Risk for Diabetes from Long Working Hours and Night Work in the United States: Prospective Associations and Machine Learning Techniques

Details

• Journal Article:
  Safety and Health at Work
• Personal Author:
  Keller, Elizabeth ; Chen, Liwei ; Gao, Feng ; Li, Jian
• Description:
  Background: Diabetes contributes significantly to death in the U.S., with many working-age individuals affected. This research determined the independent and joint associations of long working hours and night work with diabetes risk in U.S. workers, and their contribution to risk prediction. Methods: This prospective study included 1,454 workers from the Midlife in the United States (MIDUS) study with 9-year follow-up. Long working hours included those working 55 or more hours per week. Night work involved those working 16 or more nights per year. Diabetes was determined by self-reported diagnosis or treatment. Multivariable Poisson regression analysis was applied to examine the prospective association of these work-related factors at baseline with incident diabetes. A gradient boosting machine learning model was used to investigate the contributions of both factors in predicting incident diabetes. Results: Long working hours (RR and 95% CI = 1.60 [1.04, 2.46], p < 0.05) and night work (RR and 95% CI = 1.66 [1.05, 2.62], p < 0.05) were independently associated with the risk for diabetes, while controlling for baseline covariates. Gradient boosting analysis suggested long working hours and night work facilitated diabetes incidence. Exposure to both long working hours and night work increased the risk for diabetes (RR and 95% CI = 3.02 [1.64, 5.58], p < 0.001), suggesting additive interaction. Conclusion: Organizations may consider reducing hours on duty and improving shift systems for primary prevention of diabetes.
• Subjects:
  Blood Glucose Diabetes Occupational Health Safety Shift Work Schedule
• Keywords:
  Author Keywords: Diabetes; Long Working Hours; Machine Learning; Night Work; Prospective Cohort Blood Sugar Disorders; Diabetes; Long Work Hours; Shift Work; Machine Learning; Cohort Studies; Work Schedules;
• Source:
  Saf Health Work 2025 Sep; 13(3):355-360
• ISSN:
  2093-7911
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; San Francisco Region [OSHA]
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  6 pdf pages
• Volume:
  16
• NIOSHTIC Number:
  nn:20071324
• Contact Point Address:
  Dr. Jian Li, Fielding School of Public Health and the School of Nursing, University of California Los Angeles, 650 Charles E Young Dr South, Los Angeles, CA 90095, United States
• Email:
  jianli2019@ucla.edu
• Federal Fiscal Year:
  2025
• Performing Organization:
  University of California Los Angeles
• Peer Reviewed:
  True
• Start Date:
  20050701
• End Date:
  20270630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:39c4941222f5df640c6dfd6bf6de290a912a3842d18f7d416f59d5812aeb7c1bb074b2cfcb57ac5d6f6c8592ac4948ef12b7bee1000f4f7c5f63823450b4e48f
• Download URL:
  https://stacks.cdc.gov/view/cdc/258800/cdc_258800_DS1.pdf
• File Type:
  [PDF - 639.32 KB ]