ArticleBMC public health2021
Obesity and occupation in Thailand: using a Bayesian hierarchical model to obtain prevalence estimates from the National Health Examination Survey.
Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Global, regional, and national burden of knee osteoarthritis: findings from the Global Burden of Disease study 2021 and projections to 2045.Journal of orthopaedic surgery and research · 2025Article
- Associations of reallocating time between movement behaviours with adiposity and physical fitness among employees: a compositional data analysis.BMC public health · 2025Article
- Factors influencing health literacy in overweight management among late adolescents in Thailand: A cross-sectional study.Belitung nursing journal · 2024Article
- National nutrition surveillance programmes in 18 countries in South-East Asia and Western Pacific Regions: a systematic scoping review.Bulletin of the World Health Organization · 2023Article
- Obesity correlated to a higher risk of acquiring periodontitis: a cross-sectional study.F1000Research · 2021Article
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Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
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Abstract
backgroundLike many developing countries, Thailand has experienced a rapid rise in obesity, accompanied by a rapid change in occupational structure. It is plausible that these two trends are related, with movement into sedentary occupations leading to increases in obesity. National health examination survey data contains information on obesity and socioeconomic conditions that can help untangle the relationship, but analysis is challenging because of small sample sizes.
methodsThis paper explores the relationship between occupation and obesity using data on 10,127 respondents aged 20-59 from the 2009 National Health Examination Survey. Obesity is measured using waist circumference. Modelling is carried out using an approach known as Multiple Regression with Post-Stratification (MRP). We use Bayesian hierarchical models to construct prevalence estimates disaggregated by age, sex, education, urban-rural residence, region, and occupation, and use census population weights to aggregate up. The Bayesian hierarchical model is designed to protect against overfitting and false discovery, which is particularly important in an exploratory study such as this one.
resultsThere is no clear relationship between the overall sedentary nature of occupations and obesity. Instead, obesity appears to vary occupation by occupation. For instance, women in professional occupations, and men who are agricultural or fishery workers, have relatively low rates of obesity.
conclusionBayesian hierarchical models plus post-stratification offers new possibilities for using surveys to learn about complex health issues.
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