Research

A Section 508–conformant HTML version of this article
is available at http://dx.doi.org/10.1289/ehp.1307868.

Predicting Hospitalization for Heat-Related Illness at the Census-Tract Level:
Accuracy of a Generic Heat Vulnerability Index in Phoenix, Arizona (USA)
Wen-Ching Chuang1 and Patricia Gober 2,3
1School

of Sustainability, and 2School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USA;
Graduate School of Public Policy, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

3Johnson-Shoyama

Background: Vulnerability mapping based on vulnerability indices is a pragmatic approach for
highlighting the areas in a city where people are at the greatest risk of harm from heat, but the
manner in which vulnerability is conceptualized influences the results.
Objectives: We tested a generic national heat-vulnerability index, based on a 10-variable indicator
framework, using data on heat-related hospitalizations in Phoenix, Arizona. We also identified
potential local risk factors not included in the generic indicators.
Methods: To evaluate the accuracy of the generic index in a city-specific context, we used factor
scores, derived from a factor analysis using census tract–level characteristics, as independent
variables, and heat hospitalizations (with census tracts categorized as zero-, moderate-, or highincidence) as dependent variables in a multinomial logistic regression model. We also compared
the geographical differences between a vulnerability map derived from the generic index and one
derived from actual heat-related hospitalizations at the census-tract scale.
Results: We found that the national-indicator framework correctly classified just over half (54%)
of census tracts in Phoenix. Compared with all census tracts, high-vulnerability tracts that were
misclassified by the index as zero-vulnerability tracts had higher average income and higher proportions of residents with a duration of residency < 5 years.
Conclusion: The generic indicators of vulnerability are useful, but they are sensitive to scale,
measurement, and context. Decision makers need to consider the characteristics of their cities to
determine how closely vulnerability maps based on generic indicators reflect actual risk of harm.
Citation: Chuang WC, Gober P. 2015. Predicting hospitalization for heat-related illness at the
census-tract level: accuracy of a generic heat vulnerability index in Phoenix, Arizona (USA). Environ
Health Perspect 123:606–612; http://dx.doi.org/10.1289/ehp.1307868

Introduction
Extreme hot weather events have become
life-threatening phenomena in cities around
the world (Anderson and Bell 2011; Baccini
et al. 2011; Harlan et al. 2013; Loughnan
et al. 2013; Sheridan et al. 2012). To estimate
the risk of heat-related health consequences
and propose adaptation strategies, researchers
have developed heat vulnerability indices
(HVIs) using composites of health, social,
and environmental factors relevant to heat
stress (Chow et al. 2012; Johnson et al. 2012;
Loughnan et al. 2013; Reid et al. 2009,
2012). Application of HVIs at the neighborhood level allows public-health practitioners
and emergency responders to identify and
locate populations at high risk of heat stress
(Reid et al. 2009). The ability to visualize the
spatial variation of heat vulnerability (i.e.,
on a map) helps local governments allocate
resources and assist people in the areas of
greatest need. However, human vulnerability
to heat is a complex and dynamic issue,
and the usefulness of a vulnerability index
can be sensitive to scale, measurement, and
context. We investigated how generic indicators of heat risk, taken from a national study
(Reid et al. 2009) are interrelated in Phoenix,
Arizona, and we analyzed the relative importance of different components of Reid et al.’s
(2009) national heat-vulnerability index in
predicting hospital admissions. Study results

606

may help Phoenix focus its emergency services
and climate-adaptation planning on neighborhoods at high risk of heat-related illness
and mortality.

Background
Vulnerability to natural hazards is a function
of physical exposure, sensitivity, and adaptive
capacity (Chow et al. 2012; Polsky et al.
2007; Turner et al. 2003; Wisner et al.
2004). “Physical exposure” is proximity to
environmental hazards, such as heat waves
or natural disasters. “Sensitivity” is a characteristic of a population that influences its
degree of susceptibility to the hazard, and
“adaptive capacity” is the ability to cope with
the impacts and aftermath of a hazardous
event. Making the concept of vulnerability
“operational” has been a challenge because
current theoretical concepts and frameworks
are abstract and lack guidelines to measure or
quantify them (Hinkel 2011). This challenge
has stimulated studies to develop measures
of vulnerability at various scales (Chow et al.
2012; Harlan et al. 2013; Johnson et al.
2012; Reid et al. 2009, 2012). Research teams
with different paradigms have focused on
different subsets of vulnerability components
(Romero-Lankao et al. 2012), which, in turn,
influenced the selection of variables used to
evaluate degrees of vulnerability (Tate 2013).
Cutter et al. (2003) were perhaps the first to
volume

develop a social vulnerability index (SoVI);
they used data from the 1990 U.S. Census
to examine vulnerability to environmental
hazards in 3,141 U.S. counties. This approach
to vulnerability indicators (Cutter and Finch
2008) continues to provide the foundation for
those seeking indicators of heat risk.
HVI conceptualization and measurement
differ from one study to another. In the past
decade, at least 13 studies (see Supplemental
Material, Table S1) produced different
HVIs that revealed the spatial distribution of
heat vulnerability for many locations. Most
of these studies follow an inductive methodology, which builds statistical models to
explain observed harm through some indicating variables (Hinkel 2011; Tate 2013).
Researchers select their indicating variables
according to empirical analysis (e.g., ethnic
minorities are usually more vulnerable than
non-Hispanic whites) or social theories (e.g.,
low social cohesion may negatively affect
health) to evaluate an area’s relative risk of
heat-related effects. Most studies include as
risk factors temperature and vegetation cover
(exposure components), age and ethnicity
(sensitivity components), and income
(adaptive capacity). The concerns of individual disciplines produce different conceptualizations of heat vulnerability indices. For
example, environmental modelers (Uejio
et al. 2011) have used indicators of the built
Address correspondence to W.-C. Chuang, Arizona
State University, P.O. Box 875402, Tempe, AZ
85287-5402 USA. Telephone: (480) 727-7807.
E-mail: Wen-Ching.Chuang@asu.edu
Supplemental Material is available online (http://
dx.doi.org/10.1289/ehp.1307868).
We thank C. Boone, D. Hondula, K. Kyle,
S. Wittlinger, R. Burnham (Arizona State University),
and D. Ruddell (University of Southern California)
for their helpful comments. We especially thank
S. Guhathakurta (Georgia Institute of Technology)
and V. Lathey for their help and guidance in
data preparation.
This work was partially funded by the
National Science Foundation (NSF) under grant
BCS‑1026865, Central Arizona-Phoenix Long-Term
Ecological Research (CAP LTER), and by NSF grant
SES-0951366, Decision Center for a Desert City II:
Urban Climate Adaptation.
Any opinions, findings, conclusions, or recommendations expressed here are those of the authors and do not
necessarily reflect the views of the funding agencies.
The authors declare they have no actual or potential
competing financial interests.
Received: 9 November 2013; Accepted: 28 January
2015; Advance Publication: 30 January 2015; Final
Publication: 1 June 2015.

123 | number 6 | June 2015 • Environmental Health Perspectives

Predicting hospitalization for heat-related illness

environment and neighborhood stability
to examine heat mortality and heat-related
emergency services. They found that neighborhoods with a high proportion of ethnic
minorities, social isolation, and vacant
housing units had the highest heat-stress
incidence. Epidemiologists emphasize health
conditions as risk factors—for example,
diabetes, which increases susceptibility to heat
(Reid et al. 2009, 2012; Rinner 2009). In
addition, other variables such as air conditioning (AC) prevalence and social infrastructure (i.e., access to health care facilities)
are used as indicators of adaptive capacity
in studies of public health, sociology, and
epidemiology (Harlan et al. 2013; Loughnan
et al. 2013; Reid et al. 2009, 2012). Data
from simulation models provide variables
to assess future risks to heat. Vescovi et al.
(2005) explored the spatial distribution of
heat vulnerability in southern Quebec,
Canada, under several future climate-change
scenarios, using the prediction from the
Canadian Regional Climate Model and socioeconomic variables. Each disciplinary perspective captures distinct elements of exposure,
sensitivity, and adaptive capacity, and therefore produces varying findings about what
determines heat vulnerability.
English et al. (2009) reviewed studies that
identified outcomes of climate change and
developed indicators for human health vulnerability assessment and found a need to test the
usefulness of these indicators. There have been
only a few attempts to evaluate the performance of heat vulnerability indices. Wolf and
McGregor (2013) used an inductive approach
to generate an HVI (and maps) covering
4,765 census units in Greater London, United
Kingdom. In their subsequent research, Wolf
et al. (2014) validated the performance of
their HVI using daily mortality and ambulance dispatch data from 1990 to 2004 and
from 1998 to 2006, respectively. The census
unit that has an above-average HVI score
and an above-average observed health impact
score (measured by the number of mortality/­
ambulance dispatches), and the census unit
that has a below-average HVI score and a
below-average health impact score are considered as accurate predictions in the work of
Wolf et al. (2014). The results showed that
the London HVI predicted ambulance calls
better than it predicted mortality. London
HVI correctly predicted the impacts
(measured by ambulance calls) in 3,441
(62.2%) census units during summer days.
Wolf et al.’s (2014) findings also suggested
that ambulance calls and mortality had
different response patterns to heat, consistent
with a previous report of contrasting patterns
of emergency room admissions and mortality
during heat waves in London (Kovats
et al. 2004).
Environmental Health Perspectives •

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In the United States, Reid et al. (2009)
developed a national HVI using a statistical
approach that integrated factors known to
be associated with risk of heat stress in the
United States. They selected six sociodemographic and economic indicators (poverty,
educational level, minority status, living
alone, elderly, and elderly living alone), two
AC variables, a measure of vegetation density,
and diabetes prevalence to create an HVI for
metropolitan statistical areas encompassing
39,794 U.S. Census tracts. They identified four dimensions of heat vulnerability:
a) social and environmental vulnerability—
the aggregation of low education level,
poverty, ethnic-minority status, and lack of
green space; b) social isolation, measured by
the proportion of people living alone; c) AC
prevalence; and d) underlying health conditions, represented by the proportion of elderly
in the population and the prevalence of
diabetes. Later, they asked whether areas with
high HVI scores at the ZIP-code scale had
higher rates of mortality and morbidity on
abnormally hot days (defined by maximum
temperature above the 95th percentile for the
30-year temperature distribution) (Reid et al.
2012). They evaluated the relationship in five
states: California, New Mexico, Washington,
Oregon, and Massachusetts. In California,
Washington, and Massachusetts, heat-related
illness was more strongly associated with
the HVI on abnormally hot days than on
other days. But in Oregon, the association
between the HVI and heat-related illness did
not differ between abnormally hot days and
other days. In New Mexico, a 1-unit increase
in the HVI was associated with a significant
decrease in heat-related hospitalization on
abnormally hot days. These findings suggest
that local characteristics may influence the
accuracy of HVI measures for predicting the
risk of adverse heat-related health outcomes
in some areas.
Two HVI studies have been conducted
in Arizona, using measures similar to those of
Reid et al. (2009, 2012). Chow et al. (2012)
constructed an HVI using seven indicators
from the three dimensions of heat vulnerability (physical exposure, adaptive capacity,
and sensitivity) at the census-tract level in
metropolitan Phoenix. They used this HVI
to investigate geographical change to heatstress risk between 1990 and 2000, and estimated changes in heat vulnerability among
different ethnic populations. They concluded
that metropolitan Phoenix had experienced
major demographic change during those
10 years, and that demographic change alone
had altered the region’s “heatscape.” Harlan
et al. (2013) examined neighborhood vulnerability indicators for 2,081 census-block
groups in Maricopa County, which includes
the Phoenix metropolitan area. Using 278

123 | number 6 | June 2015

heat-death cases as dependent variables, they
used binary logistic regression to validate a set
of HVIs with different combinations of indicators. They concluded that socioeconomic
vulnerability, being elderly or isolated, and
surface temperature were strong predictors of
death from heat exposure.

Aim and Scope of This Study
Measurement, scale, and context all influence
the identification of risk factors. Different
combinations of risk factors can produce
different “vulnerability landscapes.” To better
understand the relationships among risk
factors and different scales, we tested Reid
et al.’s (2009) national indicators in Phoenix,
one of the nation’s hottest cities. We applied
Reid et al.’s (2009) variables at the censustract scale, but measured a few of them differently. We evaluated how accurately the model
reflects actual risk of harm locally. At this
fine scale, we expected our findings to differ
from those of Reid et al. (2009, 2012) and
the local research described above (Harlan
et al. 2013). We asked where, and what kind
of, neighborhoods are at risk of heat-related
illness caused by factors beyond social and
economic vulnerability, inadequate green
space, social isolation, and diabetes. Using
a multinomial (polytomous) logistic regression model, with hospital admissions for heat
stress modeled as a three-category dependent
variable (zero-, moderate-, or high-incidence
census tracts), our study explored several
questions: a) How well does a national HVI
explain heat-related hospitalizations in the
city of Phoenix? Analyzing the census tracts
within the municipal boundary of Phoenix
is highly relevant for interventions, because it
is the scale at which local governments determine resource allocation and enforce policies.
b) What is the relative importance of physical
exposure, adaptive capacity, and sensitivity
to hospitalization incidence, given Phoenix’s
hot climate and high prevalence of air conditioning? c) In which kinds of neighborhoods
is the incidence of heat-related hospitalization
explained well or poorly by the HVI? d) Are
there neighborhood characteristics that are
not included in the HVI that predict heatrelated hospitalizations in Phoenix?

Methods
Our dependent variable (hospital admissions for heat stress) came from the Arizona
Department of Health Services’ hospital
discharge databases for 2004 and 2005. This
data set contains a disease code [from the
International Classification of Diseases, 9th
Revision–Clinical Modification (ICD-9-CM)]
and the census tract number of the patient’s
residence. We used ArcGIS 10 (ESRI,
Redlands, CA) to calculate the rate of heatrelated illness for each census tract and map

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Chuang and Gober

460 heat-related hospitalizations (ICD-9-CM
codes 992.0–992.9, effects of heat and light),
including heat stroke, heat exhaustion, and
other less common heat-related outcomes in
362 census tracts. We normalized the heatrelated hospitalizations between 2004 and
2005 by census-tract population estimates for
2010. Rates of hospitalization varied between
0 and 0.76%; the average was 0.03% (see
Supplemental Material, Figure S1).
The variables in Reid et al.’s (2009) study
were our independent variables. Poverty, low
education level, AC prevalence, and social
isolation were indicators of adaptive capacity;
ethnicity, age, and diabetes prevalence were
indicators of a population’s sensitivity to
heat; and density of green space indicated
both physical exposure and adaptive capacity.
Vegetation density has been shown to have
a negative relationship with neighborhood
temperatures (Jenerette et al. 2007), and it
could mitigate the urban heat island effects
(Gober 2010; Stone and Norman 2006).
We used data from the 2010 Census
(http://factfinder.census.gov/faces/nav/jsf/
pages/index.xhtml) for our socioeconomic
and demographic variables, which included
the percentage of population living below
the poverty line (poverty), > 65 years of age
(elderly), ethnicity other than non-Hispanic
white (minority), having less than a high
school diploma (low education), living alone
(all ages living alone), and living alone and
> 65 years old (elderly living alone) at the
census-tract level. To determine poverty, the
U.S. Census Bureau uses a set of annualincome thresholds that vary by family size
and composition. The poverty threshold for
a household in Phoenix with two adults is
$14,218 (U.S. Census Bureau 2013a).
We measured diabetes rates differently
from Reid et al. (2009, 2012). Whereas Reid
et al. (2009, 2012) estimated diabetes prevalence based on age, race, and sex of a county’s
population and applied the diabetes incidence
rate of each group, we thought this method
might miss small-scale effects of diabetes.
Thus, we used the diabetes hospitalization

rate as an indication of diabetes-related
morbidity, and we felt it would provide
a better measure of health inequality at the
neighborhood level. Using the principal diagnosis code (ICD-9-CM codes 250.0–250.9,
diabetes mellitus) and the associated censustract numbers, we mapped 7,727 cases of
diabetes. We used census-tract population
estimates for 2010 as the denominator and
hospitalizations for diabetes during 2004 and
2005 to calculate census tract–level hospitalization rates for diabetes. The rates varied
from 0 to 5.52%; the average was 0.50%.
Fifty-three (14.64%) of the census tracts had
no hospital admissions for diabetes.
To determine AC prevalence, we
aggregated parcel-level residential AC data
from the Maricopa County Assessor’s Office to
the census-tract level. We obtained vegetation
index using a high-resolution (15 m/pixel)
ASTER image [NASA Land Processes
Distributed Active Archive Center (LP
DAAC); https://lpdaac.usgs.gov/data_access].
We combined three images taken on 16 June
2005 and 6 July 2006 to represent Phoenix’s
summer vegetation. The Normalized Difference
Vegetation Index (NDVI) was calculated
using red and near-infrared bands in ERDAS
IMAGINE 2011 (download.intergraph.com/
downloads/erdas-imagine-2011), a remotesensing image-processing software.
Statistical analysis. A flow chart that
illustrates our research steps can be found in
Supplemental Material, Figure S2. Factor
analysis was conducted using IBM SPSS
version 19. We used factor scores from
this analysis as independent variables in
the multi­nomial logistic regression (MLR),
with health outcomes as dependent variables. A valid regression model that uses
geographical/spatial data should consider the
effect of spatial autocorrelation/­dependency
(Ward and Gleditsch 2008). We used
global Moran’s I to test the distribution of
our dependent variable and model residuals. The spatial pattern of the dependent
variable was very close to a random distribution (Moran’s I = 0.10, p = 0.00), and the

Moran’s I for residuals was 0.02, p = 0.00.
We divided 362 census tracts into three
groups of heat-related health outcomes: zero
(146 tracts, 40.33%), moderate (109 tracts,
30.11%), and upper 30th percentile (high
incidence, 107 tracts, 29.56%). The deviance
and chi-square value are both significant,
providing evidence of good fit for the model.

Results
Correlation matrix. Spearman’s correlation
coefficients show the relationships among
each of the 10 census-tract level vulnerability
indicators (Table 1). Diabetes hospitalization
rates were significantly and positively correlated with several indicators of socioeconomic
disadvantage, including the proportions of
the population that were race/ethnicity other
than non-Hispanic white, below the poverty
line, and that did not have a high school
diploma. Reid et al. (2009) found a weaker
correlation between diabetes prevalence and
these variables (coefficients < 0.3). Use of
different methods for the measurement of
diabetes and the demographic structure of
Phoenix may have affected our findings.
Another location-specific condition that
did not stand out in Reid et al.’s analyses
(2009, 2012) is AC prevalence. On the
national level, AC variables showed no strong
associations (coefficient < 0.02) with poverty
and minority status. However, in Phoenix, AC
variables have significant positive associations
with poverty (coefficient > 0.5) and proportion of minority (coefficient > 0.42). AC is
vital to life and comfort in Phoenix, where
temperatures average 41°C in July (Cerveny
1996). Although Phoenix’s AC prevalence is
> 90%, including central AC and window
AC units (U.S. Census Bureau 2013b), the
nearly 10% of housing units without AC are
concentrated in economically disadvantaged
neighborhoods in central Phoenix.
Table 1 also shows that the proportion
of elderly was negatively associated with less
than high school diploma (–0.42), poverty
(–0.33), and low vegetation (–0.35) in
Phoenix. These relationships were stronger

Table 1. Spearman’s correlation for vulnerability variables.

Variable
Diabetes
Race/ethnicity other than non-Hispanic white
Age > 65 years
Living alone
Elderly living alone
Below poverty line
Less than high school diploma
Low vegetation cover
No central AC
No AC of any kind

Diabetes
1.00
0.63**
–0.13*
0.34**
0.27**
0.73**
0.67**
0.32**
0.51**
0.52**

Race/ethnicity
other than
non-Hispanic white

Age > 65
years

1.00
–0.54**
0.06
–0.07
0.79**
0.91**
0.34**
0.43**
0.42**

1.00
0.15**
0.52**
–0.33**
–0.42**
–0.35**
–0.05
–0.05

Live alone

Elderly
living
alone

Below
poverty
line

1.00
0.57**
0.31**
0.09
0.12*
0.25**
0.26**

1.00
0.16**
0.04
–0.03
0.17**
0.17**

1.00
0.83**
0.35**
0.51**
0.50**

Less than
No AC
high school
Low
No central of any
diploma vegetation
AC
kind

1.00
0.38**
0.45**
0.43**

1.00
0.23**
0.26**

1.00
0.93**

1.00

Spatial unit: census tract; n = 362.
*p < 0.05. **p < 0.01.

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123 | number 6 | June 2015 • Environmental Health Perspectives

Predicting hospitalization for heat-related illness

than the data at the national scale (with coefficients between –0.03 and –0.11). We can
therefore interpret that the census tracts in
Phoenix with a higher proportion of elderly
residents were likely to be wealthier, greener,
and better educated than what Reid et al.
(2009) found at the county scale for the
nation overall. This difference may be attributable to the influx of wealthy retirees into
the Phoenix area, and the related proliferation of retirement communities featuring golf
courses and outdoor recreational activities
(Gober 2006).
Spatial pattern of heat stress in Phoenix.
The map of heat-related hospitalization
(Figure 1A) reveals an uneven rate pattern,
with higher rates in the urban core. Urbanfringe neighborhoods in northeast, northwest,
and south Phoenix had relatively low rates
of heat-related hospitalization. Of the three
neighborhoods with the highest hospitalization rates, one (no. 3 in Figure 1A), which
Heat hospitalization rate
< –0.50 SD
–0.50 to 0.50 SD
0.50 to 1.5 SD
1.5 to 2.5 SD
> 2.5 SD

1

sits directly west of Sky Harbor Airport, is
a low-income neighborhood with a median
household income of $20,488 and a Hispanic
population of almost 90%. However, the
other two (nos. 1 and 2 in Figure 1A) are
middle-class (with median household incomes
of $40,104 and $37,514) neighborhoods, and
Hispanic populations of 25.7% and 52.3%,
respectively.
Factor analysis. Like Reid et al. (2009),
we applied a Varimax rotation in the factor
analysis to minimize the number of original
variables that load highly on any one factor
and increase the variation among factors. We
retained three factors (Table 2) with eigenvalues higher than one: a) poverty, ethnic
minority, and low education; b) lack of AC
and vegetation; and c) diabetes and social
isolation, including elderly living alone.
Factor 1 explained the highest amount of
variance (44.7%); factors 2 and 3 explained
19.98% and 10.46%, respectively. Together
Vulnerability
index scores
0
1 to 10
11 to 12
13 to 14
15 to 16

2

N

3
0 1.5 3

6

9

12

Miles

Figure 1. (A) Spatial distribution of heat-related hospitalization rate. Census-tract-level hospitalization
rates for heat-related illness are hospitalizations for heat-related illness between 2004 and 2005 divided by
census-tract population estimates for 2010 times 100 (percent). Nos. 1, 2, and 3 are the top three census
tracts with high heat hospitalization rates (> 2.5 SD). (B) Heat vulnerability index (HVI; sum of three factor
scores) in the city of Phoenix. Each census tract was assigned a score for each factor ranging from 0 to 6
based on SD above or below mean. HVI scores range from 0 to 16.
Table 2. Factor analysis of 10 variables.
Variable
Below poverty line
Race/ethnicity other than non-Hispanic white
Less than high school diploma
Age > 65 years
No central AC
No AC of any kind
Low NDVI
Age > 65 years living alone
Living alone
Diabetes

Factor 1
0.78
0.93
0.90
–0.65
0.19
0.18
0.44
–0.06
0.13
0.54

Factor 2
0.30
0.10
0.18
–0.15
0.92
0.92
0.45
0.10
0.23
0.27

Factor 3
0.32
0.04
0.14
0.49
0.27
0.27
–0.14
0.89
0.63
0.59

Factor 1: poverty, race/ethnic minority and low education; factor 2: lack of AC and vegetation; factor 3: diabetes and
social isolation.

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123 | number 6 | June 2015

they explained 75.14% of the total variance,
similar to the results of Reid at al. (2009) in
which 75% of total variance was explained by
four factors.
Poverty and minority status are important factors in heat vulnerability, locally and
nationally, and are included in factor 1 (see
Supplemental Material, Figure S3A). Also
included is a negative relationship with elderly
populations: Disadvantaged neighborhoods
in Phoenix tend to have a large number of
children and relatively few elderly residents.
Factor 2 combines lack of AC with lack of
vegetation, and can be considered a location
factor; it is associated with inner-city neighborhoods (see Supplemental Material,
Figure S3B). Residents of the inner city are
at higher risk from heat than residents elsewhere. Factor 3 (see Supplemental Material,
Figure S3C) combines social isolation
(especially of elderly people) with diabetes
hospitalization. In Phoenix, demographic characteristics make this combination an important
factor. The elderly population is at high risk
for diabetes (Arizona Department of Health
Services 2008). In 2010, according to the
U.S. Census Bureau (2011), 121,943 people
> 65 years old lived in Phoenix, and about
27% of them lived alone—a higher proportion
than in cities neighboring Phoenix. The long
history of retirement migration to Phoenix
may have resulted in a large proportion of
elderly living alone, and this population is at
high risk of diabetes hospitalization.
Each census tract was assigned a score for
each factor ranging from 0 to 6, where 0 was
assigned to tracts with values ≥ 2 SD below
the mean for the study area as a whole, and
6 was assigned to tracts with values > 2 SD
above the mean. The individual factor scores
were then summed to derive the HVI for
each tract, with each factor score given an
equal weight (Figure 1B), as in other vulnerability studies (Cutter et al. 2003; Harlan
et al. 2006; Schmidtlein et al. 2008; Wolf and
McGregor 2013). Areas with high HVI scores
were clustered in the downtown Phoenix
central business district and along the south
side of the industrial corridor.
MLR models. The results of the MLR
show that only factor 1 (poverty and minority
status) was a statistically significant predictor
(p < 0.05) of a moderate-incidence versus
zero-incidence tract [odds ratio (OR) = 2.00;
95% confidence interval (CI): 1.50, 2.65
for a 1-unit increase in the factor 1 score)]
(Table 3). Factor 1 was also a significant
predictor of a high-incidence versus zeroincidence tract (OR = 2.74; 95% CI:
2.03, 3.69), along with factor 3 (OR = 2.00;
95% CI: 1.44, 2.76). These results suggest
that census tracts with higher proportions of
residents living in poverty and ethnic minorities (factor 1), and tracts with higher rates of

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Chuang and Gober

hospitalization for diabetes and higher proportions of residents > 65 years of age living alone
(factor 3) are more vulnerable to heat stress
than other census tracts.
We used the factor scores to predict the
category (zero, moderate, and high incidence)
of heat-related health outcomes in the MLR
model. We then compared the predicted and
observed values. From the classification table
(Table 4), we found that the scores of HVI did
a better job in predicting nonvulnerable areas
than vulnerable areas. HVI accurately classified
zero-incidence census tracts as zero-incidence
tracts 79% of the time, but was less accurate
for classifying moderate tracts as moderate
versus zero or high incidence (27%) or for classifying high-incidence tracts as high-incidence
versus moderate- or zero-incidence (48%).
The overall accuracy rate in predicting heatrelated outcomes was only 54%, suggesting
that accounting for additional factors beyond
those in the standard vulnerability index would
improve risk prediction.
Factor 2, with high loadings on lack of
AC, was not a significant predictor of heat
hospitalization in Phoenix (Table 3). AC has
been recommended as a mitigation strategy
to reduce heat impacts on health, because
many studies find that AC prevalence is
negatively associated with adverse health
outcomes, especially on extremely hot days
(Keatinge 2003; McGeehin and Mirabelli
2001; Semenza et al. 1996, 1999). However,
having an AC unit does not automatically
mean being able to use it. According to a
2009 survey that interviewed 359 households
in three socially vulnerable neighborhoods in
Phoenix, many families cannot afford to turn
on their AC in the hottest season: 33–50%
of respondents who have AC indicated that
they avoid using AC to reduce electricity bills
(Hayden et al. 2011).
Unpredictable neighborhoods. We looked
at the neighborhood characteristics of the
14% of census tracts that were oppositely
misclassified by the model—35 neighborhoods predicted to be zero-incidence neighborhoods that were actually high-incidence
Table 3. Odds ratios (ORs) and 95% CIs for associations between a 1-unit increase in each factor
and census tracts with moderate or high incidence of hospitalization for heat-related illness
relative to zero-incidence census tracts based on
multinomial logistic regression.
Predictor
Moderate-incidence tract
Factor 1
Factor 2
Factor 3
High-incidence tract
Factor 1
Factor 2
Factor 3

OR (95% CI)

p-Value

2.00 (1.50, 2.65)
0.84 (0.56, 1.27)
1.18 (0.85, 1.64)

0.00
0.41
0.32

2.74 (2.03, 3.69)
1.20 (0.90, 1.60)
2.00 (1.44, 2.76)

0.00
0.21
0.00

Reference category: zero-incidence tract.

610

neighborhoods (Table 5, group 1), and 17
neighborhoods predicted to be high-incidence
areas that were actually zero-incidence areas
(Table 5, group 2). Many group 1 neighborhoods were wealthy neighborhoods on the
urban fringe (see Supplemental Material,
Figure S4). Group 2 tracts were scattered in
central and south Phoenix, and many of them
were low-income, and their proportion of
Hispanics and the diabetes rate there were
higher than the city’s average.
To better understand risk factors beyond
the scope of the national HVI, we looked at
variables from other heat vulnerability studies
which were not included in the study by Reid
et al. (2009). These variables included the
size of a census tract’s noncitizen population,
the proportion of renters, residents living in
the same residence < 5 years, unemployment
rate, vacancy rate, and nighttime temperature (Chow et al. 2012; Harlan et al. 2013;
Klinenberg 2002). The first variable is a
proxy for newcomers who may have limited
access to warnings, medical support, and
resources that can help them gain relief from
heat stress (Chow et al. 2012). Proportions
of renters and new residents are measures of
population mobility. Short-term renters and
newcomers are likely to lack social support
and assistance in their neighborhoods (Chow
et al. 2012; U.S. Environmental Protection
Agency 2006). Unemployment and vacancy
rates are typically used as proxies for social
stability of a neighborhood; unemployment rate captures the population’s lack of
stable economic resources and vacancy rate
explains a neighborhood’s prosperity. High
unemployment, vacancy, and a high crime
rate hinder residents from seeking help in

their neighborhoods (Klinenberg 2002). We
were not able to acquire crime-rate data at
the census-tract scale for Phoenix, but we
believe that unemployment and vacancy rates
are adequate proxies for social stability. The
final factor, nighttime temperature, represents
the intensity of the urban heat island effect.
We estimated nighttime surface temperatures
using the thermal band of three ASTER
satellite images (LP DAAC) that cover the
entire Phoenix City. The images were taken
in June 2003.
The above factors varied widely for the
two groups of misclassified neighborhoods,
so taking the averages of these variables for
the two groups may not adequately represent the groups’ characteristics. We used the
city’s average numbers for these variables as
thresholds, and calculated the percentage of
neighborhoods above the city average for each
variable (Table 5). Group 1 neighborhoods,
with much higher observed hospital admissions than the HVI predicted, have higher
neighborhood mobility (43%) than group 2
neighborhoods (35% mobility). This finding
suggests that high neighborhood mobility
measured by residency status < 5 years may
be associated with higher risk of heat-related
illness. In addition, many of the neighborhoods in group 1 were in low-density areas
on the urban fringe. The low-density environment offers a different lifestyle than does the
urban core, one that may be associated with
health outcomes of residents. However, more
work is required to understand why these
tracts differ from the ones that were better
predicted by HVI. Many neighborhoods in
group 2 were located in the city core, and
had higher population densities and a higher

Table 4. Accuracy assessment (classification table).
Observed 0
Observed 1
Observed 2
Percent correct

Predicted 0
115
56
35
56.90

Predicted 1
14
29
21
17.70

Predicted 2
17
24
51
25.40

Percent correct
78.80
26.60
47.70
53.90

0 = zero-incidence; 1 = moderate-incidence; 2 = high-incidence census tracts.

Table 5. Characteristics of census tracts with misclassified heat vulnerability based on the HVI compared
with average values for all census tracts in Phoenix City.
Characteristic
Median household incomea
Non-Hispanic white (%)
Diabetes (%)
Noncitizens (%)
Unemployment (%)
Proportion of renters (%)
Proportion living in the same residence < 5 years (%)
Vacancy rate (%)
Average surface temperatureb

Group 1
n = 35
$52,972
97
34
11
23
49
43
46
26.1°C

Group 2
n = 17
$27,216
6
94
71
59
65
35
71
26.6°C

Phoenix City average
n = 362
$48,750
49
0.5
16
7.5
42
46
13
25.7°C

The percentage for groups 1 and 2 refer to census tracts, not household. The percentages for Phoenix City are citywide
average. Group 1: high-incidence census tracts predicted to be zero-incidence census tracts. Group 2: zero-incidence
census tracts predicted to be high-incidence census tracts.
aGroup 1: 60% of census tract > average; group 2: 0% of census tract > average. bGroup 1: 54% of census tract
> average; group 2: 76% of census tract > average.

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123 | number 6 | June 2015 • Environmental Health Perspectives

Predicting hospitalization for heat-related illness

proportion of Hispanic residents than neighborhoods in group 1, but experienced no
heat-related hospitalization.

Discussion
Our findings suggest that low socioeconomic
status, as well as the proportion of adults
> 65 years of age living alone, percentage of
adults living alone, and the rate of hospitalization for diabetes, predict vulnerability to heat
at the census-tract level. This finding coincides with studies that found a strong association between poverty, minority, and adverse
health outcomes (Curriero et al. 2002; Harlan
et al. 2006; Uejio et al. 2011) and studies
showing that diabetes was associated with
higher risk of heat-related illness (Schwartz
2005; Semenza et al. 1999).
The proportion of dwellings with AC
was not a significant predictor of heat-related
hospital admissions in Phoenix, perhaps
because the incidence of AC is so high or
because having AC does not imply using it.
Some heat-related illness occurs in those who
work outside or engage in outdoor activity.
Thus, having an AC at home does not eliminate the risk of heat-related health problems.
Therefore, reducing the risk of heat-related
hospitalizations requires more than increasing
home AC units. It also requires a) more
effective risk mitigation for people who
work or recreate outside; b) identification
of socially isolated, diabetic patients; and
c) awareness of the concentration of effects in
disadvantaged neighborhoods.
From a political economic perspective,
the process of marginalization is a fundamental factor making some urban residents
(i.e., low income) more vulnerable to natural
or environmental hazards (Browning et al.
2006; Klinenberg 1999). However, there
are other social characteristics, such as social
capital or social networks, not measured by
common social vulnerability indicators, that
could offset the impact of environmental
hazards on low-income or minority populations (Romero-Lankao et al. 2012). Several
studies have found that some socioeconomically disadvantaged groups and immigrants
have strong internal social networks that
foster social cohesion and fast recovery from
disasters (Chamlee-Wright and Storr 2009;
Klinenberg 2002; Li et al. 2010). Klinenberg
(2002) suggested that strong social networks,
pedestrian-friendly streets, and shops, restaurants, and community organizations are
sources of resilience that can save lives from
heat stress. Living in a neighborhood with
a robust social infrastructure that provides
an environment for mutual assistance could
reduce negative health impacts, especially
during disasters (Sampson 2011).
High socioeconomic status does not
necessarily mean low heat vulnerability, and
Environmental Health Perspectives •

volume

vice versa. Our misclassified neighborhoods
included wealthy, non-Hispanic white neighborhoods with higher hospital admissions
than the HVI would have predicted. Many
of these neighborhoods had a higher proportion of households that have relocated to the
neighborhood in the past 5 years than the
city average. Programs that enhance residents’
awareness of heat risks might also reduce the
incidence of negative health outcomes in
transient neighborhoods.
Our findings provide information that
can help the city government plan effective
interventions. We recommend a two-stage
strategy to reduce heat-related hospital admissions in Phoenix. The first stage should focus
on immediate and short-term heat-mitigation
among socioeconomically disadvantaged
populations, especially in central Phoenix.
We suggest that the municipal government
relocate resources to neighborhoods with high
HVI scores in the urban core. Interventions
might include opening cooling centers during
extreme heat events, providing information
about how to prevent heat-related illness to
disadvantaged populations, and increasing the
efficiency and affordability of residential AC.
The second-stage policy should focus on longterm planning. Because high social isolation
is associated with higher risk of heat-related
illness, programs to care for people living
alone or making warning information accessible to those living alone are likely to reduce
heat-related hospital admissions.
Good planning practices that improve
health can bring co-benefits to the residents.
It has been shown that changing urban design
to reduce automobile dependency and carbon
dioxide emissions (for example, creating a
comfortable, pedestrian-friendly environment
that increases walkability in neighborhoods)
can also reduce the risks of cardiovascular
disease, obesity, and diabetes (Lathey et al.
2009), all of which that exacerbate the
outcomes of heat stress.
We acknowledge that this study has
several limitations. For the present analysis
we used ICD‑9‑CM code 992 as the only
outcome because this category is a straightforward measurement of heat impact on
human health. However, using only this data
set, we might underestimate heat impacts on
human health because a) this data set records
only serious cases that require hospitalization, and, b) there are other human-health
problems relevant to excessive heat, such as
cardiovascular disease and respiratory diseases
(Reid et al. 2012). The second limiting factor
is that we assume the heat-related illness
will have an equal probability of resulting in
hospitalization in any census tract. However,
compared with other residents, low-income
people without health insurance and non-U.S.
citizens may be less likely to seek medical care,

123 | number 6 | June 2015

and less likely to be hospitalized if they do
seek care, even if they have the same severity
of heat-related illness. Furthermore, the neighborhood mobility indicators (e.g., residence
for < 5 years) may not necessarily represent the
actual social conditions, such as lack of social
cohesion. Moreover, data used to characterize
the predictors and the outcomes are defined
at the census-tract scale. Although group-level
associations are informative and relevant for
planning group-level interventions, associations with group-level characteristics cannot
be assumed to represent associations with
the same characteristics defined at individual
level. Last, we used 2010 U.S. Census data to
define HVI, but health outcomes were from
2004 to 2005. Although this may not result
in substantial bias or misclassification, this
remains a potential limitation.

Conclusions
Generic indicator systems can predict the risk
of heat-related health problems adequately
and provide a useful picture of the spatial
distribution of risk, but they are sensitive to
scale, measurement, and context. Decision
makers need to reflect on the particular characteristics of their cities to determine how
well the vulnerability maps reflect actual
risk of harm. In Phoenix, the variables used
on a national scale allowed us to accurately
classify only about 54% of the census tracts
based on heat hospitalizations. There is,
however, a larger story about heat stress that
is not captured by the standard vulnerability
measures. There is no one-size-fits-all vulnerability indicator. Different types of problems
and concerns require multiple strategies to
evaluate the degree of vulnerability. Our
study demonstrated that researchers need to
take into account the wide institutional and
social context that determines vulnerability,
as expressed by the concept of “contextual
vulnerability” (Hinkel 2011). In addition,
vulnerability studies should not be limited to
just the identification of vulnerable people and
places, but should also include the exploration of the sources of resilience in communities. Further research can build upon our
heat vulnerability map to identify the source
of resilience to heat in Phoenix and further
investigate the factors that put neighborhoods
at risk of heat-related illness.
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123 | number 6 | June 2015 • Environmental Health Perspectives