title: Analysis of spatial variations in the effectiveness of graduated driver's licensing (GDL) program in the state of Michigan.
abstract: Injury resulting from motor vehicle crashes is the leading cause of death among teenagers in the US. Few programs or policies have been found to be effective in reducing the risk of fatal car crashes for young novice drivers. One effective policy that has been widely implemented is Graduated Driver Licensing (GDL). Published articles have mostly reported on the temporal effectiveness of GDL in the US. This article reports on the development of spatial statistical modeling approaches to evaluate and compare the effectiveness of GDL policy across eighty-three counties in the state of Michigan. Data were gathered from several publicly available databases, including the US Fatality Analysis Reporting System (FARS), US Census Bureau, US Bureau of Labor Statistics, and US Department of Agriculture. To account for spatial dependence among crash counts from adjacent counties we invoke spatial random effects, which we provide with a Conditionally AutoRegressive (CAR) prior. Our analysis confirms previous findings that GDL in Michigan is an effective policy that significantly reduces the risk of fatal car crashes among novice teenage drivers. In addition, it indicates that rurality is an important contextual variable associated with spatial differences in GDL effectiveness across the state of Michigan. Finally, our findings provide information that can be used to strengthen GDL policy and its implementation to further enhance teenage-driver safety.
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, United States.
authors.forename: Yu
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, United States. Electronic address: berrocal@umich.edu.
authors.forename: Veronica J
authors.initials: VJ
authors.lastname: Berrocal
authors.affiliation: Transportation Research Institute, University of Michigan, Ann Arbor, MI 48109, United States.
authors.forename: C Raymond
authors.initials: CR
authors.lastname: Bingham
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, United States.
authors.forename: Peter X K
authors.initials: PX
authors.lastname: Song
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality / Q000517:prevention & control / Q000706:statistics & numerical data; D000293:Adolescent; D001334:Automobile Driving / Q000331:legislation & jurisprudence*; D005260:Female; D006801:Humans; D008004:Licensure / Q000331:legislation & jurisprudence*; D008297:Male; D008824:Michigan / Q000453:epidemiology; D015397:Program Evaluation; D011640:Public Policy; D062211:Spatio-Temporal Analysis; D015599:Trauma Severity Indices; D014947:Wounds and Injuries / Q000209:etiology / Q000401:mortality / Q000503:physiopathology / Q000517:prevention & control
keywords: Bayesian hierarchical model; Conditionally AutoRegressive (CAR) model; Contextual variable; Death; Markov Chain Monte Carlo; Spatial random effects