Table 3.
Sources of uncertainty concerning the input data and affecting the CRA of hypothyroidism (CAG‐TCF) and C‐cell hypertrophy, hyperplasia and neoplasia (CAG‐TCP). Uncertainties relating to exposure were included in group 1 and uncertainties relating to toxicology were included in group 2
| Assessment input | Type of uncertainty | Description of the uncertainty |
|---|---|---|
| Consumption data (all group 1) | Excluded data | Consumption data of animal commodities and plant commodities not in the list of the 30 selected commodities and their processed derivatives have not been used |
| Ambiguity | The consumption data do not always discriminate between different commodities of a same group (e.g. tomatoes and cherry tomatoes are considered as tomatoes) | |
| Accuracy | The accuracy of the reported amount of food consumed in surveys may be affected by methodological limitations or psychological factors | |
| Sampling variability | Sample size (number of consumers in the 10 populations). A small number of consumers may affect the reliability of risk estimates at the 99.9th percentile of exposure | |
| Sampling bias | Representativeness of the consumption data (selection bias) for the whole population | |
| Use of fixed values | One invariable recipe and conversion factor are used to convert the amount of food consumed into the respective amount of raw primary commodity (RPC) | |
| Occurrence data (all group 1) | Missing data | Active substance/commodity combinations, for which occurrence data are missing and extrapolation from another commodity is not possible, were excluded |
| Excluded data | The contribution of all metabolites and degradation products to the effect has not been considered | |
| Ambiguity | The occurrence data do not always discriminate between different commodities of a same group (e.g. tomatoes and cherry tomatoes are considered as tomatoes) | |
| Accuracy | Laboratory analytical uncertainty | |
| Sampling variability | Sample size (number of occurrence data). A small number of occurrence data may affect the reliability of risk estimates at 99.9th percentile of exposure. This number varies from one pesticide/commodity combination to the other | |
| Sampling bias | Representativeness of the monitoring data (selection bias) | |
| Extrapolation uncertainty | Extrapolation of occurrence data between crops | |
| Extrapolation uncertainty | Extrapolation of occurrence data between countries | |
| Assumption | Assumption of the active substance present on the commodity in case of unspecific residue definition for monitoring | |
| Assumption | Left‐censored data: Assumption of the authorisation status of all pesticide/commodity combinations | |
| Assumption | Left‐censored data: Assumption of the use frequency for authorised pesticide/commodity combinations | |
| Assumption | Left‐censored data: Assumption on the residue level (½ LOQ as imputed value) when an active substance is used, and its residues are below the LOQ | |
| Assumption | Occurrence of residues in drinking water | |
| Processing factors (all group 1) | Assumption | Pesticide residues are transferred without any loss to processed commodities when processing factors are not available |
| Ambiguity | Application of processing factors, derived from a limited number of standardised studies, to the EFSA food classification and description system (FoodEx) | |
| Accuracy | Laboratory analytical uncertainty | |
| Accuracy | Calculation of processing factors is affected by residue levels below the LOQ | |
| Use of fixed values | The value of processing factors used in the calculations is the median value of a limited number of independent trials | |
| Excluded data | Some processing factors are not considered (e.g. peeling and washing of commodities with edible peel) | |
| NOAELs (all group 2) | Adequacy of the CAG | Uncertainty on whether the CAG contains all the active substances causing the effect |
| Adequacy of the CAG | Uncertainty on whether the CAG contains only the active substances causing the effect | |
| Accuracy | Uncertainties affecting the characterisation of active substances included in the CAG (quality of data and NOAEL setting process). This includes uncertainties affecting:
|