1. Latent Class Analysis of Maternal Vaccine Attitudes and Beliefs
- Author
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Dudley, Matthew Z., Limaye, Rupali J., Salmon, Daniel A., Omer, Saad B., O'Leary, Sean T., Ellingson, Mallory K., Spina, Christine I., Brewer, Sarah E., Bednarczyk, Robert A., Malik, Fauzia, Frew, Paula M., and Chamberlain, Allison T.
- Abstract
Background: Maternal vaccine coverage is suboptimal, and a substantial proportion of parents have concerns about vaccines. Most parents seek out vaccine information during and immediately after their first pregnancy. No study to our knowledge has analyzed survey data to identify homogeneous groups of pregnant women based on their vaccine attitudes and beliefs. Aims: To identify homogeneity among groups of pregnant women based on their vaccine attitudes and beliefs to facilitate audience segmentation and targeting of tailored educational interventions. Method: Between June 2017 and July 2018, we surveyed 2,196 pregnant women recruited from geographically and sociodemographically diverse prenatal care practices in Georgia and Colorado. We then performed a latent class analysis to identify homogeneity among groups of pregnant women. Results: Our latent class analysis produced three groups of pregnant women: vaccine supporters (36% of women), vaccine acceptors (41%), and vaccine skeptics (23%). Discussion: The major difference between the supporters and the acceptors were whether they mostly "strongly agreed" or just "agreed" to Likert-type scale survey items assessing their vaccine attitudes and beliefs. The skeptics most frequently chose "disagree" or "don't know" for items assessing attitudinal constructs such as confidence in vaccine safety and efficacy and disease susceptibility. However, even skeptics often chose "agree" for items assessing constructs such as disease severity and self-efficacy. Conclusions: This article provides useful insight into the homogeneity among groups of pregnant women based on their vaccine attitudes and beliefs. This knowledge should help facilitate audience segmentation and targeting of tailored educational interventions among this population.
- Published
- 2020
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