Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a Supervised Weighted Overfitted Latent Class Analysis

TL;DR

This paper introduces a Bayesian weighted latent class analysis method to identify dietary patterns linked to hypertension in low-income women, accounting for complex survey design to improve accuracy and generalizability.

Contribution

The study develops a novel supervised weighted overfitted latent class analysis (SWOLCA) that incorporates survey weights and complex sampling design into dietary pattern analysis.

Findings

SWOLCA accurately estimates dietary patterns associated with hypertension.

The method adjusts for survey design features like stratification and clustering.

Application to NHANES data reveals specific dietary patterns linked to hypertensive outcomes.

Abstract

Poor diet quality is a key modifiable risk factor for hypertension and disproportionately impacts low-income women. \sw{Analyzing diet-driven hypertensive outcomes in this demographic is challenging due to the complexity of dietary data and selection bias when the data come from surveys, a main data source for understanding diet-disease relationships in understudied populations. Supervised Bayesian model-based clustering methods summarize dietary data into latent patterns that holistically capture relationships among foods and a known health outcome but do not sufficiently account for complex survey design. This leads to biased estimation and inference and lack of generalizability of the patterns}. To address this, we propose a supervised weighted overfitted latent class analysis (SWOLCA) based on a Bayesian pseudo-likelihood approach that integrates sampling weights into an exposure-outcome model for discrete data. Our model adjusts for stratification, clustering, and informative sampling, and handles modifying effects via interaction terms within a Markov chain Monte Carlo Gibbs sampling algorithm. Simulation studies confirm that the SWOLCA model exhibits good performance in terms of bias, precision, and coverage. Using data from the National Health and Nutrition Examination Survey (2015-2018), we demonstrate the utility of our model by characterizing dietary patterns associated with hypertensive outcomes among low-income women in the United States.