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

doi:10.19723/j.issn.1671-167X.2021.03.017

摘要/Abstract

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

张佳伟, 韩沛恩, 杨莉. 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[J]. 北京大学学报（医学版）, 2021, 53(3): 543-548.

ZHANG Jia-wei, HAN Pei-en, YANG Li. Spatial accessibility of fever clinics for multi-tiered prevention and control on COVID-19 in Beijing[J]. Journal of Peking University (Health Sciences), 2021, 53(3): 543-548.

图/表 4

图1

表1

图2

图3

参考文献 23

[1]	陈慧, 姜淮芜, 刘静梅. 传染病定点医院新冠肺炎疫情期间发热门诊分区分级接诊策略[J]. 寄生虫病与感染性疾病, 2020,18(3):141-144.
[2]	Noll NB, Aksamentov I, Druelle V, et al. COVID-19 scenarios: an interactive tool to explore the spread and associated morbidity and mortality of SARS-CoV-2[J]. (2020-05-12)[2020-12-20]. https://doi.org/10.1101/2020.05.05.20091363.
[3]	Li Q, Guan X, Wu P, et al. Early transmission dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia[J]. N Engl J Med, 2020,382(13):1199-1207. doi: 10.1056/NEJMoa2001316
[4]	中国疾病预防控制中心. 新型冠状病毒肺炎流行病学特征分析[J]. 中华流行病学杂志, 2020,41(2):145-151.
[5]	Yang Y, Lu QB, Liu MJ, et al. Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China[J]. (2020-02-21)[2020-11-21]. https://doi.org/10.1101/2020.02.10.20021675
[6]	Qin J, You C, Lin Q, et al. Estimation of incubation period distribution of COVID-19 using disease onset forward time: A novel cross-sectional and forward follow-up study[J]. Sci Adv, 2020,6(33):1202.
[7]	Pan A, Liu L, Wang C, et al. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China[J]. JAMA, 2020,323(19):1915-1923. doi: 10.1001/jama.2020.6130 pmid: 32275295
[8]	Wang F. Why public health needs GIS: a methodological overview[J]. Ann GIS, 2020,26(1):1-12. doi: 10.1080/19475683.2019.1702099
[9]	Mcgrail MR. Spatial accessibility of primary health care utilising the two step floating catchment area method: an assessment of recent improvements[J]. Int J Health Geogr, 2012,11:50. doi: 10.1186/1476-072X-11-50
[10]	Luo W, Qi Y. An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians[J]. Health Place, 2009,15(4):1100-1107. doi: 10.1016/j.healthplace.2009.06.002
[11]	陶卓霖, 程杨. 两步移动搜寻法及其扩展形式研究进展[J]. 地理科学进展, 2016,35(5):589-599. doi: 10.18306/dlkxjz.2016.05.006
[12]	Wang X, Yang H, Duan Z, et al. Spatial accessibility of primary health care in China: a case study in Sichuan Province[J]. Soc Sci Med, 2018,209:14-24. doi: 10.1016/j.socscimed.2018.05.023
[13]	Wang X, Pan J. Assessing the disparity in spatial access to hospital care in ethnic minority region in Sichuan Province, China[J]. BMC Health Serv Res, 2016,16(1):399. doi: 10.1186/s12913-016-1643-8
[14]	Vadrevu L, Kanjilal B. Measuring spatial equity and access to maternal health services using enhanced two step floating catchment area method (E2SFCA): a case study of the Indian Sundarbans[J]. Int J Equity Health, 2016,15(1):87. doi: 10.1186/s12939-016-0376-y
[15]	Nakamura T, Nakamura A, Mukuda K, et al. Potential accessibi-lity scores for hospital care in a province of Japan: GIS-based ecological study of the two-step floating catchment area method and the number of neighborhood hospitals[J]. BMC Health Serv Res, 2017,17(1):438. doi: 10.1186/s12913-017-2367-0 pmid: 28651532
[16]	Tao Z, Cheng Y, Zheng Q, et al. Measuring spatial accessibility to healthcare services with constraint of administrative boundary: a case study of Yanqing District, Beijing, China[J]. Int J Equity Health, 2018,17(1):7. doi: 10.1186/s12939-018-0720-5
[17]	陶卓霖, 程杨, 戴特奇, 等. 公共服务设施布局优化模型研究进展与展望[J]. 城市规划, 2019,43(8):60-68, 88.
[18]	程敏, 黄维维. 基于高斯两步移动搜索法的上海市养老设施空间可达性评价[J]. 复旦学报(自然科学版), 2020,59(2):129-136.
[19]	Cheng G, Zeng X, Duan L, et al. Spatial difference analysis for accessibility to high level hospitals based on travel time in Shenzhen, China[J]. Habitat Int, 2016,53:485-494. doi: 10.1016/j.habitatint.2015.12.023
[20]	Peeters D, Thomas I. Distance predicting functions and applied location-allocation models[J]. J Geogr Syst, 2000,2(2):167-184. doi: 10.1007/PL00011453
[21]	Siegel M, Koller D, Vogt V, et al. Developing a composite index of spatial accessibility across different health care sectors: A German example[J]. Health Policy, 2016,120(2):205-212. doi: 10.1016/j.healthpol.2016.01.001
[22]	中共北京市委办公厅, 北京市人民政府办公厅. 关于印发《加强首都公共卫生应急管理体系建设三年行动计划(2020—2022年)》的通知[J]. 北京市人民政府公报, 2020(32):26-47.
[23]	左克强, 金逸, 焦岳龙, 等. 新冠肺炎疫情后发热门诊及门急诊诊疗流程优化之策[J]. 中国医院院长, 2020,16(21):81-83.

Metrics

Viewed

Full text

194

HTML			PDF

Just accepted	Online first	Issue	Just accepted	Online first	Issue
0	0	9	0	0	185

From	Others	local

Times	25	169
Rate	13%	87%

Abstract

780

Just accepted	Online first	Issue

0	0	780

From	Others	local

Times	772	8
Rate	99%	1%

Cited

Web of Science	Crossref	ScienceDirect	Search for Citations in Google Scholar >>


This page requires you have already subscribed to WoS.

Shared

Discussed

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