scholarly journals Multi-province epidemiological research using administrative data in Canada: Challenges and opportunities

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Amanda Butler ◽  
Mark Smith ◽  
Wayne Jones ◽  
Carol Adair ◽  
Simone Vigod ◽  
...  
Keyword(s):  
Health Care ◽  
Administrative Data ◽  
Data Access ◽  
Health Data ◽  
National Standards ◽  
Epidemiological Research ◽  
Data Infrastructure ◽  
Administrative Health Data ◽  
Challenges And Opportunities ◽  
Time Required

IntroductionCanada has a publicly-funded universal health care system with information systems managed by 13 provinces and territories. This context creates inconsistencies in data collection and challenges for epidemiological research conducted at the national or multi-jurisdictional level. Objectives and ApproachUsing a recent five-province research project as a case study (BC, AB, MB, ON, QC), we will discuss the strengths and challenges of using Canadian administrative health data in a multi-jurisdictional context. Our goal is to contribute to a better understanding of these challenges and the development of a more integrated and harmonized approach to conducting multi-jurisdictional research. ResultsMulti-jurisdictional data work is feasible but requires detailed coordination and extensive cooperation from all involved. There were noteable variations across provinces in this multi-province study. For example, time required to access the data varied greatly across the five provinces (from 4 to 9 months), and thus there were sequencing challenges, with some provinces being well into the analysis stage while others were still waiting for data. Access to human resources varied across provinces and in some cases led to delays in data abstraction. Cost of data (or analytic support) also varied across provinces, from $12,000 – $15,000. Critical to the success of the project was a coordinating group with expertise in both administrative health data and cross-provincial project coordination. Conclusion/ImplicationsThis project demonstrated the value of comparable data infrastructure with equitable access policies. Many of the disadvantages to multi-province projects using health care administrative data, such as potential coding errors and inconsistencies, can be managed by developing national standards and protocols, and tools that are shared for data cleaning and validation.

scholarly journals Challenges Associated with Cross-Jurisdictional Analyses using Administrative Health Data and Primary Care Electronic Medical Records in Canada

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Alan Katz ◽  
Jennifer Enns ◽  
Sabrina T Wong ◽  
Tyler Williamson ◽  
Alexander Singer ◽  
...  
Keyword(s):  
British Columbia ◽  
Social Services ◽  
Administrative Data ◽  
Data Access ◽  
Health Data ◽  
Quality Data ◽  
Data Governance ◽  
Quality Health Care ◽  
Data Repositories ◽  
Administrative Health Data

Over the last 30 years, public investments in Canada and many other countries have created clinical and administrative health data repositories to support research on health and social services, population health and health policy. However, there is limited capacity to share and use data across jurisdictional boundaries, in part because of inefficient and cumbersome procedures to access these data and gain approval for their use in research. A lack of harmonization among variables and indicators makes it difficult to compare research among jurisdictions. These challenges affect the quality, scope, and impact of work that could be done. The purpose of this paper is to compare and contrast the data access procedures in three Canadian jurisdictions (Manitoba, Alberta and British Columbia), and to describe how we addressed the challenges presented by differences in data governance and architecture in a Canadian cross-jurisdictional research study. We characterize common stages in gaining access to administrative data among jurisdictions, including obtaining ethics approval, applying for data access from data custodians, and ensuring the extracted data is released to accredited individuals in secure data environments. We identify advantages of Manitoba’s flexible ‘stewardship’ model over the more restrictive ‘custodianship’ model in British Columbia, and highlight the importance of communication between analysts in each jurisdiction to compensate for differences in coding variables and poor quality data. Researchers and system planners must have access to and be able to make effective use of administrative health data to ensure that Canadians continue to have access to high-quality health care and benefit from effective health policies. The considerable benefits of collaborative population-based research that spans jurisdictional borders have been recognized by the Canadian Institutes for Health Research in their recent call for the creation of a National Data Platform to resolve many of the issues in harmonization and validation of administrative data elements.

scholarly journals Detection of adverse drug events in e-prescribing and administrative health data: a validation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bettina Habib ◽  
Robyn Tamblyn ◽  
Nadyne Girard ◽  
Tewodros Eguale ◽  
Allen Huang
Keyword(s):  
Administrative Data ◽  
Gold Standard ◽  
Adverse Drug Events ◽  
Study Drug ◽  
Health Data ◽  
Diagnostic Code ◽  
Administrative Health Data ◽  
Potential Ades ◽  
Patient Reported

Abstract Background Administrative health data are increasingly used to detect adverse drug events (ADEs). However, the few studies evaluating diagnostic codes for ADE detection demonstrated low sensitivity, likely due to narrow code sets, physician under-recognition of ADEs, and underreporting in administrative data. The objective of this study was to determine if combining an expanded ICD code set in administrative data with e-prescribing data improves ADE detection. Methods We conducted a prospective cohort study among patients newly prescribed antidepressant or antihypertensive medication in primary care and followed for 2 months. Gold standard ADEs were defined as patient-reported symptoms adjudicated as medication-related by a clinical expert. Potential ADEs in administrative data were defined as physician, ED, or hospital visits during follow-up for known adverse effects of the study medication, as identified by ICD codes. Potential ADEs in e-prescribing data were defined as study drug discontinuations or dose changes made during follow-up for safety or effectiveness reasons. Results Of 688 study participants, 445 (64.7%) were female and mean age was 64.2 (SD 13.9). The study drug for 386 (56.1%) patients was an antihypertensive, and for 302 (43.9%) an antidepressant. Using the gold standard definition, 114 (16.6%) patients experienced an ADE, with 40 (10.4%) among antihypertensive users and 74 (24.5%) among antidepressant users. The sensitivity of the expanded ICD code set was 7.0%, of e-prescribing data 9.7%, and of the two combined 14.0%. Specificities were high (86.0–95.0%). The sensitivity of the combined approach increased to 25.8% when analysis was restricted to the 27% of patients who indicated having reported symptoms to a physician. Conclusion Combining an expanded diagnostic code set with e-prescribing data improves ADE detection. As few patients report symptoms to their physician, higher detection rates may be achieved by collecting patient-reported outcomes via emerging digital technologies such as patient portals and mHealth applications.

scholarly journals Multi-province epidemiological research using linked administrative data: a case study from Canada.

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Amanda Leanne Butler ◽  
Mark Smith ◽  
Wayne Jones ◽  
Carol E Adair ◽  
Simone Vigod ◽  
...  
Keyword(s):  
Information Systems ◽  
Administrative Data ◽  
Research Process ◽  
Health Data ◽  
Provincial Government ◽  
Data Systems ◽  
Administrative Health Data ◽  
Healthcare Data ◽  
Research Activities

BackgroundCanada has a publicly-funded universal healthcare system with information systems managed by 13 different provinces and territories. This context creates inconsistencies in data collection and challenges for research or surveillance conducted at the national or multi-jurisdictional level. ObjectiveUsing a recent Canadian research project as a case study, we document the strengths and challenges of using administrative health data in a multi-jurisdictional context. We discuss the implications of using different health information systems and the solutions we adopted to deal with variations. Our goal is to contribute to better understanding of these challenges and the development of a more integrated and harmonized approach to conducting multi-jurisdictional research using administrative data. Context and ModelUsing data from five separate provincial healthcare data systems, we sought to create and report on a set of provincially-comparable mental health and addiction services performance indicators. In this paper, we document the research process, challenges, and solutions. Finally, we conclude by making recommendations for investment in national infrastructure that could help cut costs, broaden scope, and increase use of administrative health data that exists in Canada. ConclusionCanada has an incredible wealth of administrative data that resides in 13 territorial and provincial government systems. Navigating access and improving comparability across these systems has been an ongoing challenge for the past 20 years, but progress is being made. We believe that with some investment, a more harmonized and integrated information network could be developed that supports a broad range of surveillance and research activities with strong policy and program implications.

scholarly journals Symposium: Distributed Networks of Federated Secure Research Data Environments – Enabling Analytics Across Multiple Platforms

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Matthias Schneider
Keyword(s):  
Population Data ◽  
Data Access ◽  
Distributed Networks ◽  
Research Data ◽  
Data Governance ◽  
Virtual Database ◽  
South Wales ◽  
Challenges And Opportunities ◽  
Storage Location ◽  
Time Required

IntroductionUsers of linked data require access to an increasing number of heterogeneous datasets from diverse domains, often held in different secure research data environments, especially for multi-jurisdictional projects. Under the traditional model of data access, projects are required to transfer and harmonise the necessary datasets in one central location before analysis can be undertaken, increasing the time required for data acquisition and preparation. Objectives and ApproachIn a federated data environment, analysts query distributed datasets held in a network of multiple secure data environments via a central virtual database, without requiring the data to move. Instead, the data is analysed as close as possible to its storage location, minimising the amount of data transfers and giving data custodians more control over their data. This symposium explores the challenges and opportunities of establishing and operating a distributed network of federated secure research data environments. Leading organisations operating data platforms in various jurisdictions present for 15 minutes each the current capabilities of their platforms, the landscape of data environments in their jurisdictions and potential approaches to key questions such as: Harmonising/federating data sources Data security Data governance Discoverability/metadata Performance The audience is the then invited to participate in discussing the topic for the remaining 30 minutes. The following individuals have been approached to represent their organisations in this symposium: Professor David Ford, Swansea University: UK Secure eResearch Platform (UK SErP) Charles Victor, Institute for Clinical Evaluative Sciences (ICES): ICES Data & Analytic Virtual Environment (IDAVE) Professor Louisa Jorm, Centre for Big Data Research in Health, University of New South Wales: E-Research Institutional Cloud Architecture (ERICA) Professor Kimberlyn McGrail, Population Data BC: Secure Research Environment (SRE) Results / Conclusion / ImplicationsThis symposium will help formulate requirements for and barriers to distributed networks of federated secure research data environments, and create a foundation for data analytics across multiple platforms.

scholarly journals Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies (Preprint)

2019 ◽  
Author(s):  
Xiaochen Zheng ◽  
Shengjing Sun ◽  
Raghava Rao Mukkamala ◽  
Ravi Vatrapu ◽  
Joaquín Ordieres-Meré
Keyword(s):  
Health Care ◽  
Internet Of Things ◽  
Data Sharing ◽  
Data Access ◽  
Health Data ◽  
Security And Privacy ◽  
Access Management ◽  
Related Data ◽  
Distributed Ledger ◽  
Health Related

BACKGROUND Huge amounts of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the health care ecosystem. Currently, most of these data are siloed and fragmented in different health care systems or public and private databases. It prevents the fulfillment of intelligent health care inspired by these big data. Security and privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, distributed ledger technologies (DLTs) provide reliable solutions such as blockchain, Ethereum, and IOTA Tangle to facilitate the health care data sharing. OBJECTIVE This study aimed to develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resistant, highly-scalable, and granularly-controllable health data exchange, as well as build a prototype and conduct experiments to verify the feasibility of the proposed solution. METHODS The health-related data are generated by 2 types of IoT devices: wearable devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA’s distributed ledger, the Tangle, which is a directed acyclic graph. Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree is used for data encryption and verification. RESULTS A prototype system was built according to the proposed solution. It uses a smartwatch and multiple air sensors as the sensing layer; a smartphone and a single-board computer (Raspberry Pi) as the gateway; and a local server for data publishing. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and flexible access management during data sharing. CONCLUSIONS DLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. This study also showed the possibility of fully decentralized health data sharing by replacing the local server with edge computing devices.

scholarly journals Linkage of whole genome sequencing with administrative health, and electronic medical record data for the study of autism spectrum disorder: Feasibility, Opportunities and Challenges

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Jennifer Brooks ◽  
Evdokia Anagnostou ◽  
Farah Rahman ◽  
Karen Tu ◽  
Lavnaya Uruthiramoorthy ◽  
...  
Keyword(s):  
Health System ◽  
Whole Genome Sequencing ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Administrative Data ◽  
Genome Sequencing ◽  
Health Data ◽  
Spectrum Disorder ◽  
Whole Genome ◽  
Administrative Health Data

IntroductionAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) that presents with a high degree of heterogeneity (e.g., co-occurrence of other NDDs and other co-morbid conditions), contributing to differential health system needs. Genetics are known to play an important role in ASD and may be associated with different disease trajectories. Objectives and ApproachIn this proof of principle project, our objective is to link >2,200 children with a confirmed diagnosis of a NDD from the Province of Ontario Neurodevelopmental (POND) Study to administrative health data and electronic medical record (EMR) data in order to identify subgroups of ASD with unique health system trajectories. POND includes detailed phenotype and whole genome sequencing (WGS) data. Identified subgroups will be characterized based on clinical phenotype and genetics. To meet this goal, consideration of WGS-specific privacy and data issues is needed to implement processes which are above and beyond traditional requirements for analyzing individual-level administrative health data. ResultsLinkage of WGS data with administrative health data is an emerging area of research. As such it has presented a number of initial challenges for our study of ASD. Privacy concerns surrounding the use of WGS data and rare-variant analysis are of particular importance. Practical issues required the need for analysts with expertise in administrative data, EMR data and genetic analyses, and specialized software and sufficient processing power to analyze WGS data. Transdisciplinary discussions of the scope and significance of research questions addressed through this linkage were crucial. The identification of genetic determinants of phenotypes and trajectories in ASD could support targeted early interventions; EMR linkage may inform algorithms to identify ASD in broader populations. These approaches could improve both patient outcome and family experience. Conclusion/ImplicationsAs the cost of genetic sequencing decreases, WGS data will become part of the routine clinical management of patients. Linkage of WGS, EMR and administrative data has tremendous potential that has largely not been realized; including population-level ASD research to improve our ability to predict long-term outcomes associated with ASD.

scholarly journals Introducing Health Data Research Network Canada (HDRN Canada): A New Organization to Advance Canadian And International Population Data Science

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Kimberlyn McGrail ◽  
Brent Diverty ◽  
Lisa Lix
Keyword(s):  
Data Science ◽  
Data Access ◽  
Indigenous Communities ◽  
Health Data ◽  
Research Strategy ◽  
Research Network ◽  
Case Definitions ◽  
Extensive Experience ◽  
Data Infrastructure ◽  
Metadata Standards

IntroductionNotwithstanding Canada’s exceptional longitudinal health data and research centres with extensive experience transforming data into knowledge, many Canadian studies based on linked administrative data have focused on a single province or territory. Health Data Research Network Canada (HDRN Canada), a new not-for-profit corporation, will bring together major national, provincial and territorial health data stewards from across Canada. HDRN Canada’s first initiative is the $81 million SPOR Canadian Data Platform funded under the Canadian Institutes of Health Research Strategy for Patient-Oriented Research (SPOR). Objectives and ApproachHDRN Canada is a distributed network through which individual data-holding centres work together to (i) create a single portal and support system for researchers requesting multi-jurisdictional data, (ii) harmonize and validate case definitions and key analytic variables across jurisdictions, (iii) expand the sources and types of data linkages, (iv) develop technological infrastructure to improve data access and collection, (v) create supports for advanced analytics and (vi) establish strong partnerships with patients, the public and with Indigenous communities. We will share our experiences and gather international feedback on our network and its goals from symposium participants. ResultsIn January 2020, HDRN Canada launched its Data Access Support Hub (DASH) which includes an inventory listing over 380 datasets, information about more than 120 algorithms and a repository of requirements and processes for accessing data. HDRN Canada is receiving requests for multi-province research studies that would be challenging to conduct without HDRN Canada. Conclusion / ImplicationsThus far, HDRN Canada services and tools have been developed primarily for Canadian researchers but HDRN Canada can also serve as a prompt for an international discussion about what has/has not worked in terms of multi-jurisdictional research data infrastructure. It can also present an opportunity for the development of metadata, standards and common approaches that support more multi-country research.

scholarly journals Evolution or Revolution? Recommendations to Improve the Swiss Health Data Framework

2021 ◽  
Vol 9 ◽  
Author(s):  
Andrea Martani ◽  
Lester Darryl Geneviève ◽  
Sophia Mira Egli ◽  
Frédéric Erard ◽  
Tenzin Wangmo ◽  
...  
Keyword(s):  
Public Health ◽  
Data Access ◽  
Health Data ◽  
General Level ◽  
Unique Identifier ◽  
Data Infrastructure ◽  
Data Framework ◽  
Swiss Health ◽  
Mind Set ◽  
Societal Preferences

Background: Facilitating access to health data for public health and research purposes is an important element in the health policy agenda of many countries. Improvements in this sense can only be achieved with the development of an appropriate data infrastructure and the implementations of policies that also respect societal preferences. Switzerland is a revealing example of a country that has been struggling to achieve this aim. The objective of the study is to reflect on stakeholders' recommendations on how to improve the health data framework of this country.Methods: We analysed the recommendations collected as part of a qualitative study including 48 expert stakeholders from Switzerland that have been working principally with health databases. Recommendations were divided in themes and subthemes according to applied thematic analysis.Results: Stakeholders recommended several potential improvements of the health data framework in Switzerland. At the general level of mind-set and attitude, they suggested to foster the development of an explicit health data strategy, better communication and the respect of societal preferences. In terms of infrastructure, there were calls for the creation of a national data center, the improvement of IT solutions and the use of a Unique Identifier for patient data. Lastly, they recommended harmonising procedures for data access and to clarify data protection and consent rules.Conclusion: Recommendations show several potential improvements of the health data framework, but they have to be reconciled with existing policies, infrastructures and ethico-legal limitations. Achieving a gradual implementation of the recommended solutions is the preferable way forward for Switzerland and a lesson for other countries that are also seeking to improve health data access for public health and research purposes.

scholarly journals Strategies for centering equity and public engagement in the ethical use of integrated administrative data

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Adelia Jenkins ◽  
Amy Hawn Nelson
Keyword(s):  
Data Integration ◽  
Administrative Data ◽  
Public Engagement ◽  
Data Access ◽  
The United States ◽  
Extensive Literature ◽  
Risk Modeling ◽  
Data Systems ◽  
Data Infrastructure ◽  
The Public

Background with rationaleData integration is undertaken for the public good, yet institutions rarely address structural bias in their history, or the ways data are biased due to systemic inequities in the administration of policies and programs. Meanwhile, the public are rarely consulted in data use. Though data infrastructure can be a powerful tool to support equity-oriented reforms, equity is rarely a stated goal for data integration. This raises fundamental concerns, as integrated data increasingly provide the raw materials for evaluation, research, and risk modeling that inform policy, practice, and resource allocation. Actionable Intelligence for Social Policy (AISP) is an initiative of the University of Pennsylvania that focuses on the development, use, and innovation of integrated data systems (IDS). We convene a network of IDS across the United States while supporting developing sites, and as such are uniquely situated to convene experts to develop guidance for centering equity within integrated data infrastructure. Main AimThis project aims to generate guidance for agencies supporting data sharing infrastructure to ensure an emphasis on equity and public engagement for ethical use. Methods/ApproachA variety of data collection methods are being used, including expert panel convenings and interviews with sites piloting or exemplifying strategies for public engagement and equitable data access and use. An extensive literature review is also in progress and will inform a suite of forthcoming products, including a white paper, communications and training materials. ResultsThe results will provide strategies for centering equity across the spectrum of data integration activities, including inclusive governance, staffing considerations, decisions about data quality, and the ethical use of data models and algorithms. Initial findings indicate there are few exemplar sites that routinely center equity within data integration efforts, yet there are promising incremental steps that sites can take to ensure ethical use. ConclusionWhile centering equity within data integration is an emerging focus, initial findings indicate the importance of such efforts, particularly in acknowledging and mitigating the risks of unacknowledged bias across use of administrative data for research and evaluation purposes.

scholarly journals Using Canadian administrative health data to measure the health of caregivers of children with and without health problems: A demonstration of feasibility.

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Jamie C Brehaut ◽  
Anne Guèvremont ◽  
Rubab G Arim ◽  
Rochelle E Garner ◽  
Anton R Miller ◽  
...  
Keyword(s):  
Maternal Health ◽  
Child Health ◽  
Administrative Data ◽  
Chronic Condition ◽  
Service Use ◽  
Health Problems ◽  
Health Data ◽  
Healthy Children ◽  
Administrative Health Data ◽  
High Service

IntroductionCaregivers of children with health problems experience poorer health than the caregivers of healthy children. To date, population-based studies on this issue have primarily used survey data. ObjectivesWe demonstrate that administrative health data may be used to study these issues, and explore how non-categorical indicators of child health in administrative data can enable population-level study of caregiver health. MethodsDyads from Population Data British Columbia (BC) databases, encompassing nearly all mothers in BC with children aged 6-10 years in 2006, were grouped using a non-categorical definition based on diagnoses and service use. Regression models examined whether four maternal health outcomes varied according to indicators of child health. Results162,847 mother-child dyads were grouped according to the following indicators: Child High Service Use (18%) vs. Not (82%), Diagnosis of Major and/or Chronic Condition (12%) vs. Not (88%), and Both High Service Use and Diagnosis (5%) vs. Neither (75%). For all maternal health and service use outcomes (number of physician visits, chronic condition, mood or anxiety disorder, hospitalization), differences were demonstrated by child health indicators. ConclusionsMothers of children with health problems had poorer health themselves, as indicated by administrative data groupings. This work not only demonstrates the research potential of using routinely collected health administrative data to study caregiver and child health, but also the importance of addressing maternal health when treating children with health problems. KeywordsPopulation data, linked data, case-mix, children with special health care needs

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