Cumulative dietary exposure assessment¶

Introduction¶

The goal of this exercise is to perform a probabilistic cumulative dietary exposure assessment, illustrating all data needed. In the example the exposure will be characterised by upper tail percentiles, and the risk driving substances and foods can be examined.

Preparation¶

If you haven’t done so, in the workspace browser (use the  icon), create a new workspace named Examples, using the  button in the right-hand bottom corner.

The data files used in the example(s) in this section, are located in the data folder Documentation-Examples / Exercise Dietary Exposure Assessment.

Example 1¶

In this example, try and calculate a cummulative chronic dietary exposure according to EFSA 2012 Optimistic settings. Use liver steatosis as a focal effect and Cyproconazole as an index substance. Also, use the DNFCS survey as a consumptions data source.

Detailed steps are as follows.

In the Examples workspace, create a new action (using )
- Then select Dietary exposures
- Name it Dietary exposures
- Use as Dietary exposures settings
  - Tier: EFSA 2012 Optimistic
  - Risk type Chronic
  - Select  Cumulative
- Press Create
Then go to the actions settings  of this action (path: Dietary exposures)
- At Scope, click Effects (path: Dietary exposures / Effects)
  - At Effects data source with  browse to the file Effect - Steatosis.xlsx and Select
  - At Effect Settings for focal effect select Steatosis-liver and press  Save Changes
  - In the green navigation bar, click Dietary exposures to go up one level.
- At Scope, click Foods (path: Dietary exposures / Foods)
  - At Foods data source with  browse to the file UserGroupDemo-Foods.xlsx and Select
  - In the green navigation bar, click Dietary exposures to go up one level
- At Scope, click Substances (path: Dietary exposures / Substances)
  - At Substances data source with  browse to the file UserGroupDemo-Substances.xlsx and Select
  - At Substance settings for Index substance select Cyproconazole and press  Save Changes
  - In the green navigation bar, click Dietary exposures to go up one level
- At Inputs, click Consumptions by food as measured (path: Dietary exposures / Consumptions by food as measured)
  - At Inputs, click Consumptions (path: Dietary exposures / Consumptions by food as measured / Consumptions)
    - At Consumptions data source with  browse to the file UserGroupDemo-Consumptions.xlsx and Select
    - At Consumption settings for Food survey select DNFCS_2003 and press  Save Changes
    - In the green navigation bar, click Consumptions by food as measured to go up one level
  - At Inputs, click Food conversions (path: Dietary exposures / Consumptions by food as measured / Food conversions)
    - At Inputs, click Foods as measured (path: Dietary exposures / Consumptions by food as measured / Food conversions / Foods as measured)
      - At Inputs, click Concentrations (path: Dietary exposures / Consumptions by food as measured / Food conversions / Foods as measured / Concentrations)
        
        At Concentrations data source with  browse to the file UserGroupDemo-ConcentrationData.xlsx and Select
        
        In the green navigation bar, click Food conversions to go up two levels
    - At Inputs, click Food recipes (path: Dietary exposures / Consumptions by food as measured / Food Food recipes)
      - At Food recipes data source, with  browse to the file UserGroupDemo-FoodRecipes.xlsx
      - In the green navigation bar, click Dietary exposures to go up three levels
- At Inputs, click Concentration models (path: Dietary exposures / Concentration models)
  - At Inputs, click Relative potency factors (path: Dietary exposures / Concentration models / Relative potency factors)
    - At Relative potency data source with  browse to the file UserGroupDemo-RelativePotencyFactors.xlsx and Select
    - In the green navigation bar, click Dietary exposures to go up two levels
- At Inputs, click Processing factors (path: Dietary exposures / Processing factors)
  - At Processing factors data source with  browse to the file UserGroupDemo-ProcessingFactors.xlsx and Select
  - In the green navigation bar, click Dietary exposures to go up one level
- At Inputs, click Active substances (optional) (path: Dietary exposures / Active substances)
  - At Inputs, click Points of departure (path: Dietary exposures / Active substances / Points of departure)
    - At Points of departure data source, with  browse to the file HazardDoses - Triazoles.xlsx
    - In the green navigation bar, click Dietary exposures to go up two levels
Now run the model, by pressing the  run icon in the grey bar.

Find the following results:

The 99% exposure percentile

The substance(s) with highest contribution to the total exposure distribution

The food(s)-as-measured with the highest contribution to the upper tail of the exposure distribution

Answers:

In the grey bar, browse to the results panel by clicking the  icon and click on the latest output (path: Results / Dietary exposures)

In the Dietary exposures tab, browse in the tree (unfold by clicking  where necessary) to  Dietary exposures  Distribution (daily intakes)  Percentiles

In the table it states that the 99% exposure percentile is at an exposure of 0.3803 µg/kg bw/day.

In the Dietary exposures tab, browse in the tree (unfold by clicking  where necessary) to  Dietary exposures  Details  Exposures by substance  Total distribution

From the pie chart it is clear that Myclobutanil contributes the most to the total exposure distribution with 37.8%. In the table below the graph more details can be found.

In the Dietary exposures tab, browse in the tree (unfold by clicking  where necessary) to  Dietary exposures  Details  Exposures by food and substance  Risk drivers upper tail

From the pie chart it is clear that Myclobutanil in oranges contributes the most (30.9%) to the upper tail exposure distribution

Example 2¶

Repeat the run of the previous task, but instead of the nominal run, now do an uncertainty analysis loop.

Click on the  icon (in the grey bar) to open the uncertainty settings panel, and check  Perform uncertainty analysis
- For Monte Carlo iterations per uncertainty run choose 100, and press  Save Changes
Now run the model, by pressing the  run icon in the grey bar.

Compare with the previous results, to find:

95% uncertainty bounds for the 99% exposure percentile

95% uncertainty bounds for the highest contribution from a substance to the total exposure distribution

95% uncertainty bounds for the highest contribution from a food to the total exposure distribution