Economic Valuation of the Global Burden of Cleft Disease Averted by a Large Cleft Charity
Bibliographic record
Abstract
BACKGROUND: This study attempts to quantify the burden of disease averted through the global surgical work of a large cleft charity, and estimate the economic impact of this effort over a 10-year period. METHODS: Anonymized data of all primary cleft lip and cleft palate procedures in the Smile Train database were analyzed and disability-adjusted life years (DALYs) calculated using country-specific life expectancy tables, established disability weights, and estimated success of surgery and residual disability probabilities; multiple age weighting and discounting permutations were included. Averted DALYs were calculated and gross national income (GNI) per capita was then multiplied by averted DALYs to estimate economic gains. RESULTS: 548,147 primary cleft procedures were performed in 83 countries between 2001 and 2011. 547,769 records contained complete data available for the study; 58 % were cleft lip and 42 % cleft palate. Averted DALYs ranged between 1.46 and 4.95 M. The mean economic impact ranged between USD 5510 and 50,634 per person. This corresponded to a global economic impact of between USD 3.0B and 27.7B USD, depending on the DALY and GNI values used. The estimated cost of providing these procedures based on an average reimbursement rate was USD 197M (0.7-6.6 % of the estimated impact). CONCLUSIONS: The immense economic gain realized through procedures focused on a small proportion of the surgical burden of disease highlights the importance and cost-effectiveness of surgical treatment globally. This methodology can be applied to evaluate interventions for other conditions, and for evidence-based health care resource allocation.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".