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      Trust in a COVID-19 vaccine in the U.S.: A social-ecological perspective

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          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.

          Highlights

          • •

            Low COVID-19 vaccine trust associated with vaccines distributed too soon.

          • •

            Social norms were strongly associated with COVID-19 vaccine trust.

          • •

            High trustworthiness in CDC as for information was linked to vaccine trust.

          • •

            Females expressed lower vaccine trust than males.

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          Most cited references59

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          Is Open Access

          Vaccine hesitancy: Definition, scope and determinants.

          Noni MacDonald (2015)
          The SAGE Working Group on Vaccine Hesitancy concluded that vaccine hesitancy refers to delay in acceptance or refusal of vaccination despite availability of vaccination services. Vaccine hesitancy is complex and context specific, varying across time, place and vaccines. It is influenced by factors such as complacency, convenience and confidence. The Working Group retained the term 'vaccine' rather than 'vaccination' hesitancy, although the latter more correctly implies the broader range of immunization concerns, as vaccine hesitancy is the more commonly used term. While high levels of hesitancy lead to low vaccine demand, low levels of hesitancy do not necessarily mean high vaccine demand. The Vaccine Hesitancy Determinants Matrix displays the factors influencing the behavioral decision to accept, delay or reject some or all vaccines under three categories: contextual, individual and group, and vaccine/vaccination-specific influences.
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            A global survey of potential acceptance of a COVID-19 vaccine

            Jeffrey V Lazarus, Scott C Ratzan, Adam Palayew … (2020)
            Several coronavirus disease 2019 (COVID-19) vaccines are currently in human trials. In June 2020, we surveyed 13,426 people in 19 countries to determine potential acceptance rates and factors influencing acceptance of a COVID-19 vaccine. Of these, 71.5% of participants reported that they would be very or somewhat likely to take a COVID-19 vaccine, and 61.4% reported that they would accept their employer’s recommendation to do so. Differences in acceptance rates ranged from almost 90% (in China) to less than 55% (in Russia). Respondents reporting higher levels of trust in information from government sources were more likely to accept a vaccine and take their employer’s advice to do so.
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              Is Open Access

              Purposeful selection of variables in logistic regression

              Zoran Bursac, C Gauss, David Williams … (2008)
              Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. Conclusion If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.
              Article 0 comments Cited 782 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Soc Sci Med
                Journal ID (iso-abbrev): Soc Sci Med
                Title: Social Science & Medicine (1982)
                Publisher: Elsevier Ltd.
                ISSN (Print): 0277-9536
                ISSN (Electronic): 1873-5347
                Publication date PMC-release: 8 January 2021
                Publication date (Print): February 2021
                Publication date (Electronic): 8 January 2021
                Volume: 270
                Page: 113684
                Affiliations
                [a ]Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, USA
                [b ]Division of Infectious Diseases, Johns Hopkins University School of Medicine, USA
                [c ]Krieger School of Arts & Sciences, Johns Hopkins University, Chicago, USA
                [d ]Division of Community Health Sciences, University of Illinois at Chicago School of Public Health, Chicago, IL, USA
                Author notes
                [∗ ]Corresponding author. Department of Health, Behavior and Society, Bloomberg School of Public Health, Division of Infectious Diseases School of Medicine, Johns Hopkins University, USA.
                Article
                Publisher Item ID: S0277-9536(21)00016-2 Publisher ID: 113684
                DOI: 10.1016/j.socscimed.2021.113684
                PMC ID: 7834519
                PubMed ID: 33485008
                SO-VID: edaffe02-3e81-407b-8a17-f60997db39ed
                Copyright © © 2021 Elsevier Ltd. All rights reserved.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date revision received : 27 December 2020
                Date accepted : 3 January 2021
                Categories
                Subject: Article

                ScienceOpen disciplines: Health & Social care
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                ScienceOpen disciplines: Health & Social care

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