Aircrew Availability: Modeling Predictors of Duties Not Including Flying Status

Abstract

Aerospace medicine practitioners track the epidemiology of conditions that limit aircrew availability and work toward prevention of these conditions. These prevention efforts should focus on those conditions that are the primary driver of aircrew non-availability. The purpose of this study was to reuse available datasets to conduct an analysis of potential predictors of U.S. Air Force aircrew non-availability in terms of being in duties not to include flying (DNIF) status. This study was a retrospective cohort analysis of U.S. Air Force aircrew on active duty during the period from 2003-2012. Predictor variables included age, Air Force Specialty Code (AFSC), clinic location, diagnosis, gender, and pay grade. The response variable was DNIF duration. Nonparametric methods were used for the exploratory analysis and parametric methods were used for model building and statistical inference. Significant associations were observed between age, AFSC, clinic, and primary diagnosis category and expected DNIF duration. While controlling for specific diagnoses, increasing age was positively associated with expected DNIF duration. Six AFSCs were associated with an increased expected DNIF duration; however, these AFSCs were not significant drivers of DNIF duration based on the Pareto analysis.

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Document Details

Document Type
Technical Report
Publication Date
Jul 25, 2017
Accession Number
AD1037848

Entities

People

  • Anthony P. Tvaryanas
  • Converse Jr Griffith

Organizations

  • United States Air Force School of Aerospace Medicine

Tags

Communities of Interest

  • Biomedical
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Cardiovascular System
  • Diseases And Disorders
  • Health Services
  • Heart Diseases
  • Hernia
  • Medical Personnel
  • Statistical Analysis
  • Warfare

Readers

  • Occupational Health and Safety.
  • Regression Analysis.
  • Women's Health and Cancer Risk Research: African American Women and Pregnancy Outcomes.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space