Journal article
Teen driver system modeling: a tool for policy analysis
Injury Epidemiology, Vol.5, 34
12/2018
DOI: 10.1186/s40621-018-0164-9
PMCID: PMC6139293
PMID: 30221317
Abstract
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.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.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.Teen risky driving improvement process is created endogenously by several feedback mechanisms. The model proposed in the present article to reflect this improvement process can spark discussion, which may pinpoint to additional processes that can benefit from further empirical research and result in improved insight.
Details
- Title: Subtitle
- Teen driver system modeling: a tool for policy analysis
- Creators
- Celestin Missikpode - 0000 0004 1936 8294 grid.214572.7 Injury Prevention and Research Center, College of Public Health University of Iowa S161 CPHB 105 River Street Iowa City IA 52242 USACorinne Peek-Asa - 0000 0004 1936 8294 grid.214572.7 Injury Prevention and Research Center, College of Public Health University of Iowa S161 CPHB 105 River Street Iowa City IA 52242 USADaniel McGehee - 0000 0004 1936 8294 grid.214572.7 University of Iowa Public Policy Center Iowa city IA USAJames Torner - 0000 0004 1936 8294 grid.214572.7 Department of Epidemiology, College of Public Health University of Iowa Iowa City IA USAWayne Wakeland - 0000 0001 1087 1481 grid.262075.4 Systems Science Program Portland State University Oregon USARobert Wallace - 0000 0004 1936 8294 grid.214572.7 Department of Epidemiology, College of Public Health University of Iowa Iowa City IA USA
- Resource Type
- Journal article
- Publication Details
- Injury Epidemiology, Vol.5, 34
- DOI
- 10.1186/s40621-018-0164-9
- PMID
- 30221317
- PMCID
- PMC6139293
- NLM abbreviation
- Inj Epidemiol
- ISSN
- 2197-1714
- eISSN
- 2197-1714
- Publisher
- Springer International Publishing; Cham
- Grant note
- R01-HD0065095 / NIH/NICHD
- Language
- English
- Date published
- 12/2018
- Academic Unit
- Public Health Administration; Occupational and Environmental Health; Epidemiology; Emergency Medicine; Surgery; Industrial and Systems Engineering; Nursing; Center for Social Science Innovation; Injury Prevention Research Center; Public Policy Center (Archive); Neurosurgery; Internal Medicine
- Record Identifier
- 9983995168802771
Metrics
26 Record Views