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Spatiotemporal access to emergency medical services in Wuhan, China: accounting for scene and transport time intervals
BACKGROUND: Access as a primary indicator of Emergency Medical Service (EMS) efficiency has been widely studied over the last few decades. Most previous studies considered one-way trips, either getting ambulances to patients or transporting patients to hospitals. This research assesses spatiotempora...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689650/ https://www.ncbi.nlm.nih.gov/pubmed/33243272 http://dx.doi.org/10.1186/s12942-020-00249-7 |
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author | Luo, Weicong Yao, Jing Mitchell, Richard Zhang, Xiaoxiang |
author_facet | Luo, Weicong Yao, Jing Mitchell, Richard Zhang, Xiaoxiang |
author_sort | Luo, Weicong |
collection | PubMed |
description | BACKGROUND: Access as a primary indicator of Emergency Medical Service (EMS) efficiency has been widely studied over the last few decades. Most previous studies considered one-way trips, either getting ambulances to patients or transporting patients to hospitals. This research assesses spatiotemporal access to EMS at the shequ (the smallest administrative unit) level in Wuhan, China, attempting to fill a gap in literature by considering and comparing both trips in the evaluation of EMS access. METHODS: Two spatiotemporal access measures are adopted here: the proximity-based travel time obtained from online map services and the enhanced two-step floating catchment area (E-2SFCA) which is a gravity-based model. First, the travel time is calculated for the two trips involved in one EMS journey: one is from the nearest EMS station to the scene (i.e. scene time interval (STI)) and the other is from the scene to the nearest hospital (i.e. transport time interval (TTI)). Then, the predicted travel time is incorporated into the E-2SFCA model to calculate the access measure considering the availability of the service provider as well as the population in need. For both access measures, the calculation is implemented for peak hours and off-peak hours. RESULTS: Both methods showed a marked decrease in EMS access during peak traffic hours, and differences in spatial patterns of ambulance and hospital access. About 73.9% of shequs can receive an ambulance or get to the nearest hospital within 10 min during off-peak periods, and this proportion decreases to about 45.5% for peak periods. Most shequs with good ambulance access but poor hospital access are in the south of the study area. In general, the central areas have better ambulance, hospital and overall access than peripheral areas, particularly during off-peak periods. CONCLUSIONS: In addition to the impact of peak traffic periods on EMS access, we found that good ambulance access does not necessarily guarantee good hospital access nor the overall access, and vice versa. |
format | Online Article Text |
id | pubmed-7689650 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-76896502020-11-27 Spatiotemporal access to emergency medical services in Wuhan, China: accounting for scene and transport time intervals Luo, Weicong Yao, Jing Mitchell, Richard Zhang, Xiaoxiang Int J Health Geogr Research BACKGROUND: Access as a primary indicator of Emergency Medical Service (EMS) efficiency has been widely studied over the last few decades. Most previous studies considered one-way trips, either getting ambulances to patients or transporting patients to hospitals. This research assesses spatiotemporal access to EMS at the shequ (the smallest administrative unit) level in Wuhan, China, attempting to fill a gap in literature by considering and comparing both trips in the evaluation of EMS access. METHODS: Two spatiotemporal access measures are adopted here: the proximity-based travel time obtained from online map services and the enhanced two-step floating catchment area (E-2SFCA) which is a gravity-based model. First, the travel time is calculated for the two trips involved in one EMS journey: one is from the nearest EMS station to the scene (i.e. scene time interval (STI)) and the other is from the scene to the nearest hospital (i.e. transport time interval (TTI)). Then, the predicted travel time is incorporated into the E-2SFCA model to calculate the access measure considering the availability of the service provider as well as the population in need. For both access measures, the calculation is implemented for peak hours and off-peak hours. RESULTS: Both methods showed a marked decrease in EMS access during peak traffic hours, and differences in spatial patterns of ambulance and hospital access. About 73.9% of shequs can receive an ambulance or get to the nearest hospital within 10 min during off-peak periods, and this proportion decreases to about 45.5% for peak periods. Most shequs with good ambulance access but poor hospital access are in the south of the study area. In general, the central areas have better ambulance, hospital and overall access than peripheral areas, particularly during off-peak periods. CONCLUSIONS: In addition to the impact of peak traffic periods on EMS access, we found that good ambulance access does not necessarily guarantee good hospital access nor the overall access, and vice versa. BioMed Central 2020-11-26 /pmc/articles/PMC7689650/ /pubmed/33243272 http://dx.doi.org/10.1186/s12942-020-00249-7 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 Luo, Weicong Yao, Jing Mitchell, Richard Zhang, Xiaoxiang Spatiotemporal access to emergency medical services in Wuhan, China: accounting for scene and transport time intervals |
title | Spatiotemporal access to emergency medical services in Wuhan, China: accounting for scene and transport time intervals |
title_full | Spatiotemporal access to emergency medical services in Wuhan, China: accounting for scene and transport time intervals |
title_fullStr | Spatiotemporal access to emergency medical services in Wuhan, China: accounting for scene and transport time intervals |
title_full_unstemmed | Spatiotemporal access to emergency medical services in Wuhan, China: accounting for scene and transport time intervals |
title_short | Spatiotemporal access to emergency medical services in Wuhan, China: accounting for scene and transport time intervals |
title_sort | spatiotemporal access to emergency medical services in wuhan, china: accounting for scene and transport time intervals |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689650/ https://www.ncbi.nlm.nih.gov/pubmed/33243272 http://dx.doi.org/10.1186/s12942-020-00249-7 |
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