A Bayesian Approach on Investigating Helicopter Emergency Medical Fatal Accidents

Abstract

INTRODUCTION: Helicopter Emergency Medical Service (HEMS) is a mode of transportation designed to expedite the transport of a patient. Compared to other modes of emergency transport and other areas of aviation, historically HEMS has had the highest accident-related fatality rates. Analysis of these accident data has revealed factors associated with an increased likelihood of accident-based fatalities. Here we report the results of an analysis on the likelihood of a fatality based on various factors as a result of a HEMS accident, employing a Bayesian framework.METHODS: A retrospective study was conducted using data extracted from the NTSB aviation accident database from April 31, 2005, to April 26, 2018. Evidence from Baker et al. (2006) was also used as prior information spanning from January 1, 1983, to April 30, 2005.RESULTS: A Bayesian logistic regression was implemented using the prior information and current data to calculate a posterior distribution confidence interval of possible values in predicting accident fatality. The results of the model indicate that flying at night (OR 3.06; 95 C.I 2.14, 4.48; PoD 100), flying under Instrument Flight Rules (OR 7.54; 95 C.I 3.94, 14.44; PoD 100), and post-crash fires (OR 18.73; 95 C.I 10.07, 34.12; PoD 100) significantly contributed to the higher likelihood of a fatality.CONCLUSION: Our results provide a comprehensive analysis of the most influential factors associated with an increased likelihood of a fatal accident occurring. We found that over the past 35 yr these factors were consistently associated with a higher likelihood of a fatality occurring.Simonson RJ, Keebler JR, Chaparro A. A Bayesian approach on investigating helicopter emergency medical fatal accidents. Aerosp Med Hum Perform. 2021; 92(7):563569.