A Bayesian approach to combining multiple information sources: Estimating and forecasting childhood obesity in Thailand
Issued Date
2022-01-01
Resource Type
eISSN
19326203
Scopus ID
2-s2.0-85123290899
Pubmed ID
35061753
Journal Title
PLoS ONE
Volume
17
Issue
1 January
Rights Holder(s)
SCOPUS
Bibliographic Citation
PLoS ONE Vol.17 No.1 January (2022)
Suggested Citation
Bryant J., Rittirong J., Aekplakorn W., Mo-Suwan L., Nitnara P. A Bayesian approach to combining multiple information sources: Estimating and forecasting childhood obesity in Thailand. PLoS ONE Vol.17 No.1 January (2022). doi:10.1371/journal.pone.0262047 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/86656
Title
A Bayesian approach to combining multiple information sources: Estimating and forecasting childhood obesity in Thailand
Author(s)
Other Contributor(s)
Abstract
We estimate and forecast childhood obesity by age, sex, region, and urban-rural residence in Thailand, using a Bayesian approach to combining multiple source of information. Our main sources of information are survey data and administrative data, but we also make use of informative prior distributions based on international estimates of obesity trends and on expectations about smoothness. Although the final model is complex, the difficulty of building and understanding the model is reduced by the fact that it is composed of many smaller submodels. For instance, the submodel describing trends in prevalences is specified separately from the submodels describing errors in the data sources. None of our Thai data sources has more than 7 time points. However, by combining multiple data sources, we are able to fit relatively complicated time series models. Our results suggest that obesity prevalence has recently starting rising quickly among Thai teenagers throughout the country, but has been stable among children under 5 years old.