Socioeconomic Disparities in the Prevalence of Disability in Iran: A Decomposition Analysis Using the Concentration Index
Bibliographic record
Abstract
Objective Various factors, including injuries, health conditions, demographics, environmental influences, and socioeconomic factors, can elevate the risk of disability.This study estimates the socioeconomic inequality associated with disability prevalence within Iran's general population. Materials & MethodsThis research used secondary data from the 2011 Iran multiple indicator demographic and health survey (IrMIDHS).The concentration index was used to evaluate socioeconomic-related inequality in disability prevalence.The outcome variable, disability prevalence, was measured as a binary indicator.The concentration index was also decomposed to identify the primary factors contributing to socioeconomic inequality in disability prevalence.Available demographic data included age, gender, location (rural or urban residence), and socioeconomic indicators, such as educational attainment and wealth index.Results A total of 86 403 Iranian individuals, aged 1 to 95 years, participated in the survey, with a mean age of 28.88 years (±0.06).Disability prevalence within the study population was 6.29% (n=5 432).The concentration index for disability was calculated at 0.15 (P<0.001),indicating that disability prevalence was disproportionately higher among those with greater socioeconomic status.The analysis highlighted the wealth index as the key factor driving this inequality, contributing to 87.41% of the total socioeconomic disparity in disability prevalence.Conclusion The findings indicated that socioeconomic-related inequality in the prevalence of disability was concentrated among well-off participants.Accordingly, identifying the cause of disabilities in groups with higher socioeconomic status is suggested to formulate policy options to prevent disabilities and provide needed support.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".