Bayesian network modelling to identify on-ramps to childhood obesity

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 direction 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 Partition Markov chain Monte Carlo. Results: We have implemented structure learning for each dataset. 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 8y, the most influential proximate causal factors explaining child BMI were birth weight and parents' BMI. After age 8y, 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, 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 socio-economic status, including through increasing high school completion rates, may be effective in reducing childhood obesity prevalence.

Competing Interest Statement

WZ was supported from Paul Ramsay Foundation for the submitted work; SC has received research grants from Paul Ramsay Foundation, LAB has been paid for presentations for Novo Nordisk, LAB has also been supported by Novo Nordisk for attending conferences. LAB has Leadership or fiduciary role in World Obesity Federation.

Funding Statement

WZ was supported from Paul Ramsay Foundation for the submitted work.

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The study used (or will use) ONLY openly available human data that were originally located at: https://growingupinaustralia.gov.au/data-and-documentation/accessing-lsac-data

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