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

Author:

Hu Jing1,Zhang Zhengbao2,Lin Senwei3,Zhang Qiuhuan4,Du Guoxia1,Zhou Ruishan1,Qu Xiaohan1,Xu Guojiang3,Yang Ying2,Cai Yongming567

Affiliation:

1. School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510006, China

2. Guangdong Province Center for Disease Control and Prevention, Guangzhou 511430, China

3. Source of Wisdom Co., Ltd., Guangzhou 510091, China

4. Guangdong Institute of Public Health, Guangzhou 511430, China

5. College of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China

6. Guangdong Provincial Traditional Chinese Medicine Precision Medicine Big Data Engineering Technology Research Center, Guangzhou 510006, China

7. Cloud-Based Computing Precision Medical Big Data Engineering Technology Research Center of Guangdong Universities, Guangzhou 510006, China

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 μg/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.

Funder

Key-Area Research and Development Program of Guangdong Province

Research on Prediction Trend of Population Infected with COVID-19 Based on Big Data

Natural Science Foundation of Guangdong Province

Natural Science Foundation of China Youth Fund

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

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