Farm Fatal Injury Trends in  Illinois from 1999 to 2019

Li, Sihan; Aby, Guy Roger; Aherin, Robert A.; Issa, Salah F.

doi:10.13031/jash.14898

Cited by 7 publications

(6 citation statements)

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“…Similar to previous studies, the results of this study show that the farmer group had significantly higher rates of ICU admission, surgery, and injury-related mortality than the non-farmer group [28][29][30][31]. However, the multivariate logistic regression analysis in the unmatched sample adjusted for age, sex, inhabitant, insurance type, and year of injury showed that the adjusted OR for in-jury-related mortality was not statistically significant.…”

Section: Discussionsupporting

confidence: 85%

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Clinical characteristics and outcomes of injuries in agricultural and nonagricultural workers visiting the emergency department: a propensity-matched analysis

Kang¹,

Song²,

Hong³

et al. 2023

Clin Exp Emerg Med

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Objective Agriculture is a hazardous industry. However, previous studies have focused on injuries to agricultural workers without comparison with injuries to nonagricultural workers. Therefore, we compared the clinical characteristics and outcomes of injuries reported at an emergency department (ED) between agricultural workers and nonagricultural workers.Methods We established a prospective ED-based agricultural injury surveillance system at a tertiary university hospital. Adult patients visiting the ED for an injury were divided into farmer and non-farmer groups depending on their engagement with agriculture. Using an adjusted multivariate analysis and propensity score matching (age, sex, inhabitant, and insurance type), we compared the clinical characteristics and outcomes of injuries between the farmer and non-farmer groups.Results In total, 38,556 injured adult patients (37,746 in the non-farmer group and 810 in the farmer group) were available for the unmatched sample analysis. The 1,620 matched subjects were equally classified after one-to-one nearest-neighbor propensity score matching. A multivariate logistic regression analysis of the unmatched sample revealed higher adjusted odds ratios (ORs) for intensive care unit admission (adjusted OR, 1.752; P=0.003) and overall surgery (adjusted OR, 1.870; P<0.001) in the farmer group. In contrast, univariate logistic regression analyses of the propensity score–matched sample found a higher OR in the farmer group only for overall surgery (OR, 1.786; P<0.001).Conclusion Injuries of agricultural workers had higher odds only of requiring surgery; differences in injury-related mortality between groups were not statistically significant in either the matched or unmatched sample analyses.

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Section: Discussionsupporting

confidence: 85%

“…These findings are somewhat surprising given that other studies have reported high mortality and morbidity rates among agricultural workers [4,7,10,17,25,[28][29][30][31]. A potential reason for this discrepancy could be the study design and setting.…”

Section: Discussionmentioning

confidence: 74%

Clinical characteristics and outcomes of injuries in agricultural and nonagricultural workers visiting the emergency department: a propensity-matched analysis

Kang¹,

Song²,

Hong³

et al. 2023

Clin Exp Emerg Med

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“…Agriculture remains the most dangerous occupation in the U.S. (BLS, 2020) [1]. Among all agricultural injuries, tractor-related injuries are the highest [3]. Several databases track all occupational incidents, such as Ag Injury News Clippings, the Fatality Assessment and Control Evaluation (FACE), and the Bureau of Labor (BOL) statistics.…”

Section: Methodsmentioning

confidence: 99%

“…Because of more efficient machinery and systems [2]. Moreover, within all types of agricultural fatal injuries (tractor, roadway, grain bins, farm equipment, Terrain Vehicle (ATV), electrocution, animals, manure storage, and others), the number of tractor-related injuries remains high, with 213 cases from 1999 to 2019 [3].…”

Section: Introductionmentioning

confidence: 99%

Application of REDECA Framework to Improve Safety and Health of Agricultural Tractor Drivers

Ashrafi,

Alaei,

Placencia

et al. 2024

Preprint

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Introduction:Despite tremendous efforts, including research, teaching, and extension, toward improving the safety of agricultural tractor drivers, the number of incidents related to agricultural tractor drivers has not declined. This evidence points out an urgent need to explore artificial intelligence (AI) solutions to improve the safety of tractor drivers.Methods:This paper uses 171 Fatality Assessment and Control Evaluation (FACE) reports related to tractor drivers and a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) to identify existing AI solutions and specific areas where AI solutions are missed and can be developed to reduce incidents and recovery time. Fatality reports of tractor drivers were categorized into six main categories, including run over, pinned by, fall, others (fire and crashes), roll over, and overturn. Each category was then subcategorized based on similarities of incident causes in the reports.Results:The application of the REDECA framework revealed potential AI solutions that could improve the safety of tractor drivers. In all categories, the REDECA framework lacks AI solutions for three elements, including the probability of reducing recovery time in R3, detecting changes between R2 and R3, and intervention to send workers to R2. Except for the run over category, all other categories were missing AI solutions for interventions to prevent entry to the R3 element of the REDECA. In addition, the fall, roll over, and overturn categories lacked AI intervention that minimized damage and recovery in R3.Conclusions:The outcome of this study shows an urgent need to develop AI solutions to improve tractor driver safety.

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“…Production agriculture is a dangerous occupation considering high rates of fatal and non-fatal injuries (National Safety Council, 2022). Machinery and tractors are among the hazards often involved in fatal injury events based on a recent analysis of detailed incident data collected in key agricultural states (Li et al, 2022). One potential benefit of removing the human operator from a mechanized system is to reduce human exposure and associated risk that contributes to high rates of injury associated with farm machinery operation.…”

Section: Reducing Injury Risk By Reducing Operator Exposurementioning

confidence: 99%

Risk Assessment Methods for Autonomous Agricultural Machines: A Review of Current Practices and Future Needs

Shutske¹,

Sandner²,

Jamieson³

2023

Applied Engineering in Agriculture

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Highlights Risk assessment for highly automated and autonomous agricultural machines must consider risks beyond operator risk. Engineering standards are a starting point for autonomous equipment risk assessment but are not yet adequate. Engineers designing highly automated equipment now assess risk holistically but need more tools and support. Education in accredited engineering programs and professional development should include risk assessment. Abstract. Technology continues to advance in agricultural machines and includes the development of highly automated, robotic, autonomous, and other types of machines used in fields, farmsteads, buildings, and other farm production locations. New engineering design and safety-related standards have been developed in the past half-decade, but safety remains a concern of key stakeholders and is a barrier that could influence widespread adoption. A survey of practicing engineers and researchers involved with highly automated and autonomous agricultural machine design will be presented that shows the methods for risk assessment and control currently in use including different frameworks for hazard and failure identification, prediction, and quantification. The use of engineering design standards (ASABE, ISO, and others) among practitioners is discussed including some important needs that go beyond obstacle detection and injury prevention for operators. These include safety and risk issues connected to animals, property, civic infrastructure, downtime, cyber, and environmental risk. Commonly used risk assessment methods such as the related failure modes and effects analysis (FMEA) or hazard analysis and risk assessment (HARA) are a useful starting point but are based on historical data and experience that can be used to estimate the probability and severity levels of undesirable failures or incidents such as injuries. These data do not yet exist as compared to risk assessment data that can be used to assess incident occurrence probability, failure, detectability, or controllability in more traditional machines. Suggestions are presented for further development of standards and practice recommendations including software needs and operational data that might be used by autonomous machines that is informed by what we do know about past farm incidents that could include accidents, injuries, and other unexpected failures. Keywords: Automation, Autonomous agricultural machinery, Engineering design standards, Farm equipment, Risk assessment, Robotics, Safety.

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Farm Fatal Injury Trends in Illinois from 1999 to 2019

Cited by 7 publications

References 0 publications

Clinical characteristics and outcomes of injuries in agricultural and nonagricultural workers visiting the emergency department: a propensity-matched analysis

Clinical characteristics and outcomes of injuries in agricultural and nonagricultural workers visiting the emergency department: a propensity-matched analysis

Application of REDECA Framework to Improve Safety and Health of Agricultural Tractor Drivers

Risk Assessment Methods for Autonomous Agricultural Machines: A Review of Current Practices and Future Needs

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