<p>Socioeconomic Inequality in Self-Medication in Iran: Cross-Sectional Analyses at the National and Subnational Levels</p>
Bibliographic record
Abstract
Background: Self-medication (SM) is a public health concern globally. This study aimed to measure socioeconomic inequality in SM and identify its main determinants among Iranian households. Methods: A total of 38,859 households from the 2018 Household Income and Expenditure Survey (HIES) were included in the study. Data on SM, household size, age, gender and education status of the head of household, monthly household’s expenditures (as a proxy for socioeconomic status), health insurance coverage and living areas and provinces were obtained for the survey. The concentration curve and the normalized concentration index ( Cn ) were used to quantify the magnitude of socioeconomic inequality in SM among Iranian households. The Cn was decomposed to identify the main determinants of socioeconomic inequality in SM in Iran. Results: The results indicated that 18.2% (95% confidence interval [CI]: 17.7% to 18.5%) of households in Iran had SM practice in the past month. The results suggested a higher concentration of SM among the rich households ( Cn = 0.0466; 95% CI= 0.0321 to 0.0612) in Iran. The concentration of SM among high SES households was also found in urban (0.0311; 95% CI=0.0112 to 0.0510) and rural (= 0.0513; 95% CI=0.0301 to 0.0726) areas. SM was concentrated among the rich households in Tehran, Qom, Esfahan, Ardebil, Golestan, and Sistan and Baluchestan provinces. In contrast, a higher concentration of SM was found among the poor households in Semnan, North Khorasan, Kerman, Bushehr, and South Khorasan provinces. The decomposition revealed SES of household, itself, as the main contributing factor to the concentration of SM among the wealthy households. Conclusion: This study demonstrated that SM is more concentrated among socioeconomically advantaged households in Iran. Thus, effective evidence-based interventions should be implemented to improve awareness about SM and its negative consequences. Further studies are required to investigate the consequences of SM practice among people. Keywords: self-medication, inequality, socioeconomic status, 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 | 0.002 |
| 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 | 0.001 |
| 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".