Socioeconomic Inequalities in Out of Pocket and Catastrophic Health Expenditures in Pakistan
Bibliographic record
Abstract
Abstract In Pakistan, health care utilization is linked to out-of-pocket (OOP) payments which has inequitable impact on household finances, leading to a vicious cycle of debt and repayments. In this study, we investigated socioeconomic inequality in OOP and catastrophic health expenditure (CHE), and the contribution of sociodemographic determinants to inequality. Three latest rounds of the Household Integrated Economic Survey (2007-08, 2011-12, and 2018-19) conducted by the Pakistan Bureau of Statistics were used. National and provincial-level socioeconomic inequalities were measured using concentration index (CI), and the slope (SII) and relative (RII) indices of inequality. Decomposition analyses were conducted using the approach proposed by Wagstaff (2005) and Erreygers (2009). We found that OOP payments increased from PKR 127 (2007-8) to PKR 250 (2018-19), with the largest increase observed in Punjab province. The gap in mean OOP payment between socioeconomic quintiles was also the largest in Punjab (2018-19). The percentage experiencing CHE in the most deprived quintile (Q1) changed from 8.3% (2007-8) to 13.7% (2018-9), and for the least deprived quintile (Q5) changed from 5.1% (2007-8) to 8.4% (2018-19). The OOP CI increased from 0.028 to 0.051 between 2007-8 and 2019-18, while SII and RII increased from 0.89 to 1.32 and 1.18 to 1.36, respectively. The CHE CI remained unchanged between 2007-8 and 2018-19, while SII become more positive and RII values became more negative. These findings suggest that the OOP expenditures increased over time for the least deprived group while CHE increased for the most deprived groups. The inequality decomposition analysis found that family size, socioeconomic position, dependency ratio and employment status were key contributing factors. We conclude that poor households should be protected from CHE by decoupling utilization from financing and extending financial risk protection through health insurance.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".