Annexes to the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software - Input and output data sets
Creators
- 1. Dutch National Institute for Public Health and the Environment (RIVM)
- 2. Wageningen Research, Biometris
Description
Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid:
- hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);
- hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).
The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (here).
The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.
Exposure estimates were obtained with Monte Carlo Risk Assessment (MCRA) software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).
Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.
The input and output data for the exposure assessment are reported in the following annexes:
- Annex A.1 – Input data for the exposure assessment of CAG-TCP
- Annex A.2 – Input data for the exposure assessment of CAG-TCF
- Annex B.1 – Output data from the Tier II exposure assessment of CAG-TCP
- Annex B.2 – Output data from the Tier II exposure assessment of CAG-TCF
Further information on the data, methodologies and interpretation of the results are provided in the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software (here).
The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (here). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.
Notes
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Additional details
Related works
- Is documented by
- 10.2903/j.efsa.2019.5801 (DOI)
- Is supplement to
- 10.2903/sp.efsa.2019.en-1707 (DOI)
Subjects
- exposure assessment
- http://id.agrisemantics.org/gacs/C29232
- pesticide residues
- http://id.agrisemantics.org/gacs/C3009
- chronic effects
- http://id.agrisemantics.org/gacs/C7446
- thyroid gland
- http://id.agrisemantics.org/gacs/C5333
- probabilistic models
- http://id.agrisemantics.org/gacs/C11871
- Monte Carlo method
- http://id.agrisemantics.org/gacs/C9384