PFAS Systematic Evidence Map (Shiny App)

The PFAS Systematic Evidence Map (Shiny App) provides a research synthesis and interactive database that maps the systematic secondary literature on per- and polyfluoroalkyl substances (PFAS) exposure across humans, animals, and environmental compartments.

Key Features:

• Systematic Evidence Mapping: The tool maps 175 systematic reviews meeting eligibility criteria, with literature content organized by biological, environmental, and medical aspects of exposure to 35 fluorinated compounds.
• Bibliometric Analysis Integration: The tool incorporates bibliometric data analysis revealing patterns and trends in academic literature, including research network clusters and geographical bias in research productivity.
• Methodological Quality Assessment: The tool employs a modified version of the AMSTAR2 checklist to evaluate the methodological rigour of included systematic reviews.
• Interactive Visual Map: The tool provides a visual representation of literature content, research gaps, and interdisciplinary connections in PFAS exposure research.

Scientific Applications:

• Research Gap Identification: The tool identifies data gaps in PFAS exposure research, particularly the relative scarcity of environmental and animal-related reviews compared to human exposure studies.
• Review Quality Evaluation: The tool enables assessment of methodological strengths and weaknesses in systematic reviews, including detailed descriptions of study selection processes and disclosure of potential conflicts of interest.
• Cross-Disciplinary Connection Exploration: The tool facilitates exploration of interdisciplinary connections in PFAS research and highlights opportunities for cross-country collaboration.
• Future Research Guidance: The tool guides future research initiatives by mapping literature content and drawing research network clusters across human health, controlled laboratory animal studies, and wildlife research.

Methodology:

Systematic evidence mapping of peer-reviewed systematic reviews, pre-prints, and theses through comprehensive searches across electronic databases and grey literature, with data extraction and synthesis involving mapping literature content and narrative descriptions, modified AMSTAR2 checklist evaluation, and bibliometric data analysis.