Predicting COVID-19 and Influenza Vaccination Confidence and Uptake in the United States
This study investigates and compares the predictors of COVID-19 and influenza vaccination confidence and uptake in the U.S. Vaccine hesitancy is defined as the reluctance or refusal (i.e., less than 100% behavioral intention) to vaccinate despite the availability of effective and safe vaccines. Vacc...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Vaccines |
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| Online Access: | https://www.mdpi.com/2076-393X/11/10/1597 |
| _version_ | 1797572071384940544 |
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| author | Lijiang Shen Daniel Lee |
| author_facet | Lijiang Shen Daniel Lee |
| author_sort | Lijiang Shen |
| collection | DOAJ |
| description | This study investigates and compares the predictors of COVID-19 and influenza vaccination confidence and uptake in the U.S. Vaccine hesitancy is defined as the reluctance or refusal (i.e., less than 100% behavioral intention) to vaccinate despite the availability of effective and safe vaccines. Vaccine hesitancy is a major obstacle in the fight against infectious diseases such as COVID-19 and influenza. Predictors of vaccination intention are identified using the reasoned action approach and the integrated behavioral model. Data from two national samples (<i>N</i> = 1131 for COVID-19 and <i>N</i> = 1126 for influenza) were collected from U.S. Qualtrics panels. Tobit regression models were estimated to predict percentage increases in vaccination intention (i.e., confidence) and the probability of vaccination uptake (i.e., intention reaching 100%). The results provided evidence for the reasoned approach and the IBM model and showed that the predictors followed different patterns for COVID-19 and influenza. The implications for intervention strategies and message designs were discussed. |
| first_indexed | 2024-03-10T20:49:34Z |
| format | Article |
| id | doaj.art-18df46de30fd457394450d914325a280 |
| institution | Directory Open Access Journal |
| issn | 2076-393X |
| language | English |
| last_indexed | 2024-03-10T20:49:34Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vaccines |
| spelling | doaj.art-18df46de30fd457394450d914325a2802023-11-19T18:25:08ZengMDPI AGVaccines2076-393X2023-10-011110159710.3390/vaccines11101597Predicting COVID-19 and Influenza Vaccination Confidence and Uptake in the United StatesLijiang Shen0Daniel Lee1Department of Communication Arts and Sciences, Pennsylvania State University, University Park, PA 16802, USADepartment of Communication Arts and Sciences, Pennsylvania State University, University Park, PA 16802, USAThis study investigates and compares the predictors of COVID-19 and influenza vaccination confidence and uptake in the U.S. Vaccine hesitancy is defined as the reluctance or refusal (i.e., less than 100% behavioral intention) to vaccinate despite the availability of effective and safe vaccines. Vaccine hesitancy is a major obstacle in the fight against infectious diseases such as COVID-19 and influenza. Predictors of vaccination intention are identified using the reasoned action approach and the integrated behavioral model. Data from two national samples (<i>N</i> = 1131 for COVID-19 and <i>N</i> = 1126 for influenza) were collected from U.S. Qualtrics panels. Tobit regression models were estimated to predict percentage increases in vaccination intention (i.e., confidence) and the probability of vaccination uptake (i.e., intention reaching 100%). The results provided evidence for the reasoned approach and the IBM model and showed that the predictors followed different patterns for COVID-19 and influenza. The implications for intervention strategies and message designs were discussed.https://www.mdpi.com/2076-393X/11/10/1597vaccinationCOVID-19influenzareasoned actionintegrated behavioral modelTobit regression |
| spellingShingle | Lijiang Shen Daniel Lee Predicting COVID-19 and Influenza Vaccination Confidence and Uptake in the United States Vaccines vaccination COVID-19 influenza reasoned action integrated behavioral model Tobit regression |
| title | Predicting COVID-19 and Influenza Vaccination Confidence and Uptake in the United States |
| title_full | Predicting COVID-19 and Influenza Vaccination Confidence and Uptake in the United States |
| title_fullStr | Predicting COVID-19 and Influenza Vaccination Confidence and Uptake in the United States |
| title_full_unstemmed | Predicting COVID-19 and Influenza Vaccination Confidence and Uptake in the United States |
| title_short | Predicting COVID-19 and Influenza Vaccination Confidence and Uptake in the United States |
| title_sort | predicting covid 19 and influenza vaccination confidence and uptake in the united states |
| topic | vaccination COVID-19 influenza reasoned action integrated behavioral model Tobit regression |
| url | https://www.mdpi.com/2076-393X/11/10/1597 |
| work_keys_str_mv | AT lijiangshen predictingcovid19andinfluenzavaccinationconfidenceanduptakeintheunitedstates AT daniellee predictingcovid19andinfluenzavaccinationconfidenceanduptakeintheunitedstates |