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

被引:28
作者
Cooray, Upul [1 ]
Watt, Richard G. [2 ]
Tsakos, Georgios [2 ]
Heilmann, Anja [2 ]
Hariyama, Masanori [5 ]
Yamamoto, Takafumi [1 ]
Kuruppuarachchige, Isuruni [6 ]
Kondo, Katsunori [7 ,8 ]
Osaka, Ken [1 ]
Aida, Jun [3 ,4 ]
机构
[1] Tohoku Univ, Dept Int & Community Oral Hlth, Grad Sch Dent, Sendai, Miyagi, Japan
[2] UCL, Dept Epidemiol & Publ Hlth, London, England
[3] Tokyo Med & Dent Univ, Grad Sch Med & Dent Sci, Dept Oral Hlth Promot, Tokyo, Japan
[4] Tohoku Univ, Grad Sch Dent, Liaison Ctr Innovat Dent, Div Reg Community Dev, Sendai, Miyagi, Japan
[5] Tohoku Univ, Grad Sch Informat Sci, Intelligent Integrated Syst Lab, Sendai, Miyagi, Japan
[6] Tohoku Univ, Grad Sch Dent, Dept Dent & Digital Forens, Sendai, Miyagi, Japan
[7] Chiba Univ, Ctr Prevent Med Sci, Chiba, Japan
[8] Natl Ctr Geriatr & Gerontol, Ctr Gerontol & Social Sci, Obu, Japan
基金
日本科学技术振兴机构; 日本学术振兴会;
关键词
Tooth loss; Prediction of tooth loss; Socioeconomic predictors; Explainable machine learning; Older adults; DISEASES; HEALTH;
D O I
10.1016/j.socscimed.2021.114486
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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.
引用
收藏
页数:8
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