Evidence map›Paper›PMID 40889287›Full record

ArticleInternational journal for quality in health care : journal of the International Society for Quality in Health Care2025

Controlling nosocomial transmission of respiratory infections in neurological wards: insights from COVID-19 pandemic data.

Wanji Xie, Chengyue Zhu, Yue Dong, Junqing Li, Yi Zang, Junhong Jiang, Xihai Zhu, Chang Shu, Yaojun Xiang, Zhijun Jie and 2 more

Abstract read
In one paragraph

Article in International journal for quality in health care : journal of the International Society for Quality in Health Care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Wanji XieDepartment of General Medicine, Hongqiao Community Health Service Center, 829 Wuzhong Rd, Minhang District, Shanghai, 201103, China.
Chengyue ZhuDepartment of Respiratory and Critical Care Medicine, Shanghai Fifth People's Hospital, Fudan University, 801 Heqing Rd, Minhang District, Shanghai, 200240, China.
Yue DongDepartment of Respiratory and Critical Care Medicine, Shanghai Fifth People's Hospital, Fudan University, 801 Heqing Rd, Minhang District, Shanghai, 200240, China.
Junqing LiDepartment of Respiratory and Critical Care Medicine, Shanghai Fifth People's Hospital, Fudan University, 801 Heqing Rd, Minhang District, Shanghai, 200240, China.
Yi ZangLingang Laboratory, 2380 Hechuan Rd, Minhang District, Shanghai, 201101, China.
Junhong JiangDepartment of Respiratory and Critical Care Medicine, the Fourth Affiliated Hospital of Soochow University, 9 Chongwen Rd, Suzhou, Jiangsu, 215000, China.
Xihai ZhuDepartment of Infection Control, Shanghai Blue Cross Brain Hospital, 2880 Qixin Rd, Minhang District, Shanghai, 201101, China.
Chang ShuDepartment of Infection Control, Shanghai Blue Cross Brain Hospital, 2880 Qixin Rd, Minhang District, Shanghai, 201101, China.
Yaojun XiangDepartment of Infection Control, Shanghai Blue Cross Brain Hospital, 2880 Qixin Rd, Minhang District, Shanghai, 201101, China.
Zhijun JieDepartment of Respiratory and Critical Care Medicine, Shanghai Fifth People's Hospital, Fudan University, 801 Heqing Rd, Minhang District, Shanghai, 200240, China.
Zhenzhen ZhangCenter of Community-Based Health Research, Fudan University, 128 Ruili Rd, Minhang District, Shanghai, 200031, China.
Jindong ShiCenter of Community-Based Health Research, Fudan University, 128 Ruili Rd, Minhang District, Shanghai, 200031, China.

Funding

Construction Plan of Key Disciplines in Shanghai Municipal Health System 2024ZDXK0017Foundation of Center of Community-Based Health Research, Fudan University 2022SJ06Medical Specialized Department Project of Minhang District 2025MWFC07Natural Science Foundation of Minhang District 2023MW67
6 · The paper itself

Abstract

backgroundTransmissible diseases pose significant risks within healthcare facilities, which can lead to accelerated viral spread and exacerbate healthcare burdens, particularly for patients with severe conditions. During the COVID-19 pandemic, numerous cases of nosocomial infections were reported, underscoring the shortcomings of current in-hospital management strategies for controlling such outbreaks. This study aims to provide insights for developing improved in-hospital management strategies for future infectious disease outbreaks.

methodsComprehensive data on the infection status and clinical profiles of all patients and healthcare staff, gathered during the COVID-19 outbreak at a Shanghai brain hospital, were analyzed to evaluate transmission dynamics and identify risk factors. Machine learning techniques were utilized to recognize infection factors, while time series analyses-including cross-correlation and Granger causality tests-were performed to explore temporal relationships.

resultsThe infection rate within the hospital significantly exceeded that of the surrounding community, confirming a heightened risk of nosocomial transmission. Notably, peak infections among healthcare workers lagged behind those among patients, with time series analysis revealing a clear patient-to-worker transmission pathway within hospital wards. Furthermore, a machine learning prediction model trained on all patient data demonstrated high accuracy in predicting (Area under curve = 1.00 for training set and 0.85 for testing set) susceptible individuals and successfully identified risk factors such as age, doctor number in ward, red blood cell count, etc.

conclusionThe study elucidated a distinct patient-to-worker transmission pattern of respiratory infectious diseases in healthcare settings and identified critical susceptibility risk factors. These findings provide valuable strategic guidance for managing and mitigating future nosocomial outbreaks, ultimately enhancing infection control and preparedness in hospital environments.

Indexed as

COVID-19Cross InfectionInfection ControlRespiratory Tract InfectionsAdultAgedChinaFemaleHumansMachine LearningMaleMiddle AgedPandemicsRisk FactorsSARS-CoV-2healthcare workersinfection controlmachine learningneurological patientsnosocomial transmission

Identifiers

PMID40889287
PMCPMC12450279

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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 OpenQuestion graph.