Using Machine Learning to Understand Injuries in Female Agricultural Operators in the Central United States

Details

• Personal Author:
  Beseler CL ; Rautiainen RH
• Description:
  The number of women choosing agriculture as an occupation is increasing. Agriculture is dangerous work, and women are at risk of serious injury, but the research on injuries in females is sparse. Women perform different types of farmwork and have different exposures than men. Studies have not assessed injury in a large group of female agricultural operators. In this study, we used XGBoost, a machine learning algorithm, and logistic regression to examine 17 factors hypothesized to be associated with injury in 1529 farm and ranch women. The sample was split into a training group of 1070, and the results were replicated in a test group of 459. The model accuracy was 88%. We compared the results of XGBoost to those of the logistic regression models and computed odds ratios to estimate effect sizes. We found that the two methods generally agreed. XGBoost identified the total number of musculoskeletal symptoms, age, sleep deprivation, high work-related stress, and exposure to respiratory irritants as being important to injury. The multivariate logistic regression model identified higher income, higher stress, younger age, and number of musculoskeletal symptoms as being significantly associated with injury. The analysis highlights the importance of musculoskeletal disorders and work strain to injury in women. [Description provided by NIOSH]
• Subjects:
  Agriculture Automation Occupational Health Risk Assessment Risk Factors Safety Women
• Keywords:
  Agricultural Industry Agricultural Injuries Artificial Intelligence Author Keywords: Agriculture Injuries Injury Risk Factors Machine Learning Risk Factors XGBoost
• ISSN:
  2313-576X
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  Kansas City Region [OSHA] ; Nebraska
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  9
• Volume:
  11
• Issue:
  1
• NIOSHTIC Number:
  nn:20070780
• Citation:
  Safety 2025 Mar; 11(1):9
• Contact Point Address:
  Cheryl L. Beseler, Department of Environmental, Agricultural and Occupational Health, University of Nebraska Medical Center, Omaha, NE 68198-4388, USA
• Email:
  chbeseler@unmc.edu
• Federal Fiscal Year:
  2025
• NORA Priority Area:
  Agriculture, Forestry and Fishing
• Performing Organization:
  University of Nebraska Medical Center - Omaha
• Peer Reviewed:
  True
• Start Date:
  20110901
• Source Full Name:
  Safety
• End Date:
  20270831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:7a0596fa49d9b579100d3fde5ce382046811791edb56aa820d73637cbb1c07dbee09f7918cfcfe31904d3a8ecb8e8d2a8863311b0e0afdc8380e582727d6de5d
• Download URL:
  https://stacks.cdc.gov/view/cdc/208937/cdc_208937_DS1.pdf
• File Type:
  [PDF - 185.46 KB ]