Latent class analysis is useful to classify pregnant women into dietary patterns.

Empirical dietary patterns are derived predominantly using principal components, exploratory factor analysis (EFA), or cluster analysis. Interestingly, latent variable models are less used despite their being more flexible to accommodate important characteristics of dietary data and despite dietary patterns being recognized as latent variables. Latent class analysis (LCA) has been shown empirically to be more appropriate to derive dietary patterns than k-means clustering but has not been compared yet to confirmatory factor analysis (CFA). In this article, we derived dietary patterns using EFA, CFA, and LCA on food items, tested how well the classes from LCA were characterized by the factors from CFA, and compared participants' direct classification from LCA on food items compared with 2 a posteriori classifications from factor scores. Methods were illustrated with the Pregnancy, Infection and Nutrition Study, North Carolina, 2000-2005 (n = 1285 women). From EFA and CFA, we found that food items were grouped into 4 factors: Prudent, Prudent with coffee and alcohol, Western, and Southern. From LCA, pregnant women were classified into 3 classes: Prudent, Hard core Western, and Health-conscious Western. There was high agreement between the direct classification from LCA on food items and the classification from the 2-step LCA on factor scores [κ=0.70 (95% CI = 0.66, 0.73)] despite factors explaining only 25% of the total variance. We suggest LCA on food items to study the effect for mutually exclusive classes and CFA to understand which foods are eaten in combination. When interested in both benefits, the 2-step classification using LCA on previously derived factor scores seems promising.

Duke Scholars

Author Amy H Herring Statistical Science