Socioeconomic-Related Inequalities in Dental Care Utilization in Northwestern Iran
Bibliographic record
Abstract
Satar Rezaei,1 Mohammad Habibullah Pulok,2 Telma Zahirian Moghadam,3 Hamed Zandian3 1Research Center for Environmental Determinants of Health, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran; 2Nova Scotia Health Authority, Halifax, Nova Scotia, Canada; 3Social Determinants of Health Research Center, Ardabil University of Medical Sciences, Ardabil, IranCorrespondence: Hamed ZandianSocial Determinants of Health Research Center, Ardabil University of Medical Sciences, Ardabil, IranTel +98 4533513775Email zandian.hamed899@gmail.comIntroduction: There have been multiple studies on socioeconomic-related inequalities in the use of dental services in Iran, but the evidence is still limited. This study measured inequality in dental care utilization by socioeconomic status and examined factors explaining this inequality among households in Ardabil, Iran in 2019.Methods: A total of 436 household heads participated in this cross-sectional study. Using a validated questionnaire, face-to-face interviews were conducted to collect data on dental care utilization, unmet needs, sociodemographic characteristics, economic status, health insurance, and oral health status of the participants. We used the concentration curve and relative concentration index (RCI) to visualize and quantify the level of inequality in dental care utilization by income. Regression-based decomposition was also applied to understand the causes of inequality.Results: About 59.2% (95% CI 54.4%– 63.7%) and 14.7% (95% CI 11.6%– 18.4%) of participants had visited a dentist for dental treatment in the previous 12 months and for 6-month dental checkups, respectively. The RCI for the probability of visiting a dentist in the last 12 months was 0.243 (95% CI 0.140– 0.346). This suggests that dental care utilization was more concentrated among the rich. The RCI for unmet dental care needs was negative, which indicates more prevalence among the poor. Monthly household income (20.9%), self-rated oral health (6.9%), regular brushing (3.2%), and dental health insurance (2.5%) were the main factors in socioeconomic inequality in dental care utilization.Conclusion: This study reveals that dental care–service utilization did not match the need for dental care, due to differences in socioeconomic status in Ardabil, Iran. Policies could be implemented to increase the coverage of dental care services among socioeconomically disadvantaged groups to tackle socioeconomic-related inequality in dental care utilization.Keywords: dental care utilization, socioeconomic status, inequality, decomposition
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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 teacher head, 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".