© 2017 Sulhee Yoon - ufdcimages.uflib.ufl.edu
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EXPLORING THE RELATIONSHIP BETWEEN URBAN FORM AND HEALTH OUTCOMES By SULHEE YOON A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2017
Transcript of © 2017 Sulhee Yoon - ufdcimages.uflib.ufl.edu
EXPLORING THE RELATIONSHIP BETWEEN URBAN FORM AND HEALTH OUTCOMES
By
SULHEE YOON
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2017
© 2017 Sulhee Yoon
To my Parents, Hyun-Mo Yoon and Kyung-Shin Kim
for your supports and unconditional love
4
ACKNOWLEDGMENTS
My sincere thanks to my advisor and committee chair, Dr. Ilir Bejleri, who has
been a source of encouragement, guidance, and patience throughout my years in
Urban Planning program. Dr. Bejleri is more than an academic advisor for me. He is my
mentor and role model and hopefully I can follow his way as a researcher and a mentor
for students.
I also want to thank my co-chair Dr. Ruth Steiner for her generosity and
responsive guidance in my PhD career. Dr. Steiner is always very responsive, and I
owe a huge debt of gratitude to her for my first teaching experience. I thank Dr. Paul
Zwick for his valuable feedback and a steady guidance in quantitative methods. I also
thank Dr. Paul Duncan that he provides me important tours in different ways, not only in
approach in urban planning but also in perception in public health. I also want to say a
special appreciation to Dr. Jeffrey Harman and Dr. Donna Neff who guided me to
understand importance of health service research.
I am so blessed to have awesome friends and colleagues over the past several
years. I can’t imagine I went through all the steps without them. Ali Komeily, thank you
for your encouragement on my every step to grow up. I also want to thank Leilei Duan,
Nahal Hakim, Ron Ratliff, Scott Noh, and other friends in this program. I can’t forget my
entire PhD journey we had together, our precious time sharing our life stories, and all
the blessed times we had together.
Finally, I dedicate this dissertation to my mom and dad. Thank you my mom and
dad for your guidance and praying for me all the time. I could not complete this journey
without you.
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TABLE OF CONTENTS
page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 9
LIST OF ABBREVIATIONS ........................................................................................... 11
ABSTRACT ................................................................................................................... 13
CHAPTER
1 INTRODUCTION .................................................................................................... 16
Problem Statement ................................................................................................. 16 Research Question ................................................................................................. 19 Significance ............................................................................................................ 19
Objective ................................................................................................................. 21 Dissertation Structure ............................................................................................. 22
2 LITERATURE REVIEW .......................................................................................... 24
Urban Form Dilemma: Compactness and Sprawl ................................................... 24
Current Characteristics of Urban Form ............................................................. 25 Urban Form Measurement by Spatial Configurations ....................................... 27 Multi-dimensional Urban Form Measurements ................................................. 29
Health Disparity and Its Determinants .................................................................... 32 Health Disparity and Health Outcome .............................................................. 33
Health Disparity and Access ............................................................................. 34 Predisposing/Enabling Factors ......................................................................... 41
Linkage among Urban Form, Built Environment and Health Outcome ................... 45
Urban Form and Health .................................................................................... 45 Empirical Linkage ............................................................................................. 46
Summary of Literature Review ................................................................................ 48
3 METHODOLOGY ................................................................................................... 54
Study Design .......................................................................................................... 54 Aim 1: Examine the magnitude of healthcare disparity in recent 10 years ....... 54 Aim 2: Examine the Relationship between Urban Form and Access to
Healthcare ..................................................................................................... 55 Aim 3: Assess the impact of SES in the relationship between urban form
and health outcome ....................................................................................... 56
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Study Area, Data Collection and Measurement ...................................................... 57
Aim 1: Health care availability and Health Status ............................................. 57 Aim 2 and 3: Urban form, health care accessibility, and health outcome ......... 58
Analytical Method ................................................................................................... 62 Aim 1: Measuring Disparity ............................................................................... 62 Aim 2: Urban Form and Healthcare Accessibility ............................................. 64 Aim 3: Urban Form and Health Outcome ......................................................... 68
4 RESULT .................................................................................................................. 76
AIM1: Health Disparity Trends ................................................................................ 76 Longitudinal Health Disparity among Census Region ...................................... 77 Correlation between Disparity and Selected Socioeconomic Characteristics
of Each State ................................................................................................. 79
AIM2: Built Environment: Urban Form and Healthcare Accessibility ....................... 80 Urban Form Components and its Geographic Variations ................................. 80
Correlation among Urban Form Components ................................................... 83 Travel Time to Healthcare Provider and its Geographic Variation .................... 84
Relationship between Urban Form and Healthcare Accessibility ..................... 85 Standard Residual Map .................................................................................... 87
AIM3: Relationship between Urban Form and Health Outcome ............................. 88
Geographic Distribution of Health Outcome ..................................................... 88 Correlation between Population Socioeconomics and Health Outcome ........... 89
Regression between Urban Form and Health Outcome Clusters ..................... 91
5 DISCUSSION AND CONCLUSION ...................................................................... 114
Conclusion of Aim One ......................................................................................... 115 Conclusion of Aim Two and Three ........................................................................ 116 Discussion ............................................................................................................ 118
Urban Form .................................................................................................... 118 Additional Findings- Logistic Regression ........................................................ 119
Policy Intervention ................................................................................................ 121 Limitation and Future Research ............................................................................ 124
LIST OF REFERENCES ............................................................................................. 128
BIOGRAPHICAL SKETCH .......................................................................................... 140
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LIST OF TABLES
Table page 2-1 Approaches and indicators to measure urban form in previous studies ............. 50
3-1 Research design and composition ...................................................................... 71
3-2 Indicators and measurements used to define urban form components .............. 72
3-3 Indicators and measurements for health determinants ....................................... 73
4-1 Descriptive statistics of health disparities ........................................................... 94
4-2 Normality test ...................................................................................................... 94
4-3 Homogeneity test ................................................................................................ 94
4-4 ANOVA result ..................................................................................................... 95
4-5 Homogeneous subsets of health status disparity (Gini Coefficient)- Tukey’s test ...................................................................................................................... 95
4-6 Homogeneous subsets of healthcare availability disparity (Gini Coefficient)- Tukey’s test ........................................................................................................ 95
4-7 Top 10 states in disparity in 2010s ..................................................................... 96
4-8 Correlation test with socioeconomics of population ............................................ 96
4-9 Correlation analysis of variables of density ........................................................ 96
4-10 PCA result over the total variance explanation related to density ....................... 96
4-11 Communality matrix and weight of the variables related to density .................... 97
4-12 Correlation analysis of variables for mixed use .................................................. 97
4-13 PCA result over the total variance explain related to the mixed use ................... 97
4-14 Communality matrix and weight of the variables related to mixed use ............... 97
4-15 Correlation analysis of variables for street network ............................................ 97
4-16 PCA result over the total variance explain related to the street network ............. 97
4-17 Communality matrix and weight of the variables related to street network ......... 98
4-18 Correlation analysis of variables for proximity .................................................... 98
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4-19 PCA result over the total variance explain related to the proximity ..................... 98
4-20 Communality matrix and weight of the variables related to proximity ................. 98
4-21 Correlation between urban form components ..................................................... 98
4-22 Census block group and population identified by network analysis to compare accessibility by each primary care provider ......................................... 99
4-23 OLS result for PCP accessibility (univariate) ...................................................... 99
4-24 OLS result for PCP accessibility (multivariate) and four urban form components ........................................................................................................ 99
4-25 OLS result for PCP accessibility (multivariate) and three urban form components ........................................................................................................ 99
4-26 OLS result for PCP accessibility and socioeconomic indicators ....................... 100
4-27 Comparison of descriptive statistics between Orlando MSA and Florida .......... 100
4-28 Correlation test with mortality rate and its cluster pattern ................................. 100
4-29 Collinearity statistics for independent and control variables ............................. 101
4-30 Summary statistics for each four independent variables (univariate before controlling SES) ................................................................................................ 101
4-31 Summary statistics for each four independent variables (univariate after controlling SES) ................................................................................................ 101
4-32 Summary statistics for three independent variables (multivariate before and after controlling age) ......................................................................................... 102
5-1 Logistic regression model for step 0, 1, and 2: classification ............................ 127
5-2 Logistic regression model (step 0): variables not in equation ........................... 127
5-3 Logistic regression model (step 1): Hosmer and Lemeshow test ..................... 127
5-4 Logistic regression model (step 1): variables in the equation ........................... 127
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LIST OF FIGURES
Figure page 2-1 Andersen model (1995) that demonstrates the factors that lead to the use
and access of the healthcare. Model describes that access to healthcare services is determined by three components: predisposing, enabling, and need factors. ....................................................................................................... 52
2-2 Gravity model. .................................................................................................... 52
2-3 Improved gravity model ...................................................................................... 53
2-4 2 Steps Float Catchment Area (2FCA) from Wang and Luo (2003) ................... 53
3-1 Simplified research framework ........................................................................... 74
3-2 Equation to define urban form components ........................................................ 74
3-3 Leading causes of death in Florida ..................................................................... 74
3-4 Lorenz curve and equations to calculate Gini Coefficient ................................... 75
4-1 Distribution of longitudinal health status and healthcare availability ................. 103
4-2 Health Disparity and healthcare availability trends ........................................... 104
4-3 Health Status and Healthcare availability disparities between 2000s and 2010s ................................................................................................................ 105
4-4 Box-plots of Health Status Disparities in 2008 and 2013 (Top), and Healthcare Availability Disparities (Bottom) in 2008 and 2016 ......................... 106
4-5 Map of four urban form components (density, mixed-use, street network, and proximity) at census block group level. Areas with high score (darker color) represent higher value ...................................................................................... 107
4-6 Map of travel time to the nearest PCP. The natural breaks in the range of values of the variable were used to identify the accessibility ............................ 108
4-7 Map of standard residuals from OLS regression using urban form components and the travel time to the nearest PCP ........................................ 109
4-8 Histogram of Standardized Residuals of urban form components and travel time to the nearest PCP ................................................................................... 110
4-9 Map of standard residuals from OLS regression using urban form components and the travel time to the nearest PCP ........................................ 111
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4-10 Map of mortality rate by cardiovascular disease and diabetes and its spatial patterns that identifies statistical clusters ......................................................... 112
4-11 Normal P-P plot to assess for normality of linear regression predicting transition ........................................................................................................... 113
4-12 Scatterplot of standardized residuals versus predicted values to assess for homoscedasticity in the linear regression predicting transition ......................... 113
11
LIST OF ABBREVIATIONS
ACA Affordable Care Act
ACS American Community Survey
AHP Analytical Hierarchy Process
AHRQ Area Health Resource File
AMA American Medical Association
ANOVA The Analysis of Variance
BRFSS Behavioral Risk Factor Surveillance System
CBD Central Business District
CHIP Children’s Health Insurance Program
DHHS Department of Human and Health Service
FCA Float Catchment Area
FDOH Florida Department of Health
FPDC Florida Parcel Data by County
GIS Geographic Information System
HIA Health Impact Assessment
HLA Hierarchical Linear Model
HRSA Health Resource and Services Administration
HPSA Health Professional Shortage Areas
KMO The Kaiser-Meyer-Olkin
LISA Local Indicator of Spatial Association
MD Medical Doctor
MPO Metro Planning Organization
MSA Metropolitan Statistical Area
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OLS Ordinary Least Square
PCA Principal Component Analysis
PCP Primary Care Physician
SES Socioeconomic Status
TAZ Transportation Analytic Zone
O-D Origin-Destination
OLS Ordinary Least Squares
UA Urbanized Area
VIF Variance Inflation Factor
13
Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
EXPLORING THE RELATIONSHIP BETWEEN URBAN FORM AND HEALTH
OUTCOMES
By
Sulhee Yoon
May 2017
Chair: Ilir Bejleri Co-chair: Ruth L. Steiner Major: Design, Construction, and Planning
Increasing evidence suggests that urban form is related to several public health
challenges. Urban sprawl, in particular, has long been considered a contributing factor
to health disparities because land use patterns and street connectivity affect
transportation decisions and limit physical access to various aspects of the built
environment. Several planning strategies have been proposed that seek to address
health issues by reshaping urban form, particularly by developing a compact city form.
However, there is limited empirical evidence that such measures produce a healthy city.
Therefore, there is a growing awareness of the need to re-examine the link between
urban form and health outcomes. Against this background, the present study seeks to:
1) explore regional trends of population health disparities from the perspectives of
health status and healthcare availability; 2) assess the physical relationship between
urban form and healthcare service accessibility; and 3) examine whether a population’s
health outcomes are impacted by urban form while controlling for the socioeconomic
status (SES) and considering clustering patterns. To examine these questions, this
study explores the meaning of health disparity by assessing the relationships among
14
urban form, access to healthcare, and health outcome associated with physical
inactivity using case study of between urban form access to healthcare, and health
status using case studies of longitudinal trends across the US from 2000 to 2010 and a
2010 snapshot of the Metropolitan Statistical Area (MSA) of Orlando, Florida. The data
are acquired from the Census Bureau, the Behavioral Risk Factor Surveillance System
(BRFSS), and the American Medical Association (AMA).
First, this dissertation finds that southern states show the highest disparity levels
and that, except in four states (i.e. Connecticut, Rhode Island, Kentucky, and Oregon),
health disparities have increased across the US between 2008 and the present.
Additionally, health disparities increase when the median household income level and
the proportion of non-Hispanic whites decrease; however, health disparities are
positively correlated with higher percentages of elderly people (people older than 65)
and people without healthcare coverage.
Second, a multivariate spatial regression analysis is applied to examine the
relationship between urban form and healthcare accessibility. The results show
significant correlations between two urban form components (mixed-use and street
network) and physical accessibility to primary healthcare. These findings support the
prevailing belief that as urban sprawl increases, access to primary care providers
decreases.
Third, this dissertation identifies geographical clusters of health outcomes in the
Orlando MSA and proves that areas with more compact urban forms have higher
physical inactivity-related mortality rates from cardiovascular diseases and diabetes
while controlling for socioeconomic variables correlated with population health
15
outcomes. Population age was identified as the most significant variable when
assessing the impact of urban form on clusters of mortality from cardiovascular
diseases and diabetes.
This study contributes to an understanding of health disparities in the US and
supports a more comprehensive understanding of the urban form factors that influence
such health outcomes as morbidity and mortality from chronic diseases associated with
physical inactivity.
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CHAPTER 1 INTRODUCTION
Problem Statement
Challenging public health problems triggered by health disparities have prompted
increased efforts to create livable communities. Health disparities have emerged as a
top agenda item as a growing body of evidence suggests that different sub-populations
are exposed to different rates of disease incidence and mortality. . In 2010, the
Department of Human and Health Services (DHHS) launched the Healthy People 2020
movement with the goal of eliminating health disparities and supporting healthy living.
Although many researchers have identified health disparities as being highly related to
individuals’ SES (e.g., low income, high unemployment, or high poverty) and
demographic characteristics (Andersen & Newman, 2005; Braveman & Cubbin, 2010;
Schulz & Northridge, 2004; Wilkinson & Marmot, 2003), studies have shown that health
behavioral changes among small groups and populations are not sufficient to promote
health equality or a healthy community. Many health professionals insist that the
physical environment is a key feature in reducing health disparities (Frank, Sallis, &
Conway, 2006; Gordon-Larsen, Nelson, Page, & Popkin, 2006; Kim & Ruger, 2010) and
creating a livable community and that the environment must be managed to address
social, economic, and demographic circumstances (Audirac, Shermyen, & Smith, 1990;
Godschalk, 2004; Manaugh, Miranda-Moreno, & El-Geneidy, 2010). Urban form and the
resulting built environment have long been treated as contributing factors for negative or
positive health outcomes. Achieving an appropriate urban form is considered the best
way to solve health disparities through environmentally friendly developments that
emphasize housing and transportation choices (Ewing & Cervero, 2010; Ewing, Pendall,
17
& Chen, 2003; Frank et al., 2006; J. Sallis, Frank, Saelens, & Kraft, 2004). An
appropriate urban form is also considered a positive way to promote public and private
investments that encourage physical activity (Ewing, Meakins, Hamidi, & Nelson, 2014;
Sallis & Glanz, 2006), cleaner and safer communities, accessible built environments
(e.g. access to healthcare and healthy foods) (Sallis & Glanz, 2009; Walker, Keane, &
Burke, 2010), and better health behaviors (Reid Ewing et al., 2014; Kim & Ruger, 2010;
Schulz & Northridge, 2004). Urban form is defined as “the spatial pattern of land uses
and their densities as well as the spatial design of transport and communication
infrastructure” (Stead & Marshall, 2001). It is concerned with the physical characteristics
of the built environment, including “everything humanly created, modified, or
constructed, humanly made, arranged, or maintained” (McClure & Bartuska, 2011).
However, as a consequence of urban form development, the US has experienced
significant urban sprawl. Originally, the primary purpose of this sprawl was to separate
land uses, keeping residents away from unpleasant and often environmentally harmful
industrial factories (Dowling, Timothy, 2000; Dupras et al., 2016). In particular, as a
result of urban sprawl, most Americans no longer walk or ride bicycles, and their
increasingly sedentary lifestyles contribute to higher levels of obesity, diabetes, and
other chronic diseases associated with low levels of physical activity (US DHHS, 1996).
In addition, Americans are now almost totally dependent on automobiles for travel,
driving to virtually all of their destinations because of a lack of other practical
transportation alternatives (Ewing, Pendall, et al., 2003; Ewing, Brownson, & Berrigan,
2006). In response, a number of planning strategies have been proposed to improve
Americans’ health by reshaping urban form. Among these planning strategies is the
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concept of the “compact city,” arguably the most ideal and influential approach for
revitalizing inner urban areas by providing better accessibility to the built environment
(Kelly Clifton, Ewing, Knaap, & Song, 2008; Howley, 2009; Lin & Yang, 2006). The
compact urban form has been employed since the late 1970s to combat urban sprawl.
Many researchers emphasize compact development, which is characterized by high
density, mixed land use, low dependency on automobiles, short travel distances, low
fuel consumption, and reduced emissions, all of which have been linked to greater
physical activity and, therefore, better health (Cao, Mokhtarian, & Handy, 2009; Clifton,
Ewing, Knaap, & Song, 2008; Ewing et al., 2014; L Frank, Bradley, Kavage, Chapman,
& Lawton, 2008). Nevertheless, some studies claim that it is possible that the compact
urban form may present high health risks because of traffic congestion (Boarnet,
McLaughlin, & Carruthers, 2011; SL Handy & Boarnet, 2002) and high levels of stress
and air pollution (Caschili, Montis, & Trogu, 2015). Therefore, in the search for
associations between urban form and health outcomes, many questions arise: Is the
sprawl or compactness of urban form directly associated with population health? How
does urban form affect access to the built environment, which, in turn, induces better
health outcomes? How does a population’s SES contribute to the relationship between
health and urban form?
To address modern public health concerns and interest in urban form, this
dissertation primarily explores the factors affecting the adoption of compact and sprawl
urban forms in order to explore their relationships with public health outcomes. The
research focuses on access to primary healthcare providers, who are the first line of
defense against poor health , critical factors in preventive care (Wei Luo & Qi, 2009; F.
19
Wang & Luo, 2005), and, therefore, crucial in improving public health. Along these lines,
the dissertation also examines provisions concerning geographical distribution and
population health to investigate whether a compact city plan is conducive to healthier
living. In so doing, it shows that it is essential for urban planners and public health policy
makers to understand the impacts of urban form and the built environment on health
outcomes.
Research Question
Although compact urban form has emerged as a solution to address the health
problems caused by urban sprawl, particularly by increasing physical activity and
access to the built environment, there is little empirical evidence supporting the viability
of this approach. This dissertation examines whether compact urban form supports
access to the active built environment (or, in other words, whether urban forms that
encourage better access to the built environment can promote positive health
outcomes). In so doing, the research attempts to answer the following research
questions:
1. What's the potential role of urban form in influencing health outcomes? 2. Can compact urban form improve access to the built environment and,
consequently improve health outcomes? 3. How do the allocation of and access to healthcare services affects health
outcomes, and how does a population’s SES contribute to health outcomes?
Significance
This dissertation provides health professionals with valuable information
concerning the status and geographical trends of health disparities. It offers insights
concerning how to manage the built environment to reduce health disparities. By
understanding the urban form factors that influence health outcomes, such as morbidity
and mortality from chronic diseases resulting from physical inactivity, local and regional
20
planners can implement appropriate policy programs (e.g. land use planning,
infrastructure expenditures, and development regulations) to reduce health disparities.
This dissertation offers three major contributions:
First, this research helps to fill the gap in the literature on the relationship
between health disparities and the environment by considering detailed and objectively
measured urban form variables. The research considers both spatial and non-spatial
measures that affect a population’s health status using Geographic Information System
(GIS) measurements and SES indicators. These detailed and disaggregated measures
help to identify precise links between health disparities and the built environment by
analyzing place-based population health perspectives, showing that population health is
shaped differently by different physical environments, and identifying conditions and
criteria for implementing specific health policies and urban development plans. The
concept of place-based population health is founded on the assumption that creating
efficient and effective interactions among people, the environment, and the economy is
critical for increasing access to areas of physical activity to promote social inclusion and
enhance social connectivity (Graham & Healey, 1999; A. C. K. Lee & Maheswaran,
2011; Lopez & Hynes, 2006). In other words, the set of social, economic, and
environmental conditions in a particular environment is an influential determinant of
population health and, thus, health behavior. Therefore, to produce urban policies and
plans to improve public health by reducing health disparities and inequalities, it is
necessary to investigate social structures, community social networks, the physical
environment, the distribution of material resources, and so on.
21
Second, this study employs statistical and spatial methods to measure urban
form. These methods include the application of: 1) a principle component analysis
(PCA) to identify the correlations among urban form components and assign them
weights; 2) a hot spot analysis to identify spatial concentrations of health disparities;
and 3) a spatial regression model to develop a global model of the relationships
between urban form measures and health outcomes. This research approach differs
from previous research in this field because it not only adds additional layers to the
evaluation of sprawl and health outcomes, but also supports the evaluation of
associations through various “paths”, rather than the evaluation of isolated variables
within simpler models.
Third, this study contributes to a better understanding of the specific built
environmental variables associated with health disparities and health status by
proposing a conceptual framework based on theoretical foundations that incorporate
general systems theory, the behavioral model of the environment, and previous
literature. Ecological theory is employed as a key theoretical basis, while general
systems theory and a behavioral model of the environment contribute to the research
conceptualization. The conceptual framework serves as a basis for the multidisciplinary
research and brings together three major research fields: urban design and planning,
public health and epidemiology, and regional science.
Objective
The relationship between urban form and health outcomes is complicated by
many empirical studies that differ in study area, study scale, research design, data
sources, statistical examinations, and methods used to measure urban form and
22
populations with health disparities. In this regard, the main objectives of this dissertation
are to:
Assess the physical relationship between urban form and access to healthcare services by using a network analysis to calculate travel time. This objective provides a multi-dimensional approach to measuring urban form within an MSA
Examine the spatial distribution and environmental attributes associated with health disparities and identify the effects of socioeconomic factors
Provide empirical evidence of the effects of compact and sprawl urban forms on health disparities
Dissertation Structure
This dissertation consists of five chapters. This chapter, Chapter 1, presents the
problem statement and the aims of the research.
Chapter 2 reviews the relevant literature to support an understanding of the
conception of urban form and the health determinants that define the meaning of health
access. The first section of this chapter briefly introduces the concept of urban form,
including its definition, indicators, and measurements, and outlines the health-related
dilemma rooted in urban form type. The second section explores the current status of
knowledge in public health, particularly in relation to the measures used to determine
and control health outcomes. The third section synthetically states the conceptual and
empirical linkage between urban form and health outcomes. Finally, the last section
summarizes the overall findings of the literature review and the importance of the
proposed conceptual framework.
Chapter 3 explains the methods and dataset used in this dissertation. It presents
a research design and hypotheses for the three study aims based on the conceptual
framework proposed at the end of the first section. The second and third sections
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describe the study area, the data collection methods, the specific measurements, and
the variables. The last section explains the data analysis procedure.
Chapter 4 reports the findings of this dissertation study. It comprises three
sections based on the study’s specific aims. The first, second, and third sections
present the results of aims one, two, and three, respectively. The first section presents
overall health disparity trends, regional differences in disparities, the ten states with the
highest disparities, and the correlations between health disparities and selected SES
variables across the US between 2000 and 2010. The second and third sections
present an exploratory analysis that provides a snapshot of the geography of focal
health components (i.e. healthcare access and health outcome clusters) and their
relationships with the components of urban form. These sections use regression models
to cover the built environmental correlation.
Chapter 5 concludes the research and summarizes the key findings of the
dissertation. It also suggests steps for future health and planning initiatives and provides
recommendations for reducing health disparities and improving overall health. This
chapter also includes a discussion, a review of study limitations, and directions for
future research.
24
CHAPTER 2 LITERATURE REVIEW
This chapter is organized in three sections. The primary focus on first section is
on the measures of urban form. It starts with the consideration of urban form at different
scales. Then characteristics of urban form at sub-metropolitan scale are discussed,
followed by the review of multi-dimensional approach in measuring urban form. The
second section examines previous efforts to determine the health disparity, particularly
exploring the health determinants as it relates both spatial and non-spatial
determinants. The third section describes the connection between urban form and
health determinants, including empirical linkage related with physical inactivity. Finally, a
summary is provided at the end of this chapter.
Urban Form Dilemma: Compactness and Sprawl
Most fundamental and traditional ways to define urban form is the spatial
configuration of areas according to the morphology of the city and it has been quantified
into two types: compact and sprawl (Dieleman & Wegener, 2004; FM Dieleman, Dijst, &
Burghouwt, 2002; Jaret, Ghadge, Reid, & Adelman, 2009; Tsai, 2005). As a post-
Industrial product, urban sprawl is characterized as low-density, auto-dependent
development, and high segregation of land uses created in suburban and its outskirts
areas. Sprawl has long been criticized as it is believed to be the contributing factor to
many environmental, social, and economic problems (Ewing, Schmid, Killingsworth, &
Zlot, 2003; Ewing et al., 2014). For an instance of its negative impact, sprawl requires
high car dependency and leads to inefficient street layouts. These disadvantages in
physical accessibility result from the fact that traditional transportation planners seldom
considered the influence of urban form on travel patterns when they built infrastructure
25
to accommodate the current and projected demand (Crane, 2000; SL Handy & Boarnet,
2002). Responding to this, a number of planning strategies have been proposed to limit
automobile use by means of reshaping urban form. Compact settlement, which was
achieved largely by “mixing land uses and getting people out of their cars” (Boarnet &
Crane, 2001), has been employed since the late 1980s to combat urban sprawl.
However, several criticisms have been identified with respect to these negative
characterizations of urban sprawl and positive characterizations of the compact city.
Nevertheless, traffic congestion derived from a higher trip frequency is likely to happen
in compact urban areas than in sprawling areas, resulting in higher levels of limited
physical accessibility and higher health risk (Audirac et al., 1990; Boarnet & Crane,
2001). Neuman (2005) also stated people living in sprawl areas confer more
advantages of choice and resources to exercise rather than denser urban core. These
critics argue that there is little evidence to support these claims, particularly because a
number of studies in 1990s and 2000s have failed to shed light on the actual
characteristics of urban form (Burton, Jenks, & Williams, 2003; S Handy, 1996;
McMillan, 2007). This dilemma remains unsolved despite recent compact city, smart
growth, healthy community, and new urbanist efforts.
Current Characteristics of Urban Form
Definition of urban form that characterized by spatial configuration is quite
successful that represents standard to explain urban shape. In terms of the relationship
with public health, morphological appearances of urban settlement is closely connected
to physical access to built environmental resources (e.g. health care, grocery store,
open space). Theoretically, most travel and employment activities in compact urban
settlement are related to a Central Business District (CBD). However, the urban spatial
26
structure of modern metropolises cannot be sufficiently explained that activities are
concentrated only in CBD (Burton et al., 2003; Sridharan, Koschinsky, & Walker, 2011).
In this type, the centering concentration of CBD is reduced relative to the spatial
correlations and travel activities which are mainly determined by the land-use type over
the entire urban area (Ewing et al., 2006). Although the compact and sprawl urban
forms are a useful classification method for simply verifying a city’s spatial
characteristics, contemporary cities are too complicated to be described by two types of
shapes. Because of this, the definition of urban form has changed and another
consideration was added to urban appearance. Anderson, Kanaroglou, & Miller, (1996)
defined urban form as, “the spatial configuration of fixed elements within a metropolitan
region. This includes the spatial pattern of land uses and their densities as well as the
spatial design of transport and communication infrastructure.” Marquez & Smith, (1999,
p. 542) defined urban form as, “the land use patterns, transport infrastructure, water and
energy infrastructure, and physical form of developments that facilitate human activities
and their interactions.” While they emphasized the physical aspects of urban form, they
didn’t forget to mention that they encompass transportation accessibility of the city. Per
the definition by Williams, Jenks, & Burton (2000, p.8), urban form is, “the morphological
attributes of an urban area at all scales”. Zhang & Guindon (2006, p. 150) defined it as,
“the pattern of development in an urban area, including aspects such as urban density;
the use of land (residential, commercial, industrial, institutional); the degree to which
urban development is contiguous or scattered at the edge.” (Dempsey et al. (2008, p.
21) also defined an urban form as, “a city’s physical characteristics.” These recent
studies demonstrate the range of perspectives from which urban form has been
27
described. The existing literature commonly emphasizes such aspects of urban areas
as density, land use, and human activities such as accessibility, in addition to physical
characteristics of urban areas. To achieve this goal, the measurements that link spatial
configuration and human activities need to be developed though empirical studies that
could accurately quantify those variables.
Urban Form Measurement by Spatial Configurations
Current studies on measures of urban form have been classified at different
geographical scales, from metropolitan to neighborhood. The varying measures reflect
the distinct public policy issues that occur at each scale public policy. Owen (1986)
groups structural variables into five scales: regional, sub-regional, individual settlement,
neighborhood, and building. Adopted from Owen (1986), Stead & Marshall (2001)
further developed urban from when studying the relationship between urban form and
travel patterns. They describe urban form at the strategic level deals with “location of
new development and the type of land use” (Stead & Marshall, 2001). At metropolitan
scale, urban form questions are concerned with size of cities, location and number of
centers of economic activity, as well as type and intensity of development. The
measures employ size of metropolitan populations, size of metropolitan areas, and
population density. At the local level, it concerns mix of land uses and the spatial
distribution of development. At the neighborhood level, street layout is included in the
analysis; yet land use type is not considered in that it is almost homogeneous at this
level (Stead & Marshall, 2001). Tsai (2005) also classified urban form indicators into
three scope: metropolitan area, city, and neighborhood. These classifications are
required because urban form variables can carry different levels that are operated by
human activity such as job-housing balance. Bramley & Power (2009) suggested eight
28
elements for use as urban forms factors: population, local structural form, residential
and job density, residential development density, street network distribution, residential
distribution, types of residence and buildings, and land use mix. Dempsey, Bramley,
Power, & Brown (2011) considered five elements of urban form (population density,
land use, transport infrastructure, housing and building type, and layout); they also
emphasized the importance of non-physical aspects of urban form.
Many of studies state the classic spatial configuration measure of urban form has
been the population density (Burton et al., 2003; Cutsinger & Galster, 2005; Galster,
Hanson, & Ratcliffe, 2001; Tsai, 2005). Although density-related information are mostly
derived from the census data at census tract or transportation analysis zone (TAZ)
level, a variety of indices have been proposed to measure density. Knaap, Song, &
Nedovic-Budic (2007) list measures of density in previous studies, including population
density (number of persons per acre), household density (number of households per
acre), employment density (number of jobs per acre), housing density (number of
housing units per acre), and total person density (number of residents plus jobs per
acre). While the metropolitan spatial structure has traditionally assumed mono-centric,
research periodically brings into question a linear form of the density by demonstrating
the existence of population and employment sub-centers, and higher-density
neighborhoods on urban sprawl. Chen & Feng (2012) criticized the density method
suggesting it was only applicable for modeling intra-urban variation, while the inverse
power function is more appropriate for analyzing the suburban areas. Rozenfeld,
Rybski, Gabaix, & Makse (2011) further pointed out that the density model in theory
assumes the population density at city center to be greatest, but it in fact overestimates
29
its true value because the city center is most often occupied by commercial land uses.
Although different measures of density are proven to be highly correlated in measuring
urban form (Tsai, 2005), Galster et al. (2001) present theoretical arguments which show
that some indices are better than others. First, using number of housing units is better
than using number of residents in that the former index can represent “physical
condition of land use”. Second, residential density is a superior indicator over
nonresidential densities, which can easily be affected by economic agglomeration and
governmental regulations. Third, developable land area is a better denominator than
total land area in calculating density, since undevelopable land, such as water body,
may generate misleading results.
In summary, it is evident that this body of research is poised with debate over
what and how to measure it and what is important to consider. One common conclusion
that emerges is that urban form is a multidimensional phenomenon that exists on a
continuum. Each dimension requires a separate examination. Consequently, depending
on the way in which it is measured, the same metropolitan area can be determined on a
different spectrum. But again, density characteristics are principal traits of urban form.
Being that they are relatively straightforward to measure and across a large number of
metropolitan areas, they are often used as the sole indicator of sprawl (Frey, 2003).
Multi-dimensional Urban Form Measurements
Current researches present various measures of urban form by using a multi-
dimensional approach, which also aims to diagnose compactness/sprawl (Kelly Clifton
et al., 2008; Cutsinger & Galster, 2005; R Ewing, Pendall, et al., 2003; Tsai, 2005).
Galster et al. (2001) using U.S. Census block data in 1990. In general, Galster et al.
(2001) develop three steps to measure urban form. They start by classifying land in
30
Urbanized Area (UA) into three land types: developed, developable, and undevelopable.
Then a grid system composed by one-quarter square mile cell is superimposed over the
area for areal unit analysis. Finally, they define and measure eight dimensions of
metropolitan structure, including: density, continuity, concentration, clustering, centrality,
nuclearity, mixed uses, and proximity. These eight dimensions are then converted into
standardized Z-scores and are summed up as a sprawl index. High z-scores indicate
low levels of sprawl. Another set of composite index is developed by Ewing et al. (2002)
using three primary sources of data: the Census of Population and Housing, the Annual
Housing Survey, and the Census Transportation Planning Package. Ewing et al. (2002)
construct a hierarchical structure to compute the overall sprawl index. They develop four
subcategories of urban form – density, mix, centering, and streets; then multiple
indicators are used to measure each of the four subcategories. Another type of
classification is morphological classification which relies on a particular set of intrinsic
traits of the area rather than on their location or density. Using 23 variables and
applying it on 6,788 parcels in Portland, OR (Song & Knaap, 2007) tried to identify
development patterns for residential neighborhoods. Their analysis which was based on
K-means resulted in five main residential classes, namely: 1) sporadic rural
development, 2) bundled rural development, 3) outer ring suburban infill, 4) downtown,
and inner and middle ring suburbs, 5) composite greenfields, and 6) partially cluster
greenfields. In another study, Mikelbank (2011) classified data for Cleveland, OH over a
40 years in 4 times periods of 1970, 1980, 1990, and 2000 to trace through time and
space the rise (or fall) or concentration (or diffusion) of any of the resulting
neighborhoods; employing analytical hierarchy process (AHP), the final outcome was
31
five classes of urban form neighborhoods: 1) struggling, 2) struggling African American,
3) stability, 4) new starts, and 5) suburbia.
While early studies determined urban form by its appearance, recent studies also
emphasize the importance of physical urban factors. This pattern indicates that the
urban form should be understood to include a mixture of physical and non-physical
aspects. Land use, street networks, the locations of urban facilities can be categorized
as physical aspects; and urban activities based on the interactions among urban
factors, socio-economic relationship between urban factors, and urban policies and their
following infrastructure such as transit systems can be categorized as non-physical
factors. Clifton et al., (2008) suggest a comprehensive framework in their multi-
dimensional review of quantitative approaches to urban form, urban form has been
examined from various disciplinary approaches, including landscape ecology, economic
structure, transportation planning, community design, and urban design, to name a few.
These measures differ with each discipline, as do the questions being asked, the
targeted audience, and the data sources:
LANDSCAPE ECOLOGY. It primarily focuses in natural landscape and measure of urban form focus primarily on types of land cover (urban, cropland, forest, etc.) not on land uses (residential, commercial, etc.). It is employed by natural scientist using remote sensing technology to examine the effects of various dimensions of urban form on environmental protection.
ECONOMIC STRUCTURE. It is often used by economist to identify impact of urban form in economic efficiency. National or metropolitan scale of census data is used with GIS technology. Employment and population is their nature of the data.
TRANSPORTATION PLANNING. It deals with urban form measures at sub-metropolitan level by transportation planners and engineers using more disaggregated GIS data to explore transportation network and following accessibility.
32
COMMUNITY DESIGN. It deals with urban form measures at neighborhood level by land use planners using more disaggregated GIS data to explore conflicting concerns: environmental conservation, economic efficiency, accessibility and mobility, and a sense of community.
URBAN DESIGN. It is the most disaggregate approach to examine effects of urban form and variation in physical activity on urban phenomena using primary data sources collected through field observation or interviews.
As a summary, the research work majorly relates to understand definition of
urban form and its impact on transport behavior and environmental, social, and
economic variables. Throughout the literature review, Table 2.1 broadly represent key
urban form characteristics and mainly attempt to study in three aspects:
Density- resident population distribution over the urban area measured by density distribution
Diversity- distribution of areas of commercial and recreational activities, services, employment, etc. within the city in relation to the place of residence; measured by diversity, mixed use; accessibility, composition, size, shape
Street network- nature of transportation network and modes people use for travel
Health Disparity and Its Determinants
Health disparity can be defined differently depending on the purpose of research
and it may bring some confusion. Healthy People 2010 from the US DHHS (2000)
defined health disparity as differences that occur by gender, ethnicity, education,
income, and disability in rural localities. The National Institutes of Health (2000) states
that health disparity can be different by health conditions, such as mortality, morbidity,
among specific population groups. The Institute of Medicine (2002) defined health
disparities as racial differences in healthcare quality. They concluded that the definition
of health disparity is different according to the areas of health, and population
subgroups. Braveman & Gruskin (2003) looked at the definition from the ethnicity point
of view. They describe the disparity as an absence of equalities in health that was
33
systematically associated with social advantages and disadvantages such as gender,
ethnicity, and religion. Carter-Pokras & Baquet (2002) merged eleven definitions derived
from various sources such as National Institutes of Health (2000), The Institute of
Medicine (2002), and The US Department of Health and Human Service (2000).
Health Disparity and Health Outcome
Since the 1960s, much of the research have been conducted to examine the
access to health care (Andersen, 1995; Andersen & Newman, 2005; Penchansky &
Thomas, 1981) and differences of health outcomes (M. Guagliardo, 2004; Gulliford et
al., 2002; Yamashita & Kunkel, 2010) to identify fundamental health disparities. Auster,
Leveson, & Sarachek (1972) first address a question “What is the region’s population
health with respect to health care services?”. They identified that a one percent increase
in health care services leads to a 0.1 percent reduction in age-adjusted mortality and
insist socioeconomic and demographic variables are found to be a useful predictor of
age-adjusted mortality. Adopted a conceptual model from Auster et al. (1972), many of
the studies used mortality or a disease specific death rate as a dependent variables
(Starfield, Shi, & Macinko, 2005; Waldorf & Che, 2010; Yamashita & Kunkel, 2010).
Yamashita & Kunkel (2010) explore the association of heart disease mortality and
access to hospitals in Ohio. Initial finding presents a positive correlation between
distance to care and mortality rate that a one percent increase in the distance to a
hospital led to three percent increase in heart disease mortality. However, after
controlling for socioeconomic variables it did not show a correlation. Also Waldorf and
Chen (2010) list socioeconomic, demographic, and behavior factors as control
variables. They identify the correlation of health outcomes (mortality, health disease
mortality, and infant mortality) and access to healthcare.
34
Health Disparity and Access
Andersen model (Figure 2-1) provides a conceptual framework that describes the
factors that lead to use of healthcare service. It was first developed by Aday & Andersen
(1974) and later revised by Andersen (1995), which is recognized as significant
framework for analyzing the factors that describe the access to the healthcare service
utilization. Andersen (1995) have suggested three factors to utilize health service in an
individual's level; 1) predisposing factors, 2) enabling factors, and 3) need factors.
Predisposing factors refer to socio-cultural characteristics of individuals that exist before
having an illness, such as social structure, health beliefs and demographics. Enabling
factors are those that allows person to make sure to visit healthcare services, such as
knowledge of how the healthcare system works, health insurance status, distance to
health services, and the quality of social integration. Need factors mainly focus on
health status, whether self-perceived or externally diagnosed. Some researchers have
criticized that this model for its overemphasis on the need and expense of health beliefs
(Wolinsky & Johnson, 1991). Nevertheless, it provides a useful start to understand the
complexity of factors surrounding healthcare service and emphasizes the access to
seek healthcare utilization.
Access to certain provider (e.g. healthcare, food service, etc.) is considered as a
critical measurement of the overall population by individual, community, regional, and
national level in US (M. Guagliardo, 2004; F. Wang & Luo, 2005). Penchansky and
Thomas (1981) defined access to healthcare as groups presenting the degree of
correlation between the individual and the system and broke down in five dimensions
including accessibility, availability, affordability, acceptability, and accommodation. Both
accessibility and availability have locational aspects. Accessibility is travel impedance
35
between origin-destination and is measured in travel time or distance measures (Wang
and Luo, 2005). Availability refers the total number of facilities in certain boundary
where users have options to choose (Langford & Higgs, 2006). Aday and Andersen
(1974) stated access in terms of accessibility, availability, and affordability. They
suggested a wide definition of access is beyond geographical/spatial approach. It
includes meanings of affordability, describing relationships between the cost of services
and the ability for people to pay for the service. Last two dimensions, acceptability and
accommodation, describes whether the organizational aspects of the system are
sufficient to meet population’ demand and the cultural and religious practices of
populations accessing care, respectively. In the healthcare geography field, many of the
literature have described aspects of healthcare access and barriers in a combined
dimension of accessibility and availability (M. Guagliardo, 2004; Meade & Emch, 2010;
F. Wang & Luo, 2005), most noticeably for the primary care provider (M. F. Guagliardo,
2004; W Luo & Qi, 2009; McGrail & Humphreys, 2014). Spatial accessibility for health
and healthcare disparity is linked to the neighborhood level. For example, people who
live in certain urban areas are more likely to experience poorer quality of care compared
to their suburban counterparts, and people who live in rural areas have less access to
healthcare services (T. Arcury & Preisser, 2005). However, researchers are more
focused for urban residents, although populations in rural areas experience the
shortage of health professionals. Spatially segregated neighborhoods also show a
spatial limitation to access healthcare and are more likely to experience greater disease
morbidity, higher mortality, and less health insurance coverage (LaVeist, 2005; Schulz &
Northridge, 2004). Pampel & Rogers (2004) stated that individuals living in residentially
36
segregated neighborhoods are more likely to experience the types of economic
disadvantage that contribute to healthcare disparities, such as access, low-paid job, and
employment without health insurance benefits.
Spatial Access. The concept of spatial accessibility to health care includes both
dimensions of accessibility and availability. In general, accessibility refers to the ease to
reach health services from the demand side while availability emphasizes choices of
local service locations from the supply side. Spatial accessibility to health services is
primarily dependent on the geographical locations of health care providers and
population in need, as well as the travel distance/time between them (T.-F. Wang, Shi,
Nie, & Zhu, 2013). Since distance decay is a fundamental aspect in understanding
spatial accessibility, the following questions were raised when developing our
methodology: [1] how to define travel distance and reflect distance decay, [2] how to
represent both health care demand and supply, and [3] how to apply the most
reasonable measure for travel distance to health care services. Network distance has
gained certain popularity in recent literature as a replacement for Euclidean distance
and Manhattan distance. It is considered to be a more accurate measurement for real
travel distance and time (Beere & Brabyn, 2006; Dai, 2010; Delmelle et al., 2013;
Ellison-Loschmann & Pearce, 2006; T.-F. Wang et al., 2013). However, Apparicio,
Abdelmajid, Riva, & Shearmur (2008) found that Euclidean and Manhattan distances
are strongly correlated with network distances. However, local variations are still
observed, notably in suburban areas.
Most existing measures of spatial accessibility are based on the potential
interaction between health care providers (e.g., primary care physicians, cancer
37
treatment centers, hospitals, etc.) and population in need, or supply and demand (M. F.
Guagliardo, 2004; Langford & Higgs, 2006; L. Wang & Tormala, 2014). One commonly
used measure is the supply-demand ratios, or provider-population ratios, which are
computed within bordered areas. The ratios are effective for gross comparisons of
supply between geographical units, and are widely applied to set minimal standards for
local supply and identify underserved areas (Cervigni, Suzuki, Ishii, & Hata, 2008; Perry
& Gesler, 2000; F. Wang, McLafferty, Escamilla, & Luo, 2008). For example, the U.S.
Department of Health and Human Services (DHHS) uses a minimum population-
physician ratio to identify Health Professional Shortage Areas (HPSA). However, this
basic measurement has difficulty capturing the border crossing of patients among
neighborhood spatial units. Detailed variations in accessibility across space and the
distance dimension of access are ignored (M. F. Guagliardo, 2004; T.-F. Wang et al.,
2013). Another basic method is to measure average travel distance to nearest providers
(Chan, Hart, & Goodman, 2006). This method applies the straight line distance between
the population point and the location of the health provider. However, travel routes are
rarely straight lines in reality. It also cannot fully represent clusters of health providers in
an urban setting and ignores the availability dimension of access. Gravity models,
initially developed for land use planning, are also utilized to account for the spatial
interaction between heath care supply and demand (Schuurman, Berube, & Crooks,
2010). The simplest formula for gravity–based accessibility Ai can be written as Figure
2-2. Ai is the index of spatial accessibility from population point i, such as a personal
residence or population centroid of certain spatial unit. Sj is the service capacity of
health facilities (e.g., the number of hospital beds or doctors) at location j. dij is the
38
distance or travel time between i and j, and β is the travel friction coefficient. n is the
number of health facilities. Spatial accessibility improves if the number of health facilities
increases, the service capacity increases, or the travel distance decreases. The
improved gravity–based accessibility model proposed by Joseph & Bantock (1982) adds
a population adjustment factor to the denominator (Figure2-3). Pk is the population at
location k, dkj is the distance or travel time between j and k, and the indexes n and m
represent the total number of facility locations and population locations, respectively.
The gravity-based accessibility model is essentially the ratio of supply to demand (W
Luo & Qi, 2009). Despite its elegance in revealing geographic variation in accessibility,
gravity models are not easy for public health professionals to interpret or implement. A
large amount of geo-coded data for the locations of both population and health facilities
are required to estimate the travel friction coefficient β. Sometimes the models also
involve great effort of computation and programming (W Luo & Whippo, 2012). Another
development in spatial accessibility modeling is the two–step floating catchment area
method (2SFCA) proposed by Luo & Wang (2003). The fundamental assumption of
2SFCA is that availability and accessibility are not mutually exclusive and they can
compensate each other. A health provider is defined as accessible if located inside the
catchment, and inaccessible if located outside of the catchment. The catchment of a
provider location is defined as a buffer area within a threshold travel distance or time
from the provider. The 2SFCA can be implemented in a GIS environment using two
steps (Figure 2-4). First for each physician location j, search all population locations k
that are within the catchment area and compute the provider – population ratio Rj. Then
for each population location i, search all provider locations j that are within the threshold
39
distance from location i, and sum up Rj derived from the first step at these locations.
Eventually the accessibility index Ai can be written as follows (Luo & Wang, 2003)
(Figure 2-3). The 2SFCA has been popular and used in a number of studies(Chen &
Feng, 2012; McGrail & Humphreys, 2014; T.-F. Wang et al., 2013; Yang, Goerge, &
Mullner, 2006). However, Luo and Wang demonstrate that their model is not
fundamentally different from the gravity-based accessibility model (Luo & Wang, 2003) .
The 2SFCA overcomes the restriction of using pre-defined geographical boundaries.
However, the limitation of 2SFCA is mainly found in assuming a health provider inside a
catchment area is accessible and one outside the catchment area is inaccessible, which
tends to be arbitrary, ignoring the possibility of overlapping areas in coverage. In
addition, potential improvements may be made to account for different transportation
options, as well as variable catchment sizes for different populations and health
services. While the above methods make significant contributions in revealing health
disparity, we seek to complement such spatial accessibility literature by providing an
alternative measure. Recognizing that spatial accessibility is a complex concept
including both accessibility and availability, we seek to develop a method that can
reveal and represent both dimensions respectively.
Non-spatial Access. As a fundamental non-spatial access factor, biological
differences can contribute to health disparities, with some individuals having a genetic
predisposition to certain conditions (Fine, 2005). Beyond the influence of biological
factors on health, racial minorities (blacks and Latino/Hispanics particularly) are most
likely to be affected by socially-determined health and healthcare disparities (LaVeist,
2005; Mahmoudi & Jensen, 2012). According to Mahmoudi and Jensen (2012), racial
40
minorities tend to be less healthy, receive poorer quality of health care, lack health
insurance, lack a healthcare provider, receive fewer medical screenings, and have
higher infant mortality rates and lower overall life expectance than whites. Age and
gender are the other non-spatial factor that people experience as healthcare barriers.
Young adults are more likely to experience healthcare disparities than children or older
adults; they are also more likely to lack health insurance (Buchmueller, Couffinhal,
Grignon, & Perronnin, 2004; Creighton, 2002; Kenney, G et al., 2012), it is also
applicable for men when compared to women (Sanchez, Sanchez, & Danoff, 2009).
Being unemployed (Schmitz, 2011; Schulz & Northridge, 2004), having a lower
education level (Kim & Ruger, 2010; Schulz & Northridge, 2004), having a low income
level (Gordon, Purciel-Hill, & Ghai, 2011; Schillinger, Barton, Karter, Wang, & Adler,
2006), living in poverty (K. E. Pickett & Pearl, 2001; Sanders, Lim, & Sohn, 2008;
Sridharan et al., 2011), and an individual’s level of stress (Grossman, Niemann,
Schmidt, & Walach, 2004; Schulz & Northridge, 2004) are examples of the factors
contributing to health and healthcare disparities. Neighborhoods with high poverty
concentration compared to affluent ones are more likely to have fewer social resources
(Lynch, Smith, Hillemeier, & Shaw, 2001) which have been consolidated with poorer
health status, higher mortality rates, and increase health disparity. Additional health
insurance coverage contributes to differences in health and healthcare, including lack of
health insurance coverage or type of health insurance coverage (Creighton, 2002).
Language (non-English speaking) and cultural differences contribute to healthcare
disparities, as well (L. Wang & Tormala, 2014), by undermining complete understanding
of how care can be accessed and receiving appropriate care when it is. This is because
41
poor interactions with health care providers, and lack of trust in the health care system
contribute to disparities in health and health care.
As described above, non-spatial characteristics are complex involving many
aspects of healthcare disparities and it is difficult to quantify for correlation analysis. To
overcome this problems, some of studies created a composite index that encompass
the concept of SES: 1) Scores with different weighting approaches such as z-score,
Delphi approach with experts’ weighting (Eibner & Sturm, 2006; L. Wang & Tormala,
2014); 2) PCA or factor analyses (Caschili et al., 2015; Petrişor, Ianoş, Iurea, &
Văidianu, 2012); 3) GIS-based analyses . Giving an overview of using an index, this
study presents a statistical procedure to create a non-spatial index.
Predisposing/Enabling Factors
This section reviews the literature on non-spatial health determinants that may
contribute to health outcome and an access to health care service. This section: 1)
reviews how these variables have controlled the health outcome results in previous
research, and 2) specifies why these variables are necessary in the development of the
correlation model employed in this dissertation.
Age. It is common knowledge that an older individual will have a worse outcome
than a younger on account of illnesses that are age-related. Waldorf & Che (2010)
examined the effect of health care services on two primary dependent variables, infant
mortality and age-adjusted mortality in the elderly who are over age 55. The results of
the study indicate the role of health care services to the elderly is small and not
statistically significant. However, this is not surprising considering that all-cause
mortality, whether that is in infants or the elderly, has been consistently shown in the
literature to be influenced greatly by a life time of health behaviors and environmental
42
factors (Auster, et al., 1969). T. A. Arcury et al. (2005) identified that there is a
significant effect between age and in the number of healthcare visits. The elderly in
particular were found to make 1.17 times more visits (Odds Ratio = 1.17, 95%
confidence interval = 1.03 to 1.34) than a non-elderly population controlling for health
status, personal characteristics, and distance. Also Goodwin and Anderson (2002) state
there is a significant relationship between age and health care utilization. The study
design employed a logistic regression and found that every year advanced in age
incurred a little more than 1 (odd ratio = 1.02, 95% confidence interval = 1.001 to 1.03)
additional physician visit. However, still age and its influence on health outcomes
related to accessibility of health care is ambiguous based on the existing literature. Due
to this ambiguity in the literature, this dissertation includes age as a controlling factor to
examine the effect of spatial accessibility of built environments on health outcomes.
Race. Mayberry, Mili, & Ofili (2000) note that Caucasians are more likely to have
higher rates of health care visits than minorities; whereas many of literature state racial
and ethnic differences found great disparities in access to health care for minorities. For
health outcome, race was found to play an important role in cardiac care in four states
examined by the researchers (Weitzman et al., 1997). Result indicate the African-
Americans were found to be much less likely to receive these treatments compared to
Caucasians but this study did not control for income and education level. Therefore, the
effect of race may be overstated. Similar to Weitzman et. al. (1997), Arcury et. al. (2005)
conclude that African-Americans are about 40% (odds ratio = 0.41, 95% confidence
interval = 0.24 to 0.71) as likely to have a routine check-up and 2.31 times more likely to
have a chronic care visit (odds ratio = 2.31, 95% confidence interval = 1.29 to 4.13)
43
compared to Caucasians. Waldorf & Che (2010) noted that race is an important
consideration in studies of spatial accessibility and health outcomes. However, they did
not include it in their analysis because their case study areas had nearly 90%
Caucasian. Given the large disparity in health care utilization between Caucasians and
minorities, race is included as a control variable in this study.
Education. Waldorf and Chen (2010) identify education level is a meaningful
predictor with respect to percentage of infants born with a low birth weight and
cardiovascular mortality in the elderly. Negative relationship were observed between
education level and mortality and birth rate that interprets higher level of education was
associated with less cardiovascular disease mortality and number of children born with
low birth weight. This study found that education was not a meaningful and significant
predictor on infant mortality, elderly mortality, and mortality from cancer. The results
suggest that education plays a meaningful and significant role in health outcomes that
are more manageable and influenced by individual behaviors. Aakvik & Holmås (2006)
also state that high levels of education were meaningful and significant predictors of
lower levels of age-adjusted mortality. They used OLS regression model and defined
education by the percentage of people in the municipality with a high school diploma by
the age of 20. Acrury et. al. (2005) concluded that for individuals with a chronic illness,
the likelihood of them visiting a health care service provider for management of the
illness increased with the level of education attained. Overall, education attainment
appears to have a significant role in health outcomes.
Income. It is not surprising that income is the most commonly used health
disparity determinants and higher income have been shown to be associated with better
44
health outcomes (Collins, Robertson, Garber, & Doty, 2012; M. F. Guagliardo, 2004; K.
Pickett & Wilkinson, 2015; Waldorf & Che, 2010). Arcury et. al. (2005) identify that lower
household incomes create a significant barrier to health care services and potentially
effect health outcomes as a result. Specifically, individuals with household incomes
greater than $20,000 made 2.93 times more visits for chronic care check-ups compared
individuals with incomes less than $40,000.
Unemployment. Following up the significance of income, employment status
considered as a key variable to maintain health outcome. Aakvik & Holmås (2006) state
unemployment was found to be significant control variables in explaining mortality while
the authors did not consider income as their variables. This dissertation employs
unemployment status (as well as income) to understand the effect these control
variables have on the model individually and collectively.
Health coverage. Another indicator of affordability is whether or not individuals
have insurance. In terms of health care access, higher rates of insurance have been
shown to have higher rates of utilization of health care services (Kullgren, McLaughlin,
Mitra, & Armstrong, 2012). Kaufman, Kelly, Rosenberg, Anderson, & Mitchell (2002)
also identify that those with health insurance were over 3 times more likely to have
visited a physician in the past 12 months (odds ratio = 3.59, p < 0.01). The effect of
insurance on health care utilization was consistent regardless of the type of insurance
(i.e. Medicaid, Medicare, private). However, Waldorf and Chen (2010) study suggest
insurance would not be an indicator of health outcome. The death rate for the elderly
sustained regardless of insurance coverage. Infant mortality measured by live births in
the health care system is unlikely to be affected by insurance coverage. From the
45
literature review, health insurance is likely to have an effect on the access of health care
services but the effect is inconclusive with respect to health outcome (Waldorf and
Chen, 2010). This dissertation includes insurance status to identify any of the
correlation.
Linkage among Urban Form, Built Environment and Health Outcome
Associations between urban form and health are not new. From 19th century,
public health practitioners realized the effects of the built environment on the public;
how the very place where people lived and worked affected their health (Perdue,
Gostin, & Stone, 2003). Unsanitary sewage and water conditions, dark airless tenement
housing, and toxic industrial wastes were all contributed to the spread of disease. In
response to such conditions, planners advocated public infrastructure, such as water
and sewer lines, building codes, and zoning plans to separate people from toxins and
reduce population concentrations. Today population suffer less from infectious diseases
due to more sanitary conditions however, physical environments continue to influence
public health outcomes.
Urban Form and Health
The settlement form of community influences health by encouraging or
discouraging routine physical activity involved in daily life. Much of the research has
well-documented that urban sprawl is one of the causes residents miss out
opportunities to have physical activities such as walking to the store, to work, or other
places as part of a daily routine (B. McCann & Ewing, 2003). Patterns of streets within
neighborhoods in suburban subdivisions increase auto-dependency and reduce the
propensity to walk. Metropolitan areas with high levels of urban sprawl tend to have
higher per capita vehicle miles traveled daily, even after controlling for factors, such as
46
income, size of metropolitan area, and location within the nation. This suggests that
people in high-sprawl areas drive more, quite possibly at the expense of daily physical
activity (Lopez & Hynes, 2006). This association between development patterns and
health outcomes can be seen as an indirect effect mediated by physical activity and
body weight. Even though some research linking sprawl with health status exists, there
is the need for more empirical research on this relationship.
Empirical Linkage
Many of the studies state that people who reside in sprawling suburban areas are
now suffering from chronic health conditions such as heart disease, asthma, and
diabetes that earlier generations did not (Perdue et al., 2003) because of the lack of
physical activity, poor diets, and air pollutants. Based on impact of physical environment
to health as above, two public health perspectives are identified to assess the extent of
empirical linkage: built environment and physical activity, and dietary patterns.
Physical Activity. One of the main determinants from the result of urban forms
is physical inactivity. Advances in motorized transportation have reduced the need for
physical activity in daily life (Foreyt & Carlos Poston, 1999; SL Handy & Boarnet, 2002;
C. Lee & Moudon, 2004). C. Lee & Moudon (2004) reviewed the public health literature
dealing with the association between the built environment and physical activity. They
found that the level of physical activity is correlated with access to recreational facilities,
local destinations, neighborhood safety, as well as the aesthetic quality of the
environment. The study concluded with the recommendation to create paths for walking,
jogging, and biking and to locate routine destinations close to residential areas to help
promote active living. Powell, Martin, & Chowdhury (2003) studied the role of the built
environment on the level of physical activity by using the Behavioral Risk Factor
47
Surveillance System (BRFSS). They categorized walking places based on time and
mode of travel. Results revealed that neighborhood streets or sidewalks (32%) were the
most commonly reported places used for physical activity, with public parks (26.8%)
coming second. These locations were most frequently reported as being safe and
convenient places for walking. It was also shown that proximity is another important
factor in determining a convenient and safe place to walk. The most commonly
mentioned locations were extremely close to the respondent’s house. Overall, proximity,
safety, and convenience factors were all important elements that encourage people to
walk. Leslie et al. (2007) identified the relationship between perceived and objective
measures of the built environment and further their correlates with physical activity in
Forsyth County, NC and Jackson County, MS. Perceived built environment data was
derived from a telephone survey (N=1,270) and found that neighborhood perceptions of
high-speed cars, heavy traffic, and a lack of crosswalks or sidewalks had negative
relationships with physical activity. On the other hand, existence of neighborhood
destinations was positively correlated with physical activity including walking. GIS
analysis derived objective built environmental factors including speed, volume, and
street connectivity. Although this study shows little agreement between the perceived
and objective built environment as calculated by kappa coefficients in either area, there
is a clear finding that the built environment is a significant correlate of physical activity.
Dietary pattern. Story, Neumark-Sztainer, & French (2002) analyzed individual
and environmental effects on adolescent eating behaviors. They considered schools,
fast food restaurants, vending machines, convenience stores, and worksites (for part-
time jobs) as important built environments which had a significant impact on
48
adolescents’ food choices and dietary patterns. Foods that were sold by these places
normally contain ingredients that were high in fat and sugar. Story et al. recommended
family and peer support for encouraging healthful eating and discouraging high fat and
high sugar foods as well as alcohol and tobacco uses at the interpersonal level.
Community level of intervention was also suggested to reduce environmental barriers to
healthy food choices and to control unhealthy foods such as soft drinks and high
sugar/high fat foods. Zenk et al. (2005) noted that the average distance from the home
to the nearest supermarket in the poorest neighborhoods was 1.1 miles further than the
distance in the richest neighborhoods. Block, Scribner, & DeSalvo (2004) investigated
how the density of fast-food restaurants related to the household income and ethnicity in
New Orleans, Louisiana. They found that the high density of fast-food restaurants was
positively correlated with low household income and a higher percentage of African
American residents. Although food cost is an important factor, the environmental factor
is another key in a low-income population’s ability to buy healthy food. As described
above, there is a clear connection between environment and dietary patterns. Since
poor diet habits are closely linked with obesity, cardiovascular disease, cancer, and
even mortality, it is necessary to promote healthy diet habits.
Summary of Literature Review
This review of literature attempts to understand the relationship between health
disparity and the built environment in two aspects.
First, this review showed that measurement methodologies from regional
disparity literature can help advance and expand health disparity research. Second, this
review confirms that the built environment is a significant contributing factor to the
increase the level of health status. These behavioral outcomes, obesity, physical
49
activity, and diet, are interconnected and the literature shows that the built environment
plays an important role in affecting the levels of obesity and increasing trends toward
active life styles.
50
Table 2-1. Approaches and indicators to measure urban form in previous studies
Author(s)
Measurement aim Urban form measurement
Siz
e
Density
Div
ers
ity
(mix
ed-u
se)
Contin
uity
Concentra
tion
Clu
ste
ring
Centra
lity
Com
pactn
ess
Nucle
arity
Stre
et
segm
en
t/ P
roxim
ity
Urb
an
Desig
n
Tra
vel p
atte
rn
Jabareen, Y.R. (2006)
Urban Form Types and their Sustainability
x x x x
Galster, G., et al. (2001)
Urban Sprawl Index x x x x x x x x x
Ewing R. et al. (2003)
Sprawl Indices for four components
x x x
Song, Y. and Knaap, G (2007)
Development patterns
x x x x
Hess et al. (2001) Relationship between site design and pedestrian travel
x x
Burton, E (2003) Relationship between density social equity
x x
Fulton, W., et al. (2001)
Trends in urban form and land consumption
Clifton et al., (2008)
Multidisciplinary measures of urban sprawl
x x x x x
51
Table 2-1: Continued
Author(s)
Measurement aim Urban form measurement
Siz
e
Density
Div
ers
ity
(mix
ed-u
se)
Contin
uity
Concentra
tion
Clu
ste
ring
Centra
lity
Com
pactn
ess
Nucle
arity
Stre
et s
egm
ent/
Pro
xim
ity
Urb
an
Desig
n
Tra
vel p
atte
rn
Tsai, Y. (2005) Measures to distinguish compactness from sprawl
x x x x x
Custsinger et al. (2004)
Common patterns of indices across metropolitan areas
x x x x x x
Stead and Marshall (2001)
Nine aspects of urban form, ranging from regional, local, and neighborhood planning
x x x x
52
Figure 2-1. Andersen model (1995) that demonstrates the factors that lead to the use and access of the
healthcare. Model describes that access to healthcare services is determined by three components: predisposing, enabling, and need factors.
Figure 2-2. Gravity model.
53
Figure 2-3. Improved gravity model
Figure 2-4. 2 Steps Float Catchment Area (2FCA) from Wang and Luo (2003)
54
CHAPTER 3 METHODOLOGY
This chapter provides a detailed description of the data collection and the
methodology that is used in this dissertation. Overall, the study is designed to answer
the fundamental question arises from literature review, what influences health
disparities and poor health using three keywords; healthcare access, health outcome,
and environmental exposure (urban form) that influence to increase populations’ health
(Table 3-1). Each keyword is interpreted into three aims and these move from
aggregated (state-level) to disaggregate (Census block group- level) geographic scope
of analyses, measured using multi-dimensional approach. Because SES is an important
mediator for quality of health, applying SES cannot be separated from the study design.
The outcome of studies for three steps will be translated into policy recommendations
for urban planning and health services to reduce geographic inequalities in healthcare
providers while promoting better physical formats of the built environments.
Study Design
Aim 1: Examine the magnitude of healthcare disparity in recent 10 years
The goal of aim one is to explore recent health disparity trends and to understand
regional differences of health disparity within the whole country. Health disparity is
examined by using two types of health indicators that represent: 1) populations’ health
status by using self-reported health status of individuals (perceived), and 2) populations’
availability of the health care availability (objective). Perceived health status has been
popularly used in previous literatures as a predictor of mortality that represents current
health quality (Wang & Luo, 2003). Advantage for using perceived health status is
because it does not rely on a medical conceptualization and employs individuals’
55
evaluations of their own health (Wagstaff, Paci, & Van Doorslaer, 1991). Heath care
availability is selected as an objective and spatial-driven indicator that represents health
access. Among health care providers, this study uses primary care physician as
healthcare perimeter. It is because primary care helps prevent illness and death,
regardless of whether the care is characterized by supply of primary care physicians, a
relationship with a source of primary care, or the receipt of important features of primary
care (Starfield et al., 2005). Further, the evidence shows that primary care (compare to
specialty care) is associated with a more equitable distribution of health in populations.
The Gini coefficient is used as the measurement method for estimating disparity in this
aim because it is commonly-used, valid, effective, and easy to compare and
understand. Historic trends between 2008 and 2016 are displayed with GIS maps and
the longitudinal trend graphs.
Another question arises as to what causes health disparities. Large number of studies
have reported that socioeconomic status (SES) is one of key factors affecting quality of
health and health disparity.
Aim 2: Examine the Relationship between Urban Form and Access to Healthcare
Urban form, the spatial pattern of urban physical objects, has a considerable
long-term influence on macro and micro scale environments. Urban form traces the
history and function of the physical manifestations that comprise urban settlements
(Frey, 2003). Also, it has long been viewed as complex systems of interacting and
resulting social, political, economic, cultural, and health outcomes(K Clifton, Ewing,
Knaap, & Song, 2008). The second aim of this dissertation explores the role urban form
plays in constraining access to healthcare as affected by land use and population
characteristics and transportation network at the census block group level. In recent
56
years, much research has examined the relationship of urban settlements to public
health (L Frank et al., 2008; LD Frank & Engelke, 2001; LD Frank et al., 2006). These
studies have shown that urban form development patterns impact access to health care
which ultimately contributes to health outcome of populations. Specifically, limited
spatial access to health care is affected by multiple factors including the number and
spatial distribution of health care providers, distribution of population, urban structure,
and the transportation infrastructures (F. Wang & Luo, 2005). Aim two considers a
major concept of geographical access, accessibility to healthcare providers.
Accessibility is defined based on the travel impedance such as driving time calculated
from actual transport network between spatial locations of users and providers.
Aim 3: Assess the impact of SES in the relationship between urban form and health outcome
To examine the spatial patterns of areas with concentrations of high or low levels
of health outcomes, this aim extends an idea to identify how urban form correlates with
high or low level of health outcomes. The third aim attempts to provide an answer for: 1)
how the allocation and access of healthcare services affect health outcomes; and 2)
how SES of population contributes to health outcomes because it varies by several
demographic factors including age, SES, employment status, family size, and health
status (Kirby & Kaneda, 2006). Although many studies have found spatial variations in
incidence and prevalence, there is a paucity of information on how the spatial
prevalence of low health outcomes may or may not be associated with the spatial
prevalence of built environment attributes. As a subset aim, this section seeks to
determine how and where low health outcome prevalence is clustered at the census
tract level. This information could allow programs and interventions to better target
57
populations and attributes of the built environment associated with chronic diseases
because of the physical inactivity.
Study Area, Data Collection and Measurement
Aim 1: Health care availability and Health Status
To develop two indicators of health disparity, datasets from the Behavioral Risk
Factor Surveillance System (BRFSS), and Area Health Resource File (AHRQ) from
Health Resources and Services Administration (HRSA) are collected. For individuals’
perceived health status, a question, “In general, would you say that your health is ___?”
is used. Its response items are a five-scale including: excellent, very good, good, and
fair. For this aim, the number of individuals who reported ‘poor’ or ‘fair’ health is
stratified to calculate the percentage of adults reporting fair or poor health. For
longitudinal approach, two sets of different years of data (survey from 2014 and 2003-
2009) were acquired. The number of primary care physicians per each county, to create
indicator of healthcare availability, are acquired from AHRQ in year 2008 and 2016.
PCPs include medical doctors (MD) specializing in general practice medicine, family
medicine, internal medicine, pediatrics, and obstetrics/gynecology. The measure
represents PCP per 100,000 populations. The US Census Bureau provided the state-
level data, including population density, age, the percentage of the population below the
poverty level, income, education, ethnicity, and car ownership, and the percentage of
the population using public transportation. The Gini coefficient is selected as the
preferred method for measuring disparity because of its efficiency, effectiveness, and
ease of interpretation. It has been ritually used to estimate levels of regional disparity
especially for income level of population(Amos, 1988; Williamson, 1965). Its values
58
range from zero (perfect equality) to one (perfect inequality). For correlation analyses,
the Pearson correlation coefficient is used.
Aim 2 and 3: Urban form, health care accessibility, and health outcome
Compared to the first aim covering the entire US to identify the overall magnitude
and the longitudinal trends of health disparity, the second and third aims focus on the
Orlando MSA. The Orlando MSA is selected as a case study area because it is one of
the fastest growing regions in Florida with a population of 2,321,418 according to the
2014 U.S. Census Bureau estimates. This area also has a large racially and ethnically
diverse population, which is ideal for analyzing socioeconomic, ethnic and geographic
disparities in access to health care. Orlando MSA is located in the central Florida and
comprises with four counties including Orange, Seminole, Osceola, and Lake County. It
is the third largest MSA in the state; composed with 834 census block groups (328
census tracts). Considering the dynamics of physical geography and our concern being
habitable areas, this study excludes areas where no people actually resides, such as
swamp and forest, to represent health supply and demand areas as accurately as
possible, using the land use data downloaded from Florida Geography Data Library
(FDGL).
Urban Form. Orlando MSA reasonably represents Florida because it has
experienced rapid population growth that has resulted in profound changes in the urban
form and patterns of residential locations. The state’s prescription is to redirect urban
growth toward a more fiscally efficient and livable compact urban form. However,
residential preference for low density lifestyles has historically been prevalent statewide
(Audirac et al., 1990). As discussed in Chapter 2, the establishment of urban form
dimensions in this study was developed based on multiple literature sources (Table 2-
59
1). Dimensions of urban form are calculated using two datasets: 1) number of
population and housing information based on census block group, and 2) location of
housing units and land uses derived from Florida Parcel Data by County (FPDC) that
provides information at the property parcel level. In order to unify the calculation of each
dimension of urban form, spatial data needs to be aggregated at the block group level.
The measures of urban form dimensions in this study are adopted from previous studies
that elaborate urban form variables, especially urban sprawl (Cutsinger & Galster, 2005;
Galster et al., 2001; Song & Knaap, 2007; Tsai, 2005). These studies mostly relate to
understanding the impact of urban form on transport behavior and environmental,
social, and economic variables. To this end, this study has selected the most commonly
used urban form measurements and grouped them into four indicators: 1) density, 2)
mixed-use, 3) street design, and 4) proximity. Each indicator is determined as the linear
combinations of the following variables in Figure 3-2. Parameters a1 to a12 are weights
that determine the importance of each factor. These weights are calculated using
Principal Component Analysis (PCA) extraction to identify whether there are latent
dimensions with the set of variables that impact urban form component. Before running
PCA, all measurements are normalized and measurements that represent negative
influence (e.g. higher distance to urban core represents lower proximity) are inversed.
Detailed definition and illustration of each indicator is provided in Table 3-2. The
estimation of urban form components are used as independent variables in all
regression modeling to identify relationship between healthcare accessibility (dependent
variable in AIM2) and health outcome of populations (dependent variable in AIM 3).
60
Healthcare Accessibility. Using Penchansky and Thomas’s definition,
accessibility is measured as the average travel time by automobile between a
residential location and the nearest health care provider. The population centroid within
each spatial unit is used to represent aggregated health care demand location. When
health care demand is aggregated, the true distance to health care services from each
individual or household is replaced by the distance from the aggregation point (Current,
Min, & Schilling, 1990). The aggregation method can reduce the complexity of location
and routing problems as well as protect the privacy of the individual or household by
masking their individual locations, especially in sensitive research. The population
centroid for each health care demand area can be obtained in a GIS environment
through preprocessing. The street network dataset acquired from ESRI 2013 provides
high quality, detailed road network data for all across the US in vector GIS format – and
most importantly the dataset provides segment-by-segment information on speed limits,
travel impactors, and restrictions, which are needed to model realistic catchments
based on travel-time calculations. Mailing addresses for 1,436 PCPs in Orlando MSA
were acquired from 2011 American Medical Association (AMA) Masterfile. All of these
locations were mapped in a point layer using GIS address geocoding. Census Block
Group level census data, obtained from FGDL, was used in this analysis to link
populations to healthcare services. Census Block Groups were chosen for analysis
because they are the finest-scale units for which population and dwelling counts are
made. Aim 2 only uses population count attribute in each census block group to
calculate how many populations are data. No socioeconomic or demographic variables
are use in aim 2.
61
Health Outcome. According to Florida Department of Health (2014),
cardiovascular diseases are one of the leading causes of death in Florida in 2010s,
followed closely by cancer. Lung disease, stroke and unintentional injury rounded out
the top five causes of death (Figure 3-3). As a measurement of health outcomes data,
aim three uses number of deaths from cardiovascular diseases and diabetes because
these are used as a proxy for physical inactivity (Blair, 2009; Lee et al., 2012). Disease-
adjusted mortality rate is used because it represents specific and objective indicator of
health risk. The literature shows that diabetes is an indicator of many common chronic
health conditions and risks. Diabetes is associated with obesity and physical inactivity;
many urban form factors such as access to built environments (e.g. healthy foods and
health care)(Reid Ewing et al., 2014; Koopman, Mainous, & Geesey, 2006), and walking
(Gregg, Gerzoff, & Caspersen, 2003) are correlated with diabetes prevalence. Total
death from diabetes are obtained from 2010 Census tract map of Florida Department of
Health.
SES related with Population Health. SES variables are used as covariate to
control variables to assess the impact of SES in relation of urban form and health
outcomes. These variables control for a population’s propensity to seek health care
services. This study considers two groups of non-spatial characteristics of populations
that are consider to impact healthcare behavior that would potentially influence health
disparity status. This framework is adopted from Field (2000) and more detailed
variables were added based on the literature. All acquired dataset are from 2012 5-year
American Community Survey (ACS) of Census Bureau in the block group or tract level.
All variables are summarized in Table 3-3:
62
Socio-economic and demographic status: population with high healthcare needs (age over 65), Non-white minority, and household with average income
Linguistic barrier and service awareness: population with non-English speaker, population without a high-school diploma, and population without a health coverage (private or public)
Analytical Method
Aim 1: Measuring Disparity
The specific goal of this section is to examine the recent 10 years of healthcare
disparity trends in US between 2000s and 2010s, and to identify the effects of
socioeconomic factors on health disparity. Calculations of Gini coefficient are used as
an inequality measurement to discuss health disparity in this chapter. The ANOVA test
is used to identify the regional differences of health disparity and a bivariate correlation
analysis is used to examine the relationship between socioeconomic factors and
disparity. The GIS maps and the longitudinal graphs describe the historic trends in
health disparity.
Gini Coefficient. To analyze health disparity, this study uses the Gini coefficient,
which is the measure of aggregated inequality and varies from zero (perfect equality) to
one (perfect inequality). It is derived from the Lorenz curve, which plots the cumulative
proportion of the population on the x-axis and the cumulative proportion of the variable
of interest on the y-axis (Figure 3-4). In order to measure health disparity, the x-axis
tracks the cumulative proportion of the population by health level and the y-axis the
cumulative proportion of the health variable (e.g. perceived health status and healthcare
availability). The Lorenz curve and Gini coefficient equations are presented in figure 3-4.
Descriptive statistics of Gini coefficients are used to describe the levels of
nationwide health disparity in United States. This step diagnoses the problem by
63
comparing the current health disparity status across the US. Then, the historic trends of
health disparities from 2000s to 2010s help illustrate the rates of changes in health
disparity over time and anticipate future trends.
In order to measure PCP inequality, the x-axis tracks the cumulative proportion of
the population and the y-axis measures the cumulative proportion of the health care
variable - in this case the number of PCPs in 30-minute driving zone. In this calculation,
the Gini coefficient formula is presented as follows (Eq.2 from Figure 3-4) adopted from
(Brown, 1994). Yi represents the cumulative proportion of the PCPs in each county, Xi
is the cumulative proportion of the population in county, and k is the total number of
counties in each state.
One-way ANOVA. The Analysis of Variance (ANOVA) test is used to identify the
regional differences of health disparity. To test the potential regional differences in
health disparity, the 50 states (not counting the District of Columbia) are grouped into
four census regions (Northeast, Midwest, South, and West) and differences among
these four regions are examined using ANOVA. The explanatory variable is each group,
and the response variable is measured as the Gini coefficient mean for each Census
region. Accordingly, in order to compare the four groups, it is reasonable to use the
ANOVA because is an assessment of the independence between the quantitative
response variable and the categorical explanatory variable. In addition to ANOVA test, a
bivariate correlation analysis is used to examine the relationship between
socioeconomic factors and disparity. As a result, the GIS maps and the longitudinal
graphs describe the historic trends in health disparity and spatial differences among the
four census regions in the following sections.
64
Hypothesis. The initial research question of this study was: Does South region
contain higher health disparity that provide less opportunities for residents to engage in
low health status? Based on this question, it was hypothesized that the Sothern census
regions would obtain higher average Gini coefficient than the other regions. In order to
verify this hypothesis, the statistical test examined whether the four census regions had
equal means. Accordingly, the null hypothesis was that each group had an identical
mean. Therefore, ANOVA is an F test for:
H0: µS=µNE=µW=µMW (where µS is the mean for the Gini coefficient of Southern states, µNE is the mean for the Gini coefficient of Northeast states, µW is the mean for the Gini coefficient of West states, and µMW is the mean for the Gini coefficient of Mid-West states)
Ha: At least three of the means are unequal
The test analyzed whether, if H0 were true, the differences observed among the sample
means could have reasonably occurred by chance (Agresti & Finlay, 2009, p. 370).
Statistic Test. For testing H0: µS=µNE=µW=µMW, the statistic uses the analysis
of variance F statistics (ANOVA F statistics). Using SPSS software, the test results can
be presented as in a table. In this table, if H0 is true, we can expect the values of F to be
near 1.0. Additionally, the significance (P-value) uses F distribution (Agresti & Finlay,
2009, p. 373). The P-value shows whether we can reject H0 or not. If we reject H0, it
means that there are differences among the three groups that are being compared.
Aim 2: Urban Form and Healthcare Accessibility
The methodology for aim 2 is applied in three steps. First, this study calculates
driving time from block centroids to the nearest PCP locations along the transportation
network. Next, a multivariate analysis is used to calculate the four components of urban
form. Each component has been aggregated from three variables with weights
65
calculated from PCA extraction. Finally, the correlation between urban form components
and accessibility to primary care providers is determined by using Ordinary Least
Squares (OLS) regression.
Travel Time to Healthcare. To assess the accessibility, driving time from each
residential area centroid point (residential parcel centroid) to the nearest health care
provider is calculated using GIS network analysis. It quantifies travel time in minutes to
the nearest provider, assuming people take the shortest/fastest path through the road
network between a resident location and the health care practice location. This tool
computes the shortest paths using an origin-destination (O-D) matrix and minimizes
travel time by favoring hierarchical routing techniques for travel impedance. Travel times
on road segments are based on average standard speed limit applied by road type
using US Census road classification codes. Speeds were reduced in urban areas to
account for congestion. However, drive times reflect average traffic conditions and not
peak, or rush hour conditions. This approach does not take into account individual
preferences. However, this potential limitation is minimized because the travel time has
been aggregated into a census block group level by calculating the average travel time.
Urban Form Component. The application of statistical methods to create an
index has started to new disciplines due to their specific adaptations to the data
requirements and interpretation of the results specific to the discipline using them
(Motulsky, 1995, p. 7). For this reason, this dissertation proposes expanding the
meaning of the term “geostatistical method”. To create urban form component, this
dissertation combines idea of PCA and GIS modeling. PCA is a statistical method
aiming for the reduction of data, identifying components that account for the overall
66
variability within the variables taken into consideration. The principal components are
linear combination of these variables accounting for the common and unique variable
explained by them. Using SPSS software, each component was calculated by repeating
same steps with three variables each: 1) correlation analysis to identify whether
variables involved are not significantly correlated, 2) the Kaiser-Meyer-Olkin (KMO)
measure of sampling adequacy and the Bartlett test to test adequacy to explain
variables using PCA, and 3) using Communality matrix to proportionate weight for
variables that consists urban form components.
Regression Model. Once the urban form components and physical accessibility
to healthcare providers are ready, Aim 2 applies the ordinary least squares (OLS)
algorithm by using ArcGIS v.10.4 software. The OLS tool is used in this study because it
is a first step in geographical regression modelling that provides a number of diagnostic
statistical measures necessary for full evaluation of the results (Mitchel, 2005). It
provides a global model of the relationship between urban form measures and the
accessibility. Before executing the OLS regression, all urban form components and
accessibility measurements are transformed by using z-scores because the variation of
variables did not follow normal distribution.
The general form of an OLS model for independent variables is Y= β0 + β1X1 +
β2𝑋2 + β3𝑋3+. . β𝑘𝑋𝑘 + µ𝑖, where Y is a value dependent on X1, X2,..., Xk, representing k
independent variables, β0, β1, ..., β𝑘 , are their corresponding regression coefficients
which to be estimated and µ𝑖, an error term. The Arc GIS OLS tool automatically
develops and computes these models and produces all relevant statistics. Complete
OLS reports for each model can be found in Chapter 4 that must be defined as below:
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Adjusted R2: The R2 statistic, also referred to as the coefficient of determination, provides a summary of how much variation in a dependent variable’s values is explained by a set of predictor variables. The adjusted R2 can be thought of as a ‘penalty’ for non-parsimoniousness, in the sense that it reduces the R2 value as more variables are added to a model. Thus, in a multivariate model, the adjusted R2 is always lower than the ‘raw’ R2. Like R2, an adjusted R2 value close to 1.0 (say 0.90) would indicate that 90% of the variability in a dependent variable is explained by changes in the set of regressors being modeled —and conversely a value of 0 would indicate that a set of predictors has no explanatory power for the observed changes in a dependent
P-value: Statistical inferences are typically made in the context of the null hypothesis. In the case of OLS regression modeling, the null hypothesis states that there is no linear relationship between a set of predictors and a dependent variable. For OLS modeling, coefficients are produced which describe the y-intercept and the linear relationship between each independent/dependent variable. If a coefficient value is too large to be due simply to random chance, the analyst makes the decision to reject the null hypothesis. The p-value provides the basis for making this decision because it quantifies the probability of obtaining a particular coefficient value when there really is no relationship between two variables (Kleinbaum, 1998). Additionally, the p-value is a measurement of the likelihood that an analyst has found a significant relationship between two variables that is actually due to random chance. Small p-values represent low probabilities of this occurring.
Variance Inflation Factor (VIF): This value represents a description of multicollinearity in a model. For models with two or more predictors there may be correlations between the predictor variables, which can result in highly unstable correlation coefficients (Kleinbaum, 1998). Thus, the larger the VIF value, the more inflation is present, and the more unstable a model becomes. As a general heuristic, a VIF of 10.0 or higher is regarded as problematic. For this study, the VIF threshold was set more conservatively at 7.5.
Additionally, there may be potential outliers (OLS reports in the appendix for
scatterplots). Outliers were not removed because they were known to be actual data
points. The objective of this study was to find correlated variables, not create a
comprehensive model, and therefore the other statistics are far more important in this
analysis – especially the P-value.
Standard Residual Map. The standard residual is the difference between the
observed and predicted value divided by the estimated standard deviation of the
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residuals. The OLS tool in ArcGIS provided additional information about model
performance (e.g., Akaike’s Information Criterion or AIC), and it also produced a map
layer of model residuals, which allowed for the visualization of the global model’s
over/under-predictions. Essentially, the standard residual is the number of standard
deviations the particular value is from the predicted value. In Aim2, standard deviation is
mapped by census block group. The further the standard deviation is from zero, the
worse the regression line is at predicting the specific value for that census block group.
Aim 3: Urban Form and Health Outcome
To identify how the urban form affect health outcomes and how SES of
population contributes to this relationship, aim three have two analytical approaches to:
1) identify spatial patterns of clusters (hot spot and cold spot with high mortality rate,
and outlier) of health outcome, then 2) examine the significant variables associated with
the formations of hot and cold spots, while controlling SES of population.
Clusters of Health Outcome. The objectives of the analyses were twofold. The
first objective is identifying geographical patterns (spatial autocorrelation) of health
outcome of population. The global Moran’s I was computed to examine spatial
autocorrelations or spatial patterns that are either clustered or dispersed distribution of
the values (Waller & Gotway, 2004). The global Moran’s I is widely used to test
geography’s first law, which states near things are more related than distant things
(Chakraborty, 2011). Therefore, the result of Moran’s I indicate the geographical
patterns (e.g., clustered, random or dispersed) of the limited literacy (prose literacy in
this study) across the U.S. to address the research question, what are the geographical
patterns in the health outcome. Moran’s I could range from -1 to 1. On one hand, if all
neighboring counties had more similar values (clustered), the Moran’s I coefficient
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becomes closer to 1. On the other hand, if the neighboring counties had more
dissimilar/diverse values (dispersed), the Moran’s I coefficient becomes closer to -1. If
the spatial distribution is completely random, the Moran’s I coefficient is 0. Also, the Z
score is calculated using the difference between observed value and expected value
(random spatial distribution), and the standard deviation of expected values.
The second objective is identifying the areas with high prevalence of low health
outcome. The local version of Moran’s I known as the Local Indicator of Spatial
Association (LISA) is used to detect the local areas with similar values (Pfeiffer, et al.,
2008). However, LISA does not show if the identified clusters of similar values are high
or low but only similar or dissimilar. Therefore, in this aim, the Getis and Ord Gi* (G-i-
Star) statistic or hot/cold spot analysis (described below) was used because it detects
areas where the significantly high (hot spot) or low (cold spot) prevalence of limited
literacy is located (Ord & Getis, 1995).
Regression Model to Determine the Significant Urban Form Component
with Health Outcome Clusters. Similar to Aim 2, Aim 3 also applied linear regression
model to assess the relationship between urban form components and health outcome
clusters. Before regression analysis began, it was necessary to produce a correlation
matrix. A correlation matrix indicates redundancy between variables. A variable is
considered “redundant” if it is strongly correlated with another (i.e., a value of 0.50 or
greater). Each value in the table represents the correlation coefficient between the
independent variables. A value of 1 would indicate that the variables represent exactly
the same data, while a value of zero would indicate none of the same values. A
negative symbol (-) in the value indicates a negative relationship between the two
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variables and the lack of a negative symbol indicates a positive relationship. This
approach also considers controlling SES variable because the R-squared value was
inevitably low in preliminary result. The univariate regression models were used by
creating separate models for each independent variable of urban form component. It
was used by creating separate models for each independent variable combined with
each dependent variable. The general equation of univariate OLS regression is
𝑌𝑖 = βX𝑖 + α, where: Yi is the value of the dependent variable, β is the slope of the
regression line, Xi is the value of the independent variable, and α is the value at which
the regression line crosses the Y axis. Control variable in this study is selected from
correlation analyses that are statistically significant with dependent variables.
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Table 3-1. Research design and composition
Step Aim One Aim Two Aim Three
Purpose
To examine aggregated longitudinal trends in health disparity among four census regions
To identify the relationship between urban form and regional accessibility to primary health care provider
To examine cross-sectional associations between urban form and health outcomes, accounting socioeconomic status
Indicator
Health outcome o Perceived- health
status o Objective-
healthcare availability
SES factors: o Education
attainment o Age <10 or >65 o Employment o Vehicle ownership
Urban form
Accessibility: travel time to the nearest primary health care (PCP)
Urban form
Health outcome – mortality rate SES factors o Education attainment o Race o Median income o Employment o Health coverage
Analysis
Compare Yearly Differences
Gini Coefficient
ANOVA
Urban form components using weighted linear combination derived from Principal Component Analysis (PCA)
Network analysis (catchment area)
OLS regression
Spatial autocorrelation (Global/local)
MANCOVA (controlling SES factors)
Spatial Unit
State (County) Census block group Census block group and aggregated into tract
Data Source
Behavioral Risk Factor Surveillance System (BRFSS) o 2000s: year 2008 o 2010s: year 2014
Area Health Resource File (AHQR) o 2000s: year 2008 o 2010s: year 2016
Census ACS 2012
AMA 2011- Primary care location
Street- ESRI street file 2013
AMA 2011- Primary care location
FDOH- FloridaCharts.com o 2006-2010
Census ACS 2012
Expected Outcome
Map showing the historic changes of urban form and health outcome
Statistical results showing the directions (positive/negative) magnitude of correlation between urban form components and health outcome
Map showing the average travel time to the nearest PCP by each census block group
Statistical results showing the coefficient (positive/negative) of urban form components with travel time to PCP
Maps showing the local spatial autocorrelation of health outcome (hot spot, low spot, and outliers)
Statistical results showing whether to accept/reject hypothesis (h0= three health outcome groups have identical means)
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Table 3-2. Indicators and measurements used to define urban form components
Urban Form Component
Measurement Description Reference Source Attribute Name
Density
Population density
# of population/Square Miles (sqmi)
Song and Knaap(2007); Galster et al. (2001); Theobald (2002)
US census 2012
POP
Single family density
Total sqmi of single family housing unit/ block group sqmi
Song and Knaap (2004)
FL parcel from FGDL 2012
SF
Housing unit density
# of housing unit/sqmi
Ewing et al, (2002); Glaster et al. (2001)
US census 2012
HSE
Mixed-use Mixed land use
Total sqmi of mixed use from future land use/sqmi
Ewing et al, (2002); Galster et al. (2001)
FL parcel from FGDL 2012
MX1
Job-population balance
Job-population balance
Ewing et al, (2002); Galster et al. (2001)
US census Longitudinal Employer-Household Dynamics 2010
JOBPOP
Mixed land use between housing and commercial land
Total sqmi of commercial/# of housing unit
Ewing et al, (2002); Song and Knaap (2004)
FL parcel from FGDL 2012
MX2
Street network
Street segments Total length of street miles
Song and Knaap (2007);
ESRI street network 2013
STRT
Internal connectivity
# of street intersections divided by sum of the # of intersections and # of cul-de-sac
Weston (2002); Song and Knaap (2007)
ESRI street network 2013
CONNCT
Length of cul-de-sac
Median length of cul-de-sac
Weston (2002); Song and Knaap (2007)
ESRI street network 2013
CULDS
Proximity (Centering)
Distance to urban core
Median distance to the nearest city hall
Galster et al. (2001); Weston (2002); Song and Knaap (2007)
FL parcel from FGDL 2012
D_CORE
Distance to the nearest commercial use
Median distance to the nearest commercial use
Weston (2002); Song and Knaap (2007)
FL parcel from FGDL 2012
D_COMM
Distance to the nearest industrial
Median distance to the nearest industrial use
Weston (2002); Song and Knaap (2007)
FL parcel from FGDL 2012
D_INDS
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Table 3-3. Indicators and measurements for health determinants
Category Indicator Measure Description Unit
Variable Type/Phase
Source
Health Disparity
Health Status (outcome)
Morbidity
Percentage of adults reporting fair or poor health (age-adjusted)
County Dependent/Aim1 BRFSS 2008, 2013
Mortality
Death from diabetes and cardiovascular diseases
Census tract
Dependent/Aim3 Florida DOH 2010
Access to Healthcare provider
Accessibility
Travel time from residential location to the nearest primary care physician practice location
Census block group
Dependent/Aim2
AMA 2011 ESRI Street network
Availability
Ratio of physician-to-population in each county
County Dependent/Aim1 AHQR 2008, 2016
Health Determi-nants
Socioeconomic status
Education attainment
% of Persons that are a High school graduate (includes equivalency) or higher for the population 25 years and over
Census tract
Control/Aim3
American Community Survey census 2012
Income
Median average household income in the past 12 months
Race % of Population that is not white
Population without health insurance
% of population that has no health insurance
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Figure 3-1. Simplified research framework
Figure 3-2. Equation to define urban form components
Figure 3-3. Leading causes of death in Florida
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Figure 3-4. Lorenz curve and equations to calculate Gini Coefficient
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CHAPTER 4 RESULT
This chapter consists of three sections including the results for the three specific
aims of the study. First part states recent 10 year longitudinal trends of health disparity
in US at the state level. Second part explores healthcare accessibility spatially then
identified the correlation between urban form components to addresses the built
environmental factors. Last part identifies whether health outcomes occurs in clustered
or randomized patterns and examines its correlation with urban form while controlling
the non-spatial health access indicators like socioeconomic characters of population.
AIM1: Health Disparity Trends
All four census regions experienced a gradual increase in both disparities (e.g.
health status and healthcare availability) through 2000s and 2010s (Table 4-1). For
health status, disparity levels for all states had increases between 2008 and 2013 but
two northeastern states (Connecticut, -0.02; Rhode Island, -0.01) showed an increase in
health status equality. For health availability, disparity levels for all states had increased
between 2008 and 2016 but three states (Kentucky, -0.01; Oregon, -0.006; and Rhode
Island, -0.01) showed an improvement in their healthcare availability disparity.
As shown in Figure 4-1 and 4-2, the overall disparity trends of health status and
healthcare availability intensified through 2000s to 2010s, consistent with the health
status trend reported by the BRFSS and AHRQ. In addition to the overall increasing
trend, there are differences in the spatial distributions of disparity. States with higher
disparities clustered in the south, whereas States with lower disparities were more
commonly found in the Midwest and Northeast regions. According to Figure 4-3, the
South showed the highest disparity level among the four census regions, while the other
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four had similar magnitudes and net increases in Gini coefficients. The magnitude
differences of healthcare availability disparity among the four census regions were
relatively small, even though the South was still the most acute in disparity. Further, the
health disparity in the West, while lower than the South, was clearly higher than the
Northeast and the Midwest when measured for both health status and healthcare
availability. Table 4-1 verifies that disparity in the South was relatively higher than other
regions.
Longitudinal Health Disparity among Census Region
ANOVA is used to test the statistical significance in the differences in the health
disparity measures across different census regions. Before running ANOVA, several
tests were performed to ensure that the data meet the assumptions of ANOVA. The box
plots in Figure 4-4 display one outlier in 2008 health status; and four outliers in 2008
healthcare availability based on the 3 standard deviation criteria in each disparity
respectively. There were no outliers in health status and healthcare availability in years
that represents 2010s (2013 and 2016 respectively). The tests of normality (Table 4-2),
Shapiro-Wilk test is used because sample size was less than 2000. For both health
disparity indicators, p-values equal were greater than .05 that indicates strong supports
of normality. However, significance of 2008 health status value was below .05 (.037),
which shows the data is deviated from a normal distribution. After log transformation,
the data met normal distribution (p-value=.102). Levene’s test was used to further verify
the assumption of equality of population variance (Table 4-3). Because the p-values of
disparity in health disparity were greater than 0.05 for all years, the standard deviations
of the four census regions could be considered equal in the analysis. However, data
transformation was required for 2013 health status because the significance value was
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below .05 (p-value= 0.14). After log transformation, the value showed significant level
(p-value= .243). Thus, ANOVA was used to test for statistically significant differences
among the four census regions.
ANOVA for comparing means among the census regions demonstrates that the
census regions were not equal in health disparity (Table 4-4). A post-hoc test was used
to investigate which census region was different from the others. Tukey’s tests were
selected and the results of homogeneous subsets of health status and healthcare
availability disparities are presented in Tables 4-5 and 4-6, respectively. The results
show that groups by health status disparity could include two, the South and West and
the rest (Northeast, and Midwest together), and disparities in the South were the highest
than the other regions. Likewise, the healthcare availability disparities in the South were
clearly higher than in other regions. The West showed the second highest level of
health disparity. It is consistent with what was observed from the longitudinal trends in
Figure 4-2. As a result, the most notable points confirmed from the longitudinal trends
and the ANOVA are that disparities have increased in all census regions, and disparities
in the South are significantly higher than the other regions.
For the individual states as of 2000s, Georgia (Gini coefficient = 0.237), Arkansas
(0.216), California (0.203), Alabama (0.202), and Kentucky (0.192) were rated the top
five in the health status disparity; Montana (0.407), Texas (0.391), North Dakota
(0.373), New Mexico (0.366), and Alabama (0.364) in the healthcare availability (Table
6). For 2010s, Kentucky (0.260), Arkansas (0.259), Georgia (0.243), California (0.230)
and Alabama (0.220) were rated the top five in the health status disparity; Texas
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(0.447), Montana (0.415), Mississippi (0.407), West Virginia (0.397) and Alabama
(0.390) in the healthcare availability.
As represented in Table 4-7, all but top 10 states in health status disparity
through 2000s to 2010s, all but four states in West including California, Utah, Oregon,
and Hawaii, were from the South. Likewise, more than half of top 10 states belonged to
South.
Correlation between Disparity and Selected Socioeconomic Characteristics of Each State
The above findings raise the question of what factors cause increasing
magnitudes and spatial differences in health disparity. Based on the literature review,
there should be a strong correlation between health outcomes and socioeconomic
variables. Specifically, many empirical studies have found that socioeconomic status is
a key factor which influences health disparity. Thus, the purpose of this empirical study
is to identify the relationship between disparity and the selected socioeconomic factors.
After controlling for demographic covariates, disparity in health status had
significant negative associations with the percentage of those with median household
income (Table 4-8). Disparity in healthcare availability was negatively correlated with
the percentage of uninsured, median household income, and the percentage of non-
Hispanic white population; it was positively correlated with the percentage of population
over age 65. Variables that did not show any significant association with health disparity
included fluency in English speaking, and percentage of unemployment.
In summary, the results of aim one of this study yielded four main points: (1) the
overall trend of health disparity had increased continuously from 2008 to current years
(2013 and 2016), (2) disparity in the South was significantly higher than in the other
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regions, and (3) selected SES variables (e.g. household income level, uninsured
population, race, and age over 65) correlated with disparity. This study further provides
a basis for more detailed and extensive investigations into better understanding the
environmental and socioeconomic conditions associated with health disparity, and to
develop policy recommendations for reducing health disparity in the US from the
urban/transportation planning and public health perspectives. Future studies should
include other urban areas and various rural communities, as well as more diverse
population groups.
AIM2: Built Environment: Urban Form and Healthcare Accessibility
Urban Form Components and its Geographic Variations
Figure 4-5 represents the urban form components modeling result. Each urban
form component (density, mixed-use, street network, and proximity) captures four
variables and weights for these are calculated through PCA.
Density. For density, findings of the correlation analysis show that the variables
involved are not significantly correlated (Table 4-9). The Kaiser-Meyer-Olkin measure of
sampling adequacy (KMO test equal to 0.438) and the Bartlett test (p-value <0.001 and
chi-square equal to 1838.62) suggests that the dataset is adequate to describe the
phenomenon through Principal Component Analysis (PCA). The analysis of the
variables that show extraction of components in Table 4-10 indicates that one principal
component can be identified and associated with one eigenvalue greater than 1 and
three variables (POP, HSE and SF) can represent “density”. This single component
explains 67.13% of total variance of information contained in the entire dataset. The
communality matrix in Table 4-11 illustrates intensity of the contribution of each variable
as the percentage of variance explained along with the principal component extracted
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for each of the three variables. This study assigns a weight 1.00 to which has the
highest value of the explained variable; the other values are assigned proportionately to
the extraction value and the highest value extracted. The functional relationship for
measurement of density uses the following formula:
D (Density) = Pop + 0.93*HSE + 0.17*SF
Mixed-use. A finding of the correlation analysis in Table 4-12 indicates a
negative relationship among mixed use variables except between multi-family
residential lands and commercial lands. The Kaiser-Meyer-Olkin measure of sampling
adequacy (KMO test equal to .512) and the Bartlett test (p-value <.001 and chi-square
equal to 40.28) point out that the dataset is adequate to describe the phenomenon
through PCA. The analysis of the variables that show the extraction of the components
in Table 4-13 indicates that one principal component can be identified and associated
with one eigenvalue greater than 1 and three variables (MIX_HSE, AVG_MF and
COMM_HSE) can represent “mixed use”. This single component explains 41.00% of
total variance of information contained in the entire dataset. The communality matrix in
Table 4-14 illustrates the intensity of the contribution of each variable as the percentage
of variance explained along with the principal component extracted for each of the three
variables. This study assigns a weight of 1.00 to which has the highest value of the
explained variable; while the other values are assigned proportionately to the extraction
value and the highest value extracted. The functional relationship that composes
density assumes following formula:
M (Mixed-use) = 0.25 *MX1 + MF + 0.88* MX2
Street Network. For street network, a finding of the correlation analysis in Table
4-15 indicates a negative relationship among the variables except between internal
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connectivity and length of cul-de-sac. The Kaiser-Meyer-Olkin measure of sampling
adequacy (KMO test equal to 0.520) and the Bartlett test (p-value <0.001 and chi-
square equal to 487.088) point out that the dataset is adequate to describe the
phenomenon through PCA. The analysis of the variables that show the extraction of
components in Table 4-16 demonstrates that one principal component can be identified
and associated with one eigenvalue greater than 1 and that three variables (STREET,
IN_CONNECT, and AVG_CULDES) can represent “street network”. Similarly to mixed
use, street network explains in single components of 48.88% of the total variance in the
entire dataset. The communality matrix in Table 4-17 illustrates the intensity of the
contribution of each variable as the percentage of variance explained along with the
principal component extracted for each of the three variables. This study assigns a
weight 1.00 to the highest value of the explained variable; while the other values are
assigned proportionately to the extraction value and the highest value extracted. The
functional relationship that composes density assumes the following formula:
S (Street network) = 0.94* STRT + 0.82*CONNCT + CULDS
Proximity. For the last urban form component, proximity, findings of the
correlation analysis verify that the variables involved are not significantly correlated
(Table 4-18). The Kaiser-Meyer-Olkin measure of sampling adequacy (KMO test equal
to 0.718) and the Bartlett test (p-value <0.001 and chi-square equal to 1254.85) suggest
that the dataset is adequate to describe the phenomenon through PCA. The analysis of
the variables that shows extraction of components in Table 4-19 confirms that one
principal component can be identified and associated with one eigenvalue greater than
1 and those three variables (AVG_CITYHA, AVG_COMMER, and AVG_INDST) can
represent “proximity”. This single component explains 78.88% of total variance of
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information contained in the entire dataset. The communality matrix in Table 4-20
illustrate the intensity of the contribution of each variable as the percentage of variance
explained along with the principal component extracted for each of the three variables.
This study assigns a weight of 1.00 to the highest value of the explained variable; while
the other values are assigned proportionately to the extraction value and the highest
value extracted. The functional relationship that composes density assumes following
formula:
P (Proximity) = 0.97*D_CORE + D_COMM + D_INDS
Correlation among Urban Form Components
A Pearson correlation analysis was conducted between 4 components of urban
form using SPSS (Table 4-21). The correlation matrix presents noteworthy patterns as
below. Since most of the urban form dimensions in this dissertation are conceptualized
and refined following the approaches developed by Clifton et al. (2008); Cutsinger &
Galster (2005); Galster et al. (2001), comparisons of findings of urban form correlations
are also provided in this section.
First, indices of three urban form dimensions – density, mixed-use, and street
network- are highly positively correlated with one another. This pattern is consistent with
the finding in Cutsinger et al. (2005), who explained that high population density results
in intensified development of land and high competition for scarce land between
different land uses. The correlation between density and mixed-use can be explained
using economic framework that people tend to be exposed to different environment and
live together for economic agglomeration.
Second, it is no surprise that density and mixed-use are positively correlated with
one another. This pattern indicates that population and housing agglomeration and land
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uses share common characteristics in different dimensions of urban form. This pattern,
however, contradicts the results in Cutsinger et al. (2005) as they found out that both
concentration indices are significantly negatively correlated with mixed-use, density and
continuity dimensions, and are significantly positively correlated with centrality,
concentration, and proximity dimensions. It could be the leapfrog pattern of Orlando
MSA. Also, another possible explanation of the different result could be using larger unit
size of the analysis in this study. Unlike using the neighborhood level, this study uses
census block group level which can include two or more characters of urban form.
Third, neither of the proximity component, but not street network, is correlated
with any of other urban components. It is consistent with the finding from Cutsinger et
al. (2005), as they found no correlation between centrality indices.
Travel Time to Healthcare Provider and its Geographic Variation
Figure 4-6 and Table 4-22 show the distribution of primary health care providers
in the study area and the results of network analysis show individual census block
groups serviced by PCP along with the total drive time of each route. PCPs are
concentrated in the center and the west side of Orange County dominated by downtown
Orlando and the city of Lake Buena Vista; while they are dispersed in the southwest
part of Seminole County that consists of suburban cities such as Altamonte Springs,
Maitland, and Winter Park. Overall, more than 90% of the population in Orlando MSA is
less than a 30 minute drive time to the nearest primary care. Considering PCPs as
primary health care providers, about 60.34% of the total population with a five-minute
drive of a PCP (M= 5.16, SD= 7.01). The results also show less accessibility along the
outskirts of the MSA boundary. Less than 1% of the total population could not reach the
closest provider within 30 minutes. The longest travel time to primary care in the
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Orlando MSA can be found in a census block group of Osceola County, which is an
average of 49.70 minutes to the nearest PCP.
Relationship between Urban Form and Healthcare Accessibility
Table 4-23 presents the result of univariate OLS regression for each urban form.
All four urban form components were statistically significant. The P-values of each of
these variables (0.038464, 0.014510, and 0.016479 respectively) are very low, and
indicate that these variables would likely be useful in a comprehensive model with many
variables.
The results indicate that it takes longer travel time to PCP when neighborhoods
are less dense, less mixed-use, and have lower proximity to urban core, commercial
and industrial areas (negative relationship) and longer street network (positive
relationship). However, in this approach, the adjusted R-squared values are inevitably
low (density: 0.23, mixed-use: 0.21, street network: 0.37, and proximity: 0.04) because
each variable was treated individually rather than as a comprehensive model with
multiple variables (multivariate) in the same regression equation. After applying a
simplistic univariate approach to determining correlation between urban form
component and healthcare accessibility, it has been clear that a multivariate regression
model is needed for an improved analysis that can state comprehensive geographic
variation.
OLS result with four urban form independent variables (multivariate) is
summarized in Table 4-24. The correlation results reveal divergent relationships
between urban form components and accessibility of primary health care measures.
Adjusted R-squared for the model indicating the proportion of the variability in drive time
explained by the urban form variable was 0.39. Consistent across all three models, only
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one urban form component- street network- is highly statistically significant. The street
network shows a positive correlation to all three outcomes (r= 3.41). However, the
assumption of multicollinearity was assessed through examination of variance inflation
factors (VIFs); any independent variables with a VIF of 10 or greater were considered to
be too related to the others. Two of the independent variables (density and mixed-use)
were found to be too related (VIF=27.378 and 26.498, respectively). As such, the
regression was modified to include three urban form components, mixed-use, street
network, and proximity, as independent variables to avoid strong collinearities.
A shown in Table 4-25, two out of three urban form components were significant
except for the proximity (p-value = 0.4480) after model modification. There were no
strong collinearities (VIF=1.398, 1.485, 1.099 respectively for mixed-use, street network,
and proximity) among the variables. This pattern suggests that places with higher
mixed-use (greater areas with mixed land use, and greater areas with multi-family) to
built environments tend to be associated with less travel time to PCP. To this end, it is
clear that the distribution of primary care providers are segregated in areas with high
population or housing density areas or urban forms with dispersal of housing units to
central districts, commercial, and industrial areas tend to have higher travel time to
PCP. This pattern can be explained by the shortage of PCPs in suburban areas and
outskirts of the city core. Also, a greater street network (longer street segments, less
internal connectivity, and longer length of cul-de-sac) tend to be associated with longer
travel time to PCP. To this end, it is clear that the distributions of primary care providers
are agglomerated in the areas where street network is shorter, which has the
characteristics of compact urban form. These results support the prevailing belief of “the
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more sprawl, the less accessibility to health care facility”. Also it supports travel
impedances do happen within the street network. Although there were no statistical
significance identified for proximity, it represents a negative correlation (coefficient= -
0.1498) that indirectly indicates that urban forms with dispersal of housing units to
central districts, commercial, and industrial areas tend to have higher travel time to
access primary health care provider.
Standard Residual Map
The spatial regression model significantly improves the observation of residuals.
Maps portraying standard residuals were prepared to facilitate visualization of potential
outliers for the four dependent variables discussed above and their relationships (Figure
4-7). For example, the regression results show a standard residual of over 2.5 for the
travel time to the nearest PCP in a census block group and the urban form components,
an indication that the physical travel time was much higher than the predicted value.
The maps show that the regression models are generally good in the center of each
county where each urban form considered having compact pattern, but are generally
not at outskirts of the county. While the residuals created a normal distribution (Figure
4-8), the resulting Global Moran’s I values (0.038) and z-scores (11.70) indicated in all
cases that residuals were clustered, and thus that models were not robust. To compare
the accuracy of the model, univariate OLS was also conducted to assess individual
relationship between each urban form component and accessibility. Likewise, residual
map resulted from univariate OLS represented the clusters of residual for each urban
form components (Figure 4-9). Both results indicate that it may be due to the missing
variables including involvement of other variables such as socioeconomic variables. As
a quick snapshot of additional OLS (Table 4-26) for healthcare accessibility and
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socioeconomic variables, negative relationship has observed for non-Hispanic white
population; whereas positive relationship exist in the elderly, and areas with household
without a vehicle ownership.
AIM3: Relationship between Urban Form and Health Outcome
According to the results from Aim 2, socioeconomic factors appear to be related
to healthcare accessibility. Although the literature has confirmed that demographic
factors influence health, it has remained unclear whether the environment, especially
the urban form environment, is associated with health disparity and which of its specific
attributes are most influential. This section of the dissertation research used bivariate
correlation analysis to identify the environmental correlates of health status of
population. The unit of analysis is the census tract area. Measures from census block
groups are aggregated up to the tract level and the mean value of each tract area is
used for the analysis.
This analysis focuses on a small number of variables due to the small number of
samples (degrees of freedom) available at the tract level. But a convenient and
informative approach with clear spatial boundaries for analysis is needed to bridge the
findings and results to policy implementations. The strength of this step of analysis is to
examine the built environment-health disparity relationship at an administrative
boundary for appropriate, easy to apply policies.
Geographic Distribution of Health Outcome
In Orlando MSA, the means of mortality rate by cardiovascular diseases and
diabetes at the census tract level is 1.23%, with the standards deviations of 1.81.
Overall, tracts in Lake and Osceola counties, further away from core of Orange and
Seminole counties, had higher mortality rate (Figure 4-10, left). Overall patterns of local
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spatial autocorrelations in the mortality rate from cardiovascular diseases and diabetes
were similar with the distribution of mortality rate (Figure 4-10, right). According to the
LISA maps in Figures 4.10, hot spots of high mortality rate agglomerate in northwest of
Lake county while cold spots were identified in the core of Orange county, City of
Orlando. These findings are consistent with the overall patterns of health disparities
described (Table 4-27).
Correlation between Population Socioeconomics and Health Outcome
This examines the relationships between socioeconomic variables, and health
outcome and its clusters tract level. Socioeconomic variables are selected based on the
literature review and also been used in Aim 1. Table 4-28 shows the result of a bivariate
correlation test which includes variables significantly related to health outcome status
and its cluster, and theoretically important variables such as demographics and
individual characteristics.
Mortality rate by cardiovascular diseases and diabetes are positively correlated
with the uninsured, and the elderly (age over 65), while it has negative significant
relationships with the percentage of employment. All variables show at the 0.05 level of
significance. Similar to correlation between mortality rates, cluster pattern of mortality
rates is positively correlated with the uninsured, the elderly, and the percentage of
population who do not speak English at all. It also has negative relationship with
percentage of employment; as well as the percentage of percentage of bachelor
degree. However, unlike correlation in mortality rate, cluster patterns of mortality rates
show statistical significant correlation with all urban form components. It has negative
association with density, mixed-use, and proximity, while positive relationship is
identified in street network.
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The correlation of the urban form components and populations’ socioeconomic
characteristics with mortality rates, and patterns of mortality rates are summarized and
interpreted as follows.
First, there are clear relationships between particular demographic variables and
mortality rates by cardiovascular diseases and diabetes. Especially, age showed higher
positive correlation than other SES variables that represent census tracts with higher
elderly population show higher death incidences from cardiovascular diseases and
diabetes; and potential covariate when identifying cause relationship between urban
form and mortality rate by cardiovascular diseases and diabetes. The percentage of
employment populations has negative association with high mortality rate. Also, the
percentage of uninsured populations has positive association with mortality rate. This is
clearly expected from the previous research based on the literature review (PA
Braveman & Cubbin, 2010; Gordon-Larsen et al., 2006; LaVeist, 2005).
Second, the result with z-score of mortality rates represents statistical significant
correlation with all urban form components, while mortality rate itself did not show
correlations. This indicates the potential causal impact between geographic clusters of
mortality rate and urban form components; high density, mixed-use, proximity (negative
correlation), and longer street network (positive correlation). These findings are
interpreted as follows. Possibly because proximately located offices, schools, and high
mixed uses produce commuting and other various trips, these destinations are
negatively related to high mortality. Higher proximity shows a negative association with
higher mortality that consistent with hot spot analysis (Figure 4-10) those urban core
areas have clusters of low mortality rate. It can be interpreted that areas near downtown
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Orlando support physical activity because it has smaller street blocks and higher
density. Also, morality has positive association with street network infrastructure such
as street length, cul-de-sac, and number of intersections. It may be interpreted that the
greater the street length, intersections, the more cars in the neighborhood and therefore
less physical activity such as walking and biking, which ultimately results in increased
mortality.
Regression between Urban Form and Health Outcome Clusters
Based on the results in correlation, the urban form is associated with trends of
health outcome at the census tract level. While the findings at the tract level offer
aggregated, bivariate, and therefore somewhat crude information on the environment-
disparity issues, its boundaries are spatially clear and therefore make it easy to
translate into interventions or policy recommendations. To identify more detailed
information about the roles of the built environment on health outcome, this phase of
analysis executes an analysis using linear regression analysis while controlling SES
factors that were correlated in previous sections.
According to the results of the hot spot analysis earlier, mortality rate has strong
spatial autocorrelations (Moran’s I: 0.34, P-value <0.05). Because of the evidence of
strong spatial autocorrelations, an OLS regression model is insufficient to explain the
urban form and the population health outcome. As the measures adjusted R squares
indicate, model has 38.3% evidences of good fit. Durbin-Watson statistics of 0.830 is
reported and showed the existence of strong autocorrelations as expected. In addition
to the hot spot analysis, the Durbin-Watson for this preliminary model described strong
evidence of spatial autocorrelations. A multi-linear regression was not suitable because
it did not meet the assumptions. Normality was assessed using a normal P-P plot
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(Figure 4-11), and this assumption was also met. The assumption of homoscedasticity
was assessed by visual examination of a residuals scatterplot (Figure 4-12), and the
plot did follow an ideal rectangular distribution. The assumption of multicollinearity was
assessed through examination of variance inflation factors (VIFs); any independent
variables with a VIF of 7.5 or greater were considered to be too related to the others.
Two of the independent variables (density and mixed-use) were found to be too related
(VIF=73.745.750 and 75.422, respectively), and the assumption did not met (Table 4-
29). As such, the regression was modified to a univariate linear regression to
compensate for the multicollinearity.
Applying univariate linear regression for each urban form component, Table 4-30
shows that four variables show a significant relationship with the difference pattern of
mortality rate. The p-values of each variable are very low (density, mixed-use, street
network = 0.000 and proximity= 0.31), whereas R square shows variations by each
urban form component (0.251, 0.281, 0.86, and 0.11 respectively). It is noteworthy that
these variables are consistent with majority of literature regarding compact urban form
increases the populations’ health outcome and their behavior.
Table 4-31 represents the result of univariate regression after controlling selected
SES variables from correlation analysis. R squares indicates that at least 10% of the
goodness fit of model increased, while street network shows the highest increase to
0.254. Likewise, all p-values were very low (all 0.000). Again, these results indicate a
negative relationship between density, mixed-use, and proximity; while positive
relationship with street network. The findings of this analysis provide evidence to
support the general hypothesis that urban form settlement is related to population’s
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health status. Especially it advocates that many positive public health outcomes can
result from a more compact urban form. Likewise, from Aim2, the need of multivariate
regression model was necessary to understand comprehensive and latent relationship
between urban form components and health outcomes. The multivariate result with
three urban form independent variables is summarized in Table 4-32. Two sets of
multivariate regression were conducted to compare the goodness of fit before and after
controlling the elderly population, which was selected in correlation matrix in Table 4-
28.
Adjusted R-squared for the models indicating the proportion of the variability in
cluster pattern of mortality rate explained by the urban form variable were 0.31 and 0.38
before and after controlling population age, respectively. These two models consistently
show no strong collinearities (VIF <7.5) three urban form variables. As shown in Table
4-32, all three urban form components represent statistical significance with mortality
rate. Clearly, higher mortality rate was associated with low mixed-use, low proximity,
and greater street network. Additionally, there were different results for the
transportation infrastructure variables before and after controlling SES variable. The
multivariate regression result after controlling SES supports the hypothesis that the
areas with street network characterized for urban form (longer street segments, high
number of cul-de-sac, and more intersections) were negatively related with health
outcome. However, transportation infrastructure variable from the multivariate analysis
did not support the hypothesis due to uncaptured variations related with socioeconomic
factors, but had intuitively correct direction of mortality.
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Table 4-1. Descriptive statistics of health disparities
Health Disparity (Gini Coefficient)
Year Census Region
N Mean Std. Deviation
Std. Error
95% Confidence Interval for Mean
Lower Bound
Upper Bound
Health Status
2008 Midwest 12 .074 .014 .004 .065 .083 Northeast 9 .089 .027 .009 .068 .111 South 16 .163 .036 .009 .144 .182 West 13 .144 .034 .009 .123 .165 Total 50 .123 .048 .006 .110 .137
2013 Midwest 12 .137 .019 .005 .125 3149 Northeast 9 .120 .033 .011 .095 .145 South 16 .181 .046 .011 .157 .206 West 13 .179 .037 .010 .157 .202 Total 50 .159 .043 .006 .147 .172
Healthcare Availability
2008 Midwest 12 .294 .035 .010 .271 .316
Northeast 9 .196 .044 .014 .161 .231 South 16 .311 .045 .011 .286 .335 West 13 .273 .071 .020 .229 .316 Total 50 .276 .064 .009 .258 .294
2016 Midwest 12 .319 .033 .009 .298 .340 Northeast 9 .245 .059 .019 .199 .291 South 16 .345 .048 .012 .319 .371 West 13 .295 .066 .018 .255 .335 Total 50 .308 .062 .009 .290 .326
Table 4-2. Normality test
Shapiro-Wilk Year Statistic df Sig.
Health Status 2008* .961 50 .102 2013 .961 50 .098
Healthcare availability
2008 .985 50 .780 2016 .992 50 .985
*: Results after Log transformation
Table 4-3. Homogeneity test
Year Levene statistics Df1 Df2 Sig.
Health Status 2008* 1.622 3 46 .197 2013* 1.441 3 46 .243
Healthcare availability
2008 1.863 3 46 .149 2016 2.351 3 46 .085
95
Table 4-4. ANOVA result
Year Sum of squares
df Mean square
F Sig.
Health Status 2008* Between Groups
1.038 3 .346 33.281 .000
Within Groups
.478 46 .010
Total 1.516 49 2013* Between
Groups .274 3 .091 8.358 .000
Within Groups
.503 46 .011
Total .777 49 Healthcare availability
2008 Between Groups
.081 3 .027 10.025 .000
Within Groups
.123 46 .003
Total .204 49 2016 Between
Groups .061 3 .020 7.286 .000
Within Groups
.129 46 .003
Total .190 49
Table 4-5. Homogeneous subsets of health status disparity (Gini Coefficient)- Tukey’s test
2008 2013 Census Region
N Subset for alpha = 0.05 Census Region
N Subset for alpha = 0.05
1 2 1 2 Northeast 9 -1.137 Northeast 9 -.938 Midwest 12 -1.064 Midwest 12 -.865 West 13 -.852 West 13 -.755 South 16 -.796 South 16 -.753 Sig. .306 .552 .329 .056
Table 4-6. Homogeneous subsets of healthcare availability disparity (Gini Coefficient)- Tukey’s test
2008 2016 Census Region
N Subset for alpha = 0.05 Census Region
N Subset for alpha = 0.05
1 2 1 2 Northeast 9 .196 Northeast 9 .245 Midwest 12 .273 Midwest 12 .296 West 13 .294 West 13 .319 South 16 .311 South 16 .345 Sig. 1.000 .290 .106 .114
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Table 4-7. Top 10 states in disparity in 2010s
Rank Health Status Disparity Census Region Healthcare Availability Disparity
Census Region
2008 2013 2008 2013 2008 2016 2008 2016 1 Georgia Kentucky South South Montana West Texas South 2 Arkansas Arkansas South South Texas South Montana West 3 California Georgia West South North
Dakota Midwest Mississippi South
4 Alabama California South West New Mexico West West Virginia
South
5 Kentucky Alabama South South Alabama South Alabama South 6 Louisiana Utah South West Missouri Midwest New
Mexico West
7 Utah Louisiana West South Mississippi South North Dakota
Midwest
8 Oklahoma Oregon South West Virginia South Hawaii West 9 Oregon Wyoming West West Tennessee South Missouri South 10 Hawaii Nevada West West Hawaii West Georgia South
Table 4-8. Correlation test with socioeconomics of population
Gini coefficient of health status
Gini coefficient of healthcare availability
Uninsured Pearson R .495** .272 P-value .000 .056
Average Household Income
Pearson R -.379** -292* P-value .007 .040
% of not fluent in speaking English
Pearson R .181 -.170 P-value .208 .237
% of non-Hispanic white Pearson R -.368** -0.75 P-value .009 .605
% of age over 65 Pearson R .334* .043 P-value .018 .768
% of unemployment Pearson R .269 .087 P-value .259 .547
Table 4-9. Correlation analysis of variables of density
POP10_SQMI HSE_SQMI SFH_SQMI
Correlation POP10_SQMI 1.000 .927 .288
HSE_SQMI .927 1.000 .129
SFH_SQMI .288 .129 1.000
Table 4-10. PCA result over the total variance explanation related to density
Initial Eigenvalues Extraction Sums of Squared Loadings
Component Total % of Variance
Cumulative %
Total % of Variance
Cumulative %
1 2.014 67.125 67.125 2.014 67.125 67.125
2 .928 30.921 98.047
3 .059 1.953 100.000
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Table 4-11. Communality matrix and weight of the variables related to density
Variable Extraction Weight in this study
POP10_SQMI .959 1.00
HSE_SQMI .896 0.93
SFH_SQMI .159 0.17
Table 4-12. Correlation analysis of variables for mixed use
MIX_HSE AVG_MF COMM_HSE
Correlation MIX_HSE 1.000 -.086 -.031
AVG_MF -.086 1.000 .200
COMM_HSE -.031 .200 1.000
Table 4-13. PCA result over the total variance explain related to the mixed use
Initial Eigenvalues Extraction Sums of Squared Loadings
Component Total % of Variance
Cumulative %
Total % of Variance
Cumulative %
1 1.230 41.000 41.000 1.230 41.000 41.000
2 .978 32.594 73.594
3 .792 26.406 100.000
Table 4-14. Communality matrix and weight of the variables related to mixed use
Variable Extraction Weight in this study
POP10_SQMI .144 0.25 HSE_SQMI .579 1.00 SFH_SQMI .507 0.88
Table 4-15. Correlation analysis of variables for street network
STREET IN_CONNECT AVG_CULDES
Correlation STREET 1.000 -.202 -.268
IN_CONNECT -.202 1.000 .228
AVG_CULDES -.268 .228 1.000
Table 4-16. PCA result over the total variance explain related to the street network
Initial Eigenvalues Extraction Sums of Squared Loadings
Component Total % of Variance
Cumulative %
Total % of Variance
Cumulative %
1 1.466 48.879 48.879 1.466 48.879 48.879
2 .805 26.827 75.706
3 .729 24.294 100.000
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Table 4-17. Communality matrix and weight of the variables related to street network
Variable Extraction Weight in this study
STREET .497 0.94
IN_CONNECT .438 0.82
AVG_CULDES .531 1.00
Table 4-18. Correlation analysis of variables for proximity
AVG_CITYHA AVG_COMMER AVG_INDST
Correlation AVG_CITYHA 1.000 .623 .764
AVG_COMMER .623 1.000 .659
AVG_INDST .764 .659 1.000
Table 4-19. PCA result over the total variance explain related to the proximity
Initial Eigenvalues Extraction Sums of Squared Loadings
Component Total % of Variance
Cumulative %
Total % of Variance
Cumulative %
1 2.366 78.876 78.876 2.366 78.876 78.876
2 .401 13.354 92.230
3 .233 7.770 100.000
Table 4-20. Communality matrix and weight of the variables related to proximity
Variable Extraction Weight in this study
AVG_CITYHA .808 0.97 AVG_COMMER .724 0.90 AVG_INDST .834 1.00
Table 4-21. Correlation between urban form components
Density Mixed-use Street network Proximity
Density 1 .990** -.597** .095 Mixed-use 1 -.577** .065 Street network 1 -.543** Proximity 1
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Table 4-22. Census block group and population identified by network analysis to compare accessibility by each primary care provider
Travel time (min) 2010 population within the travel time
# %
To the nearest primary health care provider
Up to 5 1,287,811 60.34
6-15 610,213 28.59
16-30 166,930 7.82
Over 30 69,457 3.25
Total 2,134,411 100
Table 4-23. OLS result for PCP accessibility (univariate)
Independent variable
Adjusted R-squared
Coefficient Standard Error t-Statistics Probability
Density 0.231369 -3.375679 0.212755 -15.866284 0.0000* Mixed-use 0.219668 -0.469686 0.030607 -15.345826 0.0000* Street network 0.370109 0.608987 0.027499 22.146150 0.0000* Proximity 0.041679 -0.206952 0.033918 -6.101527 0.0000*
* An asterisk next to a number indicates a statistically significant p-value (p < 0.01).
Table 4-24. OLS result for PCP accessibility (multivariate) and four urban form components
Independent variable
Adjusted R-squared
Coefficient Standard Error
t-Statistics Probability VIF
Intercept 0.398 5.613878 .0188 29.832 0.00* Density -1.097 0.9846 -1.114 0.265535 27.3788 Mixed-use -0.3636 0.9687 -0.375 0.7074 26.4986 Street network
3.411 0.2345 14.543 0.00* 1.5538
Proximity -0.1709 0.19826 -0.862 0.3888 1.110
Table 4-25. OLS result for PCP accessibility (multivariate) and three urban form components
Independent variable
Adjusted R-squared
Coefficient Standard Error
t-Statistics Probability VIF
0.398 Intercept 5.613878 .0188 29.832 0.00* Mixed-use -1.414 0.222 -6.353 0.00* 1.398 Street network
3.466 0.229 15.113 0.00* 1.485
Proximity -0.1498 0.1973 -0.7590 0.4480 1.099
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Table 4-26. OLS result for PCP accessibility and socioeconomic indicators
Independent variable
Adjusted R-squared
Coefficient Standard Error
t-Statistics Probability VIF
0.108 Intercept 3.516089 1.2320 2.8539 0.004431* Median income
-0.0001 0.0001 -0.87995 0.379127 1.4818
% of non-Hispanic white
-0.0034 0.0129 2.6514 0.00816* 1.628775
% of population age over 65
0.03625 0.020315 1.78465 0.04468* 1.222121
% of no vehicle
0.106079 0.032574 3.25697 0.00188* 1.509984
% of population who does not speak English at all
-0.108707 0.116136 -0.93603 0.34951 1.197673
% of no education
0.100912 0.082079 1.229451 0.219256 1.42261
Table 4-27. Comparison of descriptive statistics between Orlando MSA and Florida
Hot spots Cold spots Orlando MSA Florida
Total Population 125,650 688,398 2,134,411 18,804,592 Avg. % of Mortality 2.37 1.20 0.685 0.687 Avg. % of White population
74.92 66.99 67.89 57.9
% of uninsured population
22.16 15.15 25.2 16.2
% of population over age 65
32.54 11.99 14.33 17.3
Avg. income 40562.82 49243.97 48223.38 47,507
Table 4-28. Correlation test with mortality rate and its cluster pattern
Measurement Mortality rate Z-score of mortality rate represents cluster
SES % of uninsured .114** .175** % of bachelor degree .097 -.116* % of employment rate -.192** -.211** % of non-Hispanic white .082 .082 % of age over 65 .371** .371** % of no-vehicle ownership .017 -.044 Average income -.24 -.032 % of no English speaking .011 .187**
Urban Form
Density -.064 -.503** Mixed-use -.088 -.532** Street network -.085 .295** Proximity .012 -.119*
**: correlation is significant at 0.01 level *: correlation is significant at the 0.05 level
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Table 4-29. Collinearity statistics for independent and control variables
Tolerance VIF
Density .014 73.745 Mixed-use .013 75.422 Street network .262 3.820 Proximity .420 2.378 Uninsured .769 1.301 Age 65 up .673 1.487 Employment .787 1.271
Table 4-30. Summary statistics for each four independent variables (univariate before controlling SES)
Adjusted R Square
Coefficient (Constant)
Coefficient t Sig. 95.0 Confidence Interval Lower bound
Upper bound
Density .251 -1.162 -.797 -10.51 .000 -.946 -.648 Mixed-use .281 -1.144 -.850 -11.34 .000 -.997 -.702 Street network .086 -1.232 .618 -17.05 .000 .402 .834 Proximity .011 -1.263 -.297 -2.167 .031 -.567 -.027
Table 4-31. Summary statistics for each four independent variables (univariate after controlling SES)
Adjusted R Square
Coefficient (Constant)
Coefficient t Sig. 95.0 Confidence Interval Lower bound
Upper bound
Density .338 .232 -.742 -9.738 .000 -2.246 -1.334 Uninsured .015 2.139 .033 .001 .029 Age 65 up .043 5.930 .000 .028 .057 Employment -.006 -.099 .036 -.012 .000 Mixed-use .346 .230 -7.76 -10.02 .000 -1.272 -1.017 Uninsured .014 2.052 .041 .001 .028 Age 65 up .039 5.403 .000 .025 .053 Employment -.005 -1.594 .112 -.011 .001 Street network .254 .247 .697 6.910 .000 .499 .896 Uninsured .003 .364 .716 -.012 .017 Age 65 up .056 7.315 .000 .041 .071 Employment -.007 -2.241 .026 -.013 -.001 Proximity .201 .254 -.624 -4.842 .000 -.878 -.370 Uninsured -.006 -.842 .400 -.021 .008 Age 65 up .058 7.152 .000 .042 .074 Employment -.008 -2.402 .017 -.014 -.001
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Table 4-32. Summary statistics for three independent variables (multivariate before and after controlling age)
Adjusted R Square
Coefficient (Constant)
Coefficient t Sig. 95.0 Confidence Interval
Collinearity statistics
Lower bound
Upper bound
Tolerance VIF
Mixed-use
.312 .064 -1.103 -10.07
.000 -1.27 -1.02 .452 2.213
Street network
.596 3.24 .016 .958 .234 .270 3.701
Proximity -.641 -3.65 .000 .025 .053 .429 2.333 Mixed-use
.383 .113 -.902 -8.265
.000 -1.16 -.687 .410 2.439
Street network
.430 2.432 .001 .778 .882 .264 3.793
Proximity -.746 -4.45 .000 -1.07 -.417 .424 2.358 Age 65 up
.293 6.061 .000 .029 .057 .820 1.219
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Figure 4-1. Distribution of longitudinal health status and healthcare availability
104
Figure 4-2. Health Disparity and healthcare availability trends
105
Figure 4-3. Health Status and Healthcare availability disparities between 2000s and 2010s
106
Figure 4-4. Box-plots of Health Status Disparities in 2008 and 2013 (Top), and Healthcare Availability Disparities (Bottom) in 2008 and 2016
107
Figure 4-5. Map of four urban form components (density, mixed-use, street network, and proximity) at census block group level. Areas with high
score (darker color) represent higher value
108
Figure 4-6. Map of travel time to the nearest PCP. The natural breaks in the range of values of the
variable were used to identify the accessibility
109
Figure 4-7. Map of standard residuals from OLS regression using urban form components and the travel
time to the nearest PCP
110
Figure 4-8. Histogram of Standardized Residuals of urban form components and travel time to the
nearest PCP
111
Figure 4-9. Map of standard residuals from OLS regression using urban form components and the travel time to the nearest PCP
112
Figure 4-10. Map of mortality rate by cardiovascular disease and diabetes and its spatial patterns that
identifies statistical clusters
113
Figure 4-11. Normal P-P plot to assess for normality of linear regression predicting transition
Figure 4-12. Scatterplot of standardized residuals versus predicted values to assess for homoscedasticity
in the linear regression predicting transition
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CHAPTER 5 DISCUSSION AND CONCLUSION
This chapter presents an overall summary of the research findings pertaining to
the specific aims of the dissertation. This chapter also discusses additional findings and
policy implications. It concludes by acknowledging the limitations of this study and
suggesting directions for future research.
Eliminating health disparities is a top priority in public health research in the US.
For this reason, many research institutes have made eliminating health disparities their
primary short-term goal, as illustrated by Healthy People 2010 and 2020. This
dissertation brings attention to the problem of health disparities in the US and adds to
the previous literature on health and physical inactivity-related health outcomes by
focusing on issues of geographic inequality. A literature review describes health
disparities as being caused not by a single factor, but by multiple individual, social, and
environmental factors. Most health disparity literature relies on simple descriptive
statistics to measure disparities. Furthermore, the regional disparity literature tends to
address much larger geographic areas and to focus on disparities in income and job
opportunities. Many studies have used the inequality index as a measure of not only
income inequalities, but also health disparities. Because of its popularity, efficiency, and
effectiveness in quantifying disparities, this empirical study employed the Gini coefficient
as a measure. The literature review demonstrates that the built environment is
significantly correlated with health outcomes, including physical activity, obesity, dietary
habits, and even health disparities. Because all these elements are interconnected, a
multidisciplinary approach was proposed, and a series of hypotheses was tested to
accomplish the specific aims of this dissertation.
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More research is needed to investigate the various disparities and unequal health
burdens related to the built environment. This dissertation pays particular attention to
the role of the built environment in creating health disparities and suggests an objective
body of evidence for proposing relevant public policies and programs.
Conclusion of Aim One
The empirical investigation of current trends in health disparity magnitudes (aim
one study) showed that all states have experienced a gradual increase in both
subjective and objective measures of health disparities. All but two northeastern states
(Connecticut and Rhode Island) exhibited a decrease in health disparities as measured
by perceived health status, and all but three states (Kentucky, Oregon, and Rhode
Island) showed reduction in health disparities as measured by healthcare availability.
These results indicate a potential relationship between public health policies and health
outcomes, since all four of these states have adopted Affordable Care Act’s (ACA)
Medicaid expansion and Children’s Health Insurance Program (CHIP) (Kaiser Family
Foundation, 2017). The ACA expansion provides publicly financed health insurance
coverage to specific underserved populations (e.g. low-income children, the elderly, and
parents of dependent children). Researchers have found that being covered by
Medicaid—and, thus, receiving more consistent primary care—increases a population’s
self-reported health status (Sommers, Blendon, & Orav, 2016; Wherry, Burns, &
Leininger, 2014). Compared to uninsured adults, Medicaid adults were 25% more likely
to report being in good to excellent health (versus fair to poor health), 40% less likely to
report health declines in the last six months, and 10% more likely to screen negative for
depression. These findings confirm that Medicaid coverage continues to be associated
with increased access to care and healthcare use and improved self-reported health.
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With respect to geographical trends in health disparities, five southern (Kentucky,
Arkansas, Georgia, Alabama, and Louisiana) and five western (California, Utah,
Oregon, Wyoming, and Nevada) states were ranked the top ten in health-related
disparities. With regard to disparities related to healthcare availability in 2016, six
southern states (Texas, Mississippi, West Virginia, Alabama, and Georgia) ranked in the
top ten. GIS maps are used to visually represent these longitudinal trends and the
relative magnitudes in health disparities across the US.
Given the relationships among socioeconomic covariates, health status
disparities are negatively correlated with median household income level and the
percentage of non-Hispanic whites in a population. Health disparities are also positively
associated with the percentage of the population without healthcare coverage and the
percentage of the population that is elderly (older than 65). Disparities in healthcare
availability are negatively correlated with median household income. Several variables,
including the unemployment rate and the percentage of the population that is non-
English-speaking, are not strongly associated with health disparities.
Conclusion of Aim Two and Three
This study’s findings provide evidence supporting the general hypothesis that
urban form settlement (i.e. sprawl or compact urban form) is related to healthcare
access and health outcomes. Methodologically, this aim proposes a refined index that
accounts for macro determinants (density, mixed-use, street network, and proximity)
referred to in Cutsinger & Galster’s (2005), Galster et al.’s (2001), and Song & Knaap’s
(2007) work on indicators of urban form. Multi-dimensional approaches to quantifying
urban form provide a detailed picture of the general characteristics of the built
environment in the Orlando MSA, including the urban, suburban, and rural contexts.
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Additionally, while this study uses a PCA to calculate weights for each component,
these weights can be assessed using other methods reflecting, for example, local
conditions, the importance of various characteristics, or expert opinions. Using a GIS
network analysis to calculate travel time as a measure of accessibility creates a
practical measure of healthcare shortage areas that differs from the current designation,
which relies on a provider-to-population ratio and provides details on regional variations
in accessibility. Furthermore, by applying spatial statistics, this dissertation identifies
geographic clusters with low health outcomes and explores the associations between
these outcomes and the built environment. Based on these findings and contributions,
this dissertation recommends that planners and health policy makers consider changing
urban form patterns to improve primary care accessibility.
The overall findings of the studies for the second and third aims can be
summarized as follows. All urban form components that support sprawl development
were significant for clusters of higher levels of mortality in relation to physical inactivity.
As people moved farther urban centers, they experienced more dispersed development,
greater street network lengths, less agglomerated built environments, and higher
population mortality. This supports the assumption that mortality from cardiovascular
disease and diabetes is positively associated with objectively measured infrastructure.
Furthermore, many socioeconomic and demographic factors that were expected to be
strongly correlated with health outcomes were not significant, particularly compared to
cluster trends of health outcomes. Variables for the uninsured population, the
employment rate, and the elderly population had strong effects on the correlation
analysis for both mortality and cluster patterns of mortality rates, while education
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attainment increased the significance of the mortality rate cluster. However, in the
regression models, only age and other socioeconomic variables were significant.
Furthermore, the goodness of fit of the regression model increased when these
socioeconomic variables were controlled. Overall, these findings confirm the hypothesis
that the role of the built environment, in combination with a population’s SES, strongly
affects health outcomes.
Discussion
As stated in the introduction of this dissertation, although an increasing number
of studies have investigated the relationship between urban form and population health
over the past two decades, no conclusive relationship has been found.
Urban Form
The variance in the findings of existing literature measuring urban components
stems from a combination of several factors, including methodologies, data, and
differences in study populations. The present study attempts to address these gaps by
coordinating several methodologies to measure urban form components developed by
recent studies (Cutsinger, Galster, & Wolman, 2005; Galster et al., 2001; Song &
Knaap, 2007) using disaggregated levels of geographical units in Florida. Some of this
dissertation’s findings are consistent with those of previous studies, as noted below:
Some of the conceptually distinct urban form dimensions, especially density and mixed-use, are highly correlated with one another
Due to high inter-correlations, PCA can be used to group urban form dimensions into fewer factors (i.e. urban form components)
Consistent with Cutsinger et al. (2005), proximity (centrality) was not correlated with other urban form components
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In addition to the major findings corresponding to the specific aims, this research
uncovered several findings relating to population health and its relationship with urban
form components, as shown below:
This study shows a strong correlation between health disparities and socioeconomic variables. Specifically, this study finds that health disparities in terms of both healthcare availability and health status are highly correlated with population income level. This finding supports previous research showing that, compared to their more affluent counterparts, low-income individuals and families experience substantial disparities in healthcare and health outcomes (Lantz et al., 2001; Lasser, Himmelstein, & Woolhandler, 2006; Schillinger et al., 2006)
Different correlations exist between urban form components and healthcare accessibility and between urban form components and health outcomes associated with physical inactivity. The study aims find that the mixed-use and street network dimensions of urban form are significantly negatively and positively correlated with health measures, respectively. This suggests that a more positive built environment agglomeration is related to less inactivity (or more activity) (Ewing et al., 2014; SL Handy & Boarnet, 2002; B. A. McCann & Ewing, 2003)
The Neighborhood District, a locally defined boundary within the Orlando MSA, showed significant clusters in terms of mortality. Average mortality rates between Downtown Orlando and Winter Park were significantly lower than the MSA average. By contrast, the mortality rate in the northwest part of Lake County was higher than the MSA average
Additional Findings- Logistic Regression
In addition to conducting linear regression models, this dissertation expanded the
methodological approach by conducting a binary logistic regression. Specifically, a
binary logistic regression model was used to validate the relationship between urban
form and health outcomes and to confirm the statistical differences between hot and
cold spots in health outcome clusters. Unlike OLS regressions, logistic regressions do
not assume a linear relationship between the dependent and independent variables.
Furthermore, the dependent variables do not need to be normally distributed, there is no
assumption of variance homogeneity (in other words, variances do not have to be the
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same within categories), normally distributed error terms are not assumed, and the
independent variables do not have to be interval-based or unbounded (Wright, 1995).
Because the distribution of the cluster pattern of health outcomes was a numeric value,
it was converted into a categorical variable through a grouping into two categories: hot
(z-score from 1.80 to 3.00) and cold (z-score from -2.84 to -1.66). Of 328 census tracts,
176 were categorized as either hot or cold following the spatial autocorrelation.
Likewise, the purpose of aim three’s binary regression was to determine which urban
form component variables predict a likelihood of high or low cluster patterns of mortality
in the Orlando MSA. In line with this, the following null hypothesis was proposed: Urban
form variables—that is, density, mixed-use, street network, and proximity—will not
significantly predict the likelihood of high or low mortality. A significance level of .05 was
specified.
Likewise the linear regression result, the findings of the logistic regression
indicate that mix-use and street network significantly predicted the likelihood of having
trends of mortality rate (p-value= 0.00 and 0.026, respectively) (Table 5-3). This result is
consistent with the findings of Frank, Andresen, and Schmid (2004), who used a logistic
regression analysis to identify correlations between urban form and travel patterns and
obesity. This research used urban form variables, including land use mix, connectivity,
and residential density, and travel pattern variables, including walking distance and time
spent in a car. Findings from this model showed that land use mixes, time spent in a
car, and walking distance were significantly correlated with obesity.
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Policy Intervention
One of the aims of this dissertation is to provide policy suggestions for improving
health, reducing obesity, and alleviating health disparities. Based on the findings of this
study, several policy implications can be suggested.
First, health disparities should be incorporated into local and national health
policies as a leading health indicator. According to Healthy People 2020, there are ten
leading health indicators for promoting health and for which the publication outlines
trends, current status, and future goals. This research used perceived health status and
healthcare availability as health indicators and showed that health disparities have
clearly increased over the last ten years. Health disparities should be added as a
measure of these ten indicators, and appropriate regulations for reducing disparity
levels should be established. Moreover, equity is an important issue in economics,
sociology, public health, and urban planning. More rigorous surveillance systems are
needed to better understand the spatial and longitudinal patterns of disparities and to
develop short- and long-term strategies to reduce health disparities. In sum, current
efforts to reduce obesity should incorporate parallel strategies to reduce disparities.
Second, federal-level efforts seem necessary to control the significantly high
prevalence of health disparities in the southern states. The aim one findings clearly
show that the highest prevalence of health disparities is in the South. Regional and
federal government actions should focus on building customized strategies to more
effectively control the prevalence of health disparities. These strategies should consider
these states’ specific socio-cultural and environmental conditions. Governments can
earmark subsidies and investments to control geographical differences in health
disparities. Further, collaborations among different governmental agencies and
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departments within local jurisdictions are important for reincorporating the health
agenda into the urban planning policy decision process.
Third, policies for balancing between urban areas (e.g. downtown districts) and
surrounding areas may be effective in reducing health disparities. According to the
regression results, the distance to the urban core, as a factor of proximity, is one of the
most significant factors affecting physical inactivity-related mortality. As the distance
from the urban core increases, hot spots and lower health outcomes become more
clustered. Major causes of differences in health disparities include differences in the
built environment and socioeconomic factors relating to the main findings of this
dissertation. Therefore, policies that facilitate investments in areas with poor
infrastructures, as well as tools to systematically assess existing infrastructure quality,
can help reduce disparities in the long term.
Fourth, as this and other studies have found, land use is significantly associated
with health disparities. Therefore, land use policies should begin considering public
health implications more seriously. Further, current land use policies, such as zoning,
are too general to effectively address public health goals. This dissertation finds that
higher density, mixed-use, and proximity are negatively correlated and that a longer
street network is positively correlated with health disparities. Therefore, land use
policies should specify land use types in much greater detail to help promote healthier
and more equitable environments. Both regulatory and incentive-based strategies are
needed to promote a healthy mix of land uses in urban neighborhoods. For example,
governments could reduce or waive development impact fees for land uses that
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promote healthy lifestyles and reduce disparities, while charging additional health
impact fees for other types of land uses.
Fifth, this dissertation research highlights the need to connect urban planning
practices and public health fields. In this context, two potentially relevant population
frameworks are transportation planning and health impact assessment (HIA). As the
Metro Planning Organization (MPO) for the greater Orlando, MetroPlan Orlando
integrates the region’s long-term plans to create healthy and livable communities. While
these are too numerous to cover here in great depth, they address the link between
planning and health primarily through safety, accessibility, physical activity, and air
quality. Moreover, the aim three results show that elements of transportation
infrastructure, such as street length and the number of signs and intersections, are
significantly correlated with health outcomes. This means that both non-motorized and
motorized transportation policies should respond to the need to reduce health
disparities. Though land use planning is also central to all of these aspects, it is
primarily a means to achieve the objectives stated above. HIA is another framework that
aims to create synergies between urban planning and public health. Firmly embedded in
the socioecological model of public health, it is simultaneously holistic and reductionist.
The World Health Organization defines HIA as a “practical approach used to judge the
potential health effects of a policy, program or project on a population, particularly on
vulnerable or disadvantaged groups(National Research Council, 2011)”. Whereas
MetroPlan conducted an HIA for State Road 50, which passes Downtown Orlando en
route to the City of Oveido in Seminole County, the framework developed in this
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dissertation offers a more comprehensive and quantitative model for the practice of
public health and transportation planning.
Limitation and Future Research
This study opens several opportunities for future research. First, other methods
for measuring health disparity can be considered. Many studies have used the Gini
coefficient as a measure of regional income inequalities and health disparities.
However, although the Gini coefficient is the most popular measure of disparity, it does
not consider socioeconomic dimensions. In addition to the Gini coefficient, therefore,
future studies could use concentration coefficients to measure health disparities.
Second, future research could study the correlation between health disparities
and economic development. Literature in regional science has identified an inverted-U
pattern between regional income disparities and economic development and an
augmented inverted-U pattern at the end of the inverted-U curve (Amos, 1988;
Williamson, 1965). These empirical findings can be applied in health disparity research
to identify whether inverted-U and/or augmented inverted-U patterns exist between
health disparities and economic development. These results could suggest relevant
policies for reducing health disparities by varying economic policies, such as the
distribution of resources and investments.
Third, other methods for post-hoc test can be used to confirm health disparity
differences occurred between census groups. Although this dissertation used Tukey’s
test because the model had different number of dependent variables, Klockars,
Hancock, & McAweeney (1995) have discussed many of the post hoc ANOVA
procedures that appear to advantages over traditional approaches (e.g. the Tuckey test
currently available in statistical software packages). The various post-hoc test methods
125
differ in their ability to properly control the overall significance level and in their relative
power (e.g. Duncan’s test does not control the overall significance level level). Below
are commonly used post-hoc tests that could be considered for further investigation
(Klockars et al., 1995):
BONFERRONI. Extremely general and simple, but often not powerful
TUKEY’S. Best for all-possible pairwise comparisons when sample sizes are unequal or confidence intervals are needed
DUNNETT’S. Appropriate for comparing one sample to each of the others. But not comparing the others to each other
SCHEFFE’S. Appropriate for unplanned contrasts among sets of means
Fourth, this research can be extended to multilevel analysis. To simultaneously
examine associations among variables measured from two different spatial units, future
research could employ a Hierarchical Linear Model (HLM) identifying the group-level
built environmental correlates of health disparities, obesity, and health status while
controlling for demographic and social environmental (individual-level) variables. These
results could offer strong insights into environmental variables that may promote good
health and reduce health disparities.
Fifth, there may be causal relations among the dependent variables. There are
sequential relations among objectively measured, subjectively measured, and
behaviorally built environmental variables correlated with health conditions. However,
common quantitative methods used in this dissertation, such as correlation tests,
ANOVA tests, and multiple regression models, cannot detect causality in cross-
sectional data. To build a causal model, future research may consider using Structural
Equation Modeling (SEM) with longitudinal data.
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Lastly, it is necessary to consider more social and cultural factors in diverse
communities. This research was examined Orlando and its surrounding areas; thus, the
study was limited to a population dominated by white individuals. Future studies could
explore more diverse rural environments and communities with higher percentages of
minorities and low-income groups.
127
Table 5-1. Logistic regression model for step 0, 1, and 2: classification
Predicted Observed Cluster-cold Cluster- hot Percentage correct
Step0 Step1 Step2 Step0 Step1 Step2 Step0 Step1 Step2 Cluster- cold 156 152 153 0 4 3 100.0 97.4 98.1 Cluster- hot 20 4 3 0 16 17 0 80.0 85.0 Overall percentage
88.6 95.5 96.6
Table 5-2. Logistic regression model (step 0): variables not in equation
Variable Score df Sig.
Mixed-use 58.971 1 .008 Street network .057 1 .000 Proximity 7.132 1 .811 Overall statistics 60.584 3 .000
Table 5-3. Logistic regression model (step 1): Hosmer and Lemeshow test
Variable Score df Sig.
Mixed-use 58.971 1 .008 Street network .057 1 .000 Proximity 7.132 1 .811 Overall statistics 60.584 3 .000
Table 5-4. Logistic regression model (step 1): variables in the equation
Variable B S.E. Wald df Sig. Exp (B)
95% Confidence Interval for Mean
Lower Bound
Upper Bound
Mixed-use -4.635 3924 25.163 1 .000 .010 .002 .059 Street network 2.875 1.293 4.940 1 .026 .056 .004 .712 Proximity -1.913 1.208 2.507 1 .113 .148 .014 1.577 Nagelkerke R Square .672 Hosmer and Lemeshow test Chi-square 7.259 df 8 Sig. .509
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BIOGRAPHICAL SKETCH
Sulhee Yoon received her Master of Art in Urban and Regional Planning from
University of Florida in 2011. During her master’s, she was actively involved in research
projects, specifically in interdisciplinary projects between urban planning and public
health. These research experiences led her to pursue a fulltime job as a transportation
planner in Jacksonville Transportation Authority (JTA). Sulhee re-joined Department of
Urban and Regional Planning at University of Florida in 2013 for pursuing her PhD
degree, where she also pursued her minor degree at the Department of Health Service
Research, Management, and Policy. She received rigorous training in the area of the
intervention of built environment into population’s health condition, accessibility to built
environment as well as transportation and land use affordability. Beyond coursework,
she served as a graduate research and teaching assistant for four years. Her research
field is focused on accessibility to built environments, social determinants of health, and
their intervention to identify health and healthcare disparities. This is exemplified by her
dissertation work “Exploring the Relationship between Urban Form and Health
Outcomes”. Sulhee also has begun a career as a data analyst/scientist at GeoAdaptive
in Boston from 2016.
