Bayesian network modelling to identify on-ramps to childhood obesity

Wanchuang Zhu; Roman Marchant; Richard W Morris; Louise A Baur; Stephen J Simpson; Sally Cripps

doi:10.1186/s12916-023-02789-8

Bayesian network modelling to identify on-ramps to childhood obesity

BMC Med. 2023 Mar 21;21(1):105. doi: 10.1186/s12916-023-02789-8.

Authors

Wanchuang Zhu^{1

2

3}, Roman Marchant^{4

5}, Richard W Morris^{6

5}, Louise A Baur^{5

7

8}, Stephen J Simpson^{5

9}, Sally Cripps^{10

5

11

12}

Affiliations

¹ Human Technology Institute, University of Technology, Sydney, Australia. wanchuang.zhu@uts.edu.au.
² Data61, CSIRO, Sydney, Australia. wanchuang.zhu@uts.edu.au.
³ Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia. wanchuang.zhu@uts.edu.au.
⁴ Data61, CSIRO, Sydney, Australia.
⁵ Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
⁶ School of Psychology and Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.
⁷ Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.
⁸ The Children's Hospital at Westmead, The University of Sydney, Sydney, Australia.
⁹ School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia.
¹⁰ Human Technology Institute, University of Technology, Sydney, Australia.
¹¹ School of Mathematics and Physical Sciences, University of Technology Sydney, Sydney, NSW, Australia.
¹² School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, Australia.

Abstract

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.

Keywords: Bayesian modelling; Causal inference; Childhood obesity; Graphical models.

Publication types

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

MeSH terms

Australia / epidemiology
Bayes Theorem
Birth Weight
Body Mass Index
Child
Female
Humans
Longitudinal Studies
Male
Pediatric Obesity* / diagnosis
Pediatric Obesity* / epidemiology