Inter-city movement pattern of notifiable infectious diseases in China: a social network analysisResearch in context
Summary: Background: Co-existence of efficient transportation networks and geographic imbalance of medical resources greatly facilitated inter-city migration of patients of infectious diseases in China. Methods: To characterize the migration patterns of major notifiable infectious diseases (NIDs) d...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | The Lancet Regional Health. Western Pacific |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666606524002554 |
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| author | Lin-Jie Yu Peng-Sheng Ji Xiang Ren Yan-He Wang Chen-Long Lv Meng-Jie Geng Jin-Jin Chen Tian Tang Chun-Xi Shan Sheng-Hong Lin Qiang Xu Guo-Lin Wang Li-Ping Wang Simon I. Hay Wei Liu Yang Yang Li-Qun Fang |
| author_facet | Lin-Jie Yu Peng-Sheng Ji Xiang Ren Yan-He Wang Chen-Long Lv Meng-Jie Geng Jin-Jin Chen Tian Tang Chun-Xi Shan Sheng-Hong Lin Qiang Xu Guo-Lin Wang Li-Ping Wang Simon I. Hay Wei Liu Yang Yang Li-Qun Fang |
| author_sort | Lin-Jie Yu |
| collection | DOAJ |
| description | Summary: Background: Co-existence of efficient transportation networks and geographic imbalance of medical resources greatly facilitated inter-city migration of patients of infectious diseases in China. Methods: To characterize the migration patterns of major notifiable infectious diseases (NIDs) during 2016–2020 in China, we collected migratory cases, who had illness onset in one city but were diagnosed and reported in another, from the National Notifiable Infectious Disease Reporting System, and conducted a nationwide network analysis of migratory cases of major NIDs at the city (prefecture) level. Findings: In total, 2,674,892 migratory cases of NIDs were reported in China during 2016–2020. The top five diseases with the most migratory cases were hepatitis B, tuberculosis, hand, foot and mouth disease (HFMD), syphilis, and influenza, accounting for 79% of all migratory cases. The top five diseases with the highest proportions of migratory cases were all zoonotic or vector-borne (37.89%‒99.98%). The network analysis on 14 major diseases identified three distinct migration patterns, where provincial capitals acted as key node cities: short distance (e.g., pertussis), long distance (e.g., HIV/AIDS), and mixed (e.g., HFMD). Strong drivers for patient migration include population mobility and labor flow intensities between cities as well as the economic development level of the destination city. Interpretation: Collaborative prevention and control strategies should target cities experiencing frequent patient migration and cater to unique migration patterns of each disease. Addressing disparity in healthcare accessibility can also help alleviate case migration and thereby reduce cross-regional transmission. Funding: National Key Research and Development Program of China. |
| format | Article |
| id | doaj-art-49a731546bfb42b8a401a5a69669d127 |
| institution | OA Journals |
| issn | 2666-6065 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | The Lancet Regional Health. Western Pacific |
| spelling | doaj-art-49a731546bfb42b8a401a5a69669d1272025-08-20T01:56:39ZengElsevierThe Lancet Regional Health. Western Pacific2666-60652025-01-015410126110.1016/j.lanwpc.2024.101261Inter-city movement pattern of notifiable infectious diseases in China: a social network analysisResearch in contextLin-Jie Yu0Peng-Sheng Ji1Xiang Ren2Yan-He Wang3Chen-Long Lv4Meng-Jie Geng5Jin-Jin Chen6Tian Tang7Chun-Xi Shan8Sheng-Hong Lin9Qiang Xu10Guo-Lin Wang11Li-Ping Wang12Simon I. Hay13Wei Liu14Yang Yang15Li-Qun Fang16State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China; Center for Disease Control and Prevention (Health Inspection Office) of Yuhang District, Hangzhou, Zhejiang, PR ChinaDepartment of Statistics, Franklin College of Arts and Science, University of Georgia, GA, United StatesDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, PR ChinaThe 968th Hospital of Joint Logistics Support Force of PLA, Jinzhou, Liaoning, PR ChinaState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR ChinaDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, PR ChinaState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR ChinaState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR ChinaState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR ChinaState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR ChinaState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR ChinaState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR ChinaDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, PR China; Corresponding author. Division of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention, 155 Chang-Bai Road, Beijing 102206, PR China.Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, United States; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States; Corresponding author. Department of Health Metrics Sciences, School of Medicine, and Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98121, USA.State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China; Corresponding author. State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, 20 Dong-Da Street, Fengtai District, Beijing 100071, PR China.Department of Statistics, Franklin College of Arts and Science, University of Georgia, GA, United States; Corresponding author. Department of Statistics, Franklin College of Arts and Science, University of Georgia, Athens, GA 30602, USA.State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China; Corresponding author. State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, 20 Dong-Da Street, Fengtai District, Beijing 100071, PR China.Summary: Background: Co-existence of efficient transportation networks and geographic imbalance of medical resources greatly facilitated inter-city migration of patients of infectious diseases in China. Methods: To characterize the migration patterns of major notifiable infectious diseases (NIDs) during 2016–2020 in China, we collected migratory cases, who had illness onset in one city but were diagnosed and reported in another, from the National Notifiable Infectious Disease Reporting System, and conducted a nationwide network analysis of migratory cases of major NIDs at the city (prefecture) level. Findings: In total, 2,674,892 migratory cases of NIDs were reported in China during 2016–2020. The top five diseases with the most migratory cases were hepatitis B, tuberculosis, hand, foot and mouth disease (HFMD), syphilis, and influenza, accounting for 79% of all migratory cases. The top five diseases with the highest proportions of migratory cases were all zoonotic or vector-borne (37.89%‒99.98%). The network analysis on 14 major diseases identified three distinct migration patterns, where provincial capitals acted as key node cities: short distance (e.g., pertussis), long distance (e.g., HIV/AIDS), and mixed (e.g., HFMD). Strong drivers for patient migration include population mobility and labor flow intensities between cities as well as the economic development level of the destination city. Interpretation: Collaborative prevention and control strategies should target cities experiencing frequent patient migration and cater to unique migration patterns of each disease. Addressing disparity in healthcare accessibility can also help alleviate case migration and thereby reduce cross-regional transmission. Funding: National Key Research and Development Program of China.http://www.sciencedirect.com/science/article/pii/S2666606524002554Human mobilityNetwork analysisDisease migration |
| spellingShingle | Lin-Jie Yu Peng-Sheng Ji Xiang Ren Yan-He Wang Chen-Long Lv Meng-Jie Geng Jin-Jin Chen Tian Tang Chun-Xi Shan Sheng-Hong Lin Qiang Xu Guo-Lin Wang Li-Ping Wang Simon I. Hay Wei Liu Yang Yang Li-Qun Fang Inter-city movement pattern of notifiable infectious diseases in China: a social network analysisResearch in context The Lancet Regional Health. Western Pacific Human mobility Network analysis Disease migration |
| title | Inter-city movement pattern of notifiable infectious diseases in China: a social network analysisResearch in context |
| title_full | Inter-city movement pattern of notifiable infectious diseases in China: a social network analysisResearch in context |
| title_fullStr | Inter-city movement pattern of notifiable infectious diseases in China: a social network analysisResearch in context |
| title_full_unstemmed | Inter-city movement pattern of notifiable infectious diseases in China: a social network analysisResearch in context |
| title_short | Inter-city movement pattern of notifiable infectious diseases in China: a social network analysisResearch in context |
| title_sort | inter city movement pattern of notifiable infectious diseases in china a social network analysisresearch in context |
| topic | Human mobility Network analysis Disease migration |
| url | http://www.sciencedirect.com/science/article/pii/S2666606524002554 |
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