Evidence map›Paper›PMID 40357415›Full record

ArticleInfection and drug resistance2025

Analysis of Hospital-Acquired Infections in a Chinese Specialized Rehabilitation Hospital: A Five-Year Surveillance Study (2020-2024).

Qianfeng Li, Dong Wang, Hanyuan Fan

Abstract read
In one paragraph

Article in Infection and drug resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

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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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Qianfeng LiDepartment of Geriatric Rehabilitation, Shenzhen Dapeng New District Nan'ao People's Hospital, Shenzhen, People's Republic of China.
Dong WangDepartment of Geriatric Rehabilitation, Shenzhen Dapeng New District Nan'ao People's Hospital, Shenzhen, People's Republic of China.
Hanyuan FanHospital-Acquired Infection Control department, Shenzhen Dapeng New District Nan'ao People's Hospital, Shenzhen, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Hospital-acquired infections (HAIs) pose significant challenges in rehabilitation hospitals, particularly affecting patients with extended stays and complex medical needs. This study analyzed HAI patterns and risk factors in a Chinese rehabilitation hospital from 2020 to 2024. Methods: A retrospective observational study was conducted at a tertiary-care rehabilitation hospital with 25 specialized wards. Data collection included patient demographics, clinical parameters, and ward-level characteristics. Statistical analysis employed Poisson and Quasi-Poisson regression models to identify risk factors, with comprehensive diagnostic evaluation. Results: The study revealed an overall infection rate of 3.64%, representing 385 infections among 10,559 inpatients. The Vegetative State Awakening Department exhibited the highest infection rate at 11.1%, followed by Geriatric Rehabilitation Department (8.2%), and Neuro Rehabilitation Department wards (5.5-7.0%). Respiratory tract infections were most common (42%), with ventilator-associated pneumonia accounting for 28% of all infections. Statistical analysis identified several significant risk factors through both Poisson and Quasi-Poisson regression models. In the more reliable Quasi-Poisson model that accounted for overdispersion, tracheal intubation emerged as the strongest predictor with a coefficient of 2.02 (p < 0.001), followed by use of glucocorticoids (coefficient: 1.78, p < 0.001). While the initial Poisson model suggested a protective effect of radiation therapy, this effect was not significant in the Quasi-Poisson model. Conclusion: The study highlights the critical role of tracheal intubation and glucocorticoid use in HAI development within rehabilitation settings. The significant ward-level variability in infection rates suggests the need for tailored infection control strategies. Implementation of targeted interventions focusing on these identified risk factors could help reduce HAI incidence in rehabilitation hospitals.

Indexed as

Chinahealthcare surveillancehospital-acquired infectionsinfection controlnosocomial infectionspatient safetyrehabilitation hospitalrisk factors

Identifiers

PMID40357415
PMCPMC12067695

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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.