Objectives: A vast data mining project called 'TRACking and moniToring Occupational Risks in agriculture' (TRACTOR) was initiated in 2017 to investigate work-related health events among the entire French agricultural workforce. The goal of this work is to present the TRACTOR project, the challenges faced during its implementation, to discuss its strengths and limitations and to address its potential impact for health surveillance.
Methods: Three routinely collected administrative health databases from the National Health Insurance Fund for Agricultural Workers and Farmers (MSA) were made available for the TRACTOR project. Data management was required to properly clean and prepare the data before linking together all available databases.
Results: After removing few missing and aberrant data (4.6% values), all available databases were fully linked together. The TRACTOR project is an exhaustive database of agricultural workforce (active and retired) from 2002 to 2016, with around 10.5 million individuals including seasonal workers and farm managers. From 2012 to 2016, a total of 6 906 290 individuals were recorded. Half of these individuals were active and 46% had at least one health event (e.g. declared chronic disease, reimbursed drug prescription) during this 5-year period.
Conclusions: The assembled MSA databases available in the TRACTOR project are regularly updated and represent a promising and unprecedent dataset for data mining analysis dedicated to the early identification of current and emerging work-related illnesses and hypothesis generation. As a result, this project could help building a prospective integrated health surveillance system for the benefit of agricultural workers.
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http://dx.doi.org/10.1093/annweh/wxab083 | DOI Listing |
NPJ Parkinsons Dis
January 2025
Univ. Grenoble Alpes, AGEIS, 38000, Grenoble, France.
The risk of Parkinson's disease (PD) associated with farming has received considerable attention, in particular for pesticide exposure. However, data on PD risk associated with specific farming activities is lacking. We aimed to explore whether specific farming activities exhibited a higher risk of PD than others among the entire French farm manager (FM) population.
View Article and Find Full Text PDFSensors (Basel)
July 2024
School of Automotive Engineering, Shandong Jiaotong University, Jinan 252100, China.
With the development of smart agriculture, autopilot technology is being used more and more widely in agriculture. Because most of the current global path planning only considers the shortest path, it is difficult to meet the articulated steering tractor operation needs in the orchard environment and address other issues, so this paper proposes a hybrid algorithm of an improved bidirectional search A* algorithm and improved differential evolution genetic algorithm(AGADE). First, the integrated priority function and search method of the traditional A* algorithm are improved by adding weight influence to the integrated priority, and the search method is changed to a bidirectional search.
View Article and Find Full Text PDFSensors (Basel)
July 2024
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Attitude determination based on a micro-electro-mechanical system inertial measurement unit (MEMS-IMU) has attracted extensive attention. The non-gravitational components of the MEMS-IMU have a significant effect on the accuracy of attitude estimation. To improve the attitude estimation of low-dynamic vehicles under uneven soil conditions or vibrations, a robust Kalman filter (RKF) was developed and tested in this paper, where the noise covariance was adaptively changed to compensate for the external acceleration of the vehicle.
View Article and Find Full Text PDFJ Agromedicine
October 2024
College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD, Australia.
Objectives: Adolescents are at-risk of fatal and non-fatal injuries in the farm environment. School-based agricultural safety and farm injury prevention education is likely to be more effective when utilizing co-designed and gamification principles; however, this needs to be tested. This study examined data from a pilot evaluation of a co-designed farm injury prevention gamified educational resource for adolescents.
View Article and Find Full Text PDFFront Plant Sci
May 2024
Xi 'an Dongyang Machinery Company Limited, Xi'an, China.
The application of autonomous navigation technology of electric crawler tractors is an important link in the development of intelligent greenhouses. Aiming at the characteristics of enclosed and narrow space and uneven ground potholes in greenhouse planting, to improve the intelligence level of greenhouse electric crawler tractors, this paper develops a navigation system of electric crawler tractors for the greenhouse planting environment based on LiDAR technology. The navigation hardware system consists of five modules: the information perception module, the control module, the communication module, the motion module, and the power module.
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