Structural equation modeling to evaluate the adult vaccine hesitancy scale for vaccines recommended during pregnancy.

INTRODUCTION

Acceptance of recommended vaccines is lower among pregnant people compared to non-pregnant adults, yet no tool has specifically measured prenatal vaccine hesitancy. We evaluated the performance of an existing adult Vaccine Hesitancy Scale (aVHS) in measuring vaccine hesitancy toward routinely recommended prenatal vaccines.

METHODS

Between December 2021 and April 2022, we conducted a cross-sectional national online survey with 917 US postpartum adults 18-49 years old who had given birth in the past six months. Vaccine hesitancy was measured using the aVHS, a 10-item scale previously validated among the adult general population. Scores range from 10 to 50, with higher scores indicating greater vaccine hesitancy. Structural equation modeling (SEM) with weighted least squares means and variances adjusted (WLSMV) estimator was used to assess the fit of the aVHS structure. Construct validity was assessed by examining the correlation between the aVHS score and the self-reported receipt of recommended prenatal vaccines.

RESULTS

SEM indicated acceptable fit (RMSEA: 0.098; CFI: 0.983; TLI: 0.978; SRMR: 0.040) of the data to the two-factor model: (1) lack of vaccine confidence, and (2) perceived vaccine risks. For the paths from the two factors to the vaccine acceptance, lack of vaccine confidence was significantly correlated with influenza vaccine acceptance (β = -0.41, p < 0.001) and COVID-19 vaccine acceptance (β = -0.64, p < 0.001), while perceived vaccine risk was significantly linked with Tdap vaccine acceptance (β = -0.57, p < 0.001) and influenza vaccine acceptance (β = -0.25, p < 0.001). Additionally, pregnant people with higher aVHS scores were less likely to receive recommended prenatal vaccines.

DISCUSSION

Although the aVHS offered acceptable measurement of prenatal vaccine hesitancy, a scale that measures pregnancy-specific concerns may offer more tailored measurement for this unique population.