Statistical methods for handling nondetected results in food chemical monitoring data to improve food risk assessments.

Chemical risk assessment is important for risk management, and estimates of chemical exposure must be as accurate as possible. Chemical concentrations in food below the limit of detection are known as nondetects and result in left-censored data. During statistical analysis, the method used for handling values below the limit of detection is important. Many risk assessors employ widely used substitution methods to treat left-censored data, as recommended by international organizations. The National Institute of Food and Drug Safety Evaluation of South Korea also recommends these methods, which are currently used for chemical exposure assessments. However, these methods have statistical limitations, and international organizations recommend more advanced alternative statistical approaches. In this study, we assessed the validity of currently used statistical methods for handling nondetects. To identify the most suitable statistical method for handling nondetection, we created virtual data and conducted simulation studies. Based on both simulation and case studies, the Maximum Likelihood Estimation (MLE) and Robust Regression on Order Statistics (ROS) methods were found to be the best options. The statistical values obtained from these methods were similar to those obtained from the commonly used 1/2 Limit of Detection (LOD) substitution method for nondetection treatment. In three case studies, we compared the various methods based on the root mean squared error. The data for all case studies were from the same source, to avoid heterogeneity. Across various sample sizes and nondetection rates, the mean and 95th percentile values for all treatment methods were similar. However, "lognormal maximum likelihood estimation" method was not suitable for estimating the mean. Risk assessors should consider statistical processing of monitoring data to reduce uncertainty. Currently used substitution methods are effective and easy to apply to large datasets with nondetection rates <80%. However, advanced statistical methods are required in some circumstances, and national guidelines are needed regarding their use in risk assessments.

Chronic health risk comparison between China and Denmark on dietary exposure to chlorpyrifos.

Chlorpyrifos is one of the most heavily used pesticides in domestic and agricultural insect prevention globally. Given the potential neurotoxicity of chlorpyrifos and its high detection rates in food and drinking water, health risks attributable to chlorpyrifos residue in Chinese drinking water and food in both China and Denmark were assessed in this study. Mixed left-censored handling models were used to deal with the non-detected values in chlorpyrifos concentrations. Results show that chronic exposure imputed to chlorpyrifos residue is much lower than the reference dose, and will thus not pose appreciable health risk to the consumer. Compared to the total exposure from chlorpyrifos in drinking water and food sources, chronic exposure from drinking water sources in China accounts for 0-4.4%. Health risk owing to chlorpyrifos in food within China is 6-7-fold higher than in Denmark, and this coincides with the fact that all application of chlorpyrifos is banned in Denmark, in contrast to China. However, the Danish consumers are still exposed from imported food items. The main health risk contributors in China are the food groups of Grains and grain-based products and Vegetable and vegetable products, while the main chronic health risk contributor in Denmark is the food group of imported fruit and fruit products.

Method of median semi-variance for the analysis of left-censored data: comparison with other techniques using environmental data.

In environmental monitoring, variables with analytically non-detected values are commonly encountered. For the statistical evaluation of these data, most of the methods that produce a less biased performance require specific computer programs. In this paper, a statistical method based on the median semi-variance (SemiV) is proposed to estimate the position and spread statistics in a dataset with single left-censoring. The performances of the SemiV method and 12 other statistical methods are evaluated using real and complete datasets. The performances of all the methods are influenced by the percentage of censored data. In general, the simple substitution and deletion methods showed biased performance, with exceptions for L/2, Inter and L/√2 methods that can be used with caution under specific conditions. In general, the SemiV method and other parametric methods showed similar performances and were less biased than other methods. The SemiV method is a simple and accurate procedure that can be used in the analysis of datasets with less than 50% of left-censored data.