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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".