Tracking COVID-19 vaccination expectancies and vaccination refusal in the United States

ABSTRACT

To identify factors that predict COVID-19 vaccination refusal and show how expectancies affect vaccination acceptance for non-vaccinated adults, we used a monthly repeated cross-sectional sample from June/2021 to October/2021 to collect data on vaccination behaviors and predictor variables for 2,116 US adults over 50 years of age. Selection bias modeling – which is required when data availability is a result of behavioral choice – predicts two outcomes: (1) no vaccination vs. vaccination for the entire sample and (2) the effects of expectancy indices predicting vaccination Refuser vs. vaccination Accepters for the unvaccinated group. Vaccine refusers were younger and less educated, endorsed common misconceptions about the COVID-19 epidemic, and were Black. Vaccination expectancies were related to vaccination refusal in the unvaccinated eligible group: negative expectancies increased vaccine refusal, while positive expectancies decreased it. We conclude that behavior-related expectancies (as opposed to more stable psychological traits) are important to identify because they are often modifiable and provide a point of intervention, not just for COVID-19 vaccination acceptance but also for other positive health behaviors.

KEYWORDS:

• Expectancy value
• COVID-19 vaccination intention
• vaccine refusers

Acknowledgments

This paper was made possible by Grant No. 3R01AG063954-02S1 from the National Institute of Aging (NIA). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIA. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIA.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. This OLS bias with simultaneous causation path analysis models had been understood by economists for decades (Wonnacott & Wonnacott, 1987, Chapter 7) but Heckman showed that the same issue applied to the sample selection situation as well. Thus, his Nobel prize in 2000.

2. The dates for each data collection period are as follows: wave 1 10/20 to 11/2 2020, wave 2 11/26 to 12/4 2020, wave 3 12/17 to 12/24, 2020, wave 4 1/20 to 1/28 2021, wave 5 2/23 to 3/3 2021, wave 6 3/25 to 4/5 2021, wave 7 4/28 to 5/10 2021, wave 8 5/25 to 6/1 2021, wave 9 6/21 to 7/2 2021, wave 107/23 to 8/3 2021, wave 118/25 to 9/6 2021, wave 129/30 to 10/18 2021.

3. In both the selection and analysis model study wave is categorical because likelihood ratio tests indicate a better fit with a discontinuous model implied by separate wave indicators than a linear model assuming study wave is a continuous variable.

Additional information

Funding

The work was supported by the National Institute on Aging [3R01AG063954-02S1]