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.
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.
Yen
Muh-Yong
MY
Taipei City Hospital, Taipei, Taiwan.
Lu
Yun-Ching
YC
Huang
Pi-Hsiang
PH
Chen
Chen-Ming
CM
Chen
Yee-Chun
YC
Lin
Yusen E
YE
D003428:Cross Infection / Q000453:epidemiology / Q000517:prevention & control*; D004196:Disease Outbreaks / Q000517:prevention & control* / Q000706:statistics & numerical data; D006282:Health Personnel / Q000706:statistics & numerical data; D006801:Humans; D017053:Infection Control / Q000379:methods / Q000592:standards; D016015:Logistic Models; D008954:Models, Biological; D015233:Models, Statistical; D045473:Severe acute respiratory syndrome-related coronavirus; D045169:Severe Acute Respiratory Syndrome / Q000453:epidemiology / Q000517:prevention & control*; D013997:Time Factors