Karisa K Harland1, Mitchell Greenan2, Marizen Ramirez2. 1. University of Iowa, College of Public Health, Department of Occupational and Environmental Health, Injury Prevention Research Center, Iowa City, IA, USA; University of Iowa, Department of Emergency Medicine, Iowa City, IA, USA. Electronic address: kari-harland@uiowa.edu. 2. University of Iowa, College of Public Health, Department of Occupational and Environmental Health, Injury Prevention Research Center, Iowa City, IA, USA.
Abstract
PURPOSE: Although approximately one-third of agricultural equipment-related crashes occur near town, these crashes are thought to be a rural problem. This analysis examines differences between agricultural equipment-related crashes by their urban-rural distribution and distance from a town. METHODS: Agricultural equipment crashes were collected from nine Midwest Departments of Transportation (2005-2008). Crash zip code was assigned as urban or rural (large, small and isolated) using Rural-Urban Commuting Areas. Crash proximity to a town was estimated with ArcGIS. Multivariable logistic regression was used to estimate the odds of crashing in an urban versus rural zip codes and across rural gradients. ANOVA analysis estimated mean distance (miles) from a crash site to a town. FINDINGS: Over four years, 4444 crashes involved agricultural equipment. About 30% of crashes occurred in urban zip codes. Urban crashes were more likely to be non-collisions (aOR=1.69[1.24-2.30]), involve ≥2 vehicles (2 vehicles: aOR=1.58[1.14-2.20], 3+ vehicles: aOR=1.68[0.98-2.88]), occur in a town (aOR=2.06[1.73-2.45]) and within one mile of a town (aOR=1.65[1.40-1.95]) than rural crashes. The proportion of crashes within a town differed significantly across rural gradients (P<0.0001). Small rural crashes, compared to isolated rural crashes, were 1.98 (95%CI[1.28-3.06]) times more likely to be non-collisions. The distance from the crash to town differed significantly by the urban-rural distribution (P<0.0001). CONCLUSIONS: Crashes with agricultural equipment are unexpectedly common in urban areas and near towns and cities. Education among all roadway users, increased visibility of agricultural equipment and the development of complete rural roads are needed to increase road safety and prevent agricultural equipment-related crashes.
PURPOSE: Although approximately one-third of agricultural equipment-related crashes occur near town, these crashes are thought to be a rural problem. This analysis examines differences between agricultural equipment-related crashes by their urban-rural distribution and distance from a town. METHODS: Agricultural equipment crashes were collected from nine Midwest Departments of Transportation (2005-2008). Crash zip code was assigned as urban or rural (large, small and isolated) using Rural-Urban Commuting Areas. Crash proximity to a town was estimated with ArcGIS. Multivariable logistic regression was used to estimate the odds of crashing in an urban versus rural zip codes and across rural gradients. ANOVA analysis estimated mean distance (miles) from a crash site to a town. FINDINGS: Over four years, 4444 crashes involved agricultural equipment. About 30% of crashes occurred in urban zip codes. Urban crashes were more likely to be non-collisions (aOR=1.69[1.24-2.30]), involve ≥2 vehicles (2 vehicles: aOR=1.58[1.14-2.20], 3+ vehicles: aOR=1.68[0.98-2.88]), occur in a town (aOR=2.06[1.73-2.45]) and within one mile of a town (aOR=1.65[1.40-1.95]) than rural crashes. The proportion of crashes within a town differed significantly across rural gradients (P<0.0001). Small rural crashes, compared to isolated rural crashes, were 1.98 (95%CI[1.28-3.06]) times more likely to be non-collisions. The distance from the crash to town differed significantly by the urban-rural distribution (P<0.0001). CONCLUSIONS: Crashes with agricultural equipment are unexpectedly common in urban areas and near towns and cities. Education among all roadway users, increased visibility of agricultural equipment and the development of complete rural roads are needed to increase road safety and prevent agricultural equipment-related crashes.
Authors: Marizen Ramirez; Ronald Bedford; Hongqian Wu; Karisa Harland; Joseph E Cavanaugh; Corinne Peek-Asa Journal: Occup Environ Med Date: 2016-07-12 Impact factor: 4.402