Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 19, 2022
- Source ID
- 10.3390/vaccines10101747
Entities
People
- Dean Hashimoto
- Elizabeth B Klerman
- Erica S Shenoy
- Gregory K. Robbins
- Karen Hopcia
- Kimberly G Blumenthal
- Sara Abbaspour
- Shibani S. Mukerji
- Wei Wang
Organizations
- United States Department of Defense