Spatial Modeling and Analysis of Heat-Related Morbidity in Maricopa County, Arizona.

The objective of the present study was to examine the effects of a confluence of demographic, socioeconomic, housing, and environmental factors that systematically contribute to heat-related morbidity in Maricopa County, Arizona, from theoretical, empirical, and spatial perspectives. The present study utilized ordinary least squares (OLS) regression and multiscale geographically weighted regression (MGWR) to analyze health data, U.S. census data, and remotely sensed data. The results suggested that the MGWR model showed a significant improvement in goodness of fit over the OLS regression model, which implies that spatial heterogeneity is an essential factor that influences the relationship between these factors. Populations of people aged 65+, Hispanic people, disabled people, people who do not own vehicles, and housing occupancy rate have much stronger local effects than other variables. These findings can be used to inform and educate local residents, communities, stakeholders, city managers, and urban planners in their ongoing and extensive efforts to mitigate the negative impacts of extreme heat on human health in Maricopa County.

Heat-Associated Mortality in a Hot Climate : Maricopa County, Arizona, 2006-2016.

OBJECTIVES: Maricopa County, Arizona (2017 population about 4.3 million), is located in the Sonoran Desert. In 2005, the Maricopa County Department of Public Health (MCDPH) established a heat-associated mortality surveillance system that captures data on circumstances of death for Maricopa County residents and visitors. We analyzed 2006-2016 surveillance system data to understand the characteristics and circumstances of heat-associated deaths. METHODS: We classified heat-associated deaths based on International Classification of Diseases, Tenth Revision codes (X30, T67.X, and P81.0) and phrases (heat exposure, environ, exhaustion, sun, heat stress, heat stroke, or hyperthermia) in part I or part II of the death certificate. We summarized data on decedents' demographic characteristics, years lived in Arizona, location of death (indoors vs outdoors), presence and functionality of air conditioning, and whether the decedent had been homeless. We examined significant associations between variables by using the Pearson χ2 tests and logistic regression. RESULTS: During 2006-2016, MCDPH recorded data on 920 heat-associated deaths, 912 of which included location of injury. Of 565 (62%) heat-associated deaths that occurred outdoors, 458 (81%) were among male decedents and 243 (43%) were among decedents aged 20-49. Of 347 (38%) heat-associated deaths that occurred indoors, 201 (58%) were among decedents aged ≥65. Non-Arizona residents were 5 times as likely as Arizona residents to have a heat-associated death outdoors (P < .001). Of 727 decedents with data on duration of Arizona residency, 438 (60%) had resided in Arizona ≥20 years. CONCLUSIONS: Ongoing evaluation of interventions that target populations at risk for both outdoor and indoor heat-associated deaths can further inform refinement of the surveillance system and identify best practices to prevent heat-associated deaths.

Evaluation of a Novel Syndromic Surveillance Query for Heat-Related Illness Using Hospital Data From Maricopa County, Arizona, 2015.

OBJECTIVES: We evaluated a novel syndromic surveillance query, developed by the Council of State and Territorial Epidemiologists (CSTE) Heat Syndrome Workgroup, for identifying heat-related illness cases in near real time, using emergency department and inpatient hospital data from Maricopa County, Arizona, in 2015. METHODS: The Maricopa County Department of Public Health applied 2 queries for heat-related illness to area hospital data transmitted to the National Syndromic Surveillance Program BioSense Platform: the BioSense "heat, excessive" query and the novel CSTE query. We reviewed the line lists generated by each query and used the diagnosis code and chief complaint text fields to find probable cases of heat-related illness. For each query, we calculated positive predictive values (PPVs) for heat-related illness. RESULTS: The CSTE query identified 674 records, of which 591 were categorized as probable heat-related illness, demonstrating a PPV of 88% for heat-related illness. The BioSense query identified 791 patient records, of which 589 were probable heat-related illness, demonstrating a PPV of 74% for heat-related illness. The PPV was substantially higher for the CSTE novel and BioSense queries during the heat season (May 1 to September 30; 92% and 85%, respectively) than during the cooler seasons (55% and 29%, respectively). CONCLUSION: A novel query for heat-related illness that combined diagnosis codes, chief complaint text terms, and exclusion criteria had a high PPV for heat-related illness, particularly during the heat season. Public health departments can use this query to meet local needs; however, use of this novel query to substantially improve public health heat-related illness prevention remains to be seen.