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)

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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

iv 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

v 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

vi

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

1

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.

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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

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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.

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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,

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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:

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J -β Ak = ∑Sj / tkj 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 β Ai = ∑ (GPj/Dj ) / dij j=1 Where: Ai = potential accessibility to services in neighbourhood i GPj= general practitioners at location j Dj = potential population demand on a doctor

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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.

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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

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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

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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

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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

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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 &

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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

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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.

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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

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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,

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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

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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

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demonstrating the use of an exploratory method to create a composite index of potential accessibility.

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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.

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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.

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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.

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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

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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

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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

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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,

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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

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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

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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.

4

3.5 2 3 2 3 1

Step 1 Æ Step 2 Æ Step 3

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.

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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.

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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

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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 -2 Æ +2 -2 Æ +2 physicians • Access to physicians -2 Æ +2 -2 Æ +2 accepting patients • Access to walk in -2 Æ +2 -2 Æ +2 clinics Total Index Range -12 Æ +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.

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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

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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.

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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

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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.

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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.

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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.

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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.

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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.

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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.

6 12 1. Applewood 2. Central Erin Mills 1 10 3. Churchill Meadows 12 4. City Centre 5. Clarkson-Lorne Park 8 4 26 6. Cooksville 7. Creditview 10 32 8. Dixie 6 2 28 9. East Credit 10. Erin Mills 11. Erindale 4 1819 12. Fairview 27 13. Gateway 2 20 23 14. Hurontario 5 7 9 17 15. Lakeview 0 16. Lisgar 17. Malton 8 22 18. Mavis-Erindale -2 19. Meadowvale 15 31 20. Meadowvale Business -4 21. Meadowvale Village 22. Mineola

Index Score (Range: -12, 12) -12, (Range: Score Index 11 23. Mississauga Valley -6 14 21 24. Northeast 1 3 25. Northeast 2 -8 26. Port Credit 27. Rathwood 28. Sheridan -10 16 24 29. Sheridan Park 30. Southdown 31. Streetsville -12 32. Western Business Park 13 25 29 30 Neighbourhood

Figure 11. Spatial Index of Accessibility.

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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.

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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.

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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.

A B

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

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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.

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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

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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

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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

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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.

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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

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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

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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

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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

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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.

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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

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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

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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

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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.

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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

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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

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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

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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

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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

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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.

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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.

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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 13.40 4709 351 Business 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 4.89 4526 925 Park Municipal Total 262 678,719 2860

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Table 2. Recent Immigrants and Population with Non-English Mother Tongue. Neighbourhood Recent Immigrant Non-English Mother (% of pop.) Tongue (% of pop.) Applewood 6635 (17.7) 21900 (58.4) Central Erin Mills 3735 (12.2) 14680 (47.4) Churchill 3645 (12.8) 16110 (56.5) Meadows City Centre 1595 (20.3) 4785 (61.3) Clarkson-Lorne 1875 (4.7) 10620 (26.7) Park 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 595 (12.7) 1755 (37.3) Business Meadowvale 2315 (10.1) 12045 (52.1) village Mineola 140 (1.5) 2485 (26.3) Mississauga 4530 (17.6) 14570 (56.0) Valley 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 395 (8.8) 1690 (37.7) Business Park Municipal Total 75,200 (9.7) 322,095 (44.2)

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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 70 185 70 85 280 100 Business 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 50 20 30 40 225 240 Park Municipal Total 7720 25,815 17,100 17,875 30,910 28,930 (1.2%) (3.6%) (2.6%) (2.7%) (4.6%) (4.4%)

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Appendix B: Raw Physician Data

Table 1: Raw Physician Data Neighbourhood Physicians Accepting Walk-In Patients Clinics Airport 6 0 0 Airport Corporate 4 0 0 Applewood 43 12 2 Central Erin Mills 105 4 1 Churchill 12 4 2 Meadows City Centre 5 2 0 Clarkson-Lorne 18 3 1 Park 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 32 1 2 Business Meadowvale 6 2 1 village Mineola 10 1 0 Mississauga 8 2 2 Valley 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 13 1 1 Business Park Municipal Total 677 117 26

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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 0.848 0.031 0.041 Business 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 0.769 0.082 0.041 Park Municipal Mean 0.859 0.169 0.032

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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 2.678 0.000 0.034 Business 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 1.854 0.231 0.168 Park Municipal Mean 0.727 0.140 0.027

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Table 3: Language-Specific Access Ratios – 3Km Driving Distances Neighbourhood Mother Tongue 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 0.000 6.576 0.600 0.000 0.337 0.000 Park 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 5.634 4.410 2.489 0.000 2.101 2.114 Business Meadowvale 5.174 1.514 5.394 0.000 0.301 0.269 Village 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 5.981 2.360 4.484 0.000 0.413 1.336 Park Municipal Mean 6.454 4.144 3.468 0.455 1.343 0.598

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Table 4: Language-Specific Access Ratios – 800m Walking Distances Neighbourhood Mother Tongue 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 0.000 0.000 0.000 0.000 0.779 0.000 Park 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 14.286 1.270 0.000 0.000 5.714 9.524 Business Meadowvale 6.897 1.226 8.966 0.000 0.000 0.000 Village 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 6.667 0.855 16.667 0.000 0.000 0.952 Park Municipal Mean 3.307 3.077 4.008 0.232 0.909 0.553

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Table 5. Access to Primary Care Physicians Accepting Patients by Recent Immigrants Access to PCPs Accepting Patients By Neighbourhood Recent immigrants 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 0 Business 0.377 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 2.782 Park 1.069 Municipal Mean 1.370 2.005

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