Estimation of Agricultural and Logging Injury Incidence in Maine Using Electronic Administrative Data Sets

Profile and risk factors in farmer injuries: a review based on Haddon matrix and 5 E's risk reduction strategy

Farmers are considered a high-risk group for intentional and unintentional injuries. This review identified significant risk factors for agricultural injuries in farmers and explored injury prevention countermeasures based on the literature. Therefore, CiteSpace software was used to analyze the relevant literature in this field. Additionally, we identified both key risk factors and countermeasures using the Haddon matrix and the 5 E's risk reduction strategies conceptual framework, respectively. The risk factors were identified from four categories (host, agent, physical environment, and social environment) corresponding to three phases (pre-event, event, and post-event). Interventions of 5 E's risk reduction strategies including education, engineering, enforcement, economic, and emergency response have been proven effective in preventing injuries or reducing their severity. Our findings provide a comprehensive foundation and research direction for the study and prevention of injuries among farmers.

Regional surveillance of medically-attended farm-related injuries in children and adolescents

Purpose

Due to numerous environmental hazards such as heavy machinery and large livestock, youth who live and work on farms are at high risk of injury, disability, and death. This study described a regional surveillance system for monitoring farm-related injuries in children and adolescents. As the risk of farm-related injuries are not exclusive to farm residents, trends in farm-related injuries over the previous 5 years were reported and compared between children/adolescents who did and did not live on farms in north-central Wisconsin.

Methods

A retrospective cohort of child and adolescent patients of the Marshfield Clinic Health System was assembled. Incident farm-related injuries, including from agricultural work or other activities in a farm environment, were extracted from medical records from 2017 through 2021. Generalized linear models were created to compare age- and sex-adjusted farm-related injury rates by year.

Results

There were 4,730 (5%) in-farm and 93,420 (95%) out-farm children and adolescents in the cohort. There were 65 incident farm-related injury cases in the in-farm group and 412 in the out-farm group. The annual incidence rate of farm-related injuries was higher in the in-farm group, but changes during the 5-year timeframe were not significant in either group. In the in-farm group, rates ranged from a high of 61.8 [95% confidence interval (CI): 38.3, 94.5] incident farm-related injuries per 10,000 children/adolescents in 2017 to a low of 28.2 (13.5, 51.9) injuries per 10,000 children/adolescents in 2018. In the out-farm group, rates ranged from 10.7 (8.3, 13.6) to 16.8 (13.7, 20.5) incident farm-related injuries per 10,000 children/adolescents per year between 2017 and 2021. The in-farm group had a higher proportion of injured males and heavy machinery injuries, while the out-farm group had more all-terrain vehicle injuries and pesticide poisonings.

Conclusion

Farm residency remains hazardous for children and adolescents, as injury rates were three times higher in the in-farm group and remained stable over 5 years. All-terrain vehicle injuries were high in both groups, and should be a priority in rural safety interventions. With additional adaptations to other states, this surveillance model could be scaled across other healthcare systems.

What about the Rest of Them? Fatal Injuries Related to Production Agriculture Not Captured by the Bureau of Labor Statistics (BLS) Census of Fatal Occupational Injuries (CFOI)

Surveillance of injuries in production agriculture is necessary to inform stakeholders about workplace hazards and risks in order to improve and advance injury prevention policies and practices for this dangerous industry. The most comprehensive fatal injury surveillance effort currently in the United States is the Bureau of Labor Statistics (BLS) Census of Fatal Occupational Injuries (CFOI), which covers occupational fatalities in all U.S. industries, including production agriculture. However, this surveillance does not include many categories of fatalities that occur during agricultural work or on production agriculture worksites. To better capture the human cost of production agriculture, the authors of this paper call for the collection of additional data with a broader scope that supplements, not replaces, the current CFOI. This paper describes challenges in surveillance, highlights key procedural gaps, and offers recommendations for advancing national surveillance of fatal traumatic injuries associated with production agriculture.

Using hospitalization data for injury surveillance in agriculture, forestry and fishing: a crosswalk between ICD10CM external cause of injury coding and The Occupational Injury and Illness Classification System

BACKGROUND

While statistics related to occupational injuries exist at state and national levels, there are notable difficulties with using these to understand non-fatal injuries trends in agriculture, forestry, and commercial fishing. This paper describes the development and testing of a crosswalk between ICD-10-CM external cause of injury codes (E-codes) for agriculture, forestry, and fishing (AFF) and the Occupational Injury and Illness Classification System (OIICS). By using this crosswalk, researchers can efficiently process hospitalization data and quickly assemble relevant cases of AFF injuries useful for epidemiological tracking.

METHODS

All 6810 ICD-10-CM E- codes were double-reviewed and tagged for AFF- relatedness. Those related to AFF were then coded into a crosswalk to OIICS. The crosswalk was tested on hospital data (inpatient, outpatient, and emergency department) from New York, Massachusetts, and Vermont using SAS9.3. Injury records were characterized by type of event, source of injury, and by general demographics using descriptive epidemiology.

RESULTS

Of the 6810 E-codes available in the ICD-10-CM scheme, 263 different E-codes were ultimately classified as 1 = true case, 2 = traumatic/acute and suspected AFF, or 3 = AFF and suspected traumatic/acute. The crosswalk mapping identified 9969 patient records either confirmed to be or suspected to be an AFF injury out of a total of 38,412,241 records in the datasets, combined. Of these, 963 were true cases of agricultural injury. The remaining 9006 were suspected AFF cases, where the E-code was not specific enough to assign certainty to the record's work-relatedness. For the true agricultural cases, the most frequent combinations presented were contact with agricultural/garden equipment (301), non-roadway incident involving off-road vehicle (222), and struck by cow or other bovine (150). For suspected agricultural cases, the majority (68.2%) represent animal-related injuries.

CONCLUSIONS

The crosswalk provides a reproducible, low-cost, rapid means to identify and code AFF injuries from hospital data. The use of this crosswalk is best suited to identifying true agricultural cases; however, capturing suspected cases of agriculture, forestry, and fishing injury also provides valuable data.

Multisource surveillance for non-fatal work-related agricultural injuries

Objectives: Development of a state-wide comprehensive surveillance system for non-fatal work-related farm injuries, since non-fatal injuries that occur to the self-employed (i.e., many farm owners/operators), family workers, federal government workers and small farms with fewer than 11 employees are not included in the Bureau of Labor Statistics employer-based survey used to produce the U.S. National statistics of work-related injuries.Methods: In 2015 and 2016, inpatient discharge summaries, emergency department, and hospital-based outpatient clinic records from all 134 of Michigan's hospitals with ICD-9 codes 989.0-.1, E827.0-.9, E849.1, E906.8, E919.0 or ICD-10 codesT65.0-.1, V80, Y92.7, W55.1-.4, W30 were reviewed to identify non-fatal work-related farm injuries.Results: We identified 1,559 non-fatal work-related farm injury incidents that occurred in 1,525 individuals, with 74% being among men. The most common parts of the body injured were an upper limb (38.2%) and a lower limb (23.7%). The most common types of injury were contusions (26.4%) and fractures (19.9%). Owners/operators accounted for 44.1% and hired hands for 42.9% of individuals injured. Injuries caused by cows were the predominant cause: 472 (31.5%) of all the injuries. Dairy farms accounted for 39.6% of all cases for which the farm type was recorded.Conclusion: A comprehensive system to identify non-fatal work-related farm injuries among all individuals who work on a farm, including owner/operators, family members and migrant and seasonal farm laborers, was implemented using hospital, emergency department and hospital-based outpatient clinic medical records. Such a system is important to be able to identify hazards and target prevention.

Using multiple coding schemes for classification and coding of agricultural injury

BACKGROUND

Agricultural safety and health researchers have used a variety of classification and coding schemes to identify and categorize injury, illness, and disease associated with agricultural hazards. This paper demonstrates how the Farm and Agricultural Injury Classification (FAIC) and Occupational Injury and Illness Classification System (OIICS) coding schemes can be used in tandem for all agricultural injuries.

METHODS

Specific cases from the AgInjuryNews.org database were selected to illustrate how the FAIC and OIICS codes can be applied to agricultural injury, and to illustrate limitations that still exist with each coding scheme.

RESULTS

Using the FAIC and OIICS together provided a clearer picture of an injury incident by combining more explicit occupational and non-occupational exposures with incident type details in a single table.

CONCLUSIONS

Both FAIC and OIICS coding schemes depend on sufficient information being available from injury reports. This paper concluded with suggestions for improving coding of agricultural injury.

Morbidity and mortality from farm tractor-related injuries in Arkansas

This study applied a text string search algorithm to ascertain suspect farm tractor or agricultural machinery-related injuries in data sources available for 2000-2014 in the state of Arkansas. The occurrences of tractor or other agricultural machinery-related injuries were compared with data available from the Centers for Disease Control and Prevention's National Center for Health Statistics (NCHS) and the Bureau of Labor Statistics' Census of Fatal Occupational Injuries (CFOI). For death certificates that assigned an external cause of death, the authors first collected all those that were coded as related to agricultural machinery, based on search strings for occupation and industry and a description of how the injury occurred. They then inspected each case individually and removed those that were likely unrelated to agricultural machinery. This approach significantly increased (by 7.8 times) the number of suspect agricultural machinery-related fatalities compared to the number reported to CFOI, but there was only a 17% (not statistically significant) increase compared to NCHS. All hospital records with any discharge diagnosis coded as related to agricultural machinery were selected. Descriptive analysis of the fatalities and hospital records showed a significantly increased risk among men above retirement age, peaks during the summer, and an increased risk in the Mississippi delta region. About one-third of the agricultural machinery-related fatalities were due to overturns. The use of the algorithm can improve ascertainment of fatal agricultural machinery-related injuries in Arkansas. The death records were found to be rich in data on the circumstances of the injuries, which can be used to screen for tractor-related fatalities and, if confirmed, translated into action to improve the safety of Arkansas farmers.

Data processing and case identification in an agricultural and logging morbidity surveillance study: Trends over time

BACKGROUND

Agriculture and logging are dangerous industries, and though data on fatal injury exists, less is known about non-fatal injury. Establishing a non-fatal injury surveillance system is a top priority. Pre-hospital care reports and hospitalization data were explored as a low-cost option for ongoing surveillance of occupational injury.

METHODS

Using pre-hospital care report free-text and location codes, along with hospital ICD-9-CM external cause of injury codes, we created a surveillance system that tracked farm and logging injuries.

RESULTS

In Maine and New Hampshire, 1585 injury events were identified (2008-2010). The incidence of injuries was 12.4/1000 for agricultural workers, compared to 10.4/1000 to 12.2/1000 for logging workers.

CONCLUSIONS

These estimates are consistent with other recent estimates. This system is limited to traumatic injury for which medical treatment is administered, and is limited by the accuracy of coding and spelling. This system has the potential to be both sustainable and low cost.

Non-fatal agricultural injury surveillance in the United States: A review of national-level survey-based systems

BACKGROUND

Currently, surveillance of non-fatal agricultural injuries in the U.S. mainly relies on national surveys, and to date, none of these surveys were formally reviewed. Our objective was to review and evaluate these survey-based systems, to identify critical gaps in them and provide recommendations to improve them.

METHODS

We used the updated Center for Disease Control and Prevention guidelines to describe each system and evaluate each system's attributes like simplicity, flexibility, data quality, timeliness, representativeness, etc.

RESULTS

Four adult and two youth national surveys collected data for non-fatal agricultural injuries in the U.S. The evaluation identified three major gaps: 1) insufficient data quality attributed to non-response, measurement errors, and underreporting; 2) untimeliness of data; and 3) lack of flexibility to integrate with other existing systems.

CONCLUSION

Improving data quality, timeliness and flexibility will provide reliable and valid injury estimates, and increase the usefulness of these surveys for surveillance and prevention of farm injuries.

Socio-environmental risk factors for medically-attended agricultural injuries in Wisconsin dairy farmers

BACKGROUND

Accidents are common in the agricultural industry, particularly among dairy farmers. How said farmers get hurt is well established, but far less is known about how distal, socio-environmental factors influence injuries. This study examined associations between medically-attended agricultural injuries and: (1) personal sociodemographic characteristics, and (2) farm environment features and general safety practices.

METHODS

A cross-sectional survey was implemented with linked data from electronic health records on prior agricultural injuries that occurred between 01/01/2002-12/31/2015. The sample included adult dairy producers who resided in north-central Wisconsin (USA) and were medically-homed to the Marshfield Clinic Health System. Multiple logistic regression was used to analyze associations between socio-environmental characteristics and agricultural injuries.

RESULTS

There were 620 dairy farmers in the analytical sample, with 50 medically-attended agricultural injuries observed during the 14-year study time period (5.7 injuries per 1000 dairy farmers per year). In the multivariable model, the odds of agricultural injury were significantly greater among farmers who have private individually-purchased health insurance (OR=4.25; 95% CI: 1.31, 13.84), do not live at their dairy operation (OR=2.91; CI: 1.27, 6.67), and do not provide safety training to their workers (OR=4.27; CI: 1.00, 18.21).

CONCLUSIONS

Dairy farmers in this analysis who did not live at their dairy operation, did not provide safety training to all their workers, or had individually-purchased health insurance were more apt to get injured, but more research is needed to confirm these findings in prospectively designed studies. How these factors can be directly addressed or otherwise used to better focus farm injury prevention initiatives should also be explored.