The Impact of Payment Source and Hospital Type on Rising Cesarean Section Rates in Brazil, 1998 to 2008
Bibliographic record
Abstract
BACKGROUND: High cesarean section rates in Brazilian public hospitals and higher rates in private hospitals are well established. Less is known about the relationship between payment source and cesarean section rates within public and private hospitals. METHODS: We analyzed the 1998, 2003, and 2008 rounds of a nationally representative household survey (PNAD), which includes type of delivery, where it took place, and who paid for it. We construct cesarean section rates for various categories, and perform logistic regression to determine the relative importance of independent variables on cesarean section rates for all births and first births only. RESULTS: Brazilian cesarean section rates were 42 percent in 1998 and 53 percent in 2008. Women who delivered publicly funded births in either public or private hospitals had lower cesarean section rates than those who delivered privately financed deliveries in public or private hospitals. Multivariate models suggest that older age, higher education, and living outside the Northeast region all positively affect the odds of delivering by cesarean section; effects are attenuated by the payment source-hospital type variable for all women and even more so among first births. CONCLUSIONS: Cesarean section rates have risen substantially in Brazil. It is important to distinguish payment source for the delivery to have a better understanding of those rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".