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日本老年人牙齿缺失的社会经济因素预测重要性:基于机器学习分析的证据。

Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis.

机构信息

Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Sendai, Japan.

Department of Epidemiology and Public Health, University College London, London, United Kingdom.

出版信息

Soc Sci Med. 2021 Dec;291:114486. doi: 10.1016/j.socscimed.2021.114486. Epub 2021 Oct 13.

Abstract

Prevalence of tooth loss has increased due to population aging. Tooth loss negatively affects the overall physical and social well-being of older adults. Understanding the role of socio-demographic and other predictors associated with tooth loss that are measured in non-clinical settings can be useful in community-level prevention. We used high-dimensional epidemiological data to investigate important factors in predicting tooth loss among older adults over a 6-year period of follow-up. Data was from participants of 2010 and 2016 waves of the Japan Gerontological Evaluation Study (JAGES). A total of 19,407 community-dwelling functionally independent older adults aged 65 and older were included in the analysis. Tooth loss was measured as moving from a higher number of teeth category at the baseline to a lower number of teeth category at the follow-up. Out of 119 potential predictors, age, sex, number of teeth, denture use, chewing difficulty, household income, employment, education, smoking, fruit and vegetable consumption, community participation, time since last health check-up, having a hobby, and feeling worthless were selected using Boruta algorithm. Within the 6-year follow-up, 3013 individuals (15.5%) reported incidence of tooth loss. People who experienced tooth loss were older (72.9 ± 5.2 vs 71.8 ± 4.7), and predominantly men (18.3% vs 13.1%). Extreme gradient boosting (XGBoost) machine learning prediction model had a mean accuracy of 90.5% (±0.9%). A visual analysis of machine learning predictions revealed that the prediction of tooth loss was mainly driven by demographic (older age), baseline oral health (having 10-19 teeth, wearing dentures), and socioeconomic (lower household income, manual occupations) variables. Predictors related to wide a range of determinants contribute towards tooth loss among older adults. In addition to oral health related and demographic factors, socioeconomic factors were important in predicting future tooth loss. Understanding the behaviour of these predictors can thus be useful in developing prevention strategies for tooth loss among older adults.

摘要

由于人口老龄化,牙齿缺失的患病率有所增加。牙齿缺失会对老年人的整体身心健康产生负面影响。了解在非临床环境中测量的与牙齿缺失相关的社会人口学和其他预测因素的作用,对于社区层面的预防可能是有用的。我们使用高维流行病学数据来研究在 6 年的随访期间预测老年人牙齿缺失的重要因素。数据来自 2010 年和 2016 年日本老年评估研究(JAGES)的参与者。共有 19407 名功能独立的社区居住老年人(年龄≥65 岁)纳入了分析。牙齿缺失的定义为在基线时具有较多牙齿数的类别在随访时转变为具有较少牙齿数的类别。在 119 个潜在预测因素中,年龄、性别、牙齿数量、义齿使用、咀嚼困难、家庭收入、就业、教育、吸烟、水果和蔬菜摄入、社区参与、上次健康检查后时间、有爱好和感到无价值感,使用 Boruta 算法进行选择。在 6 年的随访期间,3013 人(15.5%)报告了牙齿缺失的发生率。经历牙齿缺失的人年龄较大(72.9±5.2 岁比 71.8±4.7 岁),且以男性为主(18.3%比 13.1%)。极端梯度提升(XGBoost)机器学习预测模型的平均准确率为 90.5%(±0.9%)。对机器学习预测的可视化分析表明,牙齿缺失的预测主要由人口统计学(年龄较大)、基线口腔健康(有 10-19 颗牙齿,戴义齿)和社会经济(家庭收入较低,体力劳动职业)变量驱动。与广泛的决定因素相关的预测因素有助于老年人牙齿缺失。除了与口腔健康相关的因素和人口统计学因素外,社会经济因素对未来牙齿缺失的预测也很重要。因此,了解这些预测因素的行为对于制定老年人牙齿缺失的预防策略可能是有用的。

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