A global country-level comparison of the financial burden of surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Approximately 30 per cent of the global burden of disease is surgical, and nearly one-quarter of individuals who undergo surgery each year face financial hardship because of its cost. The Lancet Commission on Global Surgery has proposed the elimination of impoverishment due to surgery by 2030, but no country-level estimates exist of the financial burden of surgical access. METHODS: Using publicly available data, the incidence and risk of financial hardship owing to surgery was estimated for each country. Four measures of financial catastrophe were examined: catastrophic expenditure, and impoverishment at the national poverty line, at 2 international dollars (I$) per day and at I$1·25 per day. Stochastic models of income and surgical costs were built for each country. Results were validated against available primary data. RESULTS: Direct medical costs of surgery put 43·9 (95 per cent posterior credible interval 2·2 to 87·1) per cent of the examined population at risk of catastrophic expenditure, and 57·0 (21·8 to 85·1) per cent at risk of being pushed below I$2 per day. The risk of financial hardship from surgery was highest in sub-Saharan Africa. Correlations were found between the risk of financial catastrophe and external financing of healthcare (positive correlation), national measures of well-being (negative correlation) and the percentage of a country's gross domestic product spent on healthcare (negative correlation). The model performed well against primary data on the costs of surgery. CONCLUSION: Country-specific estimates of financial catastrophe owing to surgical care are presented. The economic benefits projected to occur with the scale-up of surgery are placed at risk if the financial burden of accessing surgery is not addressed in national policies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it