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. 2021 Feb 10;19(2):e06392. doi: 10.2903/j.efsa.2021.6392

Table 17.

Sources of uncertainty concerning the input data and affecting the CRA of chronic erythrocyte AChE inhibition (CAG‐NCN)

Assessment input Type of uncertainty Uncertainty number Description Area of expertise
Consumption data Excluded data U1 Consumption data of animal commodities and plant commodities not in the list of the 35 selected commodities and their processed derivatives have not been considered in the assessment. Exposure
Ambiguity U2 The consumption data do not always discriminate between different commodities of a same group as defined in part B of annex I to Regulation (EC) No 396/2005 (e.g. tomatoes and cherry tomatoes are considered as tomatoes). Exposure
Accuracy U3 The accuracy of the reported amount of food consumed in surveys may be affected by methodological limitations or psychological factors. Exposure
Sampling variability U4 The reliability of risk estimates at the 99.9th percentile of exposure in the 10 populations under consideration is affected by the sample size (number of consumers) of the respective surveys. Exposure
Sampling bias U5 Selection bias of consumers in food consumption surveys affects the representativeness of consumption data of the respective populations. Exposure
Use of fixed values U6 In the RPC model, one invariable recipe and conversion factor are used to convert the amount of food consumed into the respective amount of raw primary commodity. Exposure
Occurrence data Missing data U7 The contribution of active substance/commodity combinations, for which occurrence data are missing and extrapolation from another commodity is not possible, was not accounted in the assessment. Exposure
Excluded data U8 The contribution to the risk of metabolites and degradation products not included in the residue definition for monitoring has not been considered. Exposure
Ambiguity U9 The occurrence data do not always discriminate between different commodities of a same group as defined in part B of annex I to Regulation (EC) No 396/2005 (e.g. tomatoes and cherry tomatoes are considered as tomatoes). Exposure
Accuracy U10 The accuracy of the quantification of residue levels above the LOQ is affected by the laboratory analytical uncertainty. Exposure
Sampling variability U11 The reliability of risk estimates at the 99.9th percentile of exposure in the 10 populations under consideration is affected by the sample size (number of occurrence data) for each pesticide/commodity combination. Exposure
Sampling bias U12 Selection bias of lots of commodities to be controlled in official monitoring programmes affects the representativeness of occurrence data. See Section 2.2.2.4 on the extraction criteria applied to the monitoring data. Exposure
Extrapolation uncertainty U13 In case of extrapolation of occurrence data between 2 commodities, it is uncertain that the residue profiles in the 2 commodities are actually identical. See Sections 2.2.3.3 and 2.2.4.1.3 regarding the extrapolation rules. Exposure
Other uncertainty U14 It is uncertain whether the use of pooled occurrence data from all EU Member states is representative of the actual residue levels to which the 10 populations under consideration are actually exposed to. Exposure
Assumption U15 The assumption used to assign occurrence data to active substances in case of unspecific residue definition for monitoring (see Sections 2.2.2.3 and 2.2.4.1.1) is subject to uncertainty. Exposure
Assumption U16 In the handling of left‐censored data, the assumption about the authorisation status of the pesticide/commodity combinations under consideration (see Section 2.2.3.2) is subject to uncertainty. Exposure
Assumption U17 In the handling of left‐censored data, the assumption about the use frequency for authorised pesticide/commodity combinations (see Section 2.2.4.1.3) is subject to uncertainty. Exposure
Assumption U18 In the handling of left‐censored data, the assumption about the residue level (1/2 LOQ as imputed value) when an active substance is used, and its residues are below the LOQ, is subject to uncertainty. Exposure
Assumption U19 The assumption about the occurrence of residues in drinking water (see Section 2.2.4.1.4) is subject to uncertainty. Exposure
Processing factors Assumption U20 The assumption that pesticide residues are transferred without any loss to processed commodities when processing factors are not available is subject to uncertainty. Exposure
Ambiguity U21 The assignment of processing factors, derived from a limited number of standardised studies, to food items of the EFSA food classification and description system (FoodEx) resulting from multiple processing techniques of the EFSA RPC‐model, is subject to uncertainty. See Section 2.2.3.4 for the principles of assignment of processing factors. Exposure
Accuracy U22 In processing studies, the accuracy of the quantification of residue levels above the LOQ in raw and processed commodities is affected by the laboratory analytical uncertainty. Exposure
Accuracy U23 Processing factors are overestimated when residue levels in the processed commodity are below the LOQ. Exposure
Use of fixed values U24 The value of processing factors used in the calculations is the median value of a limited number of independent trials. Exposure
Excluded data U25 Some processing factors are not considered in the assessment (e.g. peeling and washing of commodities with edible peel). Exposure
NOAELs Adequacy of the CAG U26 It is uncertain whether the CAG contains all the OPs and NMC insecticides causing the effect. Toxicology
Adequacy of the CAG U27 There is uncertainty about the contribution of pesticides other than OPs and NMCs to erythrocyte AChE inactivation through oxidative stress and further inhibition of enzyme activity. Toxicology
Adequacy of the CAG U28 It is uncertain whether the CAG contains only the active substances causing the effect. Toxicology
Accuracy U29 The accuracy of the NOAEL‐setting is affected by the original studies/data quality (e.g. study conducted under Good Laboratory Practice GLP or not, guidelines referred to or not, statistical analysis performed or not, overall quality of reporting). Toxicology
Accuracy U30 The accuracy of the NOAEL‐setting is affected by the data collection methodology (interpretation of raw data by the assessors, transfer of information from original studies to source documents, and from source documents to working documents (excel spreadsheets). Toxicology
Accuracy U31 The accuracy of the NOAEL‐setting is affected by the assessment methodology and principles (i.e. how the available information was assessed to derive NOAELs for erythrocyte AChE inhibition). Toxicology
Accuracy U32 The accuracy of the NOAEL‐setting is affected by the study design of the critical study (e.g. dose selection and spacing, study duration, route of administration, analytical methods…). Toxicology