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Spatial Analysis of Medical Services in Dhi-Qar Province Using GIS Tools
Muna.R. Harbi1, Loay. E George 2
Emails: [email protected], [email protected]
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 13738.95 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 important 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 closest medical service, 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. This study applied GIS tools (IDW) for the spatial distribution of primary-care centers and registered citizens of the medical centers in Dhi-Qar Governorate, by scales health services positions and population density. In a descriptive and statical analysis. According to the preliminary results, the spatial distribution of health centers and health services in Dhi Qar needs to be updated to take into account population changes.
Keywords: ArcGIS 10.7, Urban, Spatial Analysis, Sustainability, Medical Service Center, IDW.
1. Introduction
This study is dedicated to get 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. Figure (1) presents the study area of Dhi-Qar Governorate.The spatial boundaries were represented by those of the Dhi- Qar Governorate for the year 2020, the astronomical location of Dhi-Qar Governorate determined between latitude (30.40 ° -32.00 °) and longitude (45.50°-47.15 °)[1]. The geographical location is bordered by the governorates: Basra (from the south and southeast), and Maysan (East), Wasit (North), AL Muthanna (Southwest), and AL_Qadisiyah (Northwest)[2]... 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. Navoni, J. A.et al (at 2014) conducted a study to calculate the expected levels of as in water from the observed water levels' geographical location, using (IDW). The work was to behavior a risk assessment by applying spatial analysis methods, re-imagining exposure as a variable dependent on time and place, to redefine the population's risk status in the studied area. The information obtained displays future scenarios to be studied, to assist in setting risk management
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priorities [3]. Zulu, et al ( at 2014) uses inverse distance weighting in GIS tools to create continuous surfaces for the spread of HIV from the data for several years, multiple correlations and regression analyzes have been used [4].Shankardass and Jerrett, M. et al., (at 2015) used spatial analysis methods for the IDW and LUR models and chi-square test to examine the geographical distribution of predictions of the main outcomes of this distribution. Conducted according to ISAAC methodology to capture the ethnically diverse study population. [5].N. D. N.
Abd Kadir and N. Adnan (at 2016) have made spatial distribution measures for high school. This study used the Inverse WeightedDistance as an interpolation method in GIS to map the trend surface of the science and mathematics score average (GPA) as a project variable [6]. Martina Calovi and Chiara Seghieri (at2018) A GIS-based approach was designed to provide support to 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 of which demonstrates three different management techniques. According to the report selecting outpatient clinics while maintaining access to the service [7].W. S. Sebti, S. M. A. El-razek, 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 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[8]. Hussein, et al (at 2020) study the spatial and temporal variation of noise in the educational campus using the field- collected data and GIS system, processing the data to create spatial maps using interpolation tools (GIS) one best type to represent noise, the results showed that there is a high level of noise that and does not match international standards [9]This research studies the spatial variation of health services in Dhi Qar Governorate, which includes (primary medical centers), using field-collected data and a geographic information system.
Figure (1) Dhi-Qar province in Iraq
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2. 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.The research works to extract the indicator and then measure it in the study area.
The flowchart of the study methodology appears is shown in Figure (2).
Figure (2) the flowchart of the study methodology
The steps of the work stages flowchart as follows:
The stages of data collection, sorting, classification, and placement points in their real locations on the map.
Process the data.Data processing in this stage, data were collected that include the names of health centers, the preparation of health personnel, knowledge of medical services in each health center, their order in Excel tables, their classification according to the study, and determine geographical locations to (217) primary health centers, hospitals provide a secondary service, specialized centers that provide a tertiary service in Dhi Qar Government. Data entry and processing, and dropping data collected on digital maps and tables to carry out an analysis this data [10].
1. The stages of data collection, sorting, classification, and placement points in their real locations on the map.
2. Process the data.Data processing in this stage, data were collected that include the names of health centers, the preparation of health personnel, knowledge of medical services in each health center, their order in Excel tables[11], their classification according to the study, and determine geographical locations to (217) primary health centers, hospitals provide a secondary service, specialized centers that provide a tertiary service in Dhi Qar Government.
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Data entry and processing, and dropping data collected on digital maps and tables to carry out an analysis this data [12][13].
3. Determine the points based on the shape file mask for each administrative unit,the polygons of the study area must be identical to the grid of points of the study area mapped in shape, size, and location[14][15].
4. The data entry stage of the Arc GIS programto download the data, click on the "Add Data"
pop-up window in the ArcGIS program. When data is uploaded, all files will be listed in the right-hand part (table of contents) that includes the data of the study area [16].
5. The stage of converting data into a vector and in CSV format. Text files, CSV files, and Excel spreadsheets are great ways to store data, CSV file also contains plain text and is versatile because it can be opened on any operating system, in any text editor, and also in spreadsheet applications, like Excel.
6. Create a dedicated database[17].
7. Use a network of uniform grid points the distance between each two points 400 meters[18].
8. Calculate the distance of nearest health center to each grid point.
9. Make interpolation using (IDW) of the health centers, services, and draw the contour line to determine the centers 'clusters in each line[19][20].
10. Classify the regions into weak, fair, & strong regions according to nearest distance in Dhi-Qar province[21].
The formula used for IDW is as follows:
𝑧 𝑠𝑜 = 𝜆𝑖 𝑧(𝑠𝑖
𝑁
𝑖=1
) , (1) Where: 𝑁 The number of sample points surrounding the prediction location𝑠°.
𝜆𝑖 The weight s assigned to each measured point these weights decrease with distance.
𝑍 The observed value at the location.
The formula used to determine eights for known values is:
𝜆𝑖 = 𝑑(𝑠𝑖 − 𝑠𝑜) 𝜌 𝑑(𝑠𝑖− 𝑠𝑜) 𝜌
𝑁𝑖=1
, 2
Where, 𝑑𝑖is the distance between 𝑠° and𝑠𝑖;
is the power parameter, which is the main factor affecting the accuracy of IDW; 𝑁 represents the number of sampled points used for the estimation [22][23].
Several components of health services inDhiQar Governorate were identified in study; the following parameters were taken into: (a) the number of primary health centers include the main, subsidiary, support centers; (b) the No. of Doctors, health professions, pharmacists[24].
The total number of primary health centers have reached (217) centers, the number of doctors
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(258), while the number of health professions reached (5482), pharmacists of 163 for the governorate. The registered citizen'spopulation is (2,263,695) million, according to the Dhi Qar Health Directorate for the year 2020, aspresented in table (1)[25].
Table (1) somedescriptive details about medical health services in Dhi Qar Governorate
2.1Classification and analysis of the health center
once the geographical database that was created through the presented study was built, the first topic was to obtain a clear understanding of the current situation regarding the locations of health centers and their distribution in Dhi Qar Governorate. There are (217) health centers in different regions of the governorate. The resulting site pattern indicates that the existing centers are located in nearly every district and are distributed to service all parts of the city.as Figure (3) distribution health centers in Dhi-Qar. Figure (4) also shows the distribution of the number of citizens registered in each district.However, it was classified into several categories, starting from strong to weak[26][27].
District Name # of Health
centers # of doctors # of Health
professions # of pharmacists
AL Nasiriyah 41 82 1151 35
AL Refai 13 15 231 10
SuqAL_Shoyiok 25 29 776 28
Garmat 18 9 320 4
AL_Ghebaish 8 3 127 2
AL_Shatra 23 30 798 12
AL Dawaya 9 9 126 0
AL_ Aslah 15 9 226 6
Said Dakhel 10 8 33 2
Gulat Suqar 8 13 314 3
AL Fuhod 9 3 234 1
AL Graff 16 13 359 37
AL Nasr 9 9 314 5
AL Fajr 7 15 210 1
AL Battha 8 11 79 17
1708 (3) distribution health center in Dhi-QarFigure (4) distribution citizens inDhi-Qar
3. Results
Based on the applied steps mentioned in the methodology section, IDW processes were applied to predict all centers and services locations relative to registered citizens in each district and the spatial distribution of health centers using GIS tools. The dot pattern of medical and health services was classified into five categories in all health centers. The distribution of the medical services map (Figure 5) has been determined, and Map (7) represents the distribution of health services in Dhi Qar Governorate. These maps explain the number of centers and the extended area of services around primary care centers. The ratio of the number of health cadres to the number of citizens registered in (5 - 20 health centers) about 2 - 9% for a strong category, indicating that there is a large discrepancy between the units that were analyzed. The lowest difference between (55-85 health centers) was 25-39%, which is a weak category. As for the middle class, the percentage stabilized at about 21% in (46 health centers). As shown in Figure (5).Services can be calculated at various levels based on the effects of the health service measurement in the (IDW) process. Figure (6) of doctors to registered citizens, demonstrating that the larger the population, the lower the operation. It has to be the right one. The medical service is proportional to the population in an indirect way.
1709 Figure (5) Distribution MedicalService Figure (6) The values of the variation
calculatedinIDWtoolfor the medical staff in the Health Centers
The ratio of the number of medical personnel to the number of citizens registered in (4-25 health centers) was about 5-32% for the strong and above-average category, which indicates the existence of a medical deficit. The lowest difference between (10-24 health centers) ranged between 13-31%, which is weak to very weak category. As for the average group, the percentage stabilized at about 18% in (14 health centers), as shown in Figure (7)
Services can be calculated at various levels based on the effects of the health service measurement in the (IDW) process. Figure (8)depict the ratiosregistered citizens health cadres to citizensdemonstrating that the larger the population, the lower the operation. It has to be the right one. The medical service is proportional to the population in an indirect way.
Figure (7) Distribution HealthService Figure (8) The values of the variation calculatedinIDWtoolforthe health staff in the Health Centers
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The Table (2) Distribution Medical Staff and (3)Distribution Health Staff below shows the classification for both the doctors' service and the medical staff according to the map legend using the IDW method.
Range Symbol Classify
0.0000181- 0.0000659
Very Weak
0.0000659-0,0000801 0.0000801- 0.0000843
Weak 0.0000843-0.0000985
0.0000985-0.000146 Moderate
0.000146 -o. ooo307
above moderate 0.000307-0.00085
0.00085-0.000268
Strong 0.000268-0.00883
The Table (2) Distribution Medical Staff
Range Symbol Classify
0.0000122- 0.00028
Very Weak 0.00028-0,000382
0.0000382- 0.000421
Weak 0.000421-0.000523
0.000523-0.000791 Moderate
0.000791 -0.0015
above moderate 0.0015-0.00335
0.00335-0.00823
Strong 0.00823-0.0211
The Table (3) Distribution Health Staff
This study obtained data related to the number of medical staff and the number of health centers and citizens registered in each primary health center in Dhi Qar. The data were included in both Tables (4) and (5). The percentages of the medical staff relative to the number of citizens were also included in Table (6).
Table (4): The medical staff shows the percentage of registered citizens in Dhi Qar Governorate
District Name X Y Families Citizens Doctors Doctors__Citizens
AL Nasiriyah 619845 3434597 103078 629981 82 0.01376
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AL Refai 605997 3509104 28739 172429 15 0.000746
SuqAL_Shoyiok 640118 3418051 43212 259269 29 0.001051
Garmat 650014 3417416 15109 90654 9 0.000388
AL_Ghebaish 690262 3427368 10123 60738 3 0.000132
AL_Shatra 610072 3475258 41171 247031 30 0.001068
AL Dawaya 631292 3484783 17752 106518 9 0.000416
AL_ Aslah 652458 3449329 9578 57472 12 0.000843
Said Dakhel 635525 3444566 8688 52126 8 0.000408
Gulat Suqar 601602 3526169 18055 108332 13 0.000343
AL Fuhod 664365 3428691 6799 40794 3 0.000143
AL Graff 617798 3464145 18433 110598 13 0.000699
AL Nasr 605468 3489679 18661 111957 9 0.000329
AL Fajr 591018 3531038 11105 66631 3 0.000088
AL Battha 583442 3442650 8535 51219 11 0.000438
Table (5): The Health staff shows the percentage of registered citizens in Dhi Qar Governorate
Districts X Y Families Citizens Medical Staff Medical Staff/Citizen AL Nasiriyah 619845 3434597 113521 699527 332.4 0.05076
AL Refai 605997 3509104 36661 219969 64.8 0.00346
SuqAL_Shoyiok 640118 3418051 53469 320810 198.2 0.020796
Garmat 650014 3417416 23749 142489 75 0.008091
AL_Ghebaish 690262 3427368 12616 75697 29.8 0.002769
AL_Shatra 610072 3475258 49017 292811 193.2 0.017094
AL Dawaya 631292 3484783 22197 133210 32 0.00248
AL_ Aslah 652458 3449329 14599 87590 59.4 0.020389
Said Dakhel 635525 3444566 13370 80209 27.4 0.002542
GulatSuqar 601602 3526169 21632 129789 77.3 0.003786
AL Fuhod 664365 3428691 11597 69577 51.1 0.007085
AL Graff 617798 3464145 30411 182431 145.7 0.009334
AL Nasr 605468 3489679 23120 138713 74.3 0.003917
AL Fajr 591018 3531038 13988 83929 45.5 0.003982
AL Battha 583442 3442650 10817 64919 33.3 0.001827
Table (6) shows the percentage of the number of Health Staffs in relation to the registered citizens
District Name Doctors__Citizens Percentage Medical Staff__Citizen Percentage
AL Nasiriyah 0.01376 65 % 0.05076 32 %
AL Refai 0.000746 4 % 0.00346 2 %
SuqAL_Shoyiok 0.001051 5 % 0.020796 13 %
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Garmat 0.000388 2 % 0.008091 5 %
AL_Ghebaish 0.000132 0.6 % 0.002769 2 %
AL_Shatra 0.001068 5 % 0.017094 11 %
AL Dawaya 0.000416 2 % 0.00248 6 %
AL_ Aslah 0.000843 4 % 0.020389 13 %
Said Dakhel 0.000408 2 % 0.002542 2 %
Gulat Suqar 0.000343 2 % 0.003786 3 %
AL Fuhod 0.000143 0.6 % 0.007085 5 %
AL Graff 0.000699 4 % 0.009334 6 %
AL Nasr 0.000329 2 % 0.003917 3 %
AL Fajr 0.000088 0.4 % 0.003982 3 %
AL Battha 0.000438 2 % 0.001827 1 %
4. Conclusion
The results of health centers' maps and service distribution for the geographical location and data collected in Dhi Qar from 2019 to 2020 revealed that medical service calculation was available in 75 primary health centers out of 217, with values ranging from 25 to 39 percent in the locations with the lowest level of medical service. The support of 21% of these centers is extremely weak.
Many primary health centers that were closed in some areas were monitored, and some did not have any medical service, although the ratio of 2-9 percent in (5-20 health centers) was seen as a strong medical service .One of the most important issues for the services provided to the community is a clear understanding of the distribution of health services. Methods are evaluated using completeness techniques through the (IDW) tool. The study showed that the fulfillment maps accurately showed the distribution of centers and identified those that lacked medical services by analysis based on data collected. In addition to representing the number of citizens registered in each district. The percentages of medical services provided in the study area varied.
Solving these problems may require imaginative extensions of an existing framework, the introduction of completely new techniques.
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