[Establishment of non-parametric probabilistic model for evaluation of Chinese dietary exposure].
OBJECTIVE
To establish a non-parametric probabilistic model for evaluation of Chinese dietary exposure and to improve the assessment accuracy while integrating into the global risk assessment on food safety.


METHODS
Contamination data was from the national food contamination monitoring program during 2000 - 2006, including heavy metals, pesticides and mycotoxins, amounting to 135 contaminants with 499 commodities and 487 819 samples. Food consumption data was obtained from the national diet and nutrition survey conducted in 2002 with three consecutive days by 24-hour recall method, and 66 172 consumers were included. Monte Carlo simulation was applied to derive the intake distribution, and the uncertainty of each percentile was estimated using the Bootstrap sampling.


RESULTS
Different non-parametric probabilistic models for dietary exposure evaluation on heavy metals, pesticides and some of the toxins were established for Chinese people, and intake distributions with 95% confidence intervals of these contaminants were estimated. Taking acephate as an example, the results of its model shows that, for the 7 - 10 year-old children, the median dietary exposure in urban and rural areas were 1.77 microg x kg(-1) x d(-1) and 2.48 microg x kg(-1) x d(-1) respectively, with a 95% confidence interval of (1.59 - 2.06) microg x kg(-1) x d(-1) and (2.33 - 2.80) microg x kg(-1) x d(-1) respectively.


CONCLUSIONS
The non-parametric probabilistic model can quantify the variability and uncertainty of exposure assessment and improve the assessment accuracy.
Sun
Jin-fang
JF
Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
Liu
Pei
P
Chen
Bing-wei
BW
Chen
Qi-guang
QG
Yu
Xiao-jin
XJ
Wang
Can-nan
CN
Li
Jing-xin
JX
D002681:China; D003257:Consumer Product Safety; D004034:Diet Surveys; D006801:Humans; D015233:Models, Statistical; D018570:Risk Assessment; D018709:Statistics, Nonparametric