Evaluating the Sensitivity of Heat Wave Definitions among North Carolina Physiographic Regions

Jagadeesh Puvvula, Azar M. Abadi, Kathryn C. Conlon, Jared J. Rennie, Hunter Jones and Jesse E. Bell ()
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Jagadeesh Puvvula: Department of Environmental, Agricultural and Occupational Health, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA
Azar M. Abadi: Department of Environmental, Agricultural and Occupational Health, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA
Kathryn C. Conlon: Department of Public Health Sciences, University of California Davis, One Shields Ave, Davis, CA 95616, USA
Jared J. Rennie: National Centers for Environmental Information, Asheville, NC 28801, USA
Hunter Jones: Medical Sciences Interdepartmental Area, Office of Graduate Studies, University of Nebraska Medical Center, Omaha, NE 68198, USA
Jesse E. Bell: Department of Environmental, Agricultural and Occupational Health, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA

IJERPH, 2022, vol. 19, issue 16, 1-13

Abstract: Exposure to extreme heat is a known risk factor that is associated with increased heat-related illness (HRI) outcomes. The relevance of heat wave definitions (HWDs) could change across health conditions and geographies due to the heterogenous climate profile. This study compared the sensitivity of 28 HWDs associated with HRI emergency department visits over five summer seasons (2011–2016), stratified by two physiographic regions (Coastal and Piedmont) in North Carolina. The HRI rate ratios associated with heat waves were estimated using the generalized linear regression framework assuming a negative binomial distribution. We compared the Akaike Information Criterion (AIC) values across the HWDs to identify an optimal HWD. In the Coastal region, HWDs based on daily maximum temperature with a threshold > 90th percentile for two or more consecutive days had the optimal model fit. In the Piedmont region, HWD based on the daily minimum temperature with a threshold value > 90th percentile for two or more consecutive days was optimal. The HWDs with optimal model performance included in this study captured moderate and frequent heat episodes compared to the National Weather Service (NWS) heat products. This study compared the HRI morbidity risk associated with epidemiologic-based HWDs and with NWS heat products. Our findings could be used for public health education and suggest recalibrating NWS heat products.

Keywords: heat wave; heat-related illness; early heat?health warning systems (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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