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Benchmarking the location of health centers at Jeddah city: A GIS approach Author Details Author 1 Name: Abdulkader Ali Murad Department: Department of Urban & Regional Planning University/Institution: King Abdulaziz University Town/City: Jeddah Country: Saudi Arabia Corresponding author: Abdulkader Ali Murad Corresponding Author’s Email: Gis_planning@yahoo.com Acknowledgments (if applicable): n/a Biographical Details (if applicable): n/a Structured Abstract: Purpose - The purpose of this paper is to discuss a GIS application created for health care planning at Jeddah city, Saudi Arabia. The application covers important health care facilities planning issues including defining accessibility to health care facilities, identifying and classifying the distribution of health demand at Jeddah city and modeling spatial variation of patient locations. Design/methodology/approach - In order to build this application a geo-database is created that covers points, lines and polygon features such as health care facility location, road network and population districts. In addition, raster surface models are produced using Kriging function which produces raster surfaces for predicting health demand values at the study area. Findings - The outputs of this application can be used to help health care planners in evaluating the existing location of health care facilities and see if these locations are concentrated at certain city districts. In addition, local health planners can use the created models in deciding on where to allocate new health care facility at Jeddah city. Originality/value - This application is considered as a spatial decision support system for health planners in Jeddah city. It can be used to define and evaluate location of health centers as well as to identify the spatial accessibility to health centers. Keywords: GIS, Health care planning, Heath demand, Health center, Jeddah Article Classification: Research paper

For internal production use only Running Heads:

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Benchmarking the location of health centers at Jeddah city: A GIS

approach

Abstract

Purpose- The purpose of this paper is to discuss a GIS application created for health

care planning at Jeddah city, Saudi Arabia. The application covers important health

care facilities planning issues including defining accessibility to health care facilities,

identifying and classifying the distribution of health demand at Jeddah city and

modeling spatial variation of patient locations.

Design/methodology/approach- In order to build this application a geo-database is

created that covers points, lines and polygon features such as health care facility

location, road network and population districts. In addition, raster surface models are

produced using Kriging function which produces raster surfaces for predicting health

demand values at the study area.

Findings- The outputs of this application can be used to help health care planners in

evaluating the existing location of health care facilities and see if these locations are

concentrated at certain city districts. In addition, local health planners can use the

created models in deciding on where to allocate new health care facility at Jeddah

city.

Originality/value- This application is considered as a spatial decision support system

for health planners in Jeddah city. It can be used to define and evaluate location of

health centers as well as to identify the spatial accessibility to health centers.

Key Words: GIS, Health care planning, Heath demand, Health center, Jeddah.

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1. Introduction

Health care facilities at any region can be divided into two main types that are known

as primary health centers and hospitals. The former provides basic health care

services and the latter provides services for specialist health treatment. Health

authorities have always aimed to provide health care for all residents using a fair

access policy that is characterized as providing the right service at the right time in the

right place (Murad, 2006). To ensure adequate health care planning, health service

planners and policy makers need accurate and reliable measures of health facilities so

that true services shortage areas can be accurately identified and resources allocated to

those needy areas to alleviate the problem. Health care planning in a given location is

influenced by many factors, including the availability of health services in the area

(supply), the number of people living in that location (demand), the population’s

health status, the socio-economic and financial resources available to the population,

people’s knowledge about health and the health care system, and geographical

impedance between population and health services (YI QI, 2009). Among the many

factors that influence health care services, two of them are critical: physician supply

and population demand. Both of these are spatially distributed, but it is rare that their

distributions perfectly match (Luo, 2004).

One of the main issues that health planners need to cover at any built up area is

related to evaluating health accessibility. Measures of geographical accessibility have

also been proposed and critiqued in the planning and medical geography literature

(Guagliardo, 2004). Such measures range from the conceptually simple counting of

the number of facilities within a specified distance from a given location to more

sophisticated spatial interaction models. These measures can be implemented using

Geographical Information Systems (GIS). GIS plays an essential role in helping

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public health organizations understand population health and make decisions. With

the powerful tools and solutions that GIS technology brings to the desktop, health

planners can improve understanding of community health needs and design effective

interventions. GIS technology offers varied solutions including ones that improve

field data collection and reporting and others that support disease surveillance and

analysis with online mapping and spatial statistics. In addition, GIS, improve the

ability to communicate with several health situation, such as environmental

contamination, to decision makers.

Geographical Information Systems (GIS) can be used for several health studies.

Examples of these studies include examining disease rates, examining variations in

health and the use of health services. Wilkinson et al. (1998) addresses the potential

applications of GIS in health geographical studies. These applications are: disease

mapping and geographical correlation studies, patterns of health service use and

access, environmental hazards and disease clusters, and the modeling of the health

impacts of environmental hazards. Jacquez, 1998, added that GIS could be used for

exposure assessment, identification of study populations, disease mapping, and public

health surveillance. There are several examples in the literature that discuss the

potential GIS application in health care facility planning. For example, GIS is used in

Used for monitoring vector borne disease, water borne diseases, environmental health,

modeling exposure to electromagnetic fields, quantifying lead hazards in a

neighborhood, predicting child pedestrian injuries and for the analysis of disease

policy and planning (Coggon et al., 1997).

Rytkonen et al. (2003), discussed an interesting GIS application for analysing the

incidence of type 1 diabetes among children in Finland. They observed the incidence

of type 1 diabetes per 100,000 persons separately in urban areas, urban-adjacent rural

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areas, rural health and remote areas. Cerrito et al. (2003) presented another GIS based

health study, investigating the relationship between environmental factors and the

need for the treatment of lung problems. It is considered an interesting case,

demonstrating how the data mining of GIS, combined with healthcare outcomes, can

be effective in modifying clinical research. One example of using GIS and GPS in

health care is found in Gesler et al. (2004), where these technologies are used to map

out residence activity spaces, using symbols and standard deviational ellipses and

sites where diabetes information has the potential to be welcomed, for a sample of

low income females and males. This example shows how ‘prevention of diabetes’

projects can use GPS and GIS tools to collect and record the activity spaces of 121

participants and demonstrates how this approach can be used by healthcare providers

and researchers to implement a community-based diabetes prevention programme.

2. GIS and health care applications: Background

One of the basic objectives of healthcare Planning in any part of the world is to have

an equivalent access to health care for all, regardless of ability to pay. This means that

every residence should have equivalent chance to go to clinics and hospitals. To meet

this objective and other ones, health authorities are required to make careful analysis

about the real demands and supplies of health care facilities at their areas. These

analysis and studies can be classified into three main groups, which are a) spatial

changes in health status, b) spatial epidemiology, and c) health care facilities

accessibility and utilization. Each one of these topics has a spatial dimension, which

means that GIS can be used for their studies. The next part will elaborate more on

each group and illustrate the possible uses of GIS on them.

2.1 Spatial Changes in Health Status

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One of the facts about health status in a micro/macro scale is that it changes across the

space. Health authorities always investigate and analyze the health status at their areas

and make sure that health needs are satisfied. Locality definition is considered as an

important issue for health care facilities planning studies. The idea here is to

determine the socio-economic classifications for the area surrounding certain health

facilities and then relate the local profiles of such an area with the health care needs.

Once the socio-economic status of any location is defined, then GIS can be used to

map and tabulate the distributions of such status. A good example of using GIS for

linking social profiles with health needs is found by Hirschfield et al., 1995, which

have produced patient profiles for a health facility catchment area. Such studies

usually involve matching point-referenced, post coded health data with area

socioeconomic data, particularly deprivation indicators (Gatrell and Senior 1999). For

example, Health status can be viewed through comparing the actual number of

moralities in an area with the national average, taking into account age and sex

variations in the area concerned (Birkin et al, 1996). Here GIS can be used

successfully for describing spatial variations of mortality at parts of any country.

Once the mortality rate of each region is entered into the GIS, the mapping and

analysis tools of GIS can be applied to present out the regions that have high rates of

mortality. The regions with high rates require more attention from health authorities

in order to improve their existing health status. In addition to mortality, there are

much other health status indicators that are used by health authorities, such as fertility

rates, which help to assess and monitor the required health services.

2.2 Spatial Epidemiology

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The second area of health care research is known as spatial epidemiology. There are

several questions that are commonly asked in spatial epidemiology studies, which

include: where are the incidences located? what are the environmental characteristics

of these areas?, what are relations between health incidences and the environment at

other locations?, and what patterns are evolving? (Nicol, 1991). GIS is considered as a

useful tool for answering the preceding questions. For example, GIS can define the

actual location of health events, then overlay analysis can be used to create new

spatial relationships and to tag the various socio-economic and environmental

information to the health data.

There are several studies that have applied GIS to these issues. For instant Brown et

al, 1991, have used GIS for the mapping of spatial variations in health care provision

in Merseyside, UK. Wrigley, 1991, have also used GIS in mapping incidence diseases

in relation to population types. Another example of GIS applications in epidemiology

is called the Health and Environment Geographical Information System (HEGIS),

which is being established in Europe by the world health organization (Nicol, 1991).

It involves the creation of European wide environment data set, and the aim is to

research relationships between health and the environment, to aid policies and

management (ibid). Most of spatial epidemiology studies must be based upon accurate

knowledge of the population. Therefore, access to details of population composition

and socio-economic characteristics are very necessary for these studies.

Spatial epidemiology studies are concerned with finding good description of spatial

incidence of diseases as well as the modeling of such incidence. One way of

describing the spatial distribution of a certain disease is by visualizing the GIS

choropleth maps that show the spatial distributions of such a disease. In such maps,

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disease rates are plotted over the base map to define the areas that are highly affected

from the related disease. Further analysis and modeling of the spatial incidence of

diseases can be carried out using for example Kernal or density estimation technique

that is used in predicting the spatial variation in diseases risk (Gatrell and Senior

1999).

Any health care study requires a huge set of data which need to be handled and

captured into GIS software. Once this step is completed, then GIS users and

researchers will move to the following step of the application, the data exploratory

step. Gatrell and Senior (1999) defined the GIS data exploratory step as the phase

which goes beyond the map, to the use of statistical tools in an informal, pattern-

seeking vein. It is considered an area in which major research efforts have been

expanded. This type of function can be applied on point, polygon or line features. For

example, focal and local GIS functions are used in modern electronic atlases of

mortality and morbidity to highlight areas where disease rates are unusually high or

low. ArcGIS software has included these functions within its spatial analysis

extension which produces a raster output in which the value at each location is a

function of the input cells in some specified neighborhood of the location (McCoy

and Johnston, 2001). In addition, ArcGIS software has a very useful extension known

as the Geostatistical Analyst, which can be used for modeling any health point based

data, such as the location of patients, and can easily create a continuous surface from

measured sample points stored in a point-feature layer. It derives a surface using the

values from the measured locations to predict values for each location in the

landscape (Johnston, et al., 2001). One of these functions is called Kriging (which is

used by the presented application) that can be used for modeling health point data. It

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is considered as one of the deterministic interpolation methods which capable of

producing a prediction surface and providing some measures of the certainty or

accuracy of a prediction. Health studies that look into the relationship between air

pollution and health status can use this function to define air quality, based on sample

measured points. The presented study has used Kriging function for the purpose of

mdeling health demand flows at Jeddah city. The results of this function is discussed

at the next section.

3. Application of GIS for health care planning

The aim of this section is to discuss how GIS can be used to analyze the location of

health care centers in Jeddah, Saudi Arabia. The application covers important health

care planning issues which are : a- defining the level of accessibility to health care

centers, b- identifying the spatial distribution of health care demand, and c- modeling

the distribution of health care demand using GIS Kriging function. The first step in

creating this application was to build the needed geo-database for health care centers.

The next section will discuss the process of building this geo-database.

3.1 The Data-base

One of the main tasks that should be looked at carefully during the building of any

GIS application, is regarding creating the required data and then integrating these data

within the GIS application. These data fall into three main GIS data features known as

points, lines and polygons. Point data are restored as a single x,y coordinate, with

attributes describing the conditions of these points. Usually geographical features that

are too small to be depicted as lines or areas, are created in GIS as points data. For

this application, the location of health centers in Jeddah city is created as a point

feature, and all attribute data about health centers which include number physicians

and number of dentist (fig 1 and 2) are saved in the attribute table of this file. The

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second main GIS data feature is the line feature, which has a one dimensional shape

that represents geographical features too narrow to depict as area (Zeiler 1999). GIS

software stores lines as a series of ordered x,y coordinates, with the relevant

attributes. For the presented application, the road network of Jeddah city is

represented as a line feature, with attributes regarding the length and type of each road

in this city. The third GIS data feature is known as polygon date set. This type of

features is modelled in GIS as a series of segments that enclose an area and form a set

of closed area (Zeiler, 1999). City districts coverage is an example of this type of GIS

data that is created for the presented study. This coverage includes attributes such as

district name and area, and size of population and households for each district(fig.3) .

3.1 Accessibility to Health supply

The literature on accessibility measures showed a need for quantitative indicators of

accessibility for different kinds of public services including health care (Murad,

2007). Such indicators would serve as instruments in the comparisons of accessibility

in different parts of the region and in the evaluation of alternative plans for new

service facilities and transportation links. Examples of accessibility indicators are :

provider-to-population ratio, distance to the nearest provider, average distance to a set

of providers and gravitational models of provider influence (Guaglirado et al, 2003).

Each one of these indicators can be used to evaluate accessibility of health centers.

The presented application has selected distance to provider method and produces

accessibility indicators to health centers in Jeddah city. One way of defining

accessibility to health centers is by knowing how far patients live from their nearest

centers. Based on local standards, every health center should cover a catchment area

extending 2 KM radius wide. In order to define the level of accessibly to health

centers, GIS proximity analysis was used and the output of this model (figure 4)

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classifies the city into deferent zones based on the distance between clinic location

and city districts. Based on this output, several parts of the city are located at areas

with more than 2 KM accessibility zone. These areas are mainly situated north and

east of the city with some to the west.

The results of this function are shown at Fig. 1 and it is clear that there is several parts

of Jeddah city that are not located within the 2 km accessibility zones. These are

mainly north and east of the city as well as some of the western parts of the city. It is

also clear from this figure that existing health centers are serving larger catchment

area than the standard size. Based on this output, there are different parts of Jeddah

city that are having low health accessibility service. These parts include

AlMohammadia district located north of Jeddah, and Alhamra district at the west of

the city.

Health planners can use this model to help them in deciding about where to build a

new health center in Jeddah city. For example, the areas that are located outside the

2Km accessibility zones can be used as a guide for allocating any new additional

health centers in Jeddah city. Health planners and officers can present this model to

the regional or national health authorities for asking about building new health centers

at several city parts.

3.2 Identifying Health demand distribution

One of the main issues that is covered is this application is related to using ArcGIS

software for describing the spatial distribution of health demand data. However,

before covering this issue it was important to decide on about the suitable spatial

resolution (unit of analysis) for the presented application. Among the first questions to

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be answered when using GIS for health research are: “what is the appropriate study

area the scale or geographic extent of the study?” and “what is the appropriate unit of

analysis the spatial resolution?”. In many cases, the answers to these questions are

determined by:

- The availability of data for all possible geographic, the known or probable

geographical extent of the problem to be studied;

- The physical integration, transportation systems, cultural factors, and social

dynamics of the particular region;

- Existing political and jurisdictional boundaries;

- The geography of the existing health care infrastructure and service areas;

- The geographic interests of the project partners, collaborators, or funders;

- The funding sources and parameters; and many other considerations and

constraints unique to each project (Maantay, 2005).

Based on the data available for this application, diabetes patient information which is

one type of health demand is aggregated to the level of health center location. There

are 39 health centers distributed at Jeddah city. Every one of those centers has records

about the size of registered diabetic patients. These records can be used in GIS to

define the spatial pattern of diabetic patients in Jeddah city. The results of these data

are very useful for identifying pattern and location of diabetic disease in Jeddah city.

These data also can be related to other physical or environmental data to identify

relationships between diabetic data and other related data.

The first step that was made at this part of the study, was to create a point coverage

showing location of all health centers at Jeddah city, and then links diabetic data to

this coverage. Once this step is covered, the following task was to use GIS

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classification methods for describing variations of diabetic patients in Jeddah city.

Fig. 5 shows the output of the spatial distribution of diabetic disease at Jeddah city.

The resulted distribution indicates that diabetes patients are concentrated mainly at

Al-Rabwah, Bani Malik and Al-Sabail districts. These areas are covering north,

central and southern city districts.

In addition to classifying diabetic patient data, GIS is used at the presented application

to make a spatial comparison (Based on mean value) between health centers to find

out centers that are having large amount of diabetes patients. The fact that health

status varies a cross space in widely known and applies at all spatial scale across the

urban and regional hierarchy. For example standard mortality ratios (SMRs) are

calculated by comparing the actual number of mortalities in an area with the national

average taking into account age and sex variations in the area concerned. If an area

was generating deaths at the national average, its SMR value would be exactly 100

(Birkin et al, 1998). The same principle is applied at this study to calculate standard

diabetic rates (SDRs) for Jeddah city. Fig 6 shows SDR values and indicates that the

areas with the highest rates trend to be in the central and southern parts of the city. In

addition, two main areas (Al Rabwa and Al Bawadi) located north of Jeddah city are

also having high SDR rates.

3.3 Modeling spatial variation of patient locations

The literature of health analysis field indicates that there are several approaches and

models that can be used to model variation in health data. For example, Bayesian

model-based approach is used at Finland to model variations in the incidence of

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childhood type I diabetes between urban/rural municipalities (Rytkonen et al, 2003).

The same modeling technique also used by Lopez-Abente, 1998, for the analysis of

emerging neoplasm in Spain. Ying and Weimin, 2006, have also used Bayesian

modeling technique in assessing spatial variations of hospitalized children and youth

in the province of British Columbia. In addition to Bayesian models, Collins, 1998

added that regression models could be used in a combination with GIS to model

health and environmental data for Huddersfield area at the UK.

Geographical information systems are developed today to include several useful

models that can be used for defining spatial variations of health data. One of these

models is known as Kriging models, which are also known as geastatistical models,

and considered as optimal interpolators that produce estimates which are unbiased and

have known minimum variance. This technique is based upon the theory of

regionalized variables and utilizes the spatial structure of the data and involves the

construction of a variogram and the fitting of an appropriate model (ibid). The

presented study has selected this technique to model the spatial variation of diabetic

disease at Jeddah city. Geostatistical methods are based on statistical models that

include auto correlation (statistical relationships among the measured points), and

have the capability of producing the diction surface and provide some measure of the

certainty or accuracy of the predictions. Kriging weights the surrounding measured

values to derive a prediction for an unmeasured location. The general formula for this

technique is as following

Z(s) = M + ε (s)

Where

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Z(s) is the predicted value at the s location,

M is a known constant mean, and

ε (s) is the random errors process

Kriging is used at the presented study within ArcGIS geostatistical analyst extension

which has advanced tool bar containing tools for exploratory spatial data analysis and

a geostatistical wizard for creating a statistically valid surface (Johnstan et al, 2001).

In this software extension, there are different kriging methods including ordinary,

simple, universal and probability Kriging. The presented application has selected

simple kriging which is based on a known constant mean. In the case of diabetic data

at Jeddah city 260 is the mean value for the collected data. Fig. 7 shows the output of

this model and it defines how diabetes patients are spread out at all city parts but with

different amounts. It also shows how the highest concentrations of diabetes which are

found at Al-Rabwah, Bani Malik, Al-Jamiah, and Al-Rowais, are also spread at the

areas closer to them. For example, an area called Al-Syliamaniah is have a remarkable

diabetes patients because it is very close to a higher diabetes location called Al-

Jamiah. In fact this result is based on the Kriging model assumption which indicates

that spatially distributed objects are spatially correlated, in other words, things that are

close together tend to have similar characteristics. Therefore, all areas located near

these four districts are getting higher values of diabetes values. Meanwhile, the

northern areas which are close to Obhur are getting lower diabetes values because

Obhur health center is having zero diabetes patients.

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This model can be used by health planners at Jeddah city to define city parts that are

more likely to have more diabetic patients. These parts can be reached for health

protection purposes and for the management of patients living these parts.

4. Conclusion

Using GIS for health care planning is considered as one of the important and useful

GIS applications. Health planners can use this technology to evaluate the location of

health services in any built up area. In order to use this technology several types of

spatial data should be collected by health planners. The presented paper has collected

points, lines, and polygons data for the purpose of evaluating the location of health

supply and demand in Jeddah city. This paper has demonstrated that GIS can be used

to identify the level of accessibility to health care facilities. The results of accessibility

analysis show that existing health centers are serving larger catchment area than the

standard size. Based on this output, there are different parts of Jeddah city that are

having low health accessibility service. In addition, the created application had

explored the patterns of health demand and predicted the spatial variation of patients

at Jeddah city. The outputs of this application indicate that diabetes patients are

located mainly at Al-Rabwah, Bani Malik, Al-Jamiah, and Al-Rowais at Jeddah city.

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Fig. 4 Accessibility to health care facilities in Jeddah city

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10000 0 10000 20000 Meters

Priximity zones0 - 20002000 - 40004000 - 60006000 - 8000

Road

# Health centersN

EW

S

Red Sea

23

Fig. 5 Spatial Distribution of Diabetes patients at Jeddah City

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Al-Rabw ah

Bani Malik

Al-Sabail

Red Sea

Al-Row ais

10000 0 10000 20000 meters

City DistrictsRoad

Diabetes Patiants# 0# 1 - 189

# 190 - 290

# 291 - 745

# 746 - 2572N

EW

S

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Fig. 6 Standards Diabetes Rates (SDRs) at Jeddah City

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Al-Rabw ah

Bani Malik

Al-Sabail

Red Sea

Al-Row ais

10000 0 10000 20000 meters

City DistrictsRoad

SDR# 0# 0 - 58.9# 58.9 - 90.3

# 90.3 - 232.1

# 232.1 - 801.2N

EW

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Fig. 7 Predicted diabetes patients spread at Jeddah city

Red SeaRed Sea

Al-Rabwah

Bani Malik

Al-Rowais Al-Jamah

20000 0 20000 meters

Predicted Diabetes

0.101 - 106.196

106.196 - 212.292

212.292 - 318.387

318.387 - 424.483

424.483 - 530.579

530.579 - 636.674

636.674 - 742.77

No Data

Road

N

EW

S