Evidence synthesis in the time of COVID-19

Environmental evidence in action: on the science and practice of evidence synthesis and evidence-based decision-making

In civil society we expect that policy and management decisions will be made using the best available evidence. Yet, it is widely known that there are many barriers that limit the extent to which that occurs. One way to overcome these barriers is via robust, comprehensive, transparent and repeatable evidence syntheses (such as systematic reviews) that attempt to minimize various forms of bias to present a summary of existing knowledge for decision-making purposes. Relative to other disciplines (e.g., health care, education), such evidence-based decision-making remains relatively nascent for environment management despite major threats to humanity, such as the climate, pollution and biodiversity crises demonstrating that human well-being is inextricably linked to the biophysical environment. Fortunately, there are a growing number of environmental evidence syntheses being produced that can be used by decision makers. It is therefore an opportune time to reflect on the science and practice of evidence-based decision-making in environment management to understand the extent to which evidence syntheses are embraced and applied in practice. Here we outline a number of key questions related to the use of environmental evidence that need to be explored in an effort to enhance evidence-based decision-making. There is an urgent need for research involving methods from social science, behavioural sciences, and public policy to understand the basis for patterns and trends in environmental evidence use (or misuse or ignorance). There is also a need for those who commission and produce evidence syntheses, as well as the end users of these syntheses to reflect on their experiences and share them with the broader evidence-based practice community to identify needs and opportunities for advancing the entire process of evidence-based practice. It is our hope that the ideas shared here will serve as a roadmap for additional scholarship that will collectively enhance evidence-based decision-making and ultimately benefit the environment and humanity.

Methodological Guidance For Incorporating Equity When Informing Rapid-Policy And Guideline Development

Objectives

We provide guidance for considering equity in rapid reviews through examples of published COVID-19 rapid reviews.

Study Design and Setting

This guidance was developed based on a series of methodological meetings, review of internationally renowned guidance such as the Cochrane Handbook and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for equity-focused systematic reviews (PRISMA-Equity) guideline. We identified Exemplar rapid reviews by searching COVID-19 databases and requesting examples from our team.

Results

We proposed the following key steps: 1. involve relevant stakeholders with lived experience in the conduct and design of the review; 2. reflect on equity, inclusion and privilege in team values and composition; 3. develop research question to assess health inequities; 4. conduct searches in relevant disciplinary databases; 5. collect data and critically appraise recruitment, retention and attrition for populations experiencing inequities; 6. analyse evidence on equity; 7. evaluate the applicability of findings to populations experiencing inequities; and 8. adhere to reporting guidelines for communicating review findings. We illustrated these methods through rapid review examples.

Conclusion

Implementing this guidance could contribute to improving equity considerations in rapid reviews produced in public health emergencies, and help policymakers better understand the distributional impact of diseases on the population.

Tools to support the automation of systematic reviews: A scoping review

OBJECTIVE

The objectives of this scoping review are to identify the reliability and validity of the available tools, their limitations and any recommendations to further improve the use of these tools.

STUDY DESIGN

A scoping review methodology was followed to map the literature published on the challenges and solutions of conducting evidence synthesis using the JBI scoping review methodology.

RESULTS

A total of 47 publications were included in the review. The current scoping review identified that LitSuggest, Rayyan, Abstractr, BIBOT, R software, RobotAnalyst, DistillerSR, ExaCT and NetMetaXL have potential to be used for the automation of systematic reviews. However, they are not without limitations. The review also identified other studies that employed algorithms that have not yet been developed into user friendly tools. Some of these algorithms showed high validity and reliability but their use is conditional on user knowledge of computer science and algorithms.

CONCLUSION

Abstract screening has reached maturity; data extraction is still an active area. Developing methods to semi-automate different steps of evidence synthesis via machine learning remains an important research direction. Also, it is important to move from the research prototypes currently available to professionally maintained platforms.

Challenges of evidence synthesis during the 2020 COVID pandemic: a scoping review

AIM

The objectives of this scoping review are to identify the challenges to conducting evidence synthesis during the COVID-19 pandemic and to propose some recommendations addressing the identified gaps.

METHODS

A scoping review methodology was followed to map the literature published on the challenges and solutions of conducting evidence synthesis using the Joanna Briggs Methodology of performing scoping review. We searched several databases from the start of the Pandemic in December 2019 until 10th June 2021.

RESULTS

A total of 28 publications was included in the review. The challenges cited in the included studies have been categorised into four distinct but interconnected themes including: upstream, Evidence synthesis, downstream and contextual challenges. These challenges have been further refined into issues with primary studies, databases, team capacity, process, resources, and context. Several proposals to improve the above challenges included: transparency in primary studies registration and reporting, establishment of online platforms that enables collaboration, data sharing and searching, the use of computable evidence and coordination of efforts at an international level.

CONCLUSION

This review has highlighted the importance of including artificial intelligence, a framework for international collaboration and a sustained funding model to address many of the shortcomings and ensure we are ready for similar challenges in the future.