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 poerformed 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). |
Compute exposures based on total diet study data |
Specifies whether exposure is based on sampling data from total diet studies. |
Express results in terms of reference substance equivalents (cumulative) |
Specifies whether the assessment involves multiple substances and results should be cumulated over all substances. |
Dietary intake calculation method |
Dietary intake calculation method: choose between point estimates and probabilistic. |
Refined method choice |
Choose foods/substances for which a dietary intake method other than the default should be specified. |
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. |
Maximize 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 food as measured 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 |
Processing factor model |
|
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 exposurs amounts as function of covariates. |
Frequency model covariates model |
Specifies whether, and how to model exposure frequency as function of covariates. |
Apply exposure screening |
Apply exposure screening results as a first step in full run, and restrict output regarding foods-as-eaten to risk drivers. |
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). |
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. |
Impute exposure distributions |
Impute exposure distributions for substances with missing concentrations. |
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) |
Exposure levels can be generated automatically or by explicit specification (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. Specify below whether these levels are absolute or relative to ARfD/ADI. |
Exposure levels are |
Specify whether exposure levels are absolute or percentages of ARfD/ADI. |
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. |