Economic valuation of the impact of a large surgical charity using the value of lost welfare approach
Bibliographic record
Abstract
BACKGROUND: The assessment of the economic burden of surgical disease is integral to determining allocation of resources for health globally. We estimate the economic gain realised over an 11-year period resulting from a vertical surgical programme addressing cleft lip (CL) and cleft palate (CP). METHODS: The database from a large non-governmental organisation (Smile Train) over an 11-year period was analysed. Incidence-based disability-adjusted life years (DALYs) averted through the programme were calculated, discounted 3%, using disability weights from the Global Burden of Disease (GBD) study and an effectiveness factor for each surgical intervention. The effectiveness factor allowed for the lack of 100% resolution of the disability from the operation. We used the value of lost welfare approach, based on the concept of the value of a statistical life (VSL), to assess the economic gain associated with each operation. Using income elasticities (IEs) tailored to the income level of each country, a country-specific VSL was calculated and the VSL-year (VSLY) was determined. The VSLY is the economic value of a DALY, and the DALYs averted were converted to economic gain per patient and aggregated to give a total value and an average per patient. Sensitivity analyses were performed based on the variations of IE applied for each country. RESULTS: Each CL operation averted 2.2 DALYs on average and each CP operation 3.3. Total averted DALYs were 1 325 678 (CP 686 577 and CL 639 102). The economic benefit from the programme was between US$7.9 and US$20.7 billion. Per patient, the average benefit was between US$16 133 and US$42 351. Expense per DALY averted was estimated to be $149. CONCLUSIONS: Addressing basic surgical needs in developing countries provides a massive economic boost through improved health. Expansion of surgical capacity in the developing world is of significant economic and health value and should be a priority in global health efforts.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".