Dataset Information

Building a framework for the evaluation of knowledge translation for the Canadian Network for Observational Drug Effect Studies.

ABSTRACT:

Purpose

The Canadian Network for Observational Drug Effect Studies (CNODES), a network of pharmacoepidemiologists and other researchers from seven provincial sites, provides evidence on the benefits and risks of drugs used by Canadians. The Knowledge Translation Team, one of CNODES' four main teams, evaluates the impact of its efforts using an iterative and emergent approach. This article shares key lessons from early evaluation phases, including identifying stakeholders and their evaluation needs, choosing evaluation theories and approaches, and developing evaluation questions, designs, and methods appropriate for the CNODES context.

Methods

Stakeholder analysis was conducted using documentary analysis to determine key contextual factors and research evidence needs of decision maker partners and other stakeholders. Selected theories and frameworks from the evaluation and knowledge translation literature informed decisions about evaluation design and implementation. A developmental approach to evaluation was deemed appropriate due to the innovative, complex, and ever-changing context.

Results

A theory of change, logic model, and potential evaluation questions were developed, informed by the stakeholder analysis. Early indicators of program impact (citation metrics, alternative metrics) have been documented; efforts to collect data on additional indicators are ongoing.

Conclusion

A flexible, iterative, and emergent evaluation approach allows the Knowledge Translation Team to apply lessons learned from completed projects to ongoing research projects, adapt its approaches based on stakeholder needs, document successes, and be accountable to funders/stakeholders. This evaluation approach may be useful for other international pharmacoepidemiology research networks planning and implementing evaluations of similarly complex, multistakeholder initiatives that are subject to constant change.

SUBMITTER: Sketris IS

PROVIDER: S-EPMC6972643 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Building a framework for the evaluation of knowledge translation for the Canadian Network for Observational Drug Effect Studies.

Sketris Ingrid S IS Carter Nancy N Traynor Robyn L RL Watts Dorian D Kelly Kim K

Pharmacoepidemiology and drug safety 20190220

<h4>Purpose</h4>The Canadian Network for Observational Drug Effect Studies (CNODES), a network of pharmacoepidemiologists and other researchers from seven provincial sites, provides evidence on the benefits and risks of drugs used by Canadians. The Knowledge Translation Team, one of CNODES' four main teams, evaluates the impact of its efforts using an iterative and emergent approach. This article shares key lessons from early evaluation phases, including identifying stakeholders and their evalua ...[more]

PMID: 30788900

Similar Datasets

Project description:BackgroundIn some current healthcare settings, there is a noticeable absence of national institutions committed to the synthesis and use of evidence in healthcare decision- and policy-making. This absence creates a need to broaden the responsibilities of healthcare providers to include knowledge brokering and advocacy in order to optimize knowledge translation to other stakeholders, especially policy-makers. However, this process requires practitioners and researchers to acquire certain types of knowledge and skills. This article introduces two innovative methods for capacity building in knowledge translation (KT).MethodsDuring a workshop aimed at preparing 21 trainers in evidence-based medicine, two innovative methods were used: (1) debate and (2) a knowledge translation project (KTP). The main objective of the debates approach was to strengthen participants' critical thinking abilities by requiring them to search for and appraise evidence and defend their arguments. The KTP was used to introduce participants to the essential steps of knowledge translation and to suggest an extended role for healthcare practitioners, i.e., using evidence to manage not only individual patients but also to a community of patients. Participants' performances were assessed according to a pre-designed scheme. At the end of the workshop, participants' opinions and experiences with the innovative teaching methods were evaluated based on their answers to a questionnaire and the results of small-group discussions.ResultsThe participants performed well in both the debate and KTP methods. During post-workshop group discussions, they indicated that the debate approach had added a new dimension to their evidence-based medicine skills by adding purpose and motivation. However, they felt that their performances would have been better if they had been offered practical demonstrations of how to conduct the debate. The participants indicated that the KTP enhanced their understanding of the relationships between evidence and implementation, and motivated them to investigate public health problems in addition to individual patient problems. However, some participants maintained that these issues fell outside the scope of their role as doctors.ConclusionDebates and evidence implementation through KTP are generally well accepted by healthcare practitioners as methods by which they can improve their skills in KT.

Project description:BackgroundGlomerulonephritis (GN) is a group of rare kidney diseases with a substantial health burden and high risk of progression to end-stage renal disease. Research in GN has been limited by poor availability of large comprehensive registries. Substantial variations in access to and administration of treatment and outcomes in GN have been described. Leveraging provincial resources and existing infrastructure, the British Columbia (BC) GN Network is an initiative which serves to combine research and clinical care objectives. The goal of the BC GN Network is to coordinate and improve health care, including robust data capture, on all patients with GN in BC, a Canadian province of over 4.6 million people. This provincial initiative will serve as a model for Canadian or other national and international endeavours.DescriptionThe BC Provincial Renal Agency (BCPRA) is the provincial governmental agency responsible for health delivery for all kidney patients in BC. The BC GN Network has been created by the BCPRA to ensure high quality and equitable access to care for all patients with GN and is a platform for evidence based clinical care programs and associated health policy. All patients with biopsy-proven GN are registered at the time of kidney biopsy into the BCPRA provincial database of kidney disease patients, forming the BC GN Registry. Thereafter, all laboratory results and renal related outcomes are captured automatically. Histology data and core clinical variables are entered into the database. Additional linkages between the GN Registry and administrative databases ensure robust capture of medications, hospital admissions, health care utilization, comorbidities, cancer and cardiac outcomes, and vital statistics.ConclusionsThe BC GN Network and Registry is a unique model in that it combines robust data capture, data linkages, and health care delivery and evaluation into one integrated system. This model utilizes existing health infrastructure to prospectively capture population level data on patients with GN, producing a rich dataset capable of real-time identification and evaluation of GN health policy initiatives, of supporting observational cohort studies and health services research in GN, and of facilitating patient recruitment into GN clinical trials.

Project description:BACKGROUND:Integrated knowledge translation (IKT) is a model of research co-production, whereby researchers partner with knowledge users throughout the research process and who can use the research recommendations in practice or policy. IKT approaches are used to improve the relevance and impact of research. As an emerging field, however, the evidence underpinning IKT is in active development. The Integrated Knowledge Translation Research Network represents a collaborative interdisciplinary team that aims to advance the state of IKT science. METHODS:In 2017, the Integrated Knowledge Translation Research Network issued a call to its members for concept papers to further define IKT, outline an IKT research agenda, and inform the Integrated Knowledge Translation Research Network's special meeting entitled, Integrated Knowledge Translation State of the Science Colloquium, in Ottawa, Canada (2018). At the colloquium, authors presented concept papers and discussed knowledge-gaps for a research agenda and implications for advancing the IKT field. We took detailed field notes, audio-recorded the meeting and analysed the data using qualitative content analysis. RESULTS:Twenty-four participants attended the meeting, including researchers (n?=?11), trainees (n?=?6) and knowledge users (n?=?7). Seven overarching categories emerged from these proceedings - IKT theory, IKT methods, IKT process, promoting partnership, definitions and distinctions of key IKT terms, capacity-building, and role of funders. Within these categories, priorities identified for future IKT research included: (1) improving clarity about research co-production/IKT theories and frameworks; (2) describing the process for engaging knowledge users; and (3) identifying research co-production/IKT outcomes and methods for evaluation. CONCLUSION:The Integrated Knowledge Translation State of the Science Colloquium initiated a research agenda to advance IKT science and practice. Next steps will focus on building a theoretical and evidence base for IKT.

Project description:Background and objectiveObservational studies may provide valuable evidence on real-world causal effects of drug effectiveness in patients with coronavirus disease 2019 (COVID-19). As patients are usually observed from hospital admission to discharge and drug initiation starts during hospitalization, advanced statistical methods are needed to account for time-dependent drug exposure, confounding and competing events. Our objective is to evaluate the observational studies on the three common methodological pitfalls in time-to-event analyses: immortal time bias, confounding bias and competing risk bias.MethodsWe performed a systematic literature search on 23 October 2020, in the PubMed database to identify observational cohort studies that evaluated drug effectiveness in hospitalized patients with COVID-19. We included articles published in four journals: British Medical Journal, New England Journal of Medicine, Journal of the American Medical Association and The Lancet as well as their sub-journals.ResultsOverall, out of 255 articles screened, 11 observational cohort studies on treatment effectiveness with drug exposure-outcome associations were evaluated. All studies were susceptible to one or more types of bias in the primary study analysis. Eight studies had a time-dependent treatment. However, the hazard ratios were not adjusted for immortal time in the primary analysis. Even though confounders presented at baseline have been addressed in nine studies, time-varying confounding caused by time-varying treatment exposure and clinical variables was less recognized. Only one out of 11 studies addressed competing event bias by extending follow-up beyond patient discharge.ConclusionsIn the observational cohort studies on drug effectiveness for treatment of COVID-19 published in four high-impact journals, the methodological biases were concerningly common. Appropriate statistical tools are essential to avoid misleading conclusions and to obtain a better understanding of potential treatment effects.

Project description:BackgroundGovernment sponsors of research and development, along with their funded investigators, are increasingly tasked with demonstrating evidence of knowledge use by nontraditional audiences. This requires efforts to translate their findings for effective communication. For technology-related knowledge, these audiences include clinicians, consumers, manufacturers, public policy agencies, and knowledge brokers. One potentially efficient approach is to communicate research findings through relevant national organizations. However, this requires an understanding of how such organizations view and treat research knowledge, which can be determined through knowledge-value mapping. Do knowledge values differ between national organizations representing different audiences? Can a deeper understanding of knowledge values help sponsors, investigators, and organizations better communicate research findings to stakeholders?MethodsA series of comparative case studies on knowledge-value mapping were derived through interviews with spokespersons for six national organizations. The semi-structured interviews followed a 10-item questionnaire to characterize different ways in which each organization engages with research-based knowledge. Each participating organization represents a particular stakeholder group, while all share a common interest in the research subject matter.ResultsEach national organization considers the value of the research knowledge in the context of their organization's mission and the interests of their members. All are interested in collaborating with researchers to share relevant findings, while they vary along the following dimensions of knowledge engagement: create, identify, translate, adapt, communicate, use, promote, absorptive capacity, and recommendations for facilitation.ConclusionsThe principles of knowledge translation suggest that investigators can increase use by tailoring the format and context of their findings to the absorptive capacity of nonscholars. Greater absorption should result in higher levels of knowledge awareness, interest, and use, which can then be documented. National organizations and their members, in turn, can strive to optimize their absorptive capacities regarding the state of the sciences. This combination will ensure the highest possible return on public investment in research activities. This knowledge-value mapping study concludes that national organizations are appropriate channels for communicating research findings and for meeting statutory requirements and general expectations for generating and documenting knowledge use.

Project description:BACKGROUND:Testing for marginal associations between numerous genetic variants and disease may miss complex relationships among variables (e.g., gene-gene interactions). Bayesian approaches can model multiple variables together and offer advantages over conventional model building strategies, including using existing biological evidence as modeling priors and acknowledging that many models may fit the data well. With many candidate variables, Bayesian approaches to variable selection rely on algorithms to approximate the posterior distribution of models, such as Markov-Chain Monte Carlo (MCMC). Unfortunately, MCMC is difficult to parallelize and requires many iterations to adequately sample the posterior. We introduce a scalable algorithm called PEAK that improves the efficiency of MCMC by dividing a large set of variables into related groups using a rooted graph that resembles a mountain peak. Our algorithm takes advantage of parallel computing and existing biological databases when available. RESULTS:By using graphs to manage a model space with more than 500,000 candidate variables, we were able to improve MCMC efficiency and uncover the true simulated causal variables, including a gene-gene interaction. We applied PEAK to a case-control study of childhood asthma with 2,521 genetic variants. We used an informative graph for oxidative stress derived from Gene Ontology and identified several variants in ERBB4, OXR1, and BCL2 with strong evidence for associations with childhood asthma. CONCLUSIONS:We introduced an extremely flexible analysis framework capable of efficiently performing Bayesian variable selection on many candidate variables. The PEAK algorithm can be provided with an informative graph, which can be advantageous when considering gene-gene interactions, or a symmetric graph, which simply divides the model space into manageable regions. The PEAK framework is compatible with various model forms, allowing for the algorithm to be configured for different study designs and applications, such as pathway or rare-variant analyses, by simple modifications to the model likelihood and proposal functions.

Dataset Information

Building a framework for the evaluation of knowledge translation for the Canadian Network for Observational Drug Effect Studies.

Purpose

Methods

Results

Conclusion

Publications

Building a framework for the evaluation of knowledge translation for the Canadian Network for Observational Drug Effect Studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets