Health & Place 30 (2014) 45–60
Contents lists available at ScienceDirect
Health & Place
journal homepage: www.elsevier.com/locate/healthplace
Intra-urban vulnerability to heat-related mortality in New York City,
1997–2006
Joyce Klein Rosenthal a,b,n,1, Patrick L. Kinney c, Kristina B. Metzger d,2
a
Harvard University Graduate School of Design, Department of Urban Planning & Design, 48 Quincy Street, Cambridge, MA 02138, USA
Columbia University Graduate School of Architecture, Planning & Preservation, Urban Planning Program, 400 Avery Hall, 1172 Amsterdam Avenue,
New York, NY 10027, USA
c
Columbia University Mailman School of Public Health, Department of Environmental Health Sciences, 722W. 168th St., New York, NY 10032, USA
d
New York City Department of Health and Mental Hygiene, Bureau of Environmental, Surveillance and Policy, 120 Worth Street, New York, NY 10013, USA
b
art ic l e i nf o
a b s t r a c t
Article history:
Received 11 November 2013
Received in revised form
24 July 2014
Accepted 28 July 2014
The health impacts of exposure to summertime heat are a significant problem in New York City (NYC)
and for many cities and are expected to increase with a warming climate. Most studies on heat-related
mortality have examined risk factors at the municipal or regional scale and may have missed the intraurban variation of vulnerability that might inform prevention strategies. We evaluated whether placebased characteristics (socioeconomic/demographic and health factors, as well as the built and
biophysical environment) may be associated with greater risk of heat-related mortality for seniors
during heat events in NYC. As a measure of relative vulnerability to heat, we used the natural cause
mortality rate ratio among those aged 65 and over (MRR65 þ ), comparing extremely hot days (maximum
heat index 100 1F þ) to all warm season days, across 1997–2006 for NYC’s 59 Community Districts and
42 United Hospital Fund neighborhoods. Significant positive associations were found between the
MRR65 þ and neighborhood-level characteristics: poverty, poor housing conditions, lower rates of access
to air-conditioning, impervious land cover, surface temperatures aggregated to the area-level, and
seniors’ hypertension. Percent Black/African American and household poverty were strong negative
predictors of seniors’ air conditioning access in multivariate regression analysis.
& 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-SA
license (http://creativecommons.org/licenses/by-nc-sa/3.0/).
Keywords:
Neighborhood characteristics
Vulnerability
Heat-related mortality
Health disparities
Housing quality
1. Introduction
The adverse health impacts of summertime heat are a significant problem in New York City (NYC) and many other cities around
the world, and are expected to increase with a warming climate
(Knowlton et al., 2007). Excessive exposure to high heat is
associated with increased rates of heat stress, heat stroke, and
premature death (O’Neill and Ebi, 2009). Heat-associated mortality
Abbreviations: CD, community district; heat wave, three or more consecutive
days of equal to or greater than 901F maximum temperatures; HI, the heat index
(HI), or apparent temperature: a measure that combines relative humidity and
ambient temperature (Steadman, 1979); MRR65 þ , mortality rate ratio of persons
aged 65 and older; UHF, United Hospital Fund; UHI, urban heat island
n
Corresponding author at: Harvard University Graduate School of Design, Department of Urban Planning & Design, 48 Quincy Street, Cambridge, MA 02138, USA.
Tel.: þ 1 617 496 2589; fax: þ 1 617 496 1292.
E-mail addresses: jkrosenthal@gsd.harvard.edu (J. Klein Rosenthal),
plk3@columbia.edu (P.L. Kinney).
1
Present address: Harvard University Graduate School of Design, Department
of Urban Planning & Design, 48 Quincy Street, Cambridge, MA 02138, USA.
Tel.: þ 1 617 496 2589; fax: þ 1 617 496 1292.
2
Present address: 4305 Wildridge Circle, Austin, TX 78759, USA.
typically presents as excess mortality due to cardiovascular or
respiratory causes during hot weather (Hoshiko et al., 2010). As a
result of extreme events such as the premature deaths of 14,800
people in France during the August 2003 heat wave (Observatoire
régional de santé (ORS), 2003), awareness of heat-related mortality has increased. As cities create climate adaptation plans to
protect vulnerable populations, understanding the causes of intraurban spatial heterogeneity of these premature deaths should help
identify locations and population groups at greatest risk while
informing the search for modifiable exposures.
A number of studies have identified individual risk factors for
vulnerability to heat waves. Those over 65 years of age and people
with pre-existing cardiovascular and/or respiratory illnesses are
especially vulnerable populations (Basu and Samet, 2002). Vulnerable populations also include young children, the obese, and those
using medications that impede thermoregulation (New York City
Department of Health and Mental Hygiene (NYCDOHMH), 2012).
There is also a growing understanding of the role of place in
creating increased risk for heat-associated mortality. Analysis of
mortality data in France indicates that deaths during the 2003
heat wave were disproportionately concentrated in poorer neighborhoods with higher levels of immigrants and substandard
http://dx.doi.org/10.1016/j.healthplace.2014.07.014
1353-8292/& 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/3.0/).
46
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
housing (Observatoire régional de santé (ORS), 2003). People at
elevated risk of mortality during a Chicago heat wave in 1995,
which led to more than 700 excess deaths, included the elderly,
the poor, those with limited mobility and little social contact, and
those with pre-existing medical or psychiatric conditions, as well
as those with place-based risk factors such as poor access to
public transportation or air-conditioned neighborhood places
(Klinenberg, 2002; O’Neill and Ebi, 2009; Semenza et al., 1996).
Risk of mortality in that event was higher in the Black community;
for people living in certain types of low income and multi-tenant
housing, such as single-room occupancy apartment buildings; and
for those living on the top floors of buildings (Klinenberg, 2002;
Semenza et al., 1996). Access to and use of home air conditioning
was protective against heat-related death and risk of heat stroke in
four U.S. cities (O’Neill et al., 2005; Semenza et al., 1996). Black
residents of these cities had one-half the access to home air
conditioning as other racial/ethnic groups, and a higher risk of
heat-mortality (O’Neill et al., 2005).
In New York, as in other cities, summertime heat can lead to
elevated mortality and morbidity rates, especially during the
extended periods of hot weather (Basu and Samet, 2002; Braga
et al., 2002; Ellis et al., 1975; Kalkstein and Greene, 1997; Marmor,
1975; McGeehin and Mirabelli, 2001). In NYC, the effects of
temperature on mortality were observable above a threshold
temperature range, with a minimum mortality temperature of
approximately 66.4 1F (Curriero et al., 2002; O’Neill and Ebi, 2009).
In a study of the daily variation in warm season natural-cause
mortality for 1997–2006 in New York City, Metzger et al. (2010)
found that the same-day maximum heat index (HI) was linearly
related to mortality risk across its range. Heat waves in July and
August 2006 in NYC were associated with 46 confirmed heat
stroke deaths within the city, with a greater proportion in Queens
neighborhoods (New York City Department of Health and Mental
Hygiene (NYCDOHMH), 2006). Additionally, approximately 100
excess deaths occurred during the July 27-August 5, 2006 heat
wave, an 8% increase over the average daily death rate (New York
City Department of Health and Mental Hygiene (NYCDOHMH),
2006). Chronic diseases such as cardiovascular disease, mental
health disorders and obesity were common comorbidities in heat
illness and deaths in NYC between 2000 and 2011 (Centers for
Disease Control and Prevention (CDC), 2013). Among hyperthermia deaths with information available, none of the deceased had
used a working air conditioner (Ibid.). “Rates of heat illness and
death increased with age, were typically higher among males than
females for those aged o65 years, and increased with neighborhood poverty” and the homeless were at greater risk for heatrelated mortality and illness (Centers for Disease Control and
Prevention (CDC), 2013, p. 618).
These health effects could worsen during the 21st century due
to a changing climate. Temperature projections for the NYC
metropolitan region using a global-to-regional climate modeling
system and two greenhouse gas emissions scenarios, A2 and B2,
yielded a mean increase of 70% in heat-related mortality rates by
the 2050s within the region compared to the 1990s (Knowlton
et al., 2007). A net increase in annual temperature-related deaths
of 15.5–31% was estimated for Manhattan, New York, in the 2080s
as compared with the 1980s, as increases in heat-related mortality
outweighed reductions in cold-related mortality using the B1 and
A2 emissions scenarios and 16 downscaled global climate models
(Li et al., 2013).
Research suggests that the physical and social characteristics of
neighborhoods are important for understanding the spatial and
social distribution and variability of heat-related mortality within
cities (Clarke, 1972; Harlan et al., 2006, 2013; Klinenberg, 2002;
Smoyer, 1998). The urban heat island effect, which leads to higher
surface and near-surface air temperatures in dense urban areas
than surrounding suburban and rural areas, may increase the
health effects of summer temperatures, as micro-urban temperature variation and elevated nighttime temperatures increase
exposure to heat for those without air conditioning and increase
the risk of heat-related disease and mortality (Patz et al., 2005;
Smargiassi et al., 2009; Uejio et al., 2011).
Heat island intensity is spatially heterogeneous in urban landscapes, so that some areas may be significantly cooler than others
during a heat wave (Harlan et al., 2006; Smoyer, 1998). The
thermal environment (microclimates) within cities varies because
of physical layout and urban design, land use mix, and vegetative
cover and street trees (Hart and Sailor, 2008; Slosberg et al., 2006).
Hart and Sailor (2008) found that roadway area density was an
important determinant of local heat island magnitudes for Portland, Oregon, while the main factor distinguishing warmer from
cooler areas in the Portland metropolitan region was tree canopy
cover. Using thermal infrared data derived from Landsat imagery,
Slosberg et al. (2006) found that spatial variability in NYC’s surface
temperatures was most associated with changes in albedo and a
measure of vegetation coverage, the Normalized Difference Vegetation Index (NDVI). The association between the thermal environment of neighborhoods and demographic risk factors for heatrelated health effects was found to be significant in the city of
Phoenix, where “lower socioeconomic and ethnic minority groups
were more likely to live in warmer neighborhoods with greater
exposure to heat stress” (Harlan et al., 2006). Jesdale et al. (2013)
found that non-Hispanic Blacks, non-Hispanic Asians and Hispanics were more likely than non-Hispanic Whites to live in block
groups with heat risk-related land cover (HRRLC), where at least
half the population “experienced the absence of tree canopy and
at least half of the ground was covered by impervious surface”
(p. 811).
Although temperature varies within cities in ways relevant for
heat exposures, little is known in NYC about how this affects
health outcomes. Because this knowledge may suggest possible
interventions to reduce heat-associated health problems, we
examined the relationship between characteristics described at
the neighborhood scale, including biophysical, demographic and
population health characteristics, and heat-related mortality rates
within New York City.
1.1. Place and health
The conceptual basis for this research is located in the growing
body of scholarship examining the influence of place-based
characteristics and context on population health. For much of
the post-World War II period, environmental health research
focused on understanding the individual-level risk factors and
their associated biological mechanisms that may lead to disease
causation and disparities in mortality rates (Corburn et al., 2006;
Diez Roux, 2001; Schwartz, 1994). More recently, recognition of
the effects of place as a determinant of the distribution of health
outcomes has increased. Researchers from medicine, epidemiology
and the social sciences are increasingly interested in understanding the cumulative effects of the spatial clustering of physical and
psychosocial hazards often experienced in low-income neighborhoods and communities of color (Bullard, 1990; Corburn et al.,
2006; Northridge et al., 2003).
The impacts of neighborhood conditions on population health
are important and should be analyzed to target climate adaptation
strategies (Rosenthal et al., 2007). Health researchers have theorized that neighborhood conditions and characteristics may exert
an effect on health through influence on behaviors, such as risktaking and levels of physical activity, or by acting to modify the
influence of environmental exposures on individual-level health,
through impacts on individual stress and the immune system
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
(Clougherty and Kubzansky, 2009). Examining a range of models
for how neighborhoods may influence health outcomes, Ellen et al.
(2001) summarized four main pathways for these effects: (1) the
availability of neighborhood institutions and resources; (2) stresses
in the physical environment; (3) stresses in the social environment; and (4) impacts on neighborhood-based networks and
norms. In New York City, neighborhood built environments and
urban design characteristics may create hotter microclimates that
enhance heat exposures, while creating more or less inviting
streetscapes that may also influence exposures and behaviors.
Health and social science researchers frequently use ecological
analysis, in which populations or groups are the units of analysis,
rather than individuals, to examine determinants of population
health (Kawachi et al., 1997; Krieger et al., 1997; McLaughlin
Centre for Population Health Risk Assessment, 2012; Susser,
1994). This study uses both aggregate population health data
and data on community properties to examine their association
with the temperature–mortality relationship in New York City
2. Methods
We evaluated the spatial association between independent
variables that describe neighborhood-scale characteristics (socioeconomic, demographic, the built and biophysical environment,
health status and risk behaviors) and senior citizens’ rates of
excess deaths during heat events in New York City.
As a measure of relative vulnerability to heat, we used the
natural cause mortality rate ratio among those aged 65 þ
(MRR65 þ ), comparing the natural deaths rate (per days) on
extremely hot days (maximum heat index 100 1Fþ) to the natural
deaths rate on all days in the warm season between May and
September. Data were pooled across the years 1997–2006 at the
neighborhood-level.
3. Data
3.1. Neighborhood boundaries
The administrative boundaries of NYC’s Community Districts
(CDs) and United Hospital Fund (UHF) areas are used as proxies for
neighborhoods in this study; they are the levels at which the
spatially-disaggregated data necessary are available. Other studies
on the effect of the built environment on health outcomes in NYC
have used the census tract as the spatial unit of analysis (Rundle
et al., 2007); that approach was not possible for this study due to
data instability caused by the relatively small number of death
counts at that finer-scale. Despite this limitation and the heterogeneity within these areas, the places and populations contained
within Community Districts and UHF areas often share common
histories, built environments and socio-economic characteristics,
and their use in ecological analysis is a much finer scale of spatial
disaggregation compared with previous ecological studies that
used either NYC or the metropolitan region as the reference spatial
unit. There are 42 United Hospital Fund designated neighborhoods
in the city, defined by several adjoining zip codes, and 59
Community Districts. We used both types of geographic areas
because each has different covariates available for analysis.
47
period 1997 through 2006 were obtained by the NYC Department
of Health and Mental Hygiene Office of Vital Statistics. We
aggregated total mortality counts at the Community District (CD)
and United Hospital Fund (UHF) area levels for all days when the
maximum heat index was 100 1F or above (very hot days) and also
for all other days during this time period. We calculated the
mortality rate ratio by dividing the natural deaths rate on
extremely hot days (maximum heat index 100 1Fþ) by the natural
deaths rate on all warm season days (May 1–Sept. 30th) for each
neighborhood area.
The heat index (HI), or apparent temperature, is a measure that
combines relative humidity and ambient temperature (Steadman,
1979). This analysis used the same meteorological data set developed by Metzger et al. (2010). Hourly meteorological data from the
National Climatic Data Center were obtained for the three New
York City stations located at Central Park, La Guardia airport, and
John F. Kennedy airport for 1997–2006. Meteorological data from
La Guardia airport was used because it had the most complete
records during the study period (Metzger et al., 2010). The heat
index (HI) was calculated using ambient temperature (F) and
relative humidity (%) for ambient temperature of 4or ¼80 1F
and relative humidity of 4or ¼ 40% (Metzger et al., 2010). There
were 49 days during the reference time period where the HI
equaled 100 1F or above. The total reference period of the entire
May–September warm seasons during the study period is
1530 days.
Associations of the mortality rate ratios (MRR65 þ ) with the
vulnerability factors described below were evaluated.
3.3. Vulnerability factors
A range of neighborhood-level characteristics that might influence the risk of heat-related mortality during excessively warm
days was examined. An inventory of over 30 independent variables
was derived from the substantial literature documenting the public
health effects of excess heat in the epidemiology, sociology, urban
climate and urban planning fields. These were categorized into
three main groups: (1) demographic and area-level socioeconomic
status and (2) health risk characteristics describing neighborhoodlevel prevalence of health conditions (e.g., diabetes, obesity, hypertension) and risk characteristics (e.g., living alone, being at risk for
social isolation), in Tables 1 and 3 factors in the built environment
(housing conditions and land-use) and characteristics describing
the neighborhood’s biophysical environment, in Table 2. These
characteristics were used as the independent variables in linear
regression and correlation analysis with the neighborhood-level
mortality rate ratio (MRR65 þ ), described above. The correlations between independent variables and MRR65 þ are shown in
Tables 1 and 2, along with the source of the data.
Sources for these data were the 2000 US Census, the New York
City Department of Health and Mental Hygiene (NYCDOHMH), the
New York City Department of City Planning (DCP), the New York
City Department of Housing Preservation & Development (HPD),
the New York City Department of Finance, the United States Forest
Service (USFS), and the National Aeronautics and Space Administration (NASA). A geographic information system (GIS) for NYC was
created, with layers corresponding to each of the independent
variables used in the analysis. Surface temperatures derived from
NASA’s Landsat 7 ETM thermal data were averaged to the Community District and UHF-neighborhood level.
3.2. Mortality data
3.4. Analysis
The dependent variable in this analysis is a measure of the
relative risk of mortality by seniors aged 65 and older on very hot
days. Daily counts of natural cause deaths at the census tract level
for persons age 65 and over from May through September for the
Bivariate and multivariate ordinary least squares (OLS) linear
regression was used to assess the relationships between the mortality rate ratio (MRR65 þ ) and the vulnerability measures noted above
48
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
impervious streets and urban canyons of NYC’s neighborhoods
could result in unhealthy exposures to heat during extreme heat
events (Clarke, 1972; Marmor, 1975), the relationship between
surface temperatures and the distribution of heat-related mortality within New York City had not been tested prior to this analysis.
The ecological scale of our study required converting Landsatderived thermal infrared data to estimated land surface temperatures, and then aggregating these data through averaging the
finer-scale (60 m) raster data to the CD and UHF-level. Highresolution data (3-foot pixels) from the analysis of NYC’s land
cover by the USFS, also averaged to the Community District and
UHF-neighborhood scale, enabled us to test relationships between
vegetative (tree and grass cover) and impervious surface cover
with the mortality rate ratios.
Along with the search for statistical significance and the
avoidance of excessive collinearity, we sought to identify
predictors of vulnerability to premature death during heat events
in NYC at the ecological scale, and develop and use new
methods of geospatial analysis of the urban thermal environment,
while providing guidance, methods and findings for further
finer-scale vulnerability research and policy interventions.
Regression analysis proceeded in a stepwise fashion, testing
variables as described above as predictors of the mortality rate
ratios and eliminating models that did not achieve significance at
po 0.10 or very close to it. Analysis of the independent variables
with the mortality rate ratios, MRR65 þ , is summarized in Tables 1,
2 and 4.
and listed in Tables 1 and 2. First, bivariate relationships between the
mortality rate ratios and each of the candidate variables were
analyzed through OLS linear regression. Correlations between explanatory variables were also assessed using Pearson’s correlation
coefficient to identify groups of variables tending to capture the
same phenomena. For example, the percent of population in poverty
and measures of educational attainment (e.g., percent adults without
a high school diploma) are so strongly correlated (r¼0.89) at the
neighborhood scale in New York City that it does not make sense to
include both variables in multivariate modeling.
The Pearson’s r correlations between independent variables
and the bivariate regression models (R-squared values) were used
to select among the correlated metrics of similar factors for use in
multivariate linear OLS regression. An examination of the statistical significance of the variables in bivariate analysis with the
mortality rate ratios (MRR65 þ ) was part of the selection process,
including all of the categories (e.g., demographic, biophysical, built
environment).
The variables significant at p o0.10 in bivariate analyses (or
close to p o0.10; we did not use a strict cut-off value) were tested
in multivariate linear OLS regression. Our variable selection was
also informed by our interest in evaluation of biophysical predictors such as surface temperatures as well as other landscape
characteristics that represent possible (known or unknown) vulnerability factors for heat-related mortality that are also potentially amenable to public intervention to reduce health impacts.
Although it has been long asserted that the temperatures of the
Table 1
Pearson’s correlation of neighborhood-level characteristics with the mortality rate ratio (MRR65 þ ), a measure of seniors (age 65 þ ) vulnerability to heat-related mortality in
New York City, 1997–2006.
Characteristics
Demographic and socioeconomic status
Seniors (age 65þ) living alone (percent of households)
Total population below poverty rate
Median household income
Mean household income
Educational attainment
Percent high school graduate and above
Percent no high school diploma
Neighborhood racial/ethnic composition
Percent Black/African-American
Percent White
Percent Hispanic (all races)
Percent Asian
Percent non-White
Measures of possible social and/or cultural isolation
Percent of households with no phone service
Health and risk characteristics
Percent age 65þ reporting hypertension diagnosis
Percent age 65þ with diabetes
Percent age 45þ at risk for social isolation
Percent age 65þ living alone
Percent age 65þ with self-reported general health status of fair/poor
Percent obese all ages (BMI Z Z 30)
Proportion current asthmatics all ages
Percent with frequent mental distress (all ages)
Pearson’s
95% CI
Scale
r
p-Value
Lower, upper
CD
CD
UHF
CD
UHF
0.074
0.255
0.395
0.213
0.288
0.578
0.051*
0.01a
0.106
0.064*
( 0.323, 0.185)
( 0.001, 0.479)
(0.104, 0.624)
( 0.444, 0.045)
( 0.544, 0.017)
b
CD
UHF
0.245
0.255
0.062*
0.103
( 0.471, 0.011)
( 0.053, 0.518)
b,c
CD
UHF
CD
UHF
CD
UHF
CD
UHF
UHF
0.117
0.162
0.122
0.238
0.161
0.260
0.305
0.232
0.238
0.377
0.305
0.36
0.129
0.222
0.096*
0.019a
0.096*
0.129
( 0.143, 0.362)
( 0.149, 0.444)
( 0.366, 0.138)
( 0.505, 0.071)
( 0.099, 0.40)
( 0.047, 0.522)
( 0.52, 0.054)
( 0.5, 0.077)
( 0.071, 0.505)
b
0.11
( 0.048, 0.442)
b
0.047a
0.071*
0.135
0.608
0.52
0.266
0.58
0.806
(0.005, 0.559)
( 0.023, 0.539)
( 0.074, 0.502)
( 0.228, 0.376)
(0.208, 0.393)
( 0.135, 0.455)
( 0.381, 0.221)
( 0.268, 0.338)
d
CD
UHF
UHF
UHF
UHF
UHF
UHF
UHF
UHF
0.21
0.308
0.281
0.234
0.082
0.102
0.176
0.088
0.039
Notes: Variables are at the Community District (CD, n¼59) and United Hospital Fund (UHF, n¼42) level.
n
Significant at po 0.10.
Significant at p o 0.05.
b
Census 2000 from NYC DCP.
c
Census 2000 from NYC DOHMH.
d
NYC DOHMH Community Health Survey 2007 (CHS).
a
Data
b,c
b,c
b,c
b,c
b
b
b
c
d
d
d
d
d
d
d
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
49
Table 2
Pearson’s correlation of neighborhood characteristics with intra-urban mortality rate ratios (MRR65 þ ); built and biophysical environment variables used in the study.
Characteristics
Housing conditions:
Pearson’s
Percent 65þ who own and use AC
Rate of total housing violations, 2000
Rate of total housing violations, 2000–2005
Rate of serious housing violations, 2000–2005
Rate of property tax delinquencies, 2005
Rate of property tax delinquencies, five-year mean
Percent of households in dilapidated or deteriorating residential buildings, 2002
Percent homes near structures rated good or excellent, 2002 & 2005 two-year mean
Housing stock and land use
Percent of housing stock that is rent- stabilized units, 2002
Percentage of housing stock that is public housing, 2002
Percent vacant housing units
Percent owner-occupied housing units
Population density
Percent of residential buildings that are walk-ups
Land cover
Vegetated land cover as percent of residential tax lots (outlier 208 removed)
Vegetated land cover (trees and grass) as percent of land area
Trees as percent of total land cover (outliers UHF 101 and CD 208 removed)
Percent impervious of total land cover
Percent impervious of total land cover (outlier UHF 101 removed)
Remotely sensed surface temperature/surface urban heat island
Landsat 7: August 14, 2002 (daytime)
Landsat 7: September 8, 2002 (daytime)
Landsat 7: September 8, 2002 (outlier UHF 101 removed)
95% CI
Scale
r
p-Value
Lower, upper
Data
UHF
CD
CD
CD
CD
CD
UHF
UHF
0.341
0.237
0.302
0.323
0.38
0.334
0.257
0.409
0.027
0.071n
0.02
0.013
0.003
0.01
0.1n
0.007
( 0.584, 0.042)
( 0.02, 0.464)
(0.05, 0.518)
(0.073, 0.534)
(0.138, 0.579)
(0.086, 0.543)
( 0.05, 0.52)
( 0.634, 0.12)
a
CD
CD
CD
UHF
CD
CD
CD
0.249
0.086
0.189
0.413
0.207
0.177
0.126
0.064n
0.517
0.152
0.007
0.116
0.180
0.342
( 0.014, 0.48)
( 0.173, 0.334)
( 0.07, 0.424)
( 0.636, 0.125)
( 0.439, 0.051)
( 0.082, 0.414)
( 0.134, 0.37)
b
CD
UHF
CD
UHF
CD
UHF
UHF
0.061
0.219
0.041
0.216
0.062
0.237
0.3
0.649
0.163
0.758
0.175
0.646
0.131
0.057n
( 0.312, 0.198)
( 0.49, 0.09)
( 0.293, 0.217)
( 0.491, 0.098)
( 0.313, 0.197)
( 0.072, 0.504)
( 0.008, 0.556)
g
UHF
CD
UHF
CD
UHF
CD
0.225
0.115
0.224
0.109
0.302
0.152
0.152
0.386
0.154
0.411
0.055n
0.255
( 0.084, 0.495)
( 0.145, 0.36)
( 0.085, 0.494)
( 0.151, 0.355)
( 0.002, 0.554)
( 0.108, 0.392)
h
b
b
c
c
d
b
b
b
e
e
f
g
g
g
h
h
Bolded variables significant at p o 0.05.
n
Significant at po 0.10.
NYC DOHMH Community Health Survey (CHS), 2007, access to air conditioning (AC).
b
NYC Housing and Vacancy Survey (HVS).
c
NYC Department of Housing Preservation and Development (HPD).
d
NYC Department of Finance.
e
Census 2000, NYC Department of City Planning (DCP).
f
NYC Dept. of City Planning (DCP), PLUTO dataset 2003.
g
US Forest Service, Northeastern Research Station, EMERGE dataset, 2001–2002.
h
NASA Goddard Institute for Space Studies (NASA GISS), Landsat 7 data.
a
Table 3
Mortality counts and hot days in New York City, 1997–2006.
Event
Frequency
Average year-round natural cause deaths/day, all ages
Average warm-season natural-cause deaths/day for age 65 þ a
Average natural-cause deaths/day for age 65 þ on very hot daysa,b
Total warm-season natural-cause deaths, 1997–2006, for New York City
Average by Community District for the warm season, 1997–2006
E 145
E 95 (95.5)
E 100 (100.3)
E 150,000
2531 (SD 1134)
a
Difference between the average warm-season and very hot day natural-cause deaths/day for age 65 þ is
significant at p-value ¼0.0015 (t-test for paired samples; t -observed value ¼ 3.396).
b
Heat index Z 100 ˚F.
To assess the interaction and effect modification of income and
neighborhood poverty rates, which are fundamental characteristics
used to describe population vulnerability to climate variability
hazards, we also stratified bivariate analyses by rates of neighborhood poverty and income measures (Cutter et al., 2009; Fothergill
and Peek, 2004). Community Districts (CDs) were stratified into two
groups, above and below the average of the median household
income for 59 CDs, and UHF-areas were stratified into two groups,
above and below the average proportion of population poverty in
UHF-areas, for use in OLS linear regression analysis with the
mortality rate ratio as the dependent variable, based on the factors
described above. The results are described below.
We were also interested in examining relationships between
characteristics that may increase the risk of heat-related mortality:
air conditioning access and the urban heat island effect,
expressed here as area-based surface temperatures, with other
50
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
Table 4
Mortality rate ratio (MRR65 þ ) models: ordinary least squares (OLS) linear regression.
Predictor variables
UHF-neighborhood models
1. Homeownership (percent)
2. Deteriorating or dilapidated buildingsa
3. Percent below poverty
4. Impervious coverb
Hypertensionb
5. Air conditioning access, age 65 þ
6. Homes near structures rated good or
excellent
Community District models
1. Property tax delinquenciesc
2. Serious housing violationsd
3. Percent below poverty
4. Percent Asian population
R2
Adj.
R2
Pr4F Unstandardized coefficient
p-Value
Std. Error
of B
95% CI for B (lower,
upper)
β
95% CI for β (lower,
upper)
0.001
0.005
0.002
0.001
0.0016
0.002
0.001
( 0.004, 0.001)
(0.003, 0.023)
(0.001, 0.007)
(0.0005, 0.006)
(0.001, 0.007)
( 0.008, 0.001)
( 0.006, 0.001)
0.413
0.399
0.395
0.346
0.376
0.341
0.409
( 0.704, 0.122)
(0.102, 0.696)
(0101, 0.688)
(0.054, 0.637)
(0.085, 0.668)
( 0.642, 0.041)
( 0.701, 0.118)
2.87
2.72
2.72
2.4
2.613
2.3
2.838
0.007
0.01
0.01
0.021
0.013
0.027
0.007
0.004
0.0005
0.001
0.002
(0.003, 0.018)
(0.0003, 0.002)
(0.00, 0.006)
( 0.008, 0.001)
0.334
0.323
0.255
0.305
(0.084, 0.584)
(0.156, 0.830)
( 0.001, 0.511)
( 0.558, 0.053)
2.677
2.577
1.991
2.422
0.01
0.013
0.051
0.019
0.007
0.01
0.01
0.008
0.117 0.094
0.168 0.147
0.027
0.007
0.003
0.013
0.004
0.003
0.004
0.004
0.003
0.112
0.104
0.065
0.093
0.01
0.013
0.051
0.019
0.01
0.001
0.003
0.005
0.096
0.089
0.049
0.077
t
B
0.15
0.138
0.135
0.186
0.17
0.159
0.156
0.23
Standardized coefficient
Note: N¼ 42 UHF-neighborhoods and 59 Community Districts. The dependent variable is the mortality rate ratio for age 65 þ (MRR65 þ ).
a
Percent of households, 2002; influential point UHF 501 removed (Port Richmond, Staten Island).
Influential point UHF 101 removed (Kingsbridge-Riverdale, the Bronx).
Five-year mean, 2000–2003 and 2006. The share of 1–3 residential unit properties (Tax Class 1) with over $500 in unpaid property tax.
d
Six-year mean, 2000–2005. The number of class C (immediately hazardous) housing code violations issued by the NYC HPD per 1,000 rental units.
b
c
neighborhood-scale characteristics. We used multivariate OLS linear and spatial error and spatial lag regression to analyze the
neighborhood-level predictors of these characteristics, using rates
of air conditioning access for seniors and area-based measures of
surface temperatures as dependent variables, with sociodemographic, built environment, and biophysical characteristics as predictors. These analyses are summarized in Tables 6 and 7 and
discussed below.
The variables used for regression models were assessed for
multicollinearity through measures of VIF and Tolerance. All individual variables in the models had a VIFo5 (the majority VIFo2; in
multiple regression models, the mean VIFo2) and an acceptable
Tolerance 40.50. The use of highly correlated variables in multivariate regression models (e.g., a Pearson’s r of over about 0.60–
0.70) was avoided. Given the high collinearity of many sociodemographic, built environment and biophysical predictors, the majority
of the mortality rate ratio models have only one independent
variable, while the air conditioning and surface temperature models
include multiple predictors. We report here only models whose
predictors are statistically significant at po0.05 or close to it.3
4. Results
All-cause mortality of seniors aged 65 and over increased
significantly in New York City during extremely hot days
(HI Z100 1F) from 1997 to 2006 (p ¼0.001). For 59 Community
Districts (CDs), the mortality rate ratio (MRR65 þ ) had a mean
weighted by senior population of 1.0479 (95% confidence interval,
1.021, 1.090). For 42 UHF areas, the MRR65 þ had a mean weighted
by senior population of 1.0464 (95% confidence interval, 1.016,
1.085). Citywide there were over 4% more deaths on days with a
Heat Index equal to or above 100 1F compared to all other warm
season days from 1997 to 2006. This finding is consistent with
other studies that have found excess mortality during high heat
days (Hoshiko et al., 2010; Metzger et al., 2010; New York City
Department of Health and Mental Hygiene (NYCDOHMH), 2006;
Semenza et al., 1996).
3
Percent below poverty has a p-value¼ 0.051 for Community Districts.
Excess mortality during heat event days was unevenly distributed in New York City’s Community Districts and United Hospital
Fund (UHF) areas during 1997 through 2006, with higher rates
in southern and western parts of the Bronx, northern Manhattan,
central Brooklyn and the eastern side of midtown Manhattan
(Figs. 1 and 2).
Significant positive associations (p o0.05) were found between
heat-mortality rates and neighborhood-level measures of poor
housing conditions, poverty, impervious land cover, seniors’
hypertension and the surface temperatures aggregated to the
UHF-area level during the warm season (Tables 1, 2 and 4). The
rates of owner-occupied housing units and the percent of homes
near structures rated good or excellent had the strongest negative
associations with the mortality rate ratios, followed by the
prevalence of residential air conditioning access and percent Asian
population. The negative association between UHF area-based
home-ownership rates and the mortality rate ratio was the
strongest identified in the study (β ¼ 0.413; p ¼0.007). Several
measures of housing quality were significantly correlated with the
mortality rate ratios (MRR65 þ ), including rates of serious housing
violations, property tax delinquencies, and deteriorating and
dilapidated buildings, suggesting that the quality of seniors’
housing is a population-level risk factor for heat-associated
mortality (Table 2).
However, bivariate regression with the mortality rate ratios
stratified by poverty rates or income levels resulted in correlations
with the vulnerability predictors (e.g., air conditioning access, low
educational attainment, surface temperatures and property tax
delinquencies) only for the set of neighborhoods with high
poverty rates, but not for neighborhoods with relatively low
poverty. For example, a negative correlation between the prevalence of air conditioning access and the MRR65 þ exists for highpoverty neighborhoods (Pearson’s r ¼ 0.317 and p ¼0.20; n ¼18),
but not for low-poverty neighborhoods (Pearson’s r¼ 0.001 and
p¼ 0.97; n ¼ 24). These differential trends in “high-poverty” versus
“low-poverty” neighborhoods were evident for a range of vulnerability predictors. While the small sample size of the stratified
analyses reduces the power of these tests, which do not achieve
statistical significance at p o0.05, the stratified analyses
strongly suggest that poverty rates and income levels act as effect
modifiers for the ecological relationship between neighborhood
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
51
Fig. 1. Mortality Rate Ratios for seniors age 65 and older (MRR65 þ ) by New York City Community District (n ¼59). The MRR65 þ compares mortality rates during very hot
days (maximum heat index ¼100 1F þ ) to all May through September days, 1997–2006.
characteristics and higher rates of heat-mortality in New York City.
This effect modification reflects the social stratification often seen
in natural disasters, where risk, resilience and vulnerability are not
evenly distributed within cities, but rather follow the “pre-existing
systems of stratification” (Fothergill and Peek, 2004, p. 89).
The lowest-income Community Districts and UHF-areas had a
trend towards higher heat-associated mortality rates (Table 5).
Low-income areas also had a general trend towards hotter surface
temperatures and a lower degree of air conditioning access for
senior citizens (Table 6).4 The hottest Community Districts and
UHF-areas generally had higher mortality rate ratios (Fig. 3); there
4
Air conditioning access is defined as owning and using home air conditioning
according to data from the 2007 Community Health Survey (CHS, DOHMH).
was a strongly significant (p o0.0001) difference between the
mean mortality rate ratio for the quartile of Community Districts
(n ¼15) with the highest surface temperatures (MRR65 þ ¼1.223,
SD¼ 0.082) and the mean mortality rate ratio in the 44 “cooler”
Community Districts (MRR65 þ ¼0.998, SD ¼0.095).5
In multivariate spatial regression, elevated surface temperatures in NYC were significantly and positively associated with
impervious cover, poverty rates and percent Black/African American, and significantly and negatively associated with percent
vegetative land cover, percent White and mean household income.
5
The relatively “hotter” and “cooler” Community Districts calculated from the
Landsat-derived daytime surface temperatures averaged to the CD-level for August
14, 2002, a heat wave day.
52
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
Fig. 2. Mortality Rate Ratios for seniors age 65 and older (MRR65 þ ) by New York City United Hospital Fund (UHF) neighborhoods (n¼ 42). The MRR65 þ shows excess
mortality during very hot days (maximum heat index ¼100 1F þ) compared to all May through September days, 1997–2006.
Table 5
Average mortality rate ratios (MRR65 þ ) by poverty ranking for NYC Community Districts (CDs).
Group by poverty
Mean population
Percent age 65 þ
(mean)
Percent below 1999
poverty level(mean)
Median household
income
MRR65 þ
(mean)
SD (mean MRR65 þ )
20 least impoverished CDs
20 median CDs
19 most impoverished CDs
All CD average
140,133
142,445
123,876
135,681
13.13
11.97
9.18
11
10.57
20.39
36.89
22.38
$55,683
$37,010
$22,645
$38,714
1.026
1.0319
1.1104
1.0552
0.111
0.092
0.172
0.134
Disparities in the prevalence of air conditioning ownership and
use in United Hospital Fund (UHF) areas among seniors aged 65
years and older were found, with nine UHF areas in which over
25% of the senior citizens were not protected by air conditioning
during the warm season in 2007 (Fig. 4). Percent Black/African
American and percent poverty by UHF area were strong negative
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
53
Table 6
Neighborhood (UHF-area) predictors for access to home air conditioning, age 65 and older: ordinary least squares (OLS) and spatial regression.
Predictors
Socioeconomic status
Percent below poverty
Model 1
Model 2
Standardized
coefficients
t
Lower and upper
bound (95% CI)
0.528nnn
( 4.597)
0.760
0.295
Homeownership
Model 3—spatial lag model
Standardized
coefficients
t
Lower and upper
bound (95% CI)
0.446nnn
(3.891)
0.214
0.677
Built environment
Percent of households near
boarded-up buildings
W_AC65a
Race/ethnicity
Black/African American
0.368nn
( 3.203)
0.6
0.135
0.484nnn
( 4.231)
0.716
0.253
Percent non-White
Adjusted R-squared
0.528
0.476
Unstandardized
coefficients
z-Value
Lower and upper
bound (95% CI)
0.366nnn
4.389
0.132n
2.028
0.534
0.198
0.000
0.263
0.112nn
2.85
0.601b
0.191
0.033
Notes: The dependent variable is access to home air conditioning, age 65 þ by UHF areas, 2007. No spatial autocorrelation was observed in Model 1 and 2. Significant spatial
dependence was observed in Model 3’s Robust LM (lag), and a spatial lag model was used to address spatial correlation and bias.The constant in Model 3 is 82.437, with an SE
of 5.871, z-value of 13.04 and p ¼ 0.000.
n
po 0.05.
p o0.01.
nnn
p o 0.0001.
a
Spatial autoregressive parameter.
b
Pseudo R-squared.
nn
Fig. 3. Mean Community District (CD) mortality rate ratios (MRR65 þ ), stratified by
the 75th percentile daytime surface temperature (August 14, 2002), with 95% CIs
(n¼ 59). Landsat-derived surface temperatures were averaged to the CD-level. The
mean MRR65 þ for the hottest quartile of CDs (Surface Temperature 4 75th) ¼
1.223; the mean MRR65 þ for the relatively cooler 44 CDs (Surface Temperature
o 75th) ¼ 0.998.
predictors of seniors’ air conditioning access in multivariate
regression (Table 6). There was a trend for an increasing mortality
rate ratio for areas with the least proportion of White population.
Percent of seniors living alone was not significantly correlated
with increasing mortality rate ratios in New York City neighborhoods, although this was found to be a risk factor in earlier studies
of high mortality events such as the Chicago 1995 heat wave
(Semenza et al., 1996).
We used GeoDa software to test for the presence of
spatial dependence and spatial autocorrelation in all significant
(p o0.05) models. GeoDa is an open source software program that
provides spatial data analysis, including spatial autocorrelation
statistics and spatial regression functions (Anselin et al., 2005).
The residuals for each OLS model were examined with Global
Moran’s I, the Lagrange Multiplier (LM, lag and error) and the
Robust LM lag and error tests with the queens contiguity weights
matrix. We used this weights matrix to assess spatial autocorrelation and dependence as it is contiguity-based and thus provides a
logical approach for considering the effects that area-based characteristics such as poor housing conditions or land cover might
have on their surrounding locations.
No spatial autocorrelation was observed in the mortality rate
ratio models (Table 4) and two of the air conditioning (AC) access
models (Table 6). However, one of the AC access models and all of
the surface temperature models were significantly autocorrelated
and required use of spatial lag or spatial error regression. All but
one of the surface temperature models required a spatial error
model, which is appropriate as a finding of spatial error can be an
indication that relevant processes may be occurring at different
scales as well as signifying spatially autocorrelated residuals
(Table 7). This is obviously true in our use of remotely-sensed
surface temperatures averaged to the neighborhood-scale in New
York City, as microclimates are influenced by land cover and
dimensions of the built environment at finer scales. Use of the
spatial error and spatial lag regression improved the fit of the
models in all measures; improving the significance of independent
variables, increasing R-squared (a pseudo-R2 in spatial regression)
and the log-likelihood, and decreasing the Akaike info criterion
(AIC) and Schwarz criterion relative to the OLS regression.
5. Discussion
Measures of housing quality that can also be interpreted as
indicators of socioeconomic status (SES) – homeownership rates, air
conditioning access, proportion of dilapidated and deteriorating
residential buildings, property tax delinquency and serious housing
violations rates – provided the strongest correlations to the areabased mortality rate ratios. These findings reaffirm prior research
54
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
Fig. 4. Air-conditioning ownership and use by United Hospital Fund neighborhoods, data from the 2007 NYCDOHMH Community Health Survey (n¼ 42).
that highlights the quality of the neighborhood built environment
and social determinants as relevant for heat-associated mortality
risk. They provide evidence for the utility of NYC’s program to
provide air conditioners to low-income seniors, and raise questions
about how planners may address the role of poor housing quality
and concentrated poverty in creating vulnerability to heatassociated mortality.
The proportion of owner-occupied housing units by UHF area
has the strongest association with the MRR65 þ , which may be an
indicator of the stronger community ties, stability and the array of
resources and capacity typically associated with homeownership
and possessed by homeowners (Dietz and Haurin, 2003). Neighborhoods with higher rates of homeownership may have a
buffering, protective effect on the risk of heat-associated mortality
due to their positive community externalities. Prior research has
found areas with high degrees of homeowners to typically be
associated with stronger locally-based social networks, greater
involvement in local community, lengthier resident tenure, and
familiarity with a great number of neighbors—all factors supported
by prior research as potentially protective against adverse heathealth outcomes (Dietz and Haurin, 2003; Kawachi and Berkman,
2003).
The metrics of property tax delinquencies and housing violations are worth examining for what they might reveal about how
the quality of the built environment may create higher risk for
seniors. The highest correlation values at the CD-scale are rates
of property tax delinquencies in Class 1 properties, which
are primarily smaller residential buildings: one-, two- and
three-family homes and condos of three stories or less. Property
tax delinquencies, generally a warning sign of foreclosures, may be
seen as a measure of neighborhood stability and economic stress
that may result in declining building conditions. Similarly, the rate
of housing violations in a neighborhood describes the quality of
housing and the physical environment and may be a metric
relevant to climate-health outcomes (Klinenberg, 2002). These
findings are notable given the recent economic recession. Housing
foreclosures increased in New York City during the 2000s, especially following 2007, affecting minority (over 50% Black and
Hispanic) and low-income neighborhoods in Queens and Brooklyn
more than others (Chan et al., 2013). The city’s goals for the
creation of new affordable housing units were not met during
the study period (1997–2006) and in subsequent years through
the Bloomberg mayoralty, and reflecting the national economic
downturn post-2008, rates of homelessness in the city rose by
over 30% (Bosman, 2010; Brash, 2011; Gross, 2009).
This suggests that health burdens resulting from housing
foreclosures and economic recession may include increased vulnerability to heat-related morbidity and mortality for affected
communities, as increased housing instability creates greater
stress in the physical and social environment for individuals and
disrupts neighborhood-based social networks and norms. It also
suggests that the development of urban climate adaptation
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
55
Table 7
Spatial models for New York City’s land surface temperature: Standardized coefficients, z-values and 95% Confidence Intervals (CI).
UHF Neighborhoods
Model 1: 8/14/02
Spatial lag modela
Pct below 200% of poverty
Pct home ownership
Impervious cover
W_AUG_14a
Model 2: 9/8/2002
Spatial error modelb
Constant
Mean household income
Impervious cover
Pct Black/African American
Lambda
Community Districts (CD)
Model 1: 9/8/02
Spatial error modelc
Constant
Impervious cover
Mean household income
Pct Black/African American
Lambda
Model 2: 7/22/2002
Coeff.
SE
z-Value
p Value
95% CI
0.752
0.201
0.134
0.156
0.086
0.026
0.028
0.026
0.015
7.658
4.810
5.899
5.779
0.000
0.000
0.000
0.000
(0.148, 0.254)
(0.077, 0.191)
(0.103, 0.209)
(0.056, 0.116)
0.971
0.000
0.011
0.005
90.519
4.647
10.598
3.551
0.000
0.000
0.000
0.000
(85.911, 89.845)
(0.000, 0.000)
(0.090, 0.134)
(0.009, 0.029)
0.883
0.010
7.29E 06
0.004
0.058
101.376
10.346
6.778
3.734
13.964
0.000
0.000
0.000
0.000
0.000
(87.754, 91.294)
(0.084, 0.124)
(0.000, 0.000)
(0.009, 0.025)
(0.690, 0.922)
0.875
87.878
3.85E 05
0.112
0.019
0.86
89.524
0.104
4.94E 05
0.017
0.806
Spatial error modeld
Constant
Impervious cover
Percent below poverty
Pct Black/African American
Lambda
Model 3: 7/22/2002
93.901
0.128
0.084
0.024
0.795
1.171
0.015
0.017
0.007
0.060
80.158
8.317
4.977
3.609
13.175
0.000
0.000
0.000
0.000
0.000
(91.553, 96.249)
(0.098, 0.158)
(0.050, 0.118)
(0.010, 0.038)
(0.675, 0.915)
Spatial error modele
Constant
Percent below poverty
Impervious cover
Pct Black/African American
Population density
Lambda
Model 4: 8/14/02
93.866
0.083
0.12
0.025
0.00003499
0.833
1.133
0.016
0.015
0.006
1.497E 05
0.051
82.807
5.208
8.071
3.908
2.338
16.352
0.000
0.000
0.000
0.000
0.019
0.000
(91.593, 96.138)
(0.051, 0.115)
(0.090, 0.150)
(0.013, 0.037)
(0.000, 0.000)
(0.731, 0.935)
Spatial error modelf
Constant
Impervious cover
Percent below poverty
Percent White
Lambda
Model 5: 7/22/2002
Spatial error modelg
Constant
Pct of CD covered by trees, grass and vegetation
Mean household income
Pct Black/African American
Lambda
Pseudo R-Squared
0.823
0.844
0.859
93.654
0.141
0.092
0.025
0.951
1.857
0.019
0.029
0.011
0.017
50.426
7.467
3.161
2.202
56.213
0.000
0.000
0.002
0.028
0.000
(89.931, 97.377)
(0.103, 0.179)
(0.034, 0.150)
( 0.047, 0.003)
(0.917, 0.985)
111.238
0.138
6.71E 05
0.023
0.778
0.795
0.016
0.0000107
0.007
0.064
139.930
8.769
6.267
3.525
12.076
0.000
0.000
0.000
0.000
0.000
(109.644, 112.832)
( 0.170, 0.106)
(0.000, 0.000)
(0.009, 0.037)
(0.650, 0.906)
0.828
Notes: The dependent variable is the mean daytime surface temperature measured by Landsat 7, averaged to the Community District and UHF-level. Data sources for the
independent variables noted in Tables 1 and 2.
a
Log Likelihood ¼ 80.569; AIC ¼171.138; Breush–Pagan test (p-value) ¼ 0.409. W_AUG_14 is the spatial autoregressive coefficient.
Log Likelihood ¼ 46.273; AIC¼ 100.547; Breush–Pagan test (p-value) ¼ 0.84. Lambda is the spatial autoregressive parameter for spatial error models.
c
Log Likelihood ¼ 64.565; AIC ¼ 137.131; Breush–Pagan test (p-value) ¼ 0.854.
d
Log Likelihood ¼ 87.774; AIC¼ 183.548; Breush–Pagan test (p-value) ¼0.220.
e
Log Likelihood ¼ 85.352; AIC¼ 180.704; Breush–Pagan test (p-value) ¼ 0.740.
f
Log Likelihood ¼ 109.088; AIC ¼ 226.176; Breush–Pagan test (p-value) ¼ 0.197.
g
Log Likelihood ¼ 86.462; AIC¼ 180.924; Breush–Pagan test (p-value) ¼ 0.798.
b
programs should explicitly consider means for addressing housing
inadequacies and instability in their planning. Beyond New York
City, research on intra-urban differences in climate-health outcomes may be especially relevant in the context of the increasing
spatial concentration of poverty and substandard housing conditions related to a weak economy and income inequality. A growing
body of scholarship documenting the relationships between measures of the built environment, particularly housing, socioeconomic and racial disparities, and intra-urban variability in heat
exposure as risk factors for heat-health impacts (Harlan et al.,
2013; Uejio et al., 2011) highlights the importance of the social
justice dimensions of urban planning for climate adaptation. We
discuss these issues of housing quality, place and risk further
below.
The positive correlation between increasing surface temperatures and heat-mortality rates for the lower-income Community
Districts and UHF-areas suggests that hot spots of summertime
surface temperatures increase the risk of heat-associated mortality
56
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
in New York City, especially in poorer neighborhoods. Further
research should incorporate methods, such as case-control studies,
that can consider the effect of microclimates at the individual
building scale and block-group level that may be more relevant to
individual exposures.
The surface urban heat island is influenced by land cover,
including the percent of tree coverage and impervious cover.
Previous research has shown both surface and ambient air
temperatures to be related to tree coverage, total vegetative cover,
the albedo (or reflectivity) of surfaces, and impervious land cover
(Slosberg et al., 2006). The multivariate model (Table 4) includes
percent impervious cover as a neighborhood-level predictor of
increased heat-mortality rates (p o0.10), suggesting that the City’s
programs to increase vegetative cover – through forestry (MillionTrees), Greenstreets and green infrastructure, vegetated swales,
and green roof incentives – may help to create a more healthful
environment.
Our analysis was informed by conceptual frameworks that
describe how interactions between different levels of social
structures – from the individual, to the family, to the neighborhood context and the macro-level characteristics of the political
economy – may express the impacts of place on health (MorelloFrosch and Shenassa, 2006; Clougherty and Kubzansky, 2009; Link
and Phelan, 1995; Phelan et al., 2004). To interpret regression
results for predictors of increased mortality rates and environmental exposures such as surface temperatures we sought insights
from the literature on health disparities and urban environmental
history.
Low-income and communities of color have borne a disproportionate exposure to pollutants and facilities with hazardous emissions, while often also sharing in fewer of the amenities such as
parks and recreational space (Maantay, 2001; Sze, 2007). We
found that at all scales examined, surface temperatures are
significantly negatively associated with household income, so that
higher-income neighborhoods tend to have cooler mean surface
temperatures, with hotter daytime surface temperatures found in
neighborhoods or census tracts with higher rates of poverty.
Racial/ethnic variables (percent non-White and Black/African
American) are included in this research as predictors in multivariate spatial models of the surface urban heat island effect.
District-level poverty rates were significant predictors of higher
surface temperatures during a heat wave (Table 7); additional
predictors include percent impervious land-cover and percent
Black/African American, while percent White of total population,
mean household income and percent vegetated land cover were
significant negative predictors of higher surface temperatures. The
predictive ability of these variables in these models is interpreted
as a representation of how decades of economic and housing
development, frequently discriminatory, manifest in the array of
land-use, land cover and urban design factors that influence the
physical phenomenon of the urban heat island (e.g., see Schill,
1995). In effect, these racial/ethnic variables may act as an index
variable in these models, measuring the ways in which the social
processes that sort out neighborhoods socially intersect with the
characteristics of the built environment. The associations between
socioeconomic status, race/ethnicity and demographics with the
intra-urban variability of temperatures reflects an emergent form
of spatial inequality in regards to climate risks—the environmental
exposures and adverse impacts of extreme weather events and
climate change. Excess heat is conceptualized as an unevenly
distributed urban pollutant that may be relatively higher in
minority and low-income neighborhoods due to the design and
maintenance of the built environment and housing conditions.
This is consistent with the literature on heat-health impacts
discussed earlier that found greater exposures and impacts for
residents of low-income neighborhoods and communities of color.
New York City has a long history of providing “cooling” to lowincome residents to ameliorate the harsh impacts of extreme heat.
The distribution of cooling to the poor as a health strategy started
during a 10-day heat wave in 1896, an innovation personally
organized by Theodore Roosevelt, then president of the Board of
Police Commissioners, who convinced the city to purchase and
distribute 350 t of ice to low-income New Yorkers during the 1896
heat wave (Kohn, 2008, p. 11). Subsequently, the city opened “ice
stations” for the poor in 1919. A modern form of this cooling
initiative continues today in the city’s distribution of free air
conditioners to low-income senior citizens, one of several programs of the NYCDOHMH to prevent heat-associated morbidity
and mortality. New York State (NYS) also has a program6 that
purchases and installs air conditioners or fans for eligible lowincome households with individuals susceptible to extreme heat.
However, the use of home air conditioning (AC) during heat
events is limited by concern over the expense of electricity bills for
some. Respondents for a recent survey of New Yorkers considered
most vulnerable to heat-health impacts7 reported several barriers
to their use of AC during hot weather; 54% without an AC said that
they could not afford AC (95% CI; 37, 69), and of those that
reported never or infrequently using AC, 12% were concerned
about their electricity bill (95% CI; 5, 26) (Lane et al., 2013, p. 6).
Given the evidence that some seniors feel they cannot afford the
use of air conditioning, even if they own one, examining the benefits
and costs of reduced-electricity rates for low-income seniors may be
a timely avenue of policy research. Although a review of this subject
is beyond the scope of this analysis, our preliminary research
indicates that the resources available for energy assistance to lowincome seniors in NYC is limited, and many households fall into the
gaps between the modest and short-term programs that do exist.
New York State does not require regulated investor-owned electric
utilities to provide reduced-cost electricity rates to low-income
seniors, such as those provided by Massachusetts’ Energy & Utility
Assistance Programs (MassResources.org, 2014). Such “lifeline” rate
programs may assist residents with long-term income instability and
provide protection against heat-health impacts to vulnerable populations. As required by the MA Department of Public Utilities, the
benefit for qualified low-income residential customers is a 25%
discount based on the total charges in their monthly electricity bill,
including supply, delivery and all charges (National Grid, 2014). In
light of the increasing importance of policies for carbon mitigation,
design and efficiency interventions that might accompany energy
assistance for low-income seniors should be investigated as concurrent approaches. Initiatives like the Philadelphia Energy Coordinating Agency’s weatherization programs, which provide cool roof
coatings, insulation, ceiling fans, energy efficient appliances and
other environmental benefits for the homes of low-income seniors
as well as job training for developing low-income energy service
delivery, are good models that integrate climate adaptation and
mitigation for multiple benefits.
5.1. Housing and the spatial concentration of risk
Given these results, we asked how housing quality and its
distribution in NYC’s diverse neighborhoods intersected with the
6
For households that meet certain low-income requirements and other
eligibility criteria, the Home Energy Assistance Program’s Cooling Assistance
Component may purchase and install an air conditioner or fan up to $800. The
website notes, however, that the funding for this program is “very limited” and
distributed on a first-come first-serve basis (The Home Energy Assistance Program
(HEAP), 2014).
7
Defined as those who “reported not having AC, or using AC either “never” or
“less than half the time” and were either 65 years þ or reported “poor” or “fair”
health status” (Lane et al., 2013, p. 6).
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
spatial concentration of seniors. Areas with higher rates of poor
quality housing (e.g., increased violations or property tax delinquencies) had significant positive associations with higher mortality rates;
neighborhoods with indicators of better quality housing (e.g., buildings rated good or excellent, rates of homeowners) had significant
negative associations with higher mortality rates (Table 4). These
data suggests that housing quality is one of the salient characteristics
through which poverty fosters risk of heat-related mortality. In this
interpretation, the characteristics of housing and the built environment may amplify or buffer the risk of heat-related health impacts.
More generally, housing conditions are a locus and expression of
population vulnerability during extreme heat events, and housing
quality a substrate through which poverty influences risk for seniors
during conditions of environmental extremes. Health disparities arise
in part due to the differential access to options for quality housing
that higher income provides and low-income limits (Bashir, 2002).
The analysis of housing affordability and assistance in NYC
reveals “a rich, complex neighborhood topography of poverty,
wealth, and housing subsidy that defies the simplistic stereotypes
of policy and popular discourse” (Wyly and DeFilippis, 2010, p. 2).
Given the long-history and complexity of these issues, we limit our
discussion here to a few key points about rent-regulated apartments, the rising concentration of immigrant seniors, the geography of Naturally Occurring Retirement Communities (NORCs), and
concerns for the future in light of present trends.
According to the NYC Rent Guidelines Board, “available rental
housing in NYC falls into three main categories”: market-rate
housing, rent regulated housing (rent controlled or rent stabilized), and subsidized housing (public housing and “Section 8”
with voucher assistance for rent in private housing)8 (New York
City Rent Guidelines Board (NYCRGB), 2014). Roughly one-half of
the city’s total rental housing stock is rent-regulated units, with
rent-stabilized units being the greatest share compared to the
relatively small number of rent-controlled units (Furman Center,
2012). In 2011, rent-stabilized units were 45.4% of all rental units,
while only 1.8% of units were rent-controlled (Ibid). Rentstabilization is intended to provide certain protections to tenants,
in addition to regulated annual rent increases, including the right
to renew one’s lease, requirements for building services and
succession rights for family members and partners (New York
City Rent Guidelines Board (NYCRGB), 2014).
To understand the uneven spatial distribution of elevated
mortality rates, we considered the location of rent-regulated
apartments. The percent of housing stock that is rent-stabilized
units had a positive (p o0.10) correlation with the mortality rate
ratio. The seven neighborhoods with the city’s highest proportion
of regulated to total rental units (4 50% in 2011) had a mean
mortality rate ratio of 1.112 (SD ¼0.083). Although this was not
significantly different (p ¼0.19; 95% CI, 1.028, 1.195) than the mean
mortality rate ratio for all CDs (1.055), it raises questions about the
conditions in rent-stabilized housing for low-income seniors in
neighborhoods where they are concentrated.
The percent of housing units that are public housing by
Community District had no association with the mortality rate
ratio (Pearson’s r ¼0.086, p ¼ 0.52). This lack of correlation should
be interpreted with caution; it may be due to a true lack of
association, or because spatial disaggregation to the census tractor individual level is necessary for a robust evaluation of the links
between risk of heat-related mortality with residence in public
housing. Higher prevalence of asthma in children was reported in
NYC public housing compared to a range of housing types in
8
Other subsidized housing includes Mitchell-Lama housing, and other rental
units include In Rem, HUD-regulated, Loft Board regulated units, and Article 4. The
NYU Furman Center for Real Estate and Urban Policy’s Data Search Tool is a
recommended resource for data on housing types and markets.
57
studies at the census tract and individual-level (Corburn et al.,
2006; Northridge et al., 2010). Public officials and non-profit
organizations described a range of maintenance deficiencies
within NYC public housing following our study period and delays
in addressing housing repairs for mold, moisture and infestations
of cockroaches and mice, as well as long waits for structural and
appliance repairs, including air conditioners (De Blasio, 2013;
Natural Resources Defense Council (NRDC), 2013), although data
on the rates of these problems were lacking. The two public
housing developments with the City’s most delayed repairs, the
Woodside Houses in Queens and Melrose Houses in the Bronx, are
located in Community Districts above the median mortality rate
ratio (1.063) and over one SD above the mean (MRR65 þ 41.098)
(De Blasio, 2013). With a largely low-income resident population –
about 60% of residents are 30% or less of the city’s median income
– a finer-scale analysis of the relationship of public housing quality
to heat-mortality vulnerability is recommended.
A demographic trend of note since 2000 is the increase in
senior immigrants. Roughly one-half (46%) of NYC’s seniors in
2013 were immigrants (Center for an Urban Future, 2013). Often
linguistically isolated, this group is growing in size and proportion
faster than native-born seniors and is potentially vulnerable due to
cultural barriers, more limited access to support services, limited
English proficiency, and limited retirement savings and use of
Social Security compared to other seniors (Center for an Urban
Future, 2013, p. 3–5). Three of the four communities with both the
city’s highest rates of rent-regulated apartments and high mortality rate ratios (MRR65 þ 4 1.10) also had above-average rates of
foreign-born seniors; these were Washington Heights/Inwood,
Manhattan (79% of seniors foreign-born, 9% of total population;
86.7% rental units regulated), University Heights/Fordham, The
Bronx (55% of seniors foreign-born, 3% of total population; 75.7%
of rental units regulated), and Astoria, Queens (63% of seniors
foreign-born, 7% of total population; 53% of rental units regulated)
(Furman Center, 2012; Center for an Urban Future, 2013). However,
across the city, there was no correlation between the percent of
households that speak a language other than English at home (by
Community District, Census 2000) and the mortality rate ratio
(Pearson’s r ¼0.000).
Finally, we were also interested in assessing the geography
of naturally occurring retirement communities, or NORCs, in NYC.
NORCs are neighborhoods or housing developments where seniors
represent a substantial proportion of the residents, in buildings
not originally designed for older persons (see the New York State
Office of the Aging for specific thresholds; often building-based
NORCs are defined as having at least 50% of units with a senior
occupant). NORCs are a nationally recognized urban phenomenon
related to concentrated “aging in place” – older adults who remain
in their home for a long tenure rather than leave them for
retirement communities or assisted living – or they may also
develop due to the arrival of older adults or the departure of young
people (Vladeck and Segel, 2010, p. 1–2). Both NYS and NYC
developed supportive service programs in NORC areas to assist
seniors who remained living at home. New York City had 43 NORC
supportive service programs in 2010 (NORC-SSP); all received
some form of public funding (Ibid, p. 3). The majority of these
programs were city-funded (34) and located in public and private
housing developments (32); only two city-funded programs were
neighborhood-based.
Our qualitative, visual assessment of these geographies using
maps of NORC developments could not discern any association
between the areas of elevated heat-mortality rates and the
location of NORC developments in NYC (Armborst et al., 2010).
With the majority of NYC NORCs in housing developments, this
may be that they are small proportions of each area’s population,
or it may relate to the availability of supportive services in many
58
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
NORC developments. According to the NYC Department for the
Aging, residents of the City’s housing development-based NORCs
“can access health and social services right in their own building
or building complex” (New York City Department for the Aging
(NYCDFA), 2014). Health analysts hope that further developing
neighborhood-based NORCs may hold the potential to integrate
the range of housing, health and social services that can aid older
New Yorkers in successful aging in place (Vladeck and Segel, 2010).
The trends for the future are concerning. As the large baby
boomer cohort ages, NYC experienced a dramatic increase in its
older population of 60 years and over between 2000 and 2010,
increasing to over 17% of the population (New York City
Department for the Aging (NYCDFA), 2012). The proportion of
those aged 65 years and over in the city is projected to expand
rapidly by 2030, to over 1.35 million (NYC Department of City
Planning (NYCDCP), 2006).
However, for the past decade at least, there has been a chronic
and severe shortage of affordable housing in NYC in general, and in
particular for seniors (Gotbaum, 2008; Lui, 2013). The discourse of
crisis is often used in discussions of the availability of affordable
housing for low-income seniors in NYC (Wyly and DeFilippis,
2010). Policies to provide affordable housing opportunities to this
group have fallen far short of need since the 1990s. Waiting lists
for public housing or other forms of subsidized housing are long, if
open at all, in NYC; it can take many years for those on the list to
be placed. For some forms of housing subsidies, even those that
receive them have no guarantee that the benefit will translate into
an ability to secure adequate housing (as, for example, many
landlords refuse to accept vouchers that subsidize rent) (DeFilippis
and Wyly, 2008).9 A recent (April 2014) New York Times article on
housing insecurity of seniors reported “About 51% of renter
households led by an older New Yorker pay at least 35% of their
income in rent, or above what the federal government considers
affordable, making residents 65 and over among the most rent
burdened in the city” (Navarro and Yee, 2014). With housing
insecurity on the rise since the 2008 recession for low-income
New Yorkers, there is concern about the potential for an increase
in homelessness among senior citizens, along with continued
housing inadequacy (Lui, 2013). Seen in this NYC context, it is
not unusual to find that measures of housing quality are consistently significant in association with the area-based mortality rate
ratios.
6. Limitations
These findings reflect the limits of an ecologic analysis: the
presence or absence of a linear relationship between neighborhoodlevel characteristics and mortality rates does not imply that such a
relationship necessarily will or will not exist at the individual level.
Disaggregating data at the intra-urban level alleviates some concerns regarding ecologic bias. Limitations of this analysis include:
Mortality has an association with warm weather at a range of
temperatures in New York City, and heat-related mortality
occurs on days below the Heat Advisory threshold of Heat
Index 4 100 1F (Curriero et al., 2002; Metzger et al., 2010).
Therefore the mortality rate ratios MRR65 þ do not represent
the full range of heat-associated mortality that actually
occurred during the study period, and it is possible that the
magnitude of associations with neighborhood characteristics
would change with use of a more inclusive exposure metric.
9
“In tight housing markets like New York City, a voucher is akin to a hunting
license: There is no guarantee that the assistance can actually be used (DeFilippis
and Wyly, 2008, p. 784).”
Similarly, although seniors are at greatest risk, heat-associated
mortality occurs in NYC at ages below 65 years, and factoring in
all age groups might influence these findings (New York City
Department of Health and Mental Hygiene (NYCDOHMH),
2006).
The health outcome measure, MRR65 þ , is based on the sameday heat index (HI), which has a linear association with daily
mortality in New York City (Metzger et al., 2010). However,
heat-related mortality in the city is best predicted by a nonlinear association that incorporates the previous 2-day temperature and the same-day heat index (Metzger et al., 2010).
Future research may use additional modeling methods to
evaluate community characteristics using a lag time with same
day and previous 1-and 2-day temperature as a predictor.
It may be useful to examine excess mortality using different
heat exposure periods (e.g., during heat waves rather than
HI Z 100 1F) and more complex spatially-stratified time series
models.
Near-surface air temperature measurements would likely provide a more health-relevant measure of the urban heat island’s
spatial variability, but were not available across the city at the
spatial resolution necessary for this analysis. As well, daily
variations in ozone and fine particulate matter (PM10 and 2.5)
are associated with daily mortality in cities (Kinney, 1999;
Koken et al., 2003; Thurston and Ito, 1999). Ozone is a
photochemical pollutant whose formation from precursor
emissions is accelerated during hot and sunny days. This
research did not control for air quality during heat events.
The administrative boundaries of CDs and UHF define different
areas, and associations between the same independent variables (e.g., owner occupied housing units) with the health
outcome metric are not identical. There was, however, no
reversal of trend at these two spatial scales; correlations at
the UHF and CD-scale agree in the direction of association and
are generally in the same range, but differ in magnitude. The
differences correspond to the expectations of the modifiable
area unit problem (MAUP)’s general influence on statistical
results—that larger sized areas tend to have a greater correlation when used in regression analysis. This methodological
concern could be alleviated by the use of multi-level studies
that incorporate individual and household-level data to test
neighborhood effects (Subramanian et al., 2009).
The multivariate models that achieved statistical significance
were limited due to the multicollinearity of independent
variables.
Finally, there is possible but unknown instability in the data
due to the limited number of days with the Heat Index equal to
or above 100 1F during the study period.
Despite the limitations noted above, these findings affirm the
importance of neighborhood characteristics and social determinants in targeting heat illness prevention and emergency response
activities. The findings suggest that planning and design strategies
for urban heat island mitigation should target resources to improve
conditions in lower-income areas and residences, and incorporate
local data on neighborhood vulnerability to reduce health impacts
of extreme heat.
7. Conclusions
Within the city, different neighborhoods experienced differential rates of excess heat-related mortality, and this variation
was correlated with neighborhood factors including rates of
poverty, air conditioning access, educational attainment, housing
quality, rates of home ownership, land cover and land surface
J. Klein Rosenthal et al. / Health & Place 30 (2014) 45–60
temperatures. These findings suggest that low-income neighborhoods should be targeted in the city’s climate adaptation planning,
and that disparities in access to residential air conditioning are
associated with heat-related mortality rates, as are metrics of
neighborhood stability, economic hardship, and building conditions in New York City neighborhoods.
Programs that provide access to cooling for seniors during
extreme hot weather, for example, through cooling centers and
air-conditioned public spaces are widely acknowledged as protective interventions. Given the importance of access to cooling
during periods of extreme heat, further research on the spatial
distribution and use of cool spaces within neighborhoods –
including parks, air conditioned stores, public buildings and pools
– may help identify and characterize resources for seniors able to
leave their homes. Our results suggest that research on the effects
of residential building design on indoor temperatures and building
thermal performance is important to inform adaptive planning,
even while outreach and prevention measures such as home air
conditioner distribution for low-income seniors will continue to
be needed for an increasing elderly NYC population.
Policies to improve the housing conditions of elderly residents
could play a role in reducing heat-related mortality in New York
City, although these policies are not yet explicitly considered as
part of climate adaptive planning. Climate adaptation and heat
island mitigation programs that seek to identify neighborhood hot
spots within cities and address economic disparities may help to
reduce the health impacts of climate extremes and variability.
Towards that end, a community-based adaptation planning process may help address the social justice dimension of the impacts
of extreme events and climate change in New York City while
increasing the effectiveness of adaptive programs and policies.
Acknowledgments
A portion of this research was supported under a cooperative
agreement from the Centers for Disease Control and Prevention
(CDC) through the Association of Schools of Public Health (ASPH)
Grant Number CD300430, a grant from the CDC through the TKC
Integration Services, LLC, and an internship with the New York City
Department of Health and Mental Hygiene (DOHMH). The contents are solely the responsibility of the authors and do not
necessarily represent the official views of DOHMH, CDC, or any
government agency. We thank Thomas Matte of DOHMH for his
input during the research and comments on earlier versions of the
manuscript, and Grant Pezeshki and Sarah Johnson of DOHMH for
their expert advice on mapping and data; Erica Blonde of the
Harvard University Graduate School of Design (GSD) for comments
on the manuscript, research assistance and help with revisions;
Meg Howard for mapping assistance; Sumeeta Srinivasan of the
Center for Geographic Analysis at Harvard University for advice on
spatial analysis; and Elliott Sclar of Columbia University for
insightful comments on the research. We gratefully acknowledge
the anonymous reviewers of the manuscript for their feedback and
valuable input.
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