Comparison of Gis-Based Interpolation Methods for Spatial Distribution of Health Services in Dhi-Qar

Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X

COMPARISON OF GIS-BASED INTERPOLATION METHODS FOR SPATIAL DISTRIBUTION OF HEALTH SERVICES IN DHI-QAR

Muna R. Harbi1, Loay EGeorge2 1 2 [email protected], [email protected] 1 College of ScienceDepartment of Physics,University of Baghdad, Iraq 2 University of Information Technology and Communication

ABSTRACT

This research aims to study the spatial distribution of medical health centers in the Dhi-Qar Governorate to determine the areas of strength and weakness for the medical service in the region. The required data was obtained from the Dhi-Qar Health Department. The Governorate is characterized by a large area (about 13738.67 km2), divided into 15 administrative units according to the official administrative division in Iraq; it contains huge swamps and ancient monuments. In this research, some critical issues have b1een evaluated, such as the nature and characteristics of the available health services in the whole Governorate are, and how it is close to the citizen in terms of nearest distance to most immediate medical assistance, the research also aims to come up with an updateable database to enable the Dhi-Qar Health Directorate to benefit from and receive it and add the necessary data in each center to build an integrated database and facilitate the circulation of information to enhance the health status of the city. The shortage of medical staff in Dhi-Qar is just a part of a more significant structural problem. However, growing the number of doctors, nurses, and midwives does not necessarily mean a more efficient regional or specialty distribution. Medical services are categorized as weak (26%) and strong (13%) in the Governorate's medical services.

On the other hand, a weak health service accounts for (33%) of the total (5 administrative units). The Governorate's health services are accounted for by the strong category (13%).Thus; the results of the maps resulting from the interpolation method (IDW) were compared with the products of the maps resulting from the representation of medical services relative to population density and the recording of the results.

Keywords: ArcGIS 10.7, Urban, Spatial Analysis, Sustainability, Medical Service Center,Population Density

I. INTRODUCTION

This study is dedicated to getting some measures about the spatial distribution of health services provided by the Ministry of Health in Dhi-Qar province. Some tools relevant to Geographic information systems have been used to collect study and analyze data [1]. Figure (1) presents the study area of the Dhi_Qar Governorate.

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Figure (1) Dhi-Qar province in Iraq

The geographic location of Dhi-Qar Governorate is determined between latitude (30.40° -32.00°) and longitude (45.50°-47.15 °)[2][3]. The geographical location is bordered by the governorates: Basra (from the south and southeast), and Misan (East), Wasit (North), AL_Mutanna(Southwest), and AL_Qadisiyah (Northwest). It consists of (15) districts and (8) sub -districts according to their administrative division. It is ranked seventh among the governorates of Iraq in terms of area, as its land area is (13738.95) km2, divided into 15 administrative units, and it also contains (900) km² of water bodies, as well as archaeological sites [4][5].

Zulu et al. (2014) use inverse distance weighting in G.I.S. tools to create continuous surfaces for the spread of H.I.V. from the data for several years; multiple correlations and regression analyses have been used[6].N.D.N. Abd Kadir and N. Adnan (2016) have made spatial distribution measures for high school. This study used the Inverse Weighted Distance as an interpolation method in G.I.S. to map the trend surface of the science and mathematics score average (G.P.A.) as a project variable [7]. Sirisena, Pdnn,et al. (2017) have referred to few G.I.S. studies conducted to determine dengue transmission dynamics in Sri Lanka. Dengue fever was registered in high numbers in districts with large populations in the dry zone. However, these findings were limited to only a few sections.

Furthermore, communities with a high population density in the intermediate area had a high dengue incidence. The relationship between dengue incidence and climatic conditions, seasonal changes in data over time, and the probability of forecasting using G.I.S. were evaluated using Spearman's correlation. Finally, this study leads to geographic information systems (G.I.S.) to map and analyze dengue fever's spatial and temporal distribution in Sri Lanka [8].

Martina Calovi and Chiara Seghieri (2018) have designed a GIS-based approach to support healthcare management with the spatial reorganization of outpatient care in Italy. Spatial data, geographic maps, and metrics for the geospatial potential accessibility index were used to provide healthcare administrators with a palpable image of outpatient clinic accessibility. The number of operations and the distance were also considered. Three cases are presented, each demonstrating three different management techniques, according to the report selecting outpatient clinics while maintaining access to the service[9].W. S. Sebti, S. M. A. El-Mrazek, and H. M. El- Bakry(at 2019) specified the use of spatial tools and analysis tools available in QGIS to measure the distance between the average population center of concentration and the closest hospitals and used the Centroids tool to determine the point of a population center and hospitals. The distance between the average a population center and the closest (private) hospitals were measured using the Buffer Tool, the results indicated a lack of interest in health centers and units distributed in residential areas and an acute shortage of doctors and health workers[10].

Wang, Fahui, et al. (2019) observed the Hu Line, which divides China into two sections with similar areas but vastly different population sizes, have long piqued researchers' interest. This study revisited the Hu Line using a GIS-automated regionalization method (Regionalization with Dynamically Constrained Agglomerative Clustering) to develop two regions with desirable properties. The approach creates parts with the highest degree of homogeneity within them. The best result is obtained by defining attribute homogeneity using the logarithmic www.turkjphysiotherrehabil.org 5910 Turkish Journal of Physiotherapy and Rehabilitation; 32(3)

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transformation of population density. The findings indicate that the logarithmic transformation of population density is a better indicator of attributive uniformity than density alone in deprived areas. The approach delineates two virtually equal regions in area size but has a more excellent contrast in population density[11].

Anna Dmowska (at 2019) reviewed the literature on the development of broad-scale population grids and reported on the R implementation of an automated system for performing asymmetric modeling to create such grids. The asymmetric model was used to build high-resolution, multi-year comparable population grids for the entire United States. The key benefits of using R to calculate asymmetric are that it requires no specialized programming skills, requires fewer processing steps than G.I.S. software, produces no intermediate layers, has improved flexibility and automation, and is easily expandable to variables other than the total population. The system was created to work with population data from the United States Decennial Census. The population model's weights are saved using the SQLite database. The other demographic grids can be generated by importing block-level data from across the United States into an SQLite database and multiplying the counts by weights[12].Hussein and et al. (at 2020) studies the spatial and temporal variation of noise in the educational campus using the field-collected data and G.I.S. system, processing the data to create spatial maps using interpolation tools (G.I.S.), one best type to represent noise; the results showed that there is a high level of noise that and does not match international standards [13].This research studies the spatial variation of health services in Dhi- Qar Governorate (primary medical centers), using field-collected data and a geographic information system.

II. METHODOLOGY

The study follows a specific methodology represented in finding the main factors for the spatial distribution of health centers; the research works to extract the indicator and then measure it in the study area. Detailed maps of population density are essential for the study and characterization of urban regions. Detailed and accurate information on population distribution is relevant for urban planning in general, particularly for the study of services [14][15].

Population density is a measurement of the number of people in a given location. It's a standard number. The population density is calculated by dividing the number of people by the area of the area. The number of inhabitants per square kilometer is commonly used as a measure of population density.

Where:Dprepresents the density of doctors; N is the number of people; A is the land area.

To gain a deeper investigation of the nature and scale of medical services, (GIS) classification techniques were used to classify health services data on the number of citizens enrolled in each health center and the number of health staff. The following equation was applied to the data to calculate the the ratio of (Medical staff) and (Health staff) to the number of citizens and analyze [16][17].

The formula used to calculate the ratio number of doctors to number of citizens:

Where; Ms represents the relative medical (doctors) staff, N.D. Represents the number of doctors,Nc is some citizens. While, the formula used to calculate the ratio number of health staff to number of citizens:

Where HS represents the relative health staff, NH is number of Health Staff, NCis Number of Citizens)[18][19].

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The number of Citizens registered in the health centers of each district is shown on the map illustrated in Figure (2).It is based on the citizen's recorded data for 2021[20]; this study analyzes the urban and rural population density gradient in the Dhi Qar regions using geographic information systems (GIS). The population was represented in a dot point form.As shown in Figure (3), the population is concentrated in both Nasiriyah, the governorate center, and the Shatra district [21][22].

Figure (2)The number of Citizens registeredFigure (3)The number of Citizens registered

in the health centersrepresented in a dot point form

III. RESULTS

Based on the applied steps mentioned in the methodology section, several mathematical functions were considered to study the change in population density with the number of health workers. Completeness (IDW) processes were applied to the spatial distribution of health centers by using GIStools and comparing health centers according to the classification of the number of Citizens served in five categories in all health centers, measuring the ratio of medical services population density in Excel program. Then it was implemented in system GIS tools. The results are identical in both methods. The study classified services in general into five categories: strong, above average, medium, weak, and very weak. The corrective (hatch) map below shows the population density. The darker the color, the higher the population density [23]. Figure (4) shows the population density in Districts Dhi Qar. This ratio is the population density. It is usually measured in persons per square kilometer [15][24].

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Figure (4) Population Density inDhi-Qar

Table (2) and Figure(4)show the relevant data used in this study;it was supplied from the medical authority in Thi Qar province. The table shows that the areas of NA2, which constitute 6% of the Governorate and SH12(%3), have the highest population density. In contrast,other regions,which include: AS6represent (%8) of the area of the Governorate, GB5(%14), and BA10 (%14)they have a lower density of populations. The population density in 2 these administrative units ranges from (28-86) residents/ km .

Table (2) descriptive details for Population Density and area in Dhi Qar Governorate

The distribution of physicians and health personnel in 15 administrative units is depicted in Figure (5), in terms of Doctors, Pharmacists, and other health professionals work in the governorate's primary health centers.

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(A) Distribution of doctors. (B) Distribution of Health staff units

Figure (5) Distribution of doctors and health personnel in administrative units

The results proved that medical services that are classified as strong category occupies around 20% of the administrative units in the study area. Therefore, the concentration of medical services for a vulnerable group occupies about (%7), and (%40) is categorized as above-Fair, while (%20) is categorized as below fair, and as moderate categories it was (%13), as shown in Figure (6). And Table (3) present descriptive details for classifying the number of medical staff in Dhi Qar Governorate. Figure (6) presenting the output of applying IDW.

Table (3) shows the level of doctors for the number of citizens in the administrative units

Code Medical Staff Class AS6 0.000004 Strong FO1 0.000002 Weak BA10 0.000016 Strong GB5 0.000004 vary weak DK7 0.000003 above fair FJ15 0.000007 strong BN4 0.000002 fair DW8 0.000004 weak NR11 0.000003 fair KA14 0.000002 Above fair GR9 0.000004 Above fair RI13 0.000006 weak SU3 0.000004 Above fair SH12 0.000002 Above fair NA2 0.000018 Above fair

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(A) IDW. of the Doctors (B) Doctors_Population Density

Figure (6) the output of applying the interpolation method (IDW)of Doctors in administrative units while (B) Representing distribution the Medical Staff of Population Density

The population density was calculated from the ratio of the number of citizens in each center to the area of each administrative unit, as in Figure (4). After that, the percentage of the medical staff to the number of citizens in each administrative unit was calculated as shown in Figure (5). Thus, it is possible to compare the ratio of doctors to the population with the ratio of the number of citizens to the area and select a map representing their analysis according to administrative units and according to the medical service provided as in Figure (7) and Table (4).

Table (4) shows the level of doctors for the number of citizens in the administrative units

Code Doctors__Citizens Doctors Density Comparison percentage Class FO1 0.000074 0.00009 0.822222 0.52 Weak NA2 0.00013 0.000017 7.647059 4.79 Above Fair SU3 0.000112 0.000004 28 17.54 Strong BN4 0.000099 0.00005 1.98 1.24 Fair GB5 0.000049 0.00015 0.326667 0.2 Weak AS6 0.000209 0.0004 0.5225 0.33 Weak DK7 0.000153 0.00013 1.176923 0.74 Below Fair DW8 0.000084 0.000008 10.5 6.58 Above Fair GR9 0.000118 0.00006 1.966667 1.23 Fair BA10 0.000215 0.000007 30.71429 19.24 strong NR11 0.00008 0.000007 11.42857 7.16 Above Fair SH12 0.000121 0.000002 60.5 37.9 Strong RI13 0.000087 0.00007 1.242857 0.78 Below Fair KA14 0.00012 0.00008 1.5 0.94 Below Fair FJ15 0.000225 0.00017 1.323529 0.83 Below Fair 159.6513 100

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Figure (7) a map showing a comparison between the number of doctors to the population density

The KNN algorithm was employed, K-Nearest method is based on measuring the distance between two entities (points). The K-Nearest Neighbor algorithmis the analog classification algorithm (K-NN). The methodology of pattern identification and classification is commonly used. It is learned by contrasting a test tuple to a collection of named training tuples that are all identical. K-NN can be used to describe learning by comparison. It is classified according to the class of its immediate neighbors. The rest of the time, more than one Neighbor is taken into account [25].

Consequently, the "k" in K-NN denotes the number of neighbors taken into account in class determination. Table (4).The flowchart below illustrates how the KNN algorithm works

Figure (8) Steps of k-NN Classifier Algorithm

Figure (7) shows the services of the health staff (doctor, health, medical, pharmacist) in Dhi Qar Governorate. The AS6area covers (%8) of the total area, has a population density of (43) residents / 2And enjoys excellent health services. This area was identified as a vital region. Therefore, the strong categories occupy (%20). In contrast, a weak category occupies (%13) in (2administrative units), above moderate category amounted to (%26) in (4 administrative units) as well, whereas, in four administrative units each of the below-average categories reached (%6). as shown in Table (4).

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(A) IDW. of the Health staff (B) Health staff_Population Density

Figure (9) Representing interpolation IDW of health staff in administrative units while (B) Representing distribution of the Health Staff of Population Density

Table (4) Descriptive details for classifying the number of Health Staff in Dhi Qar Governorate

Code Health staff Health Staff__Citizen Percentage (%) Class AS6 37.2 0.000425 9 Strong FO1 27.3 0.000392 8 Below Fair BA10 25.4 0.000391 8 Below Fair GB5 16.9 0.000223 4 Below Fair DK7 19.7 0.000246 5 Below Fair FJ15 36.5 0.000435 9 Above Fair BN4 43 0.000302 6 Fair DW8 20.5 0.000154 3 Weak NR11 42.9 0.000309 6 Above Fair KA14 45.9 0.000354 7 Above Fair GR9 88.6 0.000486 10 Strong RI13 42.4 0.000193 4 Weak SU3 120.6 0.000376 8 Above Fair SH12 114.6 0.000391 8 Above Fair NA2 209.7 0.000300 6 Above Fair 0.004976 100

The percentage of the Health staff to the number of citizens in each administrative unit was calculated as shown in Figure (9). Thus, it is possible to compare the ratio of health staff to the population with the ratio of the number of citizens to the area and select a map representing their analysis according to administrative units and according to the medical service provided as in Figure (10) and Table (5).

The comparison results of the health staff in the study area can be summarized if (6%) occupies a strong category in one administrative unit. In comparison, the percentage (20%) is a weak group and (p. 33%) occupies below average, while 13% occupies a below-average rate.

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Figure (10) shows comparing the number of health care workers to the population density.

Health Staff__Citizen Code Health staff Percentage Class Density AS6 37.2 0.019809 21.38 Strong FO1 27.3 0.003658 3.95 Above Fair BA10 25.4 0.033346 35.99 Strong GB5 16.9 0.017706 19.11 Strong DK7 19.7 0.001365 1.47 Below Fair FJ15 36.5 0.002048 2.21 Above Fair BN4 43 0.001072 1.16 Fair DW8 20.5 0.001678 1.81 Fair NR11 42.9 0.003216 3.47 Above Fair KA14 45.9 0.001799 1.94 Fair GR9 88.6 0.002401 2.59 Above Fair RI13 42.4 0.002481 2.68 Fair SU3 120.6 0.001447 1.56 Fair SH12 114.6 0.000247 0.27 Weak NA2 209.7 0.000367 0.40 Weak 0.092641 100.00

Table (5) Result for the medical health services in Dhi Qar Governorate

Area Population The Percentage of Percentage of District Name Percentag densityPerce Doctor Health personnel e ntage NA2 % 6 % 30 % 32 % 20 RI13 % 10 % 8 %6 % 4 SH12 % 6 % 11 %11 %14 BN4 % 3 % 3 % 3 % 6 GB5 % 14 % 3 % 1 % 2 SH12 % 3 % 12 % 12 %14 DW8 % 6 % 4 % 3 % 2 AS6 % 8 % 2 % 3 % 4 DK7 % 4 % 3 % 3 % 1 KA14 % 5 % 5 % 5 % 6 www.turkjphysiotherrehabil.org 5918 Turkish Journal of Physiotherapy and Rehabilitation; 32(3)

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FO1 % 4 % 3 % 1 % 4 GR9 % 5 % 6 % 5 % 10 NR11 % 7 % 5 % 3 % 6 FJ15 % 4 %3 % 6 % 4 BA10 % 14 % 2 % 4 % 1

IV. CONCLUSION

Themap's findings showed a detailed distribution of primary health centers in the study area and practical classification values based on population and health center services. The concentrations varied greatly. Since it defines the location of the center with the medical service classification. The shortage of medical staff in Dhi-Qar is just a part of a more significant structural problem. The overall number of practicing physicians and nurses, the quality of primary health care, and access to specialty medical services can be increased. However, growing the number of doctors, nurses, and other healthcare professionals does not always mean a more efficient or equitable distribution of physicians, nurses, and other healthcare professionals, either geographically or in specialty distribution. Whether expanding medical schools and then recruiting more doctors improves patient health is a topic of debate in the literature. The study's most prominent recommendations include the need to improve health and medical facilities in areas where they are lacking and the need to expand health services.

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