Background: Thoroughbred horse farm workers self-report a high frequency of work-related injuries and pain. However, an analysis of Thoroughbred horse farm workers' compensation injury claims is absent from the literature, yet may benefit worker safety.
Methods: We analyzed workers' compensation insurance firm data containing 2276 claims filed between 2008 and 2015. Injury frequency, cost, and lost time per cause, nature, and body part injured were examined qualitatively and via univariate tests. Factors associated with high cost and high duration lost time claims were modeled via multivariable logistic regression.
Results: The average Thoroughbred worker claim cost $4,198 and accrued 10 days lost time, involving strikes (57% of total claims), sprains/strains (34%), and wrist/hand injuries (18%). Injuries primarily occurred on mornings (54%), weekdays (79%), and during the transition from breeding to sales (23%). Jobs with a high level of horse contact had significantly higher cost ($6,487) and higher duration lost time (16.8 days) claims, with significantly higher cost claims on the weekends ($6,471) and from the oldest workers ($7466), vs reference groups. Logistic models indicate significantly increased odds of a high-cost injury among high horse contact jobs (OR = 1.87; 95% C.I. = 1.53-2.29) and older age tertiles (1.38; 1.08-1.75; 1.70, 1.32-2.18). The odds of a high duration lost time injury are significantly increased among high horse contact jobs (1.91; 1.53-2.39) and males (1.50, 1.13-1.98), with significantly reduced odds among the most tenured workers (0.74; 0.56-0.99).
Conclusions: Our findings elucidate factors to reduce injury frequency, cost, and lost time among Thoroughbred horse farm workers.
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http://dx.doi.org/10.1002/ajim.23159 | DOI Listing |
Mil Med
January 2025
Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA.
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Unitat de Suport a la Recerca Terres de l'Ebre, Fundació Institut Universitari per a la Recerca al'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 43500 Tortosa, Spain.
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Research Service, Department of Medicine, Raymond G. Murphy Veterans Affairs Medical Center, University of New Mexico School of Medicine, Albuquerque, NM 87108, USA.
Hyperglycemic emergencies cause significant losses of body water, sodium, and potassium. This report presents a method for computing the actual losses of water and monovalent cations in these emergencies. We developed formulas for computing the losses of water and monovalent cations as a function of the presenting serum sodium and glucose levels, the sum of the concentrations of sodium plus potassium in the lost fluids, and body water at the time of hyperglycemia presentation as measured by bioimpedance or in the initial euglycemic state as estimated by anthropometric formulas.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computer Science, Northeast Electric Power University, Jilin 132012, China.
Satellites frequently encounter atmospheric haze during imaging, leading to the loss of detailed information in remote sensing images and significantly compromising image quality. This detailed information is crucial for applications such as Earth observation and environmental monitoring. In response to the above issues, this paper proposes an end-to-end multi-scale adaptive feature extraction method for remote sensing image dehazing (MSD-Net).
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December 2024
Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059, Krakow, Poland.
In this study, a predictive maintenance (PdM) system focused on feature selection for the detection and classification of simulated defects in wind turbine blades has been developed. Traditional PdM systems often rely on numerous, broadly chosen diagnostic indicators derived from vibration data, yet many of these features offer little added value and may even degrade model performance. General feature selection methods might not be suitable for PdM solutions, as information regarding observed faults is often misinterpreted or lost.
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