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Updated: Jun 14, 2026

Oral Health Assessment by Lay Personnel for Older Adults

Oral Health Assessment by Lay Personnel for Older Adults

Published on: February 2, 2020

Predicting tooth loss for older adults with special needs.

Xi Chen¹, James S Hodges, Stephen K Shuman

¹Department of Dental Ecology, University of North Carolina, Chapel Hill, NC, USA Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA Department of Primary Dental Care, University of Minnesota, Minneapolis, Minnesota Health Informatics, Department of Lab Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.

Community Dentistry and Oral Epidemiology

|April 1, 2010

Summary

Older adults with special needs (OASN) face higher risks of tooth loss. A new evidence-based model identifies key predictors like age and mobility to forecast tooth loss in OASN, improving oral healthcare quality.

Keywords:

older adults risk assessment tooth loss

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Last Updated: Jun 14, 2026

Oral Health Assessment by Lay Personnel for Older Adults

Oral Health Assessment by Lay Personnel for Older Adults

Published on: February 2, 2020

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Published on: July 12, 2022

Area of Science:

Geriatric Dentistry
Oral Health Epidemiology
Predictive Modeling in Healthcare

Background:

Older Adults with Special Needs (OASN) exhibit greater oral health challenges than their independent counterparts.
Tooth loss is a significant indicator of diminished oral function, yet risk factors for OASN remain poorly understood.
Current risk assessments for OASN tooth loss rely on clinical judgment rather than robust scientific evidence.

Purpose of the Study:

To develop and validate an evidence-based quantitative model for predicting tooth loss in OASN.
To establish a scientifically grounded approach for assessing tooth loss risk in this vulnerable population.
To enhance the quality of oral care for OASN through improved risk prediction.

Main Methods:

Retrospective analysis of 491 dentate older adults, including 235 from long-term care facilities.
Utilized medical and dental records for risk factor identification and tooth loss event tracking.
Employed multivariate negative-binomial regression and Akaike's Information Criterion for model development and refinement.

Main Results:

A final predictive model incorporating eight key predictors was established.
Significant predictors of tooth loss included age, initial dental status (decayed/broken teeth), anticholinergic medication burden, and physical mobility.
Internal validation confirmed the model's satisfactory predictive fit.

Conclusions:

An eight-predictor, evidence-based model was successfully developed to quantitatively predict individual tooth loss risk in OASN.
This model offers a scientific basis for proactive oral health management in OASN.
The findings support a shift towards evidence-driven risk assessment for improved OASN oral healthcare outcomes.