Department of Preventive Dentistry, Peking University School and Hospital of Stomatology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Beijing, People's Republic of China.
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
BMJ Open. 2020 Oct 26;10(10):e039599. doi: 10.1136/bmjopen-2020-039599.
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.
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.
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.
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.
本研究旨在将个体聚类为具有不同口腔健康特征的组，并对不同组间的效果差异进行统计推断，以通过分析第四次全国口腔健康流行病学调查的数据，识别中国青少年风险因素的异质性。
对于恒牙龋失补牙数（DMFT），采用统计学方法分析其与所有相关变量不同类别的关系。由于 DMFT 得分只有离散值，因此采用泊松混合回归来模拟关联中的异质性和复杂模式，并检测亚组。贝叶斯信息准则（BIC）用于确定最佳亚组数量。一系列 Wald 检验用于探索包括交互效应和 DMFT 数量在内的风险因素之间的关系。
共分析了 100986 名 12-15 岁的个体。该模型将不同个体聚类为三个亚组，并同时构建三个亚模型进行详细的统计推断。三个亚组的个体数分别为 52576（52.1%）、41969（41.5%）和 6441（6.4%）。三个亚组的 DMFT 均值（SD）分别为 0.50（1.05）、0.99（1.21）和 5.59（2.50）。模型拟合结果表明，所有风险因素对 DMFT 的影响在三个亚组中似乎不同。在控制混杂效应后，我们的分析表明，区域不平等是中国大陆青少年龋齿的主要原因。
不同特征组中龋齿的风险因素存在异质性。泊松混合回归模型可以将个体聚类为组，并识别不同组间风险因素的异质效应。研究结果支持针对不同口腔健康特征的群体采取不同的针对性干预和预防措施的必要性。
