Socioeconomic inequality in dental care utilization in Iran: a decomposition approach
Bibliographic record
Abstract
Abstract Purpose Socioeconomic inequalities in dental care utilization in Iran are rarely documented. This study aimed to provide insight into socioeconomic inequalities in dental care utilization and its main contributing factors among Iranian households. Design/methodology/approach A total of 37,860 households from the 2017 Household Income and Expenditure Survey (HIES) were included in the study. Data on dental care utilization, age, gender and education attainment of the head of household, socioeconomic status of households, health insurance coverage, living areas and provinces were obtained for the survey. The concentration curve and the normalized concentration index ( C n ) was used to illustrate and quantify socioeconomic inequalities in dental care utilization among Iranian households. The C n was decomposed to identify the main determinants of the observed socioeconomic inequality in dental care utilization in Iran. Findings The study indicated that the prevalence of dental care utilization among Iranian’s households was 4.67% (95% confidence interval [CI]: 4.46 to 4.88%). The results suggested a higher concentration of dental care utilization among socioeconomically advantaged households ( C n = 0.2522; 95% CI: 0.2258 to 0.2791) in Iran. Pro-rich inequality in dental care utilization also found in rural ( C n = 0.2659; 95%CI: 0.2221 to 0.3098) and urban ( C n = 0.0.2504; 95% CI: 0.0.2159 to 0.2841) areas. The results revealed socioeconomic status of households, age and education status of head of households and residing provinces as the main contributing factors to the concentration of dental care utilization among the wealthy households. Originality/value This study revealed pro-rich inequalities in dental care utilization among households in Iran and its provinces. Thus, health policymakers should focus on designing effective evidence-based interventions to improve healthcare utilization among household with the older head of households, lower education status, and living in relatively poor provinces to reduce socioeconomic inequality in dental care utilization in Iran.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.002 | 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".