Multimodal Strategies for the Implementation of Infection Prevention and Control (IPC) Interventions - Update of a Systematic Review for the WHO Guidelines on Core Components of IPC Programmes at the Facility Level
Clinical Microbiology and Infection(2025)SCI 1区SCI 2区
Clinic for Infectious Diseases and Hospital Hygiene | University Library
Abstract
Background Healthcare-associated infections (HAIs) remain a significant challenge worldwide, and the use of multimodal strategies is recommended by the World Health Organization (WHO) to enhance infection prevention. Objectives To update the systematic review on facility-level infection prevention and control (IPC) interventions on the WHO Core Component of using multimodal strategies. Methods Data Sources: Medline (via PubMed), EMBASE, CINAHL, and the Cochrane library.Study Eligibility Criteria: Randomized controlled studies (RCTs), interrupted time series (ITS), and before-after studies in acute care settings, from 24 November 2015 to 30 June 2023.Participants: Both paediatric and adult populations.Interventions: IPC interventions implemented with at least three WHO multimodality elements.Primary outcomes: HAI, HAI caused by antimicrobial-resistant microorganisms, and hand hygiene (HH) compliance.Assessment of Risk of Bias: Effective practice and organisation of care (EPOC) and integrated quality criteria for review of multiple study designs (ICROMS) tools.Methods of data Synthesis: Descriptive data synthesis. Results Of 5678 identified titles and abstracts, 32 publications were eligible for data extraction and analysis. Five non-controlled before-after studies (NCBA) were excluded due to an insufficient ICROMS score. Of the remaining 27 studies, nine reported on the effect of multimodal strategies to reduce device-associated HAIs, four on surgical site infections, eight on infections due to AMR and six on HH compliance. Eleven were controlled studies (RCTs or controlled before-after studies (CBAs)), nine ITS and seven NCBA studies. Twenty-two of the studies originated from high-income countries and the overall quality was medium to low. Twenty studies showed either significant HAI-reductions or HH improvement. Conclusion Most studies demonstrate a significant effect on HAI prevention and HH improvement after applying a multimodal strategy. However, the quality of evidence remains low to moderate with few studies from low- or middle-income countries. Future research should focus on higher quality studies in resource limited settings.
MoreTranslated text
Key words
antimicrobial resistance,core components,healthcare-associated infection,Infection prevention and control,multimodal strategies,systematic review,update
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper