bluuebunny/crossref_metadata_2025_split huggingface dataset:
Child Health Clinical Outcome Review Programme: The Mental Healthcare of Young People and Young Adults
Authors: Wood Sophie , Rees Sarah , Wang Ting , Marchant Amanda , Akbari Ashley , John Ann 
Published: September 2018

IntroductionThe diagnosis, management and services available for mental disorders are of growing concern and controversy in the UK. Transitional care between child and adult services and the interface between primary and secondary/ specialist services is often disjointed and thresholds for referral to Child and Adolescent Mental Health Services are high.
Objectives and ApproachRoutinely collected healthcare datasets and data linkage were used to identify patterns of healthcare utilisation by young people and young adults with mental health disorders across the four UK Nations. We explored the extent to which routinely collected datasets can contribute to an assessment of the health needs and the quality of care that children and young people with mental health disorders receive.
Data was requested from the national data providers in each country. A series of descriptive analyses were performed and methods were developed for cross- national comparisons to be made (e.g. Four Nation Person Spell).
ResultsIt is feasible to explore healthcare utilisation across the four countries of the UK using routine data. However the recording, availability and access varied considerably between countries, making meaningful comparisons challenging.
Descriptive analyses showed strong deprivation gradients in the diagnoses and care provided for young people and young adults with mental health disorders. Depression and anxiety were the most commonly recorded mental health conditions in primary care. In secondary care drug/alcohol disorders and self-harm were the most commonly recorded. Re-admissions to emergency departments were higher for those admitted for self-harm or psychiatric conditions.
Conclusion/ImplicationsRoutine data has the potential to make a difference to care. However collection and access needs to be standardised in order to improve efficiency and effectiveness in improving the care for children and young people with mental health disorders. MQ has funded an Adolescent Data Platform to facilitate this.
Methods for identifying health state transitions from administrative data: the case of metastasis in prostate cancer
Authors: Mitsakakis Nicholas , Bremner Karen , Croxford Ruth , Abrahamyan Lusine , Ryan Welson , Carcone Steven , Krahn Murray 
Published: September 2018

IntroductionHealth administrative data are a rich source of population-based information, useful for building state transition models for medical decision making. These models require identification of health state transitions and associated times. Indirect methods are needed to predict this information, as it is rarely available in administrative data.
Objectives and ApproachWe considered a set of criteria to identify transitions to metastasis for prostate cancer patients in administrative data, utilizing dates of diagnostic and medical billing codes for secondary malignancy, palliative radiation therapy, chemotherapy and bone disorders or procedures. We evaluated the criteria using the true date of metastasis from medical charts of 195 patients linked to health care administrative data in Ontario, Canada. We also built a recursive partitioning tree to optimally combine these criteria and construct rules for identifying metastatic patients. For the evaluation, both misclassification and discrepancy between true and predicted dates for the true positives were considered.
ResultsCriteria involving chemotherapy drugs or hospital visits with secondary malignancy ICD10 diagnosis gave the best results, with high sensitivity and specificity. Criteria involving bone related problems, radiation therapy or diagnosis of metastatic cancer in physician billing data were very specific but not sensitive. The criterion involving prescriptions for narcotics was sensitive but not specific. The fitted tree was parsimonious involving only two of the criteria, while improving the accuracy over individual criteria. Most criteria gave a “delayed” prediction, with criterion based on chemotherapy giving on average the smallest delay, as well as exhibiting the least variability. Criteria involving narcotics and bone related problems predicted metastasis date very prematurely, probably triggered by conditions other than prostate cancer.
Conclusion/ImplicationsSeveral criteria from administrative databases satisfactorily classified prostate cancer patients with metastasis. A classification tree was built and improved the results over single criteria, demonstrating the added benefits in using advanced statistical learning methods for this task. However, “transition to metastasis” dates were predicted inaccurately, often with significant delay.
Internal and External Data Linkage of Complex Relational Database: Results from CorHealth Ontario
Authors: Fang Jiming , Qiu Feng , Wang Xuesong , Fischer Hadas , Tu Jack 
Published: September 2018

IntroductionCorHealth Ontario, formerly Cardiac Care Network (CCN), maintains a registry of patients undergoing select cardiac procedures/surgeries in Ontario, Canada. This population-based database contains over 35 datasets with complex structure, linked by unique primary key or multiple keys.
Objectives and ApproachWe aimed to simplify the complex CorHealth database so that research analysts could create study cohorts more efficiently and effectively, and to enrich the study cohort by getting more clinical information through database linkage. Through internal linkage, we could combine clinical fields from multiple CorHealth datasets. While the CorHealth dataset may not have all the clinical information needed for a given study, we may link the CorHealth study cohort externally to other administration databases to obtain additional fields via the probability matching (i.e., identical patient ID, hospital ID and procedure/surgery date).
ResultsAfter identifying the primary keys on the relational database flowchart, we designed new data structures by combining similar topic datasets. The total number of datasets was reduced from 35 to 13. This simplified CorHealth dataset includes one main CorHealth dataset (including demographic information, referral data, comorbidities) plus 12 other linkable specific datasets (including stent, vessel, TAVI, STEMI). Through internal linkage, we can get the stent numbers, lengths and types of Percutaneous Coronary Interventions from the Stent dataset. Linking to Discharge Abstract Database (DAD), we can get the hospital length of stay and the episode of care of hospital transfer for each procedure; linking to The Ontario Health Insurance Plan database (OHIP), we can find the graft numbers and vessel types of Coronary Artery Bypass Graft.
Conclusion/ImplicationsTo improve the research capacity and increase the value of the CorHealth database, analysts could create enhanced cardiovascular study cohorts derived from the simplified CorHealth database, plus internal linkage from other CorHealth datasets, and external data linkage from population-based administrative sources. We have accomplished three reports (PCI/CABG/TAVI) accordingly in 2017/18.
Patient-Reported Outcomes Improves the Prediction of In-patient and Emergency Department Readmission Risks in Coronary Artery Disease
Authors: Southern Danielle , Norris Colleen , Quan Hude , Santana Maria , James Matthew , Wilton Stephen , Lawal Oluwaseyi , Liang Zhiyang , Awosoga Oluwagbohunmi , Graham Michelle , Ghali William , Knudtson Merril , Sajobi Tolu 
Published: September 2018

IntroductionCoronary Artery Disease (CAD) patients are known to report higher healthcare resource use, such as inpatient [IP] and emergency department [ED] readmissions, than the general population. We investigate if the patient reported outcome measures (PROMs) improve the accuracy of readmissions risk prediction models in CAD.
Objectives and ApproachPatients enrolled in the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) registry between 1995 and 2014 who received catheterization (CATH) and completed baseline PROMs were linked to discharge abstract data and national ambulatory data. Logistic regression (LR) was used to develop 30-day and 1-year readmissions risk prediction models adjusting for patients’ demographic, clinical, and self-reported characteristics. PROM was measured using the 19-item Seattle Angina Questionnaire (SAQ). The discriminatory performance of each prediction model was assessed using the Harrel’s c-statistic for LR.
ResultsOf the 13,264 patients who completed baseline SAQ, 59 (0.3%) had IP readmissions or ED visits within 30 days, and up to 356 (1.9%) within 1 year of baseline survey. The C-statistics for one-year readmissions risk prediction models that only adjusted for demographic and clinical variables only ranged between 56.4% and 61.2%. The prognostic improvement in the discrimination of these models ranged between 2% to 10% when patient-reported SAQ was included as predictor. The addition of SAQ improves the model discrimination in all types of admission.
Conclusion/ImplicationsThe addition of PROMs improves the moderate accuracy of readmissions risk prediction models. These findings highlight the need for routine collection of PROMs in clinical settings and their potential use for aiding clinical and policy decision-making and post-discharge outcomes monitoring in the management of cardiovascular diseases.
Social Class and Hospitalization in Canada
Authors: Godley Jenny , Tang Karen , Ghali William 
Published: September 2018

IntroductionDespite the existence of a universal health care system in Canada, there remains an inverse relationship between social class and health (Frohlich 2006). Those who identify as lower social class (operationalized with various indicators, including education, income, and occupation) have poorer outcomes across multiple health measures (Tang 2016).
Objectives and ApproachThis study examines the link between social class and health care utilization, specifically hospitalization, in Canada. First, we examine the relationship between different indicators of social class and rates of hospitalization; next, we look at cause-specific hospitalizations. Using the unique dataset that contains the linked data for the 2006 Census with the Discharge Abstract Database for 2006-9, we explore the following research questions:

Are the three main indicators of social class, education, income, and occupation, individually correlated with hospitalization rates overall, controlling for age and gender?
Are certain indicators of social class more highly correlated with hospitalization rates, controlling for other indicators?

ResultsWe access the linked files provided by Statistics Canada in the Prairie Research Data Centre. The long-form Census represents approximately 20\% of the Canadian population. The DAD includes data on hospitalizations in acute care facilities in Canada, with the exception of those in the province of Quebec. Approximately 4,650,000 long-form respondents were eligible for linkage to the DAD, and approximately 5.3\% of Census respondents were linked to at least one DAD record between 2006 and 2009. Our analyses are ongoing, but initial results suggest an inverse relationship between hospitalization and various measures of social class. Full results will be made available for presentation following vetting by Statistics Canada personnel.
Conclusion/ImplicationsThis data provides us with a unique opportunity to examine the relationship between the detailed and rich measures of social class collected in the long-form Census and the comprehensive hospitalization data provided by the DAD records. Results will have implications for hospital health care provision across Canada.
Unconsented linkage between dormant trials and administrative data: practical and regulatory implications
Authors: Verfuerden Maximiliane , Fewtrell Mary , Kennedy Kathy , Lucas Alan , Jerrim John , Harron Katie , Gilbert Ruth 
Published: September 2018

IntroductionHalf of all infants are fed formula milk. However, attrition biases evidence on the long-term safety of formula ingredients. We used unconsented linkage between administrative education and health records of young people who were randomised as infants to formula milks, to determine long-term safety and efficacy.
Objectives and ApproachWe used record level data from a series of 9 historical randomised controlled trials (RCTs) conducted in 1982-2002 (n=3,500 participants), which are key to the evidence-base around formula-composition. All later follow-ups are biased by attrition leading to limited evidence around the long-term effects of formula ingredients on cognition and metabolic and cardiovascular health. We sought permissions from data providers and regulatory agencies for unconsented linkage to education and hospital records, as proxy measures for cognitive and health development. We discuss the steps that were implemented to safeguard the participants' privacy and achieve ethical and multi-institutional approval for this project.
ResultsAchieving provisional ethical approval took 41 days. Achieving agreement in principle to match trial data to individual level education records took 4 months and 2 weeks, while agreement to match trial data to individual level hospital records is still underway (5.5 months in February 2018). Delays in institutional approval were largely due to unharmonised data security certificates between the two government departments holding the health and education records. Digitising and cleaning all handwritten RCT participant identifiers prior to linkage took 9 months of full-time researcher time. Maintaining separation of identifiers and attribute data required specific secure haven provision. Results on the success of linkage between RCTs and education records will be presented at the conference.
Conclusion/ImplicationsWhile directly contributing to the evidence around infant-formula-composition, this project will also act as a proof-of-concept study. Unconsented linkage between dormant RCTs and administrative data could be a novel and cost-effective method to generate evidence on the long-term efficacy and safety of interventions.
Lessons from the past: A window on the future
Authors: Katz Alan , Brownell Marni , Smith Mark 
Published: September 2018

IntroductionThe Manitoba Centre for Health Policy has provided international leadership in organizing and accessing administrative databases, linking and analyzing data and translating the findings of research into policy for three decades. During this period, MCHP has addressed numerous challenges in each of these areas.
Objectives and ApproachLinked data research is expanding rapidly in terms of access to new data sources, different types of data, sharing of data across jurisdictions, and advances in data analytics. Technical advances such as computing power and artificial intelligence support these developments while governance structures and ethical issues challenge them. This presentation will describe some of the challenges MCHP has met with a view to gaining insight into how solutions evolved and how experience can guide the future of linked data research.
ResultsThe scaling up of linked data research will need to address specific challenges including de-identification of free text, accessing and linking data from private enterprise such as wearables, and interdisciplinary collaboration to incorporate new techniques developed by computer scientists. Cross-jurisdictional data analysis presents challenges in addressing differences in data architecture. Inter-jurisdictional and international data sharing create ethical and governance challenges. Experience has demonstrated the critical role that relationship building plays in addressing each of these. These relationships are different depending on the partners. They are all based on the development of common use of language, understanding the motivation and concerns of each party, clearly articulating the benefits of the relationship and data use and attention to the cultural and political environment.
Conclusion/ImplicationsLessons from the past can guide us in addressing challenges posed by the exciting opportunities available to us all. While many of these challenges will be solved with technical solutions, we should not overlook the importance of human relationships in building a culture of trust and collaboration as we move
Facilitating Patient Recruitment Process for Research
Authors: Li Bing , Manns Braden , Raso Jim , Saunders Terry , Bakal Jeff 
Published: September 2018

IntroductionThe Assessing outcomes of enhanced Chronic disease Care through patient Education and a value-based formulary Study (ACCESS) conducted from the University of Calgary trial is seeking 4700 low-income Albertans over the age of 65 years at high risk for cardiovascular morbidity and mortality. Recruitment efforts using advertising, conventional methods including posters and brochures in pharmacies have been challenging. The use of admail was attempted but fewer than 260 people (out of nearly 122,000 letters mailed) were enrolled.
Objectives and ApproachThe objective was to determine if linking data collected by Alberta Health Service (AHS) could identify eligible patients and facilitate recruitment for the study.
We extracted cohorts of data based ICD codes. These patient’s data were linked with Admission, Discharge and Transfer (ADT) and Master Patient Index (MPI) data to pull patient’s names, addresses and postal codes. Deceased and previously contacted patients were eliminated. The final patient name-list from the Analytics team was merged with a notification letter from Research Administration and sent by the data communication team to candidate patients. Interested patients contacted the researchers. Once informed consent was obtained, the data communication team sent the study questionnaire to the patients directly.
Results30,343 eligible patients were identified in Calgary and 23,305 in Edmonton. Out of 13825 people contacted, 304 people were enrolled into the study – a significantly higher rate than using other mail-based methods.
Conclusion/ImplicationsBy linking various health administrative data, we assisted researchers to identify potential participants who would otherwise be inaccessible and geographically dispersed across Alberta. This effectively facilitated the recruitment process and enabled patients from across the province to participate with minimal investments.
Is uptake of disability-related social security benefits modified by demographic, social and area-level factors?
Authors: O'Reilly Dermot , Rosato Michael , Maguire Aideen 
Published: September 2018

IntroductionIn the UK Disability Living Allowance (DLA) provided a non means-tested contribution towards the disability-related costs of living for disabled people aged
Objectives and ApproachThree 2011 census-based measures of self-reported health (number of chronic disabilities; activity limitation (a little; a lot); and chronic poor mental health) were linked to 2011 DLA records. Census returns provided individual demographic, socio-economic, social and area-level characteristics. Overall, 92.5% of DLA records were matched to 1.4 million Census records.
ResultsInterim analysis analyses confirms health as the main determinant of DLA uptake, but that for a given level of health, uptake was… higher amongst non-married and those of lower socio-economic status ( OR 1.76; (95%CI 1.68, 1.84) most deprived vs. least deprived), lower amongst ethnic minorities (OR 0.87 (95%CIs 0.78, 0.97) non-white vs. white), migrants (OR 0.37; (95%CI 0.34, 0.39) migrants vs. non migrants) and slightly lower in rural communities (OR 0.95; (95%CI 0.93, 0.97) rural vs. urban).
Conclusion/ImplicationsPoor health is the predominant determinant of disability benefits uptake but other social and socioeconomic factors are influential. Results of these analyses might assist in better targeting of benefits.
Validating epilepsy diagnoses in routinely collected data
Authors: Fonferko-Shadrach Beata , Lacey Arron , Pickrell Owen , Rees Mark , Lyons Ronan , Sawhney Inder , White Cathy , Powell Robert , Smith Phillip , Kerr Michael 
Published: September 2018

IntroductionPrimary healthcare records are used for studies within large data repositories. One of the limitations of using these routinely collected data for epilepsy research is the possibility of including incorrectly recorded diagnoses. To our knowledge, the accuracy of UK GP diagnosis codes for epilepsy has only partially been validated.
Objectives and ApproachWe aimed to validate the accuracy of case ascertainment algorithms in identifying people with epilepsy in routinely collected Welsh healthcare data.
A reference population of 150 people with definite epilepsy and 150 people without epilepsy was ascertained from hospital records and linked to records held within the Secure Anonymised Information Linkage (SAIL) databank in Wales. We used three different algorithms to identify the reference population: a) individuals with an epilepsy diagnosis code and two consecutive AED prescription codes; b) individuals with an epilepsy diagnosis code only; c) individuals with two consecutive AED prescription codes only.
ResultsWe applied the algorithms to all patients and to adults and children separately. For all patients, combining diagnosis and AED prescription codes had a sensitivity of 84% (95% ci 77–90) and specificity of 98% (95–100) in identifying people with epilepsy; diagnosis codes alone had a sensitivity of 86% (80–91) and a specificity of 97% (92–99); and AED prescription codes alone achieved a sensitivity of 92% (70–83) and a specificity of 73% (65–80). Using AED codes only was more accurate in children, achieving a sensitivity of 88% (75–95) and specificity of 98% (88–100). This can be explained by the widespread use of AEDs for indications other than epilepsy in adults, which is not the case for children.
Conclusion/ImplicationsGP epilepsy diagnosis and AED prescription codes can be used to identify people with epilepsy using anonymised healthcare records in Wales. In children using AED prescription codes alone is an accurate way to identify epilepsy cases. These results are generalizable to other studies that use UK primary care records.