Assessing a conceptual model with both oral health and health related quality of life in community-dwelling elders

Linking patient-reported oral and general health-related quality of life

Background

The relationship between oral and overall health is of interest to health care professionals and patients alike. This study investigated the correlation between oral health-related quality of life (OHRQoL) and health-related quality of life (HRQoL) in a general adult population.

Methods

This cross-sectional study used a convenience sample of adult participants (N = 607) attending the 2022 Minnesota County and State fairs in USA, the 5-item Oral Health Impact Profile (OHIP-5) assessed OHRQoL, and the 10-item PROMIS v.1.2 Global Health Instrument assessed HRQoL. Spearman and Pearson correlations were used to summarize the bivariable relationship between OHRQoL and HRQoL (both physical and mental health dimensions). A structural equation model determined OHRQoL-HRQoL correlations (r). Correlations' magnitude was interpreted according to Cohen's guidelines (r = 0.10, 0.30, and 0.50 to demarcate "small," "medium," and "large" effects, respectively).

Results

OHRQoL and HRQoL correlated with r = 0.52 (95% confidence interval, CI: [0.50-0.55]), indicating that the two constructs shared 27% of their information. According to Cohen, this was a "large" effect. OHRQoL, and the physical and mental HRQoL dimensions correlated with r = 0.55 (95% CI: [0.50-0.59]) and r = 0.43 (95% CI: [0.40-0.46]), respectively, indicating a "large" and a "medium" effect. OHRQoL and HRQoL were substantially correlated in an adult population.

Conclusion

Using OHIP-5 to assess their dental patients' oral health impact allows dental professionals to gain insights into patients' overall health-related wellbeing.

Prediction of Frailty and Dementia Using Oral Health Impact Profile from a Population-Based Survey

Oral health and dentition have been associated with cognitive ability and frailty, but an applicable screening tool has not yet been developed. This study aimed to establish risk prediction models for dementia and frailty. A sample of 2905 community-dwelling older adults aged ≥58 years using the Taiwan Longitudinal Study on Aging (TLSA) survey was adapted and analyzed for this study. Risk scores were estimated by stepwise logistic regression. In models adjusted for covariates, increased age, female sex, no dental prosthesis (adjusted Odds ratio [adjOR], 1.61; 95% confidence interval [CI], 1.11–2.35), diabetes mellitus, chronic kidney disease, and an increased Oral Health Impact Profile (OHIP)-7T Q3 score (adjOR, 1.33; 95% CI, 1.19–1.49) were all significantly associated with frailty. In addition to these factors, an inability to self-report height or weight (adjOR, 4.52; 95% CI, 3.52–5.81) and an increased OHIP-7T Q7 score (adjOR, 1.21; 95% CI, 1.06–1.37) were significantly associated with dementia. The cut-off points of the risk scores for frailty and dementia were 80 (sensitivity, 80.0%; specificity, 81.2%) and 77 (sensitivity, 83.4%; specificity, 71.5%), respectively. The findings highlighted a number of composite risk factors of frailty and dementia. Importantly, the developed prediction models were easily applicable to screen for frailty and dementia in communities or dental clinics.