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Reducing exposure to COVID-19 by improving access to fever clinics: an empirical research of the Shenzhen area of China
BACKGROUND: The current 2019 coronavirus disease (COVID-19) pandemic is hitting citizen’s life and health like never before, with its significant loss to human life and a huge economic toll. In this case, the fever clinics (FCs) were still preserved as one of the most effective control measures in C...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8435565/ https://www.ncbi.nlm.nih.gov/pubmed/34517862 http://dx.doi.org/10.1186/s12913-021-06831-4 |
Sumario: | BACKGROUND: The current 2019 coronavirus disease (COVID-19) pandemic is hitting citizen’s life and health like never before, with its significant loss to human life and a huge economic toll. In this case, the fever clinics (FCs) were still preserved as one of the most effective control measures in China, but this work is based on experience and lacks scientific and effective guidance. Here, we use travel time to link facilities and populations at risk of COVID-19 and identify the dynamic allocation of patients’ medical needs, and then propose the optimized allocation scheme of FCs. METHODS: We selected Shenzhen, China, to collect geospatial resources of epidemic communities (ECs) and FCs to determine the ECs’ cumulative opportunities of visiting FCs, as well as evaluate the rationality of medical resources in current ECs. Also, we use the Location Set Covering Problem (LSCP) model to optimize the allocation of FCs and evaluate efficiency. RESULTS: Firstly, we divide the current ECs into 3 groups based on travel time and cumulative opportunities of visiting FCs within 30 min: Low-need communities (22.06%), medium-need communities (59.8%), and high-need communities (18.14%) with 0,1–2 and no less than 3 opportunities of visiting FCs. Besides, our work proposes two allocation schemes of fever clinics through the LSCP model. Among which, selecting secondary and above hospitals as an alternative in Scheme 1, will increase the coverage rate of hospitals in medium-need and high-need communities from 59.8% to 80.88%. In Scheme 2, selecting primary and above hospitals as an alternative will increase the coverage rate of hospitals in medium-need and high-need communities to 85.29%, with the average travel time reducing from 22.42 min to 17.94 min. CONCLUSIONS: The optimized allocation scheme can achieve two objectives: a. equal access to medical services for different types of communities has improved while reducing the overutilization of high-quality medical resources. b. the travel time for medical treatment in the community has reduced, thus improving medical accessibility. On this basis, during the early screening in prevention and control of the outbreak, the specific suggestions for implementation in developing and less developed countries are made. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-021-06831-4. |
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