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Urban–sub-urban–rural variation in the supply and demand of emergency medical services

BACKGROUND: Emergency medical services (EMSs) are a critical component of health systems, often serving as the first point of contact for patients. Understanding EMS supply and demand is necessary to meet growing demand and improve service quality. Nevertheless, it remains unclear whether the EMS su...

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Autores principales: Li, Yue, Li, Ji, Geng, Jiayu, Liu, Tao, Liu, Xin, Fan, Haojun, Cao, Chunxia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9905235/
https://www.ncbi.nlm.nih.gov/pubmed/36761335
http://dx.doi.org/10.3389/fpubh.2022.1064385
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author Li, Yue
Li, Ji
Geng, Jiayu
Liu, Tao
Liu, Xin
Fan, Haojun
Cao, Chunxia
author_facet Li, Yue
Li, Ji
Geng, Jiayu
Liu, Tao
Liu, Xin
Fan, Haojun
Cao, Chunxia
author_sort Li, Yue
collection PubMed
description BACKGROUND: Emergency medical services (EMSs) are a critical component of health systems, often serving as the first point of contact for patients. Understanding EMS supply and demand is necessary to meet growing demand and improve service quality. Nevertheless, it remains unclear whether the EMS supply matches the demand after the 2016 healthcare reform in China. Our objective was to comprehensively investigate EMS supply–demand matching, particularly among urban vs. sub-urban vs. rural areas. METHODS: Data were extracted from the Tianjin Medical Priority Dispatch System (2017–2021). From supply and demand perspectives, EMS resources and patient characteristics were analyzed. First, we performed a descriptive analysis of characteristics, used Moran's I to explore the spatial layout, and used the Gini coefficient to evaluate the equity of EMS supply and demand. Second, we analyzed urban–sub-urban–rural variation in the characteristics of EMS supply and demand by using the chi-square test. Finally, we examined the association between the EMS health resource density index and the number of patients by using the Spearman correlation and divided supply–demand matching types into four types. RESULTS: In 2021, the numbers of medical emergency stations and ambulances were 1.602 and 3.270 per 100,000 population in Tianjin, respectively. There were gradients in the health resource density index of the number of emergency stations (0.260 vs. 0.059 vs. 0.036; P = 0.000) in urban, sub-urban, and rural areas. There was no spatial autocorrelation among medical emergency stations, of which the G values by population, geographical distribution, and the health resource density index were 0.132, 0.649, and 0.473, respectively. EMS demand was the highest in urban areas, followed by sub-urban and rural areas (24.671 vs. 15.081 vs. 3.210 per 1,000 population and per year; P = 0.000). The EMS supply met the demand in most districts (r = 0.701, P = 0.003). The high supply–high demand types with stationary demand trends were distributed in urban areas; the low supply–high demand types with significant demand growth trends were distributed in sub-urban areas; and the low supply–low demand types with the highest speed of demand growth were distributed in rural areas. CONCLUSION: EMS supply quantity and quality were promoted, and the supply met the demand after the 2016 healthcare reform in Tianjin. There was urban–sub-urban–rural variation in EMS supply and demand patterns.
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spelling pubmed-99052352023-02-08 Urban–sub-urban–rural variation in the supply and demand of emergency medical services Li, Yue Li, Ji Geng, Jiayu Liu, Tao Liu, Xin Fan, Haojun Cao, Chunxia Front Public Health Public Health BACKGROUND: Emergency medical services (EMSs) are a critical component of health systems, often serving as the first point of contact for patients. Understanding EMS supply and demand is necessary to meet growing demand and improve service quality. Nevertheless, it remains unclear whether the EMS supply matches the demand after the 2016 healthcare reform in China. Our objective was to comprehensively investigate EMS supply–demand matching, particularly among urban vs. sub-urban vs. rural areas. METHODS: Data were extracted from the Tianjin Medical Priority Dispatch System (2017–2021). From supply and demand perspectives, EMS resources and patient characteristics were analyzed. First, we performed a descriptive analysis of characteristics, used Moran's I to explore the spatial layout, and used the Gini coefficient to evaluate the equity of EMS supply and demand. Second, we analyzed urban–sub-urban–rural variation in the characteristics of EMS supply and demand by using the chi-square test. Finally, we examined the association between the EMS health resource density index and the number of patients by using the Spearman correlation and divided supply–demand matching types into four types. RESULTS: In 2021, the numbers of medical emergency stations and ambulances were 1.602 and 3.270 per 100,000 population in Tianjin, respectively. There were gradients in the health resource density index of the number of emergency stations (0.260 vs. 0.059 vs. 0.036; P = 0.000) in urban, sub-urban, and rural areas. There was no spatial autocorrelation among medical emergency stations, of which the G values by population, geographical distribution, and the health resource density index were 0.132, 0.649, and 0.473, respectively. EMS demand was the highest in urban areas, followed by sub-urban and rural areas (24.671 vs. 15.081 vs. 3.210 per 1,000 population and per year; P = 0.000). The EMS supply met the demand in most districts (r = 0.701, P = 0.003). The high supply–high demand types with stationary demand trends were distributed in urban areas; the low supply–high demand types with significant demand growth trends were distributed in sub-urban areas; and the low supply–low demand types with the highest speed of demand growth were distributed in rural areas. CONCLUSION: EMS supply quantity and quality were promoted, and the supply met the demand after the 2016 healthcare reform in Tianjin. There was urban–sub-urban–rural variation in EMS supply and demand patterns. Frontiers Media S.A. 2023-01-25 /pmc/articles/PMC9905235/ /pubmed/36761335 http://dx.doi.org/10.3389/fpubh.2022.1064385 Text en Copyright © 2023 Li, Li, Geng, Liu, Liu, Fan and Cao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Li, Yue
Li, Ji
Geng, Jiayu
Liu, Tao
Liu, Xin
Fan, Haojun
Cao, Chunxia
Urban–sub-urban–rural variation in the supply and demand of emergency medical services
title Urban–sub-urban–rural variation in the supply and demand of emergency medical services
title_full Urban–sub-urban–rural variation in the supply and demand of emergency medical services
title_fullStr Urban–sub-urban–rural variation in the supply and demand of emergency medical services
title_full_unstemmed Urban–sub-urban–rural variation in the supply and demand of emergency medical services
title_short Urban–sub-urban–rural variation in the supply and demand of emergency medical services
title_sort urban–sub-urban–rural variation in the supply and demand of emergency medical services
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9905235/
https://www.ncbi.nlm.nih.gov/pubmed/36761335
http://dx.doi.org/10.3389/fpubh.2022.1064385
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