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Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach

Side effects of COVID-19 or other vaccinations may affect an individual’s safety, ability to work or care for self or others, and/or willingness to be vaccinated. Identifying modifiable factors that influence these side effects may increase the number of people vaccinated. In this observational stud...

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Autores principales: Abbaspour, Sara, Robbins, Gregory K., Blumenthal, Kimberly G., Hashimoto, Dean, Hopcia, Karen, Mukerji, Shibani S., Shenoy, Erica S., Wang, Wei, Klerman, Elizabeth B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9608090/
https://www.ncbi.nlm.nih.gov/pubmed/36298612
http://dx.doi.org/10.3390/vaccines10101747
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author Abbaspour, Sara
Robbins, Gregory K.
Blumenthal, Kimberly G.
Hashimoto, Dean
Hopcia, Karen
Mukerji, Shibani S.
Shenoy, Erica S.
Wang, Wei
Klerman, Elizabeth B.
author_facet Abbaspour, Sara
Robbins, Gregory K.
Blumenthal, Kimberly G.
Hashimoto, Dean
Hopcia, Karen
Mukerji, Shibani S.
Shenoy, Erica S.
Wang, Wei
Klerman, Elizabeth B.
author_sort Abbaspour, Sara
collection PubMed
description Side effects of COVID-19 or other vaccinations may affect an individual’s safety, ability to work or care for self or others, and/or willingness to be vaccinated. Identifying modifiable factors that influence these side effects may increase the number of people vaccinated. In this observational study, data were from individuals who received an mRNA COVID-19 vaccine between December 2020 and April 2021 and responded to at least one post-vaccination symptoms survey that was sent daily for three days after each vaccination. We excluded those with a COVID-19 diagnosis or positive SARS-CoV2 test within one week after their vaccination because of the overlap of symptoms. We used machine learning techniques to analyze the data after the first vaccination. Data from 50,484 individuals (73% female, 18 to 95 years old) were included in the primary analysis. Demographics, history of an epinephrine autoinjector prescription, allergy history category (e.g., food, vaccine, medication, insect sting, seasonal), prior COVID-19 diagnosis or positive test, and vaccine manufacturer were identified as factors associated with allergic and non-allergic side effects; vaccination time 6:00–10:59 was associated with more non-allergic side effects. Randomized controlled trials should be conducted to quantify the relative effect of modifiable factors, such as time of vaccination.
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spelling pubmed-96080902022-10-28 Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach Abbaspour, Sara Robbins, Gregory K. Blumenthal, Kimberly G. Hashimoto, Dean Hopcia, Karen Mukerji, Shibani S. Shenoy, Erica S. Wang, Wei Klerman, Elizabeth B. Vaccines (Basel) Article Side effects of COVID-19 or other vaccinations may affect an individual’s safety, ability to work or care for self or others, and/or willingness to be vaccinated. Identifying modifiable factors that influence these side effects may increase the number of people vaccinated. In this observational study, data were from individuals who received an mRNA COVID-19 vaccine between December 2020 and April 2021 and responded to at least one post-vaccination symptoms survey that was sent daily for three days after each vaccination. We excluded those with a COVID-19 diagnosis or positive SARS-CoV2 test within one week after their vaccination because of the overlap of symptoms. We used machine learning techniques to analyze the data after the first vaccination. Data from 50,484 individuals (73% female, 18 to 95 years old) were included in the primary analysis. Demographics, history of an epinephrine autoinjector prescription, allergy history category (e.g., food, vaccine, medication, insect sting, seasonal), prior COVID-19 diagnosis or positive test, and vaccine manufacturer were identified as factors associated with allergic and non-allergic side effects; vaccination time 6:00–10:59 was associated with more non-allergic side effects. Randomized controlled trials should be conducted to quantify the relative effect of modifiable factors, such as time of vaccination. MDPI 2022-10-19 /pmc/articles/PMC9608090/ /pubmed/36298612 http://dx.doi.org/10.3390/vaccines10101747 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Abbaspour, Sara
Robbins, Gregory K.
Blumenthal, Kimberly G.
Hashimoto, Dean
Hopcia, Karen
Mukerji, Shibani S.
Shenoy, Erica S.
Wang, Wei
Klerman, Elizabeth B.
Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach
title Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach
title_full Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach
title_fullStr Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach
title_full_unstemmed Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach
title_short Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach
title_sort identifying modifiable predictors of covid-19 vaccine side effects: a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9608090/
https://www.ncbi.nlm.nih.gov/pubmed/36298612
http://dx.doi.org/10.3390/vaccines10101747
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