Doctoral Dissertation Research: Climate-Health Vulnerability: Identifying Climate Thresholds for Heat-Related Illness

Heat is the leading cause of weather-related death in the United States. General circulation models of climate change project heat waves to increase in severity, duration, and intensity, placing future populations at greater risk for both heat-related illnesses and deaths. This project focuses on identifying the local to regional scale patterns of heat-related illness and the temperature thresholds that control these patterns. Temperature thresholds represent climate values (e.g., temperature, heat index, and humidity) beyond which heat-related illness emergency department admissions increase significantly. Identifying a temperature threshold allows for the activation of an early heat warning system or emergency response plan. Heat-related illness can be easily preventable through hydration or relocation to a cooler environment; therefore, interventions are a key component in reducing heat-related illness. This study will also identify high-risk populations that experience a disproportionate number of heat-related illness emergency department admissions. Previous research has investigated heat-related mortality and morbidity in urban cities. However, it has relatively neglected adverse health effects of heat in rural US locations. This study identifies high-risk populations and risk factors in both urban and rural locations (including those with at-risk agricultural and farm workers).

This objective of this study is to understand the spatiotemporal patterns of heat-related illness and how they correspond to the spatial patterns of temperature thresholds. This research employs a multidisciplinary approach to understanding the climatic, environmental, and population factors that affect patterns of heat-related illness. It draws from the human - ecological approach of vulnerability theory, spatial statistics from geography, and climate threshold identification from climatology to determine the locations and factors by which climate impacts heat-related illness. The explicit spatiotemporal integration of available fine-grained morbidity data, census, and weather data in the methodology will be a novel contribution to climate-public health research and provide new findings about the environmental and socioeconomic factors that influence high rates of heat-related illness. A wide array of spatiotemporal relationships is identified for different time periods, ages, genders, and spatial locations. These relationships are established through a geographic information system (GIS) framework, an automated freeware (python) program to identify temperature thresholds, and several regression techniques, including as geographically weighted regression, spatial regression, and multi-level modelling. Though set in North Carolina and centered on heat-related illness, the methodology utilized in this study will also be applicable to other climate-related diseases (e.g. waterborne or respiratory diseases) and can also be used as a model to assess climate-health vulnerability in other geographic locations. In addition to academic dissemination, the results will be distributed to the NC Division of Public Health, Carolina's Integrated Science Assessments, the National Weather Service, NC Farmworker Ministry Committee and the North Carolina Farmworker Institute.