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      Time trends, factors associated with, and reasons for COVID-19 vaccine hesitancy: A massive online survey of US adults from January-May 2021

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          Abstract

          Importance

          COVID-19 vaccine hesitancy has become a leading barrier to increasing the US vaccination rate.

          Objective

          To evaluate time trends in COVID-19 vaccine intent during the US vaccine rollout, and identify key factors related to and self-reported reasons for COVID-19 vaccine hesitancy in May 2021.

          Design, participants and setting

          A COVID-19 survey was offered to US adult Facebook users in several languages yielding 5,088,772 qualifying responses from January 6 to May 31, 2021. Data was aggregated by month. Survey weights matched the sample to the age, gender, and state profile of the US population.

          Exposure

          Demographics, geographic factors, political/COVID-19 environment, health status, beliefs, and behaviors.

          Main outcome measures

          “If a vaccine to prevent COVID-19 were offered to you today, would you choose to get vaccinated.” Hesitant was defined as responding probably or definitely would not choose to get vaccinated (versus probably or definitely would, or already vaccinated).

          Results

          COVID-19 vaccine hesitancy decreased by one-third from 25.4% (95%CI, 25.3, 25.5) in January to 16.6% (95% CI, 16.4, 16.7) in May, with relatively large decreases among participants with Black, Pacific Islander or Hispanic race/ethnicity and ≤high school education. Independent risk factors for vaccine hesitancy in May (N = 525,644) included younger age, non-Asian race, < 4 year college degree, living in a more rural county, living in a county with higher Trump vote share in the 2020 election, lack of worry about COVID-19, working outside the home, never intentionally avoiding contact with others, and no past-year flu vaccine. Differences in hesitancy by race/ethnicity varied by age (e.g., Black adults more hesitant than White adults <35 years old, but less hesitant among adults ≥45 years old). Differences in hesitancy by age varied by race/ethnicity. Almost half of vaccine hesitant respondents reported fear of side effects (49.2% [95%CI, 48.7, 49.7]) and not trusting the COVID-19 vaccine (48.4% [95%CI, 48.0, 48.9]); over one-third reported not trusting the government, not needing the vaccine, and waiting to see if safe. Reasons differed by degree of vaccine intent and by race/ethnicity.

          Conclusion

          COVID-19 vaccine hesitancy varied by demographics, geography, beliefs, and behaviors, indicating a need for a range of messaging and policy options to target high-hesitancy groups.

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          Most cited references19

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          A modified poisson regression approach to prospective studies with binary data.

          G Zou (2004)
          Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100. The method is illustrated with two data sets.
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            Attitudes Toward a Potential SARS-CoV-2 Vaccine: A Survey of U.S. Adults

            Once a vaccine for coronavirus disease 2019 becomes available, it will be important to maximize vaccine uptake and coverage. This national survey explores factors associated with vaccine hesitancy. The results suggest that multipronged efforts will be needed to increase acceptance of a coronavirus disease 2019 vaccine.
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              Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio

              Background Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. Methods We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. Results Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by χ2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. Conclusions Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                21 December 2021
                2021
                21 December 2021
                : 16
                : 12
                : e0260731
                Affiliations
                [1 ] Department of Epidemiology, Graduate School of Public Health University of Pittsburgh, Pittsburgh, PA, United States of America
                [2 ] Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, United States of America
                [3 ] Heinz College, Carnegie Mellon University, Pittsburgh, PA, United States of America
                Drexel University, UNITED STATES
                Author notes

                Competing Interests: Drs. King, Mejia and Mr. Rubenstein have no conflict of interest to report. Dr. Reinhart received salary support from an unrestricted gift from Facebook described in the funding section of the paper.

                Author information
                https://orcid.org/0000-0002-6658-514X
                Article
                PONE-D-21-27471
                10.1371/journal.pone.0260731
                8691631
                34932583
                0d9463f9-b102-4d46-9470-34cb4035b506
                © 2021 King 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
                : 24 August 2021
                : 15 November 2021
                Page count
                Figures: 3, Tables: 3, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100005801, Facebook;
                Award ID: Unrestricted gift
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000130, National Center for Chronic Disease Prevention and Health Promotion;
                Award ID: U01IP001121
                Funding/Support: This material is based upon work supported by Facebook (unrestricted gift) and a cooperative agreement from the Centers for Disease Control and Prevention (U01IP001121). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of Facebook or the Centers for Disease Control and Prevention. Role of the Funder: Facebook was involved in the design and conduct of the study. The CDC provided funding only. Neither Facebook nor the Centers for Disease Control and Prevention had a role in the collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
                Categories
                Research Article
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Infectious Disease Control
                Vaccines
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Biology and Life Sciences
                Immunology
                Vaccination and Immunization
                Medicine and Health Sciences
                Immunology
                Vaccination and Immunization
                Medicine and Health Sciences
                Public and Occupational Health
                Preventive Medicine
                Vaccination and Immunization
                People and Places
                Population Groupings
                Age Groups
                Adults
                People and Places
                Population Groupings
                Age Groups
                Research and Analysis Methods
                Research Design
                Survey Research
                Surveys
                People and Places
                Population Groupings
                Ethnicities
                Hispanic People
                Social Sciences
                Sociology
                Education
                Schools
                Custom metadata
                Survey microdata is not publicly available because survey participants only consented to public disclosure of aggregate data, and because the legal agreement with Facebook governing operation of the survey prohibits disclosure of microdata without confidentiality protections for respondents. The microdata is available to researchers under a nondisclosure agreement permitting research uses. Access can be requested at https://cmu-delphi.github.io/delphi-epidata/symptom-survey/. Requests are reviewed by the Carnegie Mellon University Office of Sponsored Programs and Facebook Data for Good. The R code for the analyses in this paper can be found at: https://github.com/mrubinst757/Vaccine-Hesitancy-Trends.
                COVID-19

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