Prediction Model Development and Validation of 12-Year Incident Edentulism of Older Adults in the United States

Using machine learning algorithms to investigate factors associated with complete edentulism among older adults in the United States

AIMS

Edentulism is an incapacitating condition, and its prevalence is unequal among different population groups in the United States (US) despite its declining prevalence. This study aimed to investigate the current prevalence, apply Machine Learning (ML) Algorithms to investigate factors associated with complete tooth loss among older US adults, and compare the performance of the models.

METHODS

The cross-sectional 2020 Behavioral Risk Factor Surveillance System (BRFSS) data was used to evaluate the prevalence and factors associated with edentulism. ML models were developed to identify factors associated with edentulism utilizing seven ML algorithms. The performance of these models was compared using the area under the receiver operating characteristic curve (AUC).

RESULTS

An overall prevalence of 11.9% was reported. The AdaBoost algorithm (AUC = 84.9%) showed the best performance. Analysis showed that the last dental visit, educational attainment, smoking, difficulty walking, and general health status were among the top factors associated with complete edentulism.

CONCLUSION

Findings from our study support the declining prevalence of complete edentulism in older adults in the US and show that it is possible to develop a high-performing ML model to investigate the most important factors associated with edentulism using nationally representative data.

Ten-year cross-sectional and longitudinal assessment and factors associated with unfavourable self-rated oral health in older adults in the United States

OBJECTIVE

To investigate the association of demographic and socio-economic characteristics with self-reported oral health (SROH) among older adults who participated in the Health and Retirement Study (HRS) in 2008, 2018, or both, and to describe temporal changes.

METHODS

Data were from the University of Michigan's Health and Retirement Study (HRS), a nationally representative longitudinal survey of Americans aged 51 and older. Responses from participants who completed the Core HRS survey and Dental Module (DM) in 2008 (n = 1310), 2018 (n = 1330), and the "common group" at both timepoints (n = 559) were analysed. Using the common group, the outcome measure was 2018 self-rated oral health (Favourable vs Unfavourable). Potential explanatory variables included 2008 self-rated oral health (SROH), sociodemographic, and dental utilisation-related factors. Survey logistic regression analysis was used to identify factors that were associated with unfavourable 2018 SROH in 2018.

RESULTS

Unfavourable SROH prevalence was 28.5% and 31.6% in 2008 and 2018, respectively. Among the common, longitudinal group, the unfavourable prevalence remained the same, 26.1% at both timepoints. A positive association was seen between 2018 unfavourable SROH and baseline variables of 2008 unfavourable SROH, male gender, less education, and lower levels of wealth.

CONCLUSIONS

Over a quarter of participants reported unfavourable SROH. There was little change in SROH during this period. Sociodemographic factors influence the SROH of the older population. Policies and programs to promote and protect the oral health of older adults should be designed and implemented to reduce social inequalities and improve the SROH of disadvantaged older adults.

Edentulism Predicts Cognitive Decline in the US Health and Retirement Cohort Study

This longitudinal cohort study examines if 1) cognitive decline varies by birth cohort, adjusting for covariates, and 2) edentulism and nonuse of dental care predict 10-y cognitive decline (2008-2018). The Health and Retirement Study (HRS) features a representative sample of US adults over age 50. Eligibility criteria included having cognitive interview data available and responding to the question, "Have you lost all of your upper and lower natural permanent teeth?" at 2+ time points between 2006 and 2018. Use of dental care in the past 2 y was assessed. Linear mixed models for repeated measures estimated the trajectories of mean cognition over time for the birth cohorts, adjusted for baseline cognition, dentition status, dental care use, and covariates (demographic characteristics, health behaviors, and medical conditions). Cohort-by-time interaction terms were included to assess if cognitive decline varied by birth cohort. Ten-year change in cognition status (measured by HRS Cogtot27)-categorized as dementia (<7); cognitive impairment, not demented (7-11) 7≤Cogtot27<12; and normal (≥12)-was also investigated according to birth cohort, dentition status, and dental care use. Mean (SD) baseline age was 63.4 (10.1) y (n = 22,728). Older birth cohorts had greater cognitive decline than younger cohorts. Linear mixed-model estimates and 95% confidence intervals for protective factors for cognitive decline included higher baseline cognition (HRS Cogtot27) (0.49; 0.48-0.50), use of dental care in the past 2 y (0.17; 0.10-0.23), and covariates such as greater household wealth and being married. Risk increased with being edentulous (-0.42; -0.56 to -0.28), history of stroke or diabetes, less education, Medicaid recipient, current smoker, loneliness, and poor/fair self-rated health. Edentulism and irregular dental care are among important predictors of cognitive decline. Tooth retention and regular dental care throughout life appear to be important for maintaining oral and cognitive health.