Using Geographic Information Systems to investigate variations in accessibility to ‘extended hours’ primary healthcare provision

Higgs, Gary; Langford, Mitchel; Jarvis, Paul; Page, Nicholas; Richards, Jonathan; Fry, Richard

doi:10.1111/hsc.12724

Cited by 22 publications

(11 citation statements)

References 41 publications

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“…Central to improving healthcare service delivery in the region under study is the issue of location. A suitably located hospital addresses important issues such as accessibility within a reasonable distance and time at a reasonable cost, availability of space that meets current service operational capacity and, at the same time, accommodates future development/emergency needs, capability for the projected target service population, and delivering community obligations [2][3][4]. From the forego, it is obvious that finding suitable sites for locating a hospital is a multicriteria problem that has numerous fundamental societal factors to be considered for the maximum benefit.…”

Section: Introductionmentioning

confidence: 99%

Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine

et al. 2021

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Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the Gaza Strip, specifically, there is inadequate spatial distribution and accessibility to healthcare facilities due to decades of conflicts. This study focuses on identifying hospital site suitability areas within the Gaza Strip in Palestine. The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). Thus, the suitability map of potential hospital sites was modeled using a support vector machine (SVM), multilayer perceptron (MLP), and linear regression (LR) models. The results of the predicted sites were validated using CFS cross-validation and the receiver operating characteristic (ROC) curve metrics. The CFS analysis shows very high correlations with R2 values of 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR models, respectively. Moreover, based on areas under the ROC curve, the MLP model produced a prediction accuracy of 84.90%, SVM of 75.60%, and LR of 64.40%. The findings demonstrate that the machine learning techniques used in this study are reliable, and therefore are a promising approach for assessing a suitable location for hospital sites for effective health delivery planning and implementation.

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Section: Introductionmentioning

confidence: 99%

Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine

et al. 2021

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“…the temporal accessibility to the nearest facility rather than availability [ 24 , 49 ]. There are other methods, mostly based on 2SFCA approaches, which also consider availability (Fransen et al [ 22 ], Higgs et al [ 27 ], Chen et al [ 16 ]).…”

Section: Introductionmentioning

confidence: 99%

Risk and space: modelling the accessibility of stroke centers using day- & nighttime population distribution and different transportation scenarios

et al. 2021

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Purpose Rapid accessibility of (intensive) medical care can make the difference between life and death. Initial care in case of strokes is highly dependent on the location of the patient and the traffic situation for supply vehicles. In this methodologically oriented paper we want to determine the inequivalence of the risks in this respect. Methods Using GIS we calculate the driving time between Stroke Units in the district of Münster, Germany for the population distribution at day- & nighttime. Eight different speed scenarios are considered. In order to gain the highest possible spatial resolution, we disaggregate reported population counts from administrative units with respect to a variety of factors onto building level. Results The overall accessibility of urban areas is better than in less urban districts using the base scenario. In that scenario 6.5% of the population at daytime and 6.8% at nighttime cannot be reached within a 30-min limit for the first care. Assuming a worse traffic situation, which is realistic at daytime, 18.1% of the population fail the proposed limit. Conclusions In general, we reveal inequivalence of the risks in case of a stroke depending on locations and times of the day. The ability to drive at high average speeds is a crucial factor in emergency care. Further important factors are the different population distribution at day and night and the locations of health care facilities. With the increasing centralization of hospital locations, rural residents in particular will face a worse accessibility situation.

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“…In recent years, optimization methods have been applied to the research of urban facility planning [ 45 , 46 , 47 ] and the Geographic Information System (GIS) approach has been widely used [ 11 , 48 , 49 ]. Nishino, T. (2017) explored the quantitative properties of the macro supply and demand structure for facilities for the elderly who require support or long-term care throughout Japan and they presented these properties as an index value [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

“…Nishino, T. (2017) explored the quantitative properties of the macro supply and demand structure for facilities for the elderly who require support or long-term care throughout Japan and they presented these properties as an index value [ 47 ]. Higgs, G. et al (2019) evaluated the potential access to primary healthcare services in Wales using GIS-based tools to examine variations in geographical accessibility to general practitioner (GP) surgeries offering appointment times outside of “core” operating hours [ 49 ]. The two-step floating catchment area (2SFCA) model and the potential model are also widely used in spatial accessibility evaluations, especially of medical resources [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ].…”

Section: Introductionmentioning

confidence: 99%

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Two-Step Floating Catchment Area Model-Based Evaluation of Community Care Facilities’ Spatial Accessibility in Xi’an, China

Liu

Wang

Zhou

et al. 2020

IJERPH

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Due to the rapid increase in the number of elderly people in Chinese cities, the development and planning of aged care facilities, and particularly community care facilities, which will gradually become the mainstream choice for the elderly in China, is becoming an important topic for urban sustainability. Previous studies have shown that the number and scale of aged care facilities in many cities are far from meeting the needs of the elderly and the overall occupation rate is low. Some of these cities are still expanding and some are undergoing urban renovation. In this process, the scientific planning of community care facilities to promote efficient use of facility resources has become an urgent problem that needs to be solved. In this study, the two-step floating catchment area (2SFCA) method and a potential model based on the Geographic Information System (GIS) were used to carry out a scientific evaluation of the spatial accessibility of community care facilities in the Beilin district of Xi’an. The aims were to explore the best quantitative research methods for assessing the distribution of Xi’an community care facilities’ spatial accessibility, provide ideas for similar studies in the future, and further the understanding of spatial allocation of urban community care facilities resources.

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Using Geographic Information Systems to investigate variations in accessibility to ‘extended hours’ primary healthcare provision

Cited by 22 publications

References 41 publications

Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine

Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine

Risk and space: modelling the accessibility of stroke centers using day- & nighttime population distribution and different transportation scenarios

Two-Step Floating Catchment Area Model-Based Evaluation of Community Care Facilities’ Spatial Accessibility in Xi’an, China

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