Potentially Avertable Child Mortality Associated with Surgical Workforce Scale‐up in Low‐ and Middle‐Income Countries: A Global Study
Bibliographic record
Abstract
BACKGROUND: Expansion of access to surgical care can improve health outcomes, although the impact that scale-up of the surgical workforce will have on child mortality is poorly defined. In this study, we estimate the number of child deaths potentially avertable by increasing the surgical workforce globally to meet targets proposed by the Lancet Commission on Global Surgery. METHODS: To estimate the number of deaths potentially avertable through increases in the surgical workforce, we used log-linear regression to model the association between surgeon, anesthetist and obstetrician workforce (SAO) density and surgically amenable under-5 mortality rate (U5MR), infant mortality rate (IMR), and neonatal mortality rate (NMR) for 192 countries adjusting for potential confounders of childhood mortality, including the non-surgical workforce (physicians, nurses/midwives, community health workers), gross national income per capita, poverty rate, female literacy rate, health expenditure per capita, percentage of urban population, number of surgical operations, and hospital bed density. Surgically amenable mortality was determined using mortality estimates from the UN Inter-agency Group for Child Mortality Estimation adjusted by the proportion of deaths in each country due to communicable causes unlikely to be amenable to surgical care. Estimates of mortality reduction due to upscaling surgical care to support the Lancet Commission on Global Surgery (LCoGS) minimum target of 20-40 SAO/100,000 were calculated accounting for potential increases in surgical volume associated with surgical workforce expansion. RESULTS: Increasing SAO workforce density was independently associated with lower surgically amenable U5MR as well as NMR (p < 0.01 for each model). When accounting for concomitant increases in surgical volume, scale-up of the surgical workforce to 20-40 SAO/100,000 could potentially prevent between 262,709 (95% CI 229,643-295,434) and 519,629 (465,046-573,919) under 5 deaths annually. The majority (61%) of deaths averted would be neonatal deaths. CONCLUSION: Scale up of surgical workforce may substantially decrease childhood mortality rates around the world. Our analysis suggests that scale-up of surgical delivery through increase in the SAO workforce could prevent over 500,000 children from dying before the age of 5 annually. This would represent significant progress toward meeting global child mortality reduction targets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".