Neighborhood Effects on Heat Deaths
Social and Environmental Predictors of Vulnerability in Maricopa County, Arizona
Bibliographic Data
| ID | 23330331 |
|---|---|
| Authors | L Harlan (0000-0001-9945-2792, Arizona State University), Sharon L Harlan (Arizona State University), Juan Declet‐Barreto (0000-0001-7101-394X, Arizona State University), Juan H Declet-Barreto (Arizona State University), William L Stefanov (0000-0002-9181-7619, Science Applications Research and Development, Jacobs/Engineering Science and Contract Group, NASA Johnson Space Center, Houston, Texas, USA), Diana B Petitti (Arizona State University) |
| Year | 2013 |
| Volume | 121 |
| Issue | 2 |
| Pages | 197-204 |
| Publication date | 2013-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Health Perspectives (JOURNAL) |
| Journal identifiers | ISSN: 0091-6765 • E-ISSN: 1552-9924 |
| Publisher | American Chemical Society (ACS) (PUBLISHER) |
| DOI | 10.1289/ehp.1104625 |
| PMID | 23164621 |
| PMCID | PMC3569676 |
| OpenAlex | W2144395796 |
| Language | EN |
| Citations received | 141 |
| References cited | 38 |
Background: Most heat-related deaths occur in cities, and future trends in global climate change and urbanization may amplify this trend. Understanding how neighborhoods affect heat mortality fills an important gap between studies of individual susceptibility to heat and broadly comparative studies of temperature–mortality relationships in cities. 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.
Census · Environmental health · Geography · Meteorology · Population · Socioeconomic status · Socioeconomics · Sociology · Urban heat island · Urbanization · Vulnerability (computing) · Climate Change and Health Impacts · Climate Change, Adaptation, Migration · Demography · Ecology · Medicine · Urban Heat Island Mitigation
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| Unique citing works | 141 |
|---|---|
| Citations per year | 11,75 |
| Citation span | 2014 - 2026 (13) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 130 |