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Modeling Predictors of Duties Not Including Flying Status

机译：建模预测的职责不包括飞行状态

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INTRODUCTION: The purpose of this study was to reuse available datasets to conduct an analysis of potential predictors of U.S. Air Force aircrew nonavailability in terms of being in "duties not to include flying" (DNIF) status.METHODS: 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, pay grade, and service component. 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.RESULTS: Out of a set of 783 potential predictor variables, 339 variables were identified from the nonparametric exploratory analysis for inclusion in the parametric analysis. Of these, 54 variables had significant associations with DNIF duration in the final model fitted to the validation data set. The predicted RESULTS of this model for DNIF duration had a correlation of 0.45 with the actual number of DNIF days. Predictor variables included age, 6 AFSCs, 7 clinic locations, and 40 primary diagnosis categories.DISCUSSION: Specific demographic (i.e., age), occupational (i.e., AFSC), and health (i.e., clinic location and primary diagnosis category) DNIF drivers were identified. Subsequent research should focus on the application of primary, secondary, and tertiary prevention measures to ameliorate the potential impact of these DNIF drivers where possible,
机译：作品简介:本研究的目的重用可用数据集进行分析潜在的预测美国空军飞行员nonavailability而言的“职责包括飞行”(DNIF)状态。美国的研究是回顾性队列分析空军飞行员在现役从2003 - 2012。包括年龄、空军专业代码(AFSC),诊所的位置、诊断、性别、薪酬等级、和服务组件。DNIF持续时间。探索性分析和参数方法被用于构建和模型统计推断。339年783潜在的预测变量,变量从非参数被确定包含的探索性分析参数分析。重要的协会与DNIF持续时间最后的模型拟合验证数据集。持续时间有相关性的0.45实际数量DNIF天。包括年龄、6 AFSCs 7诊所位置和40初步诊断类别。人口(即年龄)、职业(例如,AFSC)和健康(例如,诊所和位置初步诊断类别)DNIF司机识别。的应用,小学,中学,和三级预防措施改善这些DNIF司机的潜在影响可能的,

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