Evidence map›Paper›PMID 32626445›Full record

ArticleEFSA journal. European Food Safety Authority2019

Using problem formulation for fit-for-purpose pre-market environmental risk assessments of regulated stressors.

Yann Devos, Wendy Craig, Robert H Devlin, Alessio Ippolito, Rosalind A Leggatt, Jörg Romeis, Richard Shaw, Claus Svendsen, Christopher J Topping

Abstract read
In one paragraph

Article in EFSA journal. European Food Safety Authority, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Open Science in regulatory environmental risk assessment.Integrated environmental assessment and management · 2021
    Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. Article
  16. Managing evidence in food safety and nutrition.EFSA journal. European Food Safety Authority · 2019
    Article
  17. Conducting fit-for-purpose food safety risk assessments.EFSA journal. European Food Safety Authority · 2019
    Article
  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yann DevosGMO Unit European Food Safety Authority (EFSA) Italy.
Wendy CraigBiosafety Group International Centre for Genetic Engineering & Biotechnology (ICGEB) Italy.
Robert H DevlinFisheries and Oceans Canada Government Canada Canada.
Alessio IppolitoPesticides Unit European Food Safety Authority (EFSA) Italy.
Rosalind A LeggattFisheries and Oceans Canada Government Canada Canada.
Jörg RomeisResearch Division Agroecology and Environment Agroscope Switzerland.
Richard ShawCentre for Agriculture and Biosciences International (CABI) United Kingdom.
Claus SvendsenEcotoxicology and Chemical Risk Group United Kingdom Research and Innovation Centre for Ecology and Hydrology (CEH) United Kingdom.
Christopher J ToppingDepartment of Bioscience - Kalø Aarhus University Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pre-market/prospective environmental risk assessments (ERAs) contribute to risk analyses performed to facilitate decisions about the market introduction of regulated stressors. Robust ERAs begin with an explicit problem formulation, which involves among other steps: (1) formally devising plausible pathways to harm that describe how the deployment of a regulated stressor could be harmful; (2) formulating risk hypotheses about the likelihood and severity of such events; (3) identifying the information that will be useful to test the risk hypotheses; and (4) developing a plan to acquire new data for hypothesis testing should tests with existing information be insufficient for decision-making. Here, we apply problem formulation to the assessment of possible adverse effects of RNA interference-based insecticidal genetically modified (GM) plants, GM growth hormone coho salmon, gene drive-modified mosquitoes and classical biological weed control agents on non-target organisms in a prospective manner, and of neonicotinoid insecticides on bees in a retrospective manner. In addition, specific considerations for the problem formulation for the ERA of nanomaterials and for landscape-scale population-level ERAs are given. We argue that applying problem formulation to ERA maximises the usefulness of ERA studies for decision-making, through an iterative process, because: (1) harm is defined explicitly from the start; (2) the construction of risk hypotheses is guided by policy rather than an exhaustive attempt to address any possible differences; (3) existing information is used effectively; (4) new data are collected with a clear purpose; (5) risk is characterised against well-defined criteria of hypothesis corroboration or falsification; and (6) risk assessment conclusions can be communicated clearly. However, problem formulation is still often hindered by the absence of clear policy goals and decision-making criteria (e.g. definition of protection goals and what constitutes harm) that are needed to guide the interpretation of scientific information. We therefore advocate further dialogue between risk assessors and risk managers to clarify how ERAs can address policy goals and decision-making criteria. Ideally, this dialogue should take place for all classes of regulated stressors, as this can promote alignment and consistency on the desired level of protection and maximum tolerable impacts across regulated stressors.

Indexed as

biodiversityecosystem servicesGMOhypothesis testingpathway to harmpesticidesprotection goals

Identifiers

PMID32626445
PMCPMC7055725

What OpenQuestion holds

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LicenceCC BY-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.