Occupational Injury and Illness in Farmers with Prior Military Service

Occupational Injury and Illness in Farmers with Prior Military Service | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Occupational Injury and Illness in Farmers with Prior Military Service Suraj Adhikari, Cheryl Beseler, Risto Rautiainen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7743020/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background A significant portion of U.S. agricultural producers have served in the military. This population may face unique occupational health challenges. This study describes and compares occupational injury and illness outcomes of agricultural producers with and without prior military service. Methods Data from the 2023 Farm and Ranch Health and Safety Survey (FRHSS) were analyzed, including 2,367 producers from seven central U.S. states. Producers were categorized as having prior military service (MS, n = 289) or no prior military service (NoMS, n = 2078). Descriptive statistics, chi-square tests, and logistic regression were used to compare demographics, farm characteristics, injury rates and characteristics, and chronic health conditions between the groups. Results The MS group was predominantly male (98.6%) and older (87.9% >60 years) compared to the NoMS group (83.5% male, 49.8% >60 years). The overall injury proportion was lower for MS producers (17.65%) than for NoMS producers (19.83%), though not statistically significant. When adjusted for age and sex, MS producers had higher odds of hearing loss (OR = 1.6, 95% CI: 1.1–2.2) and skin disease (OR = 1.3, 95% CI: 0.9–1.8), including skin cancer (24.4% vs. 13.4%). The primary cause of injury for all producers was livestock. MS producers had a higher proportion of injuries involving power tools, while NoMS producers had more tractor-related injuries. Conclusions Agricultural producers with prior military service exhibit a distinct demographic profile and experience a significant burden of specific chronic health conditions, particularly hearing loss and skin disorders. While their overall injury rate was slightly lower, targeted safety interventions addressing their unique health vulnerabilities and promoting the use of protective equipment are essential to improving their long-term occupational health outcomes. Agricultural Safety Occupational Injuries Veterans Military Service Epidemiology Figures Figure 1 Figure 2 Figure 3 Background Agriculture ranks among the most hazardous industries worldwide. According to the Bureau of Labor Statistics (BLS), farming, fishing, and forestry occupations had the highest fatality rate of 20.3 per 100,000 full-time equivalent (FTE) workers in 2023 [ 1 ]. Similarly, the incidence rate of nonfatal occupational injury and illness in this industry is among the highest, with 4.0 reported cases per 100 FTE workers in 2023 [ 2 ]. The fatal and nonfatal agricultural injury rates of BLS have declined in recent decades, but this trend has plateaued in recent years. Additionally, national BLS nonfatal injury surveillance excludes self-employed producers and hired workers on small family farms, leaving a major gap in understanding the full burden of agricultural injuries in the U.S. [ 3 ]. Many studies have explored injuries and illnesses in agriculture; some of them address specific populations, such as youth, elderly, migrants, and immigrants, but studies of injury and illness among producers with military service are limited [ 4 – 7 ]. The United States Department of Agriculture (USDA) Census of Agriculture included questions about prior military service for the first time in 2017. In that year, 370,000 producers, accounting for 11% of total US producers, were military veterans or service members. Furthermore, 17% of all farms had one or more producers who had served or were currently serving in the U.S. military [ 8 ]. One-third (34%) of the farms operated by producers with military services specialized in cattle and dairy, which is largely concentrated in the central state region of the U.S. [ 9 ]. While military veterans make up a notable portion of agricultural employment and establish many new farming operations, they also face injury and illness risks as they transition into farming occupations. Studies suggest that veterans have poor overall health, health-related functional limitations, and chronic health conditions (e.g., cardiovascular disease, arthritis, cancer, depression, and anxiety) more frequently than civilians do [ 10 ]. Agricultural hazards can be compounded in veterans due to their existing health conditions. Rudolphi et al. [ 11 ] reported that veterans entering agriculture often encounter unique challenges, such as limited prior farming experience and inadequate safety training, which increase their vulnerability to workplace injuries. This underscores the need for targeted safety education and support to help veterans adapt safely to agricultural work. Many of them struggle with mental health issues after serving in the military while they enter another occupation where the environment is unpredictable and stressful. Posttraumatic stress disorder (PTSD) is one of the most prevalent mental disorders among U.S. veterans and is associated with an increased risk for several psychiatric conditions and suicidality [ 12 – 14 ]. Individuals with a history of mental health problems commonly abuse alcohol and other drugs to blunt the emotional pain they are experiencing. Meloni et al. [ 15 ] reported that 5.1% of the world’s injury and disease burden is attributed to alcohol consumption, as calculated by the Disability Adjusted Lost Life Years (DALY). Mental health issues and other psychological conditions can impact physical well-being and increase the risk of incidents and injuries. Individuals experiencing mental health challenges may be more prone to accidents because of impaired concentration and decision-making [ 16 ]. The goal of this study was to document the frequency and characteristics of injuries and work-related illnesses of producers with military service and compare the injury, demographic, work, and exposure characteristics of producers with and without prior military service using surveillance data. Methods Study population and design The Central States Center for Agricultural Safety and Health (CS-CASH) conducted surveys to gain a comprehensive understanding of the burden of occupational injury and illness in seven central US states: Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota [ 17 , 3 ]. This study used data on producer characteristics, injuries, illnesses and exposures from the 2023 Farm and Ranch Health and Safety Surveys (FRHSS). These surveys aim to identify key risk factors and inform targeted interventions to improve safety and health outcomes for agricultural workers in the region. A stratified random sample of farms (approximately 17,500; 2,500 per state) was drawn from the US Farm Data from their database. US Farm Data are a private agricultural information services company that has data for practically all farm enterprises in the US [ 18 ]. For inclusion, the farm size was limited to having 5,000 USD or greater estimated gross farm income to avoid marginally small enterprises. The sample included producers aged 19 and older, regardless of sex or race. The University of Nebraska Medical Center’s Institutional Review Board determined that this surveillance project is exempt under 45 CFR 46:104(d), Category 2, research that includes only survey procedures. (IRB # 0071-23-EX) Data collection The 2023 CS-CASH FRHSS survey form was modified from previous years to identify MS participants. The survey questions included information on general demographics, military-specific demographics, agricultural injuries, illnesses, exposures, and preventive practices for up to three producers (principal producers, producers 2, and 3). Military-specific demographics included ever serving in the military, branch of service, total years of service, and deployment status. In this study, producers with prior military service are referred to as “MS”, whereas those without such service are designated as “NoMS”. The injury questions addressed body parts injured, types of professional medical care received, lost farm/ranch worktime, out-of-pocket medical costs, and costs paid by insurance in dollars. These injury characteristic questions were asked only about the most serious injury during the past 12 months to make the survey brief. The first survey was mailed in June 2023, and a second reminder survey was sent to nonrespondents in November to increase the response rate. The survey form is available online [ 19 ]. The primary dependent variable (outcome) describing injury experience was measured by the following question: “How many farm-related injuries occurred to each producer during the past 12 months?”. “Injury” was defined as “the result of a sudden, unexpected, forceful event, which has an external cause and results in bodily damage or loss of consciousness”. "Farm-related" was defined as “work and leisure activities on this operation plus commuting, transport, and business trips for this operation”. Each producer could report 0, 1, 2, or 3 or more injuries, with "3 or more" being counted as three. The chronic health questions covered respiratory diseases, hearing loss, skin disorders, work-related strain symptoms, and musculoskeletal discomfort, along with information on exposure risks and protective measures used to prevent these conditions. The primary independent variable describing prior military service was measured through the following question: “Have you ever served on active duty in the U.S. armed forces?” Producers were able to respond yes or no. Other independent variables included demographic variables from the FRHSS survey data and farm production variables from data purchased from U.S. Farm Data. Individual-level factors included the following: producer’s age (aged 18–44, 45–64, and 65 years), sex (male, female), producer status (principal, 2nd, and 3rd ), primary occupation (farm/ranch, other), and percentage of total work time spent on the farm/ranch work (vs. off-farm work) (0–24%, 25–49%, 50–74%, 75–99%, and 100%, respectively). Farm-level independent variables included operation type, total acres, total farms owned, total livestock heads, and operation’s estimated revenue. Analyses were conducted from a producer-level dataset where each producer in operation had the same farm production data. Data analysis Descriptive analysis involved calculating injury counts, proportions, and rates for MS and NoMS producers in 2023. The injury proportions (%) were calculated by dividing the number of injured producers (with one or more injuries) by the number of all producers in the group, multiplied by 100. The injury rate was calculated by dividing the total number of injuries by the total number of producers in the group, multiplied by 100. Injury proportions and rates were calculated separately for MS and NoMS producers. Descriptive statistics were compiled to summarize the characteristics of the respondents, exposures, and outcomes separately for the MS and NoMS respondents. Injury and illness proportions were compared between the MS and NoMS groups via the chi-square test. Univariable and multivariable logistic regression analyses were used to compute odds ratios (ORs) and 95% confidence intervals (95% CIs) to identify significant risk factors associated with both acute and chronic health conditions. Results The 2023 FRHSS surveys were mailed to 17,497 farms and ranch operations, with one repeat mailing to nonrespondents. Usable responses were received from 1,768 operations, resulting in a response rate of 10.1%. The returned surveys contain data for 2,367 individual producers, 289 (12.2%) of whom have an MS background. A total of 98.6% (282) of the MS patients were male, 87% (254) were over 60 years old, and 48% of the NoMS patients were over 60 years old. Only 1.4% of the MS patients were female, whereas 16.5% of the NoMS patients were female. A statistically significant association was observed between gender and military service background ( Χ 2 = 45.89, p < 0.05) (Table 1 ). Among the MS participants, 93% (269) were primary producers, but only 43% (122) dedicated 100% of their work time to farming (vs. other occupations). In terms of operation type, 76% of the MS were involved in farming, 9% in ranching, and the rest in both. Approximately 60% of the respondents were from three central states: Nebraska (28.7%), Minnesota (16.3%), and Iowa (14.9%). Significant associations were identified between military service background and respondents’ age group, producer status, and type of operation, whereas no significant associations were observed with main occupation or percentage of time devoted to farming (p < 0.05) (Table 1 ). Table 1 Demographics and farm characteristics of military and nonmilitary agricultural producers, 2023 Characteristics Military background Chi-square (p-value) Demographics MS NoMS n = 289 (%) n = 2078 (%) Sex Male 282 (98.6) 1665 (83.4) 45.8 (< .001) Female 4 (1.4) 330 (16.5) Missing 86 Age group 35 years and younger 11 (3.9) 214 (10.6) 148.5 (< .001) 36–60 years 24 (8.3) 797 (39.5) More than 60 years 254 (87.8) 1003 (49.8) Missing 64 Main occupation Farm/ranch 231 (83.0) 1505 (79.1) 2.3 (0.128) Other 47 (16.9) 396 (20.8) Missing 188 Operator Primary 269 (93.0) 1462 (72.5) 56.8 (< .001) Secondary 16 (5.5) 452 (22.4) Tertiary 4 (1.3) 100 (4.9) Missing 64 Percentage time 100% 122 (43.4) 981 (51.0) 6.4 (0.168) 75–99% 37 (13.1) 209 (10.8) 50–74% 54 (19.2) 342 (17.7) 25–49% 32 (11.3) 169 (8.7) 0–24% 36 (12.8) 221 (11.5) Missing 164 Operation type Farm 173 (75.5) 1278 (70.6) 6.1 (0.045) Ranch 20 (8.7) 122 (6.7) Both 36 (15.7) 408 (22.5) Missing 330 State IA 43 (14.8) 356 (17.6) 14.4 (0.02) KS 26 (9.0) 218 (10.8) MN 47 (16.2) 298 (14.8) MO 33 (11.4) 356 (17.6) ND 23 (7.9) 252 (12.5) NE 83 (28.7) 430 (21.3) SD 34 (11.7) 271 (13.4) Missing Farm Characteristics Total acres 999 33 (11.5) 384 (19.1) Missing 78 Total livestock head 1 to 100 102 (60.7) 525 (45.9) 13.7 (0.001) 101 to 250 63 (37.5) 603 52.8) > 250 3 (1.7) 14 (1.2) Missing 1057 Estimated revenue 1 MM 50 (17.3) 517 (25.6) Missing 65 MS: Producers with prior military service NoMS: Producers without prior military service Approximately 60.7% (102) of these producers had a livestock headcount between 1 and 100, while the rest had more than 100. The majority (82.6%) reported estimated revenues of less than 1 million. Farm characteristics such as total acres, estimated revenue, and livestock had significant associations with military service background (p < 0.05) (Table 1 ). In our study, approximately 12% (289) of the producers had a military service background (MS). Among the total MS producers, 75% (230) served in the Army while remaining in the Air Force, Navy and Marines. More than 80% (260) reported a total service duration of six years or less. Fewer than 30% (142) indicated that they had been deployed during their period of service. Table 1 . Demographics and farm characteristics of military and nonmilitary agricultural producers, 2023. Occupational injuries Compared with the NoMS participants, the producers with MS had a lower injury rate (22.49 injuries/100 producers) (26.61/100 producers). When considering injuries that meet OSHA recordable criteria (i.e., injuries or illnesses causing one or more missed workdays), the injury rate for producers with MS was 8.99/100, whereas it was 10.58/100 for NoMS (Table 2 ). The differences were not statistically significant. Table 2 Farm injury counts and rates by military service status Total responses Total injured Total Injuries Injury Proportion Injury rate for all injury OSHA Recordable injury count and rate All Producers 2367 463 618 19.5% 26.1 246 (10.3) Without prior military service (NoMS) 2078 421 553 19.8% 26.6 220 (10.5) With prior military service (MS) 289 51 65 17.6% 22.4 26 (8.9) OSHA: Occupational Safety and Health Administration Table 2 . Farm injury counts and rates by military service status The most commonly injured body parts for both groups of producers were the arms/shoulders, legs/knees/hips, and back. For MS, the arms/shoulders were the top body parts injured, whereas for those NoMS, the legs/knees/hips were the most frequently injured (Fig. 1 ). Figure 1 . Distribution of body parts injured among producers The primary cause of injury for all producers was livestock, accounting for 24.5% of the cases. The second leading cause was machinery, followed by ground/floor/surface. Collectively, these three causes accounted for more than 50% of all injuries. Power tools were a significant source of injury, occurring three times more frequently among MSs than among NoMS. Conversely, tractor-related injuries were three times more common among NoMS (Fig. 2 ). Figure 2 . Distribution of injury causes among producers Among those with MS who sustained injuries, 54% sought doctor care, 24% required hospital care, and 22% received no professional medical care. Similarly, among the NoMS, 58% visited a doctor, 15% sought hospital care, and 27% did not need professional care. A greater proportion of MS participants (76%) sought some form of professional medical care than did NoMS participants (60%), but the difference was not statistically significant. In this study, medical costs include both out-of-pocket expenses and insurance-covered costs. The total medical cost for injuries amounts to $ 7,579,536 ( $ 2,572,908 out-of-pocket and $ 5,006,628 insurance paid). On average, the estimated medical cost per injury case was $ 13,718 for MS participants, whereas it was $ 16,904 for NoMS participants. Producers with military service (MS) reported higher rates of hearing loss (76.7% vs. 57.6%) and had 1.6 times greater odds of hearing impairment after adjustment for age and sex. Work strain was slightly less common among MS patients (37%) than among NoMS patients (41.8%), while MSD symptoms were similar (70%), although MS patients more frequently used preventive measures. Respiratory disease (27.8% vs. 22.5%) and skin conditions (33.1% vs. 21%) were more prevalent among MS patients, including higher rates of skin cancer (24.4% vs. 13.4%), with MS patients having 1.3 times the odds of any skin disease (Table 3 ). Table 3 Chronic health conditions in military and nonmilitary agricultural producers, 2023 Military Yes No Chi-square (p-value) OR a (95% CI) Hearing loss n % n % Yes 214 76.7 1126 57.6 37.1 (< .001) 1.6 (1.1–2.2) No 65 23.3 829 42.4 ref Work strain symptoms Yes 97 36.2 771 41.8 3.00 (0.08) 0.94 (0.6–1.3) No 171 63.8 1075 58.2 ref Musculoskeletal Disorder Yes 194 70.0 1350 69.8 0.004 (0.94) 0.8 (0.6–1.1) No 83 30.0 583 30.2 ref Respiratory disease Yes 75 27.8 431 22.6 3.6 (0.057) 1.2 (0.8–1.7) No 195 72.2 1480 77.4 ref Skin disease Yes 91 33.1 404 21.1 19.8 (< .001) 1.3 (0.9–1.8) No 184 66.9 1513 78.9 ref ORa: Adjusted Odds ratio CI: Confidence interval Table 3 . Chronic health conditions in military and nonmilitary agricultural producers, 2023 Occupational safety behaviors Hearing protection usage was slightly greater among MSs (63.8%) than among NoMS (59.55%). However, compared with their nonmilitary counterparts, MS patients tended to use respiratory protection and personal protective equipment (PPE) during chemical handling (Fig. 3 ). Significant associations were not identified between military service background and the respondents’ use of hearing protection, respiratory protection or PPE during chemical handling (p < 0.05). Figure 3 . Occupational safety behaviors Discussion This study provides critical insights into the demographics, injury patterns, and risk factors among producers with MS, a subpopulation that has received limited attention in farm safety research. The most notable findings in this study were as follows: Prior military experience may contribute to an elevated risk of auditory impairment in agricultural settings, potentially reflecting cumulative exposure to high noise levels in both occupations. The higher prevalence of skin disease among military producers suggests that prior service may contribute to increased dermatological vulnerability. Respiratory disease was reported more frequently among military agricultural producers, with the difference approaching statistical significance One notable finding is the greater prevalence of hearing loss, particularly moderate and severe hearing loss, among producers with military backgrounds than among their nonmilitary counterparts. Studies have shown that hearing loss and tinnitus are among the most common service-related disabilities among veterans. For example, the U.S. Department of Veterans Affairs (VA) highlights that hearing-related conditions are the most frequently reported service-connected disabilities [ 20 ]. According to the CDC [ 21 ], veterans are more likely to experience hearing difficulties than nonveterans are, and these issues often continue after their military service. Additionally, agricultural workers, including veterans, have a greater prevalence of hearing loss than does the general population, largely because of occupational noise exposure [ 22 , 23 ]. Veterans transitioning into agriculture may face an increased burden of noise exposure, further increasing their risk of severe hearing loss over time. Military personnel are often exposed to diverse environmental stressors, including extreme weather conditions, chemical agents, and prolonged use of protective gear, all of which may predispose individuals to chronic skin conditions. Following service, agricultural work may exacerbate these risks through regular contact with pesticides, solvents, fertilizers, and physical irritants such as dust, plants, and livestock. The combined exposure profile could contribute to the observed increase in skin disease among military producers. Military personnel are often exposed to respiratory hazards such as dust from deployment environments, burn pit emissions, chemical agents, and confined-space exposures, all of which have been linked to chronic respiratory conditions in veteran cohorts. In agricultural contexts, exposure to organic dust, animal dander, pesticides, and grain particulates further increases the risk of respiratory morbidity. The combination of preservice and postservice exposures may partly explain the elevated, although not statistically significant, prevalence observed among military producers. The incidence of injury was slightly lower among producers with military service (17.7%) than among those without military service (19.6%). While the difference was modest, several factors may have contributed to this pattern. Military training often emphasizes situational awareness, risk assessment, and adherence to safety protocols, which could translate into safer practices in agricultural work. In addition, the skills and discipline developed during service may foster greater resilience and hazard recognition in high-risk occupational environments. However, demographic factors also warrant consideration. The military producers in our study were disproportionately older and predominantly male, groups that may have different injury risk profiles than younger or more gender-diverse agricultural populations. The observed difference in injury rates may therefore partly reflect these demographic characteristics rather than military experience alone. Veterans often bring a strong sense of discipline, risk awareness, and technical expertise to their agricultural roles, which may lead to safer work practices and lower injury rates. Military training fosters skills such as situational awareness, strict adherence to protocols, and the ability to manage complex machinery in high-pressure situations—qualities that are highly applicable to farming [ 24 , 25 ]. Furthermore, veterans are more likely to be familiar with safe equipment and procedures, which can help minimize accidents [ 26 ]. The small difference in injury rates (17.65% vs. 19.59%) indicates that while veterans may have some advantages in terms of injury prevention, both groups remain exposed to significant risks in agricultural work. The study revealed that 88% of veteran farm workers are over 60 years old, whereas only 48% of nonveteran farm workers are over 60 years old. This difference may stem from veterans transitioning to farming later in life, often after completing their military service and possibly pursuing other careers first. Military service typically involves long-term commitment, and many veterans retire from active duty in their 40s or 50 s before choosing agriculture as a second career or retirement activity [ 27 ]. The U.S. Department of Veterans Affairs [ 28 ] reported that the median age of veterans is increasing, with a significant portion now in their 60s or older, which naturally results in an older demographic of veteran farm producers. In contrast, NoMS include a greater proportion of younger individuals, such as migrant workers, seasonal laborers, and those entering agriculture directly after high school or college, contributing to the lower average age in this group [ 29 , 30 ]. The analysis revealed that MS patients were more prone to specific types of outcomes, such as moderate to severe hearing loss and skin diseases, primarily skin cancer. In our study, 24% of the participants reported having skin cancer, whereas 13% reported having NoMS, highlighting the vulnerability of this group. Military personnel often experience significant sun exposure during training and deployment, which increases their risk of skin damage and skin cancer [ 31 ]. When veterans transition to agricultural work, they face additional UV exposure, creating a cumulative effect that increases their risk. Injuries caused by machinery, ATVs, power tools, and similar equipment were more common among producers with a military background than among those without. Additionally, body parts such as the arm/shoulder, leg/knee/hip, and eye/head/neck were injured more frequently among MS patients. This trend may be linked to the physically demanding nature of military training and service, which could increase susceptibility to chronic physical conditions or influence risk perception in agricultural environments [ 32 ]. Study limitations Given that the study was conducted among agricultural producers in the central region of the United States, the findings may not be generalizable to agricultural producers outside the surveillance area or those who hire agricultural workers in general. The sampling frame also excluded the smallest farms, below $ 5,000 in gross income. The response rate was low (10.1%), which could introduce response biases. In an earlier study of the 2018 survey round [ 33 ], the differences in farm characteristics between respondents and nonrespondents were small. Reliance on self-reported information regarding military service, injury and chronic health outcomes could introduce recall and other biases that we were unable to control. Another limitation is the sample size, particularly the small number of veteran producers, which limits the statistical power to detect differences in outcomes that are rare or where the group differences are relatively small. The study also does not differ by type, duration, or intensity of military service, which are factors that may significantly affect physical resilience and susceptibility to injury. Conclusions In conclusion, this study provides valuable insights into the injury characteristics of MS and NoMS agricultural producers, emphasizing the need for tailored safety interventions and improved access to healthcare. By addressing the unique challenges faced by the MS population, stakeholders can increase the well-being of veterans in agriculture and contribute to a safer and more sustainable agricultural sector. Military service is a risk factor for hearing loss, any skin disease, skin cancer, and respiratory disease. These conditions may increase when veterans transition to farming because of continued exposure to loud noise, chemicals, the sun, and air contaminants. Addressing these issues requires a multifaceted approach, including education, access to protective equipment, and regular health screenings. Targeted safety interventions and continued support for all farmers, including veterans, are essential to further reduce injury rates and promote occupational health. Abbreviations BLS Bureau of Labor Statistics CS-CASH Central States Center for Agricultural Safety and Health DALY Disability Adjusted Lost Life Years FRHSS Farm and Ranch Health and Safety Surveys FTE Full-time equivalent MS Producers with prior military service NoMS Producers without prior military service PTSD Posttraumatic stress disorder USDA United States Department of Agriculture VA United States Department of Veterans Affairs Declarations Ethics approval and consent to participate The University of Nebraska Medical Center’s Institutional Review Board determined that this surveillance project is exempt by under 45 CFR 46:104(d), Category 2, research that only includes survey procedures. No formal waiver of consent was provided by the University’s IRB because under IRB guidelines, a waiver is not required for projects deemed to be exempt from Human Subjects’ Research because they do not fall under the above-referenced regulations. (IRB # 0071-23-EX) Consent for publication Not applicable Competing interests The authors declare that they have no competing interests Funding This research was funded by the CDC/National Institute for Occupational Safety and Health, grant number U54OH010162. Author Contribution Suraj Adhikari (SA), Risto Rautiainen (RR), Cheryl Beseler (CB)The conceptualization and study design were from SA, intellectually supported by RR. SA, RR and CB equally contributed to formal analysis and interpretation of the data. Methodology for the manuscript was developed by SA and reviewed and approved by RR and CB. The initial draft was prepared by SA and critically reviewed and revised by CB and RR. SA completed data curation, calculations, and reconciliation of any errors or differences in interpretations. RR was responsible for project administration and funding acquisition.Conceptualization and Study design, RR, SA; Methodology, RR, SA, CB; Formal Analysis, SA, RR; Resources, RR; Data Curation, SA; Writing – Original Draft Preparation, SA; Writing – Review & Editing, RR, CB; Visualization, SA; Supervision, RR and CB; Project Administration, RR; Funding Acquisition, RR Acknowledgement This study was conducted as part of the Central States Center for Agricultural Safety and Health (CS-CASH) Surveillance project, funded by the Centers for Disease Control and Prevention (CDC) cooperative agreement award. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Bureau of Labor Statistics. Census of Fatal Occupational Injuries Summary. 2023. Washington, DC: U.S. Department of Labor; 2025. Available from: https://www.bls.gov/iif/fatal-injuries-tables.htm Bureau of Labor Statistics. 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Moore K, Lowe BD, Krieg EF, Brandt V. Hearing loss and hearing protector use among US farmers. J Occup Environ Hyg. 2022;19(1):19–29. Chazkel A. Valuing Veteran Skills Post-Military Service. America Succeeds; 2023. Available from: https://americasucceeds.org/valuing-veteran-skills-post-military-service Hamilton SL. Entrepreneurship Strategies for Reintegrating African American Male US Military Veterans After Military Service. Doctoral dissertation, Walden University; 2024. Stempak J. Safety Professionals Share Lessons Learned from Their Time in the Military. EHS Today; 2024. Available from: https://www.ehstoday.com/safety-leadership/article/55241880/safety-professionals-share-lessons-learned-from-their-time-in-the-military Hartal Y, Shor R, Glick SM. From battlefield to farm: a program for the rehabilitation of disabled veterans through agriculture. Isr J Psychiatry Relat Sci. 2015;52(1):15–21. U.S. Department of Veterans Affairs. (2021). Annual benefits report: Service-connected disabilities. 2021. Available from: https://www.benefits.va.gov/REPORTS/abr/docs/2021_compensation.pdf U.S. Department of Agriculture. 2022 Census of Agriculture: Producers with Military Service. 2022. Available from: https://www.nass.usda.gov/Publications/AgCensus/2022/Full_Report/Volume_1,_Chapter_1_US/ U.S. Department of Agriculture. 2022 Census of Agriculture: Farm Producers. 2022. Available from: https://www.nass.usda.gov/Publications/AgCensus/2022/Full_Report/Volume_1,_Chapter_1_US/ Rezaei SJ, Kim J, Onyeka S, Swetter SM, Weinstock MA, Asch SM, Linos E. Skin cancer and other dermatologic conditions among US veterans. JAMA dermatology. 2024;160(10):1107–11. Williamson ML, Stover RS, Kerns RD, et al. Chronic pain in OEF/OIF/OND veterans: a systematic review. Pain Med. 2019;20(6):1068–77. Beseler CL, Rautiainen RH. Assessing nonresponse bias in farm injury surveillance data. J Agric Saf Health. 2021;27(4):215–27. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Dec, 2025 Reviews received at journal 12 Nov, 2025 Reviews received at journal 03 Nov, 2025 Reviews received at journal 28 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviewers agreed at journal 18 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 12 Oct, 2025 Editor invited by journal 07 Oct, 2025 Editor assigned by journal 05 Oct, 2025 Submission checks completed at journal 05 Oct, 2025 First submitted to journal 29 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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15:12:48","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104555,"visible":true,"origin":"","legend":"","description":"","filename":"ac5582074d3b475981f99f6f77852b4e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7743020/v1/ec5bcd41b14b5eb28a6a99d5.xml"},{"id":94464804,"identity":"c1d4ee3a-04a6-41a4-b112-bbd54c46f1f0","added_by":"auto","created_at":"2025-10-27 15:13:36","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116686,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7743020/v1/4c586993c1d48228e1193301.html"},{"id":94464695,"identity":"0a7473d9-52ff-45da-bf11-f24c1ba43ff3","added_by":"auto","created_at":"2025-10-27 15:12:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201265,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of body parts injured among producers\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7743020/v1/5c709e93c8379586c94e76c5.jpg"},{"id":94464808,"identity":"3eceaa87-0eb0-4f75-b7ff-29ce12916bb7","added_by":"auto","created_at":"2025-10-27 15:13:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":194092,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of injury causes among producers\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7743020/v1/cb1f709a5de4eb91a5e8de27.jpg"},{"id":94464741,"identity":"b5f4f3ed-9012-41f0-80b5-bce3022551d2","added_by":"auto","created_at":"2025-10-27 15:13:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":220905,"visible":true,"origin":"","legend":"\u003cp\u003eOccupational safety behaviors\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMSD: Musculoskeletal Disorders\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7743020/v1/80c137f11a5638a3bb8d8ace.jpg"},{"id":94473363,"identity":"a9109378-10f2-4d38-b60d-47e05aff1482","added_by":"auto","created_at":"2025-10-27 15:44:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1480680,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7743020/v1/6f6ecd42-19bf-4849-ae25-931792ea4074.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Occupational Injury and Illness in Farmers with Prior Military Service","fulltext":[{"header":"Background","content":"\u003cp\u003eAgriculture ranks among the most hazardous industries worldwide. According to the Bureau of Labor Statistics (BLS), farming, fishing, and forestry occupations had the highest fatality rate of 20.3 per 100,000 full-time equivalent (FTE) workers in 2023 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Similarly, the incidence rate of nonfatal occupational injury and illness in this industry is among the highest, with 4.0 reported cases per 100 FTE workers in 2023 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The fatal and nonfatal agricultural injury rates of BLS have declined in recent decades, but this trend has plateaued in recent years. Additionally, national BLS nonfatal injury surveillance excludes self-employed producers and hired workers on small family farms, leaving a major gap in understanding the full burden of agricultural injuries in the U.S. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMany studies have explored injuries and illnesses in agriculture; some of them address specific populations, such as youth, elderly, migrants, and immigrants, but studies of injury and illness among producers with military service are limited [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The United States Department of Agriculture (USDA) Census of Agriculture included questions about prior military service for the first time in 2017. In that year, 370,000 producers, accounting for 11% of total US producers, were military veterans or service members. Furthermore, 17% of all farms had one or more producers who had served or were currently serving in the U.S. military [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. One-third (34%) of the farms operated by producers with military services specialized in cattle and dairy, which is largely concentrated in the central state region of the U.S. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile military veterans make up a notable portion of agricultural employment and establish many new farming operations, they also face injury and illness risks as they transition into farming occupations. Studies suggest that veterans have poor overall health, health-related functional limitations, and chronic health conditions (e.g., cardiovascular disease, arthritis, cancer, depression, and anxiety) more frequently than civilians do [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Agricultural hazards can be compounded in veterans due to their existing health conditions.\u003c/p\u003e\u003cp\u003eRudolphi et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] reported that veterans entering agriculture often encounter unique challenges, such as limited prior farming experience and inadequate safety training, which increase their vulnerability to workplace injuries. This underscores the need for targeted safety education and support to help veterans adapt safely to agricultural work. Many of them struggle with mental health issues after serving in the military while they enter another occupation where the environment is unpredictable and stressful. Posttraumatic stress disorder (PTSD) is one of the most prevalent mental disorders among U.S. veterans and is associated with an increased risk for several psychiatric conditions and suicidality [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Individuals with a history of mental health problems commonly abuse alcohol and other drugs to blunt the emotional pain they are experiencing. Meloni et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] reported that 5.1% of the world\u0026rsquo;s injury and disease burden is attributed to alcohol consumption, as calculated by the Disability Adjusted Lost Life Years (DALY). Mental health issues and other psychological conditions can impact physical well-being and increase the risk of incidents and injuries. Individuals experiencing mental health challenges may be more prone to accidents because of impaired concentration and decision-making [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe goal of this study was to document the frequency and characteristics of injuries and work-related illnesses of producers with military service and compare the injury, demographic, work, and exposure characteristics of producers with and without prior military service using surveillance data.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population and design\u003c/h2\u003e\u003cp\u003eThe Central States Center for Agricultural Safety and Health (CS-CASH) conducted surveys to gain a comprehensive understanding of the burden of occupational injury and illness in seven central US states: Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This study used data on producer characteristics, injuries, illnesses and exposures from the 2023 Farm and Ranch Health and Safety Surveys (FRHSS). These surveys aim to identify key risk factors and inform targeted interventions to improve safety and health outcomes for agricultural workers in the region.\u003c/p\u003e\u003cp\u003eA stratified random sample of farms (approximately 17,500; 2,500 per state) was drawn from the US Farm Data from their database. US Farm Data are a private agricultural information services company that has data for practically all farm enterprises in the US [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For inclusion, the farm size was limited to having 5,000 USD or greater estimated gross farm income to avoid marginally small enterprises. The sample included producers aged 19 and older, regardless of sex or race.\u003c/p\u003e\u003cp\u003e The University of Nebraska Medical Center\u0026rsquo;s Institutional Review Board determined that this surveillance project is exempt under 45 CFR 46:104(d), Category 2, research that includes only survey procedures. (IRB # 0071-23-EX)\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe 2023 CS-CASH FRHSS survey form was modified from previous years to identify MS participants. The survey questions included information on general demographics, military-specific demographics, agricultural injuries, illnesses, exposures, and preventive practices for up to three producers (principal producers, producers 2, and 3). Military-specific demographics included ever serving in the military, branch of service, total years of service, and deployment status. In this study, producers with prior military service are referred to as \u0026ldquo;MS\u0026rdquo;, whereas those without such service are designated as \u0026ldquo;NoMS\u0026rdquo;. The injury questions addressed body parts injured, types of professional medical care received, lost farm/ranch worktime, out-of-pocket medical costs, and costs paid by insurance in dollars. These injury characteristic questions were asked only about the most serious injury during the past 12 months to make the survey brief. The first survey was mailed in June 2023, and a second reminder survey was sent to nonrespondents in November to increase the response rate. The survey form is available online [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe primary dependent variable (outcome) describing injury experience was measured by the following question: \u0026ldquo;How many farm-related injuries occurred to each producer during the past 12 months?\u0026rdquo;. \u0026ldquo;Injury\u0026rdquo; was defined as \u0026ldquo;the result of a sudden, unexpected, forceful event, which has an external cause and results in bodily damage or loss of consciousness\u0026rdquo;. \"Farm-related\" was defined as \u0026ldquo;work and leisure activities on this operation plus commuting, transport, and business trips for this operation\u0026rdquo;. Each producer could report 0, 1, 2, or 3 or more injuries, with \"3 or more\" being counted as three.\u003c/p\u003e\u003cp\u003eThe chronic health questions covered respiratory diseases, hearing loss, skin disorders, work-related strain symptoms, and musculoskeletal discomfort, along with information on exposure risks and protective measures used to prevent these conditions.\u003c/p\u003e\u003cp\u003eThe primary independent variable describing prior military service was measured through the following question: \u0026ldquo;Have you ever served on active duty in the U.S. armed forces?\u0026rdquo; Producers were able to respond yes or no. Other independent variables included demographic variables from the FRHSS survey data and farm production variables from data purchased from U.S. Farm Data. Individual-level factors included the following: producer\u0026rsquo;s age (aged 18\u0026ndash;44, 45\u0026ndash;64, and 65 years), sex (male, female), producer status (principal, 2nd, and 3rd ), primary occupation (farm/ranch, other), and percentage of total work time spent on the farm/ranch work (vs. off-farm work) (0\u0026ndash;24%, 25\u0026ndash;49%, 50\u0026ndash;74%, 75\u0026ndash;99%, and 100%, respectively). Farm-level independent variables included operation type, total acres, total farms owned, total livestock heads, and operation\u0026rsquo;s estimated revenue. Analyses were conducted from a producer-level dataset where each producer in operation had the same farm production data.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eDescriptive analysis involved calculating injury counts, proportions, and rates for MS and NoMS producers in 2023. The injury proportions (%) were calculated by dividing the number of injured producers (with one or more injuries) by the number of all producers in the group, multiplied by 100. The injury rate was calculated by dividing the total number of injuries by the total number of producers in the group, multiplied by 100. Injury proportions and rates were calculated separately for MS and NoMS producers. Descriptive statistics were compiled to summarize the characteristics of the respondents, exposures, and outcomes separately for the MS and NoMS respondents.\u003c/p\u003e\u003cp\u003eInjury and illness proportions were compared between the MS and NoMS groups via the chi-square test. Univariable and multivariable logistic regression analyses were used to compute odds ratios (ORs) and 95% confidence intervals (95% CIs) to identify significant risk factors associated with both acute and chronic health conditions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe 2023 FRHSS surveys were mailed to 17,497 farms and ranch operations, with one repeat mailing to nonrespondents. Usable responses were received from 1,768 operations, resulting in a response rate of 10.1%. The returned surveys contain data for 2,367 individual producers, 289 (12.2%) of whom have an MS background.\u003c/p\u003e\u003cp\u003eA total of 98.6% (282) of the MS patients were male, 87% (254) were over 60 years old, and 48% of the NoMS patients were over 60 years old. Only 1.4% of the MS patients were female, whereas 16.5% of the NoMS patients were female. A statistically significant association was observed between gender and military service background (\u003cem\u003eΧ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;45.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the MS participants, 93% (269) were primary producers, but only 43% (122) dedicated 100% of their work time to farming (vs. other occupations). In terms of operation type, 76% of the MS were involved in farming, 9% in ranching, and the rest in both. Approximately 60% of the respondents were from three central states: Nebraska (28.7%), Minnesota (16.3%), and Iowa (14.9%). Significant associations were identified between military service background and respondents\u0026rsquo; age group, producer status, and type of operation, whereas no significant associations were observed with main occupation or percentage of time devoted to farming (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographics and farm characteristics of military and nonmilitary agricultural producers, 2023\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMilitary background\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChi-square (p-value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNoMS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;289 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;2078 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e282 (98.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1665 (83.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.8 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e330 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35 years and younger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e214 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e148.5 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36\u0026ndash;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e797 (39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than 60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e254 (87.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1003 (49.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMain occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm/ranch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e231 (83.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1505 (79.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.3 (0.128)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e396 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269 (93.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1462 (72.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.8 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e452 (22.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercentage time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122 (43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e981 (51.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.4 (0.168)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e75\u0026ndash;99%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e209 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;74%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e342 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;49%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e169 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;24%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e221 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperation type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173 (75.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1278 (70.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.1 (0.045)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRanch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e408 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e356 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.4 (0.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e218 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e298 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e356 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e252 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e430 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e271 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal acres\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (37.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e666 (33.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.9 (0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e250\u0026ndash;999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 (50.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e954 (47.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e384 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal livestock head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 to 100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102 (60.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e525 (45.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.7 (0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e101 to 250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e603 52.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstimated revenue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;400K\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e123 (42.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e728 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.1 (0.006)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e400K to 1MM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115 (39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e769 (28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1 MM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e517 (25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eMS: Producers with prior military service\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNoMS: Producers without prior military service\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eApproximately 60.7% (102) of these producers had a livestock headcount between 1 and 100, while the rest had more than 100. The majority (82.6%) reported estimated revenues of less than 1\u0026nbsp;million. Farm characteristics such as total acres, estimated revenue, and livestock had significant associations with military service background (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, approximately 12% (289) of the producers had a military service background (MS). Among the total MS producers, 75% (230) served in the Army while remaining in the Air Force, Navy and Marines. More than 80% (260) reported a total service duration of six years or less. Fewer than 30% (142) indicated that they had been deployed during their period of service.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eDemographics and farm characteristics of military and nonmilitary agricultural producers, 2023.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eOccupational injuries\u003c/h3\u003e\n\u003cp\u003eCompared with the NoMS participants, the producers with MS had a lower injury rate (22.49 injuries/100 producers) (26.61/100 producers). When considering injuries that meet OSHA recordable criteria (i.e., injuries or illnesses causing one or more missed workdays), the injury rate for producers with MS was 8.99/100, whereas it was 10.58/100 for NoMS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The differences were not statistically significant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFarm injury counts and rates by military service status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTotal responses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal injured\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eTotal Injuries\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eInjury Proportion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eInjury rate for all injury\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOSHA Recordable\u003c/p\u003e\u003cp\u003einjury count\u003c/p\u003e\u003cp\u003eand rate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAll Producers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e19.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e246 (10.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWithout prior military service (NoMS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e19.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e220 (10.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWith prior military service (MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e17.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e26 (8.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eOSHA: Occupational Safety and Health Administration\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cem\u003eFarm injury counts and rates by military service status\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe most commonly injured body parts for both groups of producers were the arms/shoulders, legs/knees/hips, and back. For MS, the arms/shoulders were the top body parts injured, whereas for those NoMS, the legs/knees/hips were the most frequently injured (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eDistribution of body parts injured among producers\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe primary cause of injury for all producers was livestock, accounting for 24.5% of the cases. The second leading cause was machinery, followed by ground/floor/surface. Collectively, these three causes accounted for more than 50% of all injuries. Power tools were a significant source of injury, occurring three times more frequently among MSs than among NoMS. Conversely, tractor-related injuries were three times more common among NoMS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cem\u003eDistribution of injury causes among producers\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAmong those with MS who sustained injuries, 54% sought doctor care, 24% required hospital care, and 22% received no professional medical care. Similarly, among the NoMS, 58% visited a doctor, 15% sought hospital care, and 27% did not need professional care. A greater proportion of MS participants (76%) sought some form of professional medical care than did NoMS participants (60%), but the difference was not statistically significant.\u003c/p\u003e\u003cp\u003eIn this study, medical costs include both out-of-pocket expenses and insurance-covered costs. The total medical cost for injuries amounts to \u003cspan\u003e$\u003c/span\u003e7,579,536 (\u003cspan\u003e$\u003c/span\u003e2,572,908 out-of-pocket and \u003cspan\u003e$\u003c/span\u003e5,006,628 insurance paid). On average, the estimated medical cost per injury case was \u003cspan\u003e$\u003c/span\u003e13,718 for MS participants, whereas it was \u003cspan\u003e$\u003c/span\u003e16,904 for NoMS participants.\u003c/p\u003e\u003cp\u003eProducers with military service (MS) reported higher rates of hearing loss (76.7% vs. 57.6%) and had 1.6 times greater odds of hearing impairment after adjustment for age and sex. Work strain was slightly less common among MS patients (37%) than among NoMS patients (41.8%), while MSD symptoms were similar (70%), although MS patients more frequently used preventive measures. Respiratory disease (27.8% vs. 22.5%) and skin conditions (33.1% vs. 21%) were more prevalent among MS patients, including higher rates of skin cancer (24.4% vs. 13.4%), with MS patients having 1.3 times the odds of any skin disease (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChronic health conditions in military and nonmilitary agricultural producers, 2023\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eMilitary\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eChi-square\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(p-value)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003csub\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHearing loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.1 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.6 (1.1\u0026ndash;2.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWork strain symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.00 (0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.94 (0.6\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMusculoskeletal Disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004 (0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8 (0.6\u0026ndash;1.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.6 (0.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.2 (0.8\u0026ndash;1.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkin disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.8 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.3 (0.9\u0026ndash;1.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eORa: Adjusted Odds ratio\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eCI: Confidence interval\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cem\u003eChronic health conditions in military and nonmilitary agricultural producers, 2023\u003c/em\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eOccupational safety behaviors\u003c/h2\u003e\u003cp\u003eHearing protection usage was slightly greater among MSs (63.8%) than among NoMS (59.55%). However, compared with their nonmilitary counterparts, MS patients tended to use respiratory protection and personal protective equipment (PPE) during chemical handling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Significant associations were not identified between military service background and the respondents\u0026rsquo; use of hearing protection, respiratory protection or PPE during chemical handling (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cem\u003eOccupational safety behaviors\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides critical insights into the demographics, injury patterns, and risk factors among producers with MS, a subpopulation that has received limited attention in farm safety research. The most notable findings in this study were as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePrior military experience may contribute to an elevated risk of auditory impairment in agricultural settings, potentially reflecting cumulative exposure to high noise levels in both occupations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe higher prevalence of skin disease among military producers suggests that prior service may contribute to increased dermatological vulnerability.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRespiratory disease was reported more frequently among military agricultural producers, with the difference approaching statistical significance\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eOne notable finding is the greater prevalence of hearing loss, particularly moderate and severe hearing loss, among producers with military backgrounds than among their nonmilitary counterparts. Studies have shown that hearing loss and tinnitus are among the most common service-related disabilities among veterans. For example, the U.S. Department of Veterans Affairs (VA) highlights that hearing-related conditions are the most frequently reported service-connected disabilities [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. According to the CDC [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], veterans are more likely to experience hearing difficulties than nonveterans are, and these issues often continue after their military service. Additionally, agricultural workers, including veterans, have a greater prevalence of hearing loss than does the general population, largely because of occupational noise exposure [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Veterans transitioning into agriculture may face an increased burden of noise exposure, further increasing their risk of severe hearing loss over time.\u003c/p\u003e\u003cp\u003eMilitary personnel are often exposed to diverse environmental stressors, including extreme weather conditions, chemical agents, and prolonged use of protective gear, all of which may predispose individuals to chronic skin conditions. Following service, agricultural work may exacerbate these risks through regular contact with pesticides, solvents, fertilizers, and physical irritants such as dust, plants, and livestock. The combined exposure profile could contribute to the observed increase in skin disease among military producers.\u003c/p\u003e\u003cp\u003eMilitary personnel are often exposed to respiratory hazards such as dust from deployment environments, burn pit emissions, chemical agents, and confined-space exposures, all of which have been linked to chronic respiratory conditions in veteran cohorts. In agricultural contexts, exposure to organic dust, animal dander, pesticides, and grain particulates further increases the risk of respiratory morbidity. The combination of preservice and postservice exposures may partly explain the elevated, although not statistically significant, prevalence observed among military producers.\u003c/p\u003e\u003cp\u003eThe incidence of injury was slightly lower among producers with military service (17.7%) than among those without military service (19.6%). While the difference was modest, several factors may have contributed to this pattern. Military training often emphasizes situational awareness, risk assessment, and adherence to safety protocols, which could translate into safer practices in agricultural work. In addition, the skills and discipline developed during service may foster greater resilience and hazard recognition in high-risk occupational environments. However, demographic factors also warrant consideration. The military producers in our study were disproportionately older and predominantly male, groups that may have different injury risk profiles than younger or more gender-diverse agricultural populations. The observed difference in injury rates may therefore partly reflect these demographic characteristics rather than military experience alone.\u003c/p\u003e\u003cp\u003eVeterans often bring a strong sense of discipline, risk awareness, and technical expertise to their agricultural roles, which may lead to safer work practices and lower injury rates. Military training fosters skills such as situational awareness, strict adherence to protocols, and the ability to manage complex machinery in high-pressure situations\u0026mdash;qualities that are highly applicable to farming [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, veterans are more likely to be familiar with safe equipment and procedures, which can help minimize accidents [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The small difference in injury rates (17.65% vs. 19.59%) indicates that while veterans may have some advantages in terms of injury prevention, both groups remain exposed to significant risks in agricultural work.\u003c/p\u003e\u003cp\u003eThe study revealed that 88% of veteran farm workers are over 60 years old, whereas only 48% of nonveteran farm workers are over 60 years old. This difference may stem from veterans transitioning to farming later in life, often after completing their military service and possibly pursuing other careers first. Military service typically involves long-term commitment, and many veterans retire from active duty in their 40s or 50 s before choosing agriculture as a second career or retirement activity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The U.S. Department of Veterans Affairs [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] reported that the median age of veterans is increasing, with a significant portion now in their 60s or older, which naturally results in an older demographic of veteran farm producers. In contrast, NoMS include a greater proportion of younger individuals, such as migrant workers, seasonal laborers, and those entering agriculture directly after high school or college, contributing to the lower average age in this group [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe analysis revealed that MS patients were more prone to specific types of outcomes, such as moderate to severe hearing loss and skin diseases, primarily skin cancer. In our study, 24% of the participants reported having skin cancer, whereas 13% reported having NoMS, highlighting the vulnerability of this group. Military personnel often experience significant sun exposure during training and deployment, which increases their risk of skin damage and skin cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. When veterans transition to agricultural work, they face additional UV exposure, creating a cumulative effect that increases their risk.\u003c/p\u003e\u003cp\u003eInjuries caused by machinery, ATVs, power tools, and similar equipment were more common among producers with a military background than among those without. Additionally, body parts such as the arm/shoulder, leg/knee/hip, and eye/head/neck were injured more frequently among MS patients. This trend may be linked to the physically demanding nature of military training and service, which could increase susceptibility to chronic physical conditions or influence risk perception in agricultural environments [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStudy limitations\u003c/h3\u003e\n\u003cp\u003eGiven that the study was conducted among agricultural producers in the central region of the United States, the findings may not be generalizable to agricultural producers outside the surveillance area or those who hire agricultural workers in general. The sampling frame also excluded the smallest farms, below \u003cspan\u003e$\u003c/span\u003e5,000 in gross income. The response rate was low (10.1%), which could introduce response biases. In an earlier study of the 2018 survey round [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], the differences in farm characteristics between respondents and nonrespondents were small. Reliance on self-reported information regarding military service, injury and chronic health outcomes could introduce recall and other biases that we were unable to control. Another limitation is the sample size, particularly the small number of veteran producers, which limits the statistical power to detect differences in outcomes that are rare or where the group differences are relatively small. The study also does not differ by type, duration, or intensity of military service, which are factors that may significantly affect physical resilience and susceptibility to injury.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study provides valuable insights into the injury characteristics of MS and NoMS agricultural producers, emphasizing the need for tailored safety interventions and improved access to healthcare. By addressing the unique challenges faced by the MS population, stakeholders can increase the well-being of veterans in agriculture and contribute to a safer and more sustainable agricultural sector. Military service is a risk factor for hearing loss, any skin disease, skin cancer, and respiratory disease. These conditions may increase when veterans transition to farming because of continued exposure to loud noise, chemicals, the sun, and air contaminants. Addressing these issues requires a multifaceted approach, including education, access to protective equipment, and regular health screenings. Targeted safety interventions and continued support for all farmers, including veterans, are essential to further reduce injury rates and promote occupational health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBLS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBureau of Labor Statistics\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCS-CASH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCentral States Center for Agricultural Safety and Health\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDALY\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisability Adjusted Lost Life Years\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFRHSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFarm and Ranch Health and Safety Surveys\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFTE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFull-time equivalent\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProducers with prior military service\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNoMS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProducers without prior military service\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePTSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePosttraumatic stress disorder\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUSDA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnited States Department of Agriculture\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnited States Department of Veterans Affairs\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e The University of Nebraska Medical Center\u0026rsquo;s Institutional Review Board determined that this surveillance project is exempt by under 45 CFR 46:104(d), Category 2, research that only includes survey procedures. No formal waiver of consent was provided by the University\u0026rsquo;s IRB because under IRB guidelines, a waiver is not required for projects deemed to be exempt from Human Subjects\u0026rsquo; Research because they do not fall under the above-referenced regulations. (IRB # 0071-23-EX)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was funded by the CDC/National Institute for Occupational Safety and Health, grant number U54OH010162.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSuraj Adhikari (SA), Risto Rautiainen (RR), Cheryl Beseler (CB)The conceptualization and study design were from SA, intellectually supported by RR. SA, RR and CB equally contributed to formal analysis and interpretation of the data. Methodology for the manuscript was developed by SA and reviewed and approved by RR and CB. The initial draft was prepared by SA and critically reviewed and revised by CB and RR. SA completed data curation, calculations, and reconciliation of any errors or differences in interpretations. RR was responsible for project administration and funding acquisition.Conceptualization and Study design, RR, SA; Methodology, RR, SA, CB; Formal Analysis, SA, RR; Resources, RR; Data Curation, SA; Writing \u0026ndash; Original Draft Preparation, SA; Writing \u0026ndash; Review \u0026amp; Editing, RR, CB; Visualization, SA; Supervision, RR and CB; Project Administration, RR; Funding Acquisition, RR\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was conducted as part of the Central States Center for Agricultural Safety and Health (CS-CASH) Surveillance project, funded by the Centers for Disease Control and Prevention (CDC) cooperative agreement award.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBureau of Labor Statistics. Census of Fatal Occupational Injuries Summary. 2023. Washington, DC: U.S. Department of Labor; 2025. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bls.gov/iif/fatal-injuries-tables.htm\u003c/span\u003e\u003cspan address=\"https://www.bls.gov/iif/fatal-injuries-tables.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBureau of Labor Statistics. Employer-Reported Workplace Injuries and Illnesses, Summary. 2023. Washington, DC: U.S. Department of Labor; 2025. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bls.gov/iif/nonfatal-injuries-and-illnesses-tables.htm\u003c/span\u003e\u003cspan address=\"https://www.bls.gov/iif/nonfatal-injuries-and-illnesses-tables.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdhikari S, Rautiainen R, Ramos AK, Earle-Richardson G. Limitations of the Bureau of Labor Statistics surveillance in capturing nonfatal injuries among self-employed agricultural producers. Am J Ind Med. 2024;67(1):45\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArcury TA, O\u0026rsquo;Hara H, Grzywacz JG, Isom S, Chen H, Quandt SA. Work safety climate, musculoskeletal discomfort, working while injured, and depression among migrant farmworkers in North Carolina. Am J Public Health. 2012;102(S2):S272\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeWit Y, Pickett W, Lawson J, Dosman J, Saskatchewan Farm Injury Cohort Team. Farm activities and agricultural injuries in youth and young adult workers. J Agromedicine. 2015;20(3):318\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKearney GD, Rodriguez G, Quandt SA, Arcury JT, Arcury TA. Work safety climate, safety behaviors, and occupational injuries of youth farmworkers in North Carolina. Am J Public Health. 2015;105(7):1336\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamos AK. Exploring injuries among cattle feedyard workers. J Agric Saf Health. 2021;27(2):63\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eU.S. Department of Agriculture. 2017 Census of Agriculture: Producers with Military Service Highlights. 2020. 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Am J Prev Med. 2012;42(5):473\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRudolphi JM, Berg RL, Rohlman DS. Occupational injuries and health among young beginning farmers with and without disabilities. J Agric Saf Health. 2020;26(1):45\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichardson LK, Frueh BC, Acierno R. Prevalence estimates of combat-related posttraumatic stress disorder: critical review. Aust N Z J Psychiatry. 2010;44(1):4\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeal KH, Bertenthal D, Miner CR, Sen S, Marmar C. Bringing the war back home: mental health disorders among 103,788 US veterans returning from Iraq and Afghanistan seen at Department of Veterans Affairs facilities. Arch Intern Med. 2007;167(5):476\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWisco BE, Marx BP, Wolf EJ, Miller MW, Southwick SM, Pietrzak RH. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.usfarmdata.com/\u003c/span\u003e\u003cspan address=\"https://www.usfarmdata.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCentral States Center for Agricultural Safety and Health (CS-CASH). Farm and Ranch Health and Safety Survey. 2025. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unmc.edu/publichealth/cscash/_documents/_research/research-farm-ranch-safety-survey.pdf\u003c/span\u003e\u003cspan address=\"https://www.unmc.edu/publichealth/cscash/_documents/_research/research-farm-ranch-safety-survey.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eU.S. Department of Veterans Affairs. Annual Benefits Report: Fiscal Year 2021. 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J Agric Saf Health. 2021;27(4):215\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Agricultural Safety, Occupational Injuries, Veterans, Military Service, Epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-7743020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7743020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eA significant portion of U.S. agricultural producers have served in the military. This population may face unique occupational health challenges. This study describes and compares occupational injury and illness outcomes of agricultural producers with and without prior military service.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData from the 2023 Farm and Ranch Health and Safety Survey (FRHSS) were analyzed, including 2,367 producers from seven central U.S. states. Producers were categorized as having prior military service (MS, n\u0026thinsp;=\u0026thinsp;289) or no prior military service (NoMS, n\u0026thinsp;=\u0026thinsp;2078). Descriptive statistics, chi-square tests, and logistic regression were used to compare demographics, farm characteristics, injury rates and characteristics, and chronic health conditions between the groups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe MS group was predominantly male (98.6%) and older (87.9% \u0026gt;60 years) compared to the NoMS group (83.5% male, 49.8% \u0026gt;60 years). The overall injury proportion was lower for MS producers (17.65%) than for NoMS producers (19.83%), though not statistically significant. When adjusted for age and sex, MS producers had higher odds of hearing loss (OR\u0026thinsp;=\u0026thinsp;1.6, 95% CI: 1.1\u0026ndash;2.2) and skin disease (OR\u0026thinsp;=\u0026thinsp;1.3, 95% CI: 0.9\u0026ndash;1.8), including skin cancer (24.4% vs. 13.4%). The primary cause of injury for all producers was livestock. MS producers had a higher proportion of injuries involving power tools, while NoMS producers had more tractor-related injuries.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eAgricultural producers with prior military service exhibit a distinct demographic profile and experience a significant burden of specific chronic health conditions, particularly hearing loss and skin disorders. While their overall injury rate was slightly lower, targeted safety interventions addressing their unique health vulnerabilities and promoting the use of protective equipment are essential to improving their long-term occupational health outcomes.\u003c/p\u003e","manuscriptTitle":"Occupational Injury and Illness in Farmers with Prior Military Service","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 13:02:21","doi":"10.21203/rs.3.rs-7743020/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-19T15:34:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-12T14:56:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T03:41:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T02:48:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78797721471218913225374506516829159876","date":"2025-10-20T05:31:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185423419135334558572325264646756577252","date":"2025-10-19T23:28:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175288303640295728010189353660606279151","date":"2025-10-18T15:10:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17107710702445434474319471556773999804","date":"2025-10-14T10:47:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T03:23:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T09:10:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T00:31:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-06T00:31:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-29T14:09:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99c0d079-2086-4fe9-809b-4744d6966f94","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-31T22:23:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 13:02:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7743020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7743020","identity":"rs-7743020","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}