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      Risk Factors for SARS Transmission from Patients Requiring Intubation: A Multicentre Investigation in Toronto, Canada

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          In the 2003 Toronto SARS outbreak, SARS-CoV was transmitted in hospitals despite adherence to infection control procedures. Considerable controversy resulted regarding which procedures and behaviours were associated with the greatest risk of SARS-CoV transmission.


          A retrospective cohort study was conducted to identify risk factors for transmission of SARS-CoV during intubation from laboratory confirmed SARS patients to HCWs involved in their care. All SARS patients requiring intubation during the Toronto outbreak were identified. All HCWs who provided care to intubated SARS patients during treatment or transportation and who entered a patient room or had direct patient contact from 24 hours before to 4 hours after intubation were eligible for this study. Data was collected on patients by chart review and on HCWs by interviewer-administered questionnaire. Generalized estimating equation (GEE) logistic regression models and classification and regression trees (CART) were used to identify risk factors for SARS transmission.


          45 laboratory-confirmed intubated SARS patients were identified. Of the 697 HCWs involved in their care, 624 (90%) participated in the study. SARS-CoV was transmitted to 26 HCWs from 7 patients; 21 HCWs were infected by 3 patients. In multivariate GEE logistic regression models, presence in the room during fiberoptic intubation (OR = 2.79, p = .004) or ECG (OR = 3.52, p = .002), unprotected eye contact with secretions (OR = 7.34, p = .001), patient APACHE II score ≥20 (OR = 17.05, p = .009) and patient Pa0 2/Fi0 2 ratio ≤59 (OR = 8.65, p = .001) were associated with increased risk of transmission of SARS-CoV. In CART analyses, the four covariates which explained the greatest amount of variation in SARS-CoV transmission were covariates representing individual patients.


          Close contact with the airway of severely ill patients and failure of infection control practices to prevent exposure to respiratory secretions were associated with transmission of SARS-CoV. Rates of transmission of SARS-CoV varied widely among patients.

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          Most cited references 23

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          Longitudinal data analysis for discrete and continuous outcomes.

           K Liang,  S Zeger,  David Shay (1986)
          Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of statistical analysis is to describe the marginal expectation of the outcome variable as a function of the covariates while accounting for the correlation among the repeated observations for a given subject. This paper proposes a unifying approach to such analysis for a variety of discrete and continuous outcomes. A class of generalized estimating equations (GEEs) for the regression parameters is proposed. The equations are extensions of those used in quasi-likelihood (Wedderburn, 1974, Biometrika 61, 439-447) methods. The GEEs have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as we often expect. A consistent variance estimate is presented. We illustrate the use of the GEE approach with longitudinal data from a study of the effect of mothers' stress on children's morbidity.
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            Heterogeneities in the transmission of infectious agents: implications for the design of control programs.

            From an analysis of the distributions of measures of transmission rates among hosts, we identify an empirical relationship suggesting that, typically, 20% of the host population contributes at least 80% of the net transmission potential, as measured by the basic reproduction number, R0. This is an example of a statistical pattern known as the 20/80 rule. The rule applies to a variety of disease systems, including vector-borne parasites and sexually transmitted pathogens. The rule implies that control programs targeted at the "core" 20% group are potentially highly effective and, conversely, that programs that fail to reach all of this group will be much less effective than expected in reducing levels of infection in the population as a whole.
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              Superspreading SARS Events, Beijing, 2003

              Superspreading events were pivotal in the global spread of severe acute respiratory syndrome (SARS). We investigated superspreading in one transmission chain early in Beijing’s epidemic. Superspreading was defined as transmission of SARS to at least eight contacts. An index patient with onset of SARS 2 months after hospital admission was the source of four generations of transmission to 76 case-patients, including 12 healthcare workers and several hospital visitors. Four (5%) case circumstances met the superspreading definition. Superspreading appeared to be associated with older age (mean 56 vs. 44 years), case fatality (75% vs. 16%, p = 0.02, Fisher exact test), number of close contacts (36 vs. 0.37) and attack rate among close contacts (43% vs. 18.5%, p < 0.025). Delayed recognition of SARS in a hospitalized patient permitted transmission to patients, visitors, and healthcare workers. Older age and number of contacts merit investigation in future studies of superspreading.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                19 May 2010
                : 5
                : 5
                [1 ]Division of Infectious Diseases, University Health Network, Toronto, Ontario, Canada
                [2 ]Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
                [3 ]Mount Sinai Hospital, Toronto, Ontario, Canada
                [4 ]Department of Laboratory Medicine and Pathology, University of Toronto, Toronto, Ontario, Canada
                [5 ]Ontario Ministry of Health and Long Term Care, Toronto, Ontario, Canada
                [6 ]Department of Anesthesia, University of Toronto, Toronto, Ontario, Canada
                [7 ]Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
                [8 ]Public Health Agency of Canada, Ottawa, Ontario, Canada
                [9 ]British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
                [10 ]Department of Medicine, University of Toronto, Toronto, Ontario, Canada
                [11 ]Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
                [12 ]Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
                [13 ]Durham Region Health Department, Whitby, Ontario, Canada
                [14 ]Department of Microbiology and Immunology, Queen's University, Kingston, Ontario, Canada
                U.S. Naval Medical Research Center Detachment/Centers for Disease Control, United States of America
                Author notes

                Conceived and designed the experiments: AM EB MC DG BH SEL MBL LCM MO SP DR DS SS AS TES MV DZ KAG. Performed the experiments: AS AM BH DR DS MV KAG. Analyzed the data: JR AS AM BH LCM SS KAG. Wrote the paper: JR AS AM EB BH SEL MO SP DR DS AS TES MV. Obtained funding: DZ.

                Raboud et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 10
                Research Article
                Virology/Emerging Viral Diseases
                Infectious Diseases/Epidemiology and Control of Infectious Diseases
                Infectious Diseases/Nosocomial and Healthcare-Associated Infections
                Infectious Diseases/Viral Infections



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