Prenatal care and child growth and schooling in four low- and medium-income countries
Bibliographic record
Abstract
BACKGROUND: The effectiveness of prenatal care for improving birth and subsequent child outcomes in low-income countries remains controversial, with much of the evidence to date coming from high-income countries and focused on early-life outcomes. We examined associations between prenatal care visits and birth weight, height-for-age at 24 months and attained schooling in four low- and middle-income countries. METHODS: We pooled data from prospective birth-cohort studies from Brazil, Guatemala, Philippines and South Africa. We created a prenatal care utilization index based on the number and timing of prenatal visits. Associations were examined between this index and birth weight, height-for-age at 24 months, and highest attained schooling grade until adulthood. RESULTS: Among 7203 individuals in the analysis, 68.9% (Philippines) to 96.7% (South Africa) had at least one prenatal care visit, with most having at least four visits. Over 40% of Brazilians and Guatemalans had their first prenatal visit in the first trimester, but fewer Filipinos (13.9%) and South Africans (19.8%) did so. Prenatal care utilization was not significantly associated with birth weight (p>0.05 in pooled data). Each unit increase in the prenatal care utilization index was associated with 0.09 (95% CI 0.04 to 0.15) higher height-for-age z-score at 24 months and with 0.26 (95% CI 0.17 to 0.35) higher schooling grades attained. Although there was some heterogeneity and greater imprecision across sites, the results were qualitatively similar among the four different populations. CONCLUSIONS: While not related to birth weight, prenatal care utilization was associated with important outcomes later in life, specifically higher height-for-age at 24 months and higher attained school grades. These results suggest the relevance of prenatal care visits for human capital outcomes important over the lifecycle.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".