The impact of anti-tobacco legislation on birth weight in Peru
Bibliographic record
Abstract
Background: Tobacco exposure remains a significant issue for public health, especially for pregnant women. It increases the risk for premature labor, low birth weight and small for gestational age (SGA), among other effects. To reduce these risks, many countries have enacted public policies to curb tobacco exposure. Peru enacted anti-tobacco laws that forbid smoking in public places, require prevention text and images in products and publicity, along with restriction of sales to adults. We evaluated the effect of the implementation of this law on newborn outcomes: birth weight, prematurity and SGA. Methods: This was a quasi-experimental study that utilized data from the Peruvian Live Birth Registry. Children born to mothers from urban areas were the intervention group, while children born to mothers from rural areas were considered the control group. Only singletons with information on birth weight and gestational age, born to mothers aged 12 to 49 years were included in the study. In addition, newborns with birth weights greater than + 4 standard deviations (SD) or less than - 4 SD from the gestational age-specific mean were excluded. To measure the effect of legislation on birth weight we performed a difference in differences analysis. Results: A total of 2,029,975 births were included in the analysis. After adjusting for characteristics of the mother and the child, and contextual variables, the anti-tobacco law in Peru reduced the incidence of prematurity by 30 cases per 10,000 live births (95% CI: 19 to 42). Conclusions: The reform had negligible effects on overall birth weights and on the incidence of SGA. This modest result suggests the need for a more aggressive fight against tobacco, prohibiting all types of advertising and promotion of tobacco products, among others measures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".