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      Facemasks, Hand Hygiene, and Influenza among Young Adults: A Randomized Intervention Trial

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          Abstract

          Limited vaccine availability and the potential for resistance to antiviral medications have led to calls for establishing the efficacy of non-pharmaceutical measures for mitigating pandemic influenza. Our objective was to examine if the use of face masks and hand hygiene reduced rates of influenza-like illness (ILI) and laboratory-confirmed influenza in the natural setting. A cluster-randomized intervention trial was designed involving 1,178 young adults living in 37 residence houses in 5 university residence halls during the 2007–2008 influenza season. Participants were assigned to face mask and hand hygiene, face mask only, or control group during the study. Discrete-time survival models using generalized estimating equations to estimate intervention effects on ILI and confirmed influenza A/B infection over a 6-week study period were examined. A significant reduction in the rate of ILI was observed in weeks 3 through 6 of the study, with a maximum reduction of 75% during the final study week (rate ratio [RR] = 0.25, [95% CI, 0.07 to 0.87]). Both intervention groups compared to the control showed cumulative reductions in rates of influenza over the study period, although results did not reach statistical significance. Generalizability limited to similar settings and age groups. Face masks and hand hygiene combined may reduce the rate of ILI and confirmed influenza in community settings. These non-pharmaceutical measures should be recommended in crowded settings at the start of an influenza pandemic.

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          Clinicaltrials.gov NCT00490633

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          Most cited references29

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          A Global Measure of Perceived Stress

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            Longitudinal data analysis for discrete and continuous outcomes.

            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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              • Article: not found

              Longitudinal Data Analysis for Discrete and Continuous Outcomes

              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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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                25 January 2012
                : 7
                : 1
                : e29744
                Affiliations
                [1 ]Department of Epidemiology, University of Michigan-School of Public Health, Ann Arbor, Michigan, United States of America
                [2 ]Center for Social Epidemiology & Population, University of Michigan-School of Public Health, Ann Arbor, Michigan, United States of America
                Fred Hutchinson Cancer Research Center, United States of America
                Author notes

                Conceived and designed the experiments: AA AM. Performed the experiments: AA AM VP RC BD. Analyzed the data: AA VP BD. Wrote the paper: AA AM VP RC BD MU.

                Article
                PONE-D-11-15596
                10.1371/journal.pone.0029744
                3266257
                22295066
                660d9f74-8c7c-4af1-9864-efee5063b4fa
                Aiello 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.
                History
                : 29 July 2011
                : 2 December 2011
                Page count
                Pages: 8
                Categories
                Research Article
                Medicine
                Clinical Research Design
                Epidemiology
                Global Health
                Infectious Diseases
                Viral Diseases
                Public Health
                Pulmonology

                Uncategorized
                Uncategorized

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