What explains socioeconomic inequality in dental caries among school children in west of Iran? A Blinder-Oaxaca decomposition
Bibliographic record
Abstract
BACKGROUND AND AIM: Dental caries among children is considered as a main public health concern in most of the countries over world and its prevalence is widespread in low-income countries like Iran. The aim of this study was to measure socioeconomic-related inequality in poor decayed, missing, filled (DMF) index and identify the determinants among school children in west of Iran. METHODS: A survey was carried out among school children aged 12 to 15 years in Kermanshah City, Iran, in 2018, to collect data on dental caries, demographic characteristics, and socioeconomic status (SES). A total of 1457 students were included in the analysis of this cross-sectional study. Logistic regression analysis examined the association of poor DMF index with the socioeconomic and behavioral determinants. We used the relative index of inequality (RII) and the slope index of inequality (SII) to measure wealth-related inequality in poor DMF index. The Blinder-Oaxaca (BO) decomposition technique was also employed to identify the factors of the difference in poor DMF prevalence between the poorest and the richest groups. RESULTS: The overall and age-adjusted prevalence of poor DMF index was 36.92% [95% confidence interval (CI): 34.48-39.43] and 37.32% (95% CI: 34.64-40.08), respectively. The SII and RII indicated that the poor DMF index was mainly prevalent among poorer children. The absolute gap (%) in the incidence of poor DMF index between children from the richest and the poorest groups was 22.50. The BO results showed that the most important factors affecting the difference in poor DMF index were mother’s education (18.23%), being girl (6.12%), and visit to dentist (2.93%). CONCLUSION: There was a significant pro-rich distribution of poor DMF index among school children in the capital of Kermanshah Province. Interventions aimed at increasing mother’s education and good oral health behavior among poorer children could contribute to decline of the difference in poor DMF index between the highest and the lowest SES groups.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".