Identifying dietary consumption patterns from survey data: a Bayesian nonparametric latent class model

Dietary assessments provide the snapshots of population-based dietary habits. Questions remain about how generalisable those snapshots are in national survey data, where certain subgroups are sampled disproportionately. We propose a Bayesian overfitted latent class model to derive dietary patterns, accounting for survey design and sampling variability. Compared to standard approaches, our model showed improved identifiability of the true population pattern and prevalence in simulation. We focus application of this model to identify the intake patterns of adults living at or below the 130% poverty income level. Five dietary patterns were identified and characterised by reproducible code/data made available to encourage further research.

Automated summary

Dietary assessments provide snapshots of population-based dietary habits, but their generalizability in national survey data is questionable due to disproportionate sampling of certain subgroups. In this study, a Bayesian overfitted latent class model was proposed to derive dietary patterns, taking into account survey design and sampling variability, and showed improved identifiability of the true population pattern and prevalence compared to standard approaches.