Obesity and occupation in Thailand: using a Bayesian hierarchical model to obtain prevalence estimates from the National Health Examination Survey

Abstract

Background: Like 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.

Methods: This 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.

Results: There 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.

Conclusion: Bayesian hierarchical models plus post-stratification offers new possibilities for using surveys to learn about complex health issues.

Keywords: Bayesian hierarchical model; Obesity; Occupation; Small area estimation; Thailand.

Fig. 1

Point estimates and 95% credible intervals for obesity prevalence, for females in urban areas in the Northeast region of Thailand, disaggregated by age, education, and occupation. Open black points denote sedentary occupations, and closed black points denote non-sedentary occupations. Lines denote 95% credible intervals. ‘x’ marks denote direct estimates

Fig. 2

Estimated obesity prevalence by age, sex, education level, and occupation, for females. Open black points denote sedentary occupations, and closed black points denote non-sedentary occupations. Lines denote 95% credible intervals. ‘x’ marks denote direct estimates

Fig. 3

Estimated obesity prevalence by age, sex, education level, and occupation, for males. Open black points denote sedentary occupations, and closed black points denote non-sedentary occupations. Lines denote 95% credible intervals. ‘x’ marks denote direct estimates

Fig. 4

Estimated obesity prevalence using European rather than Asian standards for defining obesity: females. Open black points denote sedentary occupations, and closed black points denote non-sedentary occupations. Lines denote 95% credible intervals. ‘x’ marks denote direct estimates

Fig. 5

Estimated obesity prevalence using European rather than Asian standards for defining obesity: males. Open black points denote sedentary occupations, and closed black points denote non-sedentary occupations. Lines denote 95% credible intervals. ‘x’ marks denote direct estimates