Cargando…
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-...
Autores principales: | , |
---|---|
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 |
_version_ | 1785041382362054656 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10180637 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT shenke usingconditionalinferencetoquantifyinteractioneffectsofsociodemographiccovariatesofuscovid19vaccinehesitancy AT kejriwalmayank usingconditionalinferencetoquantifyinteractioneffectsofsociodemographiccovariatesofuscovid19vaccinehesitancy |