row per pupil and reducing 7 years of data and 24 billion rows to 18,241.
Conclusion/ImplicationsWe successfully linked 95% of the cohorts’ household/school pollution data to their corresponding health and education data. This demonstrates data linking retrospective exposures for total populations using multiple daily locations, and extends our analysis platform for natural experiments to include daily exposure. Future work includes adding modelled route exposures.
Trends in Socioeconomic Inequalities in Ischemic Heart Disease, 2000-2012
Authors: Smith Brendan , Ramraj Chantel , Smith Peter , Chen Hong , Tu Jack , Manson Heather , Rosella Laura 
Published: September 2018

IntroductionLow socioeconomic position (SEP) is an important risk factor for ischemic heart disease (IHD). Current surveillance methods use area-based SEP measures to monitor trends in socioeconomic inequalities in IHD. The extent to which these methods underestimate individual-level socioeconomic inequalities in IHD is unclear.
Objectives and ApproachThe study objective was to estimate socioeconomic trends in IHD by household income and material deprivation in Ontario from 2000 to 2012. A pooled cross-sectional study was conducted using data from 6 Canadian Community Health Survey (CCHS) cycles (2000-2012) linked to the Discharge Abstract Database (n= 119,529 over 35 years of age, 55% female). Relative-weighted Poisson regression models were used to estimate IHD prevalence rates (adjusted for age, sex, ethnicity and immigration) across quintiles of equivalized household income and area-level material deprivation. Socioeconomic inequalities were estimated using the slope index of inequality (SII) and relative index of inequality (RII).
ResultsSocioeconomic inequalities in IHD were observed across income and material deprivation quintiles. Measured using the SII, adjusted IHD rates were 345 per 10,000 (95%CI: 207,483) higher at the bottom of the income distribution than the top in 2000, decreasing to 167 per 10,000 (95%CI: 40,293) by 2012. These differences represented 2.52 (95%CI: 1.58,3.46) times higher IHD rates in 2000, an increased risk that remained in 2012 (RII: 1.80, 95%CI: 0.97,2.63). A similar pattern was observed across material deprivation quintiles, however with smaller absolute and relative inequalities observed in 2000 (SII:195 per 10,000, 95%CI:79,312; RII:1.64, 95%CI:1.16,2.11) and 2012 (SII:142 per 10,000, 95%CI:16,268; RII:1.54, 95%CI:0.94,2.14).
Conclusion/ImplicationsConsistent socioeconomic inequalities in IHD were observed in Ontario, with an absolute reduction between 2000 and 2012. Area-level material deprivation underestimated individual-level socioeconomic inequalities in IHD.
Developing trauma mortality prediction models to measure injury severity
Authors: Walters Angharad , Cook Alan , Gabbe Belinda , Lyons Ronan 
Published: September 2018

IntroductionInjury severity measurement is integral to meaningful benchmarking and injury prevention strategies. Numerous injury mortality prediction methods have been developed and advanced, however, no consensus on the best model has been reached.
Objectives and ApproachThe International Collaborative Effort (ICE) on Injury Statistics propose to develop a set of trauma mortality models for use in diverse settings by deriving and validating models with data from member countries including Wales, Australia, New Zealand and USA. The ICE team will create a definitive list of injuries required to identify trauma patients using ICD-10 codes. Models will be developed using Welsh data then validated with data from other member nations. The outcomes of interest are in-hospital and 30-day mortality. Models will be used for country benchmarking by comparing the distribution of injury severity and outcomes between nations.
ResultsInitial results from replicating the model published by Wada, et al., as closely as possible using 348,433 cases held in SAIL for the years 2000 to 2013 achieved an AUROC value of 0.908 (95% CI 0.905-0.911). This model included 38 injury indicator variables, age, sex and comorbidity score. From 2009 onwards, the estimated number of deaths exceeded the actual number of deaths indicating improving risk adjusted survival. We aim to further enhance these models with additional covariates by linking with critical care data to enable us to determine the level of support patients received during their hospital stay, linking with laboratory data to provide indications of multiple organ dysfunction, acute physiological response and infection, and with GP data to incorporate measures of frailty.
Conclusion/ImplicationsUsing multi-sourced population based linked data allows us to develop a suite of enhanced mortality models for use in observational and interventional research. Applying these methods to data from different countries will allow comparisons to be made of trends in severity and outcomes and support collaborative research.
Integrated Data Enabling Statistics and Analysis (IDEAS) for UK Statistics
Authors: Wroth-Smith Jonathan 
Published: September 2018

IntroductionThe Office for National Statistics (ONS) is transforming the way it produces statistics and how it facilitates policy and academic research. Legislative change will enable access to administrative and commercial data to underpin this transformation but only through an innovative record linkage will the full benefits of transformation be realised.
Objectives and ApproachGreater access to non-survey data brings with it huge opportunities for both social and economic statistics in the UK - significant improvements in timeliness and geographic fidelity. There are also risks in working with large, complex datasets which have not been compiled for statistical analysis. ONS are developing the Integrated Data Enabling Analysis and Statistics (IDEAS) initiative to provide ONS staff to linked datasets, without constructing a large database of everything. The approach involves routinely linking data to three indices (person, address, business) and assigning unique reference numbers. Data are then de-identified but can be re-joined for defined projects.
ResultsThe session will set out case studies of linkage work that either have already been taken forward using IDEAS or those which are planned. Particular emphasis will be given to how IDEAS can facilitate research across a range of statistical research topics by linking on person, address and business.
By making anonymised linked data available in a safe setting the IDEAS framework also has the potential to facilitate research across government and across academic research. ONS have ambitious plans to use this approach to draw together expertise from across both sectors to be able to realise the benefits of linked where knowledge is shared on data quality, methods and previous research undertaken.
Conclusion/ImplicationsThe IDEAS framework is as step change in the production of official statistics. With appropriate legal, ethical and security controls, linked data within IDEAS can be repeated and refined for different applications. The framework enables collaboration across disciplines and has the potential to be using across government and academia.
Designing and Implementing a Privacy Preserving Record Linkage Protocol
Authors: Gee Tom , Behan Brendan , Lefaivre Shannon , Azimaee Mahmoud , Dharsee Moyez , El Emam Khaled , Yang Julie , Vaccarino Anthony , Evans Kenneth , Victor J. Charles , Theriault Elizabeth 
Published: September 2018

IntroductionThe Ontario Brain Institute has developed Brain-CODE, an informatics platform, to support the acquisition, storage, management and analysis of multi-modal data. The standardized research data within Brain-CODE spans several brain disorders, allowing for integrative analyses, while also providing the opportunity to leverage existing clinical administrative data holdings through external linkages.
Objectives and ApproachWithin Ontario, the majority of individuals who access the healthcare system have a unique identifier, the Ontario Health Insurance Plan (OHIP) number. The OHIP number can facilitate linkages with administrative data holdings, such as those at the Institute for Clinical Evaluative Sciences (ICES). Given that OBI is not permitted under Ontario’s privacy legislation to hold OHIP numbers, identifiers for consented participants are encrypted using a public key mechanism upon entry into Brain-CODE, where the private key is inaccessible. To facilitate linkages involving OHIP numbers between Brain-CODE and ICES, Brain-CODE Link software was co-developed by members of the Indoc Consortium.
ResultsBrain-CODE Link allows a deterministic linkage between encrypted identifiers (OHIP numbers), without revealing participant identity. The same homomorphic encryption algorithm applied to identifiers upon entry to Brain-CODE, is applied to relevant identifiers within ICES data holdings. Encrypted identifiers from Brain-CODE are securely transferred to ICES, where a comparison computation calculates differences between the encrypted sets. These differences are sent to a semi-trusted third party, who has no access to the original data, to decrypt the differences using the private key. A zero difference indicates a set of matching identifiers. One of the main challenges during testing and development of Brain-CODE Link was ensuring the software was capable of scaling to a population level, performing a large number of comparisons, in a computationally efficient manner.
Conclusion/ImplicationsOngoing pilot projects within the areas of epilepsy, neurodevelopment disorders, and neurodegeneration will be the first examples of linkages between Brain-CODE and ICES. Brain-CODE Link has successfully performed several billion test comparisons, indicating its suitability to function as a scalable privacy preserving record linkage to support comprehensive analyses.
Identification of Frailty using EMR and Admin data: A complex issue
Authors: Katz Alan , Wong Sabrina , Williamson Tyler , Taylor Carole , Peterson Sandra 
Published: September 2018

IntroductionFrailty is a state of vulnerability to diverse stressors emphasizing the importance of identifying the frail to support them. The burden of frailty in Canada is steadily growing. Today, approximately 25% of people over age 65 and 50% past age 85 – over one million Canadians – are medically frail.
Objectives and ApproachTo develop an administrative data definition of frailty to facilitate clinical and health system planning. We will validate the definition by linking the administrative data to electronic medical records (EMR) data. The EMR definition is based on a Machine Learning binarized frailty flag for patients with a Rockwood Clinical Frailty Score > 5 on physician chart audit. The sensitivity of the Machine Learning was disappointing: 28% (95% CI: 21% to 36%).specificity was: 94% (95% CI: 93% to 96%), positive predictive value: 53% (95% CI: 42% to 64%), negative predictive value: 86% (95% CI: 83% to 88%).
ResultsThere was little overlap between the EMR and administrative data definitions using the same population. Of the 29,382 eligible administrative data community dwelling patients over 65 years old, with a linkable EMR record, 2398 (8.15%) were identified as frail using the administrative data definition, but only 16.1% of these were frail according to the EMR definition. Of the 2396 who were identified as frail in EMR data, only 375 (15.7%) were identified as frail using the administrative data definition.
Conclusion/ImplicationsWe are not yet able to develop a reliable administrative data definition of frailty to identify community living individuals to support health service planning. The lack of agreement between the results obtained from EMR and administrative data definitions suggests that further refinement is necessary. Identification of frailty remains complex.