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Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women's Survey

Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women's Survey
Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women's Survey
There is increasing interest in modelling longitudinal dietary data and classifying individuals into subgroups (latent classes) who follow similar trajectories over time. These trajectories could identify population groups and timepoints amenable to dietary interventions. This paper aimed to provide a comparison and overview of two latent class methods: group-based trajectory modelling (GBTM) and growth mixture modelling (GMM). Data from 2963 mother-child dyads from the longitudinal Southampton Women’s Survey were analysed. Continuous diet quality indices (DQIs) were derived using principal component analysis from interviewer-administered food frequency questionnaires collected in mothers pre-pregnancy, at 11- and 34-weeks’ gestation, and in offspring at 6 and 12 months and 3, 6-7 and 8-9 years. A forward modelling approach from 1-6 classes was used to identify the optimal number of DQI latent classes. Models were assessed using the Akaike and Bayesian Information Criteria, probability of class assignment, ratio of the odds of correct classification, group membership, and entropy. Both methods suggested that five classes were optimal; with a strong correlation (Spearman’s=0.98) between class assignment for the two methods. The dietary trajectories were categorised as stable with horizontal lines and were defined as poor (GMM=4%, GBTM=5%), poor-medium (23%, 23%), medium (39%, 39%), medium-better (27%, 28%) and best (7%, 6%). Both GBTM and GMM are suitable for identifying dietary trajectories. GBTM is recommended as it is computationally less intensive, but results could be confirmed using GMM. The stability of the diet quality trajectories from pre-pregnancy underlines the importance of promotion of dietary improvements from preconception onwards.
Group-based trajectory modelling, Growth mixture models, Keywords:, diet quality, lifecourse epidemiology, trajectory modelling
0007-1145
Dalrymple, Kathryn V.
8ef94198-4e90-44a9-b77d-19d35d013cde
Vogel, Christina
768f1dcd-2697-4aae-95cc-ee2f6d63dff5
Godfrey, Keith M.
0931701e-fe2c-44b5-8f0d-ec5c7477a6fd
Baird, Janis
f4bf2039-6118-436f-ab69-df8b4d17f824
Hanson, Mark
1952fad1-abc7-4284-a0bc-a7eb31f70a3f
Cooper, Cyrus
e05f5612-b493-4273-9b71-9e0ce32bdad6
Inskip, Hazel
5fb4470a-9379-49b2-a533-9da8e61058b7
Crozier, Sarah
9c3595ce-45b0-44fa-8c4c-4c555e628a03
Dalrymple, Kathryn V.
8ef94198-4e90-44a9-b77d-19d35d013cde
Vogel, Christina
768f1dcd-2697-4aae-95cc-ee2f6d63dff5
Godfrey, Keith M.
0931701e-fe2c-44b5-8f0d-ec5c7477a6fd
Baird, Janis
f4bf2039-6118-436f-ab69-df8b4d17f824
Hanson, Mark
1952fad1-abc7-4284-a0bc-a7eb31f70a3f
Cooper, Cyrus
e05f5612-b493-4273-9b71-9e0ce32bdad6
Inskip, Hazel
5fb4470a-9379-49b2-a533-9da8e61058b7
Crozier, Sarah
9c3595ce-45b0-44fa-8c4c-4c555e628a03

Dalrymple, Kathryn V., Vogel, Christina, Godfrey, Keith M., Baird, Janis, Hanson, Mark, Cooper, Cyrus, Inskip, Hazel and Crozier, Sarah (2022) Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women's Survey. British Journal of Nutrition. (doi:10.1017/S000711452200263X).

Record type: Article

Abstract

There is increasing interest in modelling longitudinal dietary data and classifying individuals into subgroups (latent classes) who follow similar trajectories over time. These trajectories could identify population groups and timepoints amenable to dietary interventions. This paper aimed to provide a comparison and overview of two latent class methods: group-based trajectory modelling (GBTM) and growth mixture modelling (GMM). Data from 2963 mother-child dyads from the longitudinal Southampton Women’s Survey were analysed. Continuous diet quality indices (DQIs) were derived using principal component analysis from interviewer-administered food frequency questionnaires collected in mothers pre-pregnancy, at 11- and 34-weeks’ gestation, and in offspring at 6 and 12 months and 3, 6-7 and 8-9 years. A forward modelling approach from 1-6 classes was used to identify the optimal number of DQI latent classes. Models were assessed using the Akaike and Bayesian Information Criteria, probability of class assignment, ratio of the odds of correct classification, group membership, and entropy. Both methods suggested that five classes were optimal; with a strong correlation (Spearman’s=0.98) between class assignment for the two methods. The dietary trajectories were categorised as stable with horizontal lines and were defined as poor (GMM=4%, GBTM=5%), poor-medium (23%, 23%), medium (39%, 39%), medium-better (27%, 28%) and best (7%, 6%). Both GBTM and GMM are suitable for identifying dietary trajectories. GBTM is recommended as it is computationally less intensive, but results could be confirmed using GMM. The stability of the diet quality trajectories from pre-pregnancy underlines the importance of promotion of dietary improvements from preconception onwards.

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evaluation_and_interpretation_of_latent_class_modelling_strategies_to_characterise_dietary_trajectories_across_early_life_a_longitudinal_study_from_the_southampton_womens_survey - Accepted Manuscript
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Accepted/In Press date: 1 August 2022
e-pub ahead of print date: 15 August 2022
Keywords: Group-based trajectory modelling, Growth mixture models, Keywords:, diet quality, lifecourse epidemiology, trajectory modelling

Identifiers

Local EPrints ID: 469523
URI: http://eprints.soton.ac.uk/id/eprint/469523
ISSN: 0007-1145
PURE UUID: a92b82a8-5ea1-40ff-8629-e5f9e486b48c
ORCID for Christina Vogel: ORCID iD orcid.org/0000-0002-3897-3786
ORCID for Keith M. Godfrey: ORCID iD orcid.org/0000-0002-4643-0618
ORCID for Janis Baird: ORCID iD orcid.org/0000-0002-4039-4361
ORCID for Mark Hanson: ORCID iD orcid.org/0000-0002-6907-613X
ORCID for Cyrus Cooper: ORCID iD orcid.org/0000-0003-3510-0709
ORCID for Hazel Inskip: ORCID iD orcid.org/0000-0001-8897-1749
ORCID for Sarah Crozier: ORCID iD orcid.org/0000-0002-9524-1127

Catalogue record

Date deposited: 16 Sep 2022 16:46
Last modified: 26 Nov 2022 02:43

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Contributors

Author: Kathryn V. Dalrymple
Author: Christina Vogel ORCID iD
Author: Janis Baird ORCID iD
Author: Mark Hanson ORCID iD
Author: Cyrus Cooper ORCID iD
Author: Hazel Inskip ORCID iD
Author: Sarah Crozier ORCID iD

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