Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis
Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis
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DOI：
10.1016/j.socscimed.2021.114486
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发表时间：
2021-10-23
影响因子：
5.4
通讯作者：
Aida, Jun
中科院分区：
文献类型：
--
作者：
Cooray, Upul;Watt, Richard G.;Aida, Jun
由于人口老龄化，牙齿脱落的发生率有所增加。牙齿脱落对老年人的整体身体和社会健康产生负面影响。了解在非临床环境中测量的与牙齿脱落相关的社会人口统计学和其他预测因素的作用，有助于社区层面的预防。我们使用高维流行病学数据调查预测老年人牙齿脱落的重要因素，随访6年。数据来自2010年和2016年日本老年学评估研究（JAGES）的参与者。共有19,407名65岁及以上的社区居住功能独立老年人被纳入分析。牙齿脱落的测量方法是在随访时从基线时较多的牙齿类别向较少的牙齿类别移动。在119个潜在的预测因素中，年龄、性别、牙齿数量、假牙使用、咀嚼困难、家庭收入、就业、教育程度、吸烟、水果和蔬菜消费、社区参与、上次健康检查时间、有爱好、感觉没有价值。在6年的随访中，3013人（15.5%）报告发生牙齿脱落。经历牙齿脱落的人年龄较大（72.9 +/- 5.2 vs 71.8 +/- 4.7），主要是男性（18.3% vs 13.1%）。极端梯度增强（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显示，牙齿脱落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、戴假牙）和社会经济（较低的家庭收入、体力职业）变量驱动。与广泛范围的决定因素相关的预测因素有助于老年人牙齿脱落。除了口腔健康相关因素和人口因素外，社会经济因素在预测未来牙齿脱落方面也很重要。因此，了解这些预测因子的行为有助于制定预防老年人牙齿脱落的策略。
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.