Spatial Analysis of the Distribution Pattern of Primary Healthcare Facilities in Ile – Ife Metropolis Using Geographic Information System

Sedenu, A; Muibi, K. H.; Alaga, A; Ajileye, O. O.; Ogbole, John; Kappo, Ayorinde; Popoola, Oladimeji Samuel; Mustapha, Oloko-Oba O.

doi:10.9734/jsrr/2016/22339

Cited by 6 publications

(14 citation statements)

References 3 publications

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“…This supports the study finding similar studies Fig. 1 Geographical distribution of health facilities providing HDP POC testing and clinics without the service and their geo-location within 10 km range from the nearest health facility providing the service in the region that assessed the distribution of health facilities in the United Arab Emirates, and Nigeria [23,24]. Our study also demonstrated poor physical access to facilities providing HDP POC testing services although the geographical distribution of the PHC clinics without HDP POC testing services suggested most of them were located within 10 km of a facility providing the services.…”

Section: Discussionsupporting

confidence: 87%

Geographical access to point-of-care testing for hypertensive disorders of pregnancy as an integral part of maternal healthcare in Ghana

Kuupiel

Adu

Bawontuo

et al. 2020

BMC Pregnancy Childbirth

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Background Hypertensive disorders of pregnancy (HDP) are associated with high maternal mortality in Ghana and globally. Evidence shows that there is poor availability of pregnancy-related point-of-care (POC) tests in Ghana’s primary healthcare (PHC) clinics (health centre or community-based health planning services facilities). Therefore, we employed geographic information systems to estimate the geographical distribution of and physical accessibility to HDP POC testing services in the Upper East Region (UER), Ghana. Methods We collected data on 100 out of 365 PHC clinics, public hospitals providing HDP testing, PHC clinic type, ownership, and availability of urine dipsticks and blood pressure (BP) devices. We also obtained the geo-located data of the PHC clinics and hospitals using the global positioning system. We employed ArcGIS 10.4 to measure the distance and travel time from the location of each PHC clinic without HDP POC testing services as well as from all locations of each district to the nearest hospital/clinic where the service is available. The travel time was estimated using an assumed motorised tricycle speed of 20 km/hour. We further calculated the spatial distribution of the hospitals/clinics providing HDP POC testing services using the spatial autocorrelation tool in ArcMap, and Stata version 14 for descriptive statistical analysis. Results Of the 100 participating PHC clinics, POC testing for HDP was available in 19% (14% health centres and 5% community-based health planning services compounds) in addition to the 10 hospitals use as referral points for the service. The findings indicated that the spatial pattern of the distribution of the health facilities providing HDP POC testing was random (z-score = -0.61; p = 0.54). About 17% of the PHC clinics without HDP POC testing service were located > 10 km to the nearest facility offering the service. The mean distance and travel time from PHC clinics without HDP POC testing to a health facility providing the service were 11.4 ± 9.9 km and 31.1 ± 29.2 min respectively. The results suggest that if every 19% of the 365 PHC clinics are offering HDP POC testing in addition to these 10 hospitals identified, then the estimated coverage (health facility-to-women in fertility age ratio) in the UER is 1: 3,869. Conclusions There is poor physical accessibility to HDP POC testing services from PHC clinics without HDP POC testing in the UER. Mothers who obtain maternal healthcare in about 17% of the PHC clinics travel long distances (> 10 km) to access the service when needed. Hence, there is a need to improve the availability of HDP POC diagnostic tests in Ghana’s rural clinics.

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Section: Discussionsupporting

confidence: 87%

Geographical access to point-of-care testing for hypertensive disorders of pregnancy as an integral part of maternal healthcare in Ghana

Kuupiel

Adu

Bawontuo

et al. 2020

BMC Pregnancy Childbirth

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“… Source : Author (s) Mean centre The mean centre is a point of average x and y coordinate values for the input centroids feature with projected data accurately measure distances with the following mathematical formula 2 - where x i and y i are the coordinates for features i and n equal to the number of features and and defines the mean centre. Locating mean centres can be useful for pursuing changes in the distribution and/or to judge the distributions of different kinds of features (Sedenu et al, 2016 ) like health centres as well as possible points or locations of features in the GIS platform. Our analysis of the output map (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…4 Shows the mean centre and Central HCF of the study area. Source : Author (s) Central features The central feature tool in GIS recognizes the most centrally sited point, line or polygon in the input database associated with the shortest accumulated distance to all other features are computed using the weighted mean centre (if weight is specified) in the dataset as well as used in distance computations by Euclidean or Manhattan distance (Sedenu et al, 2016 ). It is useful for finding the centre necessitate to minimize distance (Euclidean or Manhattan distance) for all features to the centre as to identify which part of the dataset is most accessible.…”

Section: Methodsmentioning

confidence: 99%

“…6 Shows the nearest slums area from HCF in the study area. Source : Author (s) Standard distance The standard distance is used to measures the magnitude to which features are concentrated or dispersed around the geographic mean centre 7 and estimate the compactness of distributed features around the centre on a map by a circle polygon with the radius equal to the standard distance (Sedenu et al, 2016 ) can be represented mathematically as: where and are coordinates and , defines the mean centre which is not concentrated around mean centre. In Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The urban area with a large population size has a demand for improved accessibility of healthcare centres and services (Murad, 2008 ; McGrail & Humphreys, 2014 ; Mansour, 2016 ). Besides, urban healthcare centres also providing services to their rural counterpart as most of the urban areas acting as a district or block headquarter, particularly in developing countries (Christaller, 1966 ; Mansour, 2016 ; Sedenu et al, 2016 ). As a result, urban dwellers are neglected from healthcare facilities as to maintaining huge streams of the population flux from the urban–rural fringe areas (Danjuma, 2015 ; Dinda et al, 2018 ; Mansour, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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Spatial analysis and modelling for primary healthcare site selection in Midnapore town, West Bengal

Dutta¹,

Das

Roy

et al. 2021

GeoJournal

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Unprecedented and chaotic growth of cities results in reducing open spaces and water bodies, worsening infrastructure facilities and changes in ecological morphology. This unregulated growth of the urban population led to uneven distribution of urban amenities, facilities and healthcare services. Considering this, the study aimed to draw attention to the existing spatial pattern of healthcare facility centres as well as to find out the possible sites for the provision of healthcare facility centres in the municipal ward (micro-scale) of Midnapore town. This prototype study was conducted using Analytical Hierarchy Process (AHP) and Ordinary Least Square (OLS) evaluation model based on various criteria through Arc GIS environment. The findings indicate that the spatial distribution patterns of existing public healthcare centres were significantly dispersed. Weights based on a set of criteria were calculated by AHP and OLS algorithm and generated suitability evaluation maps classified from 1 (poor suitable) to 4 (most suitable). According to the employed criteria in this study unveil those existing hospitals and primary healthcare centres have not been located in the appropriate locations. The model is found to be valid for the given study area and there is no significant difference between AHP and OLS results. Further, it can be used for preparing the suitability map for the other areas with similar geo-environmental conditions for the proviso of healthcare services as well as will be most effective in preventing disease progression and reducing healthcare inequality on a large scale. Supplementary Information The online version contains supplementary material available at 10.1007/s10708-021-10528-w.

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Spatial accessibility modeling to healthcare facilities in the case of health shocks of Midnapore municipality, India

Das

Dutta²,

Roy

et al. 2023

GeoJournal

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This research aims to identify the accessibility of the entire population, especially the slum population to existing healthcare facilities (HCF) as well as the slum neighborhoods having low geographic accessibility, and finally, to provide an analytical model for the people living in areas that are outside the coverage range of existing healthcare facilities (HCF) across the study area. Spatial data has been collected and used based on the road network, elevation, location of HCF, municipal boundary, slum point, and satellite images from various sources. Also, non-spatial data such as socioeconomic variables are collected from questionnaires survey within a particular period. The spatial analysis tool like as near, network analysis, and predictive analysis in the ArcGIS platform was used to examine geographic accessibility. The results of the spatial analysis show that the distribution of public healthcare facility centers in the study area has not been uniformly distributed. Across 84% of areas in the study area have sound spatial accessibility with traveling time coverage is about 12 min. However, 16% of areas have a traveling time of 12 to 30 min under low accessibility with existing slum neighborhoods. Therefore, the low spatial accessibility areas are demanding new healthcare facilities in the study area. The Analytical Hierarchy Process (AHP) is employed to find the most optimal and efficient locational suitability for building new healthcare facility centers. The finding of AHP analysis for site suitability of healthcare facilities revealed five major classes as most suitable (2%), suitable (5%), moderate (35%), poor (54%), and very poor (4%) in the study area. Moreover, the realistic framework of this study helps to measure geographic accessibility and suitability in any geographical area.

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Spatial Analysis of the Distribution Pattern of Primary Healthcare Facilities in Ile – Ife Metropolis Using Geographic Information System

Cited by 6 publications

References 3 publications

Geographical access to point-of-care testing for hypertensive disorders of pregnancy as an integral part of maternal healthcare in Ghana

Geographical access to point-of-care testing for hypertensive disorders of pregnancy as an integral part of maternal healthcare in Ghana

Spatial analysis and modelling for primary healthcare site selection in Midnapore town, West Bengal

Spatial accessibility modeling to healthcare facilities in the case of health shocks of Midnapore municipality, India

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