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      Cohort profile: a national, community-based prospective cohort study of SARS-CoV-2 pandemic outcomes in the USA—the CHASING COVID Cohort study

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          Abstract

          Purpose

          The Communities, Households and SARS-CoV-2 Epidemiology (CHASING) COVID Cohort Study is a community-based prospective cohort study launched during the upswing of the USA COVID-19 epidemic. The objectives of the cohort study are to: (1) estimate and evaluate determinants of the incidence of SARS-CoV-2 infection, disease and deaths; (2) assess the impact of the pandemic on psychosocial and economic outcomes and (3) assess the uptake of pandemic mitigation strategies.

          Participants

          We began enrolling participants from 28 March 2020 using internet-based strategies. Adults≥18 years residing anywhere in the USA or US territories were eligible. 6740 people are enrolled in the cohort, including participants from all 50 US states, the District of Columbia, Puerto Rico and Guam. Participants are contacted regularly to complete study assessments, including interviews and dried blood spot specimen collection for serologic testing.

          Findings to date

          Participants are geographically and sociodemographically diverse and include essential workers (19%). 84.2% remain engaged in cohort follow-up activities after enrolment. Data have been used to assess SARS-CoV-2 cumulative incidence, seroincidence and related risk factors at different phases of the US pandemic; the role of household crowding and the presence of children in the household as potential risk factors for severe COVID-19 early in the US pandemic; to describe the prevalence of anxiety symptoms and its relationship to COVID-19 outcomes and other potential stressors; to identify preferences for SARS-CoV-2 diagnostic testing when community transmission is on the rise via a discrete choice experiment and to assess vaccine hesitancy over time and its relationship to vaccine uptake.

          Future plans

          The CHASING COVID Cohort Study has outlined a research agenda that involves ongoing monitoring of the incidence and determinants of SARS-CoV-2 outcomes, mental health outcomes and economic outcomes. Additional priorities include assessing the incidence, prevalence and correlates of long-haul COVID-19.

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          Most cited references24

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          Defining the Epidemiology of Covid-19 — Studies Needed

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            Review: a gentle introduction to imputation of missing values.

            In most situations, simple techniques for handling missing data (such as complete case analysis, overall mean imputation, and the missing-indicator method) produce biased results, whereas imputation techniques yield valid results without complicating the analysis once the imputations are carried out. Imputation techniques are based on the idea that any subject in a study sample can be replaced by a new randomly chosen subject from the same source population. Imputation of missing data on a variable is replacing that missing by a value that is drawn from an estimate of the distribution of this variable. In single imputation, only one estimate is used. In multiple imputation, various estimates are used, reflecting the uncertainty in the estimation of this distribution. Under the general conditions of so-called missing at random and missing completely at random, both single and multiple imputations result in unbiased estimates of study associations. But single imputation results in too small estimated standard errors, whereas multiple imputation results in correctly estimated standard errors and confidence intervals. In this article we explain why all this is the case, and use a simple simulation study to demonstrate our explanations. We also explain and illustrate why two frequently used methods to handle missing data, i.e., overall mean imputation and the missing-indicator method, almost always result in biased estimates.
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              The Proportion of SARS-CoV-2 Infections That Are Asymptomatic

              Asymptomatic infection seems to be a notable feature of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the prevalence is uncertain. This review summarizes available evidence to estimate the proportion of persons infected with SARS-CoV-2 who never develop symptoms.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2021
                21 September 2021
                21 September 2021
                : 11
                : 9
                : e048778
                Affiliations
                [1 ]City University of New York (CUNY) Institute for Implementation Science in Population Health , New York, New York, USA
                [2 ]departmentEnvironmental Health Sciences , Graduate School of Public Health and Health Policy, City University of New York , New York, New York, USA
                [3 ]departmentCommunity Health and Social Sciences , Graduate School of Public Health and Health Policy, City University of New York , New York, New York, USA
                [4 ]Maternal and Child Health, University of North Carolina at Chapel Hill Gillings School of Global Public Health , Chapel Hill, North Carolina, USA
                [5 ]departmentEpidemiology and Biostatistics , Graduate School of Public Health and Health Policy, City University of New York , New York, New York, USA
                Author notes
                [Correspondence to ] Dr Denis Nash; denis.nash@ 123456sph.cuny.edu
                Author information
                http://orcid.org/0000-0002-8426-6572
                http://orcid.org/0000-0002-9817-2868
                http://orcid.org/0000-0002-3280-5386
                Article
                bmjopen-2021-048778
                10.1136/bmjopen-2021-048778
                8458000
                34548354
                2e7f0051-7bdc-4518-b645-e09097ae02be
                © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 08 January 2021
                : 05 August 2021
                Funding
                Funded by: CUNY Institute for Implementation Science in Population Health;
                Award ID: No grant number
                Funded by: CUNY Graduate School of Public Health and Health Policy;
                Award ID: COVID-19 Grant Program, no grant number
                Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: 3UH3AI133675-04S1
                Funded by: FundRef http://dx.doi.org/10.13039/100000071, National Institute of Child Health and Human Development;
                Award ID: P2C HD050924
                Categories
                Epidemiology
                1506
                2474
                1692
                Cohort profile
                Custom metadata
                unlocked

                Medicine
                infectious diseases,epidemiology,mental health,public health
                Medicine
                infectious diseases, epidemiology, mental health, public health

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