Demographics, social position, dental status and oral health-related quality of life in community-dwelling older adults

Abstract

Purpose: To identify demographic, socioeconomic and dental clinical predictors of oral health-related quality of life (OHRQoL) in elderly people.

Methods: Cross-sectional study involving 613 elderly people aged 65-74 years in Manaus, Brazil. Interviews and oral examinations were carried out to collect demographic characteristics (age and sex) and socioeconomic data (income and education), dental clinical measures (DMFT, need of upper and lower dentures) and OHRQoL (GOHAI questionnaire). Structural equation modelling was used to estimate direct and indirect pathways between the variables.

Results: Being older predicted lower schooling but higher income. Higher income was linked to better dental status, which was linked to better OHRQoL. There were also indirect pathways. Age and education were linked to OHRQoL, mediated by clinical dental status. Income was associated with dental clinical status via education, and income predicted OHRQoL via education and clinical measures.

Conclusion: Our findings elucidate the complex pathways between individual, environmental factors and clinical factors that may determine OHRQoL and support the application of public health approaches to improve oral health in older people.

Keywords: Elderly people; Oral health; Quality of life; Socioeconomic status; Structural equation modelling.

Fig. 1

The Wilson and Cleary model linking clinical variables with quality of life

Fig. 2

Full theoretical model on the relationships between demographic and socioeconomic characteristics, dental clinical measures and OHRQoL in older people according to Wilson and Cleary conceptual model

Fig. 3

Confirmatory factor analysis of the 2-factor 6 items (measurement model) obtained through bootstrap item loadings (SE/BC 95 % CI)

Fig. 4

Parsimonious model of associations between clinical measures, age, education, income and OHRQoL. The variable “gender” was removed from this model as it was not statistically correlated with any variables. *<0.05; **P < 0.01; ***P < 0.001. Solid lines direct effects, dashed lines indirect effects [the total indirect effects were calculated as follows (all figures are standardised beta coefficients): (1) age to OHRQoL: age—clinical measures—OHRQoL = 0.079 × −0.172 = −0.014; (2) education to OHRQoL: education—clinical measures—OHRQoL = −0.223 × −0.172 = 0.038; (3) income to clinical measures: income—education—clinical measures = 0.350 × −0.223 = −0.078; (4) income to OHRQoL: income—education—clinical measures—OHRQoL = 0.350 × −0.223 × −0.172 = 0.013]