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Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility
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 However, in current practice, these assumptions are often based on outdated methods (e.g. Hill-Burton Formula).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) me...
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Published in: | International journal of health geographics 2020-07, Vol.19 (1), p.29-11, Article 29 |
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description | 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 However, in current practice, these assumptions are often based on outdated methods (e.g. Hill-Burton Formula).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.
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%.
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 |
doi_str_mv | 10.1186/s12942-020-00223-3 |
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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%.
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).
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.</description><identifier>ISSN: 1476-072X</identifier><identifier>EISSN: 1476-072X</identifier><identifier>DOI: 10.1186/s12942-020-00223-3</identifier><identifier>PMID: 32718317</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Accessibility ; Analysis ; Attraction ; Catchment Area, Health ; Catchment areas ; Congestive heart failure ; Correlation analysis ; Emergency medical services ; Epilepsy ; Error analysis ; Error correction ; Fibrillation ; Floating catchment area ; Flutter ; Germany ; Health care ; Health care access ; Health care policy ; Health insurance ; Health planning ; Health Services Accessibility ; Heart ; Hospital visits ; Hospitals ; Humanitarianism ; Humans ; Inpatients ; Internal medicine ; Medical care quality ; Medicine ; Methods ; Need ; Neurology ; Patient care ; Population ; Prediction ; Spatial accessibility ; Surgery</subject><ispartof>International journal of health geographics, 2020-07, Vol.19 (1), p.29-11, Article 29</ispartof><rights>COPYRIGHT 2020 BioMed Central Ltd.</rights><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c597t-49e05acec52a21e8c8c9cb806251ab31216b2a9b27d6e50ea6e9da7ae964698f3</citedby><cites>FETCH-LOGICAL-c597t-49e05acec52a21e8c8c9cb806251ab31216b2a9b27d6e50ea6e9da7ae964698f3</cites><orcidid>0000-0001-6267-9731</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384227/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2435293197?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25731,27901,27902,36989,44566,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32718317$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bauer, J</creatorcontrib><creatorcontrib>Klingelhöfer, D</creatorcontrib><creatorcontrib>Maier, W</creatorcontrib><creatorcontrib>Schwettmann, L</creatorcontrib><creatorcontrib>Groneberg, D A</creatorcontrib><title>Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility</title><title>International journal of health geographics</title><addtitle>Int J Health Geogr</addtitle><description>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.
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%.
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).
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. 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Klingelhöfer, D ; Maier, W ; Schwettmann, L ; Groneberg, D A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c597t-49e05acec52a21e8c8c9cb806251ab31216b2a9b27d6e50ea6e9da7ae964698f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Accessibility</topic><topic>Analysis</topic><topic>Attraction</topic><topic>Catchment Area, Health</topic><topic>Catchment areas</topic><topic>Congestive heart failure</topic><topic>Correlation analysis</topic><topic>Emergency medical services</topic><topic>Epilepsy</topic><topic>Error analysis</topic><topic>Error correction</topic><topic>Fibrillation</topic><topic>Floating catchment area</topic><topic>Flutter</topic><topic>Germany</topic><topic>Health care</topic><topic>Health care access</topic><topic>Health care policy</topic><topic>Health insurance</topic><topic>Health planning</topic><topic>Health Services Accessibility</topic><topic>Heart</topic><topic>Hospital visits</topic><topic>Hospitals</topic><topic>Humanitarianism</topic><topic>Humans</topic><topic>Inpatients</topic><topic>Internal medicine</topic><topic>Medical care quality</topic><topic>Medicine</topic><topic>Methods</topic><topic>Need</topic><topic>Neurology</topic><topic>Patient care</topic><topic>Population</topic><topic>Prediction</topic><topic>Spatial accessibility</topic><topic>Surgery</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bauer, J</creatorcontrib><creatorcontrib>Klingelhöfer, D</creatorcontrib><creatorcontrib>Maier, W</creatorcontrib><creatorcontrib>Schwettmann, L</creatorcontrib><creatorcontrib>Groneberg, D A</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Environmental Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Environmental Science Collection</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>International journal of health geographics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bauer, J</au><au>Klingelhöfer, D</au><au>Maier, W</au><au>Schwettmann, L</au><au>Groneberg, D A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility</atitle><jtitle>International journal of health geographics</jtitle><addtitle>Int J Health Geogr</addtitle><date>2020-07-27</date><risdate>2020</risdate><volume>19</volume><issue>1</issue><spage>29</spage><epage>11</epage><pages>29-11</pages><artnum>29</artnum><issn>1476-072X</issn><eissn>1476-072X</eissn><abstract>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.
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%.
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).
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.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>32718317</pmid><doi>10.1186/s12942-020-00223-3</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-6267-9731</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accessibility Analysis Attraction Catchment Area, Health Catchment areas Congestive heart failure Correlation analysis Emergency medical services Epilepsy Error analysis Error correction Fibrillation Floating catchment area Flutter Germany Health care Health care access Health care policy Health insurance Health planning Health Services Accessibility Heart Hospital visits Hospitals Humanitarianism Humans Inpatients Internal medicine Medical care quality Medicine Methods Need Neurology Patient care Population Prediction Spatial accessibility Surgery |
title | Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility |
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