| Autor: |
Skyles, Ty J., Stevens, Harlan P., Davis, Spencer C., Obray, Acelan M., Miner, Dashiell S., East, Matthew J., Davis, Tyler, Hoelzer, Haley, Piccolo, Stephen R., Jensen, Jamie L., Poole, Brian D. |
| Předmět: |
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| Zdroj: |
Vaccines; Oct2024, Vol. 12 Issue 10, p1164, 11p |
| Abstrakt: |
Background: Seasonal influenza vaccination rates are very low among teenagers. Objectives: We used publicly available data from the NIS-Teen annual national immunization survey to explore factors that influence the likelihood of a teen receiving their seasonal flu shot. Methods: Traditional stepwise multivariable regression was used in tandem with machine learning to determine the predictive factors in teen vaccine uptake. Results and Conclusions: Age was the largest predictor, with older teens being much less likely to be vaccinated than younger teens (97.48% compared to 41.71%, p < 0.0001). Provider participation in government programs such as Vaccines for Children and the state vaccine registry positively impacts vaccine uptake (p < 0.0001). Identifying as non-Hispanic Black was a small, negative predictor of teen vaccine uptake (78.18% unvaccinated compared to 73.78% of White teens, p < 0.0001). The state quartile for COVID-19 vaccine uptake also strongly predicted flu vaccine uptake, with the upper quartile of state COVID-19 vaccine uptake being significantly more likely to also get vaccinated for influenza (76.96%, 74.94%, 74.55%, and 72.97%, p < 0.0001). Other significant factors are the number of providers, education of the mother, poverty status, and having a mixed provider facility type. Additionally, the multivariable regression analysis revealed little difference in the predictive factors of vaccine uptake between pre- and post-pandemic datasets. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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