Age and sex differences in the association of dental visits with inadequate oral health and multimorbidity: Findings from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
BACKGROUND: Dental attendance is important for the prevention, diagnosis, and treatment of oral diseases. In this study, we aimed to assess the extent of the association between dental visits, inadequate oral health, and multimorbidity (MM), and whether this association differs by age and sex. METHODS: We conducted a cross-sectional analysis of the first follow-up wave (2018) of the Canadian Longitudinal Study on Aging (CLSA). Poor self-reported oral health (SROH), oral health problems, and edentulism were used to indicate inadequate oral health. MM was defined as having 2 or more chronic conditions out of cancer, cardiovascular diseases, chronic respiratory diseases, diabetes, and mental illnesses. Dental visiting was determined as the number of visits to a dental professional within the past 12 months. Covariates included socioeconomic, behavioural factors, and the availability of dental insurance. We constructed multivariable Poisson and logistic regression models with interactions terms and estimated the relative excess risk due to interaction prevalence ratio (RERIPR) to assess the effect measure modification of age and sex on the associations of interest. We conducted sensitivity analyses and estimated E-values for unmeasured confounding. RESULTS: In this sample (n = 44,815), dental visiting was inversely associated with inadequate oral health and MM in adjusted models, reducing the odds/prevalence of poor SROH (OR 0.41, 95% CI 0.34, 0.51), oral health problems (PR 0.89, 95% CI 0.79, 0.94), edentulism (OR 0.10, 95% CI 0.06, 0.15), and MM (PR 0.86, 95% CI 0.79, 0.92). These associations were stronger in older age and females. CONCLUSION: Dental visiting may contribute to better oral health and reduced chronic diseases in the middle-aged and older population. Our findings suggest the need for age and sex-specific targeted interventions to optimize oral and overall health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".