Identifying heterogeneity in the risk factors of dental caries status in Chinese adolescents using Poisson mixture regression

OBJECTIVE: The purpose of this study was to cluster individuals into groups with different dental health characteristics and make statistical inferences on the effect differences among different groups simultaneously to identify the heterogeneity of risk factors in Chinese adolescents by analysing the data from the 4th Chinese National Oral Health Survey. METHODS: For decayed, missing and filled permanent teeth (DMFT), mean values were statistically analysed for their relationships with different categories of all involved variables. As DMFT scores only have discrete values, Poisson mixture regression was adopted to model the heterogeneity and complex patterns in the association and to detect the subgroup. The Bayesian information criterion (BIC) was used to determine the optimal number of subgroups. A series of Wald tests were used to explore the relationship between risk factors including the interaction effects and the number of DMFT. RESULTS: A total of 100 986 individuals aged 12–15 years old were analysed. The model clustered different individuals into three subgroups and built three submodels for detailed statistical inference simultaneously. The number of individuals in the three subgroups were 52 576 (52.1%), 41 969 (41.5%) and 6441 (6.4%), respectively. The mean (SD) of DMFT of the three subgroups was 0.50 (1.05), 0.99 (1.21), 5.59 (2.50). The model fitting results indicated that the effects of all risk factors on DMFT appear to be different in three subgroups. Controlling the confounding effects, our analysis implied that the regional inequality was the main contributing factor to dental caries among adolescents in Chinese mainland. CONCLUSIONS: The risk factors of dental caries exhibited heterogeneity in groups with different characteristics. The Poisson mixture regression model could cluster individuals into groups and identify the heterogeneous effects of risk factors among different groups. The findings support the need for different targeted interventions and prevention measures in groups with different dental health characteristics.