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. 2014 Dec;17(12):2674-86.
doi: 10.1017/S1368980014000111. Epub 2014 Feb 25.

Capturing changes in dietary patterns among older adults: a latent class analysis of an ageing Irish cohort

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Capturing changes in dietary patterns among older adults: a latent class analysis of an ageing Irish cohort

Janas M Harrington et al. Public Health Nutr. 2014 Dec.

Abstract

Objective: Data-driven approaches to dietary patterns are under-utilized; latent class analyses (LCA) are particularly rare. The present study used an LCA to identify subgroups of people with similar dietary patterns, explore changes in dietary patterns over a 10-year period and relate these dynamics to sociodemographic factors and health outcomes.

Design: The 1998 baseline and 2008 follow-up of the Cork and Kerry Diabetes and Heart Disease Study. Diets were assessed with a standard FFQ. LCA, under the assumption of conditional independence, was used to identify mutually exclusive subgroups with different dietary patterns, based on food group consumption.

Setting: Republic of Ireland.

Subjects: Men and women aged 50-69 years at baseline (n 923) and at 10-year follow-up (n 320).

Results: Three dietary classes emerged: Western, Healthy and Low-Energy. Significant differences in demographic, lifestyle and health outcomes were associated with class membership. Between baseline and follow-up most people remained 'stable' in their dietary class. Most of those who changed class moved to the Healthy class. Higher education was associated with transition to a healthy diet; lower education was associated with stability in an unhealthy pattern. Transition to a healthy diet was associated with higher CVD risk factors at baseline: respondents were significantly more likely to be smokers, centrally obese and to have hypertension (non-significant).

Conclusions: LCA is useful for exploring dietary patterns transitions. Understanding the predictors of longitudinal stability/transitions in dietary patterns will help target public health initiatives by identifying subgroups most/least likely to change and most/least likely to sustain a change.

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Figures

Fig 1
Fig 1
Model fit for latent class models including two to eight latent classes (BIC, Bayesian Information Criterion; BICa, BIC adjusted for the number of parameters in the model; AIC, Akaike Information Criterion; LL, log likelihood)

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