6
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Analysing different exposures identifies that wearing masks and establishing COVID-19 areas reduce secondary-attack risk in aged-care facilities

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          The COVID-19 epidemic has spread rapidly within aged-care facilities (ACFs), where the infection-fatality ratio is high. It is therefore urgent to evaluate the efficiency of infection prevention and control (IPC) measures in reducing SARS-CoV-2 transmission.

          Methods

          We analysed the COVID-19 outbreaks that took place between March and May 2020 in 12 ACFs using reverse transcription–polymerase chain reaction (RT–PCR) and serological tests for SARS-CoV-2 infection. Using maximum-likelihood approaches and generalized linear mixed models, we analysed the proportion of infected residents in ACFs and identified covariates associated with the proportion of infected residents.

          Results

          The secondary-attack risk was estimated at 4.1%, suggesting a high efficiency of the IPC measures implemented in the region. Mask wearing and the establishment of COVID-19 zones for infected residents were the two main covariates associated with lower secondary-attack risks.

          Conclusions

          Wearing masks and isolating potentially infected residents appear to be associated with a more limited spread of SARS-CoV-2 in ACFs.

          Related collections

          Most cited references20

          • Record: found
          • Abstract: found
          • Article: not found

          Estimates of the severity of coronavirus disease 2019: a model-based analysis

          Summary Background In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. Methods We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. Findings Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–7·6) in those aged 80 years or older. Interpretation These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. Funding UK Medical Research Council.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Generalized linear mixed models: a practical guide for ecology and evolution.

            How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              The Mathematics of Infectious Diseases

                Bookmark

                Author and article information

                Journal
                Int J Epidemiol
                Int J Epidemiol
                ije
                International Journal of Epidemiology
                Oxford University Press
                0300-5771
                1464-3685
                21 June 2021
                21 June 2021
                : dyab121
                Affiliations
                [1 ] Maladies Infectieuses et Vecteurs: Écologie Génétique Évolution Contrôle (MIVEGEC), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD), Université de Montpellier , France
                [2 ] Department of Geriatrics, Montpellier University Hospital, Montpellier University , France
                [3 ] Pathogenesis & Control of Chronic Infections, Institut National de la Santé et de la Recherche Médicale (INSERM), U1058, Établissement Français du Sang (EFS), Montpellier University and Laboratory of Virology, Centre Hospitalier Universitaire de Montpellier , France
                [4 ] Charité, Universitätsmedizin Berlin, Humboldt-Universität Berlin , Berlin, Germany
                [5 ] Department of Dermatology and Allergy, Berlin Institute of Health, Comprehensive Allergy Center , Berlin, Germany
                [6 ] Combattre les Maladies Chroniques Pour un Vieillissement Actif (MACVIA)-France , Montpellier, France
                Author notes
                Corresponding author. MIVEGEC, 911 av. Agropolis, 34394 Montpellier Cedex 5, France. E-mail: samuel.alizon@ 123456cnrs.fr

                Hubert Blain and Samuel Alizon contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-4499-0435
                https://orcid.org/0000-0002-0779-9543
                Article
                dyab121
                10.1093/ije/dyab121
                8344874
                34151958
                15f1d3c1-e047-441c-b966-305aee31436b
                © The Author(s) 2021; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association

                This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                : 04 December 2020
                : 13 May 2021
                : 23 May 2021
                Page count
                Pages: 7
                Categories
                Original Article
                AcademicSubjects/MED00860
                Custom metadata
                PAP

                Public health
                covid-19,aged-care facilities,mask wearing,generalized linear mixed models,secondary-attack risk

                Comments

                Comment on this article