PLoS ONE (Jan 2023)

Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research

  • Melanie Cousins,
  • E. Jane Parmley,
  • Amy L. Greer,
  • Elena Neiterman,
  • Irene A. Lambraki,
  • Tiscar Graells,
  • Anaïs Léger,
  • Patrik J. G. Henriksson,
  • Max Troell,
  • Didier Wernli,
  • Peter Søgaard Jørgensen,
  • Carolee A. Carson,
  • Shannon E. Majowicz

Journal volume & issue
Vol. 18, no. 8

Abstract

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Background Antimicrobial Resistance (AMR) is a global problem with large health and economic consequences. Current gaps in quantitative data are a major limitation for creating models intended to simulate the drivers of AMR. As an intermediate step, expert knowledge and opinion could be utilized to fill gaps in knowledge for areas of the system where quantitative data does not yet exist or are hard to quantify. Therefore, the objective of this study was to identify quantifiable data about the current state of the factors that drive AMR and the strengths and directions of relationships between the factors from statements made by a group of experts from the One Health system that drives AMR development and transmission in a European context. Methods This study builds upon previous work that developed a causal loop diagram of AMR using input from two workshops conducted in 2019 in Sweden with experts within the European food system context. A secondary analysis of the workshop transcripts was conducted to identify semi-quantitative data to parameterize drivers in a model of AMR. Main findings Participants spoke about AMR by combining their personal experiences with professional expertise within their fields. The analysis of participants’ statements provided semi-quantitative data that can help inform a future of AMR emergence and transmission based on a causal loop diagram of AMR in a Swedish One Health system context. Conclusion Using transcripts of a workshop including participants with diverse expertise across the system that drives AMR, we gained invaluable insight into the past, current, and potential future states of the major drivers of AMR, particularly where quantitative data are lacking.