bluuebunny/crossref_metadata_2025 huggingface dataset:
A bayesian way to correct for measurement error in drug risk estimates from EHR data.
Authors: Lavigne Maxime , Goulden Robert , Habib Bettina , Joseph Lawrence , Girard Nadyne , Buckeridge David , Tamblyn Robyn
Published: September 2018

IntroductionData from electronic medical records is now readily available and records information needed in pharmacoepidemiological studies not usually found in administrative data such as risk factors and biometrics. Yet, EMR data leads to measurement error due to primary non-adherence. Bayesian bias correction could provide corrected estimates from administrative data.

Objectives and ApproachWe present a method for correcting risk estimates from EMR data using linked data. In our example, we estimate the risk of cardiovascular events from oral-hypoglycemics in patients with type-2 diabetes in Boston, Quebec, and the UK between 2009 and 2012. Using linked EMR and administrative data in Quebec, we compute a positive and negative predicting value of prescription on dispensation for each class of oral-hypoglycemics. The cardiovascular risk is then analysed using a bayesian Weibull survival model adjusted for potential confounders. A similar model is then computed that accounts for exposure measurement error using the PPV and NPV.

ResultsThe Quebec and Boston cohorts have similar sizes with 1197 and 2346 patients, but the UK was bigger at 41370 patients. In Quebec's data, there were important differences in PPV and NPV by class of oral-hypoglycemics with PPVs for Biguanides at 0.81, Sulphonylureas at 0.65, and others at 0.50. The pattern for NPV differed with the same classes having respectively values of 0.56, 0.97, and 0.99. Estimates from the naïve model are typical of similar analysis but compared to their correction, they were generally overprecise and biased towards the null. The adjusted estimated were adequately representing the increased uncertainty with hazard ratios for Sulphonylureas going from 1.72 (1.22, 2.41) to 3.19 (1.36, 5.93), and from 1.09 (0.86, 1.39) to 1.05 (0.45, 2.16) for no drugs

Conclusion/ImplicationsBayesian adjustment for measurement error allowed us to use linked data to regenerate uncertainty and to correct the bias in our risk estimates. Our approach was impacted by the observed low predictive value of prescribing, by reduced transportability of our PPV and NPV estimates, and other sources of bias.
Identify Patients with Congestive Heart Failure through Analyzing Free-Text Clinical Notes
Authors: Yann Margot , Stukel Therese , Jaakkimainen Liisa , Tu Karen
Published: September 2018

IntroductionA number of challenges exist in analyzing unstructured free text data in electronic medical records (EMRs). EMR text are difficult to represent and model due to their high dimensionality, heterogeneity, sparsity, incompleteness, random errors and the presence of noise.

Objectives and ApproachStandard Natural Language Processing (NLP) tools make errors when applied to clinical notes due to physician use of unconventional language, involving polysemy, abbreviations, ambiguity, misspelling, variations, and negation.

This paper presents a novel NLP framework, “Clinical Learning On Natural Expression” (CLONE), to automatically learn from a large primary care EMR database, analyzing free text clinical notes from primary care practices. CLONE’s predictive clinical models using text mining and neural network approach to extract features to identify patterns. To demonstrate effectiveness, we evaluate CLONE’s ability in a case study to identify patients with a specific chronic condition: congestive heart failure (CHF).

ResultsA random selected sample of 7500 patients from Electronic Medical Record Administrative data Linked Database (EMRALD) is used. In this dataset, each patient’s medical chart includes a reference standard, manually reviewed by medical practitioners. Prevalence of CHF is approximately 2%. The low prevalence leads to another challenging problem in machine learning: imbalanced datasets. After pre-processing, we build deep learning models to represent and extract important medical information from free text to identify CHF patients through analyzing patient charts. We evaluated the effectiveness of CLONE by comparing the predicted labels with the standard references on a holdout test dataset. Comparing it with a number of alternative algorithms, we improve the overall accuracy to over 90% on a test dataset.

Conclusion/ImplicationsAs the role of NLP in EMR data expands, the CLONE natural language processing framework can lead to substantial reduction in manual processing, while improving predictive accuracy.
Commercial use of health data in the dock – results of a public “trial”
Authors: Tully Mary , Hassan Lamiece , Oswald Malcolm , Ainsworth John
Published: September 2018

IntroductionSurveys suggest that there is a dichotomy in how citizens view research for public good and research for commercial gain. As a consequence, the idea that a research initiative, such as a learning health system, for both public and commercial benefit may be controversial and reduce public trust.

Objectives and ApproachThis study aims to investigate what informed citizens considered to be appropriate uses of health data in a learning health system. Two paired four-day juries were run, with different jurors but the same purpose, expert witnesses and facilitators. Overall, 694 people applied to be jurors; 36 were selected to match criteria based on national demographics and their prior privacy views. Jurors considered whether and why eight exemplar data uses of depersonalised patient data were acceptable. The exemplars were data uses planned by the learning health system initiative to improve care pathways (planned uses), and possible unplanned data uses.

ResultsAll planned uses were considered appropriate by most, but not all, jurors, as they had the potential of benefitting the public through improving care. Positive health outcomes were more acceptable than improved efficiency of services, given jurors prior beliefs about how the NHS operates raising concerns about whether improving efficiency would lead to inequitable distribution or closure of services. The potential uses were considered appropriate where there were improvements in drugs, treatments, or lower NHS costs. Some jurors became more accepting of commercial uses as they understood them better. Commercial uses that prioritised generating profit and did not produce health benefits for the public were unacceptable, regardless of any safeguards for the data. Commercial gain that occurred secondary to achieving public benefit were generally accepted.

Conclusion/ImplicationsJuries elicit more informed and nuanced judgement from citizens than surveys. Jurors tended to be more accepting of data sharing to both private and public sector after the jury process. Many jurors accept commercial gain if public benefit is achieved. Some were suspicious of data sharing for efficiency gains.
Monitoring health service use at the end of life in the Calgary Zone of Alberta: a Population-level analysis linking multiple administrative datasets
Authors: Cai Pin , Fong Andrew , Sinnarajah Aynharan
Published: September 2018

IntroductionAs part of the Alberta Health Services (AHS) Calgary Zone Healthcare planning, a Palliative and End of Life Care Program (PEOLC) dashboard was developed and face validity of the indicators was examined by key stakeholders such as clinicians and decision makers.

Objectives and ApproachAn internal dashboard was developed to explore End of Life (EOL) indicators that could provide evidence to support local PEOLC planning. Multiple administrative datasets available to AHS were used to estimate population needs of palliative care, current state of resource use, and EOL quality indicators. Underlying cause of death in Vital Statistics data was used to calculate minimal and maximal population estimates of palliative care needs between 2000 and 2014. Trends in acute care use during the last year of life were analyzed. Overall rates and geographic variations of selected indicators in Calgary Zone were reported.

ResultsOver the period 2000 to 2014, number of adult deaths increased in Calgary Zone, from 5,094 in 2000 to 6,823 in 2014. In 2015/16, about half of all 10,848 hospital discharges in the last year of life were incurred in the last 60 days of life, and about 40 percent were incurred in the last 30 days. Overall, 11% of decedents visited ED more than once, 7% were discharged from hospital more than once, 19% spent more than 14 days in hospital. According to the ED and inpatient data, 40.7% of decedents, roughly 3,000 people, died in hospital. We observed an urban rural continuum gradient in most of these indicators, with rates varying more than two-fold for ED and hospital discharge related indicators.

Conclusion/ImplicationsThe project demonstrates the feasibility of using existing data to generate information to support the PEOLC program planning in Calgary Zone. With early stakeholder engagement in dashboard design, analysis, interpretation, and dissemination, the dashboard was well received and will be updated as more recent data becomes available.
Factors associated with the breast cancer diagnostic interval across five Canadian provinces: a CanIMPACT study
Authors: McBride Mary , Groome Patti , Decker Kathleen , Grunfeld Eva , Jiang Li , Kendell Cynthia , Urquhart Robin , Vu Khanh , Winget Marcy
Published: September 2018

IntroductionA long breast cancer diagnostic process can affect patient anxiety and survival. Variations in the length of the diagnostic interval for similar patient presentations can indicate health system inequities and/or inefficiencies.

Objectives and ApproachWe describe the breast cancer diagnostic interval across Canada and factors associated with its length.

We studied breast cancer patients diagnosed from 2004/7 to 2010/11/12 in the Canadian provinces: British Columbia, Alberta, Manitoba, Ontario, and Nova Scotia. Using administrative data, we created parallel population-based, provincial-level datasets and ran common analyses. The diagnostic interval was defined from the screening mammogram to the diagnosis for screen-led and from the first referral/test ordering date to the diagnosis for diagnostic-led patients. Stratified by these two diagnostic routes, we describe the variation in the interval across provinces and report on the province-specific associations between the diagnostic interval and: patient age, comorbid disease burden, socioeconomic status combined with rural residence, and continuity of primary care while controlling for cancer stage.

ResultsThe median diagnostic interval varied by 6 days (29 to 35 days) across provinces. Screen-led patients were diagnosed more quickly (median 2-12 days quicker). The 90th percentile diagnostic interval was 84-126 days longer in diagnostic-led patients. In the diagnostic-led group, increasing comorbid burden was consistently associated with longer diagnostic intervals and being >70 was associated with a shorter interval at the 90th percentile in Manitoba and Ontario. There was no evidence of a clear rural or low socioeconomic status effect and patients without a primary care physician had shorter intervals. In the screen-led group, patients age 40-49 and those in the medium or low income rural areas waited longer for a diagnosis.

Conclusion/ImplicationsDiagnostic wait times differ across Canada and are variably associated with comorbidity, age, area-level socioeconomic status and rural residence. These results point to practice and system-level effects that warrant further study.
Linking pharmacy dispensing data to other administrative health datasets to measure the compliance and effectiveness of RSV immunoprophylaxis
Authors: Moore Hannah , Strunk Tobias , Abdalla Tasnim , Snelling Tom , Richmond Peter , Keil Anthony , De Klerk Nicholas
Published: September 2018

IntroductionRespiratory Syncytial Virus (RSV) causes considerable morbidity in children. RSV vaccines are in development, but the only current preventive measure is immunoprophylaxis with monoclonal antibody, palivizumab. Australia has no uniform palivizumab guidelines. In Western Australia palivizumab is licensed for use in high risk children but compliance and effectiveness is unknown.

Objectives and ApproachWe conducted a retrospective cohort study using palivizumab data from multiple pharmacy dispensing datasets which had been linked with routine laboratory, hospital morbidity, emergency department presentations, deaths and perinatal data for a cohort of infants admitted to Level 3 Neonatal Intensive Care Units (NICU) between 2002 and 2013. We identified palivizumab eligible infants as those who were extremely premature (<28 weeks gestation) with bronchopulmonary dysplasia and/or who identified as Indigenous and were NICU inpatients during the annual winter RSV season (May-October). We describe the use of palivizumab in infants that did and did not fit the eligibility criteria.

ResultsThe NICU cohort included 24,367 infants, of which 1754 had at least 1 RSV-confirmed infection before age 5 years. A total of 686 (2.8%) cohort infants were eligible for palivizumab. Palivizumab dispensing data were amalgamated from 5 pharmacy datasets. Overall, 173 of the palivizumab eligible infants (25.2%) had at least 1 palivizumab dose (27% 1 dose, 34% 2 doses, 28% 3 doses and 11% 4 or more doses). From 2011 when palivizumab guidelines were formalised, 143 (75%) had at least 1 dose. Compliance with at least 1 palivizumab dose was highest in 2011 (84.9%). From 2002-2013, 98 infants were given palivizumab outside eligibility criteria (33% 1 dose, 33% 2 doses, 34% 3 or more doses) with annual use increasing since 2008.

Conclusion/ImplicationsThis is the first time pharmacy dispensing data have been linked to other datasets to measure use and effectiveness. Compliance with palivizumab guidelines was high from 2011. These data will be used to measure the effectiveness of palivizumab against RSV-confirmed infections and respiratory infection-related hospitalisations up to age 5 years.
Development and validation of case-finding algorithms for recurrence of breast cancer using routinely collected administrative data
Authors: Xu Yuan , Kong Shiying , Quan May Lynn
Published: September 2018

IntroductionRecurrence free survival is frequently investigated in cancer outcome studies, however is not explicitly documented in cancer registry data that is widely used for research. Patterns of events after initial treatment such as oncology visits, re-operation, chemotherapy or radiation may herald recurrence.

Objectives and ApproachThis study aimed to develop and validate algorithms for identifying breast cancer recurrence using large administrative data.Two cohorts with high recurrence rates were used: 1) all young (≤ 40 years) breast cancer patients (2007-2010), and 2) all neoadjuvant chemotherapy patients (2012-2014) in Alberta, Canada. Health events after primary treatment were obtained from the Alberta cancer registry, physician billing claims, and vital statistics databases. Positive recurrence status (defined as either locoregional, distant or both) was ascertained by primary chart review. The cohort was divided into a developing (60%) and validating (40%) set. Development of algorithms geared towards high sensitivity, PPV and accuracy respectively were performed using classification and regression tree (CART) models. Key variables in the models included: a new round of chemotherapy, a second mastectomy, and a new cluster of radiologist, oncologist or general surgeon visits occurring after the primary treatment. Compared with chart review data, the sensitivity, specificity, PPV, NPV and accuracy of the algorithms were calculated.

ResultsOf 606 patients, 121 (20%) had recurrence after a median follow-up 4 years. The high sensitivity algorithm had 94.2% (95% CI: 90.1-98.4%) sensitivity, 92.8% (90.5-95.1%) specificity, 76.5% (70.0-88.3%) PPV, 98.5% (97.3-99.6%) NPV and 93.1% (91.0-95.1%) accuracy. The high PPV algorithm had 74.4% (66.6-82.2%) sensitivity, 97.8% (96.5-99.2%) specificity, 90.0% (84.1-95.9%) PPV, 93.6% (91.4-95.7%) NPV and 92.9% (90.9-95.0%) accuracy. The high accuracy algorithm had 88.4% (82.7-94.1%) sensitivity, 97.1% (95.6-98.6%) specificity, 88.4% (82.7-94.1%) PPV, 97.1% (95.6-98.6%) NPV and 95.4% (93.7-97.1%) accuracy.

Conclusion/ImplicationsThe proposed algorithms achieved favourably high validity for identifying recurrence using widely available administrative data. Further study may be needed for improving sensitivity and PPV, and validating the algorithms in larger data for widespread use.
Prevalence and incidence of diagnosed hypertension in Alberta, Canada
Authors: Holitzki Hannah , Quan Hude , Clement Fiona , Leung Alex , Liang Zhiyang
Published: September 2018

IntroductionThe prevalence of diagnosed hypertension in Canada is projected to increase despite the incidence rate decreasing. Previous work around the world has utilized survey data to provide estimates of prevalence and incidence. Administrative data is population-level, and may provide more reliable estimates of provincial prevalence and incidence than could be achieved using survey data. 

Objectives and Approach

To produce age and sex-specific prevalence and incidence estimates of diagnosed hypertension in Alberta from 2007 to 2015,

To project estimates to the fiscal year of 2019/2020.

Data from the Discharge Abstract Database, physician claims database, National Ambulatory Care Reporting System, and provincial health insurance registry will be linked using unique anonymous personal identifier and gender. A validated case definition of diagnosed hypertension for use in administrative datasets will be used to identify annual prevalent and incident cases from claims data. Obstetric cases will be excluded. The provincial health insurance registry will be used to estimate denominator values.

ResultsResults of this analysis are not available for the time of abstract submission as the timeline for this analysis projects completion in April 2018.

Conclusion/ImplicationsMaintained surveillance of diagnosed hypertension is important to inform health policy and spending decisions, to monitor efficacy of public health interventions, and to inform patient care. Furthermore, diagnosis guidelines have been updated since 2017. Providing estimates for the prevalence of diagnosed hypertension in Alberta five years into the future to compare to actual prevalence estimates may indicate whether changes in prevalence are due to actual changes in health status or to changes in diagnosis guidelines.
Primary health care engagement among marginalized people who use drugs in Ottawa, Canada
Authors: Donelle Jessy , Bayoumi Ahmed , Boucher Lisa , Martin Alana , Pineau Dave , Diliso Nicola , Renaud Brad , Boyd Rob , Oickle Pam , Marshall Zack , LeBlanc Sean , Tyndall Mark , Kendall Claire
Published: September 2018

IntroductionEngagement in primary health care may be lower among marginalized people who use drugs (PWUD) compared to the general population, despite having greater mental and physical healthcare needs as evidenced by higher co-morbidity, and more frequent use of emergency department care.

Objectives and ApproachWe investigated which socio-structural factors were related to primary care engagement among PWUD using rich survey data from the Participatory Research in Ottawa: Understanding Drugs cohort study; these data were deterministically linked to several robust provincial-level health administrative databases held at the Institute for Clinical Evaluative Sciences. We defined primary care engagement over the 2 years prior to survey completion (March-December 2013) as: not engaged (<3 outpatient visits to the same family physician) versus engaged in care (3+ outpatient visits to the same family physician). Multi-variable logistic regression was used to identify factors associated with primary health care engagement.

ResultsAmong 663 participants, characteristics include: mean age of 41.4 years, 75.6% male, 66.7% in the lowest two income quintiles, and 51.1% with 6+ co-morbidities. 372 (56%) were engaged in primary care, with a mean of 15.97 visits per year (SD=20.18). Engagement was significantly associated with the following factors: receiving drug benefits from either the Ontario Disability Support Program (adjusted odds ratio [AOR] 4.48; 95% confidence interval [95%CI] 2.64 to 7.60) or Ontario Works (AOR 3.41; 95%CI 1.96 to 5.91), having ever taken methadone (AOR 3.05; 95%CI 1.92 to 4.87), mental health co-morbidity (AOR 2.93; 95%CI 1.97 to 4.36), engaging in sex work in the last 12 months (AOR 2.05; 95%CI 1.01 to 4.13), and having stable housing (AOR 1.98; 95%CI 1.30 to 3.01).

Conclusion/ImplicationsNearly half of PWUD are not engaged in primary care, representing missed opportunities to improve health. Engagement in primary care may reflect both an increased need for health care, such as mental health disability, and increased access to primary care through other health and social services, such as housing support.
Comparing five comorbidity indices to predict mortality in chronic kidney disease
Authors: McArthur Eric , Bota Sarah , Sood Manish , Nesrallah Gihad , Kim Joseph , Garg Amit , Dixon Stephanie
Published: September 2018

IntroductionMany reports which use healthcare databases adjust the results for patient comorbidity. Several different indices were developed in the general population to summarize patient comorbidity. How well these indices predict one-year all-cause mortality in individuals with chronic kidney disease (CKD) is not well known.

Objectives and ApproachWe accrued three groups of patients in Ontario, Canada between the years 2004 and 2014, at the time they first received a kidney transplant, received maintenance dialysis, or were confirmed to have an estimated glomerular filtration rate (eGFR) less than 45 mL/min per 1.73 m2. We compared five comorbidity indices: Charlson comorbidity index, end-stage renal disease-modified Charlson comorbidity index, Johns Hopkins’ Aggregated Diagnosis Groups score, Elixhauser score, and Wright-Khan index. Each group was randomly divided 100 times into derivation and validation samples. Model discrimination was assessed using median c-statistics from logistic regression models, and calibration was evaluated graphically using calibration plots.

ResultsWe identified 4,111 kidney transplant recipients, 23,897 individuals receiving maintenance dialysis, and 181,425 individuals with moderate CKD. Within one year, 108 (2.6%), 4,179 (17.5%), and 17,898 (9.9%) in each group had died, respectively. In the validation sample, model discrimination was inadequate for all five comorbidity indices, with median c-statistics less than 0.7 for all three groups. Calibration was also poor for all models.

Conclusion/ImplicationsExisting comorbidity indices do not accurately predict one-year mortality in patients with CKD. Current indices could be modified with additional risk factors to improve their performance in CKD, or a new index could be developed for this population.
Distributed NLP Framework to create new federated data-sets
Authors: Thompson Simon
Published: September 2018

IntroductionAlthough healthcare systems generate significant amounts of structured data, there remains a untapped wealth of unstructured narrative data. In the UK, 70% of all NHS digital information is in unstructured form. The NHS has no plans to computerise this data, as it is simply would not be cost effective.

Objectives and ApproachOur aim was to make all digitised free text within partner organisations accessible for NLP processing for research, while overcoming information governance challenges. We developed a distributed GATE-based NLP platform enabling NLP models to be automatically distributed and materialised against the free text data in each organisation to create new conventional datasets, which can then be transmitted back using an established governance model.

This work adds NLP capability to the UK’s National Research Data Appliances, deployed throughout Wales and beyond and uses many open source components enabling a deployment without additional software licence costs, leading to increased potential use cases.

ResultsWe have been able to demonstrate a fully federated network of analytical nodes into NHS Wales, which takes the analytical NLP model to the free text data, as opposed to the data having to travel. Under a common, acceptable, governance model, an approval system enables organisations such as health boards to give permission for projects and NLP models to be used against their data.

In a proof of concept project, we have run a number of NLP models over large numbers of documents, which the platform has ingested, converted and analysed.

We have developed a proposal for a common NLP model definition format to enable models to be interchangeable between different research groups and systems. Sharing/discovery of established NLP models is key deliverable.

Conclusion/ImplicationsThe implications of being able to send the query to the data, enables access to this untapped data source, finally enabling the realisation of new datasets, while abiding by any IG framework. The low cost and simplicity will enable a many research opportunities, some of which are already being realised.
Trends in systematic recording errors of blood pressure and association with outcomes in Canadian and UK primary care data: a retrospective observational study
Authors: Kalia Sumeet , Greiver Michelle
Published: September 2018

IntroductionEnd digit preference (EDP) or systematic bias in the recording of blood pressure (BP) measurement is prevalent in primary care: up to 60% of BP readings end in zero. High blood pressure (BP) is a leading cause of increased morbidity in adults and errors in measurement may contribute to increased rate of adverse cardiovascular outcomes.

Objectives and ApproachWe studied EDP trends, uptake of Automated Office BP (AOBP) measurement, and cardiovascular outcomes in the UK and Canada.This is a retrospective observational study using routinely collected Electronic Medical Record data for patients age 18 or more.

We used bootstrap method to estimate the odds ratios where logistic regression was fitted on one thousand independently sampled replicates of the CPCSSN and RCGP datasets. We implemented the unsupervised algorithm of k-nearest neighbor across all sites to find the optimal decision boundary to classify the sites into the three categories: (1) strong EDP; (2) some EDP; (3) no EDP.

ResultsThe mean rate of end digit zero for both systolic and diastolic BP decreased from 26.6% in 2006 to 15.4% in 2015 in Canada and from 24.2% in 2001 to 17.3% in 2015 in the U.K. There was a gradual decline in EDP in the three years following the purchase of an AOBP machine. Sites categorized as having high levels of EDP had lower mean sBP levels than sites with potentially no EDP in both Canada and UK. Patients in sites with high levels of EDP had higher yearly prevalence of stroke (Standardized morbidity ration or SMR 1.11), myocardial infarcts (SMR 1.15), and angina (SMR 1.27) than patients in sites with no EDP.

Conclusion/ImplicationsThere is systematic recording errors including rounding down of BP readings associated with higher rates of EDP and presumably more use of manual BP measurement. Higher rates of EDP were associated with greater prevalence of adverse cardiovascular outcomes. Consideration should be given to using AOBP machines in primary care.
Scalable Secure Privacy-Preserving Record Linkage (PPRL) Methods Using Cloud-based Infrastructure
Authors: Ong Toan , Lazrig Ibrahim , Ray Indrajit , Ray Indrakshi , Kahn Michael
Published: September 2018

IntroductionBloom Filters (BFs) are a scalable solution for probabilistic privacy-preserving record linkage but BFs can be compromised. Yao’s garbled circuits (GCs) can perform secure multi-party computation to compute the similarity of two BFs without a trusted third party. The major drawback of using BFs and GCs together is poor efficiency.

Objectives and ApproachWe evaluated the feasibility of BFs+GCs using high capacity compute engines and implementing a novel parallel processing framework in Google Cloud Compute Engines (GCCE). In the Yao’s two-party secure computation protocol, one party serves as the generator and the other party serves as the evaluator. To link data in parallel, records from both parties are divided into chunks. Linkage between every two chunks in the same block is processed by a thread. The number of threads for linkage depends on available computing resources. We tested the parallelized process in various scenarios with variations in hardware and software configurations.

ResultsTwo synthetic datasets with 10K records were linked using BFs+GCs on 12 different software and hardware configurations which varied by: number of CPU cores (4 to 32), memory size (15GB – 28.8GB), number of threads (6-41), and chunk size (50-200 records). The minimum configuration (4 cores; 15GB memory) took 8,062.4s to complete whereas the maximum configuration (32 cores; 28.8GB memory) took 1,454.1s. Increasing the number of threads or changing the chunk size without providing more CPU cores and memory did not improve the efficiency. Efficiency is improved on average by 39.81% when the number of cores and memory on the both sides are doubled. The CPU utilization is maximized (near 100% on both sides) when the computing power of the generator is double the evaluator.

Conclusion/ImplicationsThe PPRL runtime of BFs+GCs was greatly improved using parallel processing in a cloud-based infrastructure. A cluster of GCCEs could be leveraged to reduce the runtime of data linkage operations even further. Scalable cloud-based infrastructures can overcome the trade-off between security and efficiency, allowing computationally complex methods to be implemented.
Using Large Data to Present Uncertainty for Risk Prediction in the Era of Precision Medicine: The RESPECT Algorithm for Predicted Death at End-of-Life
Authors: Spruin Sarah , Hsu Amy , Talarico Robert , Sequeira Yulric , Taljaard Monica , Bennett Carol , Tanuseputro Peter , Manuel Doug
Published: September 2018

IntroductionIn Ontario, only 52% of people received palliative care in their last year of life, with only 20\% of those receiving it at home, which can improve the dying experience. Existing algorithms identifying people at end-of-life can potentially improve access to palliative care but are difficult for patients to understand.

Objectives and ApproachTo predict and communicate risk of death for community dwelling older adults using a pre-specified and published approach (Trial registration NCT02779309). All assessments from community-dwelling Ontarians (N = 488,636) who received at least one home care assessment from the residential assessment instrument – home care (RAI HC) from 2007 to 2013 (N=1,331,273) were included. The algorithm used a two-step approach by rank ordering participants into 61 groups based on six-month probability of death (from Cox-proportional hazard models) and generated Kaplan-Meier five-year survival curves for each group. Median Survival time is reported with uncertainties expressed with 25th to 75th percentiles.

ResultsThe median predicted probability of death within six-months was 0.1095 (0.1093-0.1097, 95% CI). Risk varied among the 61 groups from 0.0158 (0.0158-0.0159) to 0.9820 (0.9810-0.9830). Median observed survival time varied from 27 days (10 to 81 days, 25th and 75th percentile) in the highest risk group to 10 years (3655 days (2111 to >3655 days)) in the lowest risk group. Discrimination and calibration were satisfactory between the derivation (2007-2012 assessments) and validation (2013 assessments) cohorts, with a C statistics of 0.77 and discrimination plot intercept 0.094, slope 0.914. The Kaplan-Meier five-year survival curves for each of the 61 groups will be visually represented in six different ways displaying the risk and uncertainty, and can be altered to yield information of interest specific to each patient/caregiver.

Conclusion/ImplicationsRESPECT is adaptive and personalized, with instantaneous feedback as the user provides a response to each question. We will present RESPECT’s development and implementation processes and set up an interactive presentation of the calculator, demonstrating RESPECT’s ability to deliver patient-comprehensible end-of-life prognoses with uncertainty to patients and their caregivers.
Creating the Framework for Cross-Sector Health Analysis for Local Communities
Authors: Richards Heather , Varas Kim , Magnus Samantha , Oh Jinhwa , Voggenreiter Christine
Published: September 2018

IntroductionA newly developed BC Ministry of Health geography classification has enabled a standardized approach for community-level analysis of health needs and service provision. An innovative methodology was developed and applied to health administrative data, creating more opportunities to identify variations in health status and utilization across the health system.

Objectives and ApproachTwo design principles informed the development of the new geographies. Firstly, they reflect where people live and the communities with which they identify, and secondly, they will assist with identifying where health services are needed for local populations. The objective was to provide the Ministry and health authorities with a framework to identify and work towards providing the optimal delivery of services at the local level. A working group was established for this project and included representatives from the Ministry, each regional health authority, Provincial Health Services Authority, First Nations Health Authority, and BC Stats.

ResultsThe building block for the geography classification is the Census Dissemination Block, the lowest unit of geography available in the Standard Geography Classification maintained by Statistics Canada. The geographies were assigned urban-rural designations based on an algorithm that considered the presence of a population centre, the size of the population centre, and the proportion of the population living in it, among other aspects. One of the main goals of the urban-rural designations was to provide meaningful peer groups for cross-jurisdictional studies.

The project also reengineered the methods to geocode addresses to improve accuracy to use street addresses (over past method that used postal codes) so that assignment to Census Dissemination Block would be precise.

The end result was 218 community geographies with urban-rural designations.

Conclusion/ImplicationsThis geography standard allows health system stakeholders to better understand of geographic variation in utilization and access to health care. The ability to link and share information to profile community health between health administrative data and Census data available from Statistics Canada is better due to improved geocoding of addresses.
Life expectancy and health-adjusted life expectancy are decreased in people living with inflammatory bowel disease: a population-based matched cohort study
Authors: Kuenzig Ellen , Manuel Doug , Donelle Jessy , Benchimol Eric
Published: September 2018

IntroductionInflammatory bowel disease (IBD) is a chronic immune-mediated disease of the gastrointestinal tract. Significant improvements in treatments for IBD have occurred in the past two decades. However, the benefits of new treatments on mortality is uncertain and no prior study has evaluated the life expectancy (LE) of patients with IBD.

Objectives and ApproachWe determined trends in the LE and health-adjusted life expectancy (HALE) in IBD. The Ontario Crohn’s and Colitis Cohort includes all patients in Ontario with IBD, identified from health administrative data using a previously validated algorithm. Cases were matched to five controls based on age, sex, rural/urban, and mean neighbourhood income quintile. Period life tables were used to calculate LE on July 1, 1996, 2000, and 2008. The Canadian National Population Health Survey (1996/97) and Canadian Community Health Survey (Cycles 1.1 and 2009/10) were used to estimate health utility index (HUI3). HALE was estimated using HUI3-weighted disability-free years lived.

ResultsLE increased from 75.5 years (y) in 1996 to 78.0y in 2008 among women with IBD (∆2.5y, 95%CI 0.8 to 4.1) and from 72.2y in 1996 to 75.1y in 2008 among men with IBD (∆2.9y, 95%CI 1.8 to 4.0). HALE decreased among men with IBD (∆3.9y, 95%CI 1.2 to 6.6; 1996: 67.0y; 2008: 63.1y) but was stable among women with IBD (∆2.0y, 95%CI -1.6 to 5.7; 1996: 62.3y; 2008: 64.3y) and female controls (∆-0.4y, 95%CI -2.3 to 1.5; 1996: 74.3y; 2008: 73.9y) and male controls (∆0.2y, 95%CI -1.7 to 2.2; 1996: 69.6y; 2008: 69.8y). LE and HALE in both men and women with IBD were significantly decreased compared to controls (LE: women ∆6-8y, men ∆5y; HALE: women ∆10-14y, men ∆3-7y).

Conclusion/ImplicationsAlthough patients with IBD experienced increases in LE at a pace similar to those without IBD, the gap in LE between cases and controls remains significant. Increases in LE have not been accompanied by increases in HALE. Treatments that increase both LE and HALE in patients with IBD are needed.
Governance Challenges to Promoting Data Readiness and Data Linkage for Not-for-Profit Organizational Service Data
Authors: Manhas Kiran Pohar , Lau Jason , Cui Xinjie
Published: September 2018

IntroductionData collected by myriad not-for-profit (NFPs) agencies while providing social services represent a significant untapped source of intelligence. While much research has been conducted on the governance and capacity to share administrative and research data, little work has been done in the area of NFP data sources.

Objectives and ApproachSix NFP agencies in Calgary participated in a data sharing project to measure various aspects of poverty. This collaboration required understanding the legal, ethical, policy and data challenges to working with data collected in the NFP sector. This case study will expose (a) the legal analysis on legal and governance issues for, and obligations of, NFPs when aiming to share and re-use information beyond service delivery in Alberta, Canada; and (b) the practical and policy challenges faced by the NFPs and repository. The legal analysis involved a systematic search of academic and grey literature; relevant legislation and case-law (especially privacy law).

ResultsThe legal analysis demonstrated three key things. First, in many circumstances an NFP is not held to any information-handling legal obligations. This does not absolve NFPs from social privacy expectations, especially involving vulnerable populations. Second, privacy best practices are available to NFPs from other more-regulated sectors and from NFP sector standards. The trifecta of purpose, reasonableness and minimal extent are critical. Third, adhering to privacy best practices would not be overly costly, more efficient, and less risky of negative publicity and lost public trust from privacy complaints. These findings informed the NFP data sharing project. The six participant NFPs varied greatly in their approach to consent, governance and information-handling, which affected their data-readiness for cross-organizational data linkage and analysis.

Conclusion/ImplicationsUnlike government, most NFPs have limited experience with data linkage. This paper informs ethical approaches to include NFP data in broad cross-sector data linkage. Appropriate data governance is imperative for data sharing amongst NFPs, researchers and public organizations, which promotes community service delivery, evaluation and improvements to benefit vulnerable families.
Childhood Mental Disorders and Subsequent Adverse Outcomes in Early Adulthood: A Population-Based Longitudinal Study
Authors: Chartier Mariette , Bolton James , Ekuma Oke , Mota Natalie , Hensel Jennifer , MacWilliam Leonard , Nie Yao , McDougall Chelsey
Published: September 2018

IntroductionThe relationship between childhood/adolescent mental disorders and adult outcomes has been studied using surveys and clinical samples. These studies are prone to selection, recall and self-reporting biases. No previous studies have used population-based administrative databases linking individual-level data that could address these biases and further elucidate this relationship.

Objectives and ApproachUsing de-identified administrative databases housed at the Manitoba Centre for Health Policy, we aimed to determine whether people diagnosed with mental disorders in childhood/adolescence, compared to those without, were at higher risk of early adverse adult outcomes. We created a birth cohort of 60,838 residents of Manitoba, Canada, born from fiscal years 1980/81 to 1984/85 and followed them to the end of study period in 2014/15. Through a scrambled health identifier, health, education, social services and justice system data were linked at an individual level. Survival analysis was used to test for differences controlling for key childhood covariates.

ResultsWe found that 16.5% of the cohort had a diagnosed mental disorder at some point in their childhood/adolescence. Having a diagnosed mental disorder in childhood/adolescence increased the risk of being diagnosed with the same disorder in early adulthood (at age 30 to 34 years old). It also increased the risk of suicidal death (hazard ratio (HR): 2.41), suicide attempts (HR: 3.05), public housing use (HR: 1.44), income assistance use (HR: 2.07), criminal accusation (HR:1.53), and criminal victimization (HR:1.54) in adulthood. Similarly, but to a greater extent, suicide attempts in adolescence increased the risk of suicidal death (HR: 3.65), suicide attempts (HR: 5.68), public housing use (HR: 1.64), income assistance use (HR: 1.68), criminal accusation (HR: 2.18), or criminal victimization (HR: 2.43) in adulthood.

Conclusion/ImplicationsYoung people’s mental health has significant influence on their health and well-being trajectories into adulthood. This knowledge could directly inform policy and practice to provide better population-based mental health promotion, prevention and early interventions for children/adolescents with mental disorders and subsequently prevent adverse adult outcomes in the future.
Privacy, Ethics and Information Sharing of New Zealand’s Integrated Data Infrastructure (IDI): A discussion of the issues, challenges and opportunities
Authors: Atkinson June
Published: September 2018

IntroductionNew Zealand’s (NZ) Integrated Data Infrastructure (IDI) has rapidly expanded since our 2016 IPDLN conference update. Researchers have available a wide range of administrative and survey data. Even major earthquake damage to a Statistics NZ building in late 2016 has not halted progress with expanding the IDI and its usage.

Objectives and ApproachTo provide an update on the current state of the IDI and its use by the NZ research community.

To highlight some key areas that need further discussion because of the IDI’s rapid growth, especially concerning :

Privacy, regulation and governance: how to make progress within appropriate regulatory constraints, especially around maintaining confidentiality, data sovereignty, data ethics and data security.

Capacity building: what are the best ways to build multidisciplinary skills for new IDI users, share code and knowledge and what types of networking options work best?

Cross-sectoral data linkage: to maximise the value of the IDI.

ResultsIn the last two years there has been a rapid expansion of external datalabs with access to the IDI (now around 33) and with the number of researchers using these datalabs (around 650). This has resulted in many useful research outputs; but it has also highlighted some areas that need more consideration including:

Data sovereignty: NZ needs to ensure a strong role of Māori (Indigenous NZers) in data governance to ensure that IDI data are used to maximise reduction in health and social inequalities.

Options for assisting new IDI users and exploring the best ways of sharing code, knowledge and networking.

To maximise value there needs more focus on use of cross-sectoral data linkage (to better understand the impact of social determinants on long-term health).

Conclusion/ImplicationsThere has been rapid expansion of the scope and use of the IDI in New Zealand. But to ensure the continuation of the success to date, even more attention needs to be paid to such issues as data governance and data protection.
Developing a new metric to capture continuity of primary care: The application of propensity score methods and threshold effects models using linked administrative data
Authors: Ha Ninh Thi , Harris Mark , Moorin Rachael
Published: September 2018

IntroductionEnhancement of primary care has been focused in many countries to make the healthcare system more productive in response to increasing burden of chronic conditions. Despite tremendous growth of linked administrative data, methods to use the administrative data to evaluate and improve the performance of primary care have been limited.

Objectives and ApproachThe study uses linked administrative data of people diagnosed with diabetes to develop and test a new metric that will facilitate better measurement of continuity of primary care named “cover metric”. Cover adds a time element into our previously developed regularity score compartmentalising days between GP visits into within and outside of a particular time period determined using threshold models that are either associated with a reduced (within) or increased (outside) the risk of hospitalisation. The number of days within is then summed over an ascertainment period of 365 days (calendar year) and an annual proportion calculated for each person.

ResultsThree cohorts of people with diabetes were identified based on complication severity index at the baseline year. The risk of hospitalisations among people with diabetes can vary across a range of primary care usage and other covariates. The use of propensity scores allowed us to assess the balance of observed covariates across different levels of primary care usage and to isolate the effect of primary care usage on the risk of hospitalisation from the covariates. The threshold effects models examined how the risk of hospitalisation varied with the length of time following a primary care visit to indicate the time interval which the risk of hospitalisation was lowest. The time interval was then used to construct the cover metric for each defined diabetes severity cohort.

Conclusion/ImplicationsThe new cover metric will significantly contribute to the sparsely available methods for the analysis of linked administrative data in evaluating continuity of primary care. Combination of propensity score and threshold effect models are showcased as useful analytic approaches in the examination of the relationship between primary care and hospitalisations.