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Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy

COVID-19 vaccine hesitancy has become a major issue in the U.S. as vaccine supply has outstripped demand and vaccination rates slow down. At least one recent global survey has sought to study the covariates of vaccine acceptance, but an inferential model that makes simultaneous use of several socio-...

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Detalles Bibliográficos
Autores principales: Shen, Ke, Kejriwal, Mayank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10180637/
https://www.ncbi.nlm.nih.gov/pubmed/37172006
http://dx.doi.org/10.1371/journal.pgph.0001151
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author Shen, Ke
Kejriwal, Mayank
author_facet Shen, Ke
Kejriwal, Mayank
author_sort Shen, Ke
collection PubMed
description COVID-19 vaccine hesitancy has become a major issue in the U.S. as vaccine supply has outstripped demand and vaccination rates slow down. At least one recent global survey has sought to study the covariates of vaccine acceptance, but an inferential model that makes simultaneous use of several socio-demographic variables has been lacking. This study has two objectives. First, we quantify the associations between common socio-demographic variables (including, but not limited to, age, ethnicity, and income) and vaccine acceptance in the U.S. Second, we use a conditional inference tree to quantify and visualize the interaction and conditional effects of relevant socio-demographic variables, known to be important correlates of vaccine acceptance in the U.S., on vaccine acceptance. We conduct a retrospective analysis on a COVID-19 cross-sectional Gallup survey data administered to a representative sample of U.S.-based respondents. Our univariate regression results indicate that most socio-demographic variables, such as age, education, level of household income and education, have significant association with vaccine acceptance, although there are key points of disagreement with the global survey. Similarly, our conditional inference tree model shows that trust in the (former) Trump administration, age and ethnicity are the most important covariates for predicting vaccine hesitancy. Our model also highlights the interdependencies between these variables using a tree-like visualization.
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spelling pubmed-101806372023-05-13 Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy Shen, Ke Kejriwal, Mayank PLOS Glob Public Health Research Article COVID-19 vaccine hesitancy has become a major issue in the U.S. as vaccine supply has outstripped demand and vaccination rates slow down. At least one recent global survey has sought to study the covariates of vaccine acceptance, but an inferential model that makes simultaneous use of several socio-demographic variables has been lacking. This study has two objectives. First, we quantify the associations between common socio-demographic variables (including, but not limited to, age, ethnicity, and income) and vaccine acceptance in the U.S. Second, we use a conditional inference tree to quantify and visualize the interaction and conditional effects of relevant socio-demographic variables, known to be important correlates of vaccine acceptance in the U.S., on vaccine acceptance. We conduct a retrospective analysis on a COVID-19 cross-sectional Gallup survey data administered to a representative sample of U.S.-based respondents. Our univariate regression results indicate that most socio-demographic variables, such as age, education, level of household income and education, have significant association with vaccine acceptance, although there are key points of disagreement with the global survey. Similarly, our conditional inference tree model shows that trust in the (former) Trump administration, age and ethnicity are the most important covariates for predicting vaccine hesitancy. Our model also highlights the interdependencies between these variables using a tree-like visualization. Public Library of Science 2023-05-12 /pmc/articles/PMC10180637/ /pubmed/37172006 http://dx.doi.org/10.1371/journal.pgph.0001151 Text en © 2023 Shen, Kejriwal https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Shen, Ke
Kejriwal, Mayank
Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy
title Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy
title_full Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy
title_fullStr Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy
title_full_unstemmed Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy
title_short Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy
title_sort using conditional inference to quantify interaction effects of socio-demographic covariates of us covid-19 vaccine hesitancy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10180637/
https://www.ncbi.nlm.nih.gov/pubmed/37172006
http://dx.doi.org/10.1371/journal.pgph.0001151
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