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 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

SUBMITTER: Zhu W 

PROVIDER: S-EPMC10031893 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature