ACCESS TO PRIMARY HEALTH CARE:
DOES NEIGHBOURHOOD OF RESIDENCE MATTER?
By
Laura Bissonnette
A thesis submitted in conformity with the requirements
for the degree of Master of Arts
Graduate Department of Geography
University of Toronto
© Copyright by Laura Bissonnette (2009)
Abstract
Access to primary health care: Does neighbourhood of residence matter?
For the degree of Master of Arts, 2009
Graduate Department of Geography
University of Toronto
Access to primary health care is an important determinant of health.
Within current research there has been limited examination of neighbourhood
level variations in access to care, despite knowledge that local contexts shape
health. The objective of this research is to examine neighbourhood-level access
to primary health care in the city of Mississauga, Ontario. Street address
locations of primary care physicians were obtained from the College of
Physicians and Surgeons of Ontario (CPSO) website and analyzed using
geographic information systems (GIS). A 'Three Step Floating Catchment Area'
(3SFCA) method was derived and used to measure multiple dimensions of
access for the population as a whole, for specific linguistic groups and for recent
immigrants. This research identifies significant neighbourhood-level variations in
access to care for each dimension of access and population subgroup studied.
The research findings contribute to a more nuanced understanding of
neighbourhood-level variability in access to health care.
ii
Acknowledgements
This research project has been highly collaborative in nature and there are
a number of individuals involved who I would like to acknowledge. I would
foremost like to express my thanks and gratitude to my graduate supervisor,
Kathi Wilson for providing the opportunity to work on this project as well as
providing a wonderful learning experience through her continual guidance and
feedback. I would like to express my thanks to Scott Bell, the principal
investigator (PI) on this project for providing direction and insight throughout the
research process. Thank you to Sarah Wakefield for serving on my thesis
committee. I appreciate the opportunity to work with you and to learn from you.
Thanks to the Canadian Institutes of Health Research (CIHR) for funding this
project. Additional acknowledgements are required to the individuals who have
provided support and help with the technical side of this research. As a new
student to geographic information systems, this help was very much appreciated.
Thanks to Andrew Nicholson and Tanya Kenesky of the library at the University
of Toronto at Mississauga for the provision of data and technical support. Thank
you to Usman Aslam and Alex Werenka at the University of Saskatchewan for
the time and effort put forth towards creating the physician database. A final
thank you is owed to my family, and especially to Mark. Thank you for your
support and your patience.
iii
Table of Contents
Chapter 1: Introduction………………………………………………………......1
1.1 Research Context and Research Question ……………………….1
1.2 Outline………………………………………………………………....8
Chapter 2: Literature Review…………………………………………………...10
2.1 Introduction…………………………………………………………...10
2.2 Neighbourhood Level Analysis of Health Data…………………...10
2.2.1 Conceptual Definitions of Neighbourhoods……………………..11
2.2.2 Operational Definitions of Neighbourhoods………………….....12
2.2.3 Neighbourhoods and Health……………………………………...16
2.3 Access to Health Care…………………………………………....….21
2.3.1 Components of ‘Access’…………………………………….....….22
2.3.2 Conceptualization of Potential Access……...................…........25
2.3.3 Measuring Potential Access………………………………….......26
2.4 Conclusion…………………………………………………………....48
Chapter 3: Data & Methods……………………………………………………..53
3.1 Introduction…………………………………………………………...53
3.2 Research Context……………………………………………………54
3.3 Data Collection……………………………………………………....56
3.4 Data Analysis………………………………………………………...59
3.4.1 Stage 1: Raw Distribution of Primary Care………………….….60
3.4.2 Stage 2: Potential Spatial Access to Care……………………...60
3.4.3 Stage 3: Cumulative Index of Accessibility………………….….65
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3.4.4 Stage 4: Aspatial Dimensions of Access to Care...………...…67
Chapter 4: Results……………………………………………...……………….70
4.1 Introduction…………………………………………...……………..70
4.2 Description of Mississauga’s Primary Care………..…………….70
4.3 Spatial Accessibility to Primary Care………………..…………....72
4.3.1 Driving Access (3Km) to Primary Care……………..………..…73
4.3.2 Walking Access (800m) to Primary Care……………..………...77
4.4 Cumulative Index of Potential Accessibility……………...............80
4.5 Aspatial Dimensions of Access to Care..............………..…….....83
4.5.1 Language-Specific Access to Primary Care……………..…..…83
4.5.2 Access to Primary Care for Recent Immigrants…………..…....90
Chapter 5: Discussion……………………………………………………….......92
5.1 Summary of Key Findings…………………….………………….....92
5.1.1 Spatial Access to Primary Care………………………………......92
5.1.2 Aspatial Dimensions of Access to Care...................……….......94
5.2 Research Contributions………………………………………..........96
5.2.1 Neighbourhood-Level Access to Health Care………….…….....96
5.2.2 Development of the 3SFCA Method…………………………......98
5.2.3 Aspatial Dimensions of Access to Care .............………….......101
5.3 Research Limitations………………………………………..…...…103
5.4 Recommendations for Future Research……………………..…..105
5.5 Policy Recommendations……………………………………….....107
5.5.1 Municipal Policy Intervention...................................................107
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5.5.2 Other Sources of Primary Care: Development of LHINs.........110
5.5.3 Constraints of Urban Form in Policy Intervention....................111
5.6 Conclusions………………………………………….……………...113
References……………………………………………………………………....114
Appendices………………………………………………………………….......127
Appendix A: Neighbourhood Demographics…………………………….......127
Appendix B: Raw Physician Data……………………………………….….....130
Appendix C: Access Ratios…………………………………………………....131
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1
Chapter 1: Introduction
1.1 Research context and research questions
There is an increasing awareness in Canada that access to primary health
care is a problem in need of attention (Crooks & Andrews, 2005: 47; Schuurman
et al, 2006). In particular there is concern that access to primary care is
decreasing and waiting times to see physicians are increasing. This has resulted
in decreasing satisfaction with the health care system amongst the Canadian
public (Sanmartin et al, 2000). Contributing to this problem is a reduction in
overall physician numbers over the past decade in Canada. Peaking in 1993,
physician numbers have steadily dropped by 5% since. Reasons for this
possible physician shortage include federal funding cuts to the provinces, cuts in
the enrollment numbers for medical school, an increase in specialist training at
the expense of family doctor training, and a reduction in the number of foreign
doctors entering Canada to practice medicine (Wharry & Sibbald, 2002). Given
that approximately 4.1 million Canadians do not have a regular family doctor
(Nabalamba & Millar, 2007), there are concerns that access to primary health
care is an increasing problem. With fewer medical students choosing family
practice, disparities in access to care are likely to increase in time as the existing
group of family physicians ages and begin to retire. This is particularly
problematic, given that the amount of primary care provision is directly
associated with public health outcomes, including the prevalence of cancer, heart
disease, stroke, infant mortality, low birth weight, life expectancy, and self-rated
health (Macinko, Starfield & Shi, 2007).
The Canada Health Act (CHA) acknowledges the extreme importance of
access to health care, and as such, mandates that all Canadians are entitled to
receive access to medically necessary services, without barriers (Library of
Parliament, 2003). However, the meaning of access is not clearly defined within
the act. According to the CHA’s “Accessibility” criterion, individuals are entitled to
have access to services, “where and as available” (Library of Parliament, 2003).
While this alludes to a physical or spatial component of access, it does not
specify the means by how physical accessibility should be measured or obtained
(Wilson & Rosenberg, 2004; Eyles, Birch & Newbold, 1995). Furthermore, it has
been argued that despite the goal of equalizing access to health services, the
CHA has failed to remediate health inequalities stemming from geographical and
other disparities in access to care (Wilson, Jerrett & Eyles, 2001; Mhatre &
Deber, 1992).
Physical access to health care has been demonstrated to act as an
important determinant of the use of health services and resulting health
outcomes. This is both a widely documented research phenomenon and an
intuitive understanding. There is a considerable body of empirical research
demonstrating that individuals are more likely to report satisfaction with services
(Young, Dobson & Byles, 2001) and utilize services when they are closer (e.g.
Arcury et al, 2005; Pierce, Williamson & Kruse, 1998). This includes accident
and emergency care (Parker & Campbell, 1998), and general practitioner
2
consultation (Haynes, Lovett & Sunnenberg, 2003; Salisbury, 1989). Proximity to
health care services acts as a significant determinant of primary health care use
(Field & Briggs, 2001; Salisbury, 1989). Additional determinants of use include
the quality of care and range of services offered (Salisbury, 1989).
Poor geographical access to health care may result in an increase in
adverse health outcomes (Gulliford, Figueroa-Munoz & Morgan, 2003: 8). For
example, decreased use of primary health care has been shown to result in
increased morbidity and mortality rates from heart disease and stroke (Starfield,
Shi & Macinko, 2005), and an increase in hospitalization rates (Saxena et al,
2006; Ryan et al, 2001). Poor geographical access to hospitals has also been
associated with delayed diagnoses of terminal illnesses (Silverstein et al, 2002)
and increase rates of mortality from asthma and cardiac infarction (Joseph &
Phillips, 1984).
Measuring access to health care is a complex task, as 'access' itself is
multidimensional and is a function of multiple interrelated elements. While there
is some consensus in the literature that access refers to the ability for an
individual to receive care when it is needed (e.g. Wellstood, Wilson & Eyles,
2006; Eyles et al, 1995; Evans, 1992), there is far less agreement on how to
measure access, and on what constitutes 'acceptable' access. It is clear,
however, that access may be viewed in spatial terms, such as whether services
are equally distributed, or in aspatial terms, such as whether services are equally
available to individuals regardless of age, culture, language or gender (Apparicio,
Abdelmajid, Riva & Shearmur, 2008). Such dimensions of access have been
3
further conceptualized in frameworks, such as that developed by Aday and
Anderson in 1974, and further modified in 1983 (Anderson et al, 1983). These
frameworks have greatly aided in the operationalization of access in the
literature. According to Aday and Anderson, access can be thought of as either
potential or realized. Measures of potential access consider barriers and
facilitators of entry into the health care system, while measures of realized
access describe the actual use of care. Potential access determines whether an
individual will be able to enter the health care system if needed and is the most
elementary and essential measure of access possible.
Within the literature, the study of potential access to health care is not
new. However, the traditional focus has been to measure disparities in potential
access to health care between rural and urban settings (Guagliardo, 2004;
Health-Canada, 1999). Fewer studies have focused on intra-urban and local
level variation in access to health care (Guagliardo et al, 2004). However, recent
research within health geography has begun to shift attention to the
neighbourhood as a site of service provision (Apparicio et al, 2008; Law et al,
2005). This shift is occurring, in part, due to the realization that access to
services should be considered at the scale that individuals identify with during
their daily activities and the scale that city planning functions on (Talen, 2003).
Current research indicates that neighbourhood social and physical contexts are
important in shaping individual health outcomes (Law et al, 2005; Sampson,
2003; Macintyre, Ellaway & Cummings, 2002; Diez-Roux, 2001). Despite this,
4
access to health care is one neighbourhood characteristic that has yet to be
deeply explored in the literature.
The limited empirical research on neighbourhood-level access to health
care can be partly attributed to inadequate working definitions of neighbourhoods
as well as to challenges related to appropriate methodology and available data.
With the emergence of geographic information systems (GIS) and rapid increase
in available digital geographic data, there is potential to provide new insight into
local level variations in access to care. As a result, there has been a recent flurry
of activity in the literature to develop new means to measure access to care but
no consensus on which is best. Thus, there is a need to review current methods
used to examine local-level variations in access to health care, and choose an
appropriate methodology for this task. There is also a need to better understand
how access to health care differs at local scales. This research intends to further
these aims by examining neighbourhood-level potential access to primary health
care in the City of Mississauga, Ontario.
The sprawling suburban city of Mississauga is an appropriate and
pertinent geographical location in which to examine access to health care
services. Mississauga originally formed in 1974 as an amalgamation of existing
towns (City of Mississauga, 2009). Following this, the city has experienced rapid
population growth and is now Canada’s sixth largest municipality (City of
Mississauga, 2009). As a suburb of the Greater Toronto Area (GTA),
Mississauga is also one of Ontario’s municipalities most affected by urban sprawl
(Abelsohn et al, 2005). The city of Mississauga is characterized by segregated
5
zoning and land-use types that are connected by wide and fast moving roads.
These characteristics are typical of urban sprawl (Frumkin, 2002), and in
Mississauga are a result of two key factors. First, given the city’s close proximity
to Toronto, Mississauga has expanded rapidly in a disconnected and
leapfrogging pattern typical of suburban development. Additionally, urban sprawl
is magnified by the fact that the city formed from existing towns and development
has been highly constrained by existing zoning and land use. These
characteristics of Mississauga may prove problematic for residents in need of
accessing services. Suburban design is generally associated with longer travel
distances to reach services, and additionally with lacking sidewalks and
pedestrian pathways (Giles-Corti, 2006). As a result, it is possible that the
design of this city may pose specific problems for individuals when attempting to
access health care. The choice of this sprawling suburban city as the geographic
context for this analysis may yield interesting and insightful results.
A focus on primary health care is imperative for this research given that
primary care encompasses many of the medically necessary services that
according to the CHA, all Canadians are entitled to receive (Library of
Parliament, 2003). Primary care can be defined as “essential health care based
on practical, scientifically sound and socially acceptable methods and technology
made universally accessible to individuals and families in the community” (WHO,
1978). In Canada, primary health care is a holistic approach to providing
services that address all elements that may impact health and well-being (Health-
Canada, 2006). Primary health care encompasses four essential aspects of
6
health care: first contact access for health care needs, long-term focused care,
comprehensive care for general health care needs, and the coordination of care
to specialists when necessary (Starfield, Shi & Macinko, 2005). A focus on
access to primary health care will greatly enhance understanding of the extent to
which inequalities in neighbourhood-level access to medically necessary services
exist in the City of Mississauga. This research is guided by the following
research question:
• Does potential access to primary health care differ at the neighbourhood-
level in the city of Mississauga, Ontario?
In addition to answering this question, the research will address the following
objectives:
1. To evaluate current methodology used to measure potential access
and devise an appropriate methodology to be used in this specific
Canadian setting.
2. To identify neighbourhood-level disparities in potential access to
primary health care in Mississauga, Ontario.
3. To explore a more nuanced and comprehensive understanding of
potential access to care including spatial and aspatial dimensions.
7
1.2 Outline
This research will be described in five chapters. The second chapter
reviews the pertinent literature on neighbourhood level access to care. This
chapter begins with a brief introduction. The next two sections cover specific
topics in access to health care. The first focuses on neighbourhoods as a setting
to study access to health care while the second section examines specific
research methods used to measure potential access to care. This chapter
concludes by identifying gaps in the existing body of literature that this research
will aim to address and fill.
The third chapter of this thesis discusses the research methodology. This
section begins by discussing the geographical context of the research. This
section discusses the data collection and development of a new method to
measure potential access. It describes the multiple spatial and aspatial
measures of access that are examined to develop a more nuanced
understanding of access to care. It also discusses the amalgamation of several
spatial measures of access into a cumulative index of accessibility.
The fourth chapter presents the results of the data analysis. It begins by
providing a description of the distribution of primary care provision in the city.
The second section identifies neighbourhood-level variability in access to health
care for all dimensions of access considered and displays a cumulative index of
accessibility that combines these measures. It then discusses several aspatial
measures of potential access.
8
The final chapter of this thesis reviews the findings of this research, and
considers the importance of this research in the context of the existing literature
on neighbourhood-level access to health care. It discusses limitations with this
project, and further describes important areas of future research that could build
upon these findings. It finishes by discussing potential policy implications of
these research findings.
9
Chapter 2: Literature Review
2.1 Introduction This chapter provides a conceptual review of the existing literature
pertaining to neighbourhood-level studies of access to health care. This review
has two main sections. The first section focuses on literature relevant to
neighbourhoods as the unit of analysis for the study of access to health care.
The second section of this review discusses the concept of access to care,
including how it is conceptualized and measured. This chapter concludes by
providing an overview of the limitations in the current body of literature,
identifying gaps in knowledge that this research attempts to fill.
2.2 Neighbourhood-Level Analysis of Health Data
Within recent decades, the focus of empirical and theoretical
developments in health research has begun to turn away from the individual
causation of sickness and health towards the environmental and structural
causes of health and disease (Macintyre et al, 2002; Diez-Roux, 1998). The has
resulted in an increasing awareness that local spaces have the potential to
influence health (Macintyre et al, 2002; Kearns, 1993) by shaping behaviours of
local residents (Lund, 2003; Ellen, Mijanovich & K-N Dillman, 2001) and differing
in the availability of resources. The result is that local areas have become an
increasing target of research analysis and policy intervention (Wilson et al, 2004;
Ellen, Mijanovich & K-N Dillman, 2001). Within this body of locally focused
research, neighbourhoods are emerging as a prominent focus (Pearce, Witten &
10
Bartie, 2006). The following section of this chapter describes how
neighbourhoods are conceptualized and operationalized in the literature, and
provides evidence on how neighbourhoods are able to shape the health of those
who live in them, warranting attention as the unit of analysis for health research.
2.2.1 Conceptual Definitions of Neighbourhoods
Individuals in urban settings reside in neighbourhoods. However, there is
little conceptual agreement on what actually constitutes a neighbourhood. Within
the literature focusing on neighbourhoods and health, neighbourhoods are
commonly defined ecologically. By this conceptualization, neighbourhoods are
viewed in physical terms as geographical areas enclosed in borders (Bernard et
al, 2007; Galster, 2001). Physical neighbourhoods have the ability to influence
health based on characteristics of the built environment, such as the quality of
housing, and the presence or lack of essential resources (Bernard et al, 2007).
Neighbourhoods may also be considered as spaces of social interaction. The
focus of this definition is on the individuals within areas and the social
interactions and networks that produce and consume space. By this definition,
the focus on physical neighbourhoods is greatly reduced (Galster, 2001). By this
viewpoint, it is often the social relations that occur within a neighbourhood that
can positively or negatively influence health outcomes (Bernard et al, 2007; Diez
Roux, 2001). These viewpoints, in turn, affect the way that neighbourhoods are
operationalized and examined in research. Examples include research into the
effects of health care provision on neighbourhood-level health outcomes
11
(Macinko et al, 2007) or how the socioeconomic context of neighbourhoods may
affect health (e.g. see Pickett & Pearl, 2001).
2.2.2 Operational Definition of Neighbourhoods
A limiting factor to the progress of neighbourhood-level health research
has been a difficulty in delineating neighbourhood boundaries (Lebel, Pampalon
& Villeneuve, 2007). Traditionally, the majority of health research has drawn
neighbourhood boundaries according to statistical (census) units, a choice that
stems from the availability of data for these units. In the UK, neighbourhood
boundaries typically correspond with electoral ward boundaries, while in the US
and Canada they are generally the boundaries of census units, such as block
groups (US) or tracts (Canada and the US) (Flowerdew et al, 2008). Such
neighbourhoods correspond with formal regions that are defined externally for
official purposes. Most usually, formal regions are based on a uniformity of
characteristics (e.g. uniform populations in census tracts). However, uniformity
may or may not be a desired neighbourhood trait (Lebel et al, 2007). Other, less
common empirical representations of neighbourhoods are based on defining
functional regions. These delineations tend to draw neighbourhood boundaries
according to the desired physical and social characteristics that are thought to
comprise local networks and activity space. For example, a neighbourhood may
involve a residential area, necessary services and amenities and public
transportation (Lebel et al, 2007). These functional neighbourhoods may be
based on residents' perceptions or based on historical settlements (e.g. villages
12
or towns) that predated the amalgamation of the municipality. While the choice
of unit should logically be reflective of the purpose of the research at hand (Diez
Roux, 2001), it is usually the presence of available data for statistical units that
causes their popularity as the choice of neighbourhoods (Flowerdew et al, 2008).
This reliance on available data is problematic. Statistical units, in many cases,
may not be an adequate choice for neighbourhoods because they are not of the
appropriate scale or boundary location for the occurrence of health related
processes, and do not make an appropriate choice for the study of health related
phenomena.
The terms natural and meaningful are used to describe neighbourhood
boundaries that are recognized by neighbourhood residents or in the case where
there are no recognized neighbourhoods, the areas that best represent the local-
level activity spaces of individuals (Ross, Tremblay & Graham, 2004). Such
neighbourhoods can be delineated in a number of ways. One method is to
examine the material and social infrastructure within a city and create
neighbourhoods that include the desired attributes. For example, a
neighbourhood may be viewed as having a key core area of shopping or
residential zoning and a transitional area that may be difficult to assign to a
neighbourhood (Flowerdew et al, 2008; Luginaah et al, 2001). An alternative
way to empirically define neighbourhoods is based on the desired mix of
residential and commercial zoning or desired population demographics (Ross et
al, 2004). The problem with the delineation of natural neighbourhoods based on
their composition is that some neighbourhoods may be characterized by a
13
particular demographic while others are defined by housing type. In addition,
some neighbourhoods may be defined based on homogeneity in population or
housing characteristics (e.g. see Flowerdew et al, 2008) while other
neighbourhoods, such as those in gentrification would be better characterized by
heterogeneity in the built and social environments. It is clear that the
determination of neighbourhoods for empirical purposes is problematic, and any
definition may be challenged (Ross et al, 2004).
The delineation of neighbourhoods is much less challenging in situations
when municipalities recognize existing neighbourhood boundaries for planning
purposes. In such cases, neighbourhood boundaries are locally defined and
based on a variety of locally relevant factors. In cases where neighbourhoods
formed from pre-existing communities prior to municipal amalgamation,
individuals may recognize those historical boundaries defining their home
community (Ross et al, 2004). The use of city planning boundaries as
neighbourhoods may be appropriate if they provide the unit of action for policy
interventions and additionally are scale-appropriate for the processes that affect
health.
A consideration of a phenomenon termed the modifiable areal unit
problem (MAUP) (see Openshaw, 1983) can be used to describe why scale and
boundary choice are of considerable importance in neighbourhood level
research. The MAUP describes the differences in empirical results that may
occur based on the choice of units used for analysis (Haynes et al, 2007). The
MAUP has two aspects. The first is termed the zonation effect, and describes
14
how empirical results are dependent upon where area boundaries are drawn. A
shift in the location of boundaries can easily cause results to change between
positive and negative in terms of health outcomes or service availability,
depending on whether boundaries include or exclude data. The second aspect
of the MAUP is termed the scale effect, and describes the change in empirical
results that may occur based on the level of aggregation of data, which in turn
depends on the scale of analysis (Flowerdew et al, 2008). A change in scale or
zonation has been demonstrated to result in significantly different empirical
outcomes (e.g. Apparicio et al, 2008), illustrating why the delineation of
neighbourhoods should be carefully undertaken. It has been argued that
although formal neighbourhood units such as census tracts offer readily available
data, they may not be at the right scale or zonation to accurately reflect or
measure health related process and outcomes (Flowerdew et al, 2008).
Although the delineation of natural neighbourhoods may mediate some of
the scale and zonation related problems associated with using administrative
areas as boundaries, there is still a possibility that the view of place in research is
too conventional given the changing way that local spaces are used by
individuals. It has been argued that today’s neighbourhoods are not the small,
close-knit communities of decades previous, but rather are larger and less easily
defined activity spaces (Cummings, Curtis, Diez-Roux & Macintyre, 2007). It is
becoming clear that individuals do not access all resources and social relations
within the rigid confines of the immediate proximity of their residences, and thus
neighbourhoods should not remain conceptualized this way (Sampson, 2001).
15
Rather, Cummings et al (2007) suggest considering relational geographies which
view space as nodes of resources in networks of travel, as places of dynamic
interaction, and boundaries as being permeable to movement. Although a
transition to such progressive views of neighbourhood has been slow in empirical
literature, there has been some progress. Newer methods in access to health
care involving GIS have accomplished facilitative advancements towards this
goal. For example, new GIS methods based on buffering analysis are now able
to accommodate for the potential movement of individuals across
neighbourhoods to access services such as health care (e.g. see Wang & Luo,
2005).
2.2.3 Neighbourhoods and Health
As of late, researchers, policy makers and individuals are becoming
increasingly aware that neighbourhoods have the ability to positively or
negatively affect the health of those who live in them (Kawachi & Berkman, 2003;
Diez-Roux, 2001). A number of health outcomes are influenced by
neighbourhood characteristics. Health outcomes include but are not limited to
low birth weight, infant mortality, perceived health (Wainwright & Surtees, 2003;
Ellaway, Macintyre & Kearns, 2001), heart disease (Diez-Roux et al, 1998) and
adult mortality (Sampson, 2003; Yen & Kaplan, 1998; Sloggett & Joshi, 1994).
Neighbourhood contexts may also influence health related behaviours, such as
smoking (Frohlich, Potvin, Chabot & Corin, 2002), consumption of alcohol,
16
dietary choices (Ecob & Macintyre, 2000) and personal safety choices (Diehr et
al, 1993).
What are the contextual elements of neighbourhoods that affect the health
of those who live in them? Neighbourhood-level social characteristics shown to
act as determinants of health outcomes include the socioecononomic context
and specifically levels of disadvantage (see Pickett & Pearl, 2001 for review), as
well as neighbourhood crime rates (Ellaway et al, 2001). These neighbourhood-
level characteristics act as determinants of health outcomes even when
individual characteristics are controlled for (Luginaah et al, 2001). The physical
infrastructure of neighbourhoods also has the ability to directly impact health
outcomes of neighbourhood residents (Witten, Exeter & Field, 2003).
Neighbourhoods can be conceptualized as sites of resource provision where the
presence of beneficial resources has the ability to positively influence health
(Flowerdew et al, 2008). These resources include parks for recreation and
activity, public transportation to travel to necessary services and amenities and
the availability of health services for use when needed (Witten et al, 2003).
However, it is clear when examining neighbourhoods that they do differ in the
abundance and quality of such resources. Not all neighbourhoods will have
sufficient availability of food services, social organizations, or green space.
Access to health care is one neighbourhood contextual element that has
the ability to directly impact health. There is some evidence from research that
differential access to care may result in reduced utilization of the health care
system (Hiscock, Pearce, Blakely & Witten, 2008; Haynes, 2003: 28), and
17
increased area-based inequities in health status (Hiscock et al, 2008; Korda,
Butler, Clements & Kunitz, 2007; Haynes, 2003: 28), indicating that access to
care, utilization of care, and overall health status may be closely related
processes that occur on local scales. Neighbourhood-level access to health care
may positively or negatively influence health in a number of ways. A disparity in
health care resources may itself be a cause of illness in a community if
individuals are unable to receive necessary preventative services. Additionally,
poor access to care can also exacerbate illness in communities where there are
additional neighbourhood attributes that may predispose a community to need
health care. These predisposing attributes may be contextual, such as the
presence of contaminants or the lack of green space to walk and exercise. They
may also be compositional, including the socioeconomic status of residents, the
age composition of the population, or the presence of crime and disorder.
Because the presence or absence of health care offers the potential to directly
impact emergent health outcomes in a neighbourhood, the neighbourhood itself
becomes a reasonable choice as the unit of analysis to examine health care
accessibility.
Within health geography there is a small and slowly growing body of
literature that examines neighbourhood-level variability in potential access to
health care. Within this body of literature, the majority of research uses statistical
(census) units as proxy for neighbourhoods (e.g. Wang & Luo, 2005; Wang,
2007; Pearce et al, 2006; Guagliardo et al, 2004). Using census tracts as units
of analysis, Wang & Luo (2005) determine that spatial access to primary care
18
physicians is uniformly high within the city of Chicago and decreases towards the
city’s peripheries and surrounding rural areas. Also using census tracts, Wang
(2007) measures access to Chinese speaking physicians for Chinese immigrants
in Toronto, Ontario. This study also determined that spatial access is highest in
central Toronto and decreases towards the city’s periphery. Using census
meshblocks, Pearce et al (2006) measure access to general practitioners and
other health facilities across New Zealand and identify significant variability
between ‘neighbourhoods’. However, because of the large study area, little
attention was given to variability within specific urban areas. Guagliardo et al
(2004) measure access to paediatricians in census block groups across the city
of Washington, DC. They determine that access to paediatricians is greatest in
central and western Washington and decreases towards the city’s periphery.
There appears to be common findings in research examining
neighbourhood-level potential access to care. This commonality is that access to
care is generally higher in neighbourhoods belonging to a core urban area, and
decreases towards municipal peripheries. This trend is similar to previous
findings in literature focusing on urban-rural differences to care that demonstrate
access to be greater in urban areas and lower in rural settings (Gatrell, 2001;
Meade & Earickson, 2000). However, there are two main concerns relating to
the MAUP previously discussed that make the results of the aforementioned
research suspect. The first problem relates to the size of the units analyzed,
while the second relates to choices of neighbourhood boundaries boundaries.
19
It has been demonstrated previously that the results of research on
access to care are highly dependent on the size of units used. While much of the
existing research tends to use census tracts (e.g. Wang, 2007; Wang & Luo,
2005), there is evidence that census tracts are too large and thus not scale
appropriate for the study of accessibility, and consequently will mask variation in
access to care within urban areas (Apparicio et al, 2008). This illustrates the
importance of considering scale in the choice of units of analysis, and a need for
evaluating intra-urban variability in access to care using smaller geographical
units. While the latter two examples discussed previously (Guagliardo et al,
2004; Pearce et al, 2006) used smaller units as neighbourhoods, there remains
question as to whether the boundaries of these units are likely to correspond with
the areas in which local residents choose to access services and amenities. It is
therefore difficult to discern whether the results are accurately measuring
neighbourhood-level access to care. This demonstrates a need for continual
research into intra-urban variability whereby the choice of neighbourhood scale
and boundaries are more carefully considered.
In addition to furthering empirical and theoretical understandings on
neighbourhood-level access to health care, there are highly pragmatic, policy-
relevant reasons for examining potential access to care in locally relevant and
meaningful neighbourhood units. As mentioned, many municipalities recognize
neighbourhood units that are used as the unit of city planning (Kallus & Law-
Yone, 2000). Such neighbourhoods may be the target of renewal projects aimed
at alleviating health inequalities. Current neighbourhood renewal projects include
20
the renewal of the Regent Park neighbourhood in the City of Toronto (TCH,
2009), the renewal of nearly two-dozen neighbourhoods in the state of Victoria,
Australia (SOV, 2007), and nine neighbourhoods in Yorkshire England (GOYH,
2009). Because neighbourhoods are increasingly becoming a policy focus for
implementing changes relevant to public health and well-being, it is logical to use
those very neighbourhoods as the unit of choice for health related analysis. A
less favourable alternative would be to generate empirical data using
administrative units and adapt the results to the neighbourhood units where
planning will occur, although this is often the case.
2.3 Access to Health Care
Access to health care is a complex and multidimensional concept.
Penchansky & Thomas (1981: 128) have defined access as a concept
representing the degree of 'fit' between the clients and the system. Alternative
definitions include spatial components, describing access as pertaining to the
relative ease by which health care can be reached from residential locations (Luo
& Wang, 2003; BTS, 1997; Allen, Liu & Singer, 1993). There is also a general
consensus that access to health care refers to the ability of an individual to
receive care when they need it, regardless of ability to pay (Hanratty, Zhang &
Whitehead, 2007). The following section helps to tease apart the multiple
dimensions of access to care. It discusses the theoretical frameworks that have
described dimensions of access to care and empirical measurements of access
that have been used.
21
2.3.1 Components of ‘Access’
Some definitions of access focus on its spatial components (Knox, 1979).
For example, access may be defined as an individual's ability to travel to care in
a timely and acceptable fashion (Apparicio et al, 2008; Wang, 2007; Talen, 2003;
Luo & Wang, 2003). Such dimensions of access are generally measured in
terms of distance or time to a provider. However, abilities and desires to utilize
health services are also highly influenced by needs, attitudes, beliefs and past
experiences with the health care system (Gulliford et al, 2003: 6). As such,
access to care also has aspatial components that might be social, cultural,
economic or other and that may supersede the distance between one’s
residence and a doctor (Penchansky & Thomas, 1981).
Access to health care becomes a much more complex concept when it is
empirically measured, and as such is difficult to operationalize. It is at this point
that it is necessary to break down 'access' into measurable dimensions. This has
proven difficult in part because there is little consensus as to whether access
refers to the potential to receive care or the actual act of receiving it (Guagliardo,
2004). The following section will cover the theoretical frameworks that discuss
the multiple dimensions of access and have served as guidelines for those
attempting to measure it.
Perhaps the most well known framework describing the dimensions of
access to health care was developed by Anderson in 1968. This framework
divided access into four dimensions: predisposing characteristics of individuals,
22
enabling resources of families, the need of individuals, and the actual use of
health services (Anderson, 1995). However, this framework was criticized
because it focused entirely on characteristics of individuals and families and
failed to consider characteristics of the health care system itself that play a large
role in accessibility. The framework was further modified by Aday and Anderson
in 1974. Under this framework, access was seen as a function of characteristics
of the delivery system itself, characteristics of the population in need, actual
utilization of services, consumer satisfaction, and health policies that affect many
of these aspects in a top-down manner. However, it was unclear whether this
framework was intended to explain health care use or describe dimensions of
access (Anderson, 1995; Mechanic, 1979; Rundhall, 1981), and as such, further
frameworks designed to explicitly describe access were developed.
Penchansky & Thomas (1981) argued that the Aday and Anderson
framework did not effectively address the dimensions of access. They assert
that access can be considered as a composite of five variables: availability,
accessibility, accommodation, affordability, and acceptability. Availability
describes whether the volume of supply is sufficient to meet the volume of
demand. Accessibility describes whether the location of supply is acceptable
relative to the location of demand. Accommodation describes whether the
organizational aspects of the system (such as waiting times) are sufficient to
meet the needs of the clients. Affordability describes the relationship between
service cost and the ability of clients to pay. Acceptability describes whether
both patients and providers are satisfied with the quality of services and the
23
service exchange. One limitation of these five dimensions of access is they are
measured by surveys and cannot be measured using readily available statistical
data. As a result, although this framework has been mentioned in the literature
for offering insight into the dimensions of access (McLaughlin & Wyszewianski,
2002; Joseph & Phillips, 1984), it is not commonly utilized in research that
empirically measures potential access.
The Aday and Anderson framework was further modified by Anderson et
al (1983). This framework has become the standard among the body of research
that refers to and measures access to health care. This version dichotomized
access into potential versus realized. Potential access measures characteristics
of the health system and of the individual that may facilitate or hinder individual
entry into the health care system. In contrast, realized access measures the
actual use of health services (Anderson et al, 1983). Both potential and realized
access are further divided into a spatial component describing the distribution of
resources, and an aspatial component that describes individual characteristics
such as age, gender, social class and income (Luo, 2004; Khan, 1992).
Although the Aday and Anderson (1974) framework remains the standard
reference in the literature on access to health care, several problems with the
framework remain. Foremost, many indicators of realized access such as travel
time and waiting time could also be viewed as measures of potential access,
because they may inhibit initial entry into the system. Furthermore, although it is
not the intention of the Anderson and Aday framework to explicitly support the
empirical measurement of access, this must be clarified before levels of
24
accessibility can be determined. As such, a more thorough review of the
dimensions of access as discussed in the related literature, and furthermore a
discussion of the techniques used to measure access are pertinent for this
research. Because potential access to care – the ability for one to get to care if
needed – is the most fundamental dimension of access, it is the focus of the
remainder of this literature review.
2.3.2 Conceptualization of Potential Access
According to Anderson et al (1983), potential access is defined as
comprising resources that facilitate or hinder individual entry into the health care
system. Within this viewpoint, dimensions of potential access consist of system
characteristics such as physicians-per-population, as well as individual
predisposing, enabling and need characteristics including age, gender and
ethnicity. Under this framework, potential access remains an active topic of
inquiry. Josephs and Phillips (1984) further specify the definition of potential
access as pertaining to system characteristics that affect the variation of health
care across geographical space. In other words, potential access is roughly
synonymous to physical geographical access, and aims to measure access to
health care as governed by the friction of distance. More specifically, Haynes
(2003: 13) describes physical accessibility as a measure of two components: the
location of services relative to the population, and the personal mobility of the
population, such as whether an individual has access to a car or uses public
transit. Khan (1992) further divides potential access into spatial and social
25
components. Spatial access describes geographic distance as a barrier to
accessing health care, whereas aspatial/social dimensions of access focus on
non-geographic barriers or facilitators, such as socioeconomic status, ethnicity,
income, age or gender (Luo & Wang, 2003; Khan, 1992; Joseph & Phillips,
1984). While these social dimensions operate independently of distance, they
themselves may display a spatial pattern of distribution (for example a
concentration of low socioeconomic status - SES).
Based on this conceptual discussion of the dimensions of potential
access, one can begin to tease apart individual measures of access that may be
empirically measured. In particular, potential access can be thought of in spatial
terms, where potential access is governed primarily by distance to health care
services, and distance is moderated by differential mobility constraints of
individuals. Potential access measures can also be thought of in aspatial terms,
where access is governed by individual characteristics such as socioeconomic
status, age, gender, ethnicity, or language capabilities. Each of these
dimensions offers the opportunity to measure potential access to care within an
area, and a number of methodologies to do so have been developed.
2.3.3 Measuring Potential Access
Joseph and Phillips (1984) divide measures of potential access into those
that calculate regional availability to health care versus those that calculate
regional accessibility. While both measures examine levels of health care
supply, they differ in complexity. Regional availability is the least complex of the
26
two. Regional availability measures determine levels of potential access to
health care within the borders of given areas. An example of such a measure
would be a provider-to-population ratio which involves counting only within the
boundaries of the study area, ignoring providers and individuals outside the study
area. As such, regional availability measures provide counts of access that are
mutually exclusive between regions.
An example of a regional availability measure and perhaps the most
commonly used measure of potential access is the ratio of health care providers
to the population within a given area (For example, see Rosenthall, Zaslavsky &
Newhouse, 2005; Brabyn & Barnett, 2004; Kindig & Movassaghi, 1989). A
comparison of these ratios to an 'ideal' standard (such as provincial or national
standards) helps determine whether areas are under- or over-served (Wing &
Reynolds, 1988). There are several variations of this technique, including
individuals-per-physician, physicians-per-person and physician-per-1,000
population. Such provider-to-population ratios have been used to identify health
care shortage areas for decades in Canada (Verhulst, Forrest & McFadden,
2007; Wharry & Sibbald, 2002) and in the United States (Guagliardo et al, 2004;
Wing & Reynolds, 1988). Physicians may be entered as a simple count, or may
be weighted by their practicing time in full time equivalencies (FTE) (Rosenthall
et al, 2005). In Canada, the standard according to the Canadian Institutes of
Health Information (CIHI) is physicians-per-100,000 population (CIHI, 2008: 37).
Actual ratios of this count at provincial and national scale are available from the
Canadian Medical Association (CMA) annually. For example, in 2007 there were
27
95 general practitioners and family physicians combined per 100,000 population
in Canada (CMA, 2009). Provider-to-population ratios are an example of a
regional availability measure because they provide ratios that are mutually
exclusive between regions.
The location quotient (LQ) is an extension of the provider-to-population
ratio. The location quotient determines whether the provider-to-population ratio
for each region is equitable relative to all regions in a study area. The location
quotient is mathematically calculated as follows:
LQ (region a) = Physicians in region a / Population in region a Physicians in all regions / Population in all regions
A location quotient greater than 1.0 indicates that a region is over-served relative
to the study area, while a quotient of less than 1.0 indicates that a region is
under-served relative to the study area (Joseph & Phillips, 1984). Because
location quotients measure mutually exclusive regions, they are measures of
regional availability.
The coefficient of localization (CL) is another extension of the provider-to-
population ratio. The CL is used to determine whether the distribution of
physicians across a region is equitable to the distribution of the population. The
CL is calculated as follows:
CL (region a) = ½ ∑ Physicians in region a – Population in region a Physicians in all regions Population in all regions
28
A coefficient value of 0.0 indicates an equitable distribution of physicians relative
to the population, while values between 0.0 and 1.0 indicate a spatial
concentration of supply relative to demand (Joseph and Phillips, 1984). While
this measure is commonly used to compare GP distribution relative to population
distribution, it can also be used to measure the distribution of any phenomenon
relative to a baseline measure. As such, the distribution of physicians can be
measured relative to other population characteristics using this index. However,
this measure does not identify whether there is an adequate provision of health
care relative to any standard. It is only designed to determine whether the
provision of health care is distributed equitably relative to the population (Joseph,
1995).
Measures of regional availability, and provider-to-population ratios in
particular have been widely employed in policy and research to determine
potential access to health care. However, there are generally three criticisms of
regional availability measures expressed in the literature (Wang & Luo, 2005;
Guagliardo et al, 2004; McLafferty, 2003; Wing & Reynolds, 1988). First, they
assume that supply or demand within one region are not affected by that in
contiguous or surrounding regions. This inherently assumes that regional
borders are impermeable and individuals do not cross them to seek health care.
While this may hold true when the regional boundaries correspond to those of
national, provincial or isolated municipalities, it is less accurate for boundaries
between smaller regions, such as neighbourhoods. The second criticism of
regional availability measures is that each region is given only one measure of
29
access which is constant across the entire region. This ignores any potential
intra-regional variability in access. However, the presence of intra-regional
variation in access is quite likely given that both supply and demand are
generally unevenly distributed. The third criticism of regional availability
measures is that they are extremely sensitive to the choice of geographical
boundaries. Resulting from these flaws, regional accessibility measures are
better suited for smaller regions such as neighbourhoods or non-isolated
municipalities (Joseph & Phillips, 1984).
In contrast to availability measures, regional accessibility measures are
often more complex and time consuming (Joseph & Phillips, 1984). However,
they are favored in the literature because they mediate one or more of the main
limitations of availability measures. Specifically, regional accessibility measures
provide counts of access for each region that incorporate the volume of health
care supply and consumer demand that exists in neighboring regions. Such
methods incorporate the concept that individuals are able to cross regional
boundaries to seek care, dealing with the first significant problem of regional
availability measures. In addition, some measures attempt to describe intra-
regional variations in accessibility to create a more sensitive description of
access at smaller scales.
Travel impedance methods measure the distance between a point of
health care supply to a point of population demand. Demand is often
represented by the geographic or population weighted centroid of an area
(Langford & Higgs, 2006). By this measure, longer distances represent
30
decreased potential accessibility. Travel impedance calculations are often used
as regional accessibility measures because they are capable of measuring
potential access beyond the study area boundary. Of travel impedance
measures, straight-line (Euclidean) distances are the simplest to calculate. This
involves calculating distance from the point of an individual health care consumer
to provider, or more commonly, between the centroid of a geographic unit to the
nearest health care provider. The choice of centroid used may either be the
geographical centroid or a population weighted centroid (Lovett, Haynes,
Sunnenberg & Gale, 2002; Haynes, 2003, p. 19).
Euclidean distances have been utilized in numerous studies to reveal
trends in health care access within regions. For example, Charreire & Combier
(2009) measured Euclidean distance from the geographical centroid of census
administrative units in the French district of Seine-Saint-Denis to the nearest
general practitioner. Their analysis revealed that maximum Euclidean distances
ranged from 386m to 1587m in different census units, which could pose a
significant hindrance in access for those walking to services in regions with the
poorest access. Their analysis also demonstrates that distance-based measures
can potentially shed light on whether health services are within walking and
driving distances. The previously mentioned provider-to-population ratios do not
shed light on this matter. However, the use of Euclidean distance as a measure
is very limited, primarily because individuals do not travel over land in straight
lines but use roads and pathways, although travel on foot and perhaps by bike
can be less constrained by the existing street network. In addition, Euclidean
31
distances fail to incorporate the demand for health care, as well as the ease, cost
and time required to travel, or differential access to transportation amongst the
population (McLafferty, 2003).
Because individuals generally do not travel using straight-line ('as the crow
flies') distances, more accurate measures of potential access include Manhattan
distances, road distances and travel time by car and bus. Such measures are
often calculated using GIS. Of these distances, road distances and travel times
are preferred because they best approximate how individuals travel. Manhattan
distances, simulating right angles of a road network, are appealing because they
do not require GIS files of actual road networks. However, recent literature has
shown them to be less accurate than Euclidean distances in approximating
distance along a road network (Apparicio et al, 2008). Road distances and travel
times have become the most frequently used distance measures in the literature
and have been made much easier to use with network analysis tools that are
built into most GIS software packages (Higgs, 2004).
GIS based network analysis has been used in multiple studies to measure
potential access to health care (for example, see Hiscock et al, 2008; Pearce et
al, 2006; Brabyn & Barnett 2004; Lovett et al, 2002). The most common
measure used in the literature is travel time by car. Hiscock et al (2008) use GIS
to explore the relationship between travel time to health care and the use of and
satisfaction with health care services. They measured the travel time along road
networks from the population weighted centroid to the nearest facility for all of
New Zealand (NZ). It was determined that travel times ranged from 2 minutes to
32
15 minutes between NZ regions and that increased travel time was significantly
negatively related to service use. Pearce et al (2006) calculate travel time from
the population weighted centroids of census areas to multiple services and
amenities including health care across NZ. They determined that the mean time
to general practitioners was 6.9 minutes, but varied from 0.04 minutes in the
highest access communities, to 8.38 minutes in the lowest access communities.
Emergency and Ambulance services showed far greater variance in potential
access, with ambulance services ranging from 0.13 to 30.57 minutes, and
emergency services ranging from 0.14 to 27.57 minutes. As a third example of
the use of distance to measure potential access, Lovett et al (2002) calculated
travel times from post codes along road networks to the nearest GP facility in
East Anglia, UK. A comparison of the population within each post code revealed
that 67% of the population was within five minutes to a GP by car.
Although road distances and travel times offer a more realistic measure of
distance compared to calculations of Euclidean distances, they are still flawed as
the sole measure of potential access. The first problem arises in measuring
distance to physicians from the centroid of a region. The centroid is used to
represent the geographic location of all individuals in the region, but in reality
does not. This degree of fit between the centroid of an area and the actual
location of individuals is highly dependent both on the size of the region and on
the distribution of its population. As regions become larger in size, a centroid is
less likely to represent the location where the majority of individuals live
(Apparicio et al, 2008). In addition, in regions where the population is unevenly
33
distributed, the centroid may not represent the actual location of any individuals
within the area. As such, this method is slightly biased to provide more accurate
results when areas are smaller and more uniformly distributed. Additionally, this
method generally does not consider the 'demand' component (Yang, George &
Mullner, 2006; Guagliardo et al, 2004). While provider-to-population ratios
consider whether supply is sufficient for the amount of demand, distance based
measures only consider the location of demand, and not the quantity of it.
Without knowing the relative demand for health care, it can only be determined
whether services are distributed unequally geographically, but not whether they
are distributed inequitably relative to the population served.
The introduction of gravity measures into the calculation of potential
access to health care has served to overcome some of the main flaws of simple
distance calculations by including increased detail about the level of supply and
demand. Gravity measures are based on distance decay concepts which state
that the choice to travel to health care is a tradeoff between the attractiveness of
services and the distance required to get to them. Such a phenomenon occurs
because of the friction of distance - the cost of time, money and effort to travel a
greater distance (Wang, 2007). In the literature, gravity calculations of potential
access are commonly referred to as 'Indices of accessibility'. Joseph & Bantock
(1982), followed by Joseph & Phillips (1984) describe several indices of
accessibility based on such concepts. The most basic gravity measure is
calculated as follows:
34
J Ak = ∑Sj / tkj
-β
j=1
Where Ak = potential access in neighbourhood k Sj = service capacity at location J Tkj = travel impedance (distance or time) between K and J, and -β = a coefficient representing the friction of distance
This measure calculates the potential accessibility at site A, which may be
the centroid of an area of interest. The accessibility of region A is determined by
the size of each facility within the entire study area, moderated by the distance to
each facility. The coefficient -β represents the friction of distance, and must be
determined empirically. The main problem with the above measure is that it only
determines geographic variation in health care supply, but does not determine
whether supply is equitable relative to demand.
Improvements made to this early gravity measure began to incorporate
spatial variation in demand for health care, and have since been widely used in
the literature (e.g. see Rosero-Bixby, 2004). Such measures incorporate three
general characteristics: the size of services, distance to services, and population
demand for them. The general equation of the measure is as follows:
n Ai = ∑ (GPj/Dj
) / dijβ
j=1
Where: Ai = potential accessibility to services in neighbourhood i GPj= general practitioners at location j Dj = potential population demand on a doctor
35
Knox (1979) modified this gravity-based index of accessibility by factoring
in two new measures: a time-based index of accessibility for neighbourhood i
(TAi), and the population potential of neighbourhoods (PP). The former factors
average travel time by car for one mile of travel, weighted by the proportion of
car-owning households in the neighbourhood, and is calculated as follows:
TAi = Ci (Ai/4.25) + (100-Ci) (Ai/16.75)
where: Ai = potential accessibility to services in neighbourhood i (calculated as above), Ci = the percentage of car-owning households in neighbourhood i 4.25 = the average times taken to travel one mile by car, and 16.75 = the average time taken to travel one mile by bus The population potential for neighbourhood i (PPi) is the potential number of
individuals living in a neighbourhood and willing to travel to health care within it.
It is calculated as follows:
PPi = ∑Pij/Dij1.52
where: Pij = population in neighbourhoods i and j Dij = Distance between neighbourhoods i and j Thus, the final index of availability (I) is: Ii = TAi(%) / PPi (%) x 100
Using this equation, Knox determined that accessibility was highest in the
downtown core neighbourhoods of the study area, Aberdeen Scotland, and
dropped sharply to the periphery of the city. In addition, the areas of high
36
accessibility were shown to be primarily in owner occupied, high income
neighbourhoods (Knox, 1979). This equation is an improvement upon previous
versions in that it considers the distribution of the population in need and their
ability to travel to services. It also accounts for the fact that individuals may seek
care outside of their neighbourhood of residence - a common flaw of availability
measures. However, this index remains problematic for several reasons. First,
this measure does not consider the ability or ease with which individuals can
walk to health care. In addition, the equation considers travel time by public
transportation, but does not consider whether such transportation exists within
the area of interest. Thirdly, this method is computationally intensive. It would
be highly difficult to implement when analyzing a large number of study areas,
and would be difficult to automate using GIS.
The use of gravity measures to determine potential access to health care
mediates many problems associated with distance-based measures. Gravity
measures accommodate for the cross-boundary travel that occurs when patients
seek care. In addition, with all but the earliest measures, gravity measures
incorporate the population served into the equation, whereas distance
calculations do not. In addition, gravity measures incorporate the friction of
distance, and offer the potential to account for alternative modes of
transportation, such as access by car and bus (Knox, 1979). As such, they begin
to more accurately approximate access based on how individuals actually do use
health care.
37
The main limitation of gravity measures that has remained through their
development is that the distance decay component (-β) that approximates the
friction of distance is often unknown and difficult to calculate (Guagliardo et al,
2004), particularly for alternative modes of transportation. Even when calculated,
it is an approximation at best. Additionally, gravity measures are often difficult
and time consuming to compute and automate. As such, gravity measures are
being used less frequently used as of late in favour of more recent GIS based
methods
The measurement of potential access to health care has a long history in
the literature. However, there has remained a gap in knowledge about how
different geographic barriers, such as the differential travel impedance faced
when driving, walking, or taking public transportation may affect health care
access, utilization and health outcomes (Guagliardo, 2004). GIS offers the
potential to overcome this by providing new methodological insights that are
much less labour intensive (Yang et al, 2006). Recently, increased use of GIS
has enabled the development of new techniques to measure potential access to
health care, as well as offering increased accuracy and sensitivity to existing
methods (Higgs, 2004). Although this review has already touched on several
methods that can be performed using GIS (e.g. distance calculations), the next
section will cover more novel methods to measure potential access to health care
that have been made possible by more specialized GIS analysis techniques.
One example of a more novel GIS method used to measure potential
access to physicians and also falling into the category of regional accessibility
38
measures is the use of surface representations to measure access to health care
services. Surface representations are three-dimensional maps. The third
dimension (height) is generated based on the variable of interest, where larger
quantities of this variable correspond to a greater height in the surface
representation. Surfaces are created in the GIS by representing the study area
as a regular array of pixels/cells in rows and columns. Existing data values are
entered, such as the number of physicians at known locations. The value of
each point is seen as an 'intensity' value, which correlates to its height in a three
dimensions. Values for all other cells in the grid are interpolated based on the
known points. These values can then be viewed as a three-dimensional surface
(Yang et al, 2006). As a result, an estimated measure of 'access' can be
obtained for any location on the map. Surfaces are similar in concept to gravity
measures, because they generate estimates of access that are weighted by both
by the amount of services available, and by distance to them (Guagliardo et al,
2004). However, surfaces of health care alone are rarely used to measure
potential access because they fail to incorporate estimates of the population at
any given location. Typically, surfaces of both health care supply and population
demand are layered over one another. The supply layer is always a surface
interpolation, while the demand layer may either be created by interpolation
techniques using census centroids, or may be based on area-based data to
avoid interpolation. Once the layers overlap, map algebra can be used to
calculate physician-to-population ratios for each grid cell, and averaged for a
39
neighbourhood. This technique combines elements of gravity measures and
traditional physician-to-population ratios (Guagliardo et al, 2004).
One common surface representation used is kernel density (KD)
interpolation. The kernel is a moving window. It counts the frequency of point
events within the window. The centre of the kernel is given an intensity value
based on that count, with point events that are nearer to the centre of the kernel
weighing higher than more distant events. The kernel is moved across a surface
to repeat calculations at evenly spaced reference points or grid cells. Each of
these points then has an associated intensity (z) value that can be mapped in
three-dimensions to create a 3D surface (Gatrell, Bailey, Diggle & Rowlingson,
1996).
Guagliardo et al (2004) use kernel density surfaces to calculate physician-
to-population ratios for Washington, DC. Using ArcGIS, they created one surface
representing a continual spectrum of physician density, and a second to
represent a continual spectrum of population density. Using map algebra in GIS,
physician to population ratios were calculated for each grid cell, and averaged for
each census tract. Using this method, physicians-per-100,000 ratios ranged
from 1 to greater than 70 per 100,000 over the study area.
Teach et al (2006) used surface methods to calculate the ratio of
pediatricians to youth under 18 years of age in Washington DC. They attribute
youth (<18 years) population to the geographic centroid of census blocks, and
apply a Gaussian smoothing of one mile outwards of these points. They then
attribute provider full time equivalent measures to the point of pediatric provider
40
service supply and smooth these values outwards to three miles - a distance
which represents acceptable distance to health care. They then calculate the
physician-to-population ratio at specific locations where survey respondents
reside using the smoothed supply and demand layers. Using this technique,
ratios ranged from 0 to greater than 90 per 100,000 over the study area.
Additional findings showed that African American youth had lower accessibility to
pediatric services using this measure, and that decreased access resulted in
decreased use of pediatric services (Teach et al, 2006).
Burns & Inglis (2007) demonstrate surface analysis techniques using
ArcGIS accessibility analyst to measure access to food outlets in Casey,
Australia. Although this study does not fit with the theme of examining access to
health care, it is worth mentioning because it is an innovative extension of
surface methodologies. The authors created three 'cost surfaces' for the city
using surface smoothing methods, representing travel time by car, by bus, and
by walking. This was accomplished by using known road locations and driving
speeds for the first surface, known bus routs and frequency of bus travel at each
location for the second surface, and inputting walking speed (3Km/hr) and terrain
slope for the third. The output produced continual shaded maps displaying the
ease of access in travel time at any given point. The results of this study
revealed that accessibility by all measures was greatest in the centre of the city,
and decreased significantly towards the periphery.
Surface representations offer several improvements to the determination
of potential access as compared to previous methods. As with gravity measures,
41
they do account for the fact that individuals can cross borders to seek care, and
as such improve upon distance measures and provider-to-population ratios
(Guagliardo et al, 2004). In addition they offer further improvement upon gravity
measures by providing measures of access that vary within regions. The main
flaw with surface representations is that the smoothing process used in
interpolation has been demonstrated to result in slight inaccuracies.
Inaccuracies occur when the technique is used to interpolate values for health
care supply, but not to interpolate the layer representing demand for care. The
interpolation process smoothes health care supply values onto grid cells that are
outside of the study area. This process of assigning values outside of the study
area decreases the actual count of values within the area, thus underestimating
health care supply. As a result, the level of potential access is seen to be overall
lower than it should be, in particular at the periphery of the study area (Yang et
al, 2006). Resulting from this flaw, there are more favored techniques that use
buffering to determine physician-to-population ratios. These will be discussed
next.
Using GIS, various applications of buffering techniques have also been
used to measure health care provision. These fall into the category of regional
accessibility measures, as they are capable of measuring supply and demand
across local boundaries. One example of a buffering technique is the floating
catchment method (FCM) (see for example, Luo & Wang, 2003; Luo, 2004). This
method requires the placement of a consistently sized buffer (circle) around the
geometric or population weighted centroid of each region in the study area. The
42
radius of the buffer represents a health care catchment, which should represent
the distance that individuals would be willing to travel to access health care. The
physician-to-population ratios for each buffer are then calculated. The physician
portion of the equation is generated from the number of physicians falling within
the catchment. The population portion of the equation is determined by the
population of the neighbourhood unit being buffered. The main improvement of
this method on traditional physician-to-population ratios is that physicians outside
of unit boundaries may be calculated in the ratio if they fall within the buffer. This
helps mediate the 'boundary permeability' problem that exists with regional
availability measures.
Luo (2004) used the FCA method to determine whether census tracts
were under- or over-served relative to the existing American standard from the
Department of Health and Human Services (DHHS) of 1 physician per 3500
persons. As an exploration of the technique, buffer radii of 5, 10, 20, 30, and 35
miles were used. Using a buffer of 5 miles revealed a significant amount of
shortage area within the study area, while increasing buffer size increased the
number of physicians in each catchment, thus reducing the number of areas that
were designated as shortage areas.
While the FCA method improves upon traditional physician-to-population
ratios by accounting for cross-boundary access to care, several limitations
remain. The most significant problem with this method is the subjectivity
inherent in the size of the buffer used, particularly as the results of the analysis
are highly sensitive to this (Higgs, 2004). While the buffer should represent a
43
logical distance that the majority of the population is willing to travel, there is little
consensus in the literature on the 'acceptable distance' to health care. Several
studies in the health geography literature use 5km to represent acceptable
walking distance (Luo, 2004; Higgs, 2004), while other literature both inside the
field of health geography (Lovett et al, 2002) as well as outside the field (Sallis,
Frank, Saelens & Kraft, 2004; Talen, 2003) generally consider 800m (roughly ½
mile) to be the standard for acceptable walking distance. Because the results of
this method are highly sensitive to the size of buffer used (Higgs, 2004), this can
influence the outcome of the research. Additionally, because population-to-
physician ratios have had a long history of use in public policy for health services
planning, it could be very problematic for individuals with reduced mobility if 5km
became the standard for walking distance. Additional problems with this method
exist as well. As with several earlier methods discussed, the FCA method
attributes the population of an area to its centroid. This is problematic because
results will be sensitive to the size of the units of analysis. For example, while a
3 mile buffer may extend beyond the boundary of a small neighbourhood and
thus act as a measure of regional accessibility, the same size buffer may not
extend beyond the boundary of a large neighbourhood, acting as a measure of
regional availability. This demonstrates the importance of insuring uniformity in
study areas analyzed. Another problem with this method is that it assumes that
access to physicians is equal at any location within the catchment area. In
reality, accessibility follows a distance decay curve (Joseph & Phillips, 1984),
whereby willingness to travel decreases linearly with increasing distance. A final
44
problem is the continual possibility that physicians outside the catchment area
might serve the population within it, and conversely that individuals within the
catchment area might travel outside to see a physician.
The FCA method was further improved upon to result in a method termed
the 'two-stage floating catchment area' (2SFCA) method. Originally developed
by Radke & Mu (2000), the 2SFCA method was designed to better account for
the cross-border travel of individuals in seeking health care. The 2SFCA method
uses two sets of catchment area buffers to calculate population-to-physician
ratios. The first set of buffers is placed around each point location of health care
supply. The radius of this buffer represents the desired travel distance or time.
The amount of health care supply along with the population demand falling within
this catchment used to calculate a physician-to-population ratio. A second set of
buffers is then placed around the centroid of each unit of analysis, again
representing the desired travel time or distance. The overall measure of access
for a given region is generated by summing the physician-to-population ratios of
all facilities that fall within the buffer surrounding the unit of analysis (Wang &
Luo, 2005; Higgs, 2004). This number is then stated to be an "accessibility ratio"
for unit in the study area (Yang et al, 2006).
Examples of the 2SFCA method can be found in Bagheri, Benwell & Holt
(2005) and Luo & Wang (2003). Using the 2SFCA method, Bagheri et al (2005)
identify disparities in access to primary care between census meshblocks of
Otago New Zealand. Using thirty minute driving time catchments, access ratios
ranging between 0.37 and 83.3 per 1,000 population were revealed. Luo &
45
Wang (2003) also use a 30 minute driving time catchment in their study of
Chicago, US, and determine accessibility to range between 0.017 and 5.91 per
1,000 population. Because the 2SFCA method improves upon the 'gold-
standard' measure of health care access - the traditional physician-to-population
ratio - it is likely to be become a much more common method used to evaluate
access to health care in the near future.
Using the state of Illinois as their study area, Yang et al (2006)
demonstrate that the 2SFCA method offers improvements to the KD analysis in
the computation of physician-to-population ratios. The KD method tends to
underestimate provider density by smoothing provider values outside of the study
area, and thus under-valuing potential access. The 2SFCA method does not
have this problem. Despite this potential to improve upon existing measures to
calculate physician-to-population ratios, the method remains problematic for
several reasons. One problem is that this method provides a given region with
one single measure of access, failing to take into account intra-regional variation
in physician and population density. In addition, as with the original FCA method,
the results of the 2SFCA are highly sensitive to the size of the unit of analysis.
This occurs because a buffer of a given size may extend beyond the boundaries
of a small unit of analysis and not extend beyond the boundaries of a large unit of
analysis. This could result in higher counts of health care supply or population
demand for smaller units of analysis.
The results of the 2SFCA method are also highly dependent on both the
method used to group population data into catchments, and the distribution of the
46
population within the geographic units used. Regarding the former, the
population within a facilities catchment can be determined based on whether the
centroid of administrative units are within the catchment, or in contrast, whether
the entire administrative unit falls within the catchment. These two methods will
yield different results, with the former yielding larger population estimates. This
choice requires careful consideration. A second, related problem is that when
population numbers are assigned to a catchment area, it is done under the
assumption that the population is evenly distributed within its census tract.
However, population is often distributed in clustered patterns, resulting in
inaccuracies.
To account for several of the major problems with the 2SFCA method,
variations of it have been developed in recent years. Langford and Higgs (2006)
discuss a 'dasymetric' method designed to account for variations in population
distribution within regions. In essence, smaller scale data on populations is used
to determine which locations in the administrative regions are populated and
which are not. The population data of the administrative unit is then distributed
evenly between the specific populated areas, and values of zero are entered for
the non-populated areas. This allows for a more accurate determination of
physician-to-population ratios when administrative data is assigned to facility
catchments. This method demonstrates lower accessibility values than with the
traditional 2SFCA method, indicating that the 2SFCA method may over-estimate
accessibility.
47
2.4 Conclusion
This review of theoretical and methodological investigations into
neighbourhood-level access to health care reveals several gaps in the existing
body of knowledge on the subject that require further investigation. Primarily,
there is only a very small body of research that attempts to measure potential
access to primary care at the neighbourhood level. Within this literature, the
majority of research uses statistical units as proxy for neighbourhoods, and there
is limited use of units that are more socially and politically relevant. As a result, it
is difficult to draw conclusions on local-level variations in access to care when the
results of existing research are shaped by the choice of units used (Apparicio et
al, 2008). There is a need for further investigation into this line of inquiry using
neighbourhoods that are perhaps better suited for an examination of access to
primary care. This research will address this gap in knowledge by examining
access to primary care within the neighbourhoods of Mississauga that are
recognized by city residents and utilized in city planning.
Additionally, the literature reveals that the current state of methods used to
measure health care access in the literature is in rapid transition. Methods to
measure access have been in constant development and evolution following the
inclusion of GIS into this stream of study. There is a need to work with existing
methods, of which the FCA based methods seem the most appropriate, in order
to best adapt them to the context in which they will be used for this analysis. If
done, such a stream of investigation has the potential to offer much-needed
insight into local-level variations in access to health care. This research will
48
address this need by working with the 2SFCA method and better adapt it for use
in the city of Mississauga.
The use of the 2SFCA method in the literature reveals that there is a need
to carefully consider the size of the buffer used. While there is recognition in the
literature that the size of the buffer is intended to represent an acceptable
distance to care, and that the distance that an individual is willing to travel will
vary based on individual characteristics such as age, mobility status, SES (Luo,
2004), there has been little effort in the literature to tailor the buffer size to
represent specific populations or travel abilities. This research will address this
knowledge gap by performing the data analysis using buffer sizes that represent
two mobility groups: those with access to a vehicle, and those without.
Based on the theoretical discussion of access to health care it is clear that
potential access has multiple dimensions. Penchansky & Thomas (1981)
describe five dimensions of access to health care: accessibility, affordability,
acceptability, availability, and accommodation. While all five could possibly be
measured as dimensions of potential access, only the first, accessibility, is
covered in research. Other measures of access that would contribute to the
study of Penchansky & Thomas’s (1981) dimensions of access could include
whether physicians are accepting patients, and the ability to see physicians
without appointments. These measures are important to address because they
may provide a more thorough investigation into potential access, recalling that
potential access aims to measure “characteristics of the individual or system that
may facilitate or inhibit entry into the health care system” (Aday & Anderson,
49
1983). This research will address this gap by exploring two additional
dimensions of access to care: access to physicians accepting patients and
access to walk-in facilities.
Additionally, the literature focusing on potential access to care typically
measures access for the entire population, and does not take into account how
access may differ based on aspatial characteristics such as age, socioeconomic
status, gender or ethnicity. However, there may be substantial subgroups of the
population with differing needs who are not adequately represented by traditional
accessibility calculations. These aspatial components of access according to
Khan (1992) comprise one-half of the concept of potential access and yet are
rarely studied. Access to care for particular population subgroups based on
ethnicity, gender, age and social class remains under-examined (but see Wang
et al, 2008; Wang, 2007; Pearson, 1989 in Rosenberg, 1995 for exceptions).
And yet, these social characteristics of the population can greatly affect ones
choice or ability to access care (McLaughlin & Wyszewianski, 2002).
Research has demonstrated that the ethnicity and language of a health
care provider can act as significant determinants of an individual’s choice in
primary care physicians (Wang, 2007). Yet, language- or ethnicity-specific
measures of potential access are rarely examined in research. Given the use of
potential access counts and particularly the use of population-to-provider ratios in
service provision, the exclusion of these aspatial dimensions of potential access
in the research on access to care is a significant gap in knowledge. The
consideration of such dimensions of access go further than simply measuring
50
spatial distance to physicians, and take steps towards measuring the goodness
of fit between the system and the population served (McLaughlin &
Wyszewianski, 2002). This research will help to fill this gap in the literature by
measuring access to care using provider-to-population ratios for important
population subgroups. This will be accomplished by determining whether
prominent linguistic groups of Mississauga are able to access physicians who
speak their language of mother tongue and whether recent immigrants are able
to access physicians who are accepting new patients.
As this research addresses multiple dimensions of potential access, it will
be of use to incorporate these additional dimensions into an overarching
comprehensive index of accessibility to provide a more holistic picture of local-
level health care accessibility. This is infrequently undertaken in the literature,
and in the course of this review, only one research article was found that did so.
Spitzer et al (1978) created a composite index describing the provision of primary
care for facilities in Ontario, Canada. This index of “accessibility, availability, and
scope of services” weighted and combined components such as the hours
services were available, the scope of services offered, whether services were
within walking and driving distances, and whether services had walk-in
capabilities and after hours. However, the index was assigned to the facilities
themselves, and not the neighbourhoods/regions of study. Such a composite
index, if assigned to the neighbourhood, could improve upon current methods
used to identify health shortage areas. This research will address this need by
51
demonstrating the use of an exploratory method to create a composite index of
potential accessibility.
52
Chapter 3: Data & Methods
3.1 Introduction
The overarching research question for this project is: Does potential
access to primary health care differ at the neighbourhood-level in the City of
Mississauga, Ontario? Additional objectives of this research are three-fold. The
first objective is to evaluate current methodology used to measure potential
access and to devise an appropriate methodology to be used in this specific
Canadian setting. The second objective is to identify neighbourhood-level
disparities in potential access to primary health care in Mississauga, Ontario.
Thirdly, this research explores additional spatial and aspatial dimensions of
access to develop a more nuanced and comprehensive understanding of access
to care in this geographical setting.
This research project employs quantitative methodology based on GIS
techniques to address its research objectives. A GIS analysis was chosen for
this study because it is optimal to address the research question of whether
access to health care differs between neighbourhoods of Mississauga by
allowing for large scale analysis of physician and population data. Within health
geography, GIS and spatial analysis have emerged as prominent tools for the
study of health care delivery, disease distribution, and resource allocation
(Apparicio et al, 2008) and is the natural choice for this study. Additionally, GIS
and spatial analysis have become increasingly popular in local "small area"
studies (Ricketts, 2003). Given this, there is an established precedent for the
use of GIS in this neighbourhood-level analysis.
53
The remainder of this chapter is divided into three sections. The first
section describes the research context, the City of Mississauga, Ontario. The
second section of this chapter describes the data collection techniques. The
third section describes the data analysis techniques that were used to quantify
levels of potential access to primary health care.
3.2 Research Context
This research project takes place in the City of Mississauga, Ontario. The
city of Mississauga is Canada’s sixth most populated municipality with a 2006
Census population of just under 700,000 (Appendix A, Table 1). As can be seen
from Figure 1, Mississauga is located on the west border of Toronto, and resides
on the north end of Lake Ontario. This setting is ideal for an examination of
access to primary health care for several key reasons. Partly resulting from its
proximity to Toronto, Mississauga is one of Canada's fastest growing cities. This
rapid population growth has occurred in a sprawling fashion, with a population
density of roughly 2860 persons/Km2 (Appendix A, Table 1). This is far less than
that of Canada’s other large cities, such as Montreal (4400/Km2), Toronto
(3900/Km2) and Vancouver (5000/Km2) (Statistics Canada, 2009). Despite
potential accessibility concerns that arise in a setting of rapid population growth
and urban sprawl, there has been little investigation into whether essential
services in the city are distributed accordingly to meet the needs of the
population.
54
Mississauga has 35 neighbourhoods that are recognized by the city
(Figure 1), and are used for municipal planning purposes. Originally forming as
smaller communities, these neighbourhoods were later amalgamated into the
municipality of Mississauga. As a result, the neighbourhood boundaries are
partially based on historical communities. They are also partly based on census
boundaries, as well as residents’ perceptions of where neighbourhood
boundaries should lie (McFadyen, D. Personal communication, January 14
2009). These 35 neighbourhoods will constitute the geographical units of study
for this research.
Mississauga’s neighbourhoods are highly variable in size and population
structure (Appendix A, Table 1). They range in size from 1.6 Km2 (Sheridan
Park), to 22.3 Km2 (Northeast 1) and range in population from 676 in the least
populated neighbourhood (Sheridan Park) to 63,577 in the most populated
neighbourhood (East Credit). Population density ranges from a low of 130
individuals per Km2 in the least densely populated neighbourhood (Northeast 1)
to 7205 individuals per Km2 in the most densely populated neighbourhood
(Mississauga Valley). There are three neighbourhoods with no population.
These are Airport, Airport Corporate and Northeast 3. These neighbourhoods
are not considered in the analysis of access to health care because there is no
demand for care within them.
55
Figure 1. The city of Mississauga, displaying its neighbourhoods and the surrounding geography.
3.3 Data Collection
The focus of this research is on primary health care. Primary health care
in Canada is a broad and encompassing approach to maintaining health and
well-being. It is concerned with providing services that address all elements that
may impact health (Health-Canada, 2006). Primary care services can be
generalized as performing two large and essential roles: acting as an individuals’
first point of contact with the health care system and coordinating all elements of
the health care system so that individual health concerns can be resolved as
56
necessary (Health-Canada, 2006). The latter role includes resolving short-term
health conditions, managing chronic conditions and referring patients to specialist
services when needed (Starfield et al, 2005). Primary care practitioners act as a
gateway into the Canadian health care system and as such, they are the most
fundamental component of this system (HCC, 2005).
There are a number of ways in which primary health care may be
received. Primary care may be received by general practitioners (GPs) or family
practitioners (FPs) at a private practice. Additionally primary care may be
received at community health centres, by phone through Telehealth Ontario, in a
walk-in or after hours clinic, by a family health team, in an urgent care centre, or
in an emergency room (MOH, 2009). GPs and FPs are the two primary care
providers who are able to perform all elements of primary care and are the focus
of this analysis. They are collectively referred to as primary care physicians
(PCPs) in this study. The role of PCPs includes taking on patients, treating and
managing illness and referring patients to specialists as needed (Starfield et al,
2005). FPs differ from GPs in that they have received an additional two years of
specialist residency training in family medicine following completion of medical
school. The FP specialization was newly implemented in Ontario in 1993 and is
now mandatory for all new physicians practicing primary care (CPSO, 2008).
The current state of primary care in Canada is cause for concern.
Roughly 1.4 million Canadians do not have a family doctor (Nabalamba & Millar,
2007), and yet the number of general practitioners has decreased over the past
two decades (Wharry & Sibbard, 2002). This indicates that access to primary
57
care may become increasingly problematic in years to come. Resulting from
concerns over access to and provision of primary services there has been a
heightened interest in the literature in examining access to primary care in
Canada, and this line of inquiry has become particularly pertinent as of recent in
health geography (e.g. See Crooks & Andrews, 2008).
Primary care physician (PCP) data was obtained from the physician
search engine on the College of Physicians and Surgeons of Ontario (CPSO)
website (CPSO, 2009). The search engine allows for the retrieval of physician
records based on the type of service provided (e.g. generalist or specialist and
type of specialty) as well as by municipality. The physician search and record
retrieval for this research was conducted in November of 2008 by research
assistants at the University of Saskatchewan. This search selected general
practitioners and physicians with FP specialties for the City of Mississauga. This
retrieved records of all GPs and FPs within the city. Each physician record
retrieved included the following information: name of physician, street address of
practice, whether the physician was accepting patients and languages spoken.
The address locations of physicians are updated as needed (when physicians
change practice locations). The data for whether physicians are accepting
patients is updated annually (CPSO, 2008). Thus, the data provides an annual
snapshot for the year of 2008 for this measure. This data was retrieved and
entered into a Microsoft Access database.
An additional measure of access of interest for this research is access to
walk-in clinics, as these facilities are particularly beneficial for individuals without
58
a family doctor and for individuals who have a difficult time making appointments
during standard hours (Brown et al, 2002). The locations of Mississauga’s walk-
in clinics were obtained from the “Health Care Connect” search engine on the
Ontario Ministry of Health and Long Term Care website (MOH, 2009b). The
search conducted specified the retrieval of all walk-in clinics within the City of
Mississauga. Thirty records were retrieved, and following a telephone call to
each, twenty-six were determined to be currently offering walk-in services and
were entered into a Microsoft Access database. The remaining four clinics were
no longer offering walk-in services and were not included in the Access
database.
A digital geographic boundary file of Mississauga’s neighbourhoods was
obtained from the City of Mississauga. Demographic data for the residents of
Mississauga was obtained from the University of Toronto at Mississauga (UTM)
as a digital geographic file of the 2006 Canadian Census, at the dissemination
area (DA) level. A CanMAP 2006 street file was obtained from UTM for
geocoding purposes.
3.4 Data Analysis
The analysis of physician data was carried out in four distinct stages.
First, physicians were mapped to their street addresses. Second the 2SFCA
method was modified to create a novel method to explore spatial patterns in
access to primary health care. Third a cumulative index of accessibility was
developed which combined multiple spatial dimensions of access. Following this,
59
aspatial dimensions of access to care were examined based on language and
immigrant status. All spatial analysis was conducted in ArcGIS 9.2.
3.4.1 Stage 1: Raw Distribution of Primary Care
The first stage of the data analysis involved the visualization of
Mississauga’s primary care physicians (PCPs). Mississauga’s PCPs were
geocoded to their street addresses and visualized as point symbols. This point
file was then used to create a secondary file by aggregating records at the same
street location. These locations represent “clinics” at which more than one PCP
delivers services. PCPs accepting patients were visualized as a subset of all
PCPs. Mississauga’s walk-in clinics were geocoded to their street addresses
and visualized as point symbols.
3.4.2: Stage 2: Potential Spatial Access to Care
In the second stage of the analysis, levels of potential access to PCPs in
Mississauga’s neighbourhoods were calculated. In the literature, potential
access can be measured in a number of ways as explored in Chapter 2. The
most common method is physician-to-population ratios. However, this method
falls into the category of regional availability measures because it fails to
consider health care demand and supply across neighbourhood boundaries. The
two-step floating catchment area method (2SFCA) as previously mentioned
calculates provider to population ratios that are counts of regional accessibility
60
and do consider cross-boundary travel of individuals seeking care. As a result, it
is a more accurate method to measure access to care in intra-urban settings.
As was discussed in Chapter 2, the 2SFCA has two main flaws that render
it unsuitable for this analysis. First, the results of the 2SFCA are highly sensitive
to differences in the sizes of units of analysis because a buffer that extends
beyond the boundaries of a small neighbourhood may not do so when placed on
a large neighbourhood. Because of the large differences in the size of
Mississauga’s neighbourhoods (see Figure 1), the use of one buffer size around
neighbourhood centroids could be problematic. Secondly, the 2SFCA is suited
for use on census units for which data is available, and has not been used on
meaningful neighbourhoods such as those of Mississauga. As a result, it was of
interest to adapt this method for use in Mississauga’s neighbourhoods. This was
done by adding a third step onto the existing 2SFCA method. The method
developed for this research is named here the ‘Three-Step Floating Catchment
Area (3SFCA)’ method. The 3SFCA calculates access ratios for each
neighbourhood, expressed as physicians-per-1,000 population.
In the literature, the physician to population ratios are often expressed as
physicians-per-100,000, physicians-per-10,000 or physicians-per-1,000
population. Ratios in this research are calculated as physicians-per-1,000
because 1,000 individuals may roughly represent the size of each physician’s
practice, given that Mississauga has 677 physicians and a population of roughly
670,000 individuals. Additional research in Ontario has determined an average
61
family physician roster size of roughly 1500 patients per physician (Anderson et
al, 2006). As a result, the ratio given in physicians-per-1,000 is suitable.
Step 1 Step 2 Step 3
2 3 2
1
3.5 3
4
Figure 2. Schematic of the Three-Step Floating Catchment Area Method
The three steps of the method are conducted as follows (see figure 2):
Step 1: Physician-to-population ratios are assigned to each point of health
care supply, represented by a red circle in Figure 2. To do this, catchment areas
are created around each PCP facility location. Catchment areas are based on
road network distances and are represented by the rough circle around the point
of supply in Figure 2. Physician-to-population ratios are then calculated by
counting the number of physicians at a given facility, and the population of all DA
centroids that fall within the catchment. The ratio is expressed as the number of
physicians per 1,000 population. In Figure 2, the ratio given to this facility is 2
physicians-per-1,000 population.
62
Step 2: Points of health care demand, represented by the blue circle in
Figure 2 are then assigned physician-to-population ratios. DA centroids are used
as the point of demand in this analysis. Ratios are assigned to DA centroids by
first creating catchment areas around them, also based on road network
distances. The ratios of all facilities falling within these DA catchments are
summed for a given DA. In Figure 2, the facility ratios of 2 and 1 are summed for
a DA ratio of 3-per-1,000.
Step 3: Neighbourhoods, represented by the purple box in Figure 2 are
then assigned an overall physician-to-population ratio. This is done by averaging
the ratios of all DAs that have centroids falling within a given neighbourhood. It is
this third step of averaging that sets this method apart from the 2SFCA and
causes this method to be less susceptible to variation in the size of
neighbourhood units. In Figure 2, the average of 3 and 4 is 3.5 physicians-per-
1,000.
The 3SFCA technique is based on placing a buffer around points of health
care supply and points of demand and so it requires choosing a radius for that
buffer that represents a desirable distance to health care. Inherent in this
technique is that individuals falling within that distance have access to care, and
individuals who fall outside of that distance do not. As such, the distance for the
catchment requires consideration. One limitation of current research identified in
Chapter 2 is that while there is acknowledgement in the literature that not
everyone will have the same idea of what an ‘acceptable’ distance to health care
is, there is little attempt to carry this through in methods used to measure access.
63
This research attempts to fill this gap by examining two distances, one that may
suffice for those with vehicle access, and a shorter distance for those who do not
have access to a vehicle. In the literature, acceptable distances to health care
range from 1 to 30 miles (1.6 – 40 Km). This research uses a distance of 3Km to
represent driving access to health care, which is similar in size to some of
Mississauga’s larger neighbourhoods, and therefore appropriate for a local
analysis of access to health care in this setting. A distance of 800m was chosen
to represent walking distance to care because 800m (or roughly ½ mile) is often
chosen in the literature as an acceptable distance to walk to local services and
amenities (Sallis et al, 2004; Lovett et al, 2002).
One limitation of the existing literature identified in chapter two is that
measures of access to care rarely consider different dimensions of primary care
such as whether physicians are actually accepting patients. This research will
help to fill this gap in knowledge by demonstrating how a more nuanced
understanding of access can be obtained using readily available data from the
CPSO and MOH websites. The three spatial measures of access considered in
this research are:
• access to all physicians
• access to physicians who are accepting patients
• access to walk-in clinics
While the first measure of access is most commonly examined in the literature,
there has been a lack of examination of the latter two. Given that the latter two
measures help to shed light on whether physicians are actually taking on new
64
patients or whether individuals can access care without appointment, they will
greatly enhance the examination of access to primary care conducted in this
research.
3.4.3 Stage 3: Cumulative Index of Accessibility
In the literature, cumulative indices of access are rarely used. Research
examining multiple measures of access rarely combines them to provide a
comprehensive picture of access for local areas. However, the creation of an
index that combines the multiple dimensions of access explored in this research
is of interest here so that neighbourhoods that repeatedly score high or low on
measures of access can be identified to target future research and potential
policy intervention. One cumulative index of accessibility was created in this
research. The index combines the three measures of access at 800m and the
three measures of access at 3Km. Recall that these three measures are:
• access to all physicians
• access to physicians accepting patients
• access to walk-in clinics.
Because there is little precedent in the literature for the creation of a
cumulative index, this particular index is highly exploratory in nature. However, it
is loosely based on research presented by Richardson et al. (2009) at the 13th
annual Medical Geography Symposium in Hamilton, Ontario. Here, researchers
organized neighbourhoods from low to high based on the presence of particular
health influencing attributes (such as the presence of parks, or access to
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necessary amenities), scored them from -1 to +1 based on rank, and added
scores for the final index. Such a method of ranking, scoring, and adding scores
is examined here in the creation of a cumulative index of access to care.
The 32 neighbourhoods of consideration were classified into quartiles
(also used in Pearce et al, 2006) based on lowest to highest ranking for each
measure of access. The quartiles were given scores ranging on a negative to
positive scale in increments of one. A negative to positive score range was
chosen so that the end index allowed neighbourhoods to be easily dichotomized
into those that have ‘poor’ access versus those that have ‘good’ access by the
particular measures of access examined here. Thus, the 8 lowest access
neighbourhoods are given a score of -2, the next highest 8 neighbourhoods given
a score of -1, the following 8 highest a score of +1, and the eight highest access
neighbourhoods a score of +2. This is done for each of the three measure of
access at each distance, providing six scores, each with a range of -2 to +2. The
six scores were added (See Table 1 for summary), resulting in scores that
ranged between +12 (highest access), and -12 (lowest access). The choices to
provide scores in increments of one and to tally the final score in an additive
format (rather than by averaging) were made so that the final scores would be
integers (rather than fractions) for simplicity.
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Table 1. Method of Calculating the Index of Accessibility
Index of Accessibility Measure of Access Score Range (3 Km) Score Range (800m)
• Access to all physicians
-2 +2 -2 +2
• Access to physicians accepting patients
-2 +2 -2 +2
• Access to walk in clinics
-2 +2 -2 +2
Total Index Range -12 +12
At this point it is pertinent to provide a caveat as to the intended use of
these indices. From the index scores it is not possible to characterize a
neighbourhood as having either 'good' or 'poor' access to care by all measures
and conceptualizations of access. However, the index does allow for the
assessment of neighbourhoods that consistently score high or low on the
particular measures of access examined in this study. This is of interest in
targeting future research and potential policy intervention to neighbourhoods that
repeatedly display disparities in the dimensions of potential access examined in
this research.
3.4.4 Stage 4: Aspatial Dimensions of Access to Care
There is limited research available that uses more innovative GIS
techniques such as FCA based methods to measure some of the aspatial
dimensions of access discussed in Khan (1992). For example, access to care
based on gender, age, ethnicity or language ability is rarely considered in GIS
analysis. However, this line of inquiry is particularly relevant for this research,
given the diversity of Mississauga’s population. Roughly one-half (44%) of
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Mississauga’s population speaks a first-language other than English, and 10% of
the population has immigrated to Canada within the last five years (Appendix A,
Table 2). Considering this level of diversity and potentially differential health care
needs, this research extends the use of the 3SFCA method to examine access to
care for two subgroups of the population in Mississauga:
• those whose mother tongue is not English
• recent immigrants to Canada
The definition of mother tongue given by Statistics Canada is: the first
language learned in the home, and still spoken at the time of the census
(Statistics Canada, 2005). According to Statistics Canada, this group of
respondents includes those who claim to speak a single non-official language
and those who speak multiple languages, with a mother tongue that is non-
official. Included in the latter category are individuals who are capable of
speaking English and/or French, but have a non-official mother tongue (Statistics
Canada, 2007). This population subgroup is examined in recognition that
language may act as significant aspatial barrier in potential access to care.
Recent literature has found that many individuals prefer to seek care in their
(non-English) mother tongue (Wang, 2007; Asanin & Wilson, 2008).
Access ratios were calculated for six of the most prominent mother
tongues spoken in Mississauga. These languages are Urdu (4.6% of the
municipal population), Polish (4.4%), Punjabi (3.6%), Tagalog (2.7%), and Arabic
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(2.6)1. French (1.2%) was also examined for a comparison of official and non-
official minority languages (data adapted from the 2006 Census). The ratios
calculate providers speaking a language of interest – per – 1,000 persons
speaking that language. Only considered in this ratio are the physicians who
state the ability to speak a given language on the CPSO website and the
individuals who reported that given language as their mother tongue in the
census. All other physicians and individuals are not. See Appendix A, Table 3
for neighbourhood and municipal level counts of each mother tongue.
The definition of recent immigrant given by Statistics Canada refers to
individuals who have arrived in the country in the past five years (2001-2005).
Recent literature has revealed that finding a family physician that is taking on
new patients can be extremely difficult for recent immigrants to Mississauga
(Asanin & Wilson, 2008). It was therefore of interest to determine whether this
measure of accessibility displays spatial disparities. Ratios of access to
physicians accepting patients for recent immigrants were calculated by counting
only the physicians who listed that they are accepting patients on the CPSO
website and only individuals who claim to be recent immigrants in the 2006
Census.
1 The Chinese (Cantonese and Mandarin) and Portuguese language groups are also both prominent in Mississauga but were not examined in this analysis.
69
Chapter 4: Results
4.1 Introduction
In presenting the results of the analysis, this chapter has three sections.
The first section explores descriptive statistics of the city’s primary care
physicians, focusing on the distribution of primary care across the city at the
neighbourhood level. The second section discusses results of the 3SFCA
analysis on the three measures of spatial accessibility explored: access to all
physicians, access to physicians accepting patients, and access to walk-in
clinics. It finishes with an index of spatial accessibility. The third section
discusses the results of the aspatial measures of access: language-specific
access to care and access to care for recent immigrants.
4.2 Description of Mississauga’s Primary Care
Data obtained from the CPSO website reveals that the City of Mississauga
has 677 PCPs2,(Figure 3, Appendix B, Table 1) . The number of physicians
practicing at each street location ranges from 1 to 43. The majority of PCPs
appear to be clustered in the centre of Mississauga, with fewer located in the
extreme north and south ends of the city. The degree of clustering is extremely
high in several neighbourhoods. For example, 46% of Mississauga’s physicians
2 There are actually only 600 PCPs in Mississauga, rather than 677. 531 PCPs practice at one location within the city, while 61 PCPs practice at two locations and 8 PCPs practice at three locations. This resulted in the 600 PCPs being displayed by a total of 677 points. Given that it would be error-prone to infer upon how practitioners may divide their time between multiple practicing locations, each location was considered once, despite that a practitioner may only spend a fraction of their time at that given location.
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(309 of 677) are located in only four neighbourhoods (Meadowvale, Central Erin
Mills, Applewood & Cooksville).
Figure 3. Distribution of primary care physicians (PCPs) in Mississauga.
Approximately 17 percent (117) of PCPs in Mississauga are accepting
new patients (Figure 4, Appendix B, Table 1). The number of physicians
accepting patients by neighbourhood ranges from a low of 0 (Sheridan Park,
Southdown) to a high of 25 (Cooksville). There are a total of 26 walk-in clinics in
the city of Mississauga (Figure 4, Appendix A, Table 2) with the total number per
neighbourhood ranging from 0 to 4. Eighteen neighbourhoods have no walk-in
71
clinics. The neighbourhood with the most clinics is Cooksville, located in the
eastern corner of Mississauga (see Figure 1), with four walk-in clinics.
Figure 4. Distribution of Physicians Accepting New Patients and Walk-in Clinics in Mississauga.
4.3 Spatial Accessibility to Primary Care
The following two sections discuss the results of stage 2 of the analysis:
the measurement of spatial accessibility to primary care at 3Km (driving distance)
and 800m (walking distance). The results are presented in map form where
accessibility is displayed in an access ratio, as physicians-per-1,000 population.
72
While it is not possible to state whether the level of provision is sufficient (or
acceptable), it is possible to compare access ratios to those that exist at multiple
scales. The municipal average is 0.88 physicians-per-1,000 population (based
on the 2008 data obtained here). Provincial and federal levels for the year 2007
were 0.85 and 0.98-per-1,000 respectively (CMA, 2009). These can be roughly
used as comparison to access ratios calculated in this analysis.
4.3.1 Driving Access (3Km) to Primary Care
The Three Step floating catchment area (3SFCA) method was adapted for
this research based on an existing two-step method (2SFCA). The method uses
road-network buffers to represent catchments around points of supply and
demand. The distance used in this research to represent acceptable driving
distance is 3Km along a road network.
The spatial access ratios based on driving distance (3km) that result from
the 3SFCA analysis are displayed in Figures 5 through 7 and in Appendix C,
Table 1. Access ratios to all physicians are displayed in Figure 5. Figure 6
displays access to physicians accepting patients and Figure 7 displays access to
walk-in clinics. These figures display accessibility ratios as graded shading,
where darker shading indicates a higher accessibility score and lighter shading a
lower accessibility score. The ratios are displayed in quartiles, which results in
an equal number of neighbourhoods falling into each of the four quartiles.
The population of each neighbourhood is displayed by graduated points
overlaying the shaded accessibility ratio of each neighbourhood. Larger points
73
indicate a larger neighbourhood-level population and smaller points a smaller
population. This was done to contrast accessibility levels by the number of
individuals (and hence amount of potential need) in a neighbourhood. While the
accessibility ratio itself does consider neighbourhood population, the raw number
of individuals facing poor accessibility remains of interest for the purposes of
targeting future research and policy intervention where need is greatest.
The total number of PCPs-per-1,000 population at 3Km is displayed in
Figure 5. The access ratio ranges from a low of 0.00-per-1,000 in Northeast 2 to
a high of 2.385-per-1,000 in Cooksville (Appendix C, Table 1). In general, the
higher access neighbourhoods are concentrated in a south-west to south-east
band around the bottom half of the city, with the exception of Malton, the
Northern-most neighbourhood which is also in this group. A visual examination
of the data reveals several neighbourhoods with low levels of access and large
populations. Hurontario and East Credit, in particular, stand out in this respect.
74
Figure 5. Physicians-Per-1,000 Population Using 3Km Catchments.
Driving-distance access ratios for PCPs accepting patients-per-1,000
population are displayed in Figure 6. The ratios range from a low of 0.00
(Northeast 2, Sheridan Park, Southdown) to a high of 0.732 (Malton). The spatial
pattern of access for this ratio is clearly distinct than for the total PCP-per-1k ratio
discussed previously. In this case, the highest access neighbourhoods are
clustered in the central/east area of the city. One exception, Malton, is a high
access neighbourhood in the north end of the city. There are a greater number
of low access neighbourhoods with high populations than with the previous
measure. These neighbourhoods of interest include Hurontario, East Credit,
Lisgar, Meadowvale, Erin Mills and Clarkson-Lorne Park.
75
Figure 6. Physicians Accepting New Patients-Per-1,000 Population Using 3Km Catchments.
Driving-distance access ratios for walk-in clinics-per-1,000 population are
displayed in Figure 7. Access ratios range from a low of 0.00 (Northeast 23,
Northeast 1, and Sheridan Park) to a high of 0.078 (Cooksville). The municipal
average is 0.032 per 1,000. The highest access neighbourhoods by this
measure tend to fall in a SW-SE band along the bottom half of the city, similar to
that seen in Figure 5. Neighbourhoods where low access corresponds with a
large population include Malton, Hurontario, East Credit and Clarkson-Lorne
Park.
3 It is noteworthy that Northeast 2 in the northern-most end of Mississauga (see Figure 1) has access ratios of 0.00 for all three measures of access examined thus far. It is of interest as being of complete inaccessibility to primary care based on a driving distance of 3Km.
76
Figure 7. Access to Walk-in Clinics-Per-1,000 Population Using 3Km Catchments.
4.3.2 Walking Access to Health care
The 3SFCA technique was also used to determine levels of walking
access to primary health care based on a walking distance of 800m (see Figures
8-10 and Appendix C, Table 2). The 800m distance was based on distance
along a road network. While it is recognized that not all roads will have
sidewalks and therefore be traversable by pedestrians, there was no digital file
available that had information on the presence of sidewalks. As a result, the
road network file was used as a proxy for sidewalks.
77
Walking access to all PCPs per 1,000 individuals is displayed in Figure 8.
The ratio of access to all physicians ranges from a low of 0.000 to a high of 2.678
per 1,000. The high access neighbourhoods fall in the central and south areas of
the city, particularly falling along the SW and SE border. This band of high
walking accessibility to all physicians follows the same pattern as that observed
for 3Km, indicating that walking and driving access to all physicians are similar.
Neighbourhoods of low access and high population are Hurontario, Rathwood,
Malton, Lisgar and Churchill Meadows.
Figure 8. Physicians-per-1,000 Population Using 800 m Catchments.
Walking access to PCPs accepting patients is displayed in Figure 9. The
access ratio ranges from a low of 0.000 to a high of 0.457 per 1,000. The high
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access neighbourhoods by this measure show a similar spatial distribution as did
the same measure at 3Km (Figure 6). Primarily, the neighbourhoods of highest
access are clustered in the east end of the city. However, the degree of
clustering is slightly less for the measure of walking as it was for driving. Two
neighbourhoods in the east of the city (Dixie and Mississauga Valley) were of
high access to PCPs accepting patients by driving distance, but no longer fall into
the highest access category by walking. These neighbourhoods would therefore
have a greater level of accessibility by driving than by walking. Neighbourhoods
of low access and high population include Hurontario, Malton, Lisgar, Churchill
Meadows, Central Erin Mills and Erin Mills.
Figure 9. Physicians Accepting New Patients-Per-1,000 Population Using 800 m Catchments.
79
Walking access to walk-in clinics is displayed in Figure 10. Access levels
range from 0.000 to 1.679 per 1,000 (Appendix C, Table 2). High accessibility
neighbourhoods by this measure are very dispersed as compared to previously
examined measures of access. Areas where access is low and population is
high include Malton, Meadowvale, Lisgar, Churchill Meadows and Central Erin
Mills.
Figure 10. Walk in Clinics -per-1,000 Population Using 800 m Catchments.
4.4 Cumulative Index of Potential Accessibility
The index of potential accessibility combines each of the three measures
of potential access at both distances. Thus, six total measures are combined in
an additive format. Because the total score for each measure ranges from -2 to
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+2 based on quartile ranking, the total neighbourhood score may range from -12
to +12 in the index.
The neighbourhood-level index of accessibility is displayed in Figure 11.
There are fourteen neighbourhoods with a positive index score, four
neighbourhoods with a neutral score of 0, and twelve neighbourhoods with a
negative score. The highest access neighbourhood is Cooksville with a score of
12, followed by Applewood, with a score of 10, and Fairview with a score of 9.
The poorest access neighbourhoods are Gateway, Northeast 2, Sheridan Park,
and Southdown, all with scores of -12. These seven neighbourhoods are
identified in this index as being the highest and lowest access in Mississauga.
The mean level of access by this index is a score of -0.5.
1
2
3
4
5
6
7
8
9
10
11
12
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14
15
16
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1819
20
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2930
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-12
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-8
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Neighbourhood
Inde
x Sc
ore
(Ran
ge: -
12, 1
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1. Applewood2. Central Erin Mills3. Churchill Meadows4. City Centre5. Clarkson-Lorne Park6. Cooksville7. Creditview8. Dixie9. East Credit10. Erin Mills11. Erindale12. Fairview13. Gateway14. Hurontario15. Lakeview16. Lisgar17. Malton18. Mavis-Erindale19. Meadowvale20. Meadowvale Business21. Meadowvale Village22. Mineola23. Mississauga Valley24. Northeast 125. Northeast 226. Port Credit27. Rathwood28. Sheridan29. Sheridan Park30. Southdown31. Streetsville32. Western Business Park
Figure 11. Spatial Index of Accessibility.
81
The index of accessibility is displayed in map format in Figure 12. With
this cumulative index, the highest access neighbourhoods, displayed in blue,
typically follow the SW to SE band of neighbourhoods that was seen for several
individual measures of access. The lower access neighbourhoods are primarily
located in the north of the city, with several other low access neighbourhoods
distributed throughout. Hurontario (51,763 population), Lisgar (30,158) and
Churchill Meadows (28,506) are of particular interest for having lower access and
large resident populations.
Figure 12. Index of Accessibility Map.
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4.5 Aspatial Dimensions of Access to Care
The following sections discuss results for stage 4 of the research analysis,
the examination of access to care based on language (i.e. mother tongue) and
recent immigrant status. The first section examines language specific access
ratios for five prominent language groups as well as for the French language.
The second section examines access to care for recent immigrants.
4.5.1 Language-Specific Access to Care
The following ratios measure access to physicians for six linguistic groups
of Mississauga. As with the spatial measures of access previously discussed,
these aspatial measures of access are calculated as physicians-per-1,000
population ratios. As discussed in Chapter 3, only the physicians who state
specific language capabilities on the CPSO website are considered in the ratio,
and only individuals who claim a given language as their mother tongue in the
2006 census are considered. Languages explored here are French, Arabic,
Tagalog, Polish, Urdu and Punjabi (See Appendix A, Table 3 for neighbourhood
and municipal level counts of each mother tongue). For example, the French
mother tongue ratio is calculated by dividing the number of physicians who state
French language proficiency on the CPSO website by the number of individuals
claiming French as their mother tongue in the 2006 Census, and multiplying this
number by one-thousand for the final ratio. These ratios are calculated using
3Km and 800m catchments for the 32 neighbourhoods in consideration for this
analysis.
83
Access ratios for these six languages are displayed in Figures 13-18 and
in Appendix C, Tables 3 and 4. Access ratios are displayed by graded shading.
Values are classified into quartiles, with roughly eight neighbourhoods in each
quartile. The neighbourhood-level population of each language group is shown
as graduated point symbols.
Access to language-specific services for French-speaking individuals is
shown in Figure 13. Driving access to care for the French mother tongue ranges
from a low of 0 to a high of 51 (Figure 13a). The highest access neighbourhoods
are slightly clustered in central Mississauga. Malton, the northernmost
neighbourhood also has high access. The far north and south ends of the city
has relatively poor access to health care for French speaking individuals.
Clarkson-Lorne Park and Lisgar are both poorly served and have large French-
speaking populations. This suggests that there may be a large number of
individuals in these neighbourhoods facing language-based barriers in access to
care within their home neighbourhoods.
Access ratios for 800m walking distances range from 0 to 14 (Figure 13B),
and are therefore much lower than based on driving distances. Dixie and
Streetsville are in the highest accessibility quartile based on driving accessibility,
and in the lowest quartile based on walking. Individuals in these neighbourhoods
without vehicle access may have difficulties accessing care. There are several
neighbourhoods with a large French-speaking population where access is
particularly low. These include Hurontario, East Credit, Lisgar, Clarkson-Lorne
park and Erin Mills.
84
A B
Figure 13. Access Ratios for French Speaking Individuals at 3Km (A) and 800m (B).
Access ratios at 3 Km for the Arabic speaking population ranges from 0 to
10 physicians per 1,000 individuals (Figure 14A). This ratio is much lower than
for the French speaking population. As with the previous ratio, the higher access
ratios are clustered in central Mississauga, with pockets of poor access in the
north and south ends of the city. Several of the neighbourhoods that are well-
served have some of the smallest populations (e.g. Dixie and Lakeview), while
one neighbourhood (Hurontario) has a low access ratio, but a large population of
Arabic speaking individuals.
Access ratios at 800m reveal a greater degree of variability than displayed
at 3Km (Figure 14B). The maximum level of accessibility is 26-per-1,000, which
is much higher than at 3Km. However, there are also more neighbourhoods with
no accessibility at 800m. Two neighbourhoods went from very high accessibility
based on driving distance to very low accessibility by walking distance; Lakeview
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and Dixie. Additionally, the entire south end of Mississauga is of very low
accessibility based on walking distances.
B A
Figure 14. Access Ratios for Arabic Speaking Individuals at 3Km (A) and 800m (B).
Access ratios for Polish speaking individuals are displayed in Figure 15.
Driving distance ratios range from a low of 0 to a high of just over 2 per 1,000
individuals (Figure 15A). This spatial distribution closely resembles that of the
general population with a SW to SE band of high accessibility (Figure 5). There
are several neighbourhoods that have very low accessibility but very high
populations. These include Hurontario and Clarkson-Lorne Park.
Access ratios at 800m demonstrate a much greater degree of variability
(Figure 15B). There are many neighbourhoods with an access ratio of 0,
indicating large disparities in access by walking across the city. Many of these
neighbourhoods, including Hurontario, East Credit, and Churchill have the largest
polish speaking communities. A number of neighbourhoods that were of high
86
accessibility based on driving distances are of the lowest level of accessibility by
walking including Sheridan and Meadowvale.
A B
Figure 15. Access Ratios for Polish Speaking Individuals at 3Km(A) and 800m(B).
Access ratios for Tagalog speaking individuals are displayed in Figure 16.
At 3Km, access ranges from a low of 0 to a high of 5 physicians per 1,000
(Figure 16A), revealing that access to physicians for this linguistic group is lower
than others examined thus far. The high access neighbourhoods are highly
clustered in the east end of the city. Several low access neighbourhoods (e.g.
Meadowvale Village) have large Tagalog speaking populations.
Accessibility ratios at 800m (Figure 16B) reveal a very large number of
neighbourhoods with no access to primary care services in Tagalog. Many of
these neighbourhoods have the largest concentrations of individuals who speak
the language. This indicates a severe disparity in access to Tagalog-specific
care for the majority of the city.
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A B
Figure 16. Access Ratios for Tagalog Speaking Individuals at 3Km (A) and 800m (B).
Access ratios for Punjabi mother tongue are displayed in Figure 17.
Driving distance ratios (Figure 17A) range from a low of 0 to a high of 18, and are
highest in a SW to SE band of the city. More northern neighbourhoods are
typically of lower access. Many of the more populated neighbourhoods (e.g.
Hurontario, Meadowvale Village, East Credit) are in the lower access quartiles.
Access ratios for walking distance (Figure 17B) are higher than for driving, and
range from 0 – 45. However, there are many more neighbourhoods displaying a
disparity in access at this distance. Higher access neighbourhoods based on
walking distance are clustered in the west end of the city and appear to be those
with the largest populations.
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A B
Figure 17. Access Ratios for Punjabi Speaking Individuals at 3Km (A) and 800m (B). Driving access ratios for Urdu mother tongue (Figure 18A) range from 0 –
9.7, and as such are similar to Arabic, lower than Punjabi, and higher than
Tagalog and Polish levels of access. The higher access neighbourhoods by
driving distance are in central Mississauga, particularly towards the western
border. Malton is also a high access neighbourhood. The majority of the
neighbourhoods in the second lowest access quartile are highly populated by
individuals of Urdu mother tongue. This indicates that there may be a large
number of individuals facing language barriers in access to care. Walking
access ratios (Figure 18B) range from 0 – 5.7. There are more low access
neighbourhoods at this distance. Sheridan in particular stands out as having no
access to Urdu-speaking physicians and yet has a large population of individuals
speaking Urdu, indicating a potentially large unmet demand for language-specific
care in this neighbourhood.
89
A B
Figure 18. Access Ratios for Urdu Speaking Individuals at 3Km (A) and 800m (B). 4.5.2 Access to Care for Recent Immigrants
Access to health care for recent immigrants is an important avenue of
investigation because recent immigrants may be less likely to have a dedicated
family doctor and may have difficulty finding physicians accepting patients in their
neighbourhoods of residence (Asanin & Wilson, 2008). To measure access for
recent immigrants, this analysis focuses on the spatial distribution of individuals
who have immigrated to Canada in the past five years relative to the distribution
of physicians accepting new patients.
Access ratios at 3Km range from 0 to just over 3 per 1,000 individuals
(Figure 19A, Appendix C, Table 5). High access neighbourhoods are strongly
clustered in the east end of the city, and moderate-high neighbourhoods are
clustered towards the south end of the city. There is a concentration of low
access neighbourhoods in the north west corner of Mississauga. Several of
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these neighbourhoods of low and moderate-low accessibility have large numbers
of recent immigrants, including Hurontario (lowest access, 6490 recent
immigrants), East Credit and Central Erin Mills (moderate-low access, 6875 and
3735 recent immigrants respectively).
The spatial pattern of access based on walking distance is much different
than based on driving distance (Figure 19B). The higher access neighbourhoods
are more disperse, with the highest access neighbourhoods distributed randomly
across the southern half of the city. The majority of extremely low access
neighbourhoods are in the north. Several neighbourhoods are of high
accessibility by driving distance but are not based on walking distance. These
include Dixie, Cooksville, and Malton. Based on walking distances, the majority
of low access neighbourhoods also have a small population of recent immigrants.
A B
Figure 19. Access to Physicians Accepting Patients by Recent Immigrants at 3Km (A) and 800m (B).
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Chapter 5: Discussion
5.1 Summary of Key Findings
The objectives of this research were three-fold. The first objective was to
evaluate current methodology used to measure potential access and devise an
appropriate methodology to be used in this specific Canadian setting. The
second objective was to identify neighbourhood-level disparities in potential
access to primary health care in Mississauga, Ontario. The third objective was to
explore alternative spatial and aspatial dimensions of potential access to health
care to develop a more nuanced understanding of potential access in this
research setting. The following discussion will highlight the key findings with
respect to the original research objectives.
5.1.1 Spatial Access to Primary Care
Preliminary investigation into access to Mississauga’s primary care
physicians reveals strong patterns of spatial clustering. The raw distribution
shows that the city’s 677 PCPs are located primarily in central and south
Mississauga, with few in the northern-most neighbourhoods. As previously
stated, the degree of clustering is so high that only four neighbourhoods (Central
Erin Mills, Cooksville, Applewood & Meadowvale) possess nearly one-half (46%)
of all physicians. The distribution of walk-in clinics and of physicians that are
accepting patients follows a similar pattern of spatial clustering, with the majority
of each located in central and south Mississauga.
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Spatial clustering of primary care becomes stronger when considering
health care supply relative to population demand using 3Km and 800m access
ratios. At 3 Km, the highest access neighbourhoods for the measure PCPs-per-
1,000 were primarily located along the south-west and south-east borders of the
city. Neighbourhoods in the highest access quartile for the second measure,
PCPs accepting-per-1,000, were primarily in the eastern-most tip of the city. The
third measure showed the same spatial distribution as that of the first, with high
access neighbourhoods situated mainly along the south-west and south-east
borders of the city, and many of the same high access neighbourhoods identified.
The spatial access ratios at 800m showed very similar distribution of high
access ratios as occurred at 3Km, although the degree of clustering was slightly
less. In several cases, neighbourhoods that were of high accessibility based on
driving distance were of low accessibility by walking distance. One such
example is Malton. This neighbourhood displays a disparity in access to care
that favours individuals with access to a vehicle.
Based on the examination of access to primary health care, there is a
clear demonstration that significant neighbourhood-level differences in potential
spatial access to care do exist. While several neighbourhoods are consistently of
high access by all measures (e.g. Cooksville, Fairview & Applewood), others are
low access by all measures (e.g. Northeast 1, Southdown). This indicates in
more general terms that neighbourhood-level variation in access to care does
exist. The index of accessibility supports these findings, given that
neighbourhood level access scores range from extreme ends of the scale of -12
93
and +12. However, a more varied picture of access emerges when considering
alternative dimensions and alternative distances. Several neighbourhoods
switch between high and low access depending on the measure of access and
distance examined. Dixie, Lakeview, Churchill Meadows and Meadowvale
Business are several that stand out in this respect.
5.1.2 Aspatial Dimensions of Access to Care
Because of the diversity of Mississauga’s population it was pertinent to
determine how access to primary care may differ amongst the population based
on aspatial/social characteristics such as language of mother tongue and
immigrant status. While there are many languages spoken in the city, this
research has focused on some of the largest by population – Arabic, Tagalog,
Polish, Punjabi and Urdu. Also examined were access levels for individuals
speaking French so that access based on official versus non-official second
languages can be compared.
This exploration of particular population subgroups reveals significant
geographic disparities in access for language-related population sub-groups. For
each mother tongue, access to physicians with language-specific capabilities
varies significantly between neighbourhoods. Access to health care for each
language explored displays some degree of spatial clustering. Individuals not
residing in or near those clusters of high access neighbourhoods may face
significant difficulties in accessing language-specific health care. To further
compound this problem, several neighbourhoods obtained low access scores for
94
all languages studied. These neighbourhoods may be severely lacking in care
that is sensitive to the particular language needs of the population residing within
them. Further investigation is required to determine whether there is a demand
for such care in these neighbourhoods, and how these needs could best be
addressed.
For some languages, low accessibility tends to correspond with high
population numbers of individuals. This was particularly demonstrated at a
distance of 800m, and most particularly for access to Tagalog speaking
physicians. This indicates that there may be large numbers of individuals in
these neighbourhoods that face language barriers in access to care. An
additional finding was that access ratios varied significantly between language
groups. Accessibility was very high for the French and Arabic languages,
moderate for Punjabi and Urdu, and very low for Polish and Tagalog. This
reveals that language appropriate care may be more obtainable for some
population groups than it is for others. Specifically, access to health care for
Tagalog and Urdu may be very problematic. While the traditional policy focus in
Canada is to equalize accessibility between the two official languages, French
and English, these findings indicate a need to focus on facilitating accessibility for
non-official linguistic groups.
The examination of access to care for recent immigrants reveals strong
disparities. Neighbourhoods with the best access by vehicle are clustered in the
east end of the city, while those with the best access for pedestrians are
generally located throughout the central and south end of the city. However, the
95
populations of recent immigrants are more heavily concentrated throughout the
northern end of the city and some central neighbourhoods, and as such, the
distribution of access does not correspond with the highest levels of potential
population demand.
5.2 Research Contributions
This examination of access to primary care serves as an example of
research that bridges research and literature in several fields including health
geography and neighbourhoods and health in particular, quantitative literature on
potential spatial access to health care and literature on primary health care in
Canada. In doing do, this research has made a number of contributions that fill
gaps in these existing bodies of literature. The methodological and theoretical
contributions of these endeavors are explored in the following section.
5.2.1 Neighbourhood-Level Access to Health Care
The study of neighbourhood-level access to health care is a relatively
small and recent field of enquiry. Within this field, research findings have
continually utilized statistical units as proxy for neighbourhoods. Such studies
typically demonstrate that access to health care is higher in urban centres and
lower in urban peripheries. Problematic with these findings is that they are highly
dependent on choices made with regards to research methodology and
neighbourhood boundaries.
96
This research contributes to the existing body of literature on
neighbourhood level access to care by identifying the existence of local level
variation in access to primary care within this particular urban setting. This is
accomplished through the use of meaningful neighbourhoods that are recognized
by Mississauga residents and used in city planning. Through this analysis it was
demonstrated that access to care showed a much more differential spatial
pattern than is typically identified. Furthermore, this pattern of accessibility is
highly dependent on the scale of analysis (e.g. 3Km vs. 800m). This
demonstrates that the investigation of intra-urban variability in access to care is a
highly relevant venue of inquiry. In addition, there may be cause to re-evaluate
previously studied urban areas using newer methodologies such as the 3SFCA
developed here so that previously undiscovered disparities in access to care may
be identified.
This research additionally contributes to the current dialogue on
neighbourhoods and access to health care by demonstrating how one
methodology can be adapted to examine multiple dimensions of access. While it
is recognized in the literature that potential access contains numerous spatial
(Penchansky & Thomas, 1981) and aspatial (Khan, 1992) components, the
majority of the literature focuses on narrow definitions of potential access. This
may be due to the fact that there has yet to be a precedent established for how
such dimensions of access can be measured using readily available data. The
measurement of access to physicians accepting patients, to walk-in clinics and
access for particular population subgroups in this analysis demonstrates how a
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more nuanced understanding of access to care can be obtained using available
data. In performing this analysis, a highly variable picture emerged whereby the
spatial pattern of accessibility differed significantly based on the dimension of
access studied. This demonstrates a need to continually investigate alternative
dimensions of potential access.
5.2.2 Development of the 3SFCA Method
Within the area of health geography focusing on access to health services
there has been heightened interest of late to develop methods to adequately
describe local-level variations in access to care (e.g. see Luo & Wang, 2003;
Luo, 2004; Langford & Higgs, 2006; Luo & Qi in press; McGrail & Humphreys in
press). However, most methods are developed for use in international contexts
based on statistical units of analysis (e.g. census tracts). Such methods are not
necessarily appropriat in contexts where more locally and politically meaningful
units of analysis are available, nor do they work on units of variable size such as
the neighbourhoods of Mississauga. Building upon previous research, this study
has advanced existing methodologies and techniques used to measure access to
health care, and better understand local level variations in access.
The Three Step Floating Catchment Area method is a significant
methodological contribution that will help further future explorations into
neighbourhood-level variations in access to health care. While the existing
2SFCA method has been viewed as an innovative and improved way to measure
access to health care, it was found here that it may not be appropriate for this
98
research. The 2SFCA method works well when study areas are roughly uniform
in size, and are not excessively large in comparison to the size of the catchment
used. While it is not the position of this research to state whether or not these
criteria were satisfied in previous research settings employing the 2SFCA
method, they were not satisfied in Mississauga. In contrast, the inclusion of a
third step into this method to create the 3SFCA method allowed catchments to be
created around the small and roughly equally sized dissemination areas and
further aggregated to the locally relevant neighbourhoods. This improvement
suits the method adequately for Canadian research where dissemination areas
are available nationwide as units of data analysis. Additionally, the third step of
this method demonstrates how access ratios can be calculated using statistical
units and readily available data and further adapted to provide measures of
accessibility for locally relevant neighbourhoods.
In addition to furthering existing methodology, the development of the
three-step floating catchment area method for this research also demonstrates
the importance of evaluating the appropriateness of existing methodology within
the context of which it is to be used. There are a large number of techniques
available for the examination of access to health care, but not every technique
will be appropriate in every setting. It became apparent early on in this project
that while the existing 2SFCA technique was a sophisticated tool to measure
access to care, it would be problematic if used in this setting. Thus, the
development of the three-step technique was necessary. It is therefore
acknowledged that the three-step method developed here may not work in all
99
geographic settings, and there is a continual need to thoroughly consider the
rigor and appropriateness of methods in the particular context they are to be
used.
Given the recent development of FCA based methods in the literature,
there has been a limited exploration of how such methods can be used to explore
access to care by multiple modes of transportation. While alternative buffer
distances have been used in the literature, and such distances have been quoted
as representing “adequate” travel distances to care, the mode of travel has yet to
have been specified. This is a considerable gap in the literature, given that not
all individuals travel by car, and a distance that is adequate for one individual by
one mean of transportation may not be adequate by another who travels by
different means. This research helps fill this gap in knowledge by demonstrating
how the size of the buffer used in the 3SFCA can be altered to represent driving
distance versus walking distance. It further demonstrates differential results
between the two scales examined. One limitation of this research is that it was
unable to consider travel distances by bus, as a detailed digital GIS file of the
Mississauga public Transit network was not available. It is recommended that
future research adapt FCA based methods for the analysis of access by public
transportation by using transit network files, when available, as the network on
which to create catchments.
An additional contribution of this research stemming from the use of the
3SFCA method and available physician data is the demonstration of how
additional dimensions of access to care can be investigated. Within the
100
literature, quantitative studies on access to care tend to focus on the spatial
dimensions of access. Furthermore, such investigations typically focus on only
one spatial dimension of access (i.e. access to all PCPs by the general
population). It is rare for research to examine additional dimensions of access,
such as access to walk-in facilities or physicians accepting patients. This
research fills a gap in the literature by developing and using a methodology that
is able to address alternative dimensions of access to care, thus providing a
more comprehensive and holistic picture of potential access.
This research is exploratory in demonstrating how multiple measures of
accessibility can be combined into cumulative indices of accessibility. There may
be several benefits in creating such an index. One underlying objective of this
project is to identify neighbourhoods of interest for future research. Perhaps the
most logical concluding step to this research is to identify neighbourhoods which
are repeatedly demonstrating poor access and those that repeatedly
demonstrate high access. One could do this by visually inspecting maps
showing separate measures of access, but this would be more greatly prone to
error (Odoi et al, 2005). The use of an index demonstrates a more conclusive
method to accomplish this goal.
5.2.3 Aspatial Dimensions of Access to Care
Within the body of literature focusing on potential access to care, there
has been little attention paid to how potential access may differ based on
aspatial/social characteristics of the population, including language and
101
immigrant status. Such inquiries are much more common in studies of realized
access where the use of care is the focus. Recently, several inquiries into
access to care for linguistic groups and immigrants have been made within
Canada (Asanin & Wilson, 2008; Wang, 2007). There is clearly a need for
additional research into this line of inquiry. This research contributes to the very
small body of literature on this subject by examining access to care for specific
linguistic groups within Mississauga as well as for recent immigrants. Such
contributions help to further the dialogue of neighbourhood-level health research
by shedding light on the relationships between spatial and social dimensions of
access to care.
It may have been expected that the spatial pattern of access would differ
between the general population and specific linguistic minority groups. However,
for the most part, this was not found. Primarily, neighbourhoods of low access
for the general population were also of low accessibility for linguistic minorities.
What is important to note is that the levels of access differed significantly
between the six linguistic groups examined. This is demonstrated by
accessibility ratios that are relatively high for the French speaking population but
quite low for other linguistic groups including Tagalog and Polish. While the
equalization of access between the official Canadian languages of English and
French remains a federal policy focus (Health-Canada, 2009), this research
demonstrates a need to address disparities in language-specific access to health
care for non official languages.
102
5.3 Research Limitations
Before concluding the discussion of this research, it is pertinent to
acknowledge that the methods chosen here do have several limitations and
assumptions inherent in their design. These limitations include the choice of
buffer distances of 3Km and 800m, problems with using small DA units of
analysis, and potential edge effects that may occur in this municipal setting.
Each of these limitations will be explored further below.
The use of a buffering technique to count provider-to-population ratios has
several limitations. Firstly, this technique inevitably makes the assumption that
individuals falling within a facility catchment have equal access, and those
outside of it do not have access at all. This is an oversimplification, where in
reality there is generally a gradation of access based on distance, and not an
absolute cut-off. Additionally, the use of buffers requires choosing a radius that
represents an ‘acceptable’ distance. While a distance of 800m is commonly
used in the literature to represent an acceptable walking distance to services
(Sallis et al, 2004; Lovett et al, 2002), it must be recognized that this distance is
not walkable by all. Individuals with mobility problems, elderly persons, and
those who are ill (and hence needing health care) may have difficulties traveling
this distance. A distance of 3 Km as a driving distance may also pose problems.
The time taken to traverse this distance may differ significantly depending on the
location in the city, the presence of traffic congestion, road types, construction,
and time of day. Such differences are impossible to take into account with the
103
available data. This illustrates why the continual production and enhancement of
digital data is essential to the accuracy of such access studies.
An additional limitation to this study is in using dissemination areas as the
unit to obtain population data from. This was done because smaller units tend to
show greater between-region variability in access to care (Apparicio et al, 2008).
However, the population numbers of DA’s, as with other census units, are
rounded to preserve confidentiality. Because there are more DA’s than census
tracts, the overall amount of rounding that occurs is greater, resulting in a greater
degree of inaccuracy. Additionally, when the population being studied is
particularly small, there is a chance the population total may be reduced
significantly or eliminated altogether through rounding. This is particularly
problematic when studying minority populations with small numbers. The
minority languages studied here comprise the city’s largest (non-English)
linguistic groups, and rounding error should be of minimal influence. However,
this problem should be kept in mind if the three-step method were to be used for
very small population subgroups.
One final limitation of this study relates to potential edge effects that may
have occurred when conducting the analysis. This study considered population
and physician data for the city of Mississauga alone, and did not consider data
for neighbouring municipalities. However, because Mississauga is bordered on
three sides by other municipalities, this could be problematic. In reality, it is
highly plausible that individuals in the peripheral neighbourhoods of the city may
choose to seek care in other municipalities rather in their neighbourhood of
104
residence. Additionally, it is plausible that individuals living outside of the city
may choose to access care within neighbourhoods of Mississauga. This may
have the effect of underestimating health care (and access ratios) for those living
in the peripheral neighbourhoods of the city on the three sides bordered by other
municipalities. Such problems would be less likely to occur in the analysis of
cities that are more isolated and surrounded by sparsely populated rural areas.
5.4 Recommendations for Future Research
This research has provided valuable information on potential access to
primary care. This information may be used in future research to further the
dialogue of neighbourhood-level access to care, as well as to further
methodology used to examine potential access. First and foremost, this research
demonstrates the importance of focusing on intra-urban variations in access to
care. While the majority of existing research has found little neighbourhood-level
variation in access to health care, these findings may result from an over-reliance
on the use of statistical units as proxy for neighbourhoods. Research should
continue to examine local level variations in access to care using neighbourhood
boundaries that are recognized by residents, used in city planning, and are more
likely to correspond to the scale that health related processes occur at.
Secondly, as a neighbourhood-level study, this research demonstrates the
importance of examining more nuanced dimensions of potential access.
Furthermore, it has demonstrated that such dimensions of access can be
examined using a readily available data set without the need to expend time and
105
money obtaining additional data. Future research should build upon this
example and begin to investigate the dimensions of access examined here in
other Canadian and possibly in international settings. Additionally, dimensions of
access that further explore the availability of physicians such as by full time
equivalencies (FTEs) and the potential need of the population as adjusted for
age, gender, ethnicity and other demographic characteristics would further this
research. Thirdly, this research has demonstrated disparities in access to care
for the city of Mississauga, Ontario. There is a large opportunity for future
studies that focus on this city, as well as other cities within Canada. While the
literature on access to care is dominated by US and other international studies,
there is a need for Canadian research so that health care provision and policy
can be amended accordingly. Lastly, it is recognized that while potential access
is a fundamental component of access to care, it is only one factor that may lead
to realized use of health services. Additional individual characteristics including
age, gender, ethnicity, socioeconomic status, beliefs about health and the actual
need for care will also determine whether and where an individual seeks care
(Gatrell, 2001: 155; Aday & Anderson, 1974). There is a need for ongoing
research demonstrating how potential access is related to realized access, and
how it is moderated by individual characteristics to influence decision making and
overall health outcomes.
106
5.5 Policy Recommendations
This study reveals a number of significant findings that could be used in
public policy to alleviate inequities in access to health care. While health care in
Canada is funded by provincial governments, it is increasingly becoming a
concern of municipalities. As previously mentioned, the state of health care in
Canada is in transition. Federal funding cuts to provinces and decreases in the
numbers of practicing family doctors in recent decades has resulted in a
restructuring in the way health care is being delivered, particularly with respect to
primary health care (Iglehart, 2000). The focus and responsibility of primary
health care delivery is increasing on the local (i.e. sub-municipal) level.
Municipalities are becoming responsible for funding a greater number of services
that were previously a provincial concern (Elliott et al, 2000). As a result,
municipalities are becomingly increasingly responsible for the quality of primary
care delivery. This following section will address some of the ways in which
municipalities can address and alleviate inequitable health care distribution.
5.5.1 Municipal Policy Intervention
In general, policy interventions that may improve access to health services
can be discussed as those that bring people to services, those that move
services closer to people and those that reduce barriers other than distance
(Haynes, 2003: 26). An example of the former would include the improvement of
existing transportation systems. For example, additional public transportation
routs to areas with abundant health care services could be added in
107
neighbourhoods with the poorest geographical access to health care, particularly
in the north end of the city. Such a strategy was undertaken in the UK, where
government subsidies for conventional bus services were increased in 1997 and
following years, in attempts to increase access for individuals without vehicles
(Haynes, 2003: 26). Additionally, new means of transportation could be created
which would focus on shuttling those in need to a point of health care. The
establishment of a community car scheme (Haynes, 2003: 27) in the poorest
access neighbourhoods could help those without transport, as well as those with
disabilities and the elderly who may have difficulty with public transportation.
This may be of particular use in the north end of Mississauga, an area which was
of low accessibility by all measures examined.
The primary strategy that could be used to bring health care closer to the
neighbourhoods in need would be a municipal effort to bring about shift in the
current distribution to one that is more equitable. Such efforts may be in the form
of positive (e.g. tax) incentives for physicians to locate in the northern
neighbourhoods where geographical access is poor, and for physicians with
second language capabilities to locate in neighbourhoods where access to those
languages is low and the population in need is the highest. These strategies, as
mentioned, would have to work around existing zoning and land use constraints.
Municipal strategies could also be regulatory in nature. Restrictions on the areas
where new physicians may practice (such as maximizing the number of
physicians who are able to practice in a medical complex) or where existing
physicians may move to can be placed so that additional primary care does not
108
locate in areas that are already of high access, and instead are funneled into
neighbourhoods that are low access. These redistributive efforts would take
time, but over the course of years, a gradual increase in the equitability of
provision should occur.
Strategies aimed at reducing social differences in access to health care
could be focused on eliminating language barriers in access to care. This
research identified that access to physicians based on language abilities is
potentially a larger problem for minority languages than it is for the French-
speaking population. Thus, there is a need for policy to shift focus from creating
equal access between the two official Canadian languages and begin to focus on
the non-official minority languages. Ways to mediate this without redistributing
physicians could involve the inclusion of interpreter services in family practice
settings (Brach and Frasierector, 2000). While such translation services may be
available in hospitals, particularly in the emergency ward, they are rarely present
in other primary care settings (Wang et al, 2008; Barr & Wanatt, 2005). The
ability to receive quality care at the neighbourhood level from a GP may reduce
the need for individuals to overuse emergency care (Asanin & Wilson, 2008).
Perhaps the most appropriate way to target such services is to identify
neighbourhoods where the language-specific access ratios are lowest and the
population speaking that language is the highest. This would then be an
appropriate location for the provision of translator services. Additionally, the
inclusion of information in non-official languages at family practices and health
care centres would enhance the quality of care for individuals speaking those
109
languages and increase the likelihood that their health care needs are met. Such
languages could be chosen based on those spoken most frequently within a
neighbourhood.
5.5.2 Other Sources of Primary Care: Development of LHINs
As mentioned in Chapter 3, there are a number of settings in which
primary health care can be administered. One such setting is the Local Health
Integration Network (LHIN). LHINs are a recent addition to local level primary
health care delivery, and have been operating in Canada since April 1 of 2006
(OLHIN, 2009). LHINs offer a much broader and comprehensive range of
primary care than do family practices by integrating and coordinating services
such as community health services, addiction and mental health counseling and
long-term care (Elson, 2006). These primary services are no longer the
responsibility of the provincial Ministry of Health, demonstrating an increasingly
municipal and local focus on primary care in Canada. However, individual
GP/FP practices remain under provincial control under this new system (Elson,
2006).
Given the multiple levels of regulatory control over primary care in
Canada, there is a need to conceptualize how different modes of primary care
delivery can work together to optimally provide services at the local level (Elson,
2006). LHINs, similar to Walk-in health care services, were created during a time
when public dissatisfaction with the quality and waiting times for GP/FP provision
was increasing. The intention with these added services was to fill a gap in the
110
provision of primary care at the local level that family practices were failing to
address. However, these alternative modes of delivery for primary care are not
intended to take the place of having a dedicated family doctor. Individuals who
use community based primary care such as walk-in services still tend to prefer to
consult with family doctors, and appreciate the added quality of service provision
that occurs when a physician knows a patient and understands their specific
medical history (Brown et al, 2002). Such benefits of GP/FP provision in family
practice are not trivial. As such, the municipal policy focus on primary care
should not ignore the ways in which family care provision can be optimized
municipally in favour of new modes of delivery such as LHINs. Instead,
municipal policy focus should begin to focus on how primary health care can be
optimized considering the multiple delivery systems that are now operating at the
local level, including family practices, walk-in clinics, and LHINs. Such a task has
yet to be adequately addressed (Levitt, McMullan & Freeman-Collins, 2005), and
will be of increasing importance in future years as the emphasis on LHINs and
municipal control over neighbourhood level health care increases.
5.5.3 Constraints of Urban Form in Policy Intervention
At this point it is pertinent to include a comment regarding constraints that
may exist when attempting to implement municipal-level policy to alleviate health
provision shortages. It was determined by this research that the provision of
health care significantly varied across Mississauga by all conceptualizations (e.g.
spatial, aspatial and by scale) examined here. There may be multiple causes of
111
this varied picture of accessibility, but all relate to the distribution of physicians
relative to the distribution of the population. There are two main determinants of
physician distribution recognized in the literature. First, the location where a
physician may practice is heavily constrained by the existing urban form of the
city, and particularly by the existing zoning and land use allowances. This zoning
may play a large role in the reasoning why several of Mississauga’s
neighbourhoods (e.g. Gateway, Northeast 2, Sheridan Park, Southdown) were
consistently of low accessibility by all measures examined. For example, if these
neighbourhoods are primarily industrial or green-space, they will not contain sites
where health care practices may be situated, nor will they contain adequate
residential settlements requiring such care. More investigation is required to
determine if this is the case. In such a scenario, municipal policy will have little
effect at redistributing health care into such areas. Additionally, there may not be
a need to do so. However, a second key factor affecting the distribution of
physicians is the actual choice of the physicians themselves in where to locate
their practice. Research indicates that physicians tend to choose to locate
practices near their place of residence. Additionally, physicians prefer to locate
in areas where support can be received, such as in a practice with other
physicians or near a hospital (Szafran, Crutcher & Chaytors, 2001). This
element of choice allows for policy intervention to mediate inequities in health
care distribution, and additional research is required to determine the optimal
way to do so.
112
5.6 Conclusions
This analysis has revealed significant intra-urban variability in potential
access to primary health care, considering multiple measures of access,
population groups, and at multiple distances. These findings were made
possible through the development of a GIS methodology that is appropriate for
this research setting, and through careful consideration of the appropriate
neighbourhood units to use. There is a need for ongoing examination of
neighbourhood-level access to primary care using appropriate methodology and
neighbourhood units. In particular, examination of access to primary care in
additional Canadian cities will help to further the understanding how access to
care differs in the context of increasing concerns over the state of primary health
care in Canada.
113
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Appendix A: Neighbourhood Demographics
Table 1. Neighbourhood Statistics and Demographics
Neighbourhood Size (Km2) Population Pop. Density Applewood 7.12 37516 5270 Central Erin Mills 9.24 30946 3350 Churchill Meadows 7.64 28506 3732 City Centre 2.69 7805 2900 Clarkson-Lorne Park 17.18 39753 2314 Cooksville 9.04 44224 4891 Creditview 2.96 9876 3338 Dixie 5.43 1983 365 East Credit 15.62 63577 4071 Erin Mills 13.16 42783 3252 Erindale 8.24 22315 2709 Fairview 2.55 17109 6697 Gateway 18.36 5883 320 Hurontario 11.09 51763 4666 Lakeview 11.44 20579 1799 Lisgar 5.89 30158 5122 Malton 6.81 41334 6071 Mavis-Erindale 1.96 819 417 Meadowvale 8.10 40244 4971 Meadowvale Business
13.40 4709 351
Meadowvale village 9.67 23115 2389 Mineola 5.29 9443 1786 Mississauga Valley 3.61 26011 7205 Northeast 1 22.35 2903 130 Northeast 2 6.06 974 161 Port Credit 2.88 10086 3506 Rathwood 7.58 31782 4191 Sheridan 7.87 15714 1997 Sheridan Park 1.62 676 418 Southdown 7.28 1807 248 Streetsville 4.99 9800 1966 Western Business Park
4.89 4526 925
Municipal Total 262 678,719 2860
127
Table 2. Recent Immigrants and Population with Non-English Mother Tongue.
Neighbourhood Recent Immigrant (% of pop.)
Non-English Mother Tongue (% of pop.)
Applewood 6635 (17.7) 21900 (58.4) Central Erin Mills 3735 (12.2) 14680 (47.4) Churchill Meadows
3645 (12.8) 16110 (56.5)
City Centre 1595 (20.3) 4785 (61.3) Clarkson-Lorne Park
1875 (4.7) 10620 (26.7)
Cooksville 7590 (17.6) 24130 (54.6) Creditview 715 (7.3) 5030 (50.9) Dixie 10 (0.5) 690 (34.8) East Credit 6875 (10.9) 36090 (56.8) Erin Mills 2645 (6.2) 14920 (34.9) Erindale 2750 (12.3) 11435 (51.2) Fairview 2660 (15.6) 10185 (59.5) Gateway 460 (7.8) 3215 (54.6) Hurontario 6490 (12.6) 30090 (58.1) Lakeview 800 (3.9) 6895 (33.5) Lisgar 2420 (8.0) 11535 (38.2) Malton 7315 (17.7) 23400 (65.5) Mavis-Erindale 120 (15.6) 445 (54.3) Meadowvale 3325 (8.3) 11960 (29.7) Meadowvale Business
595 (12.7) 1755 (37.3)
Meadowvale village
2315 (10.1) 12045 (52.1)
Mineola 140 (1.5) 2485 (26.3) Mississauga Valley
4530 (17.6) 14570 (56.0)
Northeast 1 185 (6.7) 1590 (54.8) Northeast 2 65 (8) 440 (45.2) Port Credit 735 (7.3) 2380 (23.6) Rathwood 2370 (7.4) 17290 (54.4) Sheridan 1685 (10.6) 6335 (40.3) Sheridan Park 15 (2.2) 125 (18.5) Southdown 30 (1.7) 345 (19.1) Streetsville 480 (4.9) 2930 (29.9) Western Business Park
395 (8.8) 1690 (37.7)
Municipal Total 75,200 (9.7) 322,095 (44.2)
128
Table 3: Minority Languages Spoken by Neighbourhood
Language Neighbourhood French Punjabi Arabic Tagalog Urdu Polish Applewood 445 335 785 1390 1775 2995 Central Erin Mills 355 870 1065 675 1690 520 Churchill Meadows 310 600 1490 1280 2585 1295 City Centre 55 155 620 150 515 155 Clarkson Lorne Park 560 160 465 630 325 1775 Cooksville 660 390 1880 1905 1730 3355 CreditView 95 430 205 560 190 300 Dixie 10 0 10 0 0 90 East Credit 520 2845 2230 2460 4500 1590 Erin Mills 695 755 730 750 1505 1780 Erindale 380 480 380 655 1315 1485 Fairview 155 510 960 675 630 710 Gateway 10 610 160 170 355 120 Hurontario 345 2565 1265 1465 2300 2035 Lakeview 220 30 85 185 160 1465 Lisgar 525 645 790 675 1755 1090 Malton 175 11305 220 305 1685 250 Mavis-Erindale 0 10 0 60 0 15 Meadowvale 685 135 645 620 1530 1285 Meadowvale Business
70 185 70 85 280 100
Meadowvale Village 210 1950 540 910 1225 530 Mineola 115 0 40 65 0 410 Mississauga Valley 240 120 1180 835 1605 1725 Northeast 1 20 50 70 55 30 100 Northeast 2 0 55 25 0 35 10 Port Credit 235 0 115 85 105 410 Rathwood 200 260 450 835 940 2155 Sheridan 260 185 280 215 1835 540 Sheridan Park 0 0 10 15 0 35 Southdown 25 0 0 35 0 50 Streetsville 95 40 305 60 65 305 Western Business Park
50 20 30 40 225 240
Municipal Total 7720 (1.2%)
25,815 (3.6%)
17,100 (2.6%)
17,875 (2.7%)
30,910 (4.6%)
28,930 (4.4%)
129
Appendix B: Raw Physician Data
Table 1: Raw Physician Data
Neighbourhood Physicians
Accepting Patients
Walk-In Clinics
Airport 6 0 0 Airport Corporate 4 0 0 Applewood 43 12 2 Central Erin Mills 105 4 1 Churchill Meadows
12 4 2
City Centre 5 2 0 Clarkson-Lorne Park
18 3 1
Cooksville 138 25 4 Creditview 4 2 0 Dixie 6 3 0 East Credit 38 7 2 Erin Mills 40 4 2 Erindale 5 3 0 Fairview 13 2 0 Gateway 5 1 0 Hurontario 28 4 1 Lakeview 19 3 0 Lisgar 9 3 0 Malton 16 4 0 Mavis-Erindale 17 4 0 Meadowvale 23 3 1 Meadowvale Business
32 1 2
Meadowvale village
6 2 1
Mineola 10 1 0 Mississauga Valley
8 2 2
Northeast 1 2 2 0 Northeast 2 17 5 1 Northeast 3 1 1 0 Port Credit 7 3 1 Rathwood 7 2 1 Sheridan 14 3 1 Sheridan Park 3 0 0 Southdown 0 0 0 Streetsville 3 1 0 Western Business Park
13 1 1
Municipal Total 677 117 26
130
Appendix C: Access Ratios
Table 1 – Spatial Access Ratios – 3km Driving Distances
Neighbourhood PCPs / 1k Accepting / 1k Walk-in / 1k Applewood 1.159 0.362 0.047 Central Erin Mills 2.029 0.128 0.049 Churchill Meadows 0.391 0.082 0.031 City Centre 1.273 0.327 0.039 Clarkson-Lorne Park 0.533 0.085 0.026 Cooksville 2.285 0.376 0.078 Creditview 0.659 0.194 0.011 Dixie 1.530 0.327 0.043 East Credit 0.735 0.12 0.029 Erin Mills 1.452 0.124 0.073 Erindale 0.671 0.169 0.008 Fairview 1.409 0.346 0.048 Gateway 0.190 0.037 0.007 Hurontario 0.509 0.095 0.023 Lakeview 0.617 0.104 0.012 Lisgar 0.468 0.094 0.028 Malton 1.368 0.732 0.026 Mavis-Erindale 0.778 0.227 0.022 Meadowvale 0.830 0.112 0.050 Meadowvale Business
0.848 0.031 0.041
Meadowvale Village 0.425 0.082 0.035 Mineola 1.252 0.194 0.041 Mississauga Valley 0.833 0.204 0.051 Northeast 1 0.401 0.147 0.000 Northeast 2 0.000 0.000 0.000 Port Credit 0.686 0.164 0.044 Rathwood 0.711 0.213 0.022 Sheridan 0.972 0.129 0.045 Sheridan Park 0.341 0.00 0.000 Southdown 0.062 0.00 0.010 Streetsville 1.287 0.114 0.029 Western Business Park
0.769 0.082 0.041
Municipal Mean 0.859 0.169 0.032
131
Table 2: Spatial Access Ratios – 800m Walking Distances
Neighbourhood PCPs / 1k Accepting / 1k Walk-in / 1k Applewood 0.820 0.301 0.056 Central Erin Mills 1.713 0.082 0.014 Churchill Meadows 0.301 0.087 0.026 City Centre 0.561 0.219 0.025 Clarkson-Lorne Park 0.701 0.145 0.037 Cooksville 2.140 0.364 0.061 Creditview 1.277 0.371 0.000 Dixie 0.000 0.000 0.000 East Credit 0.550 0.112 0.029 Erin Mills 0.936 0.087 0.098 Erindale 0.446 0.126 0.000 Fairview 0.803 0.186 0.038 Gateway 0.286 0.000 0.000 Hurontario 0.360 0.053 0.029 Lakeview 1.819 0.355 0.000 Lisgar 0.220 0.070 0.000 Malton 0.329 0.098 0.006 Mavis-Erindale 0.914 0.457 0.000 Meadowvale 0.811 0.124 0.021 Meadowvale Business
2.678 0.000 0.034
Meadowvale Village 0.228 0.072 0.033 Mineola 0.514 0.059 0.000 Mississauga Valley 0.318 0.085 0.024 Northeast 1 0.000 0.000 0.000 Northeast 2 0.000 0.000 0.000 Port Credit 1.064 0.269 0.086 Rathwood 0.517 0.133 0.049 Sheridan 0.673 0.208 0.024 Sheridan Park 0.000 0.000 0.000 Southdown 0.000 0.000 0.000 Streetsville 0.422 0.178 0.000 Western Business Park
1.854 0.231 0.168
Municipal Mean 0.727 0.140 0.027
132
Table 3: Language-Specific Access Ratios – 3Km Driving Distances Mother Tongue Neighbourhood
French Punjabi Arabic Tagalog Urdu Polish Applewood 13.318 4.595 4.293 0.957 0.693 1.119 Central Erin Mills 14.524 5.433 8.049 0.644 2.062 0.897 Churchill Meadows 3.173 1.232 3.445 0.050 0.613 0.061 City Centre 6.359 3.971 4.324 1.157 2.392 0.345 Clarkson Lorne Park
0.000 6.576 0.600 0.000 0.337 0.000
Cooksville 8.829 13.758 3.005 1.474 3.391 1.354 CreditView 0.999 0.606 2.607 0.000 0.823 0.030 Dixie 10.404 5.535 7.452 0.407 1.220 0.896 East Credit 3.890 2.487 2.820 0.058 1.045 0.247 Erin Mills 12.300 3.347 9.915 0.176 1.028 2.014 Erindale 2.997 1.418 2.994 0.172 1.014 0.222 Fairview 6.872 4.270 3.473 1.342 2.317 0.639 Gateway 0.426 0.245 0.844 0.000 0.302 0.000 Hurontario 3.213 1.050 2.190 0.010 0.858 0.006 Lakeview 4.091 1.597 5.064 0.013 0.039 0.261 Lisgar 2.014 6.037 4.684 0.000 1.618 0.501 Malton 51.167 3.328 4.396 5.128 9.762 0.000 Mavis-Erindale 4.659 0.585 3.691 0.000 0.978 0.178 Meadowvale 3.576 10.192 6.185 0.000 3.176 0.963 Meadowvale Business
5.634 4.410 2.489 0.000 2.101 2.114
Meadowvale Village
5.174 1.514 5.394 0.000 0.301 0.269
Mineola 4.319 13.504 2.373 0.287 1.010 0.422 Mississauga Valley 6.051 6.404 2.278 0.866 1.501 0.575 Northeast 1 1.600 0.000 0.000 0.645 0.000 0.229 Northeast 2 0.000 0.000 0.000 0.000 0.000 0.000 Port Credit 2.787 18.250 4.651 0.022 0.020 0.000 Rathwood 7.499 3.299 1.388 0.977 0.497 0.830 Sheridan 5.294 1.592 3.446 0.000 0.865 1.228 Sheridan Park 0.000 0 0.000 0.000 0.000 0.000 Southdown 0.000 0 0.000 0.000 0.000 0.000 Streetsville 9.377 5.021 4.452 0.168 2.593 2.387 Western Business Park
5.981 2.360 4.484 0.000 0.413 1.336
Municipal Mean 6.454 4.144 3.468 0.455 1.343 0.598
133
Table 4: Language-Specific Access Ratios – 800m Walking Distances Mother Tongue Neighbourhood
French Punjabi Arabic Tagalog Urdu Polish Applewood 7.353 0.801 6.522 0.242 0.274 0.925 Central Erin Mills 5.128 10.256 9.375 2.564 2.882 0.570 Churchill Meadows 3.159 0.000 3.130 0.000 0.206 0.000 City Centre 4.000 0.000 2.925 0.000 0.114 0.422 Clarkson Lorne Park
0.000 0.000 0.000 0.000 0.779 0.000
Cooksville 7.590 24.148 2.134 1.875 2.899 1.193 CreditView 0.000 1.149 1.905 0.000 3.509 0.000 Dixie 0.000 0.000 0.000 0.000 0.000 0.000 East Credit 6.646 1.515 4.484 0.000 1.307 0.000 Erin Mills 3.899 1.345 3.672 0.000 0.219 1.502 Erindale 1.210 0.000 17.972 0.000 0.000 0.000 Fairview 8.000 0.000 1.202 0.000 0.091 0.844 Gateway 0.000 0.000 0.000 0.000 1.333 0.000 Hurontario 4.464 1.917 1.918 0.000 1.068 0.000 Lakeview 0.368 0.000 0.000 0.000 0.000 0.840 Lisgar 1.754 3.509 1.537 0.000 4.511 0.000 Malton 9.992 0.380 0.000 1.311 2.056 0.000 Mavis-Erindale 0.000 0.000 0.000 0.000 0.000 0.000 Meadowvale 6.992 45.283 1.451 0.000 1.769 0.000 Meadowvale Business
14.286 1.270 0.000 0.000 5.714 9.524
Meadowvale Village
6.897 1.226 8.966 0.000 0.000 0.000
Mineola 0.000 0.000 0.000 0.000 0.000 0.000 Mississauga Valley 2.667 3.704 0.300 0.000 0.352 0.578 Northeast 1 0.000 0.000 0.000 0.000 0.000 0.000 Northeast 2 0.000 0.000 0.000 0.000 0.000 0.000 Port Credit 3.947 0.000 26.316 0.000 0.000 0.000 Rathwood 0.808 1.111 17.778 1.415 0.000 0.335 Sheridan 0.000 0.000 0.000 0.000 0.000 0.000 Sheridan Park 0.000 0.000 0.000 0.000 0.000 0.000 Southdown 0.000 0.000 0.000 0.000 0.000 0.000 Streetsville 0.000 0.000 0.000 0.000 0.000 0.000 Western Business Park
6.667 0.855 16.667 0.000 0.000 0.952
Municipal Mean 3.307 3.077 4.008 0.232 0.909 0.553
134
135
Table 5. Access to Primary Care Physicians Accepting Patients by Recent Immigrants
Access to PCPs Accepting Patients By
Recent immigrants Neighbourhood
3 Km 800m Applewood 2.633 2.092 Central Erin Mills 1.259 4.416 Churchill Meadows 0.735 0.617 City Centre 2.066 1.176 Clarkson Lorne Park 1.227 0.572 Cooksville 2.392 1.667 CreditView 1.416 4.489 Dixie 2.268 0 East Credit 1.167 1.572 Erin Mills 1.634 1.379 Erindale 1.304 0.656 Fairview 2.183 1.915 Gateway 0.335 0 Hurontario 0.746 0.714 Lakeview 0.948 17.647 Lisgar 1.114 2.886 Malton 3.355 0.596 Mavis-Erindale 1.674 2.667 Meadowvale 1.354 0.665 Meadowvale Business 0.377
0
Meadowvale Village 0.783 0.493 Mineola 1.982 5.556 Mississauga Valley 1.303 1.137 Northeast 1 1.47 0 Northeast 2 0 0 Port Credit 2.608 2.486 Rathwood 1.762 1.119 Sheridan 1.533 3.154 Sheridan Park 0 0 Southdown 0 0 Streetsville 1.137 1.705 Western Business Park 1.069
2.782
Municipal Mean 1.370 2.005
