A Statistical Framework to Evaluate Extreme Weather Definitions from a Health Perspective: A Demonstration Based on Extreme Heat Events

Abstract

ssuance of alerts, prior to or during extreme weather events, can be critical to preventing adverse health outcomes. In this paper, data describing episodes of extreme heat are used to demonstrate the application of a statistical framework for evaluating event definitions within the context of human health impacts. Numerous extreme heat event (EHE) definitions appear in the literature but there is a lack of scientific consensus on consistent identification of periods of extreme heat having the potential for such impacts. Ninety-two EHE definitions were operationalized for this region-specific demonstration covering the United States, using station-based meteorological data for the years 1999?2009. Hierarchical cluster analysis was used to group definitions into homogeneous sets, and a representative definition was then selected from each set. The representative definitions were combined with different exposure offsets (e.g., a 1-day lag) and evaluated against daily heat mortality data, using a negative binomial rate regression modeling approach. The EHE definition and exposure offset combinations most closely associated with heat mortality were found to vary with climate region. Those involving high but not extreme meteorological thresholds, and employing a 1-day lag or no lag, showed the strongest associations with heat mortality for all climate regions except the South and Southwest. The framework presented in this study could be applied to other geographic areas, provided the necessary meteorological and health data are available. Additionally, this framework could help regional and national weather offices, in partnership with local and state health departments, identify definitions that are well suited to issuing alerts and health advisories related to other types of severe weather.