407 Evaluating Heatwave Definitions Using Heat-Related Health Outcomes

Monday, 13 January 2020
Hall B (Boston Convention and Exhibition Center)
Jagadeesh Puvvula, Univ. of Nebraska Medical Center, Omaha, NE; and A. M. Abadi and J. E. Bell

According to the Intergovernmental Panel on Climate Change (IPCC), the rise in earth’s temperature is expected to increase the frequency of heatwaves in the future. A more significant proportion of the population is estimated to be exposed to heatwaves, which could potentially amplify the heat-related morbidity and mortality. In the United States, extreme weather events have a higher death toll than any other environmental disasters. The Center for Disease Control and Prevention (CDC), estimated about 600 people are killed each year due to extreme heat events. The exposure to extreme temperature is a preventable risk factor by developing heatwave warning systems. Based on the existing literature, there is no consistent heatwave definition that could be applied to the country. The temperature variability across a region would play a key role in human adaptability to the temperature. Central and western regions of North America are expected to be exposed to the highest levels of warming for extremely hot days. So, our study focused on evaluating the heatwave definitions by attributing to heat-related illness (emergency department) hospital admissions in North Carolina. We extracted daily minimum/maximum/mean temperature data in three geophysical regions in North Carolina; Coastal, Piedmont, and Mountains, from the NOAA Global Historical Climatological Network (GHCN). We formulated 18 scenarios for defining heatwaves using daily heat metric, duration, and threshold intensity. The daily heat-related illness hospital admission for the summer season (May - September) from 2011 to 2016, was obtained from NC DETECT syndromic surveillance database. We calculated the frequency tables by region, based on each heatwave definition. Additionally, to understand the temporal trend, distribution of hospital admissions by days of the week, we conducted descriptive statistics by different temperature metrics specific to the region. We will also apply the Generalized Additive Model and Distributed Lag Non-Linear Model to identify the heatwave definition that could predict the heat-related illness hospital admissions with a minimal prediction error. The results of this study will help to identify a heatwave definition that could be used to develop heatwave alerts during the summer season in North Carolina.
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