Incidence Rates and Trend of Serious Farm-Related Injury in Minnesota, 2000–2011

Understanding Stakeholder Dissemination Preferences for an Agriculture, Forestry, and Fishing Injury Surveillance System

Researchers and epidemiologists are working to improve the capture of agriculture, forestry, and fishing (AgFF) injuries in a variety of ways. A critical component of any surveillance system is the dissemination of information. The purpose of this paper is to report on a survey conducted with AgFF injury surveillance stakeholders to understand preferred dissemination strategies. The survey was distributed using REDCap via web link to organizational stakeholders, which included advisory board members, safety trainers, industry managers and workers, and research collaborators. In total, there were 75 respondents (21% response rate). Occupation and industry influenced preference in update methods. Regarding the length and breadth of updates, 63% of respondents prefer reports (one to five pages), followed by 57% desiring a summary (less than one page), while only 24% wanted a detailed analysis. Social media and news preferences were also different among stakeholders. Surveillance data were desired for 1) trend analysis, 2) tailoring activities and solutions for education, training, outreach and interventions and 3) for research purposes such as grant proposals and evaluation. The dissemination of injury surveillance data should be tailored to the intended audience. Greater attention needs to be paid to the ways in which we share our findings.

Beyond Age and Cause: A Multidimensional Characterization of Fatal Farm Injuries in Ireland

The study seeks to enhance understanding of the complex nature of fatal farm injuries and thereby support the development of targeted safety messages or interventions. The research analyses fatal farm injuries surveillance data collected by the Health and Safety Authority (Ireland) for the period 2004-2018. Utilizing Non-Linear Canonical Correlation Analysis (NLCCA) to identify similarities and differences between 274 fatal injuries, multiple interactions and correlations between two sets of variables were identified. The analysis identified two major patterns of injury. The first relates to older farmers (60+) who were killed due to being struck by an object in motion, i.e. a tractor or machine. These injuries occurred mainly during the summer or winter between 13:00 and 18:00 in the Southern-Eastern regions. The second pattern also relates to older farmers, however these fatalities are associated with beef production and the primary cause of injury was being kicked or crushed by livestock. These injuries commonly occur in the Border, Midland, and Western regions where fatalities are prevalent during spring and outside of normal working hours. Assessing interactions between variables demonstrates to whom, how, when, and where fatal injuries affect different groups within the farm population. These findings provide regulators and farm safety stakeholders with key insights into background characteristics of fatal injuries including type of enterprise, time of day, season, and regional location that should be used to develop safety awareness, training, and targeted interventions to at-risk populations.

Towards a Better Understanding of Farm Fatalities: Identification and Estimation of Farming Fatality Rates

OBJECTIVE

Occupational fatality rates are useful in communicating key safety challenges associated with different industries or affecting different populations within those industries. Though seemingly intuitive, the calculation and use of fatality rates associated with farming needs to be carefully considered. This paper highlights difficulties and variability in Farm Fatality Rate (FFR) measures, proposes a series of rates appropriate for assessments of farm safety, and demonstrates their usage through an analysis of farm workplace deaths in Ireland between 2008 and 2016.

METHODS

Six FFRs are calculated including: the Farm Household Rate (FHR); Farm Operator Rate (FOR); Family Worker Rate (FWR), Worker Rate (WR), Seasonal Fatality Rate (SFR) and, Enterprise Fatality Rate (EFR). To assess trends over time we calculate the rate using appropriate numerators and denominators in three sub-periods.

RESULTS

FFRs vary considerably depending on which numerator and denominator are deployed. Over the period, the FHR, FOR, and FWR increased due to growth in the number of fatalities and a slight decrease in the denominator populations. The WR has declined substantially over the full period. The QFR increased substantially for Q2-Q4 whilst the EFR highlights workers on dairy or beef enterprises being particularly at risk.

CONCLUSION

The characteristics of farm workforces present many challenges to the calculation of occupational fatality rates. We propose six alternatives that provide insights into the risks faced by farm households, farm operators, farm workers, seasonally and by type of farm enterprise. We recommend that, regardless of the rate used, the limitations associated with all fatality rates are clearly explained, particularly when engaging with media and occupational health and safety stakeholders.

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.

Changing Patterns in Midwestern Farm Trauma: A Retrospective Comparative Analysis

OBJECTIVES

The objective is to determine if injury patterns on agricultural workplaces have changed over time.

METHODS

Retrospective chart review of farm trauma in Fargo, ND, from 2006 to 2020. Results were compared to historical results from La Crosse, WI, from 1978 to 1983. Patient charts with ICD location and external cause code relating to "farm" were included in the study. Frequencies and relative percentages were computed for each categorical variable. Chi-square tests were performed to determine which categories were significantly different from one another.

RESULTS

Injuries on farms from 395 patients from 2006 to 2020 were compared to injuries from 375 patients from 1978 to 1983. Average age of patients in 2006-2020 was 48 compared to 36 for 1978-1983. There were fewer ISS 1-9, more ISS 10-24, and similar ISS > 25 from 2006 to 2020 compared to 1978-1983. Falls doubled in 2006-2020, 132 compared to 67 in 1978-1983. Injuries from tractors were fewer in 2006-2020, and 63 compared to 89 in 1978-1983. Neurological injuries almost doubled in 2006-2020 compared to 1978-1883, 126 and 61, respectively. All the aforementioned temporal differences were statistically significant with P value < .0001. There were 9 deaths from 2006 to 2020 and 8 deaths from 1978 to 1983.

CONCLUSION

Injuries on farms today occur in older patients with higher injury severity scores and are more likely to have neurological injuries compared to data from 1978 to 1983. These changing patterns in injuries can help to provide education, direct farm safety programs, and help triage resources to critical access hospitals that care for this patient population.

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.

Analysis of agriculture-related life-threatening injuries presenting to emergency departments of rural generalist hospitals in Southern Queensland

OBJECTIVE

Agricultural industries are among the most dangerous in Australia posing significant public health risks. This study analyses the nature and management of agriculture-related injuries presenting to EDs in selected hospitals in Southern Queensland.

METHODS

Data on agricultural injury presentations over a 6 month period was collected at four rural hospitals by a dedicated onsite hospital data coordinator. Additionally, in two of the participating hospitals all injury presentations over the same 6 month period were recorded. A pre-tested survey instrument, modified for rural settings and designed and developed to export the abstracted data using an iPad application was used as the survey tool.

RESULTS

The incidence of agriculture-related injuries was 11% of all injuries, most were males (73%), averaging 40 years. On presentation, 66.5% (n = 234) were categorised as imminently or potentially life threatening with 44% of those patients presenting to hospital ED >3 h after the injury. Large animals were more commonly reported as involved in the aetiology of the presenting injury, particularly using horses and handling cattle.

CONCLUSIONS

Agricultural injuries are a significant group of primary care presentations to rural hospitals and training and resourcing for rural hospitals should reflect this. A better understanding of common injury types can lead to efficient allocation of available resources in rural hospitals and potentially improve ED practices. The delay in presentation must be considered in response planning both by farmers and hospital EDs.

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.

Geographic distribution of burn in an Australian setting

OBJECTIVE

To investigate the geographic distribution and temporal trends of burn admissions in an Australian setting.

METHODS

Health administrative data of all persons hospitalised for a first burn in Western Australia for the period 2000-2012 were used. Crude and standardised incident rates were generated for each region. Maps of crude rates were generated for state regions and postcode-suburbs of Perth, the capital city. Standardised incidence rates were generated for Western Australia, total and regions, and for sub-cohorts defined by age (<20years; ≥20 years), TBSA burn severity and major causes of burns (fire, scalds and contact). Negative binomial regression was used to examine temporal changes and generate incidence rate ratios (IRR) with 95% confidence intervals (CI).

RESULTS

Perth had the lowest burn admission rate per population; clusters of suburbs of lower social advantage and higher immigrant settlement were identified as being at high risk. While the highest observed admission rates were found in Kimberley and Goldfields (remote) regions, after adjustment for the regional demographic structures, the Wheatbelt and Mid-West (rural) regions were found to have the highest adjusted rates of burn admissions. Significant annual declines in admission rates were found for the Kimberley, Pilbara and Goldfields (remote regions); however, stable admission rates were identified for all other regions.

CONCLUSIONS

The Mid-West and Wheatbelt rural regions were found to have the highest risk of burn admissions raising concerns about farming-related injury. Safety awareness and burn prevention strategies need to be continued, with specific attention to these high risk areas, to reduce burn admissions in Western Australia.

The Economic Impact for Farm Injury in Minnesota, 2004-2010

Only 2% of Minnesota's employed population worked in agriculture between the years 2005 and 2012. However, this small portion of the state's employed population accounted for 31% of total work-related deaths in the state during that same time period. During a similar time period, 2007-2013, the contribution of agriculture to Minnesota's gross domestic product increased from approximately 1.5% to about 2.3%. This article describes the economic impact of injuries related to farm work between the years 2004 and 2010. Using hospital discharge data and the Census of Fatal Occupational Injuries (CFOI), estimates of the number of injuries and fatalities related to agricultural work were compiled. A cost of illness model was applied to these injury and fatality estimates to calculate the related indirect and direct costs in 2010 dollars. Estimated total costs, in 2010 dollars, ranged between $21 and $31 million annually over the 7-year study period. The majority of the costs were attributable to indirect costs, such as lost productivity at work and home. Fatal injuries accrued the largest proportion of the estimated costs followed by hospitalized and nonhospitalized injuries. A sensitivity analysis was performed to evaluate the impact each selected data source had upon the cost estimate. The magnitude of the costs associated with these injuries argues for better surveillance of injury related to agriculture to prioritize resources and evaluate intervention and prevention programs.