Evidence map›Paper›PMID 39759426›Full record

ArticleThe Lancet regional health. Western Pacific2025

Inter-city movement pattern of notifiable infectious diseases in China: a social network analysis.

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 and 7 more

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Lin-Jie YuState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.
Peng-Sheng JiDepartment of Statistics, Franklin College of Arts and Science, University of Georgia, GA, United States.
Xiang RenDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, PR China.
Yan-He WangThe 968th Hospital of Joint Logistics Support Force of PLA, Jinzhou, Liaoning, PR China.
Chen-Long LvState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.
Meng-Jie GengDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, PR China.
Jin-Jin ChenState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.
Tian TangState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.
Chun-Xi ShanState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.
Sheng-Hong LinState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.
Qiang XuState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.
Guo-Lin WangState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.
Li-Ping WangDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, PR China.
Simon I HayDepartment of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, United States.
Wei LiuState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.
Yang YangDepartment of Statistics, Franklin College of Arts and Science, University of Georgia, GA, United States.
Li-Qun FangState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.

Funding

NCEZID CDC HHS U01 CK000696
6 · The paper itself

Abstract

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.

Indexed as

Disease migrationHuman mobilityNetwork analysis

Identifiers

PMID39759426
PMCPMC11700286

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.