Dietary exposures settings¶
Calculation settings¶
| Name | Description |
|---|---|
Dietary exposure calculation tier
|
A tier is a pre-specified set of model configurations. By selecting a model tier, MCRA automatically sets all model settings in this module according to this tier. Note that currently tier setting may need to be performed separately in sub-modules. Use the Custom tier when you want to manually set each model setting. |
Risk type
|
The type of exposure considered in the assessment; acute (short term) or chronic (long-term). |
Total diet study concentration data
|
Specifies whether exposure is based on sampling data from total diet studies. |
Multiple substances analysis
|
Specifies whether the assessment involves multiple substances. |
Express results in terms of reference substance equivalents (cumulative)
|
Specifies whether the assessment involves multiple substances and results should be cumulated over all substances. |
Sample based
|
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
|
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
|
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
|
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
|
Probabilistic specifications of processing factors will be used |
Ignore processing factors less than 1
|
This setting will suppress the use of processing factors lower than 1 (it is used in the EFSA 2012 Pessimistic tier). |
Use unit variability
|
Controls whether to use unit variability. |
Unit variability model
|
Describes variation between single units when concentration data are from composite samples. |
Estimates nature
|
Simulated unit concentrations can be higher or lower than composite value (realistic) or only equal or higher (conservative). |
Unit variability parameter
|
Use Coefficient of variation or Variability factor, specified in VariabilityFactor table. |
Mean of LogNormal simulated values (biasing)
|
Unbiased: correct unit simulations for difference between median and mean. |
Default variability factor for unit weight <= 25g
|
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). |
Default variability factor for unit weight > 25g
|
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
|
The parametric model for between-and within-individual variation, and possibly covariates. |
Model-then-add
|
Specifies whether to create separate exposure models for specific groups of foods-as-measured (model-then-add). |
Covariate modelling
|
Specifies whether to model exposures as a function of covariates at individual level. |
Amount model covariate model
|
Specifies whether, and how to model exposures amounts as function of covariates. |
Frequency model covariates model
|
Specifies whether, and how to model exposure frequency as function of covariates. |
Use occurrence patterns for generating simulated samples
|
When selected, this simulated samples will be based on occurrence patterns. |
Details level dietary exposures
|
Level of detail for summarizing dietary exposure/intakes. |
Iterate survey
|
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
|
The number of iterations for Monte Carlo simulations, e.g. 100.000 (maximum is 100.000). |
Impute exposure distributions
|
Impute exposure distributions for substances with missing concentrations. |
Allow conversion using food extrapolations
|
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. |
Non-detects replacement
|
How to replace non-detects (when not co-modelled, as in censored models). |
Function
|
Functional relation between exposure and covariable. |
Maximum degrees of freedom
|
Order of function. Determines the maximum degree of complexity of the function. |
Minimum degrees of freedom
|
Order of function. Determines the minimum degree of complexity of the function. |
Testing level
|
Significance level for testing the degree of the function. e.g., 0.05. |
Testing method
|
Starting from a full model (backward) or empty model (forward). |
Default concentration model
|
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. |
Output settings¶
| Name | Description |
|---|---|
Include drill-down on 9 individuals around specified percentile.
|
Specifies whether drilldown on 9 individuals is to be included in the output. |
Summarize simulated data
|
Specifies whether a summary of the simulated consumptions and concentrations should be included in the output. |
Store simulated individual day exposures
|
Store the simulated individual day exposures. If unchecked, no additional output will be generated. If checked, the output will contain an additional section with the simulated individual day exposures. |
Show percentiles for
|
Give specific percentiles of exposure distribution (%), e.g. 50 90 95 97.5 99 (space separated). |
Percentage for drilldown
|
Gives detailed output for nine individuals near this percentile of the exposure distribution. |
Percentage for upper tail
|
Gives detailed output for this upper percentage of the exposure distribution. |
Show % of population below level(s)
|
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
|
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
|
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
|
Specify specific prediction levels in addition to the automatically generated prediction levels (space separated). |
Lower percentage for variability (%)
|
The default value of 25% may be overruled. |
Upper percentage for variability (%)
|
The default value of 75% may be overruled. |
Report consumptions and exposures per individual instead of per kg body weight
|
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. |
Cutoff for ratio total exposure/ maximum (MCR plot)
|
For selection of individual(day) exposures specify cutoff for ratio total exposure/ maximum. |
Show tail percentiles (MCR plot) for
|
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)
|
Set minimum percentage contribution per substance to the tail exposure. |