Dietary exposures settings
Selection settings
| Name | Type | Description |
|---|---|---|
Scenario analysis foods
|
AlphaNumeric |
The foods of interest for the scenario analysis. |
Calculation settings
| Name | Type | Description |
|---|---|---|
Selected tier
|
SettingsTemplateType |
Specifies all module settings should be set according to a pre-defined tier or using custom settings. |
Exposure type
|
ExposureType | The type of exposure considered in the assessment; acute (short term) or chronic (long-term). |
Total diet study concentration data
|
Boolean |
Specifies whether exposure is based on sampling data from total diet studies. |
Multiple substances analysis
|
Boolean |
Specifies whether the assessment involves multiple substances. |
Compute cumulative exposures
|
Boolean |
Specifies whether the assessment involves multiple substances and results should be cumulated over all substances. |
Sample based
|
Boolean |
Include co-occurrence of substances in samples in simulations. If checked, substance residue concentrations are sampled using the correlations between values on the same sample. If unchecked, any correlation between substances is ignored, substance residue concentrations are sampled ignoring the correlations between values on the same sample. |
Consumptions on the same day come from the same sample
|
Boolean |
If checked, in procedure of EFSA Guidance 2012, section 4.1.1, all consumptions of a raw commodity of an individual on the same day are assumed to come from the same sample. If unchecked, all consumptions of a raw commodity of an individual on the same day are assumed to come from different samples. |
Maximise co-occurrence of high values in simulated samples
|
Boolean |
Within each pattern of substance presence. If checked, substance residue concentrations are sorted within co-occurrence patterns of substances on the same samples. After sorting, high residue values occur more frequently on the same sample. This choice is conservative. If unchecked, substance residue concentrations are sampled at random, ignoring any co-occurrence patterns of substances on the same samples. This choice is less conservative. |
Apply processing factors
|
Boolean |
Specified in table ProcessingFactor. If checked, processing factors are applied. Concentrations in the consumed food may be different from concentrations in the modelled food in monitoring programs (typically raw food) due to processing, such as peeling, washing, cooking etc. If unchecked, no processing information is used. This is in most (though not all) cases a worst-case assumption |
Use distribution
|
Boolean |
Probabilistic specifications of processing factors will be used |
Ignore processing factors less than 1
|
Boolean |
This setting will suppress the use of processing factors lower than 1 (it is used in the EFSA 2012 Pessimistic tier). |
Perform MCR analysis
|
Boolean |
Perform a Maximum Cumulative Ratio (MCR) analysis to determine co-exposure between substances. |
Substance weighting in mixtures
|
ExposureApproachType | Risk based: exposures in equivalents of the reference substance; standardised: standardised exposures per substance have variance 1; or unweighted exposures: RPFs are equal to 1. |
Use unit variability
|
Boolean |
Controls whether to use unit variability. |
Unit variability model
|
UnitVariabilityModelType | Describes variation between single units when concentration data are from composite samples. |
Estimates nature
|
EstimatesNature | Simulated unit concentrations can be higher or lower than composite value (realistic) or only equal or higher (conservative). |
Unit variability parameter
|
UnitVariabilityType | Use Coefficient of variation or Variability factor, specified in VariabilityFactor table. |
Mean of LogNormal simulated values (biasing)
|
MeanValueCorrectionType | Unbiased: correct unit simulations for difference between median and mean. |
Default variability factor for unit weight <= 25g
|
Numeric |
Default variability factor 1 (unit weight <= 25 g, small crops). Still requires specification of unit weight (FoodProperties table) and, in case of beta model, also the Number of units in a composite sample (UnitVariability table). |
Correlation between substances on the same units
|
UnitVariabilityCorrelationType | Specifies the type of correlation between substances on the same units; no correlation or full correlation. |
Default variability factor for unit weight > 25g
|
Numeric |
Default variability factor 5 (unit weight > 25 g, medium/large crops). Still requires specification of unit weight (FoodProperties table) and, in case of beta model, also the Number of units in a composite sample (UnitVariability table). |
Model type
|
IntakeModelType | The parametric model for between-and within-individual variation, and possibly covariates. |
Model-then-add
|
Boolean |
Specifies whether to create separate exposure models for specific groups of modelled foods (model-then-add). |
Covariate modelling
|
Boolean |
Specifies whether to model exposures as a function of covariates at individual level. |
Model-then-add sub-category models
|
IntakeModelPerCategory |
Sub-model specifications for foods groups that should be modelled separately. |
Grid precision frequency model
|
Numeric |
The discrete frequency distribution (ISUF) is approximated via a number of classes. |
Number of iterations (x 1000)
|
Numeric |
The number of iterations that is used to estimate the discrete frequency distribution for the ISUF model. |
Spline-fit
|
Boolean |
To achieve a better normality, a second transformation is performed: a spline function is fitted to the logarithmically or power transformed data as a function of the normal Blom scores. |
Amount model covariate model
|
CovariateModelType | Specifies whether, and how to model exposures amounts as function of covariates. |
Function
|
FunctionType | Functional relation between exposure and covariable. |
Transformation
|
TransformType | The data transformation used to approximate normality for amounts. |
Testing level
|
Numeric |
Significance level for testing the degree of the function. e.g., 0.05. |
Testing method
|
TestingMethodType | Starting from a full model (backward) or empty model (forward). |
Maximum degrees of freedom
|
Numeric |
Order of function. Determines the maximum degree of complexity of the function. |
Minimum degrees of freedom
|
Numeric |
Order of function. Determines the minimum degree of complexity of the function. |
Amounts model: variance ratio (between/within)
|
Numeric |
Estimate of the ratio of the variance components of the amounts model (only relevant for data with only 1 day per individual) |
Frequency model covariates model
|
CovariateModelType | Specifies whether, and how to model exposure frequency as function of covariates. |
Function
|
FunctionType | Functional relation between exposure and covariable. |
Testing level
|
Numeric |
Significance level for testing the degree of the function. e.g., 0.05. |
Testing method
|
TestingMethodType | Starting from a full model (backward) or empty model (forward). |
Minimum degrees of freedom
|
Numeric |
Order of function. Determines the minimum degree of complexity of the function. |
Maximum degrees of freedom
|
Numeric |
Order of function. Determines the maximum degree of complexity of the function. |
Frequency model dispersion
|
Numeric |
Frequency model dispersion estimate for (only relevant for data with only 1 day per individual). |
Use occurrence patterns for generating simulated samples
|
Boolean |
When selected, this simulated samples will be based on occurrence patterns. |
Associate the unspecified percentage with no-occurrence for foods with at least one specified occurrence
pattern
|
Boolean |
If checked, for foods with at least one specified occurrence pattern, unspecified occurrence patterns for the same food are assumed to be associated with no use. If unchecked, all substances are considered to be authorised (potentially present in samples). Note that this setting cannot be used for foods that have no specified AUs. These foods have 100% potential presence of all substances. To declare all AUs on such a food un-authorised, include an empty AU with percentage 100% in the AU data table (i.e., use an AU for this food, without specifying substances in the AU Substances table) |
Details level dietary exposures
|
DietaryExposuresDetailsLevel | Level of detail for summarizing dietary exposure/intakes. |
Iterate survey
|
Boolean |
Instead of (re-)sampling the individual days, loop over the entire survey (= 1 iteration). The number of iterations for a survey is calculated as round (number of Monte Carlo iterations /(number of individuals * surveys days)). |
Monte Carlo iterations
|
Numeric |
The number of iterations for Monte Carlo simulations, e.g. 100,000 (maximum is 100,000). |
Impute exposure distributions
|
Boolean |
Impute exposure distributions for substances with missing concentrations. |
Include diagnostics analysis for variability
|
Boolean |
For each percentile the variability (standard deviation) of the estimated percentiles versus sample size are plotted. |
Cofactor name
|
AlphaNumeric |
Specify the name of the cofactor. |
Covariable name
|
AlphaNumeric |
Specify the name of the covariable. |
Allow conversion using food extrapolations
|
Boolean |
Step 3c: try to find read across codes. If unchecked, read across table is ignored, default is ‘Use read across info’. E.g. for pineapple no measurements are found but by specifying that pineapple is converted to FruitMix (with a default proportion of 100%), the TDS sample concentration value of FruitMix will be used for pineapple (as-eaten or as ingredient). If successful, restart at step 1. |
Censored values replacement
|
NonDetectsHandlingMethod | How to replace censored values (when not co-modelled, as in censored models). |
Default concentration model
|
ConcentrationModelType | The concentration model type that will be used as default for all food/substance combinations. If this model type cannot be fitted, e.g., due to a lack of data, a simpler model will be chosen automatically as a fall-back. |
Apply reduction-to-limit scenario
|
Boolean |
Total diet study: specify reduction-to-limit scenario. If unchecked, all residue values are taken as such (base scenario: apply no reduction factors). If checked, reduction factors are applied for selected foods. Select foods where a reduction is assumed (only foods with Percentile > Limit are shown). Only foods with reduction factors > 1 (percentile / limit) are shown. |
Cutoff MCR
|
Numeric |
For selection of individual(day) exposures with maximum cumulative ratio (MCR = total exposure/maximum) above the cutoff. |
Cutoff percentage in population ranked on total exposure
|
Numeric |
For selection of individual(day) exposures above the cutoff percentage in the set of individual(day)s ranked on total exposure. |
Display ratio total exposure/ maximum (in MCR plot)
|
Numeric |
For MCR plot: specify ratio total exposure/ maximum for individual(day) exposures . |
Show tail percentiles (MCR plot) for:
|
Numeric |
Give specific percentiles of exposure distribution (%), e.g. 97.5 99 (space separated). |
Set minimum percentage contribution per substance to the tail exposure (MCR plot)
|
Numeric |
Set minimum percentage contribution per substance to the tail exposure. |
Target level
|
TargetLevelType | Select to express hazard characterisations at external or internal exposure level. For an aggregate assessment, that is dietary and nondietary exposure data are combined, the target dose level is always internal. When only dietary exposures are available, the target dose level is optional, i.c. external or internal. |
Seed for pseudo-random number generator
|
Numeric |
A value of 0 will use a pseudo-random seed in each run, a value > 0 will provide the same results in a repeated run. |
Output settings
| Name | Type | Description |
|---|---|---|
Exclude privacy sensitive data from outputs
|
Boolean |
Use this setting to not report the parts of the results (i.e., figures, tables, or sections) that are marked as (potentially) privacy sensitive. |
Include drill-down on 9 individuals around specified percentile
|
Boolean |
Specifies whether drilldown on 9 individuals is to be included in the output. |
Show percentiles for
|
Numeric |
Give specific percentiles of exposure distribution (%), e.g. 50 90 95 97.5 99 (space separated). |
Percentage for drilldown
|
Numeric |
Gives detailed output for nine individuals near this percentile of the exposure distribution. |
Percentage for upper tail
|
Numeric |
Gives detailed output for this upper percentage of the exposure distribution. |
Show % of population below level(s)
|
ExposureMethod | This setting is used for reporting the percentages of individuals (chronic) or individual days (acute) exceeding certain exposure levels. These exposure levels can be generated automatically based on the observed exposures (Automatic, default) or specified explicitly (Manual). |
Exposure levels
|
Numeric |
Specify exposure levels for which to give the percentage of exposure below these levels, e.g. 1 10 50 100 200 500. |
Number of levels of covariable to predict exposure
|
Numeric |
Specify the number of levels, e.g. 20. The range of the covariable is divided by the number of levels: range = (max - min)/levels. For these covariable levels exposures are predicted. |
Predict exposure at extra covariable levels
|
Numeric |
Specify specific prediction levels in addition to the automatically generated prediction levels (space separated). |
Lower percentage for variability (%)
|
Numeric |
The default value of 25% may be overruled. |
Upper percentage for variability (%)
|
Numeric |
The default value of 75% may be overruled. |
Report consumptions and exposures per individual instead of per kg body weight
|
Boolean |
Specifies whether body weights should be ignored and consumptions and exposures should be expressed per individual. Otherwise, the consumptions and exposures are per kg body weight. |
Uncertainty settings
| Name | Type | Description |
|---|---|---|
Resample imputation exposure distributions
|
Boolean |
Specifies whether to resample the imputated exposure distributions. |
Resample portion sizes
|
Boolean |
Specifies whether portion sizes should be resampled based on food consumption quantification data, see (Souverein et al. 2011). |
Perform uncertainty analysis
|
Boolean |
In probabilistic risk assessment of dietary exposure, distributions describe the variability in consumption within a given population of individuals and the variability of the occurrence and level of substances in the consumed foods. However, these calculations do not consider the amount of uncertainty that is due to the limited size of the underlying datasets. |
Iterations uncertainty analysis
|
Numeric |
Specifies the number of uncertainty cycles (default 100). |
Monte Carlo iterations per uncertainty run
|
Numeric |
Specifies the number of Monte Carlo iterations in each uncertainty cycle (default 10,000). |
Lower uncertainty limit (%)
|
Numeric |
Percentage lower bound, e.g. 2.5%. |
Upper uncertainty limit (%)
|
Numeric |
Percentage upper bound, e.g. 97.5%. |