Programs and Communities

Objectives: We estimated neighborhood effects of population characteristics and built and natural environments on deaths due to heat exposure in Maricopa County, Arizona (2000–2008).

Methods: We used 2000 U.S. Census data and remotely sensed vegetation and land surface temperature to construct indicators of neighborhood vulnerability and a geographic information system to map vulnerability and residential addresses of persons who died from heat exposure in 2,081 census block groups. Binary logistic regression and spatial analysis were used to associate deaths with neighborhoods.

Results: Neighborhood scores on three factors—socioeconomic vulnerability, elderly/isolation, and unvegetated area—varied widely throughout the study area. The preferred model (based on fit and parsimony) for predicting the odds of one or more deaths from heat exposure within a census block group included the first two factors and surface temperature in residential neighborhoods, holding population size constant. Spatial analysis identified clusters of neighborhoods with the highest heat vulnerability scores. A large proportion of deaths occurred among people, including homeless persons, who lived in the inner cores of the largest cities and along an industrial corridor.

Conclusions: Place-based indicators of vulnerability complement analyses of person-level heat risk factors. Surface temperature might be used in Maricopa County to identify the most heat-vulnerable neighborhoods, but more attention to the socioecological complexities of climate adaptation is needed.

In this study we characterized the relationship between temperature and mortality in central Arizona desert cities that have an extremely hot climate. Relationships between daily maximum apparent temperature (ATmax) and mortality for eight condition-specific causes and all-cause deaths were modeled for all residents and separately for males and females ages <65 and ≥65 during the months May–October for years 2000–2008. The most robust relationship was between ATmax on day of death and mortality from direct exposure to high environmental heat. For this condition-specific cause of death, the heat thresholds in all gender and age groups (ATmax = 90–97 °F; 32.2‒36.1 °C) were below local median seasonal temperatures in the study period (ATmax = 99.5 °F; 37.5 °C). Heat threshold was defined as ATmax at which the mortality ratio begins an exponential upward trend. Thresholds were identified in younger and older females for cardiac disease/stroke mortality (ATmax = 106 and 108 °F; 41.1 and 42.2 °C) with a one-day lag. Thresholds were also identified for mortality from respiratory diseases in older people (ATmax = 109 °F; 42.8 °C) and for all-cause mortality in females (ATmax = 107 °F; 41.7 °C) and males <65 years (ATmax = 102 °F; 38.9 °C). Heat-related mortality in a region that has already made some adaptations to predictable periods of extremely high temperatures suggests that more extensive and targeted heat-adaptation plans for climate change are needed in cities worldwide.

In an extreme heat event, people can go to air-conditioned public facilities if residential air-conditioning is not available. Residences that heat slowly may also mitigate health effects, particularly in neighborhoods with social vulnerability. We explored the contributions of social vulnerability and these infrastructures to heat mortality in Maricopa County and whether these relationships are sensitive to temperature. Using Poisson regression modeling with heat-related mortality as the outcome, we assessed the interaction of increasing temperature with social vulnerability, access to publicly available air conditioned space, home air conditioning and the thermal properties of residences. As temperatures increase, mortality from heat-related illness increases less in census tracts with more publicly accessible cooled spaces. Mortality from all internal causes of death did not have this association. Building thermal protection was not associated with mortality. Social vulnerability was still associated with mortality after adjusting for the infrastructure variables. To reduce heat-related mortality, the use of public cooled spaces might be expanded to target the most vulnerable.

Mortality from environmental heat is a significant public health problem in Maricopa County, especially because it is largely preventable. Maricopa County has conducted heat surveillance since 2006. Each year, the enhanced heat surveillance season usually begins in May and ends in October. The main goals of heat surveillance are to identify the demographic characteristics of heat-associated deaths (e.g., age and gender) and the risk factors for mortality (e.g., homelessness). Sharing this information helps community stakeholders to design interventions in an effort to prevent heat-associated deaths among vulnerable populations.

The two main sources of data for heat surveillance are: preliminary reports of death (PRODs) from the Office of the Medical Examiner (OME) and death certificates from the MCDPH Office of Vital Registration.

Heat-associated deaths are classified as heat-caused or heat related. Heat-caused deaths are those in which environmental heat was directly involved in the sequence of conditions causing deaths. Heat-related deaths are those in which environmental heat contributed to the deaths but was not in the sequence of conditions causing these deaths. For more information on how heat-associated deaths are classified, see the definitions in Appendix. For more information on MCDPH’s surveillance system, see Background and Methodology.

Background: Extreme heat is a public health challenge. The scarcity of directly comparable studies on the association of heat with morbidity and mortality and the inconsistent identification of threshold temperatures for severe impacts hampers the development of comprehensive strategies aimed at reducing adverse heat-health events.

Objectives: This quantitative study was designed to link temperature with mortality and morbidity events in Maricopa County, Arizona, USA, with a focus on the summer season.

Methods: Using Poisson regression models that controlled for temporal confounders, we assessed daily temperature–health associations for a suite of mortality and morbidity events, diagnoses, and temperature metrics. Minimum risk temperatures, increasing risk temperatures, and excess risk temperatures were statistically identified to represent different “trigger points” at which heat-health intervention measures might be activated.

Results: We found significant and consistent associations of high environmental temperature with all-cause mortality, cardiovascular mortality, heat-related mortality, and mortality resulting from conditions that are consequences of heat and dehydration. Hospitalizations and emergency department visits due to heat-related conditions and conditions associated with consequences of heat and dehydration were also strongly associated with high temperatures, and there were several times more of those events than there were deaths. For each temperature metric, we observed large contrasts in trigger points (up to 22°C) across multiple health events and diagnoses.

Conclusion: Consideration of multiple health events and diagnoses together with a comprehensive approach to identifying threshold temperatures revealed large differences in trigger points for possible interventions related to heat. Providing an array of heat trigger points applicable for different end-users may improve the public health response to a problem that is projected to worsen in the coming decades.

Maricopa County, Arizona, anchor to the fastest growing megapolitan area in the United States, is located in a hot desert climate where extreme temperatures are associated with elevated risk of mortality. Continued urbanization in the region will impact atmospheric temperatures and, as a result, potentially affect human health. We aimed to quantify the number of excess deaths attributable to heat in Maricopa County based on three future urbanization and adaptation scenarios and multiple exposure variables.

Two scenarios (low and high growth projections) represent the maximum possible uncertainty range associated with urbanization in central Arizona, and a third represents the adaptation of high-albedo cool roof technology. Using a Poisson regression model, we related temperature to mortality using data spanning 1983–2007. Regional climate model simulations based on 2050-projected urbanization scenarios for Maricopa County generated distributions of temperature change, and from these predicted changes future excess heat-related mortality was estimated. Subject to urbanization scenario and exposure variable utilized, projections of heat-related mortality ranged from a decrease of 46 deaths per year (− 95%) to an increase of 339 deaths per year (+ 359%).

Projections based on minimum temperature showed the greatest increase for all expansion and adaptation scenarios and were substantially higher than those for daily mean temperature. Projections based on maximum temperature were largely associated with declining mortality. Low-growth and adaptation scenarios led to the smallest increase in predicted heat-related mortality based on mean temperature projections. Use of only one exposure variable to project future heat-related deaths may therefore be misrepresentative in terms of direction of change and magnitude of effects. Because urbanization-induced impacts can vary across the diurnal cycle, projections of heat-related health outcomes that do not consider place-based, time-varying urban heat island effects are neglecting essential elements for policy relevant decision-making.

The maps in this chartbook describe the food environment in Trenton in terms of access to supermarkets, smaller grocery stores, convenience stores, and limited service restaurants. Research shows that when residents have access to healthy food outlets, they tend to eat healthy.

•Food environment maps were created using geo-coded commercially available data of food outlets (InfoUSA, 2008 and Trade Dimensions, 2008) in Trenton and in a 1 mile buffer area around Trenton.

•Using the commercial data and additional investigation, food outlets were classified into different categories based on their likelihood of carrying healthy choices: supermarkets carry most healthy choices; smaller grocery stores carry fewer healthy choices; convenience stores and limited service restaurants are likely to carry mostly unhealthy choices.

• Access to different types of food outlets was computed at the census block group level based on concentration of stores / restaurants per unit area and is reported as food outlet densities.

•Food outlet density maps are compared with Census 2000 data to visualize accessibility of healthy foods in neighborhoods with different characteristics.

Data Sources: Info USA food outlet 2008 data

Trade Dimensions food outlet 2008 data

Census 2000 data

New Jersey Department of Education 2008-2009 data