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Objectives <p>This study aimed to explore the latent factors associated to functional dentition (FD), quantify their clustering across probability levels, and derive precision prevention strategies.</p> Materials and methods <p>A cross-sectional study of 423 adults aged 65–74 were conducted using data from a 2021 national surveillance project in China. After preliminary selection of potential variables through multivariable logistic regression, latent class analysis (LCA) was performed in R. Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and entropy determined the optimal number of latent classes. Bootstrap resampling and a confusion matrix assessed model performance.</p> Results <p>Participants were classified into a low-probability group (Class 1, <i>N</i> = 136, 32.2%) and a high-probability group (Class 2, <i>N</i> = 287, 67.8%), representing the best fit (AIC = 2700.36, BIC = 2744.88, Entropy = 0.733). Compared to Class 2, Class 1 exhibited lower toothbrushing frequency and oral health knowledge level, but higher proportions of root caries, AL &gt; 5&#xa0;mm, and unrestored tooth loss. The model showed good stability (average agreement rate = 0.867) and strong predictive performance (Kappa = 0.806, Accuracy = 0.915, Sensitivity = 0.862, Specificity = 0.940, Precision = 0.875, F1-score = 0.869).</p> Conclusions <p>LCA identified distinct subgroups at varying FD probability and quantified clustered associated factors, supporting the accurate identification of high-risk populations and the development of targeted prevention strategies.</p>

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Identifying subtypes of functional dentition in older adults: a population-based regression and latent class analysis

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Linxin Jiang,
  • Simin Li,
  • Daniel R. Reissmann,
  • Shaonan Hu,
  • Xiangyu Huang,
  • Gerhard Schmalz,
  • Jianbo Li

摘要

Objectives

This study aimed to explore the latent factors associated to functional dentition (FD), quantify their clustering across probability levels, and derive precision prevention strategies.

Materials and methods

A cross-sectional study of 423 adults aged 65–74 were conducted using data from a 2021 national surveillance project in China. After preliminary selection of potential variables through multivariable logistic regression, latent class analysis (LCA) was performed in R. Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and entropy determined the optimal number of latent classes. Bootstrap resampling and a confusion matrix assessed model performance.

Results

Participants were classified into a low-probability group (Class 1, N = 136, 32.2%) and a high-probability group (Class 2, N = 287, 67.8%), representing the best fit (AIC = 2700.36, BIC = 2744.88, Entropy = 0.733). Compared to Class 2, Class 1 exhibited lower toothbrushing frequency and oral health knowledge level, but higher proportions of root caries, AL > 5 mm, and unrestored tooth loss. The model showed good stability (average agreement rate = 0.867) and strong predictive performance (Kappa = 0.806, Accuracy = 0.915, Sensitivity = 0.862, Specificity = 0.940, Precision = 0.875, F1-score = 0.869).

Conclusions

LCA identified distinct subgroups at varying FD probability and quantified clustered associated factors, supporting the accurate identification of high-risk populations and the development of targeted prevention strategies.