Near-miss narratives from the fire service: a Bayesian analysis

Details

• Personal Author:
  Lacovara AV ; Lehto M ; Pandian R ; Smith GS ; Taylor JA
• Description:
  Background: In occupational safety research, narrative text analysis has been combined with coded surveillance, data to improve identification and understanding of injuries and their circumstances. Injury data give, information about incidence and the direct cause of an injury, while near-miss data enable the identification of various hazards within an organization or industry. Further, near-miss data provide an opportunity for surveillance and risk reduction. The National Firefighter Near-Miss Reporting System, (NFFNMRS) is a voluntary reporting system that collects narrative text data on near-miss and injurious events within the fire and emergency services industry. In recent research, autocoding techniques, using Bayesian models have been used to categorize/code injury narratives with up to 90% accuracy, thereby reducing the amount of human effort required to manually code large datasets. Autocoding, techniques have not yet been applied to near-miss narrative data. Methods: We manually assigned mechanism of injury codes to previously un-coded narratives from the NFFNMRS and used this as a training set to develop two Bayesian autocoding models, Fuzzy and Naïve. We calculated sensitivity, specificity and positive predictive value for both models. We also evaluated the effect of training set size on prediction sensitivity and compared the models' predictive ability as related to injury outcome. We cross-validated a subset of the prediction set for accuracy of the model predictions. Results: Overall, the Fuzzy model performed better than Naïve, with a sensitivity of 0.74 compared to 0.678. Where Fuzzy and Naïve shared the same prediction, the cross-validation showed a sensitivity of 0.602. As the number of records in the training set increased, the models performed at a higher sensitivity, suggesting that both the Fuzzy and Naïve models were essentially "learning". Injury records were predicted with greater sensitivity than near-miss records. Conclusion: We conclude that the application of Bayesian autocoding methods can successfully code both near misses and injuries in longer-than-average narratives with non-specific prompts regarding injury. Such coding allowed for the creation of two new quantitative data elements for injury outcome and injury mechanism. [Description provided by NIOSH]
• Subjects:
  Accidents, Occupational Epidemiology Firefighters Occupational Health Quality Control Safety
• Keywords:
  Accident-potential Analytical-instruments Analytical-models Author Keywords: Text-mining Bayesian Models Fire-fighters Fire Fighter Injury Information-retrieval-systems Injuries Mathematical-models Near-miss Narratives Performance-capability Quality-control Quantitative-analysis Sensitivity-testing Surveillance-programs

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• ISSN:
  0001-4575
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Indiana ; Maryland ; OSHA Region 3 ; OSHA Region 5 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  119-129
• Volume:
  62
• NIOSHTIC Number:
  nn:20044767
• Citation:
  Accid Anal Prev 2014 Jan; 62:119-129
• Contact Point Address:
  Jennifer A. Taylor, Department of Environmental & Occupational Health, Drexel University School of Public Health, 1505 Race Street, MS 1034, Philadelphia, PA 19102, USA
• Email:
  Jat65@drexel.edu
• Federal Fiscal Year:
  2014
• NORA Priority Area:
  Public Safety
• Performing Organization:
  Drexel University, Philadelphia, Pennsylvania
• Peer Reviewed:
  True
• Start Date:
  20110901
• Source Full Name:
  Accident Analysis and Prevention
• End Date:
  20130831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:b5e89ee8f2b3092c57e6e229d58db32caaf24942c0f324f22e3c7db85d1cf700fc24890546c719094a1e55736bcdceae77647218263008b21b75e3a8e0767cc1
• Download URL:
  https://stacks.cdc.gov/view/cdc/200401/cdc_200401_DS1.pdf
• File Type:
  [PDF - 1.17 MB ]