Heterogeneous Association of Tooth Loss with Functional Limitations

Tooth loss is prevalent in older adults and associated with functional capacity decline. Studies on the susceptibility of some individuals to the effects of tooth loss are lacking. This study aimed to investigate the heterogeneity of the association between tooth loss and higher-level functional capacity in older Japanese individuals employing a machine learning approach. This is a prospective cohort study using the data of adults aged ≥65 y in Japan (N = 16,553). Higher-level functional capacity, comprising instrumental independence, intellectual activity, and social role, was evaluated using the Tokyo Metropolitan Institute of Gerontology Index of Competence (TMIG-IC). The scale ranged from 0 (lowest function) to 13 (highest function). Doubly robust targeted maximum likelihood estimation was used to estimate the population-average association between tooth loss (having <20 natural teeth) and TMIG-IC total score after 6 y. The heterogeneity of the association was evaluated by estimating conditional average treatment effects (CATEs) using the causal forest algorithm. The result showed that tooth loss was statistically significantly associated with lower TMIG-IC total scores (population-average effect: -0.14; 95% confidence interval, -0.18 to -0.09). The causal forest analysis revealed the heterogeneous associations between tooth loss and lower TMIG-IC total score after 6 y (median of estimated CATEs = -0.13; interquartile range = 0.12). The high-impact subgroup (i.e., individuals with estimated CATEs of the bottom 10%) were significantly more likely to be older and male, had a lower socioeconomic status, did not have a partner, and had poor health conditions compared with the low-impact subgroup (i.e., individuals with estimated CATEs of the top 10%). This study found that heterogeneity exists in the association between tooth loss and lower scores on functional capacity. Implementing tooth loss prevention policy and clinical measures, especially among vulnerable subpopulations significantly affected by tooth loss, may reduce its burden more effectively.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:103

Enthalten in:

Journal of dental research - 103(2024), 4 vom: 30. März, Seite 369-377

Sprache:

Englisch

Beteiligte Personen:

Matsuyama, Y [VerfasserIn]
Aida, J [VerfasserIn]
Kondo, K [VerfasserIn]
Shiba, K [VerfasserIn]

Links:

Volltext

Themen:

Big data
Dental care
Dentistry
Epidemiology
Journal Article
Machine learning
Public health

Anmerkungen:

Date Completed 28.03.2024

Date Revised 28.03.2024

published: Print

Citation Status MEDLINE

doi:

10.1177/00220345241226957

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM370231961