JA:2021-33. Logging Truck Crashes in the SW Agricultural Region: Examining Severity and Associated Factors

• Published in: Journal of agromedicine Vol. 25; no. 3; pp. 257 - 258
• Main Authors: Shipp, Eva M., Trueblood, Amber B., Kum, Hye-Chung, Garcia, Tanya, Vasudeo, Shubhangi, Perez, Marcelina
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 02.07.2020; Taylor & Francis Ltd
• Subjects: agriculture; Approximation; crash; Crashes; Forestry; Health risks; Injuries; injury; Logging; Occupational health; Occupational safety; Public rights of way; Regression analysis; Risk analysis; Risk factors; Roads; traffic; Transportation; Travel demand; Trucks; Vehicles; Louisiana; United States > US; Arkansas; Texas
• ISSN: 1059-924X; 1545-0813
• DOI: 10.1080/1059924X.2020.1765589

Purpose: The rate for fatal injuries in the agriculture, forestry, and fishing (AFF) sector far exceeds the rate for all sectors combined in the United States, based on the National Census of Fatal Occupational Injury (CFOI). Data from CFOI also illustrate that transportation crashes are among the top causes of fatal occupational injuries in the United States. However, little is known about transportation crashes involving AFF vehicles, specifically logging trucks in the southwest (SW) region (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas). The purpose of the ongoing SW AgCrash project is to develop an integrated database of AFF crashes that occurred on roadways in the SW region. These data are being assessed to determine their usefulness for supporting injury surveillance and the identification of injury risk factors. Methods: Crash records covering approximately the period of 2010 to 2018 were obtained from the department of transportation in each state. Logging truck vehicles were identified based on coded data fields pertaining to vehicle and cargo body styles. Logging truck-related crashes were characterized over time using descriptive statistics and geospatial plots. Multiple and multinomial logistic regression is being used to identify factors associated with more versus less severe crashes in Texas. Results/Findings: Louisiana, followed by Texas, and Arkansas recorded the largest numbers of logging truck-related crashes overall, which corresponds to their greater logging activity. Crash rates per total vehicle miles travelled were highest in Arkansas and Louisiana. Based on preliminary analyses, factors associated with more severe logging truck related crashes in Texas included overturned vehicle, failure to yield right of way, lack of restraint, and speeding. Practical Application: Although crash records have limitations, they can be a valuable source of data for monitoring the frequency of crashes involving logging trucks, as well as for identifying potential risk factors and countermeasures. Products constructed through the SW AgCrash project support targeted outreach and education for reducing crash risk.