Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States

A tree model identified adults age ≤34 years, Johnson & Johnson primary series recipients, people from racial/ethnic minority groups, residents of nonlarge metro areas, and those living in socially vulnerable communities in the South as less likely to be boosted. These findings can guide clinica...

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Autores principales: Meng, Lu, Fast, Hannah E, Saelee, Ryan, Zell, Elizabeth, Murthy, Bhavini Patel, Murthy, Neil Chandra, Lu, Peng-Jun, Shaw, Lauren, Harris, LaTreace, Gibbs-Scharf, Lynn, Chorba, Terence
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Brief Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452182/
https://www.ncbi.nlm.nih.gov/pubmed/36131845
http://dx.doi.org/10.1093/ofid/ofac446
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author Meng, Lu
Fast, Hannah E
Saelee, Ryan
Zell, Elizabeth
Murthy, Bhavini Patel
Murthy, Neil Chandra
Lu, Peng-Jun
Shaw, Lauren
Harris, LaTreace
Gibbs-Scharf, Lynn
Chorba, Terence
author_facet Meng, Lu
Fast, Hannah E
Saelee, Ryan
Zell, Elizabeth
Murthy, Bhavini Patel
Murthy, Neil Chandra
Lu, Peng-Jun
Shaw, Lauren
Harris, LaTreace
Gibbs-Scharf, Lynn
Chorba, Terence
author_sort Meng, Lu
collection PubMed
description A tree model identified adults age ≤34 years, Johnson & Johnson primary series recipients, people from racial/ethnic minority groups, residents of nonlarge metro areas, and those living in socially vulnerable communities in the South as less likely to be boosted. These findings can guide clinical/public health outreach toward specific subpopulations.
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spelling pubmed-94521822022-09-09 Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States Meng, Lu Fast, Hannah E Saelee, Ryan Zell, Elizabeth Murthy, Bhavini Patel Murthy, Neil Chandra Lu, Peng-Jun Shaw, Lauren Harris, LaTreace Gibbs-Scharf, Lynn Chorba, Terence Open Forum Infect Dis Brief Report A tree model identified adults age ≤34 years, Johnson & Johnson primary series recipients, people from racial/ethnic minority groups, residents of nonlarge metro areas, and those living in socially vulnerable communities in the South as less likely to be boosted. These findings can guide clinical/public health outreach toward specific subpopulations. Oxford University Press 2022-09-01 /pmc/articles/PMC9452182/ /pubmed/36131845 http://dx.doi.org/10.1093/ofid/ofac446 Text en Published by Oxford University Press on behalf of Infectious Diseases Society of America 2022. This work is written by (a) US Government employee(s) and is in the public domain in the US.
spellingShingle Brief Report
Meng, Lu
Fast, Hannah E
Saelee, Ryan
Zell, Elizabeth
Murthy, Bhavini Patel
Murthy, Neil Chandra
Lu, Peng-Jun
Shaw, Lauren
Harris, LaTreace
Gibbs-Scharf, Lynn
Chorba, Terence
Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States
title Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States
title_full Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States
title_fullStr Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States
title_full_unstemmed Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States
title_short Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States
title_sort using a cloud-based machine learning classification tree analysis to understand the demographic characteristics associated with covid-19 booster vaccination among adults in the united states
topic Brief Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452182/
https://www.ncbi.nlm.nih.gov/pubmed/36131845
http://dx.doi.org/10.1093/ofid/ofac446
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