The impact and cost-effectiveness of the Amref Health Africa-Smile Train Cleft Lip and Palate Surgical Repair Programme in Eastern and Central Africa
Bibliographic record
Abstract
INTRODUCTION: Cleft lip with or without cleft palate (CLP) is a congenital malformation that causes significant morbidity in low and middle income countries. Amref Health Africa has partnered with Smile Train to provide CLP surgeries since 2006. METHODS: We analyzed anonymized data of 37,274 CLP patients from the Smile Train database operated on in eastern and central Africa between 2006 and 2014. Cases were analyzed by age, gender, country and surgery type. The impact of cleft surgery was determined by measuring averted Disability-Adjusted Life Years (DALYs) and delayed averted DALYs. We used mean Smile Train costs to calculate cost-effectiveness. We calculated economic benefit using the human capital approach and Value of Statistical Life (VSL) methods. RESULTS: The median age at time of primary surgery was 5.4 years. A total of 207,879 DALYs were averted at a total estimated cost of US$13 million. Mean averted DALYs per patient were 5.6, and mean cost per averted DALY was $62.8. Total delayed burden of disease from late age at surgery was 36,352 DALYs. Surgical correction resulted in $292 million in economic gain using the human capital approach and $2.4 billion using VSL methods. CONCLUSION: Cleft surgery is a cost-effective intervention to reduce disability and increase economic productivity in eastern and central Africa. Dedicated programs that provide essential CLP surgery can produce substantial clinical and economic benefits.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".