Knowledge Translation (KT) research investigates methods to promote the uptake of research by practitioners, managers and policy-makers. Rooted in decades of interdisciplinary scholarship showing that evidence use is shaped by social sense‑making, institutions and politics, KT has moved beyond a linear “research to policy” model. Yet, persistent gaps between evidence and decision‑making, as well as uneven institutional capacity and fragmented KT research motivated the development of WHO’s Global Research Agenda: to prioritize rigorous, context‑sensitive KT research that addresses systemic, governance and practical barriers to sustained evidence‑informed policy-making (EIP). From October 2023 to March 2025, a structured five-step approach was undertaken, starting with synthesizing existing evidence on KT strategies and priorities, and complemented by primary data from a global survey. These inputs were used to develop a conceptual framework to organize KT research priority areas. This framework guided a global consultative process, which engaged diverse interest-holders through online consultations and Delphi surveys to jointly identify research gaps, opportunities and priority areas for inclusion in the final research agenda. The initial step of evidence synthesis identified 120 research areas. Through the global consultative process, these were refined to 19 priority research areas organized into three domains: (1) research on KT/EIP interventions, (2) research on barriers, facilitators and opportunities for KT/EIP and (3) research on KT/EIP methods, standards, measurement, theories and frameworks. Specific research areas include strategies to institutionalize KT, contextual factors influencing evidence uptake and exploring innovative technologies such as Artificial Intelligence. This study proposes a prioritized research agenda to guide future KT/EIP research and inform funding decisions. The agenda requires sustained engagement with interest-holders to maximize its impact. Future research should validate and refine the priorities, and ensure relevance, utility and effective implementation across diverse settings. The GRA is more than a technical checklist; it is a strategic roadmap for navigating the political and institutional dimensions of evidence use, enabling a shift beyond supply-side fixes toward a relational, politically aware KT/EIP approach. This shift is essential to embed evidence use in routine decision-making, strengthen system resilience and advance health equity through sustained institutional reform.

Background The effective translation of evidence into policy requires strategic engagement among interest-holders to identify current knowledge gaps, align funding, and minimize research duplication. This study outlines the methods and results of a multi-stage process to develop WHO’s first Global Research Agenda (GRA) on Knowledge Translation and Evidence-informed Policy-making (KT/EIP), aimed at improving research efficiency, guiding funding, increasing evidence use, fostering collaboration, and raising awareness of KT research. Methods From October 2023 to March 2025, a structured five-step approach was undertaken, starting with synthesizing existing evidence on KT strategies and priorities and complemented by primary data from a global survey. These inputs were used to develop a conceptual framework to organize research priority areas. This framework guided a global consultative process, which engaged diverse interest-holders through online consultations and Delphi surveys to jointly identify research gaps, opportunities, and priority areas for inclusion in the final research agenda. Results The initial step of evidence synthesis identified 120 research areas. Through the global consultative process, these were refined to 19 priority research areas organized into three domains: 1) Research on KT/EIP interventions, 2) Research on barriers, facilitators, and opportunities for KT/EIP, and 3) Research on KT/EIP methods, standards, measurement, theories, and frameworks. Specific research areas include strategies to institutionalize KT, targeted approaches for public health emergencies, contextual factors influencing KT/EIP uptake, and the exploration of innovative technologies like Artificial Intelligence. Conclusions This study proposes a prioritized research agenda to guide future KT/EIP research and inform funding decisions. This resource for researchers, policy-makers, and funders requires sustained engagement with interest-holders to maximize its impact. Future research should validate and refine this agenda, and ensure relevance, utility, and effective implementation across diverse settings.

Background and objectives Without strategic actions in its support, the translation of scientific research evidence into health policy is often absent or delayed. This review systematically maps and assesses national-level strategic documents in the field of knowledge translation (KT) for health policy, and develops a practical template that can support Evidence-informed Policy Network (EVIPNet) Europe countries in producing national strategies for evidence-informed policy-making.Methods Websites of organizations with strategic responsibilities in KT were electronically searched, on the basis of pre-defined criteria, in July-August 2017, and an updated search was carried out in April-June 2021. We included national strategies or elements of national strategies that dealt with KT activities, as well as similar strategies of individual institutions with a national policy focus. Two reviewers screened the strategies for inclusion. Data were analysed using qualitative content analysis.Results A total of 65 unique documents were identified, of which 17 were eligible and analysed for their structure and content. Of the 17, 1 document was a national health KT action plan and 6 documents were institution-level KT strategies. The remaining 10 strategies, which were also included were 2 national health strategies, 5 national health research strategies and 3 national KT strategies (not specific to the field of health alone). In all, 13 structural elements and 7 major themes of health policy KT strategies were identified from the included documents.Conclusion KT in health policy, as emerged from the national strategies that our mapping identified, is based on the production and accessibility of policy-relevant research, its packaging for policy-making and the activities related to knowledge exchange. KT strategies may play different roles in the complex and context-specific process of policy-making. Our findings show that the main ideas of health-specific evidence-informed policy literature appear in these strategies, but their effectiveness depends on the way stakeholders use them. Specific knowledge-brokering institutions and organizational capacity, advocacy about the use of evidence, and close collaboration and co-decision-making with key stakeholders are essential in furthering the policy uptake of research results.

EDITORIAL article Front. Public Health, 30 March 2023Sec. Health Economics Volume 11 - 2023 | https://doi.org/10.3389/fpubh.2023.1163995

EDITORIAL article Front. Public Health, 12 August 2022Sec. Health Economics Volume 10 - 2022 | https://doi.org/10.3389/fpubh.2022.942087

Knowledge translation (KT) is increasingly acknowledged to have the potential to improve policy-making. The value of health information (HI), as part of the KT context, is now also increasingly understood. This paper aims to identify existing tools for the translation of HI into policy-making and to develop a related framework facilitating future application of these identified tools. Updating and building upon a scoping review undertaken for the Health Evidence Network (HEN) Synthesis Report No. 54, commissioned by the World Health Organization (WHO) Regional Office for Europe in 2017, a literature search was conducted using the same databases (PubMed and Scopus) and the same keywords as in the WHO/HEN scoping review. All papers elaborating on tools enhancing the use of HI in policy-making were included. Of the 2549 records screened, 17 publications were included in this study. This review identified four different types of tools: 1) Visualisation and modelling tools, 2) Information packaging and synthesis tools, 3) Communication and dissemination tools and 4) Information linkage and exchange tools. The distinctions between these are fluid as different tools can be combined or incorporated into one another to complement each other. Our framework shows that communication/dissemination or linkage tools are crucial to effectively inform policy decisions through HI. This study helps to understand and guide the processes of KT of HI.

This overview aimed to synthesize existing systematic reviews to produce a draft framework of evidence-informed health priority setting that supports countries in identifying appropriate steps and methods when developing and implementing national research agendas. We searched Ovid MEDLINE® and the WHO Institutional Repository for Information Sharing from 2010 to 2020 for critical or systematic reviews that evaluated research priority setting exercises. We adapted the AMSTAR checklist to assess the quality of included reviews and used adapted frameworks for data extraction and analysis. The search resulted in 2395 titles, of which 31 were included. Populations included in the reviews typically involved patients, families and carers, researchers, clinicians, policymakers and research funders. The topics covered in the reviews varied from specific diseases or conditions, approaches for healthcare practice or research priority setting methods itself. All the included systematic reviews were of low or critically low quality. The studies were thematically grouped based on their main focus: identifying and engaging with stakeholders; methods; context; and health area. Our overview of reviews has reconfirmed aspects of existing frameworks, but has also identified new concepts for countries to consider while developing their national research agendas. We propose a preliminary framework for consideration that highlights four key phases: (1) preparatory, (2) priority setting, (3) follow-up phase and (4) sustainability phase, which have thirteen sub-domains to consider.

Background The use of research evidence as an input for health decision-making is a need for most health systems. There are a number of approaches for promoting evidence use at different levels of the health system, but knowledge of their effectiveness is still scarce. The objective of this overview was to evaluate the effectiveness of knowledge communication and dissemination interventions, strategies or approaches targeting policy-makers and health managers. Methods This overview of systematic reviews used systematic review methods and was conducted according to a predefined and published protocol. A comprehensive electronic search of 13 databases and a manual search in four websites were conducted. Both published and unpublished reviews in English, Spanish or Portuguese were included. A narrative synthesis was undertaken, and effectiveness statements were developed, informed by the evidence identified. Results We included 27 systematic reviews. Three studies included only a communication strategy, while eight only included dissemination strategies, and the remaining 16 included both. None of the selected reviews provided “sufficient evidence” for any of the strategies, while four provided some evidence for three communication and four dissemination strategies. Regarding communication strategies, the use of tailored and targeted messages seemed to successfully lead to changes in the decision-making practices of the target audience. Regarding dissemination strategies, interventions that aimed at improving only the reach of evidence did not have an impact on its use in decisions, while interventions aimed at enhancing users’ ability to use and apply evidence had a positive effect on decision-making processes. Multifaceted dissemination strategies also demonstrated the potential for changing knowledge about evidence but not its implementation in decision-making. Conclusions There is limited evidence regarding the effectiveness of interventions targeting health managers and policy-makers, as well as the mechanisms required for achieving impact. More studies are needed that are informed by theoretical frameworks or specific tools and using robust methods, standardized outcome measures and clear descriptions of the interventions. We found that passive communication increased access to evidence but had no effect on uptake. Some evidence indicated that the use of targeted messages, knowledge-brokering and user training was effective in promoting evidence use by managers and policy-makers.

BACKGROUND: Selective reporting of trial results is common. OBJECTIVE: To study selective reporting in clinical study reports, company trial registers and publications of quality of life in placebo-controlled trials of antidepressants. METHODS: We compared clinical study reports of four antidepressants (fluoxetine, duloxetine, paroxetine and sertraline) obtained from two European drug regulators, data from online company registers, and publications received or retrieved from Eli Lilly and GlaxoSmithKline. Pfizer was also contacted but did not provide any publications. RESULTS: We included 15 trials (19,015 pages) and 4717 patients. Six trials had used SF-36, seven EQ-5D and two both instruments. Nine of the 15 CSRs (60%) displayed selective reporting. In the companies' online registers, there was selective reporting for all 15 trials (100%). We received 20 publications from Eli Lilly and retrieved six from the GlaxoSmithKline register. There was selective reporting in 24 of the 26 publications (92%). Despite extensive selective reporting, we found only small differences between placebo and active drugs. CONCLUSIONS: Access to the full raw data from clinical trials and to case report forms for all patients are needed to evaluate the effect of antidepressants on quality of life. Regulatory agencies should refuse to approve drugs or new indications based on incomplete reporting.

EDITORIAL article Front. Public Health, 18 June 2021Sec. Health Economics https://doi.org/10.3389/fpubh.2021.692926

Since its inception in 2003, Cost Effectiveness and Resource Allocation journal has come a long way over the past 18 years. Possibly much longer than many of its contemporaries in the blossoming science of health economics might have anticipated. Today, entering 2020 it celebrates the Age of Maturity. We believe that in the third decade of XXI century the interdisciplinary science of health economics, will rejuvenate and come back to us younger than ever from its early historical roots almost a century ago. The spreading of economic globalization in several distinctive ways, either led by multinational business corporations or newly emerged Asian leadership, or both, is likely to make challenges for contemporary health systems far more serious. The fourth industrial revolution (cyber physical systems and artificial intelligence technology) and accelerated innovation in the field of E-Health and digital health, will probably change the workflow in medical and health care, and inevitably transform the labour market in the upcoming decades. So, let us be up to the task. Let us provide academic centres, industry-sponsored pharmaceutical and medical device innovation hubs, and governing authorities alike, with a powerful forum for debate on cost-effective resource allocation in the years to come.

OBJECTIVE:To study the drop-out rates in trials of selective serotonin and serotonin-norepinephrine reuptake inhibitors (SSRIs and SNRIs). METHODS:This study is a systematic review and meta-analysis of trials. The main outcome measure: Overall drop-out rate. Secondary outcomes were drop-outs due to adverse events and lack of effect. We obtained clinical study reports (CSRs) of five antidepressant drugs from the European Medicines Agency and the UK's Medicines and Healthcare products Regulatory Agency. The eligibility criteria for selecting studies: double-blind randomised, placebo-controlled trials for any indication. Data extraction and analysis: The primary outcome was extracted by two researchers independently and meta-analysed using the Mantel-Haenszel method (fixed effect model). The secondary outcomes were extracted by one researcher and checked by another. Sensitivity analyses were performed using Peto's odds ratio and beta binomial methods, due to presence of null events, and by excluding unreliable trials. RESULTS:We included 71 CSRs (67,319 pages) with information on 73 trials (11,057 patients on SSRI or SNRI drugs, and 7,369 on placebo). There were minor discrepancies within the CSRs when a modified intention to treat principle was used and patients lost to follow up early in the trial were not accounted for. Significantly more patients dropped out on active drug than on placebo, risk ratio 1.08 (95% CI 1.03 to 1.13), with no difference between adults and children/ adolescents, RR = 1.08 (1.03 to 1.13) and 1.07 (0.95 to 1.21), respectively. When three trials with a prior single-blind phase on active drug were removed, the difference was a risk ratio of 1.12 (1.07 to 1.18), whereas the result was the same after removal of three trials with fraudulent data or other issues with data validity, risk ratio 1.08 (1.03 to 1.13). There were more drop-outs due to adverse events on active drug than on placebo, risk ratio 2.63 (2.33 to 2.96). There were fewer drop-outs due to lack of effect, risk ratio 0.47 (0.43 to 0.53). However, this result is biased; when more people drop out due to adverse effects, fewer can drop out because of lack of effect. CONCLUSIONS:By using CSRs, we were able to demonstrate for the first time that more patients dropped out on active drug than on placebo. As it can be argued that the drop-out rate reflects the patients' overall assessment of the balance between benefits and harms, our review adds to the growing concern that SSRIs and SNRIs might not have the desired effect. Our review also highlights the importance of using CSRs for undertaking reviews of drugs.

Delays in diagnosis and treatment of pulmonary tuberculosis are a major set-back to global tuberculosis control. There is currently no global evidence on the average delays thus, the most important contributor to total delay is unknown. We aimed to estimate average delay measures and to investigate sources for heterogeneity among studies assessing delay measures. Systematic review of studies reporting mean (± standard deviation) or median (interquartile range, IQR) of patient, doctor, diagnostic, treatment, health system and/or total delays in journal articles indexed in PubMed. We pooled mean delays using random-effects inverse variance meta-analysis, investigated for variations in pooled estimates in subgroup analyses and explored for sources of heterogeneity using pre-specified explanatory variables. The systematic review included 198 studies (831,724 patients) from 78 countries. The median number of patients per study was 243 (IQR; 160–458) patients. Overall, the pooled mean total delay was 87.6 (95% CI: 81.4–93.9) days. The most important and largest contributor to total delay was patient delay with a pooled mean delay of 81 (95% CI: 70–92) days followed by doctor’s delay and treatment delay with pooled mean delays of 29.5 (95% CI: 25.9–33.0) and 7.9 (95% CI: 6.9–8.9) days respectively. There was considerable heterogeneity in all pooled analyses (I2 > 95%). In the meta-regression models of mean delays, studies excluding extra-pulmonary tuberculosis patients reported increased mean doctor’s delay by 45 days on average, non-use of chest x-ray and conducting studies in high income countries decreased mean treatment delay by 20 and 22 days on average, respectively. Strategies to address patients’ delay could have important implications for the success of the global tuberculosis control programmes.

Background The National Institute for Health and Care Excellence (NICE) was established in 1999 and provides national guidance and advice to improve health and social care. Several steps in the research cycle have been identified that can support the reduction of waste that occurs in biomedical research. The first step in the process is ensuring appropriate research priority setting occurs so only the questions that are needed to fill existing gaps in the evidence are funded. This paper summarises the research priority setting processes at NICE. Methods NICE uses its guidance production processes to identify and prioritise research questions through systematic reviews, economic analyses and stakeholder consultations and then highlights those priorities by engagement with the research community. NICE also highlights its methodological areas for research to ensure the appropriate development and growth of the evidence landscape. Results NICE has prioritised research questions through its guidance production and methodological work and has successfully had several research products funded through the National Institute for Health Research and Medical Research Council. This paper summarises those activities and results. Conclusions This activity of NICE therefore reduces research waste by ensuring that the research it recommends has been systematically prioritised through evidence reviews and stakeholder input.

Objective: The objectives of this review were to identify vaccine economic evaluations that include herd immunity and describe the methodological approaches used. Methods: We used Kim and Goldie's search strategy from a systematic review (1976-2007) of modelling approaches used in vaccine economic evaluations and additionally searched PubMed/MEDLINE and Embase for 2007-2015. Studies were classified according to modelling approach used. Methods for estimating herd immunity effects were described, in particular for the static models. Results: We identified 625 economic evaluations of vaccines against human-transmissible diseases from 1976 to 2015. Of these, 172 (28%) included herd immunity. While 4% of studies included herd immunity in 2001, 53% of those published in 2015 did this. Pneumococcal, human papilloma and rotavirus vaccines represented the majority of studies (63%) considering herd immunity. Ninety-five of the 172 studies utilised a static model, 59 applied a dynamic model, eight a hybrid model and ten did not clearly state which method was used. Relatively crude methods and assumptions were used in the majority of the static model studies. Conclusion: The proportion of economic evaluations using a dynamic model has increased in recent years. However, 55% of the included studies used a static model for estimating herd immunity. Values from a static model can only be considered reliable if high quality surveillance data are incorporated into the analysis. Without this, the results are questionable and they should only be included in sensitivity analysis. (C) 2017 The Author(s). Published by Elsevier Ltd.

INTRODUCTION:We aimed to identify the validity and robustness of effect estimates for serious rare adverse events in clinical study reports of antidepressant trials, across different meta-analysis methods for rare binary events data (1,2).METHODS:Four serious rare adverse events (all-cause mortality, suicidality, aggressive behaviour and akathisia) were meta-analyzed using different methods (3). The Yusuf-Peto odds ratio (OR), which ignores studies with no events in the treatment arms, was compared with the alternative approaches of generalized linear mixed models (GLMM), conditional logistic regression, a Bayesian approach using Markov Chain Monte Carlo (MCMC) and a beta-binomial regression model.RESULTS:Though the estimates for the four outcomes did not change substantially across the different analysis methods, the Yusuf-Peto method underestimated the treatment harm and overstimated its precision, especially when the estimated odds ratio (OR) deviated greatly from 1. For example the OR for suicidality for children and adolescents was 2.39 (95 percent Confidence Interval, CI 1.32 to 4.33, using the Yusuf-Peto method), but increased to 2.64 (95 percent CI 1.33 to 5.26) using conditional logistic regression, to 2.69 (95 percent CI 1.19 to 6.09) using beta-binomial, to 2.73 (95 percent CI 1.37 to 5.42) using the GLMM and finally to 2.87 (95 percent CI 1.42 to 5.98) using the MCMC approach.CONCLUSIONS:The method used for meta-analysis of rare events data influences the estimates obtained and the exclusion of double zero-event studies can give misleading results. To ensure reduction of bias and erroneous inferences, sensitivity analyses should be performed using different methods and we recommend that the Yusuf-Peto approach should no longer be used. Other methods, in particular the beta-binomial method that was shown to be superior, should be considered instead.