Sub-Optimal Oral Health, Multimorbidity and Access to Dental Care
Bibliographic record
Abstract
ABSTRACT INTRODUCTION Emerging research on the links between sub-optimal oral health and multimorbidity (MM), or the co-existence of multiple chronic conditions, has raised queries on whether enhancing access to dental care may mitigate the MM burden, especially in older age. Here, we aim to assess the association between sub-optimal oral health and MM and whether access to dental care can mitigate the risk of MM in individuals with sub-optimal oral health. METHODS We conducted a cross-sectional analysis using data from the Canadian Longitudinal Study on Aging (CLSA) (n=44,815, 45-84 years old). Edentulism, self-reported oral health (SROH), and other oral health problems (e.g., toothache, bleeding gums), were each used as indicators of sub-optimal oral health. MM was defined according to the Public Health Agency of Canada as having 2 or more chronic conditions out of cancer, cardiovascular diseases, chronic respiratory diseases, diabetes, and mental illnesses. Variables for access to dental care included the number of dental visits within the last year, dental insurance status, and cost barriers to dental care. We constructed multivariable step-wise logistic regression models and interaction terms with 95% confidence intervals and estimated prevalence ratio (PR) to assess the associations of interest, adjusting for a priori determined sociodemographic and behavioural factors. RESULTS Each of the sub-optimal oral health indicators were significantly associated with MM (edentulism PR=1.48, 95%CI 1.31, 1.68; poor SROH PR=1.81, 95%CI 1.62, 2.01; other oral health problems PR = 1.91, 95%CI 1.78, 2.06). The magnitude of this association was exacerbated in individuals who lacked dental insurance, could not afford dental care, and those who reported fewer dental visits within the last year. CONCLUSION The association between sub-optimal oral health and MM may be exacerbated by the lack of access to dental care. Policies aiming to enhance access to dental care may help mitigate the risk of MM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".