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Neighborhood effects on heat deaths: social and environmental predictors of vulnerability in Maricopa County, Arizona.

Harlan SL, Declet-Barreto JH, Stefanov WL, Petitti DB - Environ. Health Perspect. (2012)

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

View Article: PubMed Central - PubMed

Affiliation: School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona 85284-2402, USA. sharon.harlan@asu.edu

ABSTRACT

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.

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Related in: MedlinePlus

Univariate analysis of the LISA-identified clusters of census block groups (CBGs) in Maricopa County, Arizona, with similar or dissimilar HVI scores (p-value ≤ 0.05). High/high areas in the map are clusters of neighboring CBGs with uniformly high vulnerability scores; low/low areas are clusters with low vulnerability scores; low/high areas represent a CBG with a low vulnerability score neighbored by high vulnerability CBGs; high/low areas represent a CBG with a high vulnerability score neighbored by low vulnerability CBGs. Entries in the legend (next to the colored boxes) also show the percentages of 2000–2008 heat-related decedents who were residents in each type of cluster.
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f2: Univariate analysis of the LISA-identified clusters of census block groups (CBGs) in Maricopa County, Arizona, with similar or dissimilar HVI scores (p-value ≤ 0.05). High/high areas in the map are clusters of neighboring CBGs with uniformly high vulnerability scores; low/low areas are clusters with low vulnerability scores; low/high areas represent a CBG with a low vulnerability score neighbored by high vulnerability CBGs; high/low areas represent a CBG with a high vulnerability score neighbored by low vulnerability CBGs. Entries in the legend (next to the colored boxes) also show the percentages of 2000–2008 heat-related decedents who were residents in each type of cluster.

Mentions: We observed a moderate degree of overall spatial clustering of vulnerability scores among neighborhoods (Global Moran’s I for HVI = 0.37; p = 0.05). LISA analysis (Figure 2), identified neighborhood clusters with similar or dissimilar scores (p ≤ 0.05). High/high (red) clusters in Figure 2 represent neighborhoods with extremely high vulnerability scores next to others with extremely high scores. One-quarter of heat decedents lived in high/high clusters. Only 9.6% of heat decedents lived in clusters with low/low vulnerability scores (blue) or dissimilar scores (e.g., purple areas, indicating low vulnerability neighborhoods next to high). Sixty percent of the 50 heat deaths among homeless people occurred in high/high vulnerability clusters [see Supplemental Material, Figure S1 (http://dx.doi.org/10.1289/ehp.1104625)].


Neighborhood effects on heat deaths: social and environmental predictors of vulnerability in Maricopa County, Arizona.

Harlan SL, Declet-Barreto JH, Stefanov WL, Petitti DB - Environ. Health Perspect. (2012)

Univariate analysis of the LISA-identified clusters of census block groups (CBGs) in Maricopa County, Arizona, with similar or dissimilar HVI scores (p-value ≤ 0.05). High/high areas in the map are clusters of neighboring CBGs with uniformly high vulnerability scores; low/low areas are clusters with low vulnerability scores; low/high areas represent a CBG with a low vulnerability score neighbored by high vulnerability CBGs; high/low areas represent a CBG with a high vulnerability score neighbored by low vulnerability CBGs. Entries in the legend (next to the colored boxes) also show the percentages of 2000–2008 heat-related decedents who were residents in each type of cluster.
© Copyright Policy - public-domain
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3569676&req=5

f2: Univariate analysis of the LISA-identified clusters of census block groups (CBGs) in Maricopa County, Arizona, with similar or dissimilar HVI scores (p-value ≤ 0.05). High/high areas in the map are clusters of neighboring CBGs with uniformly high vulnerability scores; low/low areas are clusters with low vulnerability scores; low/high areas represent a CBG with a low vulnerability score neighbored by high vulnerability CBGs; high/low areas represent a CBG with a high vulnerability score neighbored by low vulnerability CBGs. Entries in the legend (next to the colored boxes) also show the percentages of 2000–2008 heat-related decedents who were residents in each type of cluster.
Mentions: We observed a moderate degree of overall spatial clustering of vulnerability scores among neighborhoods (Global Moran’s I for HVI = 0.37; p = 0.05). LISA analysis (Figure 2), identified neighborhood clusters with similar or dissimilar scores (p ≤ 0.05). High/high (red) clusters in Figure 2 represent neighborhoods with extremely high vulnerability scores next to others with extremely high scores. One-quarter of heat decedents lived in high/high clusters. Only 9.6% of heat decedents lived in clusters with low/low vulnerability scores (blue) or dissimilar scores (e.g., purple areas, indicating low vulnerability neighborhoods next to high). Sixty percent of the 50 heat deaths among homeless people occurred in high/high vulnerability clusters [see Supplemental Material, Figure S1 (http://dx.doi.org/10.1289/ehp.1104625)].

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

View Article: PubMed Central - PubMed

Affiliation: School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona 85284-2402, USA. sharon.harlan@asu.edu

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus