Although effective preventive measures for heat-related illness have been recommended and mandated for military personnel, there continues to be incident cases. In 2016, there were 401 incident cases of heat stroke and 2,135 incident cases of âother heat illnessâ among all active component service members. Current military guidelines utilize the wet bulb globe temperature (WBGT) index to measure heat risk, guiding work/rest and hydration practices. The WBGT requires calibrated instrumentation and is based on fixed cutoff values. We propose using readily available meteorological data inputs and EHI cases to identify and validate an EHI risk prediction model. Prior studies have found that combinations of WBGT and the previous dayâs WBGT and relative humidity and temperature have predictive value for EHI. We build upon prior work by using generalized additive models (GAMs).
Objective:
To identify predictors of the risk of developing exertional heat illness (EHI) among basic training populations in the Department of Defense.