Additive Exposure with FACET

Transcription

1 Additive Exposure with FACET Cronan McNamara Cian O Mahony Creme Global ILSI Europe Uncertainty Workshop, February 2014

2 Overall Aim of FACET Databases Harmonised database on nationally available food intake data Databases on occurrence of target food additives in foods Databases on food additive use levels reported by industry Models/Tools Probabilistic exposure models to estimate the distribution of exposure across Europe Exposure Software for estimating exposure to target food chemicals

3 Dietary Exposure STATISTICAL BACKGROUND

4 Statistical Background Exposure is usually calculated in a population; therefore variable Generally a distribution of exposure is estimated, taking into account the variability in the population Once the distribution of exposure is calculated, it is described using various statistics

5 Exposure Statistics Mean: the arithmetic mean or average exposure in the population Percentile: Value of a variable below which a certain per cent of observations fall E.g. the P20 is the value below which 20% of the exposures fall In risk assessment, the upper percentiles are typically most important, e.g. the P95 of exposure.

6 Relevant Questions for an Exposure Assessment What is the mean exposure in the total population? What is the P95 of exposure? What is the acceptable/tolerable/recommended daily intake? How certain are we about these estimates?

7 Probabilistic Dietary Exposure THE EXPOSURE MODEL AND DATA INPUTS

8 Basic Principle Amount of Food Dietary Exposure Concentration of Substance

9 Basic Principle Amount of Food Dietary Exposure Concentration of Substance

10 Required inputs Food Consumption Data Food consumption diaries/databases Nationally representative surveys of food consumption Total diet of 1,000+ consumers recorded for 1 or more days Typically 100,000+ lines of consumption data Subject demographics needed also

11 Dietary Surveys Hungary Italy France Poland UK Finland Ireland FACET Food Codes Portugal

12 Additive Exposure Algorithm 1. Choose Additive of interest 2. Find all foods that contains that additive 3. Find all consumption events in the Diary involving those foods 4. Determine the exposure to the additive at each eating event 5. Collate exposures from each consumption event to give the distribution of exposure to the additive in the population

13 FACET Food Codes System is tiered, e.g. A.05: Chocolate products and confectionary A.05.1: Cocoa and chocolate products A05.1.2: Cocoa-based spreads A.05.2: Confectionary including hard and soft candy A : Glucose syrup-based confectionery A : Sugar confectionary More information specific to food can be included via additive flags: Nutritional information (e.g. Low fat, sugar reduced) Topping (e.g. Chocolate topping, whipped cream) Coating Filling

14 Additive Exposure Algorithm 1. Choose Additive of interest 2. Find all foods that contains that additive 3. Find all consumption events in the Diary involving those foods 4. Determine the exposure to the additive at each eating event 5. Collate exposures from each consumption event to give the distribution of exposure to the additive in the population

15 Concentration Sources Exposure Foodquanta consumed [Food Amount] [Concentration of Additive] Regulatory data Maximum Permitted Levels (MPLs) Industry Data Typical Min Typical Max Extreme Min Extreme Max Fitted distribution Can be assessed with or without probability of occurrence

16 Frequency Fitted Distribution Distribution of Concentration Lognormal(a, b) Ext Min P5 Typ Min P25 Typ Max P75 Ext Max P95

17 Additive Exposure Algorithm 1. Choose Additive of interest 2. Find all foods that contains that additive 3. Find all consumption events in the Diary involving those foods 4. Determine the exposure to the additive at each eating event 5. Collate exposures from each consumption event to give the distribution of exposure to the additive in the population

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19 FACET Additive Exposure UNCERTAINTY ANALYSIS

20 Assessing Uncertainties 150 (!) separate sources of uncertainty were identified across flavours, additives and food packaging How to communicate these uncertainties and their impact on an exposure assessment? Uncertainty tables and expert judgement

21 Uncertainty Process 1. State source of uncertainty 2. Identify variable effected 3. Estimate impact uncertainty has on variable 4. Estimate impact uncertainty has on population exposure: a) On mid-range exposures b) On highly exposed consumers 5. Describe overall uncertainty

22 Uncertainty Tables

23 Expert Models for Decision Makers TM Thank you Cronan McNamara Cian O Mahony Creme Global