Bayesian network modelling to identify on-ramps to childhood obesity.

Male Female Child Humans Pediatric Obesity / diagnosis Longitudinal Studies Birth Weight Bayes Theorem Australia / epidemiology Body Mass Index
Bayesian modelling Causal inference Childhood obesity Graphical models

Journal

BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723

Informations de publication

Date de publication:
21 03 2023
Historique:
received: 01 08 2022
accepted: 15 02 2023
entrez: 22 3 2023
pubmed: 23 3 2023
medline: 24 3 2023
Statut: epublish

Résumé

When tackling complex public health challenges such as childhood obesity, interventions focused on immediate causes, such as poor diet and physical inactivity, have had limited success, largely because upstream root causes remain unresolved. A priority is to develop new modelling frameworks to infer the causal structure of complex chronic disease networks, allowing disease "on-ramps" to be identified and targeted. The system surrounding childhood obesity was modelled as a Bayesian network, using data from The Longitudinal Study of Australian Children. The existence and directions of the dependencies between factors represent possible causal pathways for childhood obesity and were encoded in directed acyclic graphs (DAGs). The posterior distribution of the DAGs was estimated using the Partition Markov chain Monte Carlo. We have implemented structure learning for each dataset at a single time point. For each wave and cohort, socio-economic status was central to the DAGs, implying that socio-economic status drives the system regarding childhood obesity. Furthermore, the causal pathway socio-economic status and/or parental high school levels → parental body mass index (BMI) → child's BMI existed in over 99.99% of posterior DAG samples across all waves and cohorts. For children under the age of 8 years, the most influential proximate causal factors explaining child BMI were birth weight and parents' BMI. After age 8 years, free time activity became an important driver of obesity, while the upstream factors influencing free time activity for boys compared with girls were different. Childhood obesity is largely a function of socio-economic status, which is manifest through numerous downstream factors. Parental high school levels entangle with socio-economic status, and hence, are on-ramp to childhood obesity. The strong and independent causal relationship between birth weight and childhood BMI suggests a biological link. Our study implies that interventions that improve the socio-economic status, including through increasing high school completion rates, may be effective in reducing childhood obesity prevalence.

Sections du résumé

BACKGROUND
When tackling complex public health challenges such as childhood obesity, interventions focused on immediate causes, such as poor diet and physical inactivity, have had limited success, largely because upstream root causes remain unresolved. A priority is to develop new modelling frameworks to infer the causal structure of complex chronic disease networks, allowing disease "on-ramps" to be identified and targeted.
METHODS
The system surrounding childhood obesity was modelled as a Bayesian network, using data from The Longitudinal Study of Australian Children. The existence and directions of the dependencies between factors represent possible causal pathways for childhood obesity and were encoded in directed acyclic graphs (DAGs). The posterior distribution of the DAGs was estimated using the Partition Markov chain Monte Carlo.
RESULTS
We have implemented structure learning for each dataset at a single time point. For each wave and cohort, socio-economic status was central to the DAGs, implying that socio-economic status drives the system regarding childhood obesity. Furthermore, the causal pathway socio-economic status and/or parental high school levels → parental body mass index (BMI) → child's BMI existed in over 99.99% of posterior DAG samples across all waves and cohorts. For children under the age of 8 years, the most influential proximate causal factors explaining child BMI were birth weight and parents' BMI. After age 8 years, free time activity became an important driver of obesity, while the upstream factors influencing free time activity for boys compared with girls were different.
CONCLUSIONS
Childhood obesity is largely a function of socio-economic status, which is manifest through numerous downstream factors. Parental high school levels entangle with socio-economic status, and hence, are on-ramp to childhood obesity. The strong and independent causal relationship between birth weight and childhood BMI suggests a biological link. Our study implies that interventions that improve the socio-economic status, including through increasing high school completion rates, may be effective in reducing childhood obesity prevalence.

Identifiants

DOI: 10.1186/s12916-023-02789-8 PMID: 36944999 PMC: PMC10031893
pubmed: 36944999
doi: 10.1186/s12916-023-02789-8
pii: 10.1186/s12916-023-02789-8
pmc: PMC10031893
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

105

Informations de copyright

© 2023. The Author(s).

Références

Obesity (Silver Spring). 2010 Feb;18 Suppl 1:S13-6
pubmed: 20107455
Int J Epidemiol. 2021 May 17;50(2):620-632
pubmed: 33330936
Int J Obes (Lond). 2007 Jul;31(7):1044-51
pubmed: 17146451
Artif Intell Med. 2020 Jul;107:101912
pubmed: 32828451
Curr Obes Rep. 2020 Dec;9(4):562-570
pubmed: 32785878
Cell. 2015 Mar 26;161(1):106-118
pubmed: 25815989
N Engl J Med. 1997 Sep 25;337(13):869-73
pubmed: 9302300
PLoS Med. 2021 Jul 27;18(7):e1003699
pubmed: 34314418
Lancet. 2021 Aug 14;398(10300):608-620
pubmed: 34119000
Lancet. 2017 Dec 9;390(10112):2602-2604
pubmed: 28622953
PLoS One. 2016 Aug 24;11(8):e0147746
pubmed: 27557132
J Obes. 2013;2013:129193
pubmed: 23710344
Int J Environ Res Public Health. 2019 Jul 16;16(14):
pubmed: 31315303
Obes Rev. 2007 Mar;8 Suppl 1:vi-ix
pubmed: 17316292
Int J Obes Relat Metab Disord. 1999 Nov;23 Suppl 8:S1-107
pubmed: 10641588
Int J Obes Suppl. 2015 Dec;5(Suppl 2):S74-9
pubmed: 27152189
Lancet. 2019 Feb 23;393(10173):791-846
pubmed: 30700377
Am Econ J Appl Econ. 2010 Jan 1;2(1):63-91
pubmed: 25254082
BMJ. 2007 Jul 28;335(7612):194
pubmed: 17591624
Am J Health Promot. 2020 Mar;34(3):303-306
pubmed: 31833396

Auteurs

Wanchuang Zhu (W)

Human Technology Institute, University of Technology, Sydney, Australia. wanchuang.zhu@uts.edu.au.
Data61, CSIRO, Sydney, Australia. wanchuang.zhu@uts.edu.au.
Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia. wanchuang.zhu@uts.edu.au.

Roman Marchant (R)

Data61, CSIRO, Sydney, Australia.
Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.

Richard W Morris (RW)

School of Psychology and Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.
Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.

Louise A Baur (LA)

Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.
The Children's Hospital at Westmead, The University of Sydney, Sydney, Australia.

Stephen J Simpson (SJ)

Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia.

Sally Cripps (S)

Human Technology Institute, University of Technology, Sydney, Australia.
Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
School of Mathematics and Physical Sciences, University of Technology Sydney, Sydney, NSW, Australia.
School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, Australia.

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