TY - JOUR
T1 - Importance of socioeconomic factors in predicting tooth loss among older adults in Japan
T2 - Evidence from a machine learning analysis
AU - Cooray, Upul
AU - Watt, Richard G.
AU - Tsakos, Georgios
AU - Heilmann, Anja
AU - Hariyama, Masanori
AU - Yamamoto, Takafumi
AU - Kuruppuarachchige, Isuruni
AU - Kondo, Katsunori
AU - Osaka, Ken
AU - Aida, Jun
N1 - Funding Information:
The authors gratefully acknowledge all participants of the JAGES survey. JAGES was supported by MEXT( Ministry of Education, Culture, Sports, Science and Technology-Japan )-Supported Program for the Strategic Research Foundation at Private Universities (2009–2013); JSPS( Japan Society for the Promotion of Science ), KAKENHI grant: JP15H01972 , JP15H04781 , JP15H05059 , JP15K03417 , JP15K03982 , JP15K16181 , JP15K17232 , JP15K18174 , JP15K19241 , JP15K21266 , JP15KT0007 , JP15KT0097 , JP16H05556 , JP16K09122 , JP16K00913 , JP16K02025 , JP16K12964 , JP16K13443 , JP16K16295 , JP16K16595 , JP16K16633 , JP16K17256 , JP16K19247 , JP16K19267 , JP16K21461 , JP16K21465 , JP16KT0014 , JP18KK0057 , JP19K24060 , JP19H03860 , JP18390200 , JP22330172 , JP22390400 , JP23243070 , JP23590786 , JP23790710 , JP24390469 , JP24530698 , JP24683018 , JP25253052 , JP25870573 , JP25713027 , JP25870881 , JP26285138 , JP26460828 , JP26780328 , JP26882010 , 23243070 , 22390400 , 24390469 , 15H01972 , and 20H00557 ; the Japanese Ministry of Health, Labour, and Welfare, Health and Labour Sciences Research Grant: H22-Choju-Shitei-008,H24-Junkanki [Seishu]-Ippan-007, H24-Chikyukibo-Ippan-009, H24-Choju-Wakate-009, H25-Kenki-Wakate-015, H25-Choju-Ippan-003, H26-Irryo-Shitei-003 [Fukkou], H26-Choju-Ippan-006, H26-Choju-Ippan-006, H27-Ninchisyou-Ippan-001, H28-Choju-Ippan- 002, H28- Ninchisyou-Ippan-002, H30-Kenki-Ippan-006 and H30-Junkankitou-Ippan-004, 19FA2001,19FA1012; AMED(the Japan Agency for Medical Research and Development ) grant: 16dk0110017h0002 , 16ls0110002h0001 , JP17dk0110017 , JP18dk0110027 , JP18ls0110002 , JP18le0110009 , JP19dk0110034 , JP20dk0110034 ; the Japanese National Center for Geriatrics and Gerontology , Research Funding for Longevity Sciences grant: 20–19 , 24–17 , 24–23 , 29–42 , 30–22 ; the World Health Organization Centre for Health Development (WHO Kobe Centre) grant: WHOAPW 2017/713981 and JST( Japan Science and Technology Agency )OPERA: JPMJOP1831 .
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Explainable machine learning
KW - Older adults
KW - Prediction of tooth loss
KW - Socioeconomic predictors
KW - Tooth loss
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UR - http://www.scopus.com/inward/citedby.url?scp=85117720792&partnerID=8YFLogxK
U2 - 10.1016/j.socscimed.2021.114486
DO - 10.1016/j.socscimed.2021.114486
M3 - Article
C2 - 34700121
AN - SCOPUS:85117720792
VL - 291
JO - Ethics in Science and Medicine
JF - Ethics in Science and Medicine
SN - 0277-9536
M1 - 114486
ER -