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

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  • Cooray, Upul
  • Watt, Richard G.
  • Tsakos, Georgios
  • Heilmann, Anja
  • Hariyama, Masanori
  • Yamamoto, Takafumi
  • Kuruppuarachchige, Isuruni
  • Kondo, Katsunori
  • Osaka, Ken
  • Aida, Jun

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.

Suggested Citation

  • Cooray, Upul & Watt, Richard G. & Tsakos, Georgios & Heilmann, Anja & Hariyama, Masanori & Yamamoto, Takafumi & Kuruppuarachchige, Isuruni & Kondo, Katsunori & Osaka, Ken & Aida, Jun, 2021. "Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis," Social Science & Medicine, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:socmed:v:291:y:2021:i:c:s0277953621008182
    DOI: 10.1016/j.socscimed.2021.114486
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    References listed on IDEAS

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    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Aida, Jun & Hanibuchi, Tomoya & Nakade, Miyo & Hirai, Hiroshi & Osaka, Ken & Kondo, Katsunori, 2009. "The different effects of vertical social capital and horizontal social capital on dental status: A multilevel analysis," Social Science & Medicine, Elsevier, vol. 69(4), pages 512-518, August.
    3. Hawazin W Elani & André F M Batista & W Murray Thomson & Ichiro Kawachi & Alexandre D P Chiavegatto Filho, 2021. "Predictors of tooth loss: A machine learning approach," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-14, June.
    4. Chenxi Huang & Karthik Murugiah & Shiwani Mahajan & Shu-Xia Li & Sanket S Dhruva & Julian S Haimovich & Yongfei Wang & Wade L Schulz & Jeffrey M Testani & Francis P Wilson & Carlos I Mena & Frederick , 2018. "Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-20, November.
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