Decomposing socioeconomic inequality in dental caries in Iran: cross-sectional results from the PERSIAN cohort study
Bibliographic record
Abstract
BACKGROUND: The current study aimed to measure and decompose socioeconomic-related inequalities in DMFT (decayed, missing, and filled teeth) index among adults in Iran. METHODS: The study data were extracted from the adult component of Prospective Epidemiological Research Studies in IrAN (PERSIAN) from 17 centers in 14 different provinces of Iran. DMFT score was used as a measure of dental caries among adults in Iran. The concentration curve and relative concentration index (RC) was used to quantify and decompose socioeconomic-related inequalities in DMFT. RESULTS: A total of 128,813 adults aged 35 and older were included in the study. The mean (Standard Deviation [SD]) score of D, M, F and DMFT of the adults was 3.3 (4.6), 12.6 (10.5), 2.1 (3.4) and 18.0 (9.5), respectively. The findings suggested that DMFT was mainly concentrated among the socioeconomically disadvantaged adults (RC = - 0.064; 95% confidence interval [CI), - 0.066 to - 0.063). Socioeconomic status, being male, older age and being a widow or divorced were identified as the main factors contributing to the concentration of DMFT among the worse-off adults. CONCLUSIONS: It is recommended to focus on the dental caries status of socioeconomically disadvantaged groups in order to reduce socioeconomic-related inequality in oral health among Iranian adults. Reducing socioeconomic-related inequalities in dental caries should be accompanied by appropriate health promotion policies that focus actions on the fundamental socioeconomic causes of dental disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".