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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Vaccines |
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| Online Access: | https://www.mdpi.com/2076-393X/10/10/1747 |
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| author | Sara Abbaspour Gregory K. Robbins Kimberly G. Blumenthal Dean Hashimoto Karen Hopcia Shibani S. Mukerji Erica S. Shenoy Wei Wang Elizabeth B. Klerman |
| author_facet | Sara Abbaspour Gregory K. Robbins Kimberly G. Blumenthal Dean Hashimoto Karen Hopcia Shibani S. Mukerji Erica S. Shenoy Wei Wang Elizabeth B. Klerman |
| author_sort | Sara Abbaspour |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T19:24:29Z |
| format | Article |
| id | doaj.art-f93fe81807e94daaa3ddb15751c96854 |
| institution | Directory Open Access Journal |
| issn | 2076-393X |
| language | English |
| last_indexed | 2024-03-09T19:24:29Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vaccines |
| spelling | doaj.art-f93fe81807e94daaa3ddb15751c968542023-11-24T03:05:22ZengMDPI AGVaccines2076-393X2022-10-011010174710.3390/vaccines10101747Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning ApproachSara Abbaspour0Gregory K. Robbins1Kimberly G. Blumenthal2Dean Hashimoto3Karen Hopcia4Shibani S. Mukerji5Erica S. Shenoy6Wei Wang7Elizabeth B. Klerman8Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USADepartment of Medicine, Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA 02114, USAHarvard Medical School, Boston, MA 02114, USAHarvard Medical School, Boston, MA 02114, USAOccupational Health Services, MassGeneralBrigham, Boston, MA 02114, USAHarvard Medical School, Boston, MA 02114, USADepartment of Medicine, Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA 02114, USADivision of Sleep Medicine, Harvard Medical School, Boston, MA 02114, USADepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USASide 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.https://www.mdpi.com/2076-393X/10/10/1747vaccinationCOVID-19side effectsallergytime-of-day-effectsmachine learning |
| spellingShingle | Sara Abbaspour Gregory K. Robbins Kimberly G. Blumenthal Dean Hashimoto Karen Hopcia Shibani S. Mukerji Erica S. Shenoy Wei Wang Elizabeth B. Klerman Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach Vaccines vaccination COVID-19 side effects allergy time-of-day-effects machine learning |
| 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 | vaccination COVID-19 side effects allergy time-of-day-effects machine learning |
| url | https://www.mdpi.com/2076-393X/10/10/1747 |
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