Measuring access to urban health services using Geographical Information System (GIS): a case study of health service management in Bandar Abbas, Iran

Masoodi, Mehdi; Rahimzadeh, Mahsa

doi:10.15171/ijhpm.2015.23

Cited by 32 publications

(17 citation statements)

References 19 publications

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“…One important parameter in measuring the spatial accessibility is the drive distance between the supply and demand (hospital and home) locations [ 30 ]. Using a travel-time threshold (d) of 5 min in the 2SFCA calculation, an acceptable spatial distance to hospitals in Iran’s metropolitan areas has been estimated at 1–1.5 km travel-distance (0.621–0.932 mile) or at 5–10 min travel-time [ 40 ]. The estimated 5 min travel-time buffer for each neighbourhood’s centroid and hospitals service area zones was derived from the ArcMap Network Analyst tool.…”

Section: Methodsmentioning

confidence: 99%

Different configurations of the two-step floating catchment area method for measuring the spatial accessibility to hospitals for people living with disability: a cross-sectional study

et al. 2021

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Background Poor spatial accessibility to hospital services is associated with higher morbidity and mortality rates among people living with disability. Improved methods to evaluate spatial accessibility are needed. This study measured the potential spatial accessibility of people living with disability by applying four configurations of the two-step floating catchment area (2SFCA) method to recommend the best model for use in health services research. Methods 2SFCA and an enhanced version (E2SFCA) were used to measure hospital accessibility for people living with disability. We also developed and embedded a non-spatial severity index into the two 2SFCA models. We used 16,186 records of people living with disability experience to evaluate the methodological performance across 68 neighbourhoods of the city of Ahvaz, located in south-western Iran. The models’ performance were measured through correlation of the four accessibility scores with the distance to closest hospital for each neighbourhood centroid. Results Among the four models used to measure spatial accessibility, the E2SFCA integrated with the severity index displayed the best performance. Most people with disabilities lived in neighbourhoods located in the South-western and central areas of the city. Interestingly, south-western neighbourhoods had poor hospital accessibility score and were identified as unmet need areas for access to health services. Conclusions Inclusion of the severity factor in the E2SFCA improved access measurements. Identifying areas with poor levels of hospital accessibility can help policymakers design tailored interventions and improve accessibility to hospital-based care in urban settings for people living with disability.

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Section: Methodsmentioning

confidence: 99%

Different configurations of the two-step floating catchment area method for measuring the spatial accessibility to hospitals for people living with disability: a cross-sectional study

et al. 2021

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“…With regard to demographics, it is expected that variations may affect target CA and also surrounding areas. Notably, recent efforts have been made to integrate accessibility and location–allocation models in geographic information systems, thus considering spatial planning ( 19 , 20 ). Outcomes not necessarily related to HF access may include a better understanding of the level of performance of healthcare on the basis of the sustained HF costs.…”

Section: Discussionmentioning

confidence: 99%

Predictive Assessment of Cancer Center Catchment Area from Electronic Health Records

Salmasi

Capobianco

2017

Front. Public Health

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Healthcare facilities (HF) may identify catchment areas (CA) by selecting criteria that depend on various factors. These refer to hospital activities, geographical definition, patient covariates, and more. The analyses that were traditionally pursued have a limiting factor in the consideration of only static conditions. Instead, some of the CA determinants involve influences occurring at both temporal and spatial scales. The study of CA in the cancer context means choosing between HF, usually divided into general hospitals versus oncological centers (OCs). In the CA context, electronic health records (EHRs) promise to be a valuable source of information, one driving the next-generation patient-driven clinical decision support systems. Among the challenges, digital health requires the re-definition of a role of stochastic modeling to deal with emerging complexities from data heterogeneity. To model CA with cancer EHR, we have chosen a computational framework centered on a logistic model, as a reference, and on a multivariate statistical approach. We also provided a battery of tests for CA assessment. Our results indicate that a more refined CA model’s structure yields superior discrimination power between health facilities. The increased significance was also visualized by comparative evaluations with ad hoc geo-localized maps. Notably, a cancer-specific spatial effect can be noticed, especially for breast cancer and through OCs. To mitigate the data distributional influences, bootstrap analysis was performed, and gains in some cancer-specific and spatially concentrated regions were obtained. Finally, when the temporal dynamics are assessed along a 3-year timeframe, negligible differential effects appear between predicted probabilities observed between standard critical values and bootstrapped values. In conclusion, for interpreting CA in terms of both spatial and temporal dynamics, sophisticated models are required. The one here proposed suggests that bootstrap can improve test accuracy. We recommend that evidences from stochastic modeling are merged with visual analytics, as this combination may be exploited by policy-makers in support to quantitative CA assessment.

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“…There are a few studies with the application of GIS in public health. For example, studies on the analysis of access to treatment centers [74][75][76]. There are also studies examining the impact of distance [77], developing a map for planning prevention of the diseases [27,34] There is no doubt some studies using GIS can be applied in substance abuse research as shown by studies by [82][83] in which applying GIS in their studies towards drug users to inform of the formation and optimization of public health intervention, hence providing a greater understanding of at risk drug user populations.…”

Section: Integration Of Gis and Multivariate Analysis In Substance Abmentioning

confidence: 99%

Combating substance abuse with the potential of geographic information system combining multivariate analysis

Fazillah¹,

Juahir²,

Toriman³

et al. 2018

J. Fundam and Appl Sci.

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Substance abuse problems have been a growing concern for people from all over the world. The objective of this study is to demonstrate the usefulness of a combination between a geographic information system and a multivariate analysis However, due to the limited studies on a combination of both these methods in the substance abuse field, we review some other studies in various fields indicating the potential of this method in future substance abuse studies. the multivariate analysis lies in results very well, we hope this method will attract researchers to applying it in their studies and can consequently and indirectly help in combating substance abuse problems

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Measuring access to urban health services using Geographical Information System (GIS): a case study of health service management in Bandar Abbas, Iran

Cited by 32 publications

References 19 publications

Different configurations of the two-step floating catchment area method for measuring the spatial accessibility to hospitals for people living with disability: a cross-sectional study

Different configurations of the two-step floating catchment area method for measuring the spatial accessibility to hospitals for people living with disability: a cross-sectional study

Predictive Assessment of Cancer Center Catchment Area from Electronic Health Records

Combating substance abuse with the potential of geographic information system combining multivariate analysis

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