Cargando…

Introducing a drift and diffusion framework for childhood growth research

Background: Growth trajectories are highly variable between children, making epidemiological analyses challenging both to the identification of malnutrition interventions at the population level and also risk assessment at individual level. We introduce stochastic differential equation (SDE) models...

Descripción completa

Detalles Bibliográficos
Autores principales: Lewis, Fraser I, Guga, Godfrey, Mdoe, Paschal, Mduma, Esto, Mahopo, Cloupas, Bessong, Pascal, Richard, Stephanie A, McCormick, Benjamin J J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7791186/
https://www.ncbi.nlm.nih.gov/pubmed/33490877
http://dx.doi.org/10.12688/gatesopenres.13123.2
_version_ 1783633556344406016
author Lewis, Fraser I
Guga, Godfrey
Mdoe, Paschal
Mduma, Esto
Mahopo, Cloupas
Bessong, Pascal
Richard, Stephanie A
McCormick, Benjamin J J
author_facet Lewis, Fraser I
Guga, Godfrey
Mdoe, Paschal
Mduma, Esto
Mahopo, Cloupas
Bessong, Pascal
Richard, Stephanie A
McCormick, Benjamin J J
author_sort Lewis, Fraser I
collection PubMed
description Background: Growth trajectories are highly variable between children, making epidemiological analyses challenging both to the identification of malnutrition interventions at the population level and also risk assessment at individual level. We introduce stochastic differential equation (SDE) models into child growth research. SDEs describe flexible dynamic processes comprising: drift - gradual smooth changes – such as physiology or gut microbiome, and diffusion - sudden perturbations, such as illness or infection. Methods: We present a case study applying SDE models to child growth trajectory data from the Haydom, Tanzania and Venda, South Africa sites within the MAL-ED cohort. These data comprise n=460 children aged 0-24 months. A comparison with classical curve fitting (linear mixed models) is also presented. Results: The SDE models offered a wide range of new flexible shapes and parameterizations compared to classical additive models, with performance as good or better than standard approaches. The predictions from the SDE models suggest distinct longitudinal clusters that form distinct ‘streams’ hidden by the large between-child variability. Conclusions: Using SDE models to predict future growth trajectories revealed new insights in the observed data, where trajectories appear to cluster together in bands, which may have a future risk assessment application. SDEs offer an attractive approach for child growth modelling and potentially offer new insights.
format Online
Article
Text
id pubmed-7791186
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher F1000 Research Limited
record_format MEDLINE/PubMed
spelling pubmed-77911862021-01-22 Introducing a drift and diffusion framework for childhood growth research Lewis, Fraser I Guga, Godfrey Mdoe, Paschal Mduma, Esto Mahopo, Cloupas Bessong, Pascal Richard, Stephanie A McCormick, Benjamin J J Gates Open Res Research Article Background: Growth trajectories are highly variable between children, making epidemiological analyses challenging both to the identification of malnutrition interventions at the population level and also risk assessment at individual level. We introduce stochastic differential equation (SDE) models into child growth research. SDEs describe flexible dynamic processes comprising: drift - gradual smooth changes – such as physiology or gut microbiome, and diffusion - sudden perturbations, such as illness or infection. Methods: We present a case study applying SDE models to child growth trajectory data from the Haydom, Tanzania and Venda, South Africa sites within the MAL-ED cohort. These data comprise n=460 children aged 0-24 months. A comparison with classical curve fitting (linear mixed models) is also presented. Results: The SDE models offered a wide range of new flexible shapes and parameterizations compared to classical additive models, with performance as good or better than standard approaches. The predictions from the SDE models suggest distinct longitudinal clusters that form distinct ‘streams’ hidden by the large between-child variability. Conclusions: Using SDE models to predict future growth trajectories revealed new insights in the observed data, where trajectories appear to cluster together in bands, which may have a future risk assessment application. SDEs offer an attractive approach for child growth modelling and potentially offer new insights. F1000 Research Limited 2020-11-26 /pmc/articles/PMC7791186/ /pubmed/33490877 http://dx.doi.org/10.12688/gatesopenres.13123.2 Text en Copyright: © 2020 Lewis FI et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Lewis, Fraser I
Guga, Godfrey
Mdoe, Paschal
Mduma, Esto
Mahopo, Cloupas
Bessong, Pascal
Richard, Stephanie A
McCormick, Benjamin J J
Introducing a drift and diffusion framework for childhood growth research
title Introducing a drift and diffusion framework for childhood growth research
title_full Introducing a drift and diffusion framework for childhood growth research
title_fullStr Introducing a drift and diffusion framework for childhood growth research
title_full_unstemmed Introducing a drift and diffusion framework for childhood growth research
title_short Introducing a drift and diffusion framework for childhood growth research
title_sort introducing a drift and diffusion framework for childhood growth research
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7791186/
https://www.ncbi.nlm.nih.gov/pubmed/33490877
http://dx.doi.org/10.12688/gatesopenres.13123.2
work_keys_str_mv AT lewisfraseri introducingadriftanddiffusionframeworkforchildhoodgrowthresearch
AT gugagodfrey introducingadriftanddiffusionframeworkforchildhoodgrowthresearch
AT mdoepaschal introducingadriftanddiffusionframeworkforchildhoodgrowthresearch
AT mdumaesto introducingadriftanddiffusionframeworkforchildhoodgrowthresearch
AT mahopocloupas introducingadriftanddiffusionframeworkforchildhoodgrowthresearch
AT bessongpascal introducingadriftanddiffusionframeworkforchildhoodgrowthresearch
AT richardstephaniea introducingadriftanddiffusionframeworkforchildhoodgrowthresearch
AT mccormickbenjaminjj introducingadriftanddiffusionframeworkforchildhoodgrowthresearch