Prediction of Periodontitis Occurrence: Influence of Classification and Sociodemographic and General Health Information

医学 牙周炎 社会经济地位 人口 多元微积分 队列 接收机工作特性 队列研究 牙科 人口学 环境卫生 内科学 工程类 控制工程 社会学
作者
Fábio Renato Manzolli Leite,Karen Glazer Peres,Loc Do,Flávio Fernando Demarco,Marco Aurélio Peres
出处
期刊:Journal of Periodontology [Wiley]
卷期号:88 (8): 731-743 被引量:27
标识
DOI:10.1902/jop.2017.160607
摘要

Background: Prediction of periodontitis development is challenging. Use of oral health–related data alone, especially in a young population, might underestimate disease risk. This study investigates accuracy of oral, systemic, and socioeconomic data on estimating periodontitis development in a population‐based prospective cohort. Methods: General health history and sociodemographic information were collected throughout the life‐course of individuals. Oral examinations were performed at ages 24 and 31 years in the Pelotas 1982 birth cohort. Periodontitis at age 31 years according to six classifications was used as the gold standard to compute area under the receiver operating characteristic curve (AUC). Multivariable binomial regression models were used to evaluate the effects of oral health, general health, and socioeconomic characteristics on accuracy of periodontitis development prediction. Results: Complete data for 471 participants were used. Periodontitis classifications with lower thresholds yielded superior predictive power. Calculus, pocket, or bleeding presence at age 24 years separately presented fair accuracy. Accuracy increased using multivariable models; for example, the Beck et al. classification AUC from 0.59 to 0.75 combining proportion of teeth with calculus, bleeding, or pocket with income; number of lost teeth; sex; education; people living in the house; prosthetic needs; or number of decayed, missing, or filled teeth (DMFT). Proportion of teeth with pocket, bleeding, or calculus; number of DMFT; toothbrushing frequency; blood pressure; sex; and income were most frequently associated. Conclusions: Choice of classification might have an impact on accuracy to predict periodontitis occurrence. Regardless of the classification, predictive value for development of periodontitis in young adults might be increased by combining periodontal information, sociodemographic information, and general health history.
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