Journal of Agromedicine最新文献

Introduction: Agricultural injuries remain a major concern in North America, with a fatal injury rate of 19.5 deaths per 100,000 workers in the United States. Numerous research efforts have sought to compile and analyze records of agricultural-related injuries and fatalities at a national level, utilizing resources, ranging from newspaper clippings and hospital records to Emergency Medical System (EMS) data, death certifications, surveys, and other multiple sources. Despite these extensive efforts, a comprehensive understanding of injury trends over extended time periods and across diverse types of data sources remains elusive, primarily due to the duration of data collection and the focus on specific subsets.

Methods: This systematic review, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, consolidates and analyzes agricultural injury surveillance data from 48 eligible papers published between 1985 and 2022 to offer a holistic understanding of trends and challenges.

Results: These papers, reporting an average of 25,000 injuries each, were analyzed by database source type, injury severity, nature of injury, body part, source of injury, event/exposure, and age. One key finding is that the top source of injury or event/exposure depends on the chosen surveillance system and injury severity, underscoring the need of diverse data sources for a nuanced understanding of agricultural injuries.

Conclusion: This study provides policymakers, researchers, and practitioners with crucial insights to bolster the development and analysis of surveillance systems in agricultural safety. The overarching aim is to address the pressing issue of agricultural injuries, contributing to a safer work environment and ultimately enhancing the overall well-being of individuals engaged in agriculture.

Objectives: There is limited research on the factors that contribute to suicides among farm and agriculture workers. The purpose of this analysis was to examine the socio-demographic characteristics and circumstances that may increase risk of suicide for a farm worker by using the detailed data from the Kansas Violent Death Reporting System (KS-VDRS).

Methods: KS-VDRS is a state-based system and was used to look at suicides among farm and agricultural workers that occurred in Kansas from 2016 to 2020. Frequencies, percentages, and age-adjusted rates were calculated to compare trends between farm/agriculture workers and other major occupational groups (non-farm/agriculture). Females were not included in this analysis due to low counts in the occupational group.

Results: In Kansas, male farm and agriculture workers die by suicide at a rate of 147.5 per 100,000 population, which is 3 times that of the average male workers (45.2 per 100,000 population). Male suicide decedents in farming and agriculture-related occupations were older with a mean age of 54 years and less educated (73% had a high school diploma or less) than their non-agriculture counterparts (mean age 44 years, and 57% had a high school diploma or less). Common circumstances around time of death were current depressed mood (58.6%), current mental health problem (41.4%), and physical health problem (31.4%).

Conclusion: KSVDRS provides valuable information on the circumstances surrounding suicides, and this can inform prevention efforts within and across states. Numerous factors may contribute to the increased risk of suicide among farmers and those who work in agriculture. Physical health problems, family relationship problems, and financial problems are more commonly reported among this population compared to other male workers. Findings from this report highlight the complexity of suicide risk within the farm and agriculture industry in Kansas and reinforce the need for state-specific data and tailored prevention efforts.

Conducting surveillance of agricultural injuries and fatalities in the United States has been an ongoing challenge, with many cases falling outside the criteria of national and local surveillance systems. In this research, capture-recapture analysis was used to estimate the number of fatal agricultural injuries in Indiana between 2016 and 2020. A limited analysis of non-fatal injuries is also provided. This analysis was possible because of two publicly available datasets containing incident descriptions with sufficient detail for case matching. The first dataset consisted of summary lists of fatal and nonfatal agricultural injuries in Indiana published in annual agricultural fatality reports produced by the Purdue Extension. The second data source was AgInjuryNews, which gathers reports of agricultural injuries and fatalities published in news media and other publicly available sources. Results of the capture-recapture analysis estimate that, every year in Indiana, the Purdue Extension misses 18% of fatal incidents and AgInjuryNews misses approximately 60%. AgInjuryNews identifies approximately 3 fatal incidents per year that are missed by Purdue Extension. Analysis of nonfatal incidents was limited by the fact that both data sources only included nonfatal injuries that were extremely severe and/or connected to a fatality. The Purdue Extension is estimated to miss 22% and AgInjuryNews is estimated to miss 25% of nonfatal agricultural injuries meeting that narrow definition. While capture-recapture analysis only provides estimates of true injury rates, the results provide evidence that Purdue Extension's surveillance captures most agricultural fatalities in the state. AgInjuryNews has been able to identify cases missed by Purdue, and this research takes an important step forward in quantifying how media reports found in this data source differ from extension surveillance. This research also highlights the continuing limitations in the surveillance non-fatal injuries and the ways in which publicly available data can aid researchers in filling gaps in surveillance.

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.

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.

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.

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.