Determining Multiple Thresholds for Thermal Health Risk Levels Using the Segmented Poisson Regression Model

Abstract

Determining the thresholds for risk assessment is critical for the successful implementation of thermal health warning systems. A risk assessment methodology with multiple thresholds must be developed to provide detailed warning information to the public and decision makers. This study developed a new methodology to identify multiple thresholds for different risk levels for heat or cold wave events by considering simultaneously impact on public health. A new objective function was designed to optimize segmented Poisson regression, which relates public health to temperature indicators. Thresholds were identified based on the values of the objective functions for all threshold candidates. A case study in identifying thresholds for cold and heat wave events in Seoul, South Korea, from 2014 to 2018, was conducted to evaluate the appropriateness of the proposed methodology. Daily minimum or maximum air temperature, mortality, and morbidity data were used for threshold identification and evaluation. The proposed methodology can successfully identify multiple thresholds to simultaneously represent different risk levels. These thresholds show comparable performance to those using the relative frequency approach.