Revision as of 10:45, 2 April 2008

RIVM and Wacheningen University have developed a monte carlo model for estimating risk from dietary intake.This page briefly summarizes what the model is about.

In a Monte Carlo dietary risk assessment the risk of exposure to pesticides or other chemicals from the diet is quantified by combining database information on food consumption with database information from monitoring programs for residues of chemicals in food.

MCRA is intended for users who want to analyze their own consumption and chemical concentration data. MCRA provides the following options
# acute (short-term) risk assessment
# chronic (long-term) risk assessment
# empirical or parametric modelling of residue levels
# modelling of processing effects, unit variability and nondetects levels
# bootstrapping to assess the uncertainty of percentiles
# comparison with deterministic point estimates (IESTI)

For questions please contact:
MailJacob van Klaveren (RIKILT/KAP, residue and food consumption data sets, applications), or
MailHilko van der Voet (Biometris, statistical methods, program development)

http://mcra.rikilt.wur.nl/mcra/mcra.html

Summary

This summary contains information adapted from manuals and reference guides available from the website:  http://mcra.rikilt.wur.nl/mcra/olddocumentation.html

MCRA provides the following options:
• acute risk assessment
• chronic risk assessment
• parametric or non-parametric modelling of residue levels
• modelling of processing effects
• modelling of sample variability
• modelling of non-detects levels
• restrictions on age and/or days
• consumers only

MCRA is available as standalone version or as internet application.

The program MCRA is composed of a set of procedures which may be arranged into four
main blocks. The main tasks of block 1 to 4 are:
1. reading of data (residue concentration data, consumption data, consumer characteristics,
processing factors, variability factors, percent crop treatment)
2. pre-processing of datastructures (age and/or day restrictions, consumers only, processing or not,
variability factors, estimation of parameters for a parametric model), determining number of
loops, chunksize, etc.
3. simulation of exposure values (parametric, non-parametric)
4. generating output (intake distribution, contribution to upper tail, characteristics of consumers with
the highest intake, etc…)