An empirical evaluation of the performance of financial protection indicators for UHC monitoring: Evidence from Burkina Faso
Bibliographic record
Abstract
Achieving Universal Health Coverage (UHC) has been recognized as one of the Sustainable Development Goals (SDGs) and includes both ensuring access to health services and providing financial protection (FP) against using these services. Currently, progress towards achieving the FP component of UHC is assessed using the catastrophic health expenditure budget share indicator, which estimates the proportion of the population with health expenditures exceeding 10% of total income or consumption. Other indicators exist, however, and are widely used in the literature, yet few studies have compared the usefulness of these indicators for UHC monitoring. Using panel data from Burkina Faso, this paper seeks to evaluate the performance of common FP indicators based on three properties: (1) their ability to identify those most at risk of financial hardship (i.e. the poor), (2) their ability to detect households with health shocks, and (3) their sensitivity to seasonal variation. Our results indicate that, while some indicators perform better in certain conditions than others, none are without limitation. Indeed, despite being the best able to differentiate households who have experienced a health shock, the official SDG indicator performs the worst at identifying the poorest group of the population and is the most sensitive to seasonal variation. As such, more research is needed in order to improve the measurement of FP such that progress towards achieving UHC can be accurately monitored.
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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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".