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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | BMC Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12916-023-02789-8 |
| _version_ | 1827983880457551872 |
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| author | Wanchuang Zhu Roman Marchant Richard W. Morris Louise A. Baur Stephen J. Simpson Sally Cripps |
| author_facet | Wanchuang Zhu Roman Marchant Richard W. Morris Louise A. Baur Stephen J. Simpson Sally Cripps |
| author_sort | Wanchuang Zhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T22:53:11Z |
| format | Article |
| id | doaj.art-b8e8c5423b3f43deb4ed1585e0c700f6 |
| institution | Directory Open Access Journal |
| issn | 1741-7015 |
| language | English |
| last_indexed | 2024-04-09T22:53:11Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj.art-b8e8c5423b3f43deb4ed1585e0c700f62023-03-22T11:33:04ZengBMCBMC Medicine1741-70152023-03-0121111310.1186/s12916-023-02789-8Bayesian network modelling to identify on-ramps to childhood obesityWanchuang Zhu0Roman Marchant1Richard W. Morris2Louise A. Baur3Stephen J. Simpson4Sally Cripps5Human Technology Institute, University of TechnologyData61, CSIROSchool of Psychology and Sydney Medical School, The University of SydneyCharles Perkins Centre, The University of SydneyCharles Perkins Centre, The University of SydneyHuman Technology Institute, University of TechnologyAbstract 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.https://doi.org/10.1186/s12916-023-02789-8Childhood obesityCausal inferenceBayesian modellingGraphical models |
| spellingShingle | Wanchuang Zhu Roman Marchant Richard W. Morris Louise A. Baur Stephen J. Simpson Sally Cripps Bayesian network modelling to identify on-ramps to childhood obesity BMC Medicine Childhood obesity Causal inference Bayesian modelling Graphical models |
| title | Bayesian network modelling to identify on-ramps to childhood obesity |
| title_full | Bayesian network modelling to identify on-ramps to childhood obesity |
| title_fullStr | Bayesian network modelling to identify on-ramps to childhood obesity |
| title_full_unstemmed | Bayesian network modelling to identify on-ramps to childhood obesity |
| title_short | Bayesian network modelling to identify on-ramps to childhood obesity |
| title_sort | bayesian network modelling to identify on ramps to childhood obesity |
| topic | Childhood obesity Causal inference Bayesian modelling Graphical models |
| url | https://doi.org/10.1186/s12916-023-02789-8 |
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