Algorithmic hospital catchment area estimation using label propagation
Abstract Background Hospital catchment areas define the primary population of a hospital and are central to assessing the potential demand on that hospital, for example, due to infectious disease outbreaks. Methods We present a novel algorithm, based on label propagation, for estimating hospital cat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-06-01
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| Series: | BMC Health Services Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12913-022-08127-7 |
| _version_ | 1828336845673463808 |
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| author | Robert J. Challen Gareth J. Griffith Lucas Lacasa Krasimira Tsaneva-Atanasova |
| author_facet | Robert J. Challen Gareth J. Griffith Lucas Lacasa Krasimira Tsaneva-Atanasova |
| author_sort | Robert J. Challen |
| collection | DOAJ |
| description | Abstract Background Hospital catchment areas define the primary population of a hospital and are central to assessing the potential demand on that hospital, for example, due to infectious disease outbreaks. Methods We present a novel algorithm, based on label propagation, for estimating hospital catchment areas, from the capacity of the hospital and demographics of the nearby population, and without requiring any data on hospital activity. Results The algorithm is demonstrated to produce a mapping from fine grained geographic regions to larger scale catchment areas, providing contiguous and realistic subdivisions of geographies relating to a single hospital or to a group of hospitals. In validation against an alternative approach predicated on activity data gathered during the COVID-19 outbreak in the UK, the label propagation algorithm is found to have a high level of agreement and perform at a similar level of accuracy. Results The algorithm can be used to make estimates of hospital catchment areas in new situations where activity data is not yet available, such as in the early stages of a infections disease outbreak. |
| first_indexed | 2024-04-13T22:06:46Z |
| format | Article |
| id | doaj.art-fe2883514d2c48f5a2722f0bff2a4dcf |
| institution | Directory Open Access Journal |
| issn | 1472-6963 |
| language | English |
| last_indexed | 2024-04-13T22:06:46Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj.art-fe2883514d2c48f5a2722f0bff2a4dcf2022-12-22T02:27:56ZengBMCBMC Health Services Research1472-69632022-06-0122111210.1186/s12913-022-08127-7Algorithmic hospital catchment area estimation using label propagationRobert J. Challen0Gareth J. Griffith1Lucas Lacasa2Krasimira Tsaneva-Atanasova3Hub for Quantitative Modelling in Healthcare, University of ExeterBristol Medical School, Population Health Sciences, University of BristolSchool of Mathematical Sciences, Queen Mary University of LondonHub for Quantitative Modelling in Healthcare, University of ExeterAbstract Background Hospital catchment areas define the primary population of a hospital and are central to assessing the potential demand on that hospital, for example, due to infectious disease outbreaks. Methods We present a novel algorithm, based on label propagation, for estimating hospital catchment areas, from the capacity of the hospital and demographics of the nearby population, and without requiring any data on hospital activity. Results The algorithm is demonstrated to produce a mapping from fine grained geographic regions to larger scale catchment areas, providing contiguous and realistic subdivisions of geographies relating to a single hospital or to a group of hospitals. In validation against an alternative approach predicated on activity data gathered during the COVID-19 outbreak in the UK, the label propagation algorithm is found to have a high level of agreement and perform at a similar level of accuracy. Results The algorithm can be used to make estimates of hospital catchment areas in new situations where activity data is not yet available, such as in the early stages of a infections disease outbreak.https://doi.org/10.1186/s12913-022-08127-7Catchment areaCovid-19 |
| spellingShingle | Robert J. Challen Gareth J. Griffith Lucas Lacasa Krasimira Tsaneva-Atanasova Algorithmic hospital catchment area estimation using label propagation BMC Health Services Research Catchment area Covid-19 |
| title | Algorithmic hospital catchment area estimation using label propagation |
| title_full | Algorithmic hospital catchment area estimation using label propagation |
| title_fullStr | Algorithmic hospital catchment area estimation using label propagation |
| title_full_unstemmed | Algorithmic hospital catchment area estimation using label propagation |
| title_short | Algorithmic hospital catchment area estimation using label propagation |
| title_sort | algorithmic hospital catchment area estimation using label propagation |
| topic | Catchment area Covid-19 |
| url | https://doi.org/10.1186/s12913-022-08127-7 |
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