A global country-level comparison of the financial burden of surgery
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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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".