Inequality in Under-Five Mortality in Iran: A National and Subnational Survey Data Analysis
Bibliographic record
Abstract
BACKGROUND: Despite substantial progress in the national average of under-five mortality rate in Iran, distribution of under-five mortality across different socioeconomic groups is unknown. This study measured socioeconomic inequality in under-five mortality in Iran and across its provinces. METHOD: Using data from provincially representative Multiple Indicator Demographic and Health Survey, conducted in Iran in 2010, we developed an accurate principal component analysis model to construct an indicator of socioeconomic status of Iranian households. Under-five mortality rates at national and subnational level were estimated using full birth history. The indicator then was used to measure inequality in under-five mortality using Wagstaff normalised concentration index (WCI) at national and subnational levels. RESULTS: Estimates of Wagstaff normalised concentration indices showed a pro-rich inequality in under-five mortality at national and subnational levels. The concentration index of under-five mortality in Iran was -0.197. Moreover, the consistent negative values of the concentration indices indicated that under-five mortality inequality disfavored the worst-off in all provinces. However, the inequality varied among provinces and ranged from -0.013 to -0.487. At national level there was a descending trend in the under-five mortality rate as one moves higher up in the socioeconomic quintiles. CONCLUSIONS: This study suggests that further reduction in under-five mortality not only requires addressing the national average but also needs considering inequality in under-five mortality. Therefore, child health policy requires looking beyond the average, putting equality and average together at both national and sub national levels.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".