Lighting and marking policies are associated with reduced farm equipment-related crash rates: a policy analysis of nine Midwestern US states

Summary of Roadway Incidents Involving Farm Equipment in Five Midwestern States Using the Fatality Analysis Reporting System (FARS)

Roadway incidents involving farm equipment is a growing area of concern among agricultural safety and health and public health professionals. The aim of this project was to evaluate the usefulness of the Fatality Analysis Reporting System (FARS) and analyze the number of roadway fatal incidents that involve farm equipment. Data collected from the FARS through the National Highway Traffic Safety Administration was used to summarize roadway incidents involving farm equipment. Cases from five midwestern states were analyzed from January to December 2020 using SPSS. Incidents involving farm equipment resulted in 25 cases with Iowa, Michigan, and Wisconsin all reporting six cases each. The most common manner of incidents were single-vehicle crashes and rear-ending incidents. Most of the events occurred during busy agricultural seasons, most often occurring in June and August with five cases each. The FARS dataset is a useful tool to identify cases, but it faces limitations, such as only reporting fatalities and lack of information on specific farm equipment involved in incidents. The results from the study are helpful to better understand roadway incidents and guide future intervention strategies.

Using motor vehicle crash records for injury surveillance and research in agriculture and forestry

PROBLEM

Fatal injuries in the agriculture, forestry, and fishing sector (AgFF) outweigh those across all sectors in the United States. Transportation-related injuries are among the top contributors to these fatal events. However, traditional occupational injury surveillance systems may not completely capture crashes involving farm vehicles and logging trucks, specifically nonfatal events.

METHODS

The study aimed to develop an integrated database of AgFF-related motor-vehicle crashes for the southwest (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) and to use these data to conduct surveillance and research. Lessons learned during the pursuit of these aims were cataloged. Activities centered around the conduct of traditional statistical and geospatial analyses of structured data fields and natural language processing of free-text crash narratives.

RESULTS

The structured crash data in each state include fields that allowed farm vehicles or equipment and logging trucks to be identified. The variable definitions and coding were not consistent across states but could be harmonized. All states recorded data fields pertaining to person, vehicle, and crash/environmental factors. Structured data supported the construction of crash severity models and geospatial analyses. Law enforcement provided additional details on crash causation in free-text narratives. Crash narratives contained sufficient text to support viable machine learning models for farm vehicle or equipment crashes, but not for logging truck narratives.

DISCUSSION

Crash records can help to fill research and surveillance gaps in AgFF in the southwest region. This supports traffic safety's evolution to the current Safe System paradigm. There is a conceptual linkage between the Safe System and Total Worker Health approaches, providing a bridge between traffic safety and occupational health.

PRACTICAL APPLICATIONS

Despite limitations, crash records can be an important component of injury surveillance for events involving AgFF vehicles. They also can be used to inform the selection and evaluation of traffic countermeasures and behavioral interventions.

Applying systems thinking to improve the safety of work-related drivers: A systematic review of the literature

INTRODUCTION

Light vehicles (<4.5 tons) driven for work purposes represent a significant proportion of the registered motor vehicles on our roads. Drivers of these vehicles have significant exposure to the dangers of the road transport environment. To optimize safety for these workers, it is critical to understand the factors contributing to risk of being involved in an incident. This information can then be used to inform the review and revision of existing risk controls and the development of targeted prevention activities.

METHOD

The aim of the study was to undertake a systematic review of the literature to identify the factors associated with work-related driving incidents. The factors identified in the review were represented within an adapted version of Rasmussen's risk management framework (Rasmussen, 1997). Fifty studies were analyzed following data screening and review of full text. The highest proportion of risk factors were categorized at the lower levels of the system, including the 'Drivers and Other Road Users' level (n = 20, 44.4%) and the 'Equipment, Environment, and Meteorological Surroundings' level (n = 19, 42.2%). There were no risk factors identified at the 'Regulatory and Government Bodies' levels of the framework, confirming the narrow investigative scope of past research and the need to acknowledge a broader range of factors within and across higher levels of the system.

CONCLUSIONS

The findings of this study inform the direction of future research and design of targeted prevention activities capable of creating system change for the safety of work-related drivers.

Community engagement in the development and implementation of a rural road safety campaign: Steps and lessons learned

INTRODUCTION

Rural crashes result in fatality rates twice as high as urban, after accounting for vehicle miles traveled, and those involving farm vehicles tend to be the most severe. Farm vehicle crash interventions have focused on the farm equipment (e.g., lighting and marking) or the farm vehicle operator (e.g., training), despite crashes being most frequently caused by other vehicle driver actions. Community-based campaigns focused on rural drivers have potential to influence driver behavior. The objective of this study was to describe the role, formation, and lessons learned from a community advisory board (CAB) in the development and dissemination of a community-based rural roadway safety campaign.

METHODS

The CAB provided campaign input through quarterly meetings and email. The campaign had three main CAB and crash data-informed messages: 1) Slow Down, 2) Leave More Space, and 3) Avoid Passing. The CAB led campaign activities to publicize the message, distribute swag, and organize event logistics. To evaluate CAB effectiveness and inform future community engagement efforts, we conducted in-depth, semi-structured telephone interviews with CAB members in July 2020. Interviews were transcribed, coded, and codes were categorized into five main themes.

RESULTS

Overall, CAB membership was described as an overwhelmingly positive experience in terms of the CAB structure, culture fostered among the group, responsibilities, and time commitment. Board members reported successful campaign implementation, gave positive feedback regarding the research team's engagement efforts, and provided valuable recommendations for future campaigns (e.g., adding social media components, expansion of CAB age and industry diversity, and increasing group bonding activities).

CONCLUSIONS

Results from this study demonstrate the instrumental role and logistics involved in engagement of community advisors for the development and implementation of a rural roadway safety campaign. Steps and lessons from this study can be applied to other community-level injury and violence prevention topics, with a particular focus on rural communities.

Farm vehicle crashes on public roads: Analysis of farm-level factors

PURPOSE

Rural public roads experience higher crash fatality rates than other roadways, with agricultural equipment adding greater risk of injury and fatality. This study set out to describe farmers' experiences with farm equipment crashes and predictors of crashes at the farm level.

METHODS

A survey of farm operators was conducted in 9 Midwestern states (IL, IA, KS, MN, MO, NE, ND, SD, and WI) in collaboration with the US Department of Agriculture's National Agricultural Statistical Service.

FINDINGS

From 1,282 farms operating equipment on public roads in 2013, 7.6% of farmers reported that equipment from their farm had ever been in a crash (n = 97). Crashes occurred most often in June-August (44.0%) and were most often reported as being during the daytime (71.3%), on dry roads (79.4%), or in clear weather (71.4%). While most farmers responded that they were driving the farm equipment at the time of the crash (52.0%), nearly half of crashes involved their employees as the driver (48.0%). Crashes often went unreported to law enforcement (28.6%).

CONCLUSION

To illustrate crash probabilities for farms with different profiles, we included farm acreage, crop farming, vehicle horsepower, annual miles driven, and the total number of farm vehicles driven on public roads in a predictive model. Large crop farms of 241+ acres, those who drove farm vehicles 1,430+ miles per year, and those with 20 or more farm vehicles had the highest probability of crash of 0.14.

Evaluation of Self-Reported Agricultural Tasks, Safety Concerns, and Health and Safety Behaviors of Young Adults in U.S. Collegiate Agricultural Programs

Young adults enrolled in collegiate agricultural programs are a critical audience for agricultural health and safety training. Understanding the farm tasks that young adults engage in is necessary for tailoring health and safety education. The project analyzed evaluation survey responses from the Gear Up for Ag Health and Safety^™ program, including reported agricultural tasks, safety concerns, frequency of discussing health and safety concerns with healthcare providers, safety behaviors, and future career plans. The most common tasks reported included operation of machinery and grain-handling. Most participants intended to work on a family-owned agricultural operation or for an agribusiness/cooperative following graduation. Reported safety behaviors (hearing protection, eye protection, and sunscreen use when performing outdoor tasks) differed by gender and education type. Male community college and university participants reported higher rates of "near-misses" and crashes when operating equipment on the roadway. One-third of participants reported discussing agricultural health and safety issues with their medical provider, while 72% were concerned about the health and safety of their family and co-workers in agriculture. These findings provide guidance for better development of agricultural health and safety programs addressing this population-future trainings should be uniquely tailored, accounting for gender and educational differences.

Crash narrative classification: Identifying agricultural crashes using machine learning with curated keywords

OBJECTIVE

Traditionally, structured or coded data fields from a crash report are the basis for identifying crashes involving different types of vehicles, such as farm equipment. However, using only the structured data can lead to misclassification of vehicle or crash type. The objective of the current article is to examine the use of machine learning methods for identifying agricultural crashes based on the crash narrative and to transfer the application of models to different settings (e.g., future years of data, other states).

METHODS

Different data representations (e.g., bag-of-words [BoW], bag-of-keywords [BoK]) and document classification algorithms (e.g., support vector machine [SVM], multinomial naïve Bayes classifier [MNB]) were explored using Texas and Louisiana crash narratives across different time periods.

RESULTS

The BoK-support vector classifier (SVC), BoK-MNB, and BoW-SVC models trained with Texas data were better predictive models than the baseline rule-based algorithm on the future year test data, with F1 scores of 0.88, 0.89, 0.85 vs. 0.84. The BoK-MNB trained with Louisiana data performed the closest to the baseline rule-based algorithm on the future year test data (F1 scores, 0.91 baseline rule-based algorithm vs. 0.89 BoK-MNB). The BoK-SVC and BoK-MNB models trained with Texas and Louisiana data were better productive models for Texas future year test data with F1 scores 0.89 and 0.90 vs. 0.84. The BoK-MNB model trained with both states' data was a better predictive model for the Louisiana future year test data, F1 score 0.94 vs. 0.91.

CONCLUSIONS

The findings of this study support that machine learning methodologies can potentially reduce the amount of human power required to develop key word lists and manually review narratives.

A Systematic Review of Large Agriculture Vehicles Use and Crash Incidents on Public Roads

Background: Agricultural vehicles are a common sight on rural public roads. However, due to their larger mass (height, width, length, and weight), there are concerns about safety. The aim of this paper is to explore crash incidents on public roads of agricultural vehicles to determine the size of the problem, risk factors, and potential prevention strategies.Methods: A systematic review using the PRISMA guidelines was undertaken of peer-reviewed literature from Medline, Agricola, Scopus, PsycInfo, Science Direct, Web of Science, and SafetyLit. Crash incident rates, risk factors, and prevention strategies were extracted from the articles, and a review of quality was undertaken using McMasters guidelines.Results: Included in the review were 30 articles, with the majority from the United States. Crash risk rates, where reported, were low relative to agricultural vehicle use and when compared to overall road crash numbers. Crash risk factors included weather and visibility, age, personal and driving characteristics, road conditions, and event characteristics. Prevention strategies proposed were targeted at drivers and operators, vehicles, road design, driving behavior, and surveillance, policy, and technology.Conclusions: Overall, reported crash numbers involving large agricultural vehicles were low. Currently, there is limited capacity to calculate exposure rates compounded by the difficulties in identifying road incidents that involve agriculture vehicles. Better surveillance systems are required to improve our understanding of exposure and crash incident rates. Future research into the multiplicity of interrelated factors involved in agriculture vehicle crashes on roads, exposure rates, and evidence for the effectiveness of the prevention strategies is required.

Prevalence of alcohol impairment and odds of a driver injury or fatality in on-road farm equipment crashes

OBJECTIVE

The objective of this article was to estimate the prevalence of alcohol impairment in crashes involving farm equipment on public roadways and the effect of alcohol impairment on the odds of crash injury or fatality.

METHODS

On-road farm equipment crashes were collected from 4 Great Plains state departments of transportation during 2005-2010. Alcohol impairment was defined as an involved driver having blood alcohol content of ≥0.08 g/100 ml or a finding of alcohol impairment as a driver contributing circumstance recorded on the police crash report. Injury or fatality was categorized as (a) no injury (no and possible injury combined), (b) injury (nonincapacitating or incapacitating injury), and (c) fatality. Hierarchical multivariable logistic regression modeling, clustered on crash, was used to estimate the odds of an injury/fatality in crashes involving an alcohol-impaired driver.

RESULTS

During the 5 years under study, 3.1% (61 of 1971) of on-road farm equipment crashes involved an alcohol-impaired driver. One in 20 (5.6%) injury crashes and 1 in 6 (17.8%) fatality crashes involved an alcohol-impaired driver. The non-farm equipment driver was significantly more likely to be alcohol impaired than the farm equipment driver (2.4% versus 1.1% respectively, P = .0012). After controlling for covariates, crashes involving an alcohol-impaired driver had 4.10 (95% confidence interval [CI], 2.30-7.28) times the odds of an injury or fatality. In addition, the non-farm vehicle driver was at 2.28 (95% CI, 1.92-2.71) times higher odds of an injury or fatality than the farm vehicle driver. No differences in rurality of the crash site were found in the multivariable model.

CONCLUSION

On-road farm equipment crashes involving alcohol result in greater odds of an injury or fatality. The risk of injury or fatality is higher among the non-farm equipment vehicle drivers who are also more likely to be alcohol impaired. Further studies are needed to measure the impact of alcohol impairment in on-road farm equipment crashes.