Your browser doesn't support javascript.
loading
Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis.
Cooray, Upul; Watt, Richard G; Tsakos, Georgios; Heilmann, Anja; Hariyama, Masanori; Yamamoto, Takafumi; Kuruppuarachchige, Isuruni; Kondo, Katsunori; Osaka, Ken; Aida, Jun.
Afiliação
  • Cooray U; Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Sendai, Japan. Electronic address: upul.cooray.15@ucl.ac.uk.
  • Watt RG; Department of Epidemiology and Public Health, University College London, London, United Kingdom.
  • Tsakos G; Department of Epidemiology and Public Health, University College London, London, United Kingdom.
  • Heilmann A; Department of Epidemiology and Public Health, University College London, London, United Kingdom.
  • Hariyama M; Intelligent Integrated Systems Laboratory, Graduate School of Information Sciences, Tohoku University, Miyagi, Japan.
  • Yamamoto T; Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Sendai, Japan.
  • Kuruppuarachchige I; Department of Dental and Digital Forensics Tohoku University Graduate School of Dentistry, Sendai, Japan.
  • Kondo K; Center for Preventive Medical Sciences, Chiba University, Chiba, Japan; Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan.
  • Osaka K; Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Sendai, Japan.
  • Aida J; Department of Oral Health Promotion, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan; Division for Regional Community Development, Liaison Center for Innovative Dentistry, Graduate School of Dentistry, Tohoku University, Sendai, Japan.
Soc Sci Med ; 291: 114486, 2021 12.
Article em En | MEDLINE | ID: mdl-34700121
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.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Perda de Dente Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Perda de Dente Idioma: En Ano de publicação: 2021 Tipo de documento: Article