Built Environment, Hospital Access, and Heat Related Illnesses in Arizona

• In Event: Multi-Level Climate Adaptation and Governance Across Individual, Household, Community, and City Levels

Abstract

Heat is a leading cause of weather-related deaths in the U.S. and worldwide. The threat from extreme heat is growing as many parts of the world continue breaking the new temperature records. While Heat Related Illnesses (HRI) is one of the most important ways to measure patients’ degree of illnesses from extreme heat by Emergency Medical Services system, in most parts of the world, this record is not systematically organized due to thin heat-health information infrastructure.The state of Arizona in the U.S. has been one of the key innovators in heat policy and governance in part because there is rapid increase in harmful health impacts. Thus, Arizona’s heat related data has been systemically tracked by local and state health officials. This study combines heat related illnesses and environmental health related data in 2024 at the most disaggregated level (community statistical area) that is publicly available. The Dependent variable is the HRI rate standardized by the size of population. Key independent variables are percentage of old housing units and hospital access data in a given community.Descriptively, HRI rate is highest among the remote/small towns and the urban centers, resembling ‘camel back’ curve shape, that is much different than ‘urban heat island effects’ focusing only on temperature aspects and thus overlooking rural areas. Preliminary regression models suggest that 1 % increase in old housing units is associated with 1.2% increase in HRI rate. Each additional mile farther from general hospital is associated with 4.3% increase in HRI rate. Finally, communities in rural areas have 62% higher HRI rate compared to urban areas.Limitations of this work include that tribal community data is largely missing from the publicly available data, which may be a significant one given the prominent presence of tribal communities in Arizona. Another limitation is that while community statistical area is the most disaggregated levels of data, but it is still coarse than census tract level data. For future work, more fine-grained data analyses may be considered while also considering the ethical and social issues related to the release of finer-grained data.

Author

• Minwoo Ahn, College of Charleston
  Presenting Author