Abstract
Objectives:
The purpose of the present study was to characterize the incidence, injury characteristics and outcomes of patients presented to a Level I adult trauma center in Fargo, North Dakota with farm machinery injuries (FMIs).
Methods:
We performed a retrospective review of the trauma registry of Sanford Medical Center Fargo (SMCF) between January 2010 and December 2020. We compared admission characteristics of FMI admissions to non-FMI admissions, identified the types of machinery that are most commonly associated with FMI and described the nature of these injuries by severity, anatomical site, type, age, sex, and length of stay (LoS). Injury severity was evaluated using Injury Severity Score (ISS).
Results:
Findings indicated that FMI admissions had a higher mean ISS, longer ICU LoS, and a higher mortality rate than non-FMI admissions. The leading cause of fatal and non-fatal FMI in this region are tractors. Males experience 91.2% of tractor injuries and individuals 65 and over account for nearly 53% of all tractor injuries (n=18). Males accounted for all deaths, tractor and otherwise. The ‘other machinery’ category was the second most common category and accounted for 50% of female patients. Additionally, 24.5% of all FMI are related to machine maintenance.
Conclusion:
The findings from this study indicate that FMI injuries represent a significant problem in the upper Midwest. Older, male farm workers experience a higher incidence of tractor-related injury and all tractor-related deaths occurred in individuals 65 years of age and older. These results underscore the need for further investigation into aging-related farm safety issues.
Keywords: agriculture, injury, farm machinery, tractor, trauma
Introduction
Agriculture ranks among the most hazardous industries worldwide, and high rates of occupational fatalities, injuries, and illnesses are observed in many studies.1–3 In 2020, the Bureau of Labor Statistics (BLS) reported that the rate of fatal agriculture, forestry, fishing and hunting was 21.5 injuries per 100,000 full-time equivalent workers, the highest of any industry.4 In 2020, the non-fatal injury rate was 4.1 injuries/100 FTE for hired farmworkers vs. 1.7 injuries/100 FTE for all industries combined. 5 However, these injury rates underrepresent the totality of injuries among agricultural populations across the United States. BLS surveillance of non-fatal injuries through the Survey of Occupational Injuries and Illnesses (SOII) excludes self-employed farmers and family members, and workers on farms with fewer than 11 employees. It was estimated that the SOII undercounts occupational agricultural injuries (AI) and illnesses by 78%.6
Additionally, the SOII is not conducted in North Dakota and South Dakota due to insufficient sample sizes .7 The Central States Center for Agricultural Safety and Health (CS-CASH) sends annual injury surveys to self-employed farmers and ranchers in seven states, including North Dakota, South Dakota and Minnesota, to assess agricultural injuries. 8 However, the self-report nature of the farm and ranch health and safety surveys and the 32% average response may limit the validity and generalizability of the results. 8 Therefore, reliable information is vital to understanding the extent of AI in the upper Midwest, specifically the role of machinery in AI.
Machinery is consistently identified in literature as the principal cause of fatal and nonfatal AI, with tractors accounting for most injuries and deaths. 9–11 Although tractors are consistently identified as a primary cause of AI, studies often present tractors and machinery as a single category, hampering comprehensive examination of machinery-related injuries. 9 Additionally, few comprehensive studies exist on the incidence and magnitude of injuries sustained from machinery such as augers, balers, and combines. 10, 12–13
The objectives of this study were to longitudinally characterize the incidence, injury characteristics, and outcomes of patients presented with farm machinery injuries (FMIs) to a Level I adult trauma center in the upper Midwest between 2010 and 2020. Data came from the trauma registry at Sanford Medical Center Fargo (SMCF). SMCF is the only Level I trauma center between Minneapolis, Seattle, Denver and Omaha, and it provides trauma care to North Dakota, Minnesota, South Dakota and Montana. SMCF was a Level II trauma center until 2018 when it became a verified Level I adult trauma center. This research contributes to the literature about machine-related fatal and non-fatal AI in the upper Midwest. Implications from the results are discussed.
Methods
We performed a retrospective review of the SMCF trauma registry between January 2010 and December 2020. The trauma registry at SMCF is maintained by a team of registrars trained in coding and abstraction of trauma patient charts. The registry includes patients admitted to SMCF following a traumatic injury, patients transferred to SMCF from another hospital due to a traumatic injury, or patients who died within SMCF resulting from their traumatic injury.
FMI were identified through ICD-9 & ICD-10 codes (Table 1) and analysis of injury descriptions entered into a free text field unique to the SMCF registry. We manually cross-referenced the ICD-9 & ICD-10 location and external cause codes with our injury details field to narrow down the list, resulting in a total of 292 AI patients. We reviewed the injury details to identify patients injured by farm machinery, for a final list of 106 patients with FMI, and manually categorized each injury by the type of machinery associated with the injury (Figure 1). Injuries related to falls, including falling on or into machinery, all-terrain vehicles, or animal handling were excluded from the analysis, but will be explored in future analyses.
TABLE 1.
List of initial ICD codes used to identify agricultural injuries
| ICD version | Code Type | Codes |
|---|---|---|
| ICD-9 | Location | E849.0 – E849.9 |
| External cause | E810 – E829 E840 – E849 E880 – E928 |
|
| ICD-10 | Location | Y92.4 – Y92.9 |
| External cause | V00-V99 W00-W99 X00-X58 |
FIGURE 1.
Decision tree used to identify patients for inclusion
Injury severity was evaluated using Injury Severity Score (ISS). The ISS ranges from 0 to 75, with an ISS equal to or greater than 16 indicating major trauma. Descriptive information obtained for each injury included: age and sex of the patient, year of injury, emergency department length of stay, ICU length of stay, inpatient length of stay, discharge disposition, and types and anatomical sites of FMI. Statistical analyses were performed using R. This study was granted exempt status by the institutional review board at SMCF.
Results
Characteristics of FMI admissions and non-FMI admissions
Between January 2010 and December 2020, a total of 106 FMI were recorded compared to 14,677 admissions for all other traumas (Table 2). The mean ages of FMI and non-FMI trauma admissions were similar (48 years vs 50 years). However, the proportions between male and female admissions (92.5% vs 7.5%) was far greater for FMI compared with non-FMI admissions (58.0% vs 42.0% p<0.01). FMI admissions had a higher mean ISS than non-FMI admissions (9.5 vs 9.1). Differences existed when we compared ISS levels distributions. About 65% of non-FMI admissions were in the minor (0–9) category compared to about 56% for FMI admissions (p<0.01). The FMI admissions mean ICU LoS was higher than non-FMI admissions (6.3 days vs 5.8 days). Also of note, non-FMI patients are more likely to be discharged to a skilled nursing facility (SNF) (22.5%) than FMI patients (11.3%). However, FMI patients have a higher mortality rate (5.7%) than non-FMI patients (3.1%) (p = 0.01).
TABLE 2.
Trauma type comparison at SMCF
| FMI (N=106) |
All other traumas (N=14,677) |
Total (N=14,783) |
p value | |
|---|---|---|---|---|
| Age | 0.35 | |||
| Mean (SD) | 48.0 (19.9) | 49.8 (27.4) | 49.8 (27.3) | |
| Gender | < 0.01 | |||
| Female | 8 (7.5%) | 6,170 (42.0%) | 6,178 (41.8%) | |
| Male | 98 (92.5%) | 8,507 (58.0%) | 8,605 (58.2%) | |
| Injury severity score | 0.41 | |||
| Mean (SD) | 9.5 (7.4) | 9.1 (8.0) | 9.1 (8.0) | |
| ISS levels | < 0.01 | |||
| 0–9 | 59 (56.2%) | 9,546 (65.1%) | 9,605 (65.0%) | |
| 10–15 | 22 (21.0%) | 2,639 (18.0%) | 2,661 (18.0%) | |
| 16–24 | 21 (20.0%) | 1,514 (10.3%) | 1,535 (10.4%) | |
| 25 and over | 3 (2.9%) | 968 (6.6%) | 971 (6.6%) | |
| ED LoS (Hours) | 0.04 | |||
| Mean (SD) | 3.2 (2.0) | 3.7 (2.8) | 3.7 (2.7) | |
| ICU LoS (Census days) | 0.59 | |||
| Mean (SD) | 6.8 (7.3) | 5.5 (6.7) | 5.5 (6.7) | |
| Inpatient LoS (Census days) | 0.75 | |||
| Mean (SD) | 6.3 (7.1) | 5.8 (7.1) | 5.8 (7.1) | |
| Discharge disposition/Outcome | 0.01 | |||
| Home | 70 (66.0%) | 8,056 (54.9%) | 8,126 (55.0%) | |
| SNF | 12 (11.3%) | 3,300 (22.5%) | 3,312 (22.4%) | |
| Mortality | 6 (5.7%) | 454 (3.1%) | 460 (3.1%) | |
| Other | 18 (17.0%) | 2,867 (19.5%) | 2,885 (19.5%) |
Injury characteristics of FMI admissions
FMI patients were predominantly male (92.5%) with a mean ISS of 10 (Table 3). The age range for FMI patients was 10 to 86 years with a mean of 48 years and a median of 51.5 years. About 31% of FMI patients were between 45 and 64 years. Individuals 65 years and older accounted for 26.4% of FMI admissions. Injuries to individuals aged 10 to 14 years are less common. Mean emergency department (ED) length of stay (LoS) is 3 hours and inpatient LoS is 6 days. Of all FMI patients presented to SMCF, 21% were admitted to the ICU. Mean ICU LoS is 7 days. After receiving treatment, 66% of patients were discharged to their homes. About 11% of patients were discharged to a SNF.
TABLE 3.
FMI Patient and Injury Characteristics
| Auger (n=24) | Baler (n=5) | Combine (n=2) | Machine maintenance (n=11) | Other machinery (n=26) | Power take-off (PTO) (n=4) | Tractor (n=34) | Total (n=106) | |
|---|---|---|---|---|---|---|---|---|
| Age | ||||||||
| Mean (SD) | 40 (14) | 47 (18) | 30 (11) | 54 (21) | 39 (19) | 54 (21) | 60 (19) | 48 (20) |
| Age groups | ||||||||
| 10 to 14 | - | - | - | - | 1 (3.8) | - | 2 (5.9) | 3 (2.8) |
| 15 to 24 | 4 (16.7) | 1 (20) | 1 (50) | - | 6 (23.1) | - | 1 (2.9) | 13 (12.3) |
| 25 to 44 | 10 (41.7) | 1 (20) | 1 (50) | 5 (45.5) | 9 (34.6) | 1 (25) | 2 (5.9) | 29 (27.4) |
| 45 to 64 | 9 (37.5) | 2 (40) | - | 2 (18.2) | 7 (26.9) | 2 (50) | 11 (32.4) | 33 (31.1) |
| 65+ | 1 (4.2) | 1 (20) | - | 4 (36.4) | 3 (11.5) | 1 (25) | 18 (52.9) | 28 (26.4) |
| Gender | ||||||||
| Female | - | 1 (20) | - | - | 4 (15.4) | - | 3 (8.8) | 8 (7.5) |
| Male | 24 (100) | 4 (80) | 2 (100) | 11 (100) | 22 (84.6) | 4 (100) | 31 (91.2) | 98 (92.5) |
| Injury severity score (ISS) | ||||||||
| Mean (SD) | 8 (6) | 6 (2) | 8 (11) | 10 (6) | 9 (8) | 9 (9) | 12 (8) | 10 (7) |
| ISS levels (n, %) | ||||||||
| 0–9 | 5 (20.8) | - | 1 (50) | - | 5 (19.2) | 2 (50) | 4 (11.8) | 17 (16) |
| 10–15 | 5 (20.8) | 1 (20) | - | 2 (18.2) | 5 (19.2) | 1 (25) | 8 (23.5) | 22 (20.8) |
| 16–24 | 2 (8.3) | - | 1 (50) | 3 (27.3) | 6 (23.1) | 1 (25) | 14 (41.2) | 27 (25.5) |
| 25+ | 12 (50) | 4 (80) | - | 6 (54.5) | 10 (38.5) | - | 8 (23.5) | 40 (37.7) |
| ED LoS (Hours) | ||||||||
| Mean (SD) | 2 (1) | 3 (1) | 2 (2) | 4 (2) | 4 (3) | 4 (2) | 3 (1) | 3 (2) |
| ICU LoS (Census days) | ||||||||
| Mean (SD) | 2 (1) | NA | NA | NA | 2 (1) | NA | 10 (8) | 7 (7) |
| Inpatient LoS (Census days) | ||||||||
| Mean (SD) | 7 (7) | 4 (1) | 4 (2) | 5 (4) | 3 (2) | 3 (4) | 9 (10) | 6 (7) |
| Discharge disposition (n, %) | ||||||||
| Death | - | - | - | - | 1 (3.8) | - | 5 (14.7) | 6 (5.7) |
| Home | 18 (75) | 4 (80) | 2 (100) | 8 (72.7) | 20 (76.9) | 4 (100) | 14 (41.2) | 70 (66) |
| Home health | 2 (8.3) | - | - | - | 2 (7.7) | - | - | 4 (3.8) |
| LTCH | 1 (4.2) | - | - | - | - | - | 1 (2.9) | 2 (1.9) |
| NA | 1 (4.2) | - | - | 1 (9.1) | 2 (7.7) | - | 4 (11.8) | 8 (7.5) |
| Rehab | - | - | - | - | - | - | 1 (2.9) | 1 (0.9) |
| SNF | 1 (4.2) | - | - | 2 (18.2) | 1 (3.8) | - | 8 (23.5) | 12 (11.3) |
| Transfer out | 1 (4.2) | 1 (20) | - | - | - | - | 1 (2.9) | 3 (2.8) |
The five most common types of machinery associated with FMI are listed in Table 3. Machine maintenance is a separate category because we isolated injuries related to the operation of machines from injuries that occurred while a machine was not in operation. The other machinery category includes farm machinery that are the cause of only one injury during this time period (e.g. circular saw, plow, potato picker, rock picker).
Tractor injuries are the most common FMI (n=34) and they have the highest mean ISS (12), the longest mean ICU LoS (10 days), and the longest mean inpatient LoS (9 days). Males experience 91.2% of tractor injuries and individuals 65 and over account for nearly 53% of all tractor injuries (n=18). All age groups are represented in this category. Tractor injuries are typically severe injuries that involve multisystem injuries that necessitate complex interventions and multispecialty care, resulting in a higher ISS and longer hospital stays. The majority of patients injured while operating tractors experienced rollover (n=8) or runover (n=13) accidents that caused trauma to the head, torso (chest/abdomen/pelvis) and upper and lower extremities, while others (n=10) were crushed between the tractor, or the agricultural implement attached to the tractor, and another object. Also, comorbidities (e.g. diabetes, hypertension, CHF) among aging farmers with tractor FMI can increase their length of stay. Tractor injuries were the cause of five of the six FMI deaths. These five individuals were males between 65 and 82 years. Of the five deaths, two individuals were run over by tractors and three individuals died after being crushed between a tractor and another object. Nearly 24% of patients with tractor FMI, the highest of all FMI categories, were discharged to a SNF. As tractor injuries are more complex and more severe than other FMI, a higher percentage are discharged to a SNF for post-hospital care.
Auger injuries are the third most common FMI and have the fourth highest mean ISS (8). The mean inpatient LoS for auger injuries is seven days, the second highest LoS, and 75% of patients were discharged to their homes. Males sustained all auger injuries and 79.2% were between the ages of 25 and 64 years. Auger injuries are often single extremity injuries, including abrasions, lacerations, fractures and degloving injuries. Amputations of the fingers and partial leg amputations are often auger-related. The majority (75%) of patients with auger injuries are discharged to their homes.
Machine maintenance injuries are the second most severe FMI with a mean ISS of 10. All patients with machine maintenance FMI were male and 36.4% were 65 years or older. Common injuries in this category included abrasions, contusions, and fractures resulting from being crushed by tractor tires or being in close proximity to a tractor tire as it exploded.
The other machinery category was the second most common category and accounted for 50% of female patients. Patients in this category presented with abrasions, contusions, lacerations, punctures and fractures. All age groups were represented in this category and nearly 77% of patients were discharged to their homes.
Combine injuries (n=2) and PTO injuries (n=4) were the least common of the five categories. PTO injuries had a higher mean ISS (9) than combine injuries (8). Amputations and fractures were common combine and PTO injuries all patients in both categories were male. However, patients with PTO injuries were generally older.
Mechanism of FMI by year
We evaluated the machine involved in each FMI between January 2010 and December 2020 (Figure 2). Tractor injuries occur in each year of this study. Auger injuries appear in all but one year (2017). Machine maintenance injuries, most of which are tractor-related, arise in most years. PTO-related FMI occurs more frequently in 2019 and 2020 than earlier years while auger injuries occur in every year but 2017. Between 2018 and 2020 the count of both tractor and auger injuries doubled. In 2020, tractor injuries are the most common injury, followed by auger injuries.
FIGURE 2.
Farm Machinery Mechanism by Year
Discussion
Reliable surveillance information is crucial to understanding the role of machinery in agricultural injuries and preventing FMI. However, limited reliable data exists regarding the extent of fatal and non-fatal FMI in the upper Midwest. The SMCF trauma registry data contributes to this gap in knowledge. This study provides pertinent information on the magnitude, severity, characteristics, and outcomes of FMI patients admitted from a four-state upper Midwest region.
Overall, our study found that FMI admissions between 2010 and 2020 had a higher mean ISS, longer ICU LoS, and a higher mortality rate than non-FMI admissions. The leading cause of fatal and non-fatal FMI in this region are tractors. This finding is consistent with previous studies.10,16–18 Moreover, tractor-related injuries are the most severe machine-related injuries and tractors are responsible for five of six deaths. Males accounted for over 91% of tractor-related injuries and all deaths, tractor and otherwise. Consistent with these findings, males have been identified as experiencing higher risk of agricultural injury and death, specifically for tractor-related injuries.10, 18
A concerning finding we noted is that 26.4% of FMI and all tractor-related deaths occurred in individuals 65 years of age and older. Previous studies indicate the single most common cause of fatal occupational injury for agricultural workers 55 years and older are tractor overturns. 14–15,19 Risk factors including reduced hearing, vision, musculoskeletal strength, dexterity, and reaction time increase the likelihood of farming-related injury. 14, 21 Studies have also shown that use of medication may impact reaction times and dexterity, increasing the possible risk of injury. 14, 21 Each of these factors contribute to a higher incidence of tractor injury and deaths in older farmers.
Although our findings support the longstanding need to focus prevention efforts on machine safety, specifically safe tractor operation, for older farm workers, unique issues arise for injury intervention and prevention among this population, including an unwillingness to improve safety practices. 22 Caffaro et al. (2018) found that aging farmers acknowledge the presence of farm injury risks related to aging, but believe that their substantial farming experience, common sense and confidence in their strength will compensate for these dangers. 22 They also found that many participants reported not wearing seatbelts, suggesting that farmers may not understand the risks associated with non-use of safety devices. 22 Moreover, older farm operators are more likely to have a higher percentage of tractors without Rollover Protective Structures (ROPS) than younger operators and they demonstrate more resistance to purchasing newer equipment or installing safety features on older equipment due to the longevity of older tractors and the financial costs of ROPS retrofits. 14, 23
Therefore, further investigations into multilevel interventions that focus on the abilities and limits, behaviors, and risk awareness of older farmers is needed to support their safety and health. Additionally, early intervention safety programs should target farmers at younger ages so appropriate safety practices can be integrated at the start of their career. 22 One safety and training intervention should focus on the use of ROPS and seat belts which, together, are 99% effective in preventing death or serious injury in the event of a tractor rollover. 26
Our findings also suggest that some FMI may be associated with seasonal patterns. For example, the increased auger injuries in 2020 may be attributed to heavy rain in the region in 2019 and 2020. Between September and October of 2019 some areas in North Dakota received over 400% of normal rainfall. 24 Due to this excess precipitation, some harvested grain may have had a moisture content unsuitable for long term bin storage. When grain is stored too wet, it becomes out of condition and can turn into moldy crusts and chunks that block the flow of grain in a grain auger. Farm workers may have removed the safety cage and reached into the auger to clear obstructions, resulting in finger trauma, hand trauma and/or amputations.
Similarly, excessively rainy conditions in 2020 may have resulted in more tractor injuries. Wet surfaces, slopes, poor visibility and poor ground conditions are hazards associated with the risk of rollover. 27 Farmers in our study who were injured while operating a tractor in wet conditions may have a lack of awareness of the risks associated with such activities or they may be prioritizing efficiency and time over safety. Given this, potential interventions may include preventing tractor overturns by helping tractor operators understand the stability limits of tractors and to prepare them to identify potential hazards they may encounter in autumn, including wet surfaces. 28 Additionally, future research that explicitly examines seasonal patterns and the incidence of FMI may inform season-based safety interventions to prevent FMI.
Additionally, our study found that 24.5% of all FMI are related to machine maintenance. This finding coincides with another study that reported that 37% of farming-related injuries in their dataset are related to adjusting or repairing machines. 29 Maintenance tasks often bring farmers into close contact with machinery hazards, including sharp objects and moving parts, which increases risk of injury. Injury prevention trainings that emphasize ways to prevent injury while conducting maintenance may benefit farm workers.
Finally, staffing shortages in North Dakota and South Dakota during the COVID-19 pandemic may have resulted in increased FMI at SMCF. In 2020 as the number of hospitalizations increased, smaller clinics in the upper Midwest were forced to close so their medical providers and nurses could treat COVID-19 patients. 25 Since many smaller, rural medical facilities were closed, FMI patients may have sought treatment at SMCF, increasing the number of FMI admissions. Future research may bring insight into the effects of the COVID-19 pandemic on farming-related injury and mortality.
Strengths and Limitations
Our study used an exclusive dataset that contains FMI presented to SMCF in a multi-state, rural region of the United States. The dataset is maintained by a team of trained registrars who adhere to quality standards established by the National Trauma Data Standards and the American College of Surgeons.
However, this dataset is not reflective of all FMI traumas in the upper Midwest. Level II centers are located throughout the region, with their own trauma registries, that provide care for FMI. Additionally, patients who were pronounced dead at the scene of a FMI are not included in the registry. Given this, mortality due to FMI may be underestimated. Lastly, this study did not analyze farm injuries related to falls, all-terrain vehicles, animals, and seasonality of injuries. Future analytic efforts will focus on these injury types. Although our dataset is not all-inclusive, it contributes to a knowledge gap about the characteristics, frequency, and severity of FMI in the region.
Conclusion
The findings from this study indicate that FMI injuries represent a significant problem in the upper Midwest. FMI admissions are generally more severe, have longer ICU LoS, and have higher mortality rates than non-FMI admissions. Although tractor rollovers are preventable and most rollover fatalities could be avoided, our findings identify tractors as the most common cause of injury and death. Older, male farm workers experience a higher incidence of tractor-related injury and all tractor-related deaths occurred in individuals 65 years of age and older. These results underscore the need for further investigation into aging-related farm safety issues in the upper Midwest. To minimize injuries for people of all ages, additional and ongoing education on farm machine safety is needed.
Acknowledgements
This project is supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number 5P20GM121341.
Contributor Information
Dr Elizabeth A Gilblom, North Dakota State University, Fargo, United States.
Dr Hilla I Sang, Sanford Health, Sioux Falls, 57117-5039 United States.
Ms Angela B Johnson, North Dakota State University, Fargo, 58108-6050 United States.
Dr Sheryl Sahr, Sanford Health, Sioux Falls, 57117-5039 United States.
Ms Melissa Misialek, Sanford Health, Sioux Falls, 57117-5039 United States.
Ms Deb Syverson, Sanford Health, Sioux Falls, 57117-5039 United States.
Dr Zachery Staskywicz, University of North Dakota School of Medicine and Health Sciences, Grand Forks, 58202-9037 United States.
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