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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9608090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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