Journal of Agromedicine最新文献

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.

Generative Artificial Intelligence (AI) provides unprecedented opportunities to improve injury surveillance systems in many ways, including the curation and publication of information related to agricultural injuries and illnesses. This editorial explores the feasibility and implication of ChatGPT integration in an international sentinel agricultural injury surveillance system, AgInjuryNews, highlighting that AI integration may enhance workflows by reducing human and financial resources and increasing outputs. In the coming years, text intensive natural language reports in AgInjuryNews and similar systems could be a rich source for data for ChatGPT or other more customized and fine-tuned LLMs. By harnessing the capabilities of AI and NLP, teams could potentially streamline the process of data analysis, report generation, and public dissemination, ultimately contributing to improved agricultural injury prevention efforts, well beyond any manually driven efforts.

Injury and fatality events that are caused by tree work and tractor-related activities contribute to the already elevated rates of work-related injuries in the agriculture and logging sectors. This brief report highlights the circumstances surrounding these events, as well as a number of surveillance sources that identify the extent to which they contribute to the injury burden in these hazardous industries. These data sources include fatality investigations, agricultural injury news reports, consumer product injury data captured from emergency rooms and near-miss reporting data captured from individuals participating in the National Rollover Protective Structure Rebate Program (NRRP). Several recommendations for further research and interventional efforts are outlined, with particular consideration of the manner in which rollbars have been involved in falling object incidents.

Objectives: This study collected data related to agricultural injuries, analyzed their causes, and suggested possible remedial actions. Few studies from developing countries have investigated this important topic. Such investigations are extremely important and are required to frame national policies.

Methods: A well-designed structured form was developed to collect and evaluate data on agricultural incidents, their causes, the severity of injuries, and possible prevention methods. The authors followed a door-to-door survey methodology to collect data with the help of local village leaders.

Results: Less educated and older adults in the age group 31 to 45 years were more prone to agricultural incidents (p < .05). The mean age of the victims was 35.02 years (95% CI 32.7 to 37.3); 124 (91.18%) of the victims were men; and 21 (15.4%) of the cases were fatal. Tractor rollovers caused most of the deaths. Chaff cutters were responsible for most of the incidents that required amputation. Eighteen (45%) of the chaff-cutter incidents occurred in the evening between 3:00 PM and 6:00 PM, and 12 (30%) of these incidents occurred due to fatigue.

Conclusions: The authors strongly recommend that rollover protective structures (ROPS) for tractors and seat belts should be made mandatory in India. Public guidelines emphasizing mandatory higher education for farmers and a model curriculum for both schools and colleges that focuses on the safety of rural populations should be developed. Special training should be provided on the safe operation of machinery and a definitive work - rest schedule should be followed to prevent fatigue and protect agricultural workers from incidents.

Farming is one of the top industries in Michigan and has the highest fatality rate. National sources of non-fatal farm injuries underestimate the burden, especially among children. In this paper, we provide a more accurate estimate of non-fatal farm injuries in Michigan by using an ongoing multi-source surveillance system. Michigan's farm-injury surveillance system includes abstractions of hospital-based medical records, poison control center calls, ambulance runs, and workers' compensation claims for individuals with a farm-related injury. For this analysis, injury onset occurred in 2015 to 2021 and included all injuries regardless of age or occupation. We identified 4,306 injuries in adults and 336 injuries in youth. For those 0-13 years of age all but two were family members. For those 14-15 and 16-17 years of age, 19% and 45%, respectively were hired hands. For adults, 51% were owner/operators, 43% were hired hands, and 5% were family members. For all ages, the most frequent injury source was cattle. Lacerations/cuts/punctures were the most common type of injury for those < 14 years of age while for older individuals it was contusions/bruises. The plurality of injuries occurred in the summer months. Children working on a farm are in unique situation and tracking injuries and identifying injury sources provide necessary information to protect their health and well-being. Michigan's farm-injury surveillance system will continue to provide a comprehensive count of work-related injuries among Michigan's farming population. The information is used to direct public health intervention both at the individual and population level.

Objective: Generation of reliable data underpins the effectiveness of Occupational Health and Safety (OHS) surveillance systems. Despite the importance of understanding OHS data systems, there are few papers that provide overviews of their structure and/or content. This paper introduces a basic framework for assessing OHS data systems that will be of use to researchers internationally. We applied this approach to assess the Irish OHS data system by undertaking a data mapping exercise.

Method: We developed a checklist based on recommendations of monitoring and measurement of OHS proposed by the National Academies of Sciences, Engineering, and Medicine (USA). An assessment of published reports that present systematic OHS surveillance data was undertaken to identify the institutions or organisations responsible for collecting and curating the data, their remit, and, associated with this, their respective case definitions. We then provide an overview of the variables collected and these are then mapped against the checklist.

Results: The assessment highlights that whilst the farm fatalities dataset provides complete coverage of all fatalities, regardless of age or employment status, the same is not true of the three non-fatal injuries datasets reviewed. There are important differences in the data collection methods and, associated with this, which populations are covered.

Practical application: The assessment approach provides valuable insights into the strengths and weaknesses of a critical element of OHS surveillance systems, namely the production of datasets. This knowledge is important for researchers as understanding the data that informs their research is fundamental to good science. It is critical for policy-makers and other stakeholders to understand the strengths and weaknesses on which OHS policy, strategies, or education and training interventions are developed.

Objective: Agriculture is a dangerous industry, with evidence indicating a disproportionate burden among Hispanic/Latinx workers. There is a need to expand the utilization of different data systems to improve the surveillance of precarious workers within agriculture. This analysis describes inclusion criteria to identify farm-related injuries and illnesses in hospital data utilizing ICD-10 codes to better assess health equity issues involving Hispanic/Latinx workers and their associated costs.

Methods: Discharge data of agriculture-related injuries and illnesses treated in Illinois hospitals and emergency departments from 2018 to 2021 were extracted using ICD-10 diagnosis and location of injury codes. Injury cause, nature, severity, and course of clinical care are stratified by ethnicity. Multivariable models were developed to assess differences in injury severity, level of care required, and cost of care.

Results: We identified 3,745 farm-related injuries and illnesses treated in Illinois hospitals between 2018 and 2021, of which 196 involved Hispanic/Latinx individuals. Hispanic/Latinx patients were substantially younger and disproportionately covered by workers' compensation insurance or uninsured. Compared to non-Hispanic/Latinx individuals, Hispanic/Latinx patients suffered injuries from different mechanisms, particularly involving animals and cutting/piercing instruments. While non-Hispanic/Latinx individuals demonstrated more severe injuries based on the descriptive statistics, after controlling for confounding (particularly age), we did not observe ethnic disparities in injury severity or level of care required. However, the cost of care was equivalent to or higher among Hispanic/Latinx persons.

Conclusion: The case definition used for this analysis identified agriculture-related cases and provided insights on the course of clinical care by ethnicity. This strategy would likely yield valuable information in states with larger and more diverse agricultural workforces. More targeted research to appropriately scope the issue and inform interventions is needed to understand differential exposure and reduce agricultural workplace hazards and address the financial burden resulting from farm-related injuries.

Background: Agriculture is a hazardous industry with undocumented injury events. Credible surveillance measures are critical for this industry, especially to guide injury prevention programs with targeted recommendations for specific commodity groups and populations. This multi-phase study explored the feasibility for two state agency databases, the Ohio Bureau of Workers' Compensation (BWC) Program and the Emergency Medical Services Incident Reporting System (EMSIRS), to augment the state's Bureau of Labor Statistics (BLS) annual reports.

Methods: BWC data described injury claims in agricultural workplaces from 1999 to 2008. State EMSIRS data described the types of medical emergencies for which EMS services were requested to Ohio farms in 2013-2014. Descriptive analyses were performed on each distinctive source.

Results: Over 14,000 BWC claims were analyzed, with primary nature of injury identified as sprains and strains of bodily extremities; falls were the most common cause of injury. The EMSIRS data provided 1,376 cases, where EMS services were requested to Ohio farms at injury onset. Some cases had possibility to be excluded in CFOI or employment claims data, with 24% patients 65 years and older and 6% children 13 years and younger. The primary cause of injury was falls, and the highest reported injury type was blunt trauma.

Conclusions: Both BWC and EMSIRS databases showed the potential to enhance Ohio's agricultural surveillance data with viable information not found in previously used systems. Each agency database had its own merits to further clarify and quantify morbidity. When used together, these sources enrich surveillance statistics to describe Ohio's agricultural injury incidents.

Objective: The purpose of this study was to investigate characteristics associated with farm vehicle and farm labor transportation-related roadway crashes in the state of Florida. Agricultural roadway crashes related to these vehicles have not been explored in Florida.

Methods: The data for this study comes from the Florida Department of Highway Safety and Motor Vehicles (FLHSMV) for the years 2013-2021. The data is recorded by the Florida Highway Patrol when a vehicular crash has occurred. The data is then coded to allow the selection of farm and agricultural-related vehicles. Specifically, this study focuses on farm labor transport vehicles and farm vehicles (e.g. farm tractors).

Results: There were 744 farm vehicle- and 209 farm labor transportation-related crashes during the 9-year study period. The farm labor transport vehicle crashes involved a total of 420 vehicles and 1,329 individuals, and the farm vehicle crashes involved 1,458 vehicles and 1,652 individuals. Injury risk for non-farm labor transportation vehicle occupants was significantly higher than injury risk for farm labor transportation vehicle occupants.

Conclusions: The average fatality rate for farm vehicle-involved (2.1 fatalities per 100 crashes) and farm labor transportation vehicle-involved crashes (2.9 fatalities per 100 crashes) were much higher than the overall FL roadway crash fatality rate (0.45 fatalities per 100 crashes).