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Record W6888871030 · doi:10.22108/gep.2022.133178.1511

Measuring Accessibility to Medical Centers in Isfahan City Using 2SFCA Method

2023· article· en· W6888871030 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsCatchment areaCensusVariety (cybernetics)Gini coefficientInequalityMetropolitan areaFunction (biology)Urban planning

Abstract

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AbstractOne of the most important challenges facing policymakers and urban planners in recent decades is the issue of accessibility to a variety of urban services. The main purpose of this study was thecalculation of the accessibility of census blocks to medical centers using the Two-Step Floating Catchment Area (2SFCA) method in Isfahan City. In the present study, according to the conditions with and without the limitations of the accessibility radii, different types of distance decay functions were used. The results showed that the 2SFCA method with the use of the cumulative opportunity negative linear function had the highest average of correlation for calculating accessibility to medical centers in comparison with other functions. Calculation of average accessibility in the 15 main regions of Isfahan City showed that the central regions (3, 1, and 5) had the highest decrease and the marginal regions (9, 8, and 11) had the highest increase in the unlimited compared to the limited mode. In general, based on the obtained results of 2SFCA method and the calculated Gini index, the level of inequality in accessibility of census blocks to health services was high in Isfahan City and this inequality increased in terms of accessibility to both hospitals and clinics. Since the extended 2SFCA method has a high capability for assessing supply and demand, as well as catchment area, application of this method can provide a great help for managers and planners in theassessment of the population’s access to a variety of services, such as emergency services and healthcare.Keywords: spatial accessibility, 2SFCA method, distance decay function, medical centers, Isfahan IntroductionOne of the most important challenges faced by policymakers and urban planners in recent decades has been the subjct of access to a variety of urban services. Hospital and clinic centers as the most important urban facilities play an important role in serving people. handeling access to healthcare requires examining the factors, such as spatial distribution of services and demands. Distribution of healthcare centers can affect ease of accessibility for applicants. As health is the basis of social, economic, political, and cultural developments of human societies, identifying deprived areas in terms of accessibility and planning for equitable accessibility to health services for all members of society are essential. MethodologyIn the present study, the Two-Step Floating Catchment Area Method (2SFCA) was employed to calculate the access of census blocks to medical centers (hospitals and clinics) in the city of Isfahan for limited and unlimited accessibility radii. To define the most appropriate distance decay function in the 2SFCA method, the average of Pearson’s correlation coefficient between the accessibility values ​​obtained from different distance decay functions was used. The distance decay function with the highest mean correlation of accessibility values compared to other functions was determined as the most appropriate function in the 2SFCA method. Also, the Lorenz curve and Gini coefficient were applied to compare inequalities of access to medical centers in Isfahan. Results and DiscussionThe results showed that the use of the negative linear cumulative opportunity distance decay function had the highest average correlation in the accessibility values compared to other functions. In the case of limited accessibility radius, the central regions and some northwest and east areas had the highest accessibility to hospitals. In the case of unlimited radius, the central areas had the most accessibility, while accessibility decreased as the distance from these areas increased. Calculation of the average accessibility in the 15 main regions of Isfahan showed that the central (3, 1 and 5) and marginal (9, 8, and 11) regions had the highest decrease and increase in the unlimited compared to the limited mode, respectively. Also, the sensitivity analysis of accessibility to hospitals showed that Al-Zahra and Hazrat Zahra hospitals in Districts 5 and 14 had the greatest impacts on the accessibility of cesus blocks to hospital services in Isfahan City. Comparing the accessibility of census blocks to both hospitals and clinics with accessibility only to hospitals showed an increase in accessibility in the central areas of the city due to the greater concentration of clinics in those areas. However, in the case of combination of hospitals and clinics, the Gini coefficient was equal to 0.60, which showed an increase of 0.04 compared to the case of accessibility only to hospitals, which indicated that inequality was higher in the combinatorial case. ConclusionConsidering the supply and demand simultaneously, the 2SFCA method can provide a more realistic assessment of the accessibility status of census blocks to medical services. In general, based on the obtained results by this method and due to considering the limited radius of accessibility and calculating the Gini index, the level of inequality in the accessibility of census blocks to health services was high in Isfahan City, while this inequality increased in the case of accessibility to both hospitals and clinics. References- Apparicio, P., Gelb, J., Dubé, A. S., Kingham, S., Gauvin, L., & Robitaille, É. (2017). The approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues. International Journal of Health Geographics, 16(1), 1-24.- Bryant Jr, J. and Delamater, P. L. (2019). Examination of spatial accessibility at micro- and macro-levels using the enhanced two-step floating catchment area (E2SFCA) method. Annals of GIS, 25(3), 219-229.- Chatterjee, S. and Hadi, A. S. (2006). Regression analysis by example. 4th Ed., John Wiley & Sons.- Chen, X. and Jia, P. (2019). A comparative analysis of accessibility measures by the two-step floating catchment area (2SFCA) method. International Journal of Geographical Information Science, 33(9), 1739-1758.- Dai, D. (2010). Black residential segregation, disparities in spatial access to health care facilities, and late-stage breast cancer diagnosis in metropolitan Detroit. Health & Place, 16(5), 1038-1052.- Dewulf, B., Neutens, T., De Weerdt, Y., & Van de Weghe, N. (2013). Accessibility to primary health care in Belgium: an evaluation of policies awarding financial assistance in shortage areas. BMC Family Practice, 14(1), 1-13.- Goswami, S., Murthy, C. A., & Das, A. K. (2018). Sparsity measure of a network graph: Gini index. Information Sciences, 462, 16-39.- Hashtarkhani, S., Kiani, B., Bergquist, R., Bagheri, N., Vafaeinejad, R., & Tara, M. (2020). An age-integrated approach to improve measurement of potential spatial accessibility to emergency medical services for urban areas. The International Journal of Health Planning and Management, 35(3), 788-798.- Jamtsho, S., Corner, R., & Dewan, A. (2015). Spatio-temporal analysis of spatial accessibility to primary health care in Bhutan. ISPRS International Journal of Geo-Information, 4(3), 1584-1604.- Kiran, K. C., Corcoran, J., & Chhetri, P. (2020). Measuring the spatial accessibility to fire stations using enhanced floating catchment method. Socio-Economic Planning Sciences, 69, 100-673.- Luo, W. (2004). Using a GIS-based floating catchment method to assess areas with shortage of physicians. Health & Place, 10(1), 1-11.- Luo, W. and Qi, Y. (2009). Health & place: An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health & Place, 15(4), 1100-1107.- Luo, W. and Wang, F. (2003). Measures of spatial accessibility to health care in a GIS environment: Synthesis and a case study in the Chicago region. Environment and Planning B: Planning and Design, 30(6), 865-884.- McGrail, M. R. and Humphreys, J. S. (2014). Measuring spatial accessibility to primary health care services: Utilising dynamic catchment sizes. Applied Geography, 54, 182-188.- Ngui, A. N. and Apparicio, P. (2011). Optimizing the two-step floating catchment area method for measuring spatial accessibility to medical clinics in Montreal. BMC Health Services Research, 11(1), 1-12.- Peng, Z. R. (1997). The jobs-housing balance and urban commuting. Urban Studies, 34(8), 1215-1235.- Park, J. and Goldberg, D. W. (2022). An Examination of the Stochastic Distribution of Spatial Accessibility to Intensive Care Unit Beds during the COVID-19 Pandemic: A Case Study of the Greater Houston Area of Texas. Geographical Analysis.- Radke, J. and Mu, L. (2000). Spatial decompositions, modeling and mapping service regions to predict access to social programs. Geographic Information Sciences, 6(2), 105-112.- Wang, F. (2000). Modeling Commuting Patterns in Chicago in a GIS Environment: A Job Accessibility Perspective. Professional Geographer, 52(1), 120-133.- Wang, L. (2007). Immigration, ethnicity, and accessibility to culturally diverse family physicians. Health and Place, 13(3), 656-671.- Wang, F. (2012). Measurement, optimization, and impact of health care accessibility: a methodological review. Annals of the Association of American Geographers, 102(5), 1104-1112.- Zhang, S., Song, X., & Zhou, J. (2021). An equity and efficiency integrated grid-to-level 2SFCA approach: spatial accessibility of multilevel healthcare. International Journal for Equity in Health, 20(1), 1-14.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.509
GPT teacher head0.622
Teacher spread0.113 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2023
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