Measuring trust in public health authorities in the United States: An item response theory analysis

Trust in public health authorities is central to effective public health governance, shaping communication, policy implementation, and collective action. Despite its importance, trust has been measured using a wide range of approaches, and comparatively few studies rely on validated instruments, limiting comparability across studies and contexts. This study applies Item Response Theory (IRT) to refine the beneficence dimension of the Trust in Public Health Authorities (TiPHA) Scale, originally developed and validated by Holroyd et al. (2021) using confirmatory factor analysis. Using cross-sectional, nationally representative data from the 2024 RAND American Life Panel Omnibus Wave 17 survey (n = 2,034), we evaluated the dimensionality and item-level performance of the eight-item TiPHA beneficence subscale. A graded response model was estimated to assess item discrimination, threshold ordering, and information. Results supported unidimensionality and high internal consistency for the full subscale (α = 0.88), but revealed substantial variation in item performance. Three negatively worded items demonstrating very low discrimination and minimal information were removed. The resulting five-item scale retained robust reliability (α = 0.88) and exhibited stronger measurement coherence, providing greater precision at moderate to high levels of trust. Posterior latent trait estimates were generated and rescaled to a 0–10 metric for interpretability. When applied descriptively across sociodemographic and socioeconomic characteristics, the refined trust score exhibited modest but interpretable variation, with higher average trust observed among older adults and individuals with higher educational attainment, consistent with prior theoretical expectations. These patterns support the scale’s use as a population-based measure of trust in public health authorities. Future research should apply this refined scale in multivariable and intersectional analyses to examine whether observed differences persist after accounting for correlated social, economic, and structural factors.