A correction factor for estimating statewide agricultural injuries from ambulance reports

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.

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.

Characteristics of Agriculture Related Motor Vehicle Crashes in Rural New York State

Objective: The Centers for Disease Control and Prevention (CDC) data have shown that agriculture, forestry, and fishing as an occupational group have the third highest rate of work-related roadway crashes. Agriculture-related crashes have been explored in the Midwest and South; however, we know little about agriculture-related crashes in the Northeast, especially in New York.Methods: To better understand this, researchers obtained motor vehicle crash data from 2010 to 2012 from the New York State Department of Motor Vehicles (NYSDMV). These data were then filtered to agriculture-related cases by both vehicle registration type and vehicle body type.Results: We identified 203 agriculture-related vehicle crashes, involving 381 vehicles and 482 people. Of the agriculture incidents, 91.6% caused property damage, while 36.0% caused injury. The case fatality rate for roadway vehicle crashes was nearly five times as great (2.0/0.4 = 5.00) for agriculture versus non-agriculture crashes (p = 0.0003).Conclusion: Using these data as a supplement to the New York Center for Agricultural Medicine and Health's existing surveillance system provided information useful in setting priorities involving roadway safety.

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.

Developing Surveillance Methodology for Agricultural and Logging Injury in New Hampshire Using Electronic Administrative Data Sets

OBJECTIVE

Agriculture and logging rank among industries with the highest rates of occupational fatality and injury. Establishing a nonfatal injury surveillance system is a top priority in the National Occupational Research Agenda. Sources of data such as patient care reports (PCRs) and hospitalization data have recently transitioned to electronic databases.

METHODS

Using narrative and location codes from PCRs, along with International Classification of Diseases, 9th Revision, external cause of injury codes (E-codes) in hospital data, researchers are designing a surveillance system to track farm and logging injury.

RESULTS

A total of 357 true agricultural or logging cases were identified.

CONCLUSIONS

These data indicate that it is possible to identify agricultural and logging injury events in PCR and hospital data. Multiple data sources increase catchment; nevertheless, limitations in methods of identification of agricultural and logging injury contribute to the likely undercount of injury events.