Financial protection in health revisited: Is catastrophic health spending underestimated for service‐ or disease‐specific analysis?
Bibliographic record
Abstract
Economists originally developed methods to assess financial catastrophe using total or aggregate out-of-pocket health spending. Aggregate out-of-pocket health spending is financially catastrophic when it exceeds a fixed proportion (i.e., threshold) of a household's total income or expenditure in a given period. However, these methods are now applied to assess financial catastrophe in disease- or service-specific rather than aggregate out-of-pocket health spending without using disease- or service-specific thresholds. This paper argues that not using disease- or service-specific thresholds for such assessments is misleading and underestimates the burden of financial catastrophe, especially among households from poorer backgrounds. It then proposed disease- or service-specific catastrophic payment thresholds, applied them to Nigeria and found that financial catastrophe was underestimated for the five service groups considered. The paper stresses the importance of using disease- or service-specific thresholds and avoiding unadjusted thresholds, which may leave poorer households behind as financially protected.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".