Cara Hamann1, Corinne Peek-Asa2, Charles F Lynch3, Marizen Ramirez4, Paul Hanley5. 1. Injury Prevention Research Center, University of Iowa, 200 Newton Rd, 2186 WL, Iowa City, IA 52242, USA. 2. Department of Occupational and Environmental Health, University of Iowa College of Public Health, 105 River St, S143 CPHB, Iowa City, IA 52242, USA. 3. Department of Epidemiology, University of Iowa College of Public Health, 105 River St, S447 CPHB, Iowa City, IA 52242, USA. 4. Department of Occupational and Environmental Health, University of Iowa College of Public Health, 105 River St, S318 CPHB, Iowa City, IA 52242, USA. 5. Department of Urban and Regional Planning, University of Iowa Public Policy Center, 310 South Grand Ave, 218 SQ, Iowa City, IA 52242, USA.
Abstract
Purpose: To identify how person, crash, environment, and population characteristics differ between bicycle-motor vehicle crashes that occur at intersections and non-intersections. Methods: The Iowa Department of Transportation crash database for the years 2001 through 2011 was used to identify bicycle-motor vehicle (BMV) crashes and associated person, crash, and environment characteristics. Population-level data were drawn from the 2010 U.S. Census and the 2010 American Community Survey. Descriptive statistics, GIS mapping, and multivariable logistic regression were used to examine factors associated with crash risk and crash location. Results: Compared to intersections, non-intersection BMV crashes had higher odds of involving young bicyclists (<10 years old; OR: 1.8, 95%CI: 1.2-2.6), location outside city limits (OR: 5.7, 95%CI: 3.9-8.3), with driver vision obscured (OR: 1.5, 95% CI: 1.2-1.8), reduced lighting on roadway (OR: 1.9, 95% CI: 1.5-2.4), and lower odds when the bicyclist (OR: 0.4, 95% CI: 0.3-0.6) or motorist (OR: 0.6, 95% CI: 0.4-0.8) failed to yield right of way. Conclusions: Environmental factors, as well as developmental (age) and behavioral factors of bicycle-motor vehicle crashes vary by location (intersection/non-intersection). Results from this study can be used to tailor and target multiple intervention approaches, such as making infrastructure changes, increasing safety behavior among both motorists and bicyclists, and identifying which age groups and locations would most benefit from intervention.
Purpose: To identify how person, crash, environment, and population characteristics differ between bicycle-motor vehicle crashes that occur at intersections and non-intersections. Methods: The Iowa Department of Transportation crash database for the years 2001 through 2011 was used to identify bicycle-motor vehicle (BMV) crashes and associated person, crash, and environment characteristics. Population-level data were drawn from the 2010 U.S. Census and the 2010 American Community Survey. Descriptive statistics, GIS mapping, and multivariable logistic regression were used to examine factors associated with crash risk and crash location. Results: Compared to intersections, non-intersection BMV crashes had higher odds of involving young bicyclists (<10 years old; OR: 1.8, 95%CI: 1.2-2.6), location outside city limits (OR: 5.7, 95%CI: 3.9-8.3), with driver vision obscured (OR: 1.5, 95% CI: 1.2-1.8), reduced lighting on roadway (OR: 1.9, 95% CI: 1.5-2.4), and lower odds when the bicyclist (OR: 0.4, 95% CI: 0.3-0.6) or motorist (OR: 0.6, 95% CI: 0.4-0.8) failed to yield right of way. Conclusions: Environmental factors, as well as developmental (age) and behavioral factors of bicycle-motor vehicle crashes vary by location (intersection/non-intersection). Results from this study can be used to tailor and target multiple intervention approaches, such as making infrastructure changes, increasing safety behavior among both motorists and bicyclists, and identifying which age groups and locations would most benefit from intervention.
Entities:
Keywords:
bicycling; environment; epidemiology; public health; traffic accidents
Authors: Patrick Morency; Lise Gauvin; Céline Plante; Michel Fournier; Catherine Morency Journal: Am J Public Health Date: 2012-04-19 Impact factor: 9.308