Application of All-Ages Lead Model Based on Monte Carlo Simulation of Preschool Children’s Exposure to Lead in Guangdong Province, China

Jing Hu, Zhengbao Zhang, Senwei Lin, Qiuhuan Zhang, Guoxia Du, Ruishan Zhou, Xiaohan Qu, Guojiang Xu, Ying Yang and Yongming Cai
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Jing Hu: School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510006, China
Zhengbao Zhang: Guangdong Province Center for Disease Control and Prevention, Guangzhou 511430, China
Senwei Lin: Source of Wisdom Co., Ltd., Guangzhou 510091, China
Qiuhuan Zhang: Guangdong Institute of Public Health, Guangzhou 511430, China
Guoxia Du: School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510006, China
Ruishan Zhou: School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510006, China
Xiaohan Qu: School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510006, China
Guojiang Xu: Source of Wisdom Co., Ltd., Guangzhou 510091, China
Ying Yang: Guangdong Province Center for Disease Control and Prevention, Guangzhou 511430, China
Yongming Cai: College of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China

Sustainability, 2023, vol. 15, issue 2, 1-15

Abstract: Introduction: Lead (Pb) poisoning in children is a major public health issue worldwide. The physiologically based pharmacokinetic model (PBPK model) has been extensively utilized in Pb exposure risk assessment and can connect external exposure with biological monitoring data. This study aimed to combine a Monte Carlo simulation with the all-ages lead model (ALLM) to quantify the heterogeneity and uncertainty of certain parameters in the population. The parameters of the all-ages lead model based on Monte Carlo simulation (ALLM + MC) were localized in Guangdong Province. Our study discusses the practicability of the application of the localized ALLM + MC in Guangdong Province. Methods: A local sensitivity analysis was used to assess the impact of pharmacokinetic parameters on the prediction of blood lead level (BLL). Environmental Pb concentration, exposure parameters, and sensitive parameters were included in the ALLM + MC, and the differences between the ALLM- and the ALLM + MC-predicted values were compared. Additionally, we localized the exposure parameters in the ALLM + MC and used them to evaluate BLL in preschool children from Guangdong Province. Finally, we compared the predictive values to those observed in the literature. Results: The predictive values of ALLM and ALLM + MC had a significant correlation (r = 0.969, p < 0.001). The predictive value of ALLM was included in the ALLM + MC prediction range. Moreover, there were no significant differences between the predictive and the observed values of preschool children from Guangdong Province (z = ?0.319, p = 0.749). Except for children aged 5–6, the difference between the predictive and the observed values was less than 1 ?g/dL. The root mean square error (RMSE) and the mean deviation (RMD) of ALLM and ALLM + MC were reduced by 24.73% and 32.83%, respectively. Conclusions: The localized ALLM + MC is more suitable for predicting the BLL of preschool children in Guangdong Province, which can be used to explain the heterogeneity and uncertainty of parameters in the population. The ALLM + MC has fewer time, space, and financial restrictions, making it more appropriate for determining the BLLs in large populations. The use of ALLM + MC would improve the feasibility of regular and long-term blood Pb detection.

Keywords: Monte Carlo; all-ages lead model; preschool children; lead exposure (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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