Disparities in periodontitis risk and healthcare use among individuals with disabilities in Korea: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: We analyzed the relationship between disability status and periodontal disease, focusing on disparities in healthcare utilisation, including outpatient visits and hospitalisation rates, among disability types and severities. METHODS: This study used data from the National Health Insurance Service(NHIS) of Korea, which includes comprehensive records of the insured population. We examined 966,200 individuals with disabilities, grouped into five categories, and applied propensity score matching to compare with a matched control population. Periodontal disease was defined by the Korean Classification of Diseases criteria, and we used chi-square tests, t-tests, multivariate logistic regression, and negative binomial regression. RESULTS: Individuals with disabilities had higher odds of hospitalisation for periodontitis (OR: 3.83, 95% CI = 3.59-4.08) but lower odds for outpatient visits (OR: 0.68, 95% CI = 0.68-0.69) and dental treatments (OR: 0.73, 95% CI = 0.72-0.73) compared to those without disabilities. The highest hospitalisation rates were among those with mental health disabilities (OR: 13.70, 95% CI = 12.26-15.30). Severe disabilities were associated with increased hospitalisation rates (OR: 7.14, 95% CI = 6.66-7.66) and fewer outpatient visits and treatments. CONCLUSION: Individuals with mental health disabilities or severe disabilities experience greater risks of hospitalisation for periodontitis and attend fewer outpatient visits and treatments. Targeted interventions are needed to improve dental care access and reduce disparities.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".