Dataset Information

Benefit-Risk Assessment of Vaccines. Part I: A Systematic Review to Identify and Describe Studies About Quantitative Benefit-Risk Models Applied to Vaccines.

ABSTRACT: INTRODUCTION:Understanding the balance between the benefits and risks of vaccination is essential to ensure informed and adequate public health decision making. Quantitative benefit-risk models (qBRm) represent useful tools to help decision makers with supporting benefit-risk assessment throughout the lifecycle of a medical product. However, few initiatives have been launched to harmonise qBRm approaches, specifically for vaccines. OBJECTIVES:The aim of this paper was to identify publications about qBRm applied to vaccines through a systematic literature review, and to describe their characteristics. METHODS:Medline, Scopus and Institute for Scientific Information Web of Knowledge databases were searched to identify articles in English, published from database inceptions up to December 2019. The search strategy included the combination of three key concepts: 'benefit-risk', 'modelling' and 'vaccines'. Data extracted included the modelling context and the methodological approaches used. RESULTS:Of 3172 publications screened, 48 original publications were included. Most of the selected studies were published over the past decade and focused on rotavirus (15), dengue (10) and influenza (6) vaccines. The majority (30) of studies reported analyses related to high-income countries. The methodology of the studies differed, particularly in modelling techniques, benefit-risk measures, and sensitivity analyses. The present work also pointed out a high level of variability in the quality of reporting across studies, with particular regard to input parameters and methodological approaches. CONCLUSIONS:This review provides an extensive list of qBRm applied to vaccines. Discrepancies across studies were identified during our review. While the number of published qBRm studies is increasing, no reporting guidance for qBRm applied to vaccines is currently available. This may affect decision makers' confidence in the results and their benefit-risk assessment(s); therefore, the development of such reporting guidance is highly needed.

SUBMITTER: Arlegui H

PROVIDER: S-EPMC7575467 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Benefit-Risk Assessment of Vaccines. Part I: A Systematic Review to Identify and Describe Studies About Quantitative Benefit-Risk Models Applied to Vaccines.

Arlegui Hugo H Bollaerts Kaatje K Salvo Francesco F Bauchau Vincent V Nachbaur Gaëlle G Bégaud Bernard B Praet Nicolas N

Drug safety 20201101 11

<h4>Introduction</h4>Understanding the balance between the benefits and risks of vaccination is essential to ensure informed and adequate public health decision making. Quantitative benefit-risk models (qBRm) represent useful tools to help decision makers with supporting benefit-risk assessment throughout the lifecycle of a medical product. However, few initiatives have been launched to harmonise qBRm approaches, specifically for vaccines.<h4>Objectives</h4>The aim of this paper was to identify ...[more]

PMID: 32914292

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Project description:Background Engaging users of health research, namely knowledge users, as partners in the research process may to lead to evidence that is more relevant to the users. This may optimize the uptake of evidence in healthcare practice, resulting in improved health outcomes or more efficient healthcare systems. However, barriers to involving knowledge users in the research process exist. Theories, models and frameworks may help guide the process of involving knowledge users and address barriers to engaging with knowledge users in research; however, there is little evidence identifying or describing the theories, models and frameworks of health research partnerships. Objectives Identify and describe theories, models and frameworks of health research partnerships. Report on concepts of knowledge user engagement represented in identified theories, models and frameworks. Methods We conducted a scoping review. Database (MEDLINE, Embase, CINAHL, PCORI) and ancestry and snowball searches were utilized. Included articles were written in English, published between January 2005 and June 2021, specific to health, a research partnership, and referred to a theory, model or framework. No critical appraisal was conducted. We developed a coding framework to extract details related to the publication (e.g. country, year) and theory, model or framework (e.g. intended users, theoretical underpinning, methodology, methods of development, purpose, concepts of knowledge user engagement). One reviewer conducted data extraction. Descriptive statistics and narrative synthesis were utilized to report the results. Results We identified 21 874 articles in screening. Thirty-nine models or frameworks were included in data analysis, but no theory. Two models or frameworks (5%) were underpinned by theory. Literature review was the method (n = 11, 28%) most frequently used to develop a model or framework. Guiding or managing a partnership was the most frequently reported purpose of the model/framework (n = 14, 36%). The most represented concept of knowledge user engagement was principles/values (n = 36, 92%). Conclusions The models and frameworks identified could be utilized by researchers and knowledge users to inform aspects of a health research partnership, such as guidance or implementation of a partnership. Future research evaluating the quality and applicability of the models and frameworks is necessary to help partners decide which model or framework to implement. Supplementary Information The online version contains supplementary material available at 10.1186/s12961-022-00877-4.

Dataset Information

Benefit-Risk Assessment of Vaccines. Part I: A Systematic Review to Identify and Describe Studies About Quantitative Benefit-Risk Models Applied to Vaccines.

Publications

Benefit-Risk Assessment of Vaccines. Part I: A Systematic Review to Identify and Describe Studies About Quantitative Benefit-Risk Models Applied to Vaccines.

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