bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing a Primary Care EMR-based Frailty Definition using Machine Learning
Authors: Aponte-Hao Sylvia , Mele Bria , Jackson Dave , Katz Alan , Leduc Charles , Lethebe Brendan , Wong Sabrina , Williamson Tyler 
Published: September 2018

IntroductionFrailty is a geriatric syndrome that is predictive of heightened vulnerability for disability, hospitalization, and mortality. Annually an estimated 250,000 frail Canadians die, and this estimate is expected to double in the next 40 years, as Canadians grow older. Currently there is no single accepted clinical definition of frailty.
Objectives and ApproachThe objective of this study was to develop an operational definition of frailty using machine learning that can be applied to a primary care electronic medical record (EMR) database.
The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is a pan-Canadian network of primary care practices that collect de-identified patient information (such as encounter diagnoses, health conditions, and laboratory data) from EMRs.
780 patients from CPCSSN have were randomly selected and assessed by physicians using the Rockwood Clinical Frailty Scale (as frail or not frail), and their clinical characteristics from CPCSSN used to develop the definition using machine-learning.
ResultsA total of 8,044 clinical features were extracted from these tables: billing, problem list, encounter diagnosis, labs, medications and referrals. A chi-squared automatic interaction detector (CHAID) approach was selected as the best approach. The bootstrapping process used a cost matrix that prioritized high sensitivity and positive predictive value. 10-fold cross validation was used for validity measures. Key features factored into the algorithm included: diagnosis of dementia (ICD-9 code 290), medications furosemide and vitamins, and use of key word “obstruction” within the billing table. The validation measures with 95% confidence intervals are as follows: sensitivity of 28% (95% CI: 21% to 36%), specificity of 94% (95% CI: 93% to 96%), positive predictive value of 53% (95% CI: 42% to 64%), negative predictive value of 86% (95% CI: 83% to 88%).
Conclusion/ImplicationsNo other primary care specific frailty screening tools have sufficient validity. These results suggest heterogeneous diseases require clearly defined features and potentially more sophisticated algorithms to account for heterogeneity. Further research utilizing continuous features and continuous frailty scores may be more suitable in the creation of a case detection algorithm.
Cross-Sectoral Data Linkage: Tracking Mental Health Service Utilization from Childhood into Adulthood
Authors: Schraeder Kyleigh , Cairney John , Carter Jeffrey , Vingilis Evelyn , Neufeld Richard , Barwick Melanie , Kurdyak Paul , Tobon Juliana 
Published: September 2018

IntroductionOf the 15-18% of children and youth in Canada with a mental health disorder, some receive specialized mental health (MH) services and need additional treatment as young adults. Lack of a shared database across child and adult sectors has prevented examining predictors of future MH health service use.
Objectives and ApproachWe examined predictors of mental health service utilization in adulthood, and compared a sample of youth who received specialized MH treatment and age-, sex-, and region- matched controls. Patient-level administrative data from five MH agencies funded by the Ministry of Children and Youth Services (MCYS) in Ontario, with population health sector datasets held at the Institute for Clinical Evaluative Sciences (ICES). We expanded previous definitions of coding a MH visit by including codes specific to long-lasting childhood MH diagnoses (e.g., Attention Deficit-Hyperactivity Disorder).
ResultsOur match rate for linking the MCYS treated youth with their population health data was 77%. Youth who received MH treatment (N= 2957) were twice as likely as matched controls (N= 8891) to have a MH visit in the medical system in adulthood (i.e., after age 18). The most common diagnostic codes for the first visit were anxiety, depressive disorders, and ADHD. The median survival time (when 50% had a visit) from age 18 to first MH visit was 3.3 years. In adjusted Cox regressions, significant predictors of having an adult MH visit included service use history in both medical and MH systems during childhood and adolescence (e.g., ongoing pattern of children’s MH service use).
Conclusion/ImplicationsThis study represents the first longitudinal, case-control cohort study in Canada to examine MH service utilization in the medical sector by youth treated for MH problems. The linkage of information from multiple datasets allowed for a broader understanding of MH service utilization across sectors of care, specific to children and youth.
Correspondence between self reported and GP coded alcohol consumption
Authors: Greene Giles , Trefan Lazlo , Farewell Daniel , Paranjothy Shantini , Akbari Ashley , Fone David 
Published: September 2018

IntroductionSelf reported data on health is often burdensome and expensive. Administrative data provides inexpensive global coverage of health and is less prone to social desirability for behaviors such as alcohol consumption. Recent studies have found varying correspondence between self-reported and administrative data for a number of chronic and acute conditions.
Objectives and ApproachWe sought to test the agreement between self-rated heavy drinking (35+ units for women, 50+ for men) and primary care physician (GP) coded (Read mapped ICD-10 codes) heavy drinking, and their association with alcohol related hospital admissions.
We assessed agreement between self-reported and GP coded heavy drinking data, from a prospective UK cohort and linked GP and hospital data using Cohen's kappa (95% CI). Then using Cox regression we the compared the time to alcohol related admission for both. Goodness-of-fit was assessed using AIC.
ResultsOf the 10,892 participants a total of 6,609 were linkable to both the Hospital Admissions database (PEDW) and the GP practice data, with no systematic differences between those linked and not. The sample were mostly female (57%) with a mean age of 48.2 years (SD = 15.5). In total there were 81 alcohol related admissions, with 2.7% classified as heavy drinkers by GPs and 31.7% self-rated. There was poor agreement between raters (K=0.044; 95% CI = 0.037, 0.050). GP coded heavy drinking was strongly associated with admission (β = 1.90 95% CI 1.34, 2.58, AIC = 1350.71), as was self-rating (β = 1.14 (5% CI 0.67, 1.62, AIC = 1358.19).
Conclusion/ImplicationsThe low levels of agreement but similar AICs suggest that self-rated and GP classification are measuring a similar construct but at different levels. This analysis suggest that routinely collected GP ratings of heavy drinking may be a cheaper and more efficient method of assessing heavy drinking at a population level.
Ontario’s stroke report cards: Cross-continuum data linkage allows evaluation of system of care
Authors: Hall Ruth , Khan Ferhana , Levi Jen , Ma Huiting , Martin Cally , Moher Cheryl , Kelloway Linda , Linkewich Elizabeth , Bayley Mark 
Published: September 2018

IntroductionReport cards or scorecards typically reflect one particular sector along the care continuum; however, stroke patients typically require acute care, inpatient rehabilitation and community care highlighting the need to link data sources to demonstrate the interdependencies between and across sectors.
Objectives and Approach1) Identify stroke best practice indicators from across the care continuum; 2) develop a one page report card that reports on the quality of the stroke system of care through data linkage and 3) visually impactful knowledge translation tool. The indicators cover five health care sectors starting with pre-hospital stroke symptom onset, then to management of the acute event, to institutional and community-based rehabilitative care, reintegration into the community and secondary prevention. The report card is a knowledge translation tool that identifies gaps in best practice, provides achievable benchmarks of regional and provincial stroke system performance to drive system change.
ResultsUsing data linkage techniques, seven administrative datasets are used to populate the 20 indicators in the annual Ontario stroke report card. Indicator performance was trended by comparing the previous 3 years’ results to the most recent year of data. Fifteen of 17 indicators improved (11 statistically significant) compared to the previous three years and 2 indicators did not change / declined. Performance benchmarks were calculated using Achievable Benchmarks of Care™ methodology and 14 of 16 performance benchmarks improved since 2014/15. There was wide variation across indicators with only 4 indicators showing a reduction in regional variation. The Ontario stroke report card can be viewed at https://www.ices.on.ca/Publications/Atlases-and-Reports/2017/Stroke-Report-Cards.
Conclusion/ImplicationsThe Ontario stroke report card spans the stroke care continuum, provides a snapshot of Ontario’s stroke system performance. Data linkage is essential for a system-wide opportunity to evaluate and influence system performance. This cross-continuum approach and report card format could be applied to other health related conditions.
Analysis of factors associated with changing general practice in the first 14 years of life in Wales using linked cohort and primary care records: implications for using primary care databanks for life course research
Authors: Akbari Ashley , Lyons Ronan , Bandyopadhyay Amrita , Bedford Helen , Brophy Sinead , Cortina-Borja Mario , Griffiths Lucy J , De Stavola Bianca , Tingay Karen , Dezateux Carol 
Published: September 2018

IntroductionPrimary care electronic health records (pcEHRs) are a valuable resource for life course research, however loss to follow up due to changing practices has received little attention. We investigated factors associated with changes in registration and record continuity in the Secure Anonymised Information Linkage (SAIL) databank, with ~80% practice coverage.
Objectives and ApproachWe analysed linked pcEHRs for 1834 (882 girls) Millennium Cohort Study (MCS) participants, resident in Wales and with parental consent to health record linkage at the age seven MCS interview. We studied time from first to next general practice (GP) registration in Wales by fitting Cox proportional hazards models, and estimated mutually-adjusted hazard ratios (aHRs) for the following factors: child (sex, ethnicity, mode of delivery, gestation, birthweight, neonatal illness, wheeze, longstanding illness); maternal (age, education, lone parent status); household (income, housing tenure, residential mobility, urban/rural residence); GP type (SAIL-contributing/-non-contributing). Analyses were weighted for survey design (Stata: Release 15; StataCorp LP).
ResultsThere were 3065 Welsh GP registrations for 1834 children. By age 5 years, 25% of children changed GP at least once, with 1070 (58.3%), 477 (26.0%) and 287 (15.7%) registered with 1, 2, 3+ GPs respectively up to 14 years of age. Children with older mothers (aHRs; 95% CI: 0.96; 0.95, 0.98; per year) or those residing in rural areas (0.75;0.56,0.99) were less likely, and those whose first registration was not with a SAIL contributing GP (2.16;1.60,2.93), whose mothers had no educational qualifications (1.40;1.15,1.71), or had recently changed address (1.62;1.21,2.16) more likely, to change GP. 305 (16.6%) children had never registered with a SAIL-contributing GP. Of 403 children initially registered with a SAIL contributing GP who then changed GP, 66.7% re-registered with a SAIL contributing GP.
Conclusion/ImplicationsGeographically contiguous primary care databanks, such as the SAIL databank, enable a high proportion of children to be reliably followed over time despite changing GP. Similar analyses of databases based on geographically disparate volunteer GPs are needed to quality assure their suitability for life course epidemiology research.
Multi-morbidity using General Practice drug chapters and the relationship with secondary healthcare utilisation in Wales, UK
Authors: Lyons Jane , Lyons Ronan , Akbari Ashley , Berridge Damon 
Published: September 2018

IntroductionMulti-morbidity and polypharmacy are increasing but are under investigated. Data linkage has much to offer in understanding trends in prevalence, inter-relationships between variables and impact on healthcare activity. We created Welsh population e-cohorts in 2000 and 2014 to study these issues, using the Secure Anonymised Information Linkage (SAIL) Databank.
Objectives and ApproachThe aim of this study was to measure changing prevalences of multimorbidity, initially through disease chapter prescribing and then to explore the relationship between the number of morbidities recorded in primary care and use of different hospital based outpatient services. Data linkage was used to create cohorts of Welsh residents registered to SAIL providing General Practices (GPs) for at least 360 days in 2000 and 2014. The 13 Read code drug chapters were used to calculate morbidity scores between 0 and 13. Proportional odds or cumulative logit models were used to relate GP recorded morbidities to outpatient attendance patterns.
ResultsThe GP cohorts included 1.6 million and 2.1 million population with 56.6% and 73.4% having at least one recorded morbidity for 2000 and 2014 respectively. In 2014, 5+ morbidities were most prevalent (61.3%) in 85+ year olds and least common (2.7%) in 5-9 year olds. Some 35% of individuals attended at least one outpatient specialty in 2014, varying from 22.4% for 5-9 year olds and 63.2% for 80-84 year olds.
Preliminary modelling results show the number of GP recorded morbidity chapters was strongly related to increasing outpatient attendances at different specialties, e.g. OR 15.3 (95%CI: 15.1-15.4) of being in a higher outpatient attendance category for the 5+ morbidity group relative to the zero morbidity group. Increasing age and female gender were associated with increased numbers of specialists attended whilst deprivation had a more modest impact.
Conclusion/ImplicationsThere has been a large increase in recorded multimorbidity across all age groups in Wales. In this exploratory cross-sectional design, multimorbidity was strongly related to increasing use of outpatient services. Further work is ongoing to define and utilise more refined multimorbidity metrics and incorporate longitudinal designs in analysis.
Intellectual Disability in Children with Congenital Heart Defects in Western Australia
Authors: Nembhard Wendy , Luo Chunqiao , Leonard Helen , Bourke Jenny , Bower Carol 
Published: September 2018

IntroductionCongenital heart defects (CHD) are diagnosed up to age 6 in 8-12/1,000 births annually in Western Australia. Recent improvements in management of infants with CHDs has significantly increased survival; approximately 85\% of infants with CHDs live beyond childhood. However, children with CHDs may have increased risk of life-long intellectual disabilities.
Objectives and ApproachWe conducted a study of 20,997 children to determine risk of intellectual disability (ID). All singleton, live born infants with CHDs born 1983-2010 were identified from the Western Australian Register for Developmental Anomalies, a statewide birth defects registry (n=6,968). Infants without CHDs born 1983-2010 were randomly selected from birth records (n=14,029). All data were linked to the Western Australia Midwives Notification System to obtain maternal and infant information. Children with ID were identified by linkage to the statewide Intellectual Disability Exploring Answers database. Risk ratios (RR) and 95% confidence intervals (CI) were calculated from multivariable logistic regression analyses.
ResultsOf 20,997 children, 965 (4.6%) had an ID; 1.3% of children without CHDs and 11.2% of children with CHDs had an ID (P<0.001). 0.2% of children without CHDs and 0.4% of children with CHDs had autism. Three percent of children with CHDs had a known biomedical cause for ID (excluding trisomy 21) and 4.4% of children with CHDs had trisomy 21. Children with CHDs had 9 times the risk of ID compared to children without CHDs (RR=9.30; 95% CI: 7.91, 10.94). Children with CHDs almost had a twofold increased risk of autism compared to children without CHDs (RR=1.78; 95% CI: 1.07, 2.95). The greatest risk of ID among children with CHDs was associated with trisomy 21 (RR=166.0; 95% CI: 78.5, 350.8).
Conclusion/ImplicationsChildren with CHDs have higher risk of ID than children without CHDs. Although the greatest risk for ID was for trisomy 21, children with CHDs still had increased risk of ID from other causes and all causes overall. Future research should elucidate the underlying etiology of ID in these children.
A Bayesian Network Model of the Relationships between Chronic Disease Indicators
Authors: Yuan Mengru , Buckeridge David 
Published: September 2018

Introduction We previous developed an informatics platform to: 1) generate large numbers of indicators of chronic conditions and determinants from heterogeneous sources, 2) present indicators in context of known causal relationships. However, the causality was defined by expert-consensus and only concerning direction. Quantitative estimates of causal effects are needed to drive public health decision-making.
Objectives and ApproachThe objective of this work is to quantify the strength of the relationships between chronic disease indicators through empirical analysis of data for a defined population.
Eight chronic diseases were explored and the individual data were obtained from linked administrative data for one million randomly sampled Montréal residents. We use Bayesian networks (BN) with our causal model based on expert consensus as a prior for the structure of the BN. In addition, we compare two networks estimated separately from individual-level data and data aggregated at the regional level, the latter being most commonly available to public health agencies.
ResultsBNs were developed using constraint-based and score-based algorithms for structure learning, and maximum likelihood for parameter estimation. We found that the BN structures and parameters learned from individual-level data differed from the one estimated from data aggregated by community health centers. Specifically, the BN structure learned from individual data contained 9 more arcs between indicators and tened to fit the data better (the Bayesian factor between two network structures was 25.55), however, the results from the aggregated data matched our prior understanding of epidemiological knowledge more closely.
Conclusion/ImplicationsConclusion: We compared BNs built using different resolutions of data as means to describe patterns among indicators for a defined population. This strategy for interpreting indicators combines prior domain knowledge with data and represents an initial step towards an intelligent decision-support tool for public health practitioners.
Establishing an International Data Linkage Repository Workgroup Toward a Benchmarking Repository
Authors: Kum Hye-Chung , Leonard Susan , Akgün Özgür , Alexander Trent , Antonie Luiza , Levenstein Margaret , O'Hara Amy 
Published: September 2018

IntroductionAccess to real data with diverse attributes is critical for effective development of any data analytic algorithm. Benchmarking data repositories have all been vital to the development of research communities focused on algorithm development. This work reports on the development of such a data repository for record linkage.
Objectives and ApproachEstablishing a common benchmarking repository of real data can propel a field to the next level of rigor by facilitating comparison of different algorithms, understanding what type of algorithms work best under certain real data conditions and problem domains, promoting transparency and replicability of research, and creating incentives for proper citations for contributions. In addition, benchmarking repositories can bring together the diverse stakeholders (e.g., computer scientists, statisticians, data custodians, data users including social, behaviour, economic, and health (SBEH) scientists) that can advance the field more effectively than could researchers from any single discipline.
ResultsIn Fall 2016, international leaders in record linkage formed a Data Linkage Repository workgroup (DLRep) to establish a benchmarking data repository for record linkage. The workgroup is working in collaboration with The Inter-university Consortium for Political and Social Research (ICPSR) to host the site data repository planned for release in Summer 2018. The repository for record linkage research will house various types of real data that require linking with metadata, unique handles for citations, proposed algorithms for evaluation criteria, and a platform for posting, sharing, and comparing results as well as citations of relevant papers. Some datasets will have the gold standard published that researchers can evaluate their results against. Other datasets will gather results to build the gold standard as a community.
Conclusion/ImplicationsRecord linkage methodology is important to domains where data needs to be integrated from multiple sources, including diverse disciplines. Establishing an international interdisciplinary research community around a benchmark data linkage repository to validate and compare linkage algorithms is crucial to fully realizing the social benefits of data about people.
Better than blocking: building and using multiple variable pattern indexes (MVPi) to screen data sources for possible matches and reduce linkage search space
Authors: Hermansen Sigurd 
Published: September 2018

IntroductionScaling up data linkage presents a challenging problem that has no straightforward solution. Lacking a prescribed ID in common between two data sources, the number of records to compare increases geometrically with data volume. Data linkers have for the main part resorted to “blocking” on demographic or other identifying variables.
Objectives and ApproachAmong the more efficient of better blocking methods, carefully constructed multiple variable pattern indexes (MVPi) offer a robust and efficient method for reduction of linkage search spaces in Big Data. This realistic, large-scale demonstration of MVPi combines 30,156 SSA Death Master File (DMF) and NDI matches on SSN with equal dates of death (true matches) and 16,332 DMF records with different or missing SSN, and links the total of 46,448 records on names, date of birth, and postal code (ignoring SSN) to >94MM DMF records. The proportion of true matches not linked tests for loss of information during the blocking phase of data linkage.
ResultsBlocking has an obvious cost in terms of completeness of linkage: any errors in a single blocking variable mean that blocking will miss true matches. Remedies for this problem usually add more blocking variables and stages, or make better use of information in blocking variables, requiring more time and computing resources. MVPi screening makes fuller use of information in blocking variables, but does so in this demonstration in one cohort (>30K) and DMF (>94MM) data sets. In this acid-test demonstration with few identifying variables and messy data, MVPi screening failed to link less than eight percent of the cohort records to its corresponding true match in the SSA DMF. MVPi screening reduced trillions of possible pairs requiring comparisons to a manageable 83MM .
Conclusion/ImplicationsThe screening phase of a large-scale linkage project reduces linkage search space to the pairs of records more likely to be true matches, but it may also lead to selectivity bias and underestimates of the sensitivity (recall) of data linkage methods. Efficient MVPi screening allows fuller use of identifying information.