Assessing the Brazilian surgical system with six surgical indicators: a descriptive and modelling study
Bibliographic record
Abstract
BACKGROUND: Brazil boasts a health scheme that aspires to provide universal coverage, but its surgical system has rarely been analysed. In an effort to strengthen surgical systems worldwide, the Lancet Commission on Global Surgery proposed a collection of 6 standardised indicators: 2-hour access to surgery, surgical workforce density, surgical volume, perioperative mortality rate (POMR) and protection against impoverishing and catastrophic expenditure. This study aims to characterise the Brazilian surgical health system with these newly devised indicators while gaining understanding on the complexity of the indicators themselves. METHODS: Using Brazil's national healthcare database, commonly reported healthcare variables were used to calculate or simulate the 6 surgical indicators. Access to surgery was calculated using hospital locations, surgical workforce density was calculated using locations of surgeons, anaesthesiologists and obstetricians (SAO), and surgical volume and POMR were identified with surgical procedure codes. The rates of protection against impoverishing and catastrophic expenditure were modelled using cost of surgical inpatient hospitalisations and a γ distribution of incomes based on Gini and gross domestic product/capita. FINDINGS: In 2014, SAO density was 34.7/100 000 population, surgical volume was 4433 procedures/100 000 people and POMR was 1.71%. 79.4% of surgical patients were protected against impoverishing expenditure and 84.6% were protected against catastrophic expenditure due to surgery each year. 2-hour access to surgery was not able to be calculated from national health data, but a proxy measure suggested that 97.2% of the population has 2-hour access to a hospital that may be able to provide surgery. Geographic disparities were seen in all indicators. INTERPRETATION: Brazil's public surgical system meets several key benchmarks. Geographic disparities, however, are substantial and raise concerns of equity. Policies should focus on stimulating appropriate geographic allocation of the surgical workforce and better distribution of surgical volume. In some cases, where benchmarks for each indicator are met, supplemental analysis can further inform our understanding of health systems. This measured and systematic evaluation should be encouraged for all nations seeking to better understand their surgical systems.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".