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Identifying dietary consumption patterns from survey data: a Bayesian nonparametric latent class model
Briana J K Stephenson1, Stephanie M Wu1, Francesca Dominici1
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
This study introduces a new Bayesian model to accurately identify dietary patterns in national surveys, even with disproportionate subgroup sampling. The method improves the generalizability of dietary habit assessments for diverse populations.
Area of Science:
- Nutritional epidemiology
- Statistical modeling
- Public health research
Background:
- Dietary assessments in national surveys offer population-level insights but face challenges in generalizability due to disproportionate subgroup sampling.
- Understanding true dietary patterns is crucial for public health interventions, yet standard methods may not fully account for complex survey designs.
Purpose of the Study:
- To develop and validate a Bayesian overfitted latent class model for deriving robust dietary patterns from national survey data.
- To improve the identifiability and generalizability of dietary pattern analysis, specifically for socioeconomically disadvantaged groups.
Main Methods:
- A novel Bayesian overfitted latent class model was proposed, incorporating survey design and sampling variability.
- The model's performance was evaluated through simulations, comparing its identifiability of true population patterns and prevalence against standard approaches.
- The model was applied to identify dietary intake patterns among adults at or below 130% of the poverty income level.
Main Results:
- The proposed Bayesian model demonstrated improved identifiability of true population dietary patterns and prevalence in simulation studies compared to standard methods.
- Five distinct dietary patterns were identified among adults living at or below 130% poverty income level.
- The study provides reproducible code and data to facilitate further research in dietary pattern analysis.
Conclusions:
- The developed Bayesian model offers a more accurate and generalizable approach to identifying dietary patterns from complex national survey data.
- This methodology enhances the understanding of dietary habits in vulnerable populations, paving the way for targeted public health strategies.
- The availability of reproducible resources encourages wider adoption and further investigation into dietary pattern analysis.
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