Data on Patient Record Trajectory for Linkage (DataPRinT Linkage).

The linkage of Electronic Medical Records, Administrative and other data sources is highly valuable for research and health system monitoring. Once linked, combined resources can be analyzed to provide the answers to a variety of health questions that otherwise could not be answered. However, legislative and administrative barriers, including lengthy processes for data sharing agreements, may preclude timely linkage which is a key requirement during pandemics. Objective To develop a method using a patient’s health trajectory to probabilistically link primary care Electronic Medical Record (EMR) data with administrative and other data, without the need to transfer large datasets or identifiable information. To determine the legislative feasibility, accuracy and validity of this linkage process. Study Design Identify data strings that do not directly identify patients and could be used as unique linkage variables. The data strings, which we are calling dataprints, are sufficiently similar over time in different databases. One example in Ontario, Canada, is the pattern of submitted health claims. For every patient seen by a family physician, there exists a unique pattern of dates/billing codes/diagnoses over time. These unique patterns are reasonably similar in EMR and administrative datasets. We will apply an algorithm which turns the string in the selected dataprints to an irreversibly hashed code for each person. The hashed code and no additional information will be provided by both data controllers to a trusted-third party who will determine which records match and send a mapping table to both. This enables analyses to be run in parallel, without divulging any direct person identifiers. Dataset Individuals contained in the University of Toronto Practice Based Research Network (UTOPIAN). Outcome Measures Linkage quality will be assessed by the number of true matches and represented by sensitivity, specificity and positive and negative predictive values. Results The method will be evaluated against a validated, deterministically linked reference standard at North York General Hospital using de-identified EMR and hospital data. Results will inform processes to enable analyses across datasets while adhering to privacy legislation

Marginal structural models using calibrated weights with SuperLearner: application to longitudinal diabetes cohort.

Although machine learning has permeated many disciplines, the convergence of causal methods and machine learning remains sparse in the existing literature. Our aim was to formulate a marginal structural model in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes: metformin, sulfonylurea and SGLT-2. We were interested in estimating “diabetes care provision” in next calendar year using a composite measure of chronic disease prevention and screening elements. We demonstrated the application of dynamic treatment regimes using the National Diabetes Action Canada Repository in which we applied a collection of mainstream statistical learning algorithms. We generated an ensemble of statistical learning algorithms using the SuperLearner based on the following base learners: (i) least absolute shrinkage and selection operator, (ii) ridge regression, (iii) elastic net, (iv) random forest, (v) gradient boosting machines, (vi) neural network. Each statistical learning algorithm was fitted using the pseudo-population with respect to the marginalization of the time-dependent confounding process. The covariate balance was assessed using the longitudinal (i.e. cumulative-time product) stabilized weights with calibrated restrictions. Our results indicated that the treatment drop-in cohorts (with respect to metformin, sulfonylurea and SGLT-2) may improve diabetes care provision in relation to treatment naïve cohort. As a clinical utility, we hope that this article will facilitate discussions around the prevention of adverse chronic outcomes associated with diabetes through the improvement of diabetes care provisions in primary care

“How we do it”: A qualitative study of strategies for adopting an exercise routine while living with type 1 diabetes

IntroductionFor people living with type 1 diabetes (T1D) the challenge of increasing daily physical activity (PA) is compounded by the increased risks of hypoglycemia and glucose variability. Little information exists on the lived experience of overcoming these barriers and adopting and maintaining an active lifestyle while living with T1D.Research Design and MethodsWe conducted a patient-led qualitative study consisting of semi-structured interviews or focus groups with 22 individuals at least 16 years old living with T1D. We used existing patient co-researcher networks and snowball sampling to obtain a sample of individuals who reported being regularly physically active and had been diagnosed with T1D for at least one year. We used an interpretive description analysis to generate themes and strategies associated with maintaining an active lifestyle while living with T1D. We involved patient co-researchers in study design, data collection, and interpretation.Results14 self-identified women and 8 self-identified men (ages 19-62, median age 32 years) completed the study, led by either a researcher, or a patient co-researcher and research assistant regarding their strategies for maintaining an active lifestyle. We identified five themes that facilitate regular sustained PA: (1) Structure and organization are important to adopt safe PA in daily life “I can’t do spontaneous exercise. I actually need a couple hours of warning minimum”; (2) Trial and error to learn how their body responds to PA and food “Once you put the time and effort into learning, you will have greater success”; (3) Psychosocial aspects of PA “…because it’s not just your body, it’s your soul, it’s your mind that exercise is for”; (4) Diabetes technology and (5) Education and peer support. Strategies to overcome barriers included (1) Technology; (2) Integrating psychosocial facilitators; (3) Insulin and carbohydrate adjustments; and (4) Planning for exercise.ConclusionsLiving an active lifestyle with T1D is facilitated by dedicated structure and organization of routines, accepting the need for trial and error to understand the personalized glycemic responses to PA and careful use of food to prevent hypoglycemia. These themes could inform clinical practice guidelines or future trials that include PA interventions

Privacy-Preserving Record Linkage: An international collaboration between Canada, Australia and Wales

ABSTRACT Objectives Linkage of “big data” can provide the answers to a variety of health questions that benefit the delivery of patient care, impact of policies, system planning and evaluation. In some jurisdictions, legal and operational barriers may prevent data linkage for research and system evaluation. Collaboration between international research institutions in Canada, Australia and Wales was formed at the Farr Institute International Conference in 2015. This partnership will test privacy-preserving record linkage (PPRL) techniques for linkage accuracy on real datasets held in a Canadian data repository. Approach Bloom filter PPRL techniques have been incorporated into a prototype linkage system. Evaluations on probabilistic linkage using Bloom filters method have shown potential for large-scale record linkage, performing both accurately and efficiently under experimental conditions. The prototype will be used to evaluate the Bloom filter PPRL techniques in 3 phases. Phase 1: 3 tests using simulated data relating to 20 million individuals will be matched to a sub-cohort of 1 million individuals. Phase 2: 100,000 people from hospital inpatient records will be matched to 18 million people in a health system registration file. These tests will inform whether the method can achieve high levels of privacy protection without negatively impacting performance and linkage quality. Performance indicators include match rate and processing efficiency based on record volumes. Results Linkage quality will be assessed by the number of true matches and non matches identified as links and non-links. This method will be evaluated using synthetic and real-world datasets, where the true match status is known. Initial performance testing linked a file of 3,000 records to 30,000 with a 100% match result. Subsequent test phases as above will continue to be evaluated and these results will be presented. Conclusion Completion of the phased tests will confirm the ability to link datasets while preserving privacy. This international collaboration will expand the utility of this prototype linkage system and expand the global knowledge bank focusing on PPRL methods in general. It will also inform how to adapt to local requirements by providing a solution to many common legal and administrative challenges

Marginal structural models using calibrated weights with SuperLearner : application to type II diabetes cohort

Funding: We like to acknowledge the SOPR-CIHR funding for NDR, and AHRQ Inspire PHC award for the application of this study. This statistical research was supported by Natural Sciences and Engineering Research Council (NSERC) PhD scholarship (CGS: 534600).As different scientific disciplines begin to converge on machine learning for causal inference, we demonstrate the application of machine learning algorithms in the context of longitudinal causal estimation using electronic health records. Our aim is to formulate a marginal structural model for estimating diabetes care provisions in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes: metformin, sulfonylurea and SGLT-2i. The binary outcome of diabetes care provisions was defined using a composite measure of chronic disease prevention and screening elements [27] including (i) primary care visit, (ii) blood pressure, (iii) weight, (iv) hemoglobin A1c, (v) lipid, (vi) ACR, (vii) eGFR and (viii) statin medication. We used several statistical learning algorithms to describe causal relationships between the prescription of three common classes of diabetes medications and quality of diabetes care using the electronic health records contained in National Diabetes Repository. In particular, we generated an ensemble of statistical learning algorithms using the SuperLearner framework based on the following base learners: (i) least absolute shrinkage and selection operator, (ii) ridge regression, (iii) elastic net, (iv) random forest, (v) gradient boosting machines, and (vi) neural network. Each statistical learning algorithm was fitted using the pseudo-population generated from the marginalization of the time-dependent confounding process. Covariate balance was assessed using the longitudinal (i.e. cumulative-time product) stabilized weights with calibrated restrictions. Our results indicated that the treatment drop-in cohorts (with respect to metformin, sulfonylurea and SGLT-2i) may have improved diabetes care provisions in relation to treatment naive (i.e. no treatment) cohort. As a clinical utility, we hope that this article will facilitate discussions around the prevention of adverse chronic outcomes associated with type II diabetes through the improvement of diabetes care provisions in primary care.PostprintPeer reviewe

Trends in diabetes medication use in Canada, England, Scotland and Australia : a repeated cross-sectional analysis (2012-2017)

Funding: This study was supported by Diabetes Action Canada which is funded, in part, through a Canadian Institutes of Health Research chronic disease network grant under the Strategy for Patient-Oriented Research (Funding Reference number: SCA 145101). Dr Greiver is supported through the Gordon F. Cheesbrough Research Chair in Family and Community Medicine from North York General Hospital. Acquisition of the PBS 10% sample data was supported by a NHMRC Centre of Research Excellence Grant (#1060407) and a Cooperative Research Centre Project Grant from the Australian Department of Industry, Innovation and Science (CRC-P-439). AH was supported by a NSW Health Early-Mid Career Fellowship.Background: Several new classes of glucose lowering medications have been introduced in the past two decades. Some, such as Sodium-glucose cotransporter 2 inhibitors (SGLT2s), have evidence of improved cardiovascular outcomes, while others, such as Dipeptidyl peptidase-4 inhibitors (DPP4s), do not. It is therefore important to identify their uptake, in order to find ways to support the use of more effective medications. Aims: We studied the uptake of these new classes amongst patients with type 2 diabetes. Design and setting: Retrospective repeated cross-sectional analysis. We compared rates of medication uptake in Australia, Canada, England and Scotland. Method: We used primary care Electronic Medical Data on prescriptions (Canada, UK) and dispensing data (Australia) from 2012 to 2017. We included persons aged 40 years or over on at least one glucose-lowering drug class in each year of interest, excluding those on insulin only. We determined proportions of patients in each nation, for each year, on each class of medication, and on combinations of classes. Results: By 2017, data from 238,609 patients were included. The proportion of patients on sulfonylureas (SUs) decreased in three out of four nations, while metformin decreased in Canada. Use of combinations of metformin and new drug classes increased in all nations, replacing combinations involving SUs. In 2017 more patients were on DPP4s (between 19.1% and 27.6%) than on SGLT2s (between 10.1% and 15.3%). Conclusions: New drugs are displacing SUs. However, despite evidence of better outcomes, the adoption of SGLT2s lagged behind DPP4s.Publisher PDFPeer reviewe

Time Course of Cocaine-Induced Behavioral and Neurochemical Plasticity

Factors that result in augmented reinstatement, including increased withdrawal period duration and high levels of cocaine consumption, may provide insight into relapse vulnerability. The neural basis of augmented reinstatement may arise from more pronounced changes in plasticity required for reinstatement and/or the emergence of plasticity expressed only during a specific withdrawal period or under specific intake conditions. In this study, we examined the impact of withdrawal period duration and cocaine intake on the magnitude of cocaine-primed reinstatement and extracellular glutamate in the nucleus accumbens, which has been shown to be required for cocaine-primed reinstatement. Rats were assigned to self-administer under conditions resulting in low (2 hours/day; 0.5 mg/kg/infusion, IV) or high (6 hours/day; 1.0 mg/kg/infusion, IV) levels of cocaine intake. After 1, 21 or 60 days of withdrawal, drug seeking and extracellular glutamate levels in the nucleus accumbens were measured before and after a cocaine injection. Cocaine-reinstated lever pressing and elevated extracellular glutamate at every withdrawal time point tested, which is consistent with the conclusion that increased glutamatergic signaling in the nucleus accumbens, is required for cocaine-induced reinstatement. Interestingly, high-intake rats exhibited augmented reinstatement at every time point tested, yet failed to exhibit higher levels of cocaine-induced increases in extracellular glutamate relative to low-intake rats. Our current data indicate that augmented reinstatement in high-intake rats is not due to relative differences in extracellular levels of glutamate in the nucleus accumbens, but rather may stem from intake-dependent plasticity