Quantitative evaluation of infection control models in the prevention of nosocomial transmission of SARS virus to healthcare workers: implication to nosocomial viral infection control for healthcare workers.
Identifieur interne : 002472 ( Main/Curation ); précédent : 002471; suivant : 002473Quantitative evaluation of infection control models in the prevention of nosocomial transmission of SARS virus to healthcare workers: implication to nosocomial viral infection control for healthcare workers.
Auteurs : Muh-Yong Yen [Taïwan] ; Yun-Ching Lu ; Pi-Hsiang Huang ; Chen-Ming Chen ; Yee-Chun Chen ; Yusen E. LinSource :
- Scandinavian journal of infectious diseases [ 1651-1980 ] ; 2010.
Descripteurs français
- KwdFr :
- Facteurs temps, Flambées de maladies (), Humains, Infection croisée (), Infection croisée (épidémiologie), Lutte contre l'infection (), Lutte contre l'infection (normes), Modèles biologiques, Modèles logistiques, Modèles statistiques, Personnel de santé (), Syndrome respiratoire aigu sévère (), Syndrome respiratoire aigu sévère (épidémiologie), Virus du SRAS.
- MESH :
- normes : Lutte contre l'infection.
- épidémiologie : Infection croisée, Syndrome respiratoire aigu sévère.
- Facteurs temps, Flambées de maladies, Humains, Infection croisée, Lutte contre l'infection, Modèles biologiques, Modèles logistiques, Modèles statistiques, Personnel de santé, Syndrome respiratoire aigu sévère, Virus du SRAS.
English descriptors
- KwdEn :
- Cross Infection (epidemiology), Cross Infection (prevention & control), Disease Outbreaks (prevention & control), Disease Outbreaks (statistics & numerical data), Health Personnel (statistics & numerical data), Humans, Infection Control (methods), Infection Control (standards), Logistic Models, Models, Biological, Models, Statistical, SARS Virus, Severe Acute Respiratory Syndrome (epidemiology), Severe Acute Respiratory Syndrome (prevention & control), Time Factors.
- MESH :
- epidemiology : Cross Infection, Severe Acute Respiratory Syndrome.
- methods : Infection Control.
- prevention & control : Cross Infection, Disease Outbreaks, Severe Acute Respiratory Syndrome.
- standards : Infection Control.
- statistics & numerical data : Disease Outbreaks, Health Personnel.
- Humans, Logistic Models, Models, Biological, Models, Statistical, SARS Virus, Time Factors.
Abstract
Healthcare workers (HCWs) are at high risk of acquiring emerging infections while caring for patients, as has been shown in the recent SARS and swine flu epidemics. Using SARS as an example, we determined the effectiveness of infection control measures (ICMs) by logistic regression and structural equation modelling (SEM), a quantitative methodology that can test a hypothetical model and validates causal relationships among ICMs. Logistic regression showed that installing hand wash stations in the emergency room (p = 0.012, odds ratio = 1.07) was the only ICM significantly associated with the protection of HCWs from acquiring the SARS virus. The structural equation modelling results showed that the most important contributing factor (highest proportion of effectiveness) was installation of a fever screening station outside the emergency department (51%). Other measures included traffic control in the emergency department (19%), availability of an outbreak standard operation protocol (12%), mandatory temperature screening (9%), establishing a hand washing setup at each hospital checkpoint (3%), adding simplified isolation rooms (3%), and a standardized patient transfer protocol (3%). Installation of fever screening stations outside of the hospital and implementing traffic control in the emergency department contributed to 70% of the effectiveness in the prevention of SARS transmission. Our approach can be applied to the evaluation of control measures for other epidemic infectious diseases, including swine flu and avian flu.
DOI: 10.3109/00365540903582400
PubMed: 20148749
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Lin</name></author></analytic><series><title level="j">Scandinavian journal of infectious diseases</title><idno type="eISSN">1651-1980</idno><imprint><date when="2010" type="published">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Cross Infection (epidemiology)</term><term>Cross Infection (prevention & control)</term><term>Disease Outbreaks (prevention & control)</term><term>Disease Outbreaks (statistics & numerical data)</term><term>Health Personnel (statistics & numerical data)</term><term>Humans</term><term>Infection Control (methods)</term><term>Infection Control (standards)</term><term>Logistic Models</term><term>Models, Biological</term><term>Models, Statistical</term><term>SARS Virus</term><term>Severe Acute Respiratory Syndrome (epidemiology)</term><term>Severe Acute Respiratory Syndrome (prevention & control)</term><term>Time Factors</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Facteurs temps</term><term>Flambées de maladies ()</term><term>Humains</term><term>Infection croisée ()</term><term>Infection croisée (épidémiologie)</term><term>Lutte contre l'infection ()</term><term>Lutte contre l'infection (normes)</term><term>Modèles biologiques</term><term>Modèles logistiques</term><term>Modèles statistiques</term><term>Personnel de santé ()</term><term>Syndrome respiratoire aigu sévère ()</term><term>Syndrome respiratoire aigu sévère (épidémiologie)</term><term>Virus du SRAS</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Cross Infection</term><term>Severe Acute Respiratory Syndrome</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Infection Control</term></keywords><keywords scheme="MESH" qualifier="normes" xml:lang="fr"><term>Lutte contre l'infection</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Cross Infection</term><term>Disease Outbreaks</term><term>Severe Acute Respiratory Syndrome</term></keywords><keywords scheme="MESH" qualifier="standards" xml:lang="en"><term>Infection Control</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Disease Outbreaks</term><term>Health Personnel</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infection croisée</term><term>Syndrome respiratoire aigu sévère</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Logistic Models</term><term>Models, Biological</term><term>Models, Statistical</term><term>SARS Virus</term><term>Time Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Facteurs temps</term><term>Flambées de maladies</term><term>Humains</term><term>Infection croisée</term><term>Lutte contre l'infection</term><term>Modèles biologiques</term><term>Modèles logistiques</term><term>Modèles statistiques</term><term>Personnel de santé</term><term>Syndrome respiratoire aigu sévère</term><term>Virus du SRAS</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Healthcare workers (HCWs) are at high risk of acquiring emerging infections while caring for patients, as has been shown in the recent SARS and swine flu epidemics. Using SARS as an example, we determined the effectiveness of infection control measures (ICMs) by logistic regression and structural equation modelling (SEM), a quantitative methodology that can test a hypothetical model and validates causal relationships among ICMs. Logistic regression showed that installing hand wash stations in the emergency room (p = 0.012, odds ratio = 1.07) was the only ICM significantly associated with the protection of HCWs from acquiring the SARS virus. The structural equation modelling results showed that the most important contributing factor (highest proportion of effectiveness) was installation of a fever screening station outside the emergency department (51%). Other measures included traffic control in the emergency department (19%), availability of an outbreak standard operation protocol (12%), mandatory temperature screening (9%), establishing a hand washing setup at each hospital checkpoint (3%), adding simplified isolation rooms (3%), and a standardized patient transfer protocol (3%). Installation of fever screening stations outside of the hospital and implementing traffic control in the emergency department contributed to 70% of the effectiveness in the prevention of SARS transmission. Our approach can be applied to the evaluation of control measures for other epidemic infectious diseases, including swine flu and avian flu.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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