Aug 2013. Vol. 3, No.4 ISSN 2305-1493 International Journal of Scientific Knowledge Computing and Information Technology
© 2012-2013 IJSK & K.A.J. All rights reserved www.ijsk.org
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ACCESSIBILITY OF HEALTH CARE INSTITUTIONS: A CASE
STUDY BY USING GIS
Fatih KARA & Dr. Istvan Oliver EGRESI
Fatih University, Department of Geography, 34500, Buyukcekmece/Istanbul, TURKEY.
ABSTRACT
Equitable provision of health care services is a major challenge for developing countries. The degree of
accessibility of health care institutions is one of the most significant indicators for measuring the
efficiency of a health care system. Accessibility is a complex indicator that reflects the number of health
care institutions, their geographical distribution and the impact of different types of barriers (economic,
social, cultural, etc.). Geographers have been mainly concerned with geographical accessibility for the
calculation of which they have tried different methods. Over the last twenty years, GIS has provided
valuable tools for the measurement of geographical accessibility. In this study, we use GIS tools to
investigate the accessibility of health care institutions in the Büyükçekmece district of Istanbul. We
found no major accessibility problems in the district as even those inhabitants living the farthest from
the health care centers can reach the closest medical institution in less than 30 minutes. Nevertheless,
these results are based on the assumption that patients always visit the closest health care center which is
not always realistic.
Keywords: medical geography, health care provision, accessibility, geographic information systems,
Istanbul, Turkey.
1. INTRODUCTION
Public provision of health care services is
among the biggest problems in developing
countries and the accessibility of health care
institutions is one of the most important factors
in constituting healthy communities. The
degree of accessibility of health care
institutions is one of the most significant
indicators for measuring the efficiency of the
health care system (Gatrell and Elliott 2009).
The access of public to health care institution
could be seriously restricted by distance (Black
and al 2004). Longer distances may affect
especially the access of elderly and of
physically-impaired people to health care. In
general, longer the distance to health care
facilities higher the risk of fatalities (Jones et al
1998; Hare and Barcus 2007), although this is
disputed by some studies (Drummer and Parker
2004).
The study of accessibility to health care has
long been of interest to medical geographers
and other social scientists (Quah 1977; Joseph
and Phillips 1984). Such studies on health care
accessibility have not been confined to the
more developed countries (Hare and Barcus
2007; Jones et al 1998; Pearce et al 2006; Ohta
et al 2007; Liu et al 2002; Kalogirou and Foley
2006; Philips et al 2000) or urban areas; a
substantial number of studies has also been
published on health care accessibility in
developing countries (Perry and Gesler 2000;
Rosero-Bixby 2004; Gibson et al 2011; Okafor
1990; Murad 2004) and rural areas (Brabyn and
Skelly 2002; Tsoka and Le Sueur 2004; Arcury
et al 2005).
There are many studies in which GIS were used
as a tool in querying the physical
(geographical) accessibility of health care
institutions (Love and Lindquist, 1995;
Wilkinson et al. 1998; Albert et al. 2000;
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Phillips et al. 2000; Black et al. 2004; Murad,
2004). In addition to these, World Health
Organization (WHO) also developed a GIS-
based model for physical accessibility of health
care institutions. Analysis, such as price
efficiency and total number of patients who
have ability to reach hospitals, can be
performed by means of this model. The subject
of this study is to analyze the accessibility of
health care institutions located in Istanbul’s
Büyükçekmece district by using the high ability
of GIS in analyzing spatial data.
2. LITERATURE REVIEW
According to Quah (1977; also in Penchansky
and Thomas 1981; Oliver and Mossialos 2004),
accessibility of health care is a complex
indicator for the health of the health care
system in a country or region and implies
adequacy in numbers, fair geographical
distribution and absence of any type of barrier
(economic, social, or cultural) to medical care.
Penchansky and Thomas (1981, p. 128) also
argue that there are several dimensions
(availability, accessibility, accommodation,
affordability and acceptability) that we need to
consider when discussing access of population
to health care facilities, while Gulliford et al
(2002, p. 186) contend that “the availability of
services, and barriers to access, have to be
considered in the context of differing
perspectives, health needs and material settings
of diverse groups in society”. Based on these
arguments, Joseph and Philips (1984), Aday
and Andersen (1974), Luo and Wang (2003)
and Guagliardo (2004) distinguished between
potential accessibility (which refers strictly to
physical accessibility or to the number of
people residing within a certain range who
could potentially use the services of these
health care facilities should they face no
barrier) and revealed accessibility (or actual
utilization of health care facilities, which takes
into account the barriers mentioned above).
Poor countries suffer from lack of or
insufficient medical infrastructure. Moreover,
the health care facilities tend to concentrate in
the capital and the biggest cities while vast
rural areas remain uncovered by medical
services. Economically and socially more
advanced countries generally have an adequate
number of health care facilities but problems
may still exist due to their unequal territorial
distribution or due to the existence of certain
barriers that may restrict the access of certain
categories of people to health care. While we
do not intend to minimize the importance of
these social, economic or cultural barriers, in
this study we focus mainly on the issue of
geographical (physical) access to health care
facilities.
Geographical accessibility is a topic that has
preoccupied medical geographers for quite
some time (Quah 1977). They have tried
different methods to evaluate accessibility.
Many authors have used basic cartographic
methods to map the availability of health care
facilities and highlight potential inequalities
(Knox 1979). They have also used
sophisticated mathematical models to
understand the effect of distance on
geographical accessibility of health care
facilities (Mitropoulos et al 2006; Knox 1979;
Koening 1980; Joseph and Bantock 1984) and
statistical methods to reveal the existence of
factors or barriers that affect the access of
population to health care services (see also
Guagliardo 2004 for an interesting review of
these models and statistical methods). For
example, Vedia Dokmeci and collaborators
(Dokmeci 2002; Dokmeci and Ozus 2004;
Şentürk et al 2011) have investigated the
distribution of different types of health care
facilities (hospitals, physician offices and
pharmacies) in Istanbul. Using a regression
analysis, they found that the most important
factors that influence the distribution of these
health care facilities are population income and
education level. Moreover, they found that,
while state hospitals are more evenly
distributed, private hospitals tend to
concentrate in high-income districts (Şentürk et
al 2011).
Over the last 20 years GIS has provided a
valuable tool for the measurement of
geographical accessibility (Gatrell and Elliott
2009; Kohli et al 1995; for an interesting
review of the literature on the use of GIS to
assess accessibility to health care services see
also Higgs 2004). GIS, just like other data
management systems (DMS), is an information
system used for obtaining, organizing, storing
and analyzing large-scale data (Black et al
2004; Aronoff, 1989; Burrough, 1986). GIS
differs from other DMSs in its possession of
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information on the location of data it analyzes,
and the capability of GIS software to perform a
wide variety of assessments related to
geographical locations by means of its high
spatial analyzing ability. GIS software receives
this ability from “topological” properties it
possesses. Topology is defined as the geometric
relationships of objects found in GIS
environment to each other and relationship
(such as proximity and inclusion) between one
object and the other object is determined due to
this property. This constitutes the basis of
spatial analysis and due to these properties GIS
are ideal for measuring spatial accessibility
(Black et al 2004; Murad 2004)
Health services planning and GIS are two
interconnected concepts that require spatial
data. The location of health care institutions,
distribution and characteristics of patients are
primary spatial data that should be considered
during the planning of local health care services
(Murad, 2004). Spatial querying tools and
related GIS capabilities such “buffer” and
“overlay” make GIS a very efficient tool in
querying the accessibility of health care
institutions both for today and in the future
(Love and Lindquist, 1995; Black et al 2004).
Accessibility can be assessed by either
measuring the distance from residence to the
health care facility (linear distance or road
distance) of by estimating travel time. In some
cases perceived distance or perceived travel
time could also be considered (Arcury et al
2005; Love and Lindquist 1995).
Travel time is the preferred indicator for most
studies because it takes into account the state of
the roads and the main mode of transportation
(which together determine average speed). In
the more developed countries, where
automobiles are ubiquitous, driving time is
generally employed (Brabyn and Skelly 2002;
Luo and Wang 2003; Philips et al 2000; Hare
and Barcus 2007; Liu et al 2002). Some
authors (mainly from Europe where public
transportation is more developed and more
efficient than in North America or Oceania)
have also included the use of public
transportation in their calculation of travel time
(Lovett et al 2002). In the less developed
countries, researchers often use walking time or
travel time by public transportation to measure
distance from the nearest hospital (Tsoka and
Le Sueur 2004; Perry and Gesler 2000; Rosero-
Bixby 2004).
What could be considered an acceptable
distance for people to travel for medical care?
There is no universally accepted range. For
example, Rovali and Kiivet (2006) put this
range at 30 minutes beyond which they found
that geographical access to inpatient care was
diminished. Hare and Barcus (2007) have
estimated that people residing at more than 45
minutes from health care facilities are more
likely to be marginalized. Finally, Brabyn and
Skelly (2002: 7) consider one hour as an
adequate range because one hour is “a
threshold that ambulance drivers talk about”.
They also warn that “people who have to travel
more than one hour are paying a high cost
(financially and emotionally) to visit a
hospital”.
However, in some of the more developed of the
developing countries (the so-called emerging
markets), such as Turkey, the situation may be
more complex and therefore it may be more
difficult to assess accessibility using travel
time. While the number of automobiles has
increased tremendously in Turkey over the last
two decades, most people still rely on public
transportation for moving around. Moreover,
due to the high population density in the
Turkish cities, many people may be within
walking distance from most objectives. In big
cities, such as Istanbul, traffic may also be an
important variable. During rush hours, traffic
may be much slower and travel time much
longer than during other times of the day. This
may constitute an important limitation for those
studies that are based on travel time.
Measuring distance following the road network
is a relatively straightforward operation and it
is very easy to do using GIS. There is no
universally accepted definition for acceptable
range. Each researcher may decide on a range
(or ranges) based on his or her experience and
knowledge of the area. (There are many factors
that should be considered, such as topography
or population density.) For example, in a
similar study done in the Shaanxi Province
(China), Gibson et al (2011) measured
accessibility of households to rural health
centers using a five- kilometer and a ten-
kilometer range.
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GIS-developed models have an important role
in facilitating mobility (Boothby and Drummer
2003). Planners benefit from GIS-based studies
by learning about those areas that are
underserved by health care institutions
(Kalogirou and Foley 2006). Using GIS, they
can also identify the optimal location of new
facilities. The main purpose would be to
allocate the right number of users to those
locations and minimize the distance patients
have to travel to use those medical facilities
(Mitropoulos et al 2006). GIS tools may also
allow planners to anticipate changes in the
demand for medical services and act
accordingly (Murad 2004). This could result in
better allocation of resources based on
population needs for health care and, therefore,
in better accessibility (Gatrell and Senior
2005).
3. DATA AND METHOD
The first step was to identify the health care
institutions that are located within the
boundaries of the research area. These data
were then transferred into a GIS environment.
Next, the road network was digitized using the
“street” base map of ArcMap 10.1 software and
Google Earth. In addition, we determined the
topographical condition of the research area
with the help of Digital Elevation Model
(DEM) obtained from the images of Aster
satellite using a 30 m resolution.
We then produced buffer zones at 1 km and 3
km around the health care institutions. As we
have seen in the preceding chapter, these
ranges are arbitrarily selected by researchers
based on their previous experience and on the
specific local conditions. We chose to draw the
first buffer zone at 1 km because this is a very
densely populated district where people are
used to walk (shorter distances) to run their
errands and this distance could be covered in
approximately 15 minutes on foot. The second
buffer zone was chosen at 3 km because this
distance could be covered by a car or by public
transportation in about 10-15 minutes during
normal traffic conditions.
Another spatial analysis we performed is the
“shortest distance” analysis from two outlying
points to the closest health care institutions.
4. STUDY AREA
Büyükçekmece became a new district of
Istanbul after separating from Çatalca in 1998
and was included within the boundaries of
Metropolitan Municipality in 2008. Today,
there are 23 neighborhoods located within the
boundaries of Büyükçekmece (3.35% of total
land area of Istanbul province) and 201.077
inhabitants reside here according to the
address-based population registration system
(ABPRS) as of 2012 (figure 1a-1b).
(a) (b)
Figure 1: (a) Buyukcekmece sub-province; (b) Neighborhoods of Buyukcekmece
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While Batikoy is the most populated
neighborhood of Buyukcekmece with a
population of 30,703, Ahmediye is the least
populated neighborhood with only 1327
persons residing here. The population density
of the district (158 km2 of land area), was
calculated at 1277 persons /km2, with Batikoy,
Dizdariye, Fatih and Ataturk being the most
densely-populated neighborhoods (figure 2).
Figure 2: Population density map of Buyukcekmece Sub-province
In terms of topography, except for the steeper
slopes towards the Marmara Sea and towards
the Büyükçekmece Lake, Büyükçekmece
district appears to be relatively flat. The highest
point of the district, situated in the south
towards the Marmara Sea, is 221 m (figure 3).
These topographical characteristics have
favored higher building and population
densities.
Figure 3: Digital Elevation Model of Buyukcekmece district.
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This topographical characteristic has also
allowed for higher road densities. The total
length of asphalt roads is 1007 km and they are
connecting all neighborhoods in the district.
The district is served by 19 health care
institutions located within the boundaries of the
research area (figure 4).
Figure 4: Hospitals and roads of Buyukcekmece.
5. RESULTS
Analyses performed in this section are based on
the measurement of the accessibility of people
residing within the boundaries of the district to
the health care institutions. Therefore, analyses
rest upon the spatial relationship between the
centers of settlement and health care
institutions.
In this regard, the proximity of residential areas
to health care institutions within the research
area was identified primarily by creating buffer
zones around these facilities. First we draw a
buffer zone at 1 km from the health care
institutions (figure 5).
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Figure 5: 1 km buffer zones for hospitals.
As can be seen from the above map, health care
institutions agglomerate in the southern part of
the district, where the population is denser. Of
the district’s total area of 158 km2, an area of
33 km2 (20.88%) is located within 1 km of
distance from the hospitals. This figure can be
said to be quite good given that the population
was concentrated within this zone. This
proximity is of great importance especially for
emergency situations. The proximity plays a
vital role in the patients who should be rushed
to hospital as soon as possible and in cases that
require immediate intervention.
Another buffer zone was drawn at 3 km of the
health care centers (figure 6).
Figure 6: 3 km buffer zones for hospitals.
The map above shows that 89 km2 (56.33%) of
the district’s territories is located within 3 km
from a health care facility. When we compare
this map with the map displaying population
density in the district it becomes clear that the
great majority of the district’s population lives
within an acceptable distance from health care
institutions.
Another analysis performed with regard to
spatial analysis of health care institutions is the
“shortest distance” analysis. We performed this
analysis for two points in the north of the
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district situated farthest from the health care
centers which are all concentrated in the south.
We selected for this analysis a point in the
north of the district (northwest from
Büyükçekmece Lake) and a point in the
northeast (figures 7 and 8).
The first point used for our analysis is situated
at 11.9 km from the nearest health care facility
(figure 8). This means that a person who
resides there could reach the closest health care
center by car in no more than 15 minutes given
the low population density and easier traffic in
this part of the district. By public transport this
may take longer but still within the 30 minutes
considered acceptable by Rovali and Kiivet
(2006).
Figure 7: Farthest distance analysis-route 1.
The second spot selected for distance analysis
is located in the northeast of the lake and this
distance is the farthest distance to the health
care institutions within the boundaries of the
district (figure 8). The distance in this case is
15 km and can be covered in a period of 20
minutes by car and 30-35 minutes by public
transport taking the traffic into account. In case
of emergency, an ambulance needs to cover the
distance twice; however, ambulances are not
restricted by speed regulation and benefit from
the right of way in traffic. Therefore, we
estimate that an ambulance responding
promptly could take a patient to the hospital in
less than 30 minutes.
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Figure 8: Farthest distance analysis-route 2.
6. CONCLUSION
Büyükçekmece has a total of 19 health care
institutions. Considering a total population of a
little over 200 thousands inhabitants results a
ratio of one health care center for 10,000
inhabitants. Although all these centers are
located in the south, this study found that there
are no major problems related to accessibility.
The great majority of people in the district lives
within three kilometers from a health care
institution, distance that can be covered by a
car or bus in about 10-15 minutes during
normal traffic conditions. People living on
almost 21% of the district’s area reside within
walking distance (one kilometer) from a health
care center. Also those areas that are not
included within the 3 km buffer zone have
much lower population densities. However,
even when considering the longest distance
from a health care facility estimated travel time
does not exceed 30 minutes which was
considered by Rovali and Kiivet (2006) the
longest acceptable range for delivering health
care services. Based on the results of this study,
we can conclude that there are no problems
related to geographical accessibility in the
Büyükçekmece district.
There are, however, a few limitations to the
results of this study which need to be
mentioned here. Firstly, we assume that
patients will always patronize the closest
medical facility to their residence. This
assumption, while very practical for the
purpose of our research could be far from
reality (see also Mitropoulos et al 2006).
Besides distance there are many barriers to
effective use of health care centers. For
example, many of these 19 health care
institutions in the Büyükçekmece district are
private and offer medical services for a fee
which not all residents could afford. The
second limitation derives from the assumption
that residents always patronize medical
facilities in their district. This is again not
realistic considering that residents are not
restricted by law from using any health care
center they want, the district boundaries are
very discreet and the districts are well
connected. As a matter of fact, often certain
neighborhoods are better connected by roads
and public transport to other districts than to
other neighborhoods in the same district.
Moreover, many residents work in other
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districts and may prefer to visit health care
centers close to their work place or somewhere
in between work and residence.
Another result of our study is that GIS proved
to be a useful tool in this medical geography
application. GIS has a variety of analysis tools
that can be used in many urban applications,
such as transportation, health, and education
where they can provide a wide range of
conveniences to urban geographers and
planners. GIS makes it also possible to achieve
better outcomes with its tools such as overlay
and proximity.
Another feature of GIS is that it is a
technology-driven system or science. In other
words, benefits of GIS will increase with the
emergence of new methods, applications and
techniques. This is visible when comparing
studies using GIS 20 years ago with recent
studies. ArcMap, which was used as research
tool for this study, can be a very good example
to this. ArcMap started to offer “basemap” to
its users together with its ten software versions,
and thus users found the opportunity to access
many maps, satellite images and aerial
photographs from all parts of the world. In this
study, “shortest path” analyses were made via
“street” basemap.
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