Spatial Distribution and Accessibility of Health Facilities in Khulna City Corporation, Bangladesh.

Transcription

1 Spatial Distribution and Accessibility of Health Facilities in Khulna City Corporation, Bangladesh. Muhammad Salaha Uddin PhD student, Spatially Integrated Social Science (SISS) Program Department of Geography and Planning, University of Toledo Abstract Understanding spatial distribution pattern of any geographic feature is considered as an important issue for physical planning, and decision making at spatial level. Health service facilities in urban area context is an important geographic feature as they are providing one of the basic services to city dwellers. However, the city dwellers are not always getting equal accessibility to health services for several reasons, and locational disparity is one of them. Spatial distribution gives an insight to understand the associated locational disparities and help to plan accordingly. This study is conducted to understand the spatial distribution pattern of health facilities in Khulna city as well as the level of accessibility of 31 wards to hospital and clinics. At the beginning of the study the facility to population ratio is calculated to have an overview of ward wise service facilities across the city area. Average nearest neighbor distance and Moran I were calculated to portray the spatial distribution pattern of health facilities in Khulna city. On the other hand, near analysis based on Euclidean distance measure is applied to figure out the level of accessibility to hospital and clinics for 31 wards. It is found that overall health facilities across the 31 wards of Khulna city are randomly distributed. This random pattern is also observed for some facilities when they are observed individually as attributes of 31 wards. The calculated Moran s Is indicate their randomness when they were analyzed as the number of facilities to corresponding wards. However, the result based on distance based analysis shows the distribution of clinic, pharmacy and diagnostic center are clustered pattern. Only the doctor s chambers show clustered pattern in both cases of Nearest Neighbor and Moran s analysis. The accessibility to hospital, and clinics in terms of distance for 31 wards are also analyzed in the study, and found that clinics are more accessible in terms of distance than the hospitals. The study is limited to explain the underlying causes and issues of different distribution pattern of health facilities. Incorporation of land use setting, socio-economic characteristics with this distribution pattern may be able to unfold the underlying reasons of different distribution pattern and accessibility. 1 P a g e

2 1.Introduction: Bangladesh is one of the most populous countries in the world. The country is still struggling to provide the basic human services to her citizens. Health service is one of them. The existing health services and associated infrastructures are not satisfying the necessity of the people. Moreover, there exists policy level disparities to deliver health services to the people. As a result, this sector is not properly working to fulfill the demand of the citizens, and the variability in the level of health services, and facilities are evidenced over the country. Besides the policy level disparities, spatial distribution of health facilities is one of the core concepts of delivering the health services to the peoples. Spatial relationship between locations of health centers and geographic accessibility to those centers have been for long time an important factor for decision makers, planners, and health care systems (Langford and Higgs, 2006). Realizing this core issue of distribution variability, this study is conducted with the object to find out the spatial distribution pattern, and accessibility to health facilities. For this purpose, Khulna the third largest divisional city of Bangladesh is selected as study area. There has been little detailed investigation of spatial characteristics of health facilities in Khulna city, particularly geographic patterns and accessibility. The main aim of this study is to provide an overview of the spatial distribution pattern of health facility and to investigate the health facility location with respect to distance within demarcated administrative area of Khulna City corporation. The study is conducted within the platform of Geographic Information System (GIS). GIS has immense capability in assessing geographic distribution of health services. Various GIS analytical tools have been used widely to recognize spatial pattern of distribution of existing health facility, and find out new optimal locations of facilities. This study is done within GIS platform by applying spatial analysis techniques. 2.Related Work The study on spatial distribution of geographic features is very common in practice to understand the spatial characteristics of features. Theoretically there are different methods to understand the spatial distribution of a particular geographic features. Average Nearest Neighbor is such a method which is widely used to understand the spatial distribution of geographic features. This method as a spatial statistical tool has been used to identify cluster, dispread, and random distribution of health facilities over space (Hazrin et al. 2013). Philip J. clark and Francis C. Evans (1954) used this method to understand the spatial distribution of plant populations. It is suitable to examine geographical and spatial patterns of health services together with planning the location of new health facilities (Dowie et.al, 1995) and also in the spatial analysis of healthcare utilization (Foley R, 2002). In this line of thinking Mälqvist et al. (2010) investigated the interrelationships between household locations, nearest health service delivery, and neonatal mortality in Vietnam. The study concluded that the households that were far away from the health facilities were bearing high risk of Neonatal death. Moran s I is another spatial statistical tool that is also applied in several studies to understand the degree of variation of spatial features and associated attribute values. Mayer (2009) used GIS based Global and local Moran s statistics to determine if CAM (Chiropractic, acupuncture, massage therapy, homeopathic and naturopathic) clinics in Ontario, Canada were significantly clustered or dispersed in their spatial distribution. The distribution characteristics of CAM were merged with digital maps containing census derived attributes of the area, such as average income, employment rates and population density. The result of this study suggested that as economic prosperity, the likelihood that the area would have a higher per capita level of CAM heal care supply (Meyer, 2009) Health accessibility of services and facilities has an important dimension of the built urban environment (Apparicio et.al, 2008). Geographical accessibility refers to the ease with which patients of a given area can reach health services and facilities (Hewko, 2002). Guagliardo (2004) in a review study reported that 2 P a g e

3 there are various spatial analysis measures of health facility accessibility including provider-to-population ratios, travel impedance to nearest provider, average travel impedance to nearest provider, and gravity models. One tranche has considered the spatial dimensions related to geographic access (distances, travel times, catchments, etc.), with data being manipulated and geographically analyzed using (GIS) before subsequent statistical analyses (Schuurman et.al, 2006; Mclafferty and Grady, 2005; Hossain and Laditka, 2009). Another body of research has explained service accessibility by considering the socio-economic aspects of access related to cost, insurance provision etc., with data collected using opinion or attitudes surveys (Nandi et.al 2008; Yu SM, 2008). 3. Study area and Data Set: 3.1 Study Area: Khulna city is situated in the south western part of Bangladesh (Map 01), and considered as major economic and industrial hub in the south western part of Bangladesh. It was established in 1882 as a district. It is situated between and north latitudes, and between and east longitudes (BBS, 2011). According to population census 2011 it s estimated that the population of Khulna City is 1.4million (1,046,341). In Khulna City Corporation, ward 13 has the lowest population of The population growth rate is 3.8%. It has been estimated that by the year of 2020 the population will be 3.19 million (KDA, 2002). The city dwellers are getting health services from numerous public and private based organizations. According to urban health atlas ( in Khulna city corporation area there are 2222 nos of health facilities which include hospital, clinics, pharmacies, diagnostic centers, delivery hut, doctor s chamber, blood bank, EPI centers etc. All of the facilities are governing at both private and public sectors. In terms of number, the private sectors are leading. However, the public sector is offering health facilities in less costs which attract mass people to the services of public sectors. The survey report of Detail Area Plan (DAP), 2012 of Khulna Development Authority (KDA) indicates that, about 51.85% households prefer government health facilities for regular treatment. However, quite a large percentage i.e % households use private health facilities for treatment. Low and lower middle income groups prefer public health care facility because of its low cost. However, for quality services solvent households are gradually shifting their option to private healthcare services. The survey report of DAP (2012) of KDA shows that about 29.49% households spend Tk and less than Tk. 1000; and 40.54% spend between Tk to Tk per month for family health purpose. Over 16% households spend between Tk to Tk.8000 per month for treatment. Over 14% households family health expenditure is Tk.8000 and above per month 3 P a g e

4 Map 01: Location of Study Area with reference to Bangladesh. 4 P a g e

5 3.2 Data and Materials: Spatial data on health facilities of Khulna city were obtained from the website of This website is first, and unique interactive web map where spatially integrated database on health facilities of seven different cities of Bangladesh were developed. Population, and other associated attribute data of health facilities were also collected from this website. For this study health facilities such as hospitals, clinics, doctor s chamber, pharmacy, diagnostic center were defined as point features with latitude and longitude to represent the respective health service locations. Another layer of administrative boundary of 31 wards of Khulna city corporation area was used as polygon feature in this study. The attribute data were aggregated, and joined within the spatial layer of ward boundary. The total number of 2222 health facilities across Khulna City Corporation area (45.1 Km 2 ) were extracted for this study. Among them 1904 number of health facilities which include hospitals (07), Clinics (409), Doctors Chamber (619), Pharmacies (823), Diagnostic centers (46) were studied to understand the spatial variability. EPI center (143), Delivery hut (17), Blood Bank (01) and some other health facilities whose types were not given in web database were not considered for this study. However, total numbers of health facilities were considered to figure out the overall service coverages of different health facilities. All of the spatial data layers were projected in Bangladesh Transverse Mercator (BTM) projected system. 4.Methods: 4.1 Under and Over Coverage Estimation Unver cover and over coverage of health facilities in Khulna City corporation area were estimated based on the facility to population ratio. For this purpose, total number of health facilities in every ward was calculated per populations. A choropleth map is prepared to show the result at spatial level. 4.2 Average Nearest Neighbor: Average nearest neighbor was applied to understand the spatial distribution pattern of different health facilities according to their classification. The average nearest neighbor is a measure of the distance between each spatial feature, and its nearest neighbor centroid. All these distances are then averaged and compared with hypothetical random distribution. Through this comparison the spatial pattern of features being analyzed is decided whether the pattern is clustered, dispersed or random. If the averaged distances of observed features are less than the average of a hypothetical expected distances the pattern is considered clustered. The value of the average nearest neighbor ratio in this case is less than one. On the other hand, if the average distance is greater than the expected distribution, the spatial pattern is considered as dispersed. In this case the average nearest neighbor ratio is larger than the one (Wong D.W.S and Lee J,2005). The average nearest neighbor ratio is calculated based on dividing the observed distances by the expected distances with the same number of features covering the same study area. The equations used to calculate the average nearest neighbor distance index are as follows: A) The observed mean nearest neighbor distance, D o = n 1 d i n Where n is the number of points and d i is the nearest neighbor distance for point i. 5 P a g e

6 B) The expected value of the nearest neighbor distance in a random pattern D e =.5 A )Where A is the area and n is the number of points. n C) The average Nearest Neighbor ratio or Nearest Neighbor Statistic (R): R = D o D e 4.3 Global Moran s I: Global Moran s I measures spatial autocorrelation based on both feature locations, and features values (ESRI). In this study Global Moran s, I calculated for some health facilities for those cases where facility distribution doesn t follow the random pattern. The object in this case was to analyze those facilities in terms of the attribute data of Wards to understand if there is any other distribution. In this study the facilities that are tested for Global Moran s I are attributed as total number to respective wards according to their locations. The formula that is used in this case is adopted from the book Wong W.S David, and Lee Jay (2005). The formula is: I = n n n W ij (x i x )(x j i=1 j=1 x) n n W ij n (x i x ) i=1 j=1 Where n is the number of features. In this study n is 31 as there are 31 wards. X i is the attribute values of 31 wards (polygon features). In the study number of respective health facilities within specific ward boundary attributed as values of X i. W ij is the assigned weight to each of the ward (polygon) based on their neighbor definition. In this case ArcGIS built-in weight assign technique is used instead of ascribing any specific criteria based weight. 4.4 Near Analysis: This analysis is done to find out the distances from each ward to the nearest Hospital and Clinics. This is the process of determining the Euclidean distances between the input features (Centroid of Wards) and the near features (Hospitals and Clinics). For this purpose, a new layer of centroids of 31 wards was created and distance from these points to nearest hospitals, and clinics were calculated by using Near Tool of ArcGIS. i=1 5. Results and discussions: 5.1 Facilities under and over coverage In this study there is no standard taken to compare where existing health facilities are sufficient or not. Sufficient is relative term, and matter of extensive investigation. Instead of extensive investigation a very straight forward and easy way was applied to investigate whether existing facilities, are under or over coverage based on the density of facilities across 31 wards, Besides, health facility distribution per 10,000 populations is explore, and mapped to understand over and under cover area. According to WHO (2010) 6 P a g e

7 this measure allows comparison between spatial units and identification of gaps in health service coverage across the study area. According to the facility population ratio ward no 23 is showing (Map:02) the highest ratio of facility to population with compare to other wards. In general, it is observable the wards which are containing the hospital are showing high facility-population ratio as there may have natural tendencies of agglomeration of health facilities around the hospitals. Ward no 23 is surrounded by four hospitals with close proximity, and is containing highest number of private doctor s chamber (65) among 31 wards. This may happen for taking the geometric accessibility facilities to the surrounded hospitals as well as to provide easy accessibility to the patients who are coming to these hospitals for health services. In terms of facility population ratio ward no 5, 6,13,17,21,22,24,29 belong to second category. Among these wards hospitals are located in ward no 17, 21, 22, 29 which can be a reason of the concentration of maximum number of health facilities. In case of ward no 17 this trend of facility agglomeration is noticeable. In ward 17 the major public hospital, and medical college named Khulna Medical college hospital is located. In relation with this location the agglomeration trend of Pharmacy is evidenced in this ward. In this ward there are 96 pharmacies which is the highest in number among 31 wards. Ward no 4 and 15 are representing lowest facility population ratio, and interestingly both of this ward is located two opposite peripheral areas of the city. Map 02: Health Facility to Population Ratio 7 P a g e

8 5.2 Spatial distribution Pattern of Health facilities: Spatial distribution patterns of health facilities in Khulna City were explored at different levels. At first, based on total number of health facilities across the 31 wards were explored to find out their overall distribution pattern whether they are clustered, dispersed or random. Next after, spatial distribution pattern of specific health facilities such as Hospital, Clinic, Pharmacy, doctor s chambers, Diagnostic center are explored to find out the arrangement of those facilities at ward level. In the following the spatial distribution pattern of health facilities have been discussed accordingly Spatial Distribution of overall Health Facilities: The distribution of total number of health facilities is examined by applying Global Moran s I. Total number of health facilities are ascribed as the attribute values to respective wards. The calculated Moran s I in this case is which indicates to the clustered natures of health facilities across the Khulna city. However, the standardized score which is 1.21, and smaller than the standard marker of 1.96 at 5% level of significance which leads to the decision regarding the clustering pattern of overall health facilities is rejected. Fig 01: The result of Global Moran S I of number of all health facilities in Khulna City Spatial Distribution Pattern of Hospital and Clinics Spatial distribution of hospital and clinic is explored by applying average nearest neighbor distance methods. In case of hospital the distribution pattern is showing the dispersed pattern. The calculated nearest neighbor statistic is 1.80 which indicates to the dispersed distribution pattern. At 5% significance level the standardized Z score is 4.59 (fig: 02) which is more than the standard marker of 1.96 in case of two tail test. This indicates that the statistical results regarding the dispersed distribution pattern of all hospitals in Khulna city is statistically significant. On the other hand, the distribution pattern of clinics showed clustered pattern according to average nearest neighbor distances analysis. The calculated nearest neighbor statistic and Z-score are 0.76 and (fig: 03) which indicate that the distribution pattern of clinics is clustered and statistically significant at 95% confidence level. Z score, and 8 P a g e

9 corresponding p value indicates that there is 0% likelihood that this clustered pattern could be the result of random chance. Fig 02: The result of Average Nearest Neighbor distance of Hospitals in Khulna City. Fig 03: The result of Average Nearest Neighbor distance of Clinics in Khulna Moran s I for Clinics Along with distance based analysis, Moran s I for clinics is calculated, and tested to understand the spatial distribution pattern in terms of numbers within the 31 wards. As there are only seven hospitals, Moran s I for hospitals is not calculated. Moran s I value of Clinics is which also indicates to clustered distribution pattern of clinics. However, calculated Z score which is 1.28 (fig 04) indicates that the decision regarding the clustered pattern of clinics over the 31 wards is not statistically significant at 95% confidence level. Fig 04: The result of Global Moran S I of number of Clinics in Khulna City 9 P a g e

10 5.2.3 Spatial Distribution Pattern of Pharmacies and Diagnostic Centers The spatial distribution of both Pharmacies and Diagnostic center shows clustered pattern according to average nearest neighbor distance analysis (Fig 05 and 06). The calculated nearest neighbor statistics are found 0.31 and 0.52 for pharmacies and diagnostic centers respectively. At 5% level of significant the result shows statistically significant as the Standardized Z-score are and for pharmacies and diagnostic centers respectively. Fig 05: The result of Average Nearest Neighbor Distance of Pharmacies in Khulna Fig 06: The result of Average Nearest Neighbor Distance of Diagnostic Center in Moran s I of Pharmacies, and Diagnostic Centers Like the Clinics the distribution pattern of Pharmacies and Clinics are showing same as random pattern in terms of number of those facilities across the 31 wards. The calculated Moran s I for Pharmacies ( ) shows mild inclination to clustered pattern which is not accepted at 95% confidence level as the calculated Z-score is.038 (fig 07) which is less than the standard markup value of On the other hand, calculated Moran s I ( ) for diagnostic centers shows the indication to clustered pattern but at 5% significance level the result is not statistically accepted as Z score is only 0.34 (fig 08) which is also less than the critical value of Fig 07: The result of Global Moran s I of number of Pharmacies in Khulna City Fig 08: The result of Global Moran s I number of Diagnostic Centers in Khulna City 10 P a g e

11 5.2.4 Spatial Distribution of Doctor s Chamber: The spatial distribution pattern of Doctors chambers represents clustered distribution pattern in both ways of average nearest neighbor distance, and Moran I. The calculated average nearest neighbor distance static is 0.45 and corresponding Z-score is which indicates that according to average nearest neighbor distance the spatial distribution of doctor s chamber is clustered and statistically significant (fig 09) at 5% significance level. On the other hand, based on the number of Doctor s chamber across the 31 wards the calculated Moran s I (0.18) also indicates to clustered distribution of doctor s chamber. However, given the z-score of 1.78 (fig 10), indicates that the result is significant at 90 % confidence level instead of 95%. This means there is a less than 10% likelihood that this clustered pattern could be the result of random chance. Fig 09: The result of Average Nearest Neighbor Distance of Doctor s Chamber in Khulna City Fig 10: The result of Global Moran s I of number of Doctor s Chamber in Khulna City Spatial Accessibility of Health Facilities One of the objective of this study is to figure out the geographic accessibility of health facilities of Khulna City Corporation area. The simplest concept of spatial accessibility for given facility locations depends on how easy it is to reach the location. In this line of concept, the spatial accessibility of Hospitals, and Clinics were analyzed by performing Near analysis in ArcGIS platform. The distance here is measured is Euclidean distance, and as a result the calculated distance is not reflecting the actual distance in reality. From this analysis it is found that 6 wards (ward no 3,4,5,8,13 and 31) are less accessible to hospitals in terms of distance to nearest Hospital location (Map 03). The distance varies for 1.8 km to 2.99 km to get access to the nearest hospital. By contrast the most accessible wards are ward no 2, 16, 23 and 29. In case of this four wards the hospital facilities are accessible within the distances of less than half km. The similar analysis is also performed for the clinics. The result indicates the accessibility to the nearest clinics for each ward is very small and in terms of measured distances they equally and highly accessible. However, as per classification the group of wards whose accessibility are less belong to the class of km which indicates very small distances. This result also indicates each wards contains significant number 11 P a g e

12 of clinics. Moreover, it also indicates the growing health service based business activities in private sectors. Map 03: Spatial Accessibility to Closest Hospitals Map 04: Spatial Accessibility to Closest Clinics 12 P a g e

13 6. Conclusion Urban health facility is one of the major issues for people in urban area. Health access to all income classes of people in any area should be ensured for equitable society. Policy makers should consider the socioeconomic, demographic and geographic condition in relation to health issues of the society to ensure equitable society. Analysis of spatial distribution of existing facilities is very basic to address the geographic phenomenon at spatial level. In this study it is tried to figure out the spatial distribution scenario of selected health facilities as well as to conceptualize the accessibility to selected health facilities of different wards. From the spatial distribution and accessibility, it is primarily assumed that all of the 31 wards in Khulna city corporation area have geographic accessibility to the existing health facilities. The disparities at spatial scale are not widely observed. However, to make a concrete conclusion the study needs to be extended at farther level where service area calculation of specific health facilities, associated distribution pattern of each type of facilities, land use and socio-economic factors can be the incorporated to understand any underlying disparities of health facilities at spatial as well as economic level. 13 P a g e

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