Measuring potential spatial access to primary health NADINE SCHUURMAN MYRIAM B´

Measuring potential spatial access to primary health NADINE SCHUURMAN MYRIAM B´
Measuring potential spatial access to primary health
care physicians using a modified gravity model
NADINE SCHUURMAN
Department of Geography, Simon Fraser University, Burnaby, Canada BC V5A 1S6 (e-mail: [email protected])
MYRIAM BÉRUBÉ
Department of Mathematics, University of British Columbia, Vancouver, Canada BC V6T 1Z2 (e-mail: [email protected])
VALORIE A. CROOKS
Department of Geography, Simon Fraser University, Burnaby, Canada BC V5A 1S6 (e-mail: [email protected])
Ensuring equity of access to primary health care
(PHC) across Canada is a continuing challenge,
especially in rural and remote regions. Despite
considerable attention recently by the World Health
Organization, Health Canada and other health policy
bodies, there has been no nation-wide study of
potential (versus realized) spatial access to PHC. This
knowledge gap is partly attributable to the difficulty
of conducting the analysis required to accurately
measure and represent spatial access to PHC. The
traditional epidemiological method uses a simple
ratio of PHC physicians to the denominator
population to measure geographical access. We
argue, however, that this measure fails to capture
relative access. For instance, a person who lives
90 minutes from the nearest PHC physician is
unlikely to be as well cared for as the individual who
lives more proximate and potentially has a range of
choice with respect to PHC providers. In this article,
we discuss spatial analytical techniques to measure
potential spatial access. We consider the relative
merits of kernel density estimation and a gravity
model. Ultimately, a modified version of the gravity
model is developed for this article and used to
L’évaluation du potentiel d’accessibilité spatiale aux
services de santé primaires à l’aide d’un modèle
gravitationnel modifié
L’organisation de la prestation des services de santé
primaires (SSP) partout au Canada sur une base
équitable demeure un enjeu de taille, notamment
dans les régions rurales et éloignées. Malgré
l’importance accordée par l’Organisation mondiale de
la santé, Santé Canada et d’autres instances
chargées des politiques de santé, aucune étude n’a
encore été menée à l’échelle nationale sur
l’accessibilité spatiale potentielle (plutôt que réelle)
aux SSP. Les défis et obstacles de la recherche visant
à évaluer et à représenter avec exactitude
l’accessibilité spatiale aux SSP pourraient expliquer
en partie ce déficit de connaissances. En
épidémiologie, l’approche traditionnelle utilisée pour
évaluer l’accessibilité géographique repose sur un
rapport simpliste entre le nombre de médecins
prodiguant des SSP et la population totale. Nous
défendons l’idée que les résultats qui en sont issus ne
rendent pas compte de l’accès relatif. Par exemple, le
niveau de soins qu’une personne qui vit à 90 minutes
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010) 29–45
DOI: 10.1111/j.1541-0064.2009.00301.x
C / Canadian Association of Geographers / L’Association canadienne des géographes
30
Nadine Schuurman, Myriam Bérubé and Valorie A. Crooks
calculate potential spatial access to PHC physicians in
the Canadian province of Nova Scotia. This model
incorporates a distance decay function that better
represents relative spatial access to PHC. The
results of the modified gravity model demonstrate
greater nuance with respect to potential access
scores. While variability in access to PHC physicians
across the test province of Nova Scotia is evident, the
gravity model better accounts for real access by
assuming that people can travel across artificial
census boundaries. We argue that this is an
important innovation in measuring potential
spatial access to PHC physicians in Canada. It
contributes more broadly to assessing the
success of policy mandates to enhance the
equitability of PHC provisioning in Canadian
provinces.
Key words: primary health care, gravity model, kernel
density estimation, spatial access, rural and remote
health
d’un médecin en SSP peut s’attendre de recevoir est
inférieur à celui d’une autre qui jouit d’une proximité
et d’un plus vaste choix de SSP. Cet article porte sur
les techniques d’analyse spatiale utilisées pour
évaluer le potentiel d’accessibilité spatiale. Il est
question d’examiner à la fois le bien-fondé relatif des
méthodes d’estimation par noyau de densité ainsi que
celui d’un modèle gravitationnel. Une modification a
finalement été apportée au modèle gravitationnel
utilisé pour le calcul du potentiel de l’accessibilité
spatiale aux médecins prodiguant des SSP dans la
province canadienne de la Nouvelle-Écosse. Ce
modèle, qui utilise une fonction décroissante de la
distance, permet de relativiser le niveau
d’accessibilité spatiale aux SSP. Les résultats de
l’application de ce modèle nuancent mieux le potentiel
d’accessibilité. Il ressort de cette étude un portrait
nuancé de l’accessibilité aux médecins en SSP en
Nouvelle-Écosse. Partant du principe que les gens
traversent les frontières arbitraires des unités de
recensement, le modèle gravitationnel constitue un
meilleur outil d’évaluation de l’accessibilité. Il
constitue également un apport novateur dans
l’évaluation du potentiel d’accessibilité aux médecins
en SSP au Canada et fournit, de manière générale,
une base sur laquelle évaluer si les politiques ont eu
une incidence positive sur le caractère équitable de la
prestation des SSP dans les provinces canadiennes.
Mots clés : Services de soins primaires, modèles
gravitationnels, estimation par noyau de la densité,
accessibilité spatiale, santé rurale et éloignée
Introduction
The problem of spatial access to primary health
care (PHC) is especially Canadian given that ours
is a largely rural and remote country from a
geographical perspective—and one that is committed to health equity. Attention has recently
been drawn to the mismatch between the spatial distribution of inhabitants and that of PHC
providers (Pong and Pitblado 2005). Spatial analysis has an important role to play in understanding this phenomenon. Spatial measures of access
to PHC are not well studied—despite some key
studies in recent years (Gatrell et al. 1996). More
importantly, they have not been well integrated
into the health services literature (Pong and Pitblado 2005; Watson et al. 2005). Instead, such
literature continues to focus on simple ratios
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
of physicians to denominator population as a
means of measuring potential spatial access to
PHC. By developing more nuanced measures of
spatial access to PHC, spatial analysis can make
a unique and important contribution to understanding equity of access to care in Canada.
This article begins by outlining the mandate
for universal access to PHC in Canada and expectations fostered by international and national
commitments to fair and equitable access. We
then outline the two primary spatial analytical
methods for calculating spatial access to physicians: kernel density estimation and the gravity
model. We then move to create a modified gravity model to best determine potential spatial access to PHC physicians and demonstrate its use
in the test province of Nova Scotia. Comparison between the modified gravity model and a
Measuring potential spatial access to primary health care physicians
simple ratio of physicians to population demonstrate the role that spatial analytics can play with
respect to modelling relative access to health
services.
Primary Health Care and Expectations
of Spatial Access in Canada
The basic tenants of PHC are that first-contact
health and social care should be given in communities, by communities and for community
members with the use of appropriate technology
in ways that may advance social development
(WHO 1978; Cueto 2004; Litsios 2004; Crooks
and Andrews 2009a). In 1978, the World Health
Organization (WHO) advanced PHC provision as
a way to achieve ‘health for all’ at the global
scale by the start of the twenty-first century.
More specifically, the WHO released the Declaration of Alma-Ata following a conference held
in the USSR in 1978 outlining its vision for
providing PHC around the world (Cueto 2004).
This Declaration is a landmark document as it
establishes access to health care as a basic human right (Werner 1995). Although the goal of
achieving health for all had not been reached by
the start of the new millennium, PHC remains an
important feature of health care systems across
countries, and the WHO has renewed its interest in advancing such care provision (Pappas and
Moss 2001).
PHC is understood to mean a number of different things depending on how it is interpreted. In
developing nations, it has been frequently interpreted selectively (also known as selective PHC),
whereby low-cost interventions to prevent the
spread of disease are prioritized as the most
important first-contact care that can be given
(Wisner 1988; Cueto 2004). In developed nations
with established care systems, health care decision makers have frequently translated the PHC
mandate into a set of system priorities, namely
that care be made affordable, universal, delivered equitably and comprehensive for all citizens
(Hall and Taylor 2003). These priorities are often applied to primary care (i.e., the entry point
in a system organized by care tiers) to facilitate the provision of comprehensive, coordinated
and first-contact medical and preventative care
(Schoen et al. 2004; Cardarelli and Chiapa 2007),
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
31
even though the PHC mandate clearly has relevance to other system tiers (Canadian Nurses Association 2005). The practice of family physicians
and general practitioners is, in fact, central to
both primary care and PHC (Agarwal 2009), and
thus they are core providers of PHC across countries and systems.
Although PHC is defined and understood in a
number of ways throughout Canada (see Crooks
and Andrews 2009a), Health Canada (2006, n.p.)
considers it to be both the ‘direct provision of
first-contact services (by providers such as family physicians . . .); and a coordination function to
ensure continuity and ease of movement across
the system . . .’. PHC is considered to be ‘the
single most important basis from which to renew
the [Canadian] health care system’ and forms
the core of the country’s health care system
(Canadian Nurses Association 2003, 1). Enhancing Canadian PHC has been identified as a significant priority in a number of important and
influential reports such as the 2003 First Ministers’ Accord on Health Care Renewal and the
10 Year Plan to Strengthen Health Care, which
was released in 2004. In fact, over one billion
dollars have been spent since 2000 on enhancing PHC in Canada throughout the process of
transitioning to a ‘renewed’ PHC system (Health
Council of Canada 2005, 2008). A focus of this
enhancement has been on implementing teambased care, creating after-hours care, using new
care technologies and changing physician payment modes (Glazier 2008). Such enhancements
are clearly designed to better facilitate Canadians’ access to first-contact care, and particularly
that provided by family physicians and general
practitioners.
Access is a core geographic concept central to
PHC that has been translated into system priorities across nations, along with community-based
and equitable provisioning (Crooks and Andrews
2009b). For example, in the Declaration of AlmaAta, mentioned above, the WHO calls for PHC
to be ‘universally accessible’ (WHO 1978). The
issue of equitable access is, however, complex.
Talen (1998) argues that it includes a number of
value judgments about which groups should benefit, what social justice looks like, and how to
arrive at decisions about allocation of resources.
Clearly, these issues are of concern to many as
equitable access usually involves taxpayer funds
32
Nadine Schuurman, Myriam Bérubé and Valorie A. Crooks
and spatial distribution of valued assets (Talen
1998). Access to health care in general can be
thought of in a number of ways, including based
on geographic location, wait-list times, availability of needed information (e.g., for system navigation) and service quality (Torgerson et al.
2006). According to Haggerty et al. (2007, 340),
first-contact accessibility in PHC pertains to ‘the
ease with which a person can obtain needed care
(including advice and support) from the practitioner of choice within a time frame appropriate
to the urgency of the problem’. Certainly such
accessibility is predicated upon having spatial access to practitioners. In fact, when care is spatially inaccessible this lessens the equitability of
PHC provisioning (Bowen 2001; Crooks and Andrews 2009a; Wong and Regan 2009).
According to the results of the 2007 National
Physician Survey (Question 1) there are just over
30,000 family physicians and general practitioners in Canada, 91.9 percent of who are involved
in full- or part-time practice. Canadian family
physicians base their practice on four main principles, that: (1) the family physician is a skilled
clinician; (2) family medicine is a communitybased discipline; (3) the family physician is a
resource to a defined practice population; and
(4) the patient–physician relationship is central
to the role of the family physician (College
of Family Physicians of Canada 2006). They,
along with general practitioners, serve as gatekeepers for referrals to specialist providers and
surgeons, provide generalized care for all body
systems and also deliver preventative care,
among other things. Thus, these physicians
have an extremely important role in maintaining
the health and well-being of Canadians through
the delivery of care and enactment of their
principles of practice. Clearly, then, having
access—including spatial access—to their care is
important. When asked to identify access issues
affecting their patients, the distance patients
must travel and related travel expenses was
noted by Canadian family physicians and general practitioners as a barrier to care (National
Physician Survey 2007, Question 25b). Interestingly, when specialist physicians were asked
about their patients’ access to family physicians
(which likely also included general practitioners),
of 3,197 survey respondents, 28.7 percent indicated it was poor, 26.5 percent indicated it was
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
fair, and only four percent indicated it was excellent (National Physician Survey 2007, Question 25a). It is clear, then, that there is concern
about having access to family physician and general practitioner services, and given the focus of
Canadian health care decision makers on renewing PHC, the analysis presented herein is needed
in order to have an understanding of the current state of spatial access to general and family
medicine care based on the best science possible.
In this article, we thus focus our analysis on general practitioners and family physicians as they
are core first-contact PHC providers in the Canadian system. We collectively refer to these two
groups in the remainder of this article as ‘PHC
physicians’.
Spatial Access Calculations: Kernel
Density Estimation versus the Gravity
Model
Access to PHC is not a well-defined concept. This
results in ‘access’ encompassing meanings that
range from the capacity to pay (Wilkinson et al.
2001), clinic operation times (Macinko et al.
2004), culturally sensitive services (WHO 2008),
feeling you have the right, ability or interest to
access services (Bernard et al. 2004), to the physical siting of PHC service sites (Tanser 2006).
Khan and Bhardwaj (1994) do, however, offer
a particularly useful conceptualization of access, which is subdivided into four types: potential spatial, potential aspatial, realized spatial
and realized aspatial (Khan and Bhardwaj 1994).
Potential access refers to the availability of a service while realized access refers to actual usage
(Aday and Andersen 1974). Spatial access is dependent on the geographic distribution of services and target populations while aspatial access
depends on social factors such as income, education, race and age (Khan 1992). More specifically,
potential spatial access to PHC depends on a patient’s travel cost (time/distance) to the physician’s office as well as the amount of demand
for a physician’s services by other clients (Joseph
and Bantock 1982). Khan further clarifies that it
‘refers to the availability of that service as moderated by space, or the distance variable . . . [and]
stems from a proposed conceptualization of access to health care in terms of a series of
Measuring potential spatial access to primary health care physicians
dichotomous dimensions’ (1992, p. 275). Recent
studies examining issues of potential spatial
access have focused not only on health care services but also on issues of access to nutritious
food (Sharkey and Horel 2008; Sharkey 2009),
among others.
In this study, we examine potential spatial access to PHC, and specifically PHC physicians, using the Canadian province of Nova Scotia as
an example. There are a number of traditional
ways to measure spatial access, including the
simple count of facilities within a defined unit,
the average time to a facility, average travel distance and the opportunity versus impedance-totravel models of access (Handy and Niemeier
1997; Talen and Anselin 1998). These methods
were conceived in a more computationally limited era. Geographic information systems (GIS)
methods now allow more facets of the environment to be included; measuring spatial access
is made more complex by numerous variables
including methods of geographical aggregation,
how residential areas are defined, and distance
measurements—all impact resultant measures of
access (Apparicio and Séguin 2006; Apparicio
et al. 2008). Ensuing approaches have sought
to limit variation in the results of access
analysis that are artefacts of the measurement
technique rather than ‘on-the-ground’ reality. Kim
and Kwan (2003) describe an enhanced space–
time prism that attempts to account for the opportunities within a specified geographical area
but also model the complexities of the spatial
environment that ultimately affect access. Their
model specifically incorporates the time of day
that activities or services are offered (Kim and
Kwan 2003). This dimensionality is further extended by Geurs and van Wee (2004), who argue
that access models should ideally incorporate
not only individual space–time constraints but
also feedback amongst access, land-use and
travel behaviour variables. They argue that these
inter-relationships are necessary in order to fully
understand transportation use decisions in different land-use scenarios. Moreover, they suggest
that the perspective/perception of an individual
regarding having access is more important than
absolute measures (Geurs and van Wee 2004).
These are important innovations in understanding and theorizing access.
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
33
In this article, we focus on potential spatial access strictly through the lens of road travel time
as we are concerned especially with rural and
remote access to an essential service (i.e., PHC
physicians). Two leading potential spatial access
modelling methods have emerged in the recent
health services literature: kernel density estimation and the gravity model. We examine each
in some detail below and ultimately argue that
the gravity model is superior for the purpose
of modelling potential spatial access to PHC in
Canada.
Kernel density estimation
In general, kernel density estimation is used
to estimate a smooth probability density function from univariate or multivariate data (Silverman 1986). For univariate data, it is the smooth
equivalent of a histogram. To create a smooth
density surface from a set of bivariate data
points, a symmetrical hump, defined by a kernel
function such as the Gaussian distribution function, is placed over each point. Each hump has a
total density (volume) of 1.0. If a point has a
population of 10, it counts as 10 points. The
kernel density estimation process is illustrated in
Figure 1.
One of the key attributes of this method that
Figure 1 illustrates is that the choice of bandwidth greatly affects the resulting density surface. A large bandwidth will create a smoother
surface and will mask local peaks or troughs.
The choice of bandwidth depends on the data
and the desired level of detail (like choosing
a bin width for a histogram). According to the
literature, choice of kernel function has little
impact on the resulting surface. Bandwidth selection, on the other hand, greatly affects the
results.
Initially, kernel density estimation was used in
health GIS to model disease risk (Gatrell et al.
1996). It was applied on a set of points of disease occurrence to create a probability density
surface of disease occurrence. This surface was
divided by a population density surface to obtain
a disease risk surface.
When using this model to create a potential spatial access surface for PHC, two kernel density surfaces are made: one for PHC
34
Nadine Schuurman, Myriam Bérubé and Valorie A. Crooks
Figure 2
The problem with using the kernel density estimation method to estimate the ratio of provider to population is that unoccupied land
distorts the ratio.
Figure 1
Kernel density estimation. The figure illustrates a set of points:
S 1 . . . Sn . The circle around a point represents its sphere of influence
and its radius is the bandwidth of the kernel function. The height of
the hump (kernel function) models the influence of the data point S
on its surroundings. This influence decreases with distance.
physician locations and one for population (or
patient) centroids (Guagliardo 2004). The first
raster is divided by the second in order to create
a PHC physician-to-population ratio raster. The
raster cell sizes and extents for the two surfaces should be the same. The raster cell values in each census polygon of choice can then
be averaged to determine the PHC physician-topopulation ratio for a census polygon.
One problem with using kernel density estimation to model the distribution of health care
services, however, is that it uses straight-line distances (circles of a specified radius around a
service point). These clearly do not correspond
to road networks. Another problem is the bandwidth (radius) used for services is often large
compared to the study area, which creates a service density layer consisting of one set of concentric rings (Guagliardo et al. 2004; Yang et al.
2006). This density distribution does not show
much information about the distribution of services; it simply shows that services near the middle of an area are close to more services in
the area than services near the periphery. The
second problem with having large bandwidths
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
is that they increase the likelihood that some
of the service supply will be lost to areas with
no population, such as a large lake (Yang et al.
2006).
Many researchers use a larger bandwidth for
physicians than for population points (Guagliardo et al. 2004; McLafferty and Grady 2004;
Yang et al. 2006). This is because the bandwidth of the provider represents the distance
from which patients are likely to come, while the
population bandwidth is the radius of the circular area which the population of a centroid occupies. Thus, portions of PHC circles end up in
lakes, airports, national parks and the like. This
‘supply density’ is lost. In the highly simplified
raster division illustrated in Figure 2, 1/4 of a
physician’s potential supply is lost due to this
problem.
Lastly, kernel density estimation is not a good
model of population density. When modelling the
kernel density of population centroids, one assumes that most of the population lives near the
centroid and that population density decreases
with distance from the centroid. This is absolutely false. In fact, it may be possible that no
one lives near the centroid, it just happens to be
the centre of the census block (the census block
is a small unit for which no demographic information is given but for which full populations
counts are available; it is approximately equivalent to a city block). The best we can assume
from census population data is that a census
block’s population is evenly distributed within
the census block polygon. Figure 3 below depicts
a census block polygon, its centroid and the centroid’s ‘circle of influence’.
Measuring potential spatial access to primary health care physicians
35
the gravity equation developed by Joseph and
Bantock to model potential spatial access to general practitioners in rural areas of Southern Ontario, Canada (Joseph and Bantock 1982). This
equation is based on Newton’s Law of Gravitation
and is used in other studies of potential spatial
access to health care (Luo and Wang 2003), as
well as studies on access to employment (Wang
2001; Wang and Minor 2002).
Ai =
Sj
j
D j d ij
β
where:
Figure 3
Census block polygon showing its centroid and the centroid’s circle
of influence.
Network kernel density estimation
The first two problems with two dimensional
(2-D) kernel density estimation identified above
can be fixed by using road network kernel density. This method distributes point densities over
a road network instead of over 2-D space. Some
advances have been made in this area in the
last several years (Borruso 2005; Xie and Yan
2008; Okabe et al. 2009). Most notable is a toolbox for spatial analysis on a network: SANET
(Okabe et al. 2006). These are upgraded regularly
and can be installed on ArcGIS Desktop. However, the third problem remains. This is because
‘PHC supply’ is not well modelled by a continuous distribution over a 2-D landscape or a road
network. It is distributed to discrete population
pockets which can be separated by nonpopulated
areas.
Gravity model
Although Guagliardo (2004) chose to use kernel
density estimation in his study of access to PHC
in Washington, DC, he argues that the gravity
model is the most reliable measure of spatial access, whether potential or realized. He presents
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
Ai = access at population point i (census block
centroid)
Sj = supply at physician location j (# of physicians at postal code centroid)
dij = distance/travel time between population
point i and physician location j
β = distance decay coefficient
Dj = demand at physician location j
Dj =
Pk
β
k
d kj
where:
Pk = population at population point k
dkj = distance/travel time between population
point k and physician location j
β = distance decay coefficient
In this model, a population block’s access is
equal to the sum of the supply and demand
ratios, weighted based on distance/travel time
from the population centroid at all physician
locations (within a reasonable distance/travel
time—we use two hours). Before calculating access, demand at each location must be determined. Physician demand is equal to the sum
of all nearby populations weighted by distance/
travel time.
The distance decay coefficient is the main
drawback to using the gravity model. It measures the relationship between actual populationservice interaction and distance, assuming other
possible factors influencing interaction are constant (Fotheringham 1981). Theoretically, it
should be calculated from actual physician
36
Nadine Schuurman, Myriam Bérubé and Valorie A. Crooks
service utilization data using regression (Shen
1998; Wang 2001) but often these data are not
available. The size of the exponent depends on
how far people are willing to travel to access
a service. A high exponent, which increases the
rate at which the distance weight increases with
increasing distance, means that people tend not
to travel far for a service, while a low exponent means that people are willing to travel farther for a service (Black 1973). This suggests
that different β values may need to be used to
measure urban and rural access to PHC physicians. A low exponent results in a smoother ‘access surface’ while a high exponent produces
sharper variation. Some authors have experimented with various exponents (Luo and Wang
2003) while others have chosen exponents based
on published precedents (Joseph and Bantock
1982).
β
The impedance function, d ij , can be replaced
by an exponential function (Shen 1998) or a step
function (Robitaille and Herjean 2008). The twostep floating catchment area method, designed
by Luo and Wang (2003) and based on the spatial decomposition method by Radke and Mu
(2000), is quite commonly employed in recent
PHC access studies (Langford and Higgs 2006;
Yang et al. 2006; McGrail and Humphreys 2009).
This method, although developed from a different direction, is equivalent to the gravity model
above with only two possible distance weights:
1 or ∞ (Luo and Wang 2003). If d ij <= D then
β
β
d ij = 1 and if d ij > D then d ij = ∞ (access = 0)
for some cutoff distance/travel time D (such as
30 minutes). This cutoff value is a bit artificial, assigning the same travel impedance to all
population centroids within a maximum travel
time/distance, and complete lack of access to
all populations outside this maximum value.
McGrail and Humphreys (2009) begin their study
of access to PHC in rural Australia with the regular 2-step floating catchment area method, later
deciding to add a distance decay function (graduated between 15 and 60 minutes).
In the present study, we employ a two-hour
cutoff when assessing potential spatial access as
a means of accommodating the vast differences
in population density and service distribution
across Canada. It is premised on the understanding that in extremely remote areas, the perceived
viable travel time to services will be longer than
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
in more compact urban areas that enjoy better
PHC. Furthermore, we believe that two hours is
a reasonable maximum one-way drive time for
a day trip. This cutoff time is also supported—
sometimes indirectly—by other research. For example, Luo and Wang (2003) tested various beta
parameters for the gravity model to determine
which one gave the preferred amount of variability for their study area (Luo and Wang 2003).
They also tested various cutoff time parameters for the two-step floating catchment method
(2SFCA). They found that larger cutoff times
(2SFCA) and smaller beta values resulted in more
gradual variations in access scores across a geographic region. Most PHC access studies, however, have been conducted on smaller areas such
as counties and metropolitan areas (Joseph and
Bantock 1982; Langford and Higgs 2006; Yang
et al. 2006). These studies use cutoff times of
30 minutes or less. McGrail and Humphreys’
(2009) study of access in rural southern Australia found that small cutoff times left too
many regions with no access, so they preferred
60 minutes.
Luo and Wang (2003) tested various beta parameters (β = 1 to 2.2) for the gravity model
to study access to primary care in the Chicago
10-county region. They found that the variances
in the access scores were similar when using the
gravity model with β = 1.8 and 2SFCA with a
cutoff time of 50 minutes. Joseph and Bantock
(1982) used β = 2 and were looking at access
within a 15-mile radius in Wellington County,
southern Ontario. McGrail and Humphreys (2009)
used β = 1.5 when studying access to care
in rural Australia within a 60-minute travel
time (McGrail 2009, personal correspondence). In
the present study, we are working with large
distances and assuming car travel times and
therefore need a smaller beta value than used
in studies for smaller rural areas surrounding
metropolitan areas where it may be financially
feasible to have PHC services within 30 minutes of most residents. We chose to assume that
travel time and PHC use were inversely proportional (β = 1): doubling travel time halves the
willingness of people to visit a PHC physician.
For our study area, this amount of variation in
access works well.
In the remainder of this article, we develop
a variation of the distance/travel time decay
Measuring potential spatial access to primary health care physicians
β
function, d ij , identified in the literature review
to accurately calculate potential spatial access to
PHC physicians in the province of Nova Scotia.
Data
1) PHC physicians
Addresses of PHC physicians practicing in
Nova Scotia were obtained from the MDSelect Canadian Medical Directory. We used only
data for physicians with either a ‘General
Practice’ or ‘Family Medicine’ designation in
the directory listing. Since not all physicians
had street addresses (some have PO Box addresses), postal codes were used to geo-code
physicians. DMTI Platinum Suite 2008 postal
codes were used. As the postal code file contains more than one point with the same
postal code, the Single Link Indicator (SLI)
field was used to select unique points for each
postal code.
2) Population
Statistics Canada 2006 census block centroids
were used as population points. 2006 census
population values were obtained with Statistic
Canada’s Geosuite 2006 software. The dissemination block (DB) is the smallest unit of census geography covering all of Canada.
2) Roads
Road network analyses were run using DMTI
2008 CanMap RouteLogistics roads for BC,
Nova Scotia and Ontario. These road data
come with detailed spatial and nonspatial
information: each road feature has a value
for distance, speed limit and direction of
travel.
37
β
the distance/travel time decay function, d ij , presented in the literature review above. We denote
this function as f (tij ); we replace dij with tij since
we use travel time, not distance. For travel times
less than or equal to 10 minutes, we do not apply any decay; for 10 to 120 minutes we use a
decay that is (β = 1) proportional to travel time.
When travel times exceed 120 minutes, we consider these locations to be inaccessible.
Gravity model:
Ai =
j
Sj
D j f (tij )
where:
Ai = access at population point i (dissemination block centroid)
Sj = supply at physician location j (# of
physicians at postal code centroid)
f (tij ) = travel time impedance function
tij = travel time from population point i to
physician location j
Dj = demand at physician location j
Dj =
k
Pk
f (tkj )
where:
Pk = population at population point k
f (tkj ) = travel time impedance function for
travel from population point k to physician location j
The following travel time impedance function
was used:
f (tij ) = 1 for tij ≤ 10 minutes
tij
10
Methods
f (tij ) =
Measuring potential spatial access to PHC: the
gravity model
f (tij ) = ∞ for tij > 120 minutes (no access)
Potential spatial access to PHC depends on distance to nearby physicians as well as the availability of these physicians (i.e., the number of
physicians [supply] relative to the demand on
these physicians from surrounding populations).
Because of this, the gravity model was chosen
to determine potential PHC access in Nova Scotia. To do this, we used a slight variation of
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
for 10 < tij ≤ 120 minutes
We assigned no travel impedance, (tij ) = 1, to
any dissemination block within 10 minutes of
a PHC physician. Dissemination block centroids
farther than 120 minutes from a physician were
considered to have no access. Between 10 and
120 minutes, impedance increased linearly (a dissemination block 40 minutes away from a PHC
physician has four times the travel impedance of
38
Nadine Schuurman, Myriam Bérubé and Valorie A. Crooks
a DB 10 minutes away). Actual PHC utilization
data were not used to determine the relationship between travel time and utilization. However, since rural areas that are commonly located
an hour or more from the nearest physician were
included in the study, a linear relationship between travel time and impedance was assumed
to be reasonable.
Figure 4 below illustrates our gravity model.
Three scenarios are presented where access for
the dissemination block i is calculated. Circles represent dissemination block centroids and
squares represent postal code centroids. The
numbers in each shape are the population count
and physician count respectively. Travel times
are indicated on the arrows. The first scenario is
the simplest. Three PHC physician locations are
within 120 minutes of DB i. Their facilities are
all within 10 minutes so there is no distance decay. One hundred other people are also within
10 minutes of all three facilities. First, the demand at each facility is calculated: 150 for all.
Access is then found by summing the supply
over demand ratio for each facility. Note that in
this case, the other population centroid has the
same access score. For the area covered by the
two DBs, the access score is equal to the total supply, four physicians, divided by the total demand, 150 people. The second scenario
shows the impact of competing populations on
a DB’s access. Here, the ‘other’ centroid theoretically does not have access to facility C. This
decreases the demand at facility C and therefore increases access to care in the DB i. The
third scenario shows the effect of the travel time
impedance function. Facility C is now 20 minutes
away from DB i. This decreases the demand at
facility C, which is beneficial to the ‘other’ DB.
However, it decreases the access of the DB i to
facility C and also, its total access score.
Calculating travel times
Road travel times for each DB-PHC physician pair
within 120 minutes drive time were calculated.
ESRI’s ArcMap 9.3 software and the Origin Destination Cost Matrix tool, which is part of the
Network Analyst extension, were used. DB centroids within 2.5 kilometres of a road and with a
Figure 4
Three iterations of the modified gravity model. In the first example on the left, potential spatial access is determined by summing the supply
over demand ratio for each facility. In the second example (middle), we illustrate the impact of a decrease in demand at one facility when it is
inaccessible to a part of the population. The third scenario on the right illustrates the effect of travel time impedance on access to PHC.
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
Measuring potential spatial access to primary health care physicians
39
Figure 5
The problem of floating ‘road bits’ which are unconnected to the major road networks. Here we show a section of Nova Scotia in the Truro area
with small floaters (circled).
population density greater than one person per
five square kilometres were inputted as origins
and physician postal codes within 2.5 kilometres
of a road were inputted as destinations.
Initially, all roads for the province were used
to create a road network data set and this network was used to generate an origin destination cost matrix (OD). However, it was found
that some DBs with high population densities
and close to other DBs with access were assigned
potential spatial access values of 0. This is because the road file contains ‘road bits’: small sections of road which are not connected to the
main road network. If a DB centroid is closer to
a road bit than the main road network, its population is lost and thus not connected to a PHC
physician. Road bits near Truro, Nova Scotia are
shown in Figure 5 as disjointed purple strands
to illustrate this problem.
In order to create a road network with no road
bits, two-hour service area roads for PHC physician postal codes were generated using the Network Analyst extension New Service Area tool
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
(service area lines were generated for selected
physician postal codes within about two hours
of each other and not located near a road bit).
These two-hour service area roads were used to
create a new road network which was then employed to generate OD matrices. An earlier version of this method is described in detail in
Schuurman et al. (2006).
Calculating access scores
The resulting OD matrix table contains fields
for the DBUIDs (unique identifying number for a
dissemination block) of the origin DBs, the postal
codes of the destination PHC physicians and the
respective travel times. The value of the travel
time impedance function, (tij ), was calculated for
each DB–PHC physician pair and added as a field.
Before calculating the DB potential spatial access
scores, a PHC physician demand table was created by querying the OD matrix. The OD matrix
was joined to a table containing 2006 DB populations and another table containing the number of
40
Nadine Schuurman, Myriam Bérubé and Valorie A. Crooks
Figure 6
Modified gravity model results using dissemination blocks, a high resolution geographical unit. Note that rural areas have a relatively low access
score.
physicians per postal code. It was then grouped
by physician postal code and, for each postal
code, the demand from each DB within two
hours of that postal code, Pi / f (tij ), was summed.
To calculate DB access scores, the OD matrix
was joined to the new physician demand table.
The OD matrix was grouped by DBUID and, for
each DB, the access values for all PHC physicians
within two hours of that DB, S j /D j f (tij ), were
summed.
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
Results
The modified gravity model was used to calculate
potential spatial access scores for PHC for the
province of Nova Scotia using first DBs (the most
granular unit of analysis possible). As illustrated
in Figure 6, Nova Scotia DBs were mapped and
coloured by an access score. Access scores were
divided into five categories using the Jenks Natural Breaks method (score distribution did not
Measuring potential spatial access to primary health care physicians
41
Figure 7
Modified gravity model results based on dissemination areas—the smallest census geography for which a full range of demographic data exist.
follow a normal distribution). Areas with no access (shown in grey) are either islands or DBs
with areas larger than 5 km2 .1 Otherwise, all
DBs in Nova Scotia are within two hours of a
PHC physician. Halifax has the highest access
scores, followed by Yarmouth and Sydney. Popu1
Limitation: DBs with areas larger than 5 km2 were found to
have no access since their centroids are usually farther than
2.5 km from a road. In the future, these centroids can be visually inspected and brought closer to an appropriate road
segment before running the OD Cost Matrix tool. Doing so
will depend on the location of a DB polygon with respect to
the road network.
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
lations that live around Truro, New Glasgow and
Antigonish have ‘average’ access scores. Other areas have relatively low potential spatial access to
PHC physicians.
Access scores are essentially physician-topopulation ratios, except that they account for
the fact that people are allowed to travel to
nearby DBs. If people in a DB must travel far
to reach a PHC physician, they are considered to
have access to a partial physician. Only DB populations within 10 minutes of a physician have
full access to the PHC physician. Like physicianto-population ratios, the population weighted
42
Nadine Schuurman, Myriam Bérubé and Valorie A. Crooks
Figure 8
Conventional measure of physician-to-population ratios using census dissemination areas. Even if this measure is calculated based on the larger
census geography unit of the census subdivision, the problem of fixed, impermeable geographic boundaries remains and confounds the metric.
Note that even in large metropolitan areas (e.g., Halifax), the ratio is relatively low.
average of all the DB access scores is equal to
the physician-to-population ratio for all of Nova
Scotia. Calculated access scores were multiplied
by 10,000 to show physician access per 10,000
people.
Figure 7 is similar to Figure 6 but is calculated based on the dissemination area (DA) unit.
As a reminder, dissemination blocks (DB) are the
smallest unit for which population counts are
available. Dissemination areas (DA) are the smallest units for which complete demographic information is available for all variables. Typically
DAs contain 400–700 people. DA access scores
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
were calculated as the population weighted average of DB scores. Comparing this map to
Figure 8, which depicts the traditional epidemiological measure of physician-to-population ratios in Nova Scotia DAs, clearly shows the
advantage of using the gravity model. The reader
can quickly discern that the ratio does not do
an adequate job of illustrating nuances of potential spatial access. For example, a person
residing within 90 minutes of a PHC physician
located in another DA or DB in rural Nova Scotia is still given an access score using the gravity model metric. Using the traditional ratio, this
Measuring potential spatial access to primary health care physicians
person would appear to have no access whatsoever. The gravity model permits more accurate and nuanced relative measurement of access
to PHC.
The regular ratio map (Figure 8) assumes that
people cannot cross DA borders. Thus, only DAs
with PHC physicians are considered to have potential spatial access. These DAs only contain 14
percent of Nova Scotia’s population. All other
DAs are considered to have no access—clearly a
gross misrepresentation. In Figures 6 and 7, calculated using our modified gravity model, 440
people are considered to have no access and 18
are excluded for low population density. This is
a much more realistic picture of potential spatial
access to PHC.
Discussion and Conclusions
Though traditional epidemiology has relied upon
using physician-to-population ratios to visualize
and assess access, it is an insufficiently nuanced
metric to appropriately describe spatial access in
a large and highly rural country such as Canada.
This article described a number of traditional
accessibility models and specifically articulated
the relative merits of kernel density estimation
and the gravity model. Based on an assessment
of the chief two candidates, we selected the gravity model as the preferable method and modified it to optimally describe spatial access to
PHC. The results allow the reader to visualize
relative geographical access to PHC—using the
province of Nova Scotia as an example. Likewise,
we demonstrated the relative paucity of description associated with the traditional ratio metric.
Spatial access to PHC is a pressing consideration for Canadians, and particularly those in
rural and remote locations (Schuurman 2009). Focus group testing indicates that lack of timely access to PHC poses challenges to rural and remote
residents and affects decisions about location of
health services (Wong and Regan 2009). While ensuring access to PHC has long been recognized
as an issue of creating health equity—clearly one
that is especially resonant in rural regions—little
attention has been paid to appropriate spatial
analysis tools available to most accurately determine access. As practice patterns of physicians
change over the next decade, measures of poten-
The Canadian Geographer / Le Géographe canadien 54, no 1 (2010)
43
tial spatial access will become more important—
and spatial analysis has an important role to
play in providing the information needed to address these challenges. Even as the landscape of
physicians and access shifts, the concept of optimal travel time distance will continue to be elusive. We suggest that optimal is perhaps a term
best used when comparing methods rather than
as a prescriptive modifier of spatial access.
Future research will use the modified gravity model to assess access to PHC across all
provinces in Canada from a health services
perspective. This article is an important first
methodological step towards this end. The
authors recognize, however, that the most important aspect of implementing gravity models
is the process of interpretation and eventual implementation for decision making (Handy and
Niemeier 1997). A key aspect of future research
is thus the translation of potential spatial access
models to policy and decision makers.
Acknowledgements
This research was funded by the Canadian Institutes of
Health Research and the Michael Smith Foundation for Health
Research.
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