北京大学学报(医学版) ›› 2021, Vol. 53 ›› Issue (3): 543-548. doi: 10.19723/j.issn.1671-167X.2021.03.017

• 论著 • 上一篇    下一篇

新型冠状病毒肺炎疫情分级防控水平下北京市发热门诊空间可及性

张佳伟,韩沛恩,杨莉Δ()   

  1. 北京大学公共卫生学院卫生政策与管理系,北京 100191
  • 收稿日期:2021-02-25 出版日期:2021-06-18 发布日期:2021-06-16
  • 通讯作者: 杨莉 E-mail:lyang@bjmu.edu.cn
  • 基金资助:
    国家自然科学基金(71673004);国家自然科学基金(71911530221)

Spatial accessibility of fever clinics for multi-tiered prevention and control on COVID-19 in Beijing

ZHANG Jia-wei,HAN Pei-en,YANG LiΔ()   

  1. Department of Health Policy and Management, Peking University School of Public Health, Beijing 100191, China
  • Received:2021-02-25 Online:2021-06-18 Published:2021-06-16
  • Contact: Li YANG E-mail:lyang@bjmu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(71673004);National Natural Science Foundation of China(71911530221)

摘要:

目的: 情景模拟北京市分别作为疫情传播和暴发城市下新型冠状病毒肺炎不同流行程度,并分析北京市发热门诊医疗资源应对能力,为优化北京市重大疫情下医疗资源的空间布局提供科学依据。方法: 应用广义SEIR(susceptible-exposed-infectious-removed)模型进行情景模拟,根据医疗资源、人口流动以及封禁措施等因素预测北京市的新型冠状病毒肺炎流行情况,并应用改进的两步移动搜寻法计算北京市发热门诊的空间可及性现状。结果: 根据SEIR模型模拟得到低、中、高防控水平下北京市作为疫情传播城市单日最高感染人数分别为8 514、183和68例,作为疫情暴发城市单日最高感染人数分别为22 803、10 868和3 725例。北京市模拟为疫情传播城市时,在全市585个居民点中,低防控水平下有17个居民点(2.91%)发热门诊可及性好,41个居民点(7.01%)的发热门诊可及性较好, 56个居民点(9.57%)的发热门诊可及性一般,62个居民点(10.60%)的发热门诊可及性较差,409个居民点(69.91%)发热门诊可及性差;中防控水平下只有房山区西部、门头沟区西部、延庆区北部、怀柔区北部和密云区北部可及性较差;高防控水平下有559个居民点(95.56%)的发热门诊可及性好,24个居民点(4.10%)的发热门诊可及性较好, 2个居民点(0.34%)的发热门诊可及性一般,现有发热门诊可以满足需求。北京市模拟为疫情暴发城市时,低防控水平下仅1个居民点(0.17%)的发热门诊可及性好, 5个居民点(0.86%)的发热门诊可及性较好,10个居民点(1.71%)的发热门诊可及性一般,12个居民点(2.05%)的发热门诊可及性较差, 557个居民点(95.21%)的发热门诊可及性差;中防控和高防控下生态涵养区可及性均较差。结论: 北京市发热门诊资源分布不均。北京市模拟作为疫情传播城市时,高防控水平下可适当减少发热门诊开放数量以避免交叉感染;中等防控水平时开设的发热门诊基本能够满足北京市发热患者的接诊需求,但生态涵养区的发热门诊可及性存在不足,应优先考虑对生态涵养区二级以上公立医院发热门诊进行建设;在防控水平为低时,生态涵养区发热门诊可及性较差,优先考虑生态涵养区发热门诊建设的同时可建立临时发热哨点,缓解门诊压力。北京市模拟作为疫情暴发城市且低防控时由于感染人数较多,需要升级防控水平减少人员流动来遏制疫情发展。

关键词: 发热门诊, 空间可及性, 两步移动搜寻法, SEIR模型

Abstract:

Objective: To simulate the different prevalence of corona virus disease 2019 (COVID-19) in Beijing as the spreading and the outbreak city and analyze the response capacity of its medical resources of fever clinics, and to provide a scientific basis for optimizing the spatial layout in Beijing under severe epidemics. Methods: The study obtained epidemiological indicators for COVID-19, factors about medical resources and population movement as parameters for the SEIR model and utilized the model to predict the maximum number of infections on a single day at different control levels in Beijing, simulated as an epidemic spreading city and an epidemic outbreak city respectively. The modified two-step floating catchment area method under ArcGIS 10.6 environment was used to analyze spatial accessibility to fever clinics services for the patients in Beijing. Results: According to the results of the SEIR model, the highest number of infections in a single day in Beijing simulated as an epidemic spreading city at low, medium and high levels of prevention and control were 8 514, 183, and 68 cases, the highest number of infections in a single day in Beijing simulated as an outbreak city was 22 803, 10 868 and 3 725 cases, respectively. The following result showed that Beijing was simulated as an epidemic spreading city: among the 585 communities in Beijing, under the low level of prevention and control, there were 17 communities (2.91%) with excellent accessibility to fever clinics, and that of 41 communities (7.01%) with fever clinics was good. Spatial accessibility of fever clinics in 56 communities (9.57%) was ranked average, and 62 communities’ (10.60%) accessibility was fair and 409 communities (69.91%) had poor accessibility; at the medium level of prevention and control, only the west region of Fangshan District and Mentougou District, the north region of Yanqing District, Huairou District and Miyun District had poor accessibility; under the high level of prevention and control, 559 communities’ (95.56%) had excellent accessibility. The accessibility in 24 communities (4.10%) was good and in 2 communities (0.34%) was average. In brief, the existing fever clinics could meet the common demand. Beijing was simulated as an outbreak city: under the low level of prevention and control, only 1 community (0.17%) had excellent accessibility to fever clinics, and 5 communities (0.86%) had good accessibility. The accessibility of fever clinics in 10 communities (1.71%) was average and in 12 communities (2.05%) was fair. The accessibility of fever clinics in 557 communities (95.21%), nearly all areas of Beijing, was poor; under the middle and high level of prevention and control, the accessibility of ecological conservation areas was also relatively poor. Conclusion: The distribution of fever clinic resources in Beijing is uneven. When Beijing is simulated as an epidemic spreading city: under the high level of prevention and control, the number of fever clinics can be appropriately reduced to avoid cross-infection; at the medium level of prevention and control, the fever clinics can basically meet the needs of patients with fever in Beijing, but the accessibility of fever clinics in ecological conservation areas is insufficient, and priority should be given to the construction of fever clinics in public hospitals above the second level in the ecological conservation areas. When the level of prevention and control is low, the accessibility of fever clinics in ecological conservation areas is poor. Priority should be given to the construction of fever clinics in ecological conservation areas, and temporary fever sentinels can be established to relieve the pressure of fever clinics. When Beijing is simulated as an outbreak city and has low prevention and control, due to a large number of infections, it is necessary to upgrade the prevention and control level to reduce the flow of people to curb the development of the epidemic.

Key words: Fever clinics, Spatial accessibility, Two-step floating catchment area method, SEIR model

中图分类号: 

  • R197.2

图1

模型示意图"

表1

北京市模拟作为疫情传播城市和暴发城市不同防控水平下模拟的感染人数"

Items Prevention and
control level
Number of people
infected 30 days
after the outbreak
Number of people
infected 120 days
after the outbreak
Highest number of
infections in a
single day
Cumulative number
of infections
Low 207 2 719 8 514 79 374
Epidemic spreading city Medium 73 133 183 8 405
High 56 6 68 328
Low 1 073 19 591 22 803 361 624
Epidemic outbreak city Medium 663 7 192 10 868 109 478
High 474 2 335 3 725 40 441

图2

北京市模拟作为疫情传播城市低、中、高防控水平下发热门诊可及性示意图"

图3

北京市模拟作为疫情暴发城市低、中、高防控水平下发热门诊可及性示意图"

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