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Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility

BACKGROUND: The adequate allocation of inpatient care resources requires assumptions about the need for health care and how this need will be met. However, in current practice, these assumptions are often based on outdated methods (e.g. Hill-Burton Formula). This study evaluated floating catchment a...

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Autores principales: Bauer, J., Klingelhöfer, D., Maier, W., Schwettmann, L., Groneberg, D. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384227/
https://www.ncbi.nlm.nih.gov/pubmed/32718317
http://dx.doi.org/10.1186/s12942-020-00223-3
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author Bauer, J.
Klingelhöfer, D.
Maier, W.
Schwettmann, L.
Groneberg, D. A.
author_facet Bauer, J.
Klingelhöfer, D.
Maier, W.
Schwettmann, L.
Groneberg, D. A.
author_sort Bauer, J.
collection PubMed
description BACKGROUND: The adequate allocation of inpatient care resources requires assumptions about the need for health care and how this need will be met. However, in current practice, these assumptions are often based on outdated methods (e.g. Hill-Burton Formula). This study evaluated floating catchment area (FCA) methods, which have been applied as measures of spatial accessibility, focusing on their ability to predict the need for health care in the inpatient sector in Germany. METHODS: We tested three FCA methods (enhanced (E2SFCA), modified (M2SFCA) and integrated (iFCA)) for their accuracy in predicting hospital visits regarding six medical diagnoses (atrial flutter/fibrillation, heart failure, femoral fracture, gonarthrosis, stroke, and epilepsy) on national level in Germany. We further used the closest provider approach for benchmark purposes. The predicted visits were compared with the actual visits for all six diagnoses using a correlation analysis and a maximum error from the actual visits of ± 5%, ± 10% and ± 15%. RESULTS: The analysis of 229 million distances between hospitals and population locations revealed a high and significant correlation of predicted with actual visits for all three FCA methods across all six diagnoses up to ρ = 0.79 (p < 0.001). Overall, all FCA methods showed a substantially higher correlation with actual hospital visits compared to the closest provider approach (up to ρ = 0.51; p < 0.001). Allowing a 5% error of the absolute values, the analysis revealed up to 13.4% correctly predicted hospital visits using the FCA methods (15% error: up to 32.5% correctly predicted hospital). Finally, the potential of the FCA methods could be revealed by using the actual hospital visits as the measure of hospital attractiveness, which returned very strong correlations with the actual hospital visits up to ρ = 0.99 (p < 0.001). CONCLUSION: We were able to demonstrate the impact of FCA measures regarding the prediction of hospital visits in non-emergency settings, and their superiority over commonly used methods (i.e. closest provider). However, hospital beds were inadequate as the measure of hospital attractiveness resulting in low accuracy of predicted hospital visits. More reliable measures must be integrated within the proposed methods. Still, this study strengthens the possibilities of FCA methods in health care planning beyond their original application in measuring spatial accessibility.
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spelling pubmed-73842272020-07-28 Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility Bauer, J. Klingelhöfer, D. Maier, W. Schwettmann, L. Groneberg, D. A. Int J Health Geogr Research BACKGROUND: The adequate allocation of inpatient care resources requires assumptions about the need for health care and how this need will be met. However, in current practice, these assumptions are often based on outdated methods (e.g. Hill-Burton Formula). This study evaluated floating catchment area (FCA) methods, which have been applied as measures of spatial accessibility, focusing on their ability to predict the need for health care in the inpatient sector in Germany. METHODS: We tested three FCA methods (enhanced (E2SFCA), modified (M2SFCA) and integrated (iFCA)) for their accuracy in predicting hospital visits regarding six medical diagnoses (atrial flutter/fibrillation, heart failure, femoral fracture, gonarthrosis, stroke, and epilepsy) on national level in Germany. We further used the closest provider approach for benchmark purposes. The predicted visits were compared with the actual visits for all six diagnoses using a correlation analysis and a maximum error from the actual visits of ± 5%, ± 10% and ± 15%. RESULTS: The analysis of 229 million distances between hospitals and population locations revealed a high and significant correlation of predicted with actual visits for all three FCA methods across all six diagnoses up to ρ = 0.79 (p < 0.001). Overall, all FCA methods showed a substantially higher correlation with actual hospital visits compared to the closest provider approach (up to ρ = 0.51; p < 0.001). Allowing a 5% error of the absolute values, the analysis revealed up to 13.4% correctly predicted hospital visits using the FCA methods (15% error: up to 32.5% correctly predicted hospital). Finally, the potential of the FCA methods could be revealed by using the actual hospital visits as the measure of hospital attractiveness, which returned very strong correlations with the actual hospital visits up to ρ = 0.99 (p < 0.001). CONCLUSION: We were able to demonstrate the impact of FCA measures regarding the prediction of hospital visits in non-emergency settings, and their superiority over commonly used methods (i.e. closest provider). However, hospital beds were inadequate as the measure of hospital attractiveness resulting in low accuracy of predicted hospital visits. More reliable measures must be integrated within the proposed methods. Still, this study strengthens the possibilities of FCA methods in health care planning beyond their original application in measuring spatial accessibility. BioMed Central 2020-07-27 /pmc/articles/PMC7384227/ /pubmed/32718317 http://dx.doi.org/10.1186/s12942-020-00223-3 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Bauer, J.
Klingelhöfer, D.
Maier, W.
Schwettmann, L.
Groneberg, D. A.
Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility
title Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility
title_full Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility
title_fullStr Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility
title_full_unstemmed Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility
title_short Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility
title_sort prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384227/
https://www.ncbi.nlm.nih.gov/pubmed/32718317
http://dx.doi.org/10.1186/s12942-020-00223-3
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