Antenatal care and caesarean sections: trends and inequalities in four population-based birth cohorts in Pelotas, Brazil, 1982–2015
Bibliographic record
Abstract
BACKGROUND: Antenatal care and correctly indicated caesarean section can positively impact on health outcomes of the mother and newborn. Our objective was to describe how coverage and inequalities for these interventions changed from 1982 to 2015 in Pelotas, Brazil. METHODS: Using perinatal data from the 1982, 1993, 2004 and 2015 Pelotas birth cohorts, we assessed antenatal care coverage and caesarean section rates over time. Antenatal care indicators included the median number of visits, the prevalence of mothers attending at least six visits and the proportion who started antenatal care in the first trimester of pregnancy and attended at least six visits. We described these outcomes according to income quintiles and maternal skin colour, to identify inequalities. We described overall, private sector and public sector caesarean section rates. Differences in prevalence were tested using chi-square testing and median differences using Kruskal-Wallis testing. RESULTS: From 1982 to 2015, the median number of antenatal care visits and the prevalence of mothers attending at least six visits increased in all income quintiles and skin colour groups. Inequalities were reduced, but not eliminated. The overall proportion of caesarean births increased from 27.6% in 1982 to 65.1% in 2015, when 93.9% of the births in the private sector were by caesarean section. Absolute income-related inequalities in caesarean sections increased over time. CONCLUSIONS: Special attention should be given to the antenatal care of poor and Black women in order to reduce inequalities. The explosive increase in caesarean sections requires radical changes in delivery care policies, in order to reverse the current trend.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".