Fatal work-related motor vehicle crashes in the United States, 2011-2014: Analysis of matched data from the Census of...

Motor vehicle crashes (MVCs) are the leading cause of work-related fatalities in the United States. The Bureau of Labor Statistics (BLS), the National Institute for Occupational Safety and Health (NIOSH), and the National Highway Traffic Safety Administration (NHTSA) matched and analyzed the Census of Fatal Occupational Injuries (CFOI) and the Fatality Analysis Reporting System (FARS) to describe work-related MVCs.

BLS matched CFOI and FARS data for 2011-2014. The matching algorithm used a series of iterative matches allowing for increasing levels of flexibility. Descriptive epidemiologic analysis was conducted to examine potential risk factors for MVCs.

Crashes: Information on 3,822 fatal work-related MVCs was successfully matched. A collision (n = 3,156, 82.5%) was most often the first injury- or damage-producing event. Collisions with motor vehicles in transport accounted for 1,769 (46.5%) of crashes. Vehicles: The match identified 3,879 vehicles. Over half (53.6%) were large trucks. In most vehicles, one fatality (n = 3,657) occurred. Multiple worker fatalities per vehicle (n = 203) were more common in heavy trucks (n = 59, 29.1%) and pick-up trucks (n = 36, 17.7%).

The match identified 4,060 workers, 3,581 (88.2%) of whom were drivers. Workers employed in transportation, warehousing, and utilities (38.2%) and services (24.4%) accounted for most fatalities. Among passengers, the highest proportions were observed in mining, quarrying, and oil and gas extraction (28.6%) and construction (20.0%). A small number of drivers were reported as impaired by being asleep or fatigued (5.2%), under the influence of alcohol, drugs, and medications (3.0%) or blacking out (1.8%). For 755 (21.1%) of the 3,581 drivers, the investigating officer indicated that speed contributed to the crash. Conclusions/Practical Applications: The CFOI/FARS matched data analysis provides a fuller understanding of work-related MVCs, thus making it possible to develop focused crash prevention recommendations for workers who drive as part of their job.