bluuebunny/crossref_metadata_2025 huggingface dataset:
Validation of mechanical ventilation coding in hospital discharge abstracts: a population data linkage study
Authors: Nie Yao , Olafson Kendiss , Sareen Jitender , Yogendran Marina
Published: September 2018

IntroductionMechanical ventilation (MV) is an important intervention used in critically ill patients. Accurately identifying MV use in Hospital Discharge Abstracts will be extremely useful in population-based research. Although Canadian Institute for Health Information collects information on MV for all hospitalization, its validity in intensive care unit (ICU) patients is unknown.

Objectives and ApproachWe validated MV use within ICU patients in Hospital Discharge Abstracts. Winnipeg Regional Health Authority (WRHA) ICU database prospectively collects use of MV by trained nurses. All patients admitted to a WRHA ICU (82 beds) between April 1, 2000 and March 31, 2012 were identified in Hospital Discharge Abstracts. MV was identified in Hospital Discharge Abstracts through International Classification of Diseases (ICD-9-CM), prior to 2004, while Canadian Classification of Health Interventions (CCI) were used 2004 onwards. Agreement between the WRHA database (gold standard) and Hospital Discharge Abstracts for invasive ventilation, non-invasive ventilation or neither was calculated at ICU encounter level.

ResultsThere were 54,680 WRHA ICU admission during the study period. The linking of these two sources was highly successful with accurate identification exceeding 99%. There were 26,083 mechanical ventilations (25,387 invasive; 696 non-invasive) from the Hospital Discharge Abstracts and 30,455 (28,315 invasive; 4,554 non-invasive) from the CIC data. Hospital Discharge Abstracts had a sensitivity of 82.8%, specificity of 96.4%, Positive Predictive Value (PPV) of 96.7%, and Negative Predictive Value (NPV) of 81.7% for identifying mechanical ventilation. For invasive ventilation, Sensitivity was 85.5%, Specificity was 95.6%, PPV was 95.4% and NPV was 86.0%. Validation of non-invasive ventilation was poor in sensitivity (9.38%) and PPV (61.35%): with specificity 99.5% and NPV 92.36%.

Conclusion/ImplicationsHospital Abstracts data are a good source to identity mechanically ventilated patients for ICU containing hospital stays especially invasive mechanical ventilations. Future research needs to explore the poor agreement with non-invasive mechanical ventilation.
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.
Ligo: An Open Source Application for the Management and Execution of Administrative Data Linkage
Authors: Lawrance Greg , Hernandez Raphael Parra , Mamakani Khalegh , Khan Suraiya , Hills Brent , Yip Harold , Marrville Caelan
Published: September 2018

IntroductionLigo is an open source application that provides a framework for managing and executing administrative data linking projects. Ligo provides an easy-to-use web interface that lets analysts select among data linking methods including deterministic, probabilistic and machine learning approaches and use these in a documented, repeatable, tested, step-by-step process.

Objectives and ApproachThe linking application has two primary functions: identifying common entities in datasets [de-duplication] and identifying common entities between datasets [linking]. The application is being built from the ground up in a partnership between the Province of British Columbia’s Data Innovation (DI) Program and Population Data BC, and with input from data scientists. The simple web interface allows analysts to streamline the processing of multiple datasets in a straight-forward and reproducible manner.

ResultsBuilt in Python and implemented as a desktop-capable and cloud-deployable containerized application, Ligo includes many of the latest data-linking comparison algorithms with a plugin architecture that supports the simple addition of new formulae. Currently, deterministic approaches to linking have been implemented and probabilistic methods are in alpha testing. A fully functional alpha, including deterministic and probabilistic methods is expected to be ready in September, with a machine learning extension expected soon after.

Conclusion/ImplicationsLigo has been designed with enterprise users in mind. The application is intended to make the processes of data de-duplication and linking simple, fast and reproducible. By making the application open source, we encourage feedback and collaboration from across the population research and data science community.
Policy advocacy to enable administrative data linking: building a civil society coalition
Authors: Lenczner Michael , McPhedran Waitzer Jonathan
Published: September 2018

IntroductionAdministrative data linking holds tremendous promise for improving understanding of social problems, enhancing service delivery, and revolutionizing impact evaluation in Canada. Embracing this opportunity at scale requires navigation of significant technical and policy challenges. The greatest challenge, though, may be a lack of political will.

Objectives and ApproachThe nonprofit sector is uniquely positioned to advocate for a strong political commitment to linked administrative data. As a sector, it could directly benefit from that data for impact evaluation and for advocacy. It is also closest to the people who are most likely to be negatively impacted by the resulting surveillance and stigmatization.

We are building a network of social service organizations, foundations, and advocacy groups to explore the possibility of creating a shared policy agenda. We’ve developed a coalition model that engages these unequally resourced stakeholders on equal footing - with the goal of enabling fully-informed and equitable participation.

ResultsThis coalition is working to develop a set of conditions for increased administrative data linking that reflect the shared interests of funders, service providers, advocacy groups, and beneficiary communities. We are also researching the legislative and policy changes required to enable that desired outcome.

In developing this agenda, and bringing it to government, we hope to provide the social license (and public pressure) required to create an enabling policy environment for increased data linking in Canada.

Beyond developing a shared agenda, this initiative also aims to deliver long-term outcomes involving increased data policy literacy among Canadian nonprofits. This coalition represents collaborative infrastructure to enable ongoing, coordinated input from the nonprofit sector on key questions of data governance and policy.

Conclusion/ImplicationsThis equity-focused, multi-stakeholder coalition approach to digital policy development represents a significant innovation in public engagement. We’re excited to share our process and key learnings with conference participants with hopes of receiving expert feedback while inviting key allies to engage in this emerging initiative.
Predicting health care utilization using CIHI's Population Grouping Methodology
Authors: Rosehart Yvonne
Published: September 2018

IntroductionCIHI’s Population Grouping Methodology uses data from multiple sectors to create clinical profiles and to predict the entire population’s current and future morbidity burden and healthcare utilization. Outputs from the grouper can be applied to healthcare decision making and planning processes.

Objectives and ApproachThe population grouping methodology starts with everyone who is eligible for healthcare, including those who haven’t interacted with the healthcare system, providing a true picture of the entire population. The grouper uses diagnosis information over a 2-year period to create health profiles and predict individuals’ future morbidity and expected use of primary care, emergency department and long-term care services.

Predictive models were developed using age, sex, health conditions and the most influential health condition interactions as the predictors. These models produce predictive indicators for the concurrent period as well as one year into the future.

ResultsThe power of the model lies in the user’s ability to aggregate the data by population segments and compare healthcare resource utilization by different geographic regions, health sectors and health status.

The presentation will focus on how CIHI’s population grouping methodology helps client’s monitor population health and conduct disease surveillance. It assists clients with population segmentation, health profiling, predicting health care utilization patterns and explaining variation in health care resource use. It can be used for risk adjustment of populations for inter-jurisdictional analysis, for capacity planning and it can also be used as a component in funding models.

Conclusion/ImplicationsCIHI’s population grouping methodology is a useful tool for profiling and predicting healthcare utilization, with key applications for health policy makers, planners and funders. The presentation will focus on how stakeholders can apply the outputs to aid in their decision-making and planning processes.
Adherence to Follow-up Care Guidelines for Breast Cancer Survivors in four Canadian provinces: a CanIMPACT study
Authors: McBride Mary , Groome Patti , Jiang Li , Whitehead Marlo , Li Dongdong , Decker Kathleen , Kendell Cynthia , Winget Marcy , Turner Donna , Urquhart Robin , Grunfeld Eva
Published: September 2018

IntroductionBreast cancer survivors are at risk for late and ongoing problems including cancer recurrence and late effects of treatment. Lack of access to quality follow-up care may affect later mortality, morbidity, and quality of life. This study examines variation in utilization of guideline-based follow-up care separately for four Canadian provinces.

Objectives and ApproachFor our retrospective population-based cohort study of breast cancer survivors diagnosed from 2007 to 2010 in British Columbia (BC), 2007-2011 in Manitoba (MB), 2007-2010 in Ontario (ON), and 2007-2012 in Nova Scotia (NS), we linked provincial cancer registries, clinical and health administrative databases, and followed cases alive at 30 months post-diagnosis to five years from diagnosis. For each province, we calculated percent adherence, overuse, and underuse of recommended follow-up care, including surveillance for recurrent and new cancer, surveillance for late effects, and general preventive care. We also examined variation among provinces and over time.

ResultsSurvivor numbers were 23,700 (ON), 9493 (BC), 2688 (MB), and 2735 (NS). Annual oncologist visit guideline compliance varied provincially (e.g. Year 2 ON=32.7%, BC=15.0%). For most provinces and follow-up years, the majority of survivors had fewer oncologist visits than recommended.  However, survivors had additional annual breast cancer-related visits to a primary care provider.  Surveillance breast imaging guideline compliance was high (e.g. Year 2, ON=81.1%, MB=72.0%, NS=52.8%, BC =49.7%), with rates declining in ON and MB (to approximately 64%), but increasing in NS and BC (to approximately 58%) by Year 5. Overuse of breast imaging was identified in NS (9.1%-20.7% overuse in follow-up years 2-5).  As per the guideline, 72.9%-79.7% (Years 2-5) of BC survivors had no imaging for metastastic disease, highest among all provinces.

Conclusion/ImplicationsProvincial and temporal variations in guideline adherence were identified. Patterns differed by guideline, and both overuse and underuse were observed. These results point to opportunities to improve survivor care and efficiencies in care delivery. In particular, regular care with a primary care physician has been shown to improve follow-up care.
Maternal assault admissions are associated with increased risk of child protection involvement
Authors: Orr Carol , Preen David B , Glauert Rebecca , O'Donnell Melissa , Fisher Colleen , Sims Scott
Published: September 2018

IntroductionAlmost half of Australian women assaulted by their partner have children in their care. Evidence suggests a link between children’s exposure to violence, and subsequent maltreatment allegations. However, this evidence is limited by small sample sizes. Linked administrative data present an opportunity to further investigate this sensitive topic.

Objectives and ApproachThis study investigated the relationship between assaults on mothers and subsequent child maltreatment allegations. The sample included all live births in Western Australia from 1990 to 2009 (N=524,534) and their parents, with follow up to 2013. Linked administrative data on child maltreatment allegations and mothers’ assault-related hospital admissions were obtained. Multivariate Cox regression estimated the risk of maltreatment allegation following maternal assault admission. Adjusted and unadjusted hazard ratios (HR) and 95\% confidence intervals (CI) were calculated for the risk of maltreatment allegation, and time (in months) between assault admission and the first maltreatment allegation.

ResultsOne in five children whose mother had an assault admission had a subsequent maltreatment allegation, increasing to more than one in three children when restricted to assault admissions in the prenatal period. More than half of the children who had a maltreatment allegation after their mother was admitted for assault were Aboriginal. After adjusting for covariates, children whose mother had an assault admission had two-fold increased risk of having a maltreatment allegation. The risk of maltreatment allegation was greatest in young children, 5.5-year-old (SD=4.6), when restricted to maternal assault admissions in the prenatal period the children were younger at 4-year-old (SD=4.1). The time from maternal assault admission to maltreatment allegation was around 12 months longer for Aboriginal children than for non-Aboriginal children.

Conclusion/ImplicationsChildren of mothers who have been assaulted are at higher risk of child maltreatment allegation. Targeted early intervention is required for families with young children, and pregnant women experiencing violence. The time to maltreatment allegation for Aboriginal children warrants community developed culturally-safe partnerships between Aboriginal communities and government services.
What is an admission? A standardised approach to classifying inpatient episode data from multiple jurisdictions
Authors: Rees Sarah , Akbari Ashley , Collins Huw , Marchant Amanda , Thayer Daniel , Wang Ting , Wood Sophie , John Ann
Published: September 2018

IntroductionInpatient datasets in the UK are primarily organised by episodes (periods of care under an individual consultant), while researchers often want to measure admissions (periods of stay in hospital). We developed a standardized method for identifying admissions in inpatient data, while accounting for differences between the four UK nations.

Objectives and ApproachAll UK inpatient datasets include date variables, permitting chronological sequencing of episodes. They include flags describing whether an episode is a transfer of care, although structures and definitions differ. Data quality is variable leading to concurrent and overlapping episodes, duplication and “orphan” or “childless” transfer episodes, where no originating or destination episode can be identified.

Our objective was to define a method for classifying individual episodes into a continuous period of stay in hospital, which would be consistently applicable to the analysis of inpatient datasets of all four UK nations, while prioritising clinical meaningfulness. Three permutations were considered.

ResultsFor each permutation, episodes for an individual were linked when they related to the same individual and met the following criteria:

Zero or one day gap between episode end and subsequent episode start

Evidence of transfer according to admission method or discharge destination variables

Episode overlapping or completely nested within another episode

Permutations:

a and b

a only

a and c

Permutation three was adopted, as it was felt to be the most clinically meaningful approach, was not dependent on accurate recording of transfers and captured nested or overlapping episodes, which may occur for example when a patient is in a long-stay psychiatric or elderly care ward but requires care from a different specialty. Importantly it permitted consistent analysis of episodes across all UK nations.

Conclusion/ImplicationsThe output of this work provides a useful guide for the classification of inpatient episodes into more clinically meaningful periods of care, and is applicable to the inpatient datasets of all four UK nations. It describes important issues to consider when classifying episodes of care, particularly relating to data quality.
Evaluating the implementation of the Inverse Care Law Programme in Wales, a population based intervention in primary care
Authors: Akbari Ashley , Song Jiao , Cox Caryn , May Leon , Watkins Williams , Thomas Sarah , Aitken Sarah , King William
Published: September 2018

IntroductionThe Inverse Care Law (ICL) programme in Wales was setup to identify people in deprived communities at risk of cardiovascular disease (CVD) through a health check; offering lifestyle and medical intervention as appropriate. Evaluation of this programme to tackle health inequalities ensuring services are available is vital.

Objectives and ApproachTo evaluate the uptake and long term outcomes of the programme, using longitudinal evidence-based results, it was necessary to develop an efficient and cost effective approach with a readily available source of data. To achieve this, the Welsh Longitudinal General Practice (WLGP) data held in Secure Anonymised Information Linkage (SAIL) databank was utilised, with programme-specific code deployed within primary care at the point of the health check, which identified the intervention, potential CVD risks, referrals and any follow-up. Lifestyle risk factors could be evaluated such as poor diet, physical inactivity, smoking and high alcohol intake.

ResultsUtilising routine data sources and reproducible SQL (Standard Query Language), we evaluated the programme initialisation between February 2015 and November 2016, and found of 55 General Practices who participated, 31 of 35 in Aneurin Bevan (AB) and 17 of 20 in Cwm Taf (CT) University Health Boards, providing data which allowed identification of the health check and associated outcomes of interest in the routine data, with 3 (2 AB, 1 CT) since delivering. There are ongoing evaluations on the various risk factors longitudinally as well as the overall implementation of the programme itself, with this collaborative approach succeeding in utilising existing powerful data linkage within the SAIL databank to identify our intervention and facilitate long-term follow-up at an individual level using robust information governance mechanisms.

Conclusion/ImplicationsLessons learned and challenges encountered are being fed back as part of our evaluation, with further work assessing the long term population level outcomes and impact of the health check and services provided across these deprivation groups, informing and refining programme delivery of similar work across Wales in the future.
Further Development and Validation of the electronic Frailty Index using the Secure Anonymised Information Linkage Databank
Authors: Hollinghurst Joe , Akbari Ashley , Rodgers Sarah , Clegg Andrew
Published: September 2018

IntroductionAging populations with increasing frailty have major implications for health services internationally, and evidence-based treatment becomes increasingly important. The development of an electronic Frailty Index (eFI) using routine primary care data facilitates implementation of evidence-based interventions. However, the eFI does not account for time restrictions regarding when information was recorded.

Objectives and ApproachOur aim is to implement and further validate the eFI using the Secure Anonymised Information Linkage (SAIL) databank, introducing refinements based on time restrictions.

Our implementation of the eFI identifies frailty based on 1574 Read codes, which are mapped amongst 36 categories known as deficits. The eFI is based on the internationally established cumulative deficit model, and each deficit contributes equally to the eFI value.

However, although each deficit is equally weighted, only one of them is currently time dependent. We therefore analyse the time at which each deficit is identified, and propose time dependent cut-points based on our findings.

ResultsWe were able to successfully implement the eFI using data from over 400,000 individuals from the Welsh population using data held in the SAIL databank. Our results agree with the baseline characteristics and distributions of frailty found in the original development of the eFI.

We also found that the percentage of individuals identified as frail increased as the number of years of records included was increased. Furthermore, the increase in percentage year by year was almost linear for a number of the deficits. This led to the identification of time bounds for particular deficits, which could help to refine future implementations of the eFI.

Conclusion/ImplicationsOur work validates and refines the eFI, which is a particularly useful resource as it uses existing primary care data to identify frailty, meaning no additional resources are required. Furthermore, our implementation is readily available, meaning that future research related to frailty is easily reproducible and achievable by others.
Characteristics of Opioid-Related Deaths in Ontario, Canada: Leveraging the Drug and Drug/Alcohol Related Death (DDARD) Database
Authors: Singh Samantha , Martins Diana , Khuu Wayne , Tadrous Mina , Gomes Tara , Juurlink David
Published: September 2018

IntroductionReview of post-mortem toxicological results is the gold standard for identifying whether a death is opioid-related. The Drug and Drug/Alcohol Related Death (DDARD) database contains abstracted information from the Office of the Chief Coroner of Ontario, for all opioid-related deaths that occurred in Ontario, Canada between 1991 and 2016.

Objectives and ApproachThe DDARD, which contains manner of death and drug concentrations from post-mortem toxicology results for opioids-related deaths in Ontario, was linked to the data repository housed at ICES. The objective of this project was to examine demographic characteristics and the type of opioid contributing to opioid-related deaths in FY2015/16. Individuals identified within DDARD who died from an opioid-related cause were linked to demographic, hospitalization and prescription drug databases to report on age, gender, neighbourhood income quintile, past health services utilization for opioid-toxicity, alcohol use disorders (AUD), mental health emergency department (ED) visits, and opioid(s) present at time of death.

ResultsWe identified 737 opioid-related deaths in FY2015/16, the majority of which involved men (n=497; 67.4%), those living in lower socioeconomic status areas (n=395; 53.6%), and those residing in urban regions (n=655; 88.9%). Nearly half (n=325; 44.1%) of opioid-related deaths occurred among those aged 45 to 65 years. We found 9.5% (n=70) of individuals had a previous hospital visit for opioid toxicity, 25.4% (n=187) had previously diagnosed AUD, and 42.5% (n=313) had a previous mental health ED visit. Overall, 250 (33.9%) individuals had an active opioid prescription at time of death with oxycodone (n=92; 36.8%) the most commonly dispensed. Among those who didn’t have an active opioid prescription at time of death (n=484; 66%), fentanyl (n=184; 37.8%) was the most commonly found opioid on post-mortem toxicology.

Conclusion/ImplicationsThis project demonstrates how data obtained through chart abstractions can be used to enhance existing administrative health datasets. Given the concern around the safety of opioids, it is important to examine the characteristics and type of opioid(s) involved at time of opioid-related death in order to develop targeted preventative strategies.
Identifying all persons in Wales with type 1 diabetes mellitus using routinely collected linked data
Authors: Rafferty James , Akbari Ashley , Atkinson Mark D , Bain Stephen , Luzio Stephen , Stephens Jeffery , Owens David , Thomas Rebecca
Published: September 2018

IntroductionType 1 diabetes mellitus (T1DM) is an autoimmune condition characterised by hyperglycaemia, caused by the destruction of insulin producing β-cells in the pancreas. Previous epidemiological population level studies of T1DM and its complications have typically used recorded T1DM diagnoses to determine diabetes status and define cohorts.

Objectives and ApproachThe objective was to identify all persons with T1DM in Wales from Primary (~70\% population coverage) and Secondary Care (100% coverage) data held in the Secure Anonymised Information Linkage (SAIL) databank. People with a coded T1DM diagnosis (using Read codes in Primary Care data and International Classification of Disease (ICD10) codes in Secondary Care data), plus either insulin prescribed shortly after diagnosis or a hospital admission for diabetic ketoacidosis were identified as having T1DM. A sub-group of this SAIL e-cohort were validated using a register of persons diagnosed with T1DM in Wales under 15 years old (Brecon cohort).

Results18,285 people had a T1DM diagnosis and 10,539 had more T1DM than type 2 diabetes mellitus (T2DM) diagnoses. 6,375 persons were identified with T1DM in Primary Care data using our criteria, with a median diagnosis age of 19.2 years (interquartile range 11.0, 35.5). 47.5\% were diagnosed under 18 years of age. 39.6% of people with a T1DM diagnosis did not have T1DM using our criteria. False positive and negative rates of 4.8% and 4.5% respectively were achieved by comparing persons in the SAIL e-cohort against the Brecon cohort. Clinician estimated false positive and negative rates were 1.4% and 3.9% respectively. The prevalence of T1DM in Wales in 2016 was 0.37% or 11,049 people.

Conclusion/ImplicationsOur criteria for identifying people with T1DM was more reliable than using diagnosis codes alone, allowing for a more accurate, efficient and reproducible means of identifying individuals with T1DM for researchers utilising the SAIL databank, and other national health repositories.