Teen driver system modeling: a tool for policy analysis

作     者：Missikpode, Celestin Peek-Asa, Corinne McGehee, Daniel V. Torner, James Wakeland, Wayne Wallace, Robert 

作者机构：Department of Epidemiology College of Public Health University of Iowa Iowa City IA United States Department of Occupational and Environmental Health College of Public Health University of Iowa Iowa City IA United States Injury Prevention and Research Center College of Public Health University of Iowa S161 CPHB 105 River Street Iowa City 52242 IA United States University of Iowa Public Policy Center Iowa city IA United States Systems Science Program Portland State University OR United States 

出 版 物：《Injury Epidemiology》 (Inj. Epidemiol.)

年 卷 期：2018年第5卷第1期

页      面：34页

学科分类：12[管理学] 1204[管理学-公共管理] 120402[管理学-社会医学与卫生事业管理(可授管理学、医学学位)] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1002[医学-临床医学] 10[医学] 

基　　金：NIH/NICHD, (R01-HD0065095) National Institutes of Health, NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development, NICHD 

主　　题：Driving events Dynamics Modeling Policy analysis Systems Teen 

摘      要：Background: Motor vehicle crashes remain the leading cause of teen deaths in spite of preventive efforts. Prevention strategies could be advanced through new analytic approaches that allow us to better conceptualize the complex processes underlying teen crash risk. This may help policymakers design appropriate interventions and evaluate their impacts. Methods: System Dynamics methodology was used as a new way of representing factors involved in the underlying process of teen crash risk. Systems dynamics modeling is relatively new to public health analytics and is a promising tool to examine relative influence of multiple interacting factors in predicting a health outcome. Dynamics models use explicit statements about the process being studied and depict how the elements within the system interact;this usually leads to discussion and improved insight. A Teen Driver System Model was developed by following an iterative process where causal hypotheses were translated into systems of differential equations. These equations were then simulated to test whether they can reproduce historical teen driving data. The Teen Driver System Model that we developed was calibrated on 47 newly-licensed teen drivers. These teens were recruited and followed over a period of 5-months. A video recording system was used to gather data on their driving events (elevated g-force, near-crash, and crash events) and miles traveled. Results: The analysis suggests that natural risky driving improvement curve follows a course of a slow improvement, then a faster improvement, and finally a plateau: that is, an S-shaped decline in driving events. Individual risky driving behavior depends on initial risk and driving exposure. Our analysis also suggests that teen risky driving improvement curve is created endogenously by several feedback mechanisms. A feedback mechanism is a chain of variables interacting with each other in such a way they form a closed path of cause and effect relationships. Conclusion