Socioeconomic Inequalities in Out-of-Pocket and Catastrophic Health Expenditures in Pakistan
Bibliographic record
Abstract
Objectives: In Pakistan, healthcare utilization is linked to out-of-pocket payments (OOP) which disproportionately affect low-income households. We investigated socioeconomic inequality in OOP and catastrophic health expenditures (CHEs), and the contribution of sociodemographic factors to these inequalities. Methods: Socioeconomic inequalities were quantified using the concentration index (CI), and the slope (SII) and relative (RII) indices of inequality using data from three rounds of Household Integrated Economic Survey (2007-08, 2011-12, and 2018-19). Decomposition analyses were conducted using the Wagstaff and Erreygers approach. Results: OOP payments increased from PKR 127 (2007-08) to PKR 250 (2018-19). CHEs in the most deprived quintile (Q1) changed from 8.3% (2007-08) to 13.7% (2018-19), and for the least deprived quintile (Q5) from 5.1% (2007-08) to 8.4% (2018-19). The OOP CI increased from 0.028 to 0.051, while the SII and RII increased from 0.89 to 1.32 and 1.18 to 1.36, respectively. Decomposition analysis showed that household size, composition, employment, and the province of residence explained much of the socioeconomic inequality in CHEs. Conclusion: Poor households experience high CHE, disproportionately impacting larger families with children and elderly members. Policymakers should implement targeted financial protection strategies to safeguard vulnerable households from the impoverishing effects of healthcare expenses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".