accessibility of health care institutions: a case study by using gis

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.

[email protected]

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;

mailto:[email protected]



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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



25

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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