Prevalence of smoking during pregnancy and associated social inequalities in developed countries over the 1995–2020 period: A systematic review
Bibliographic record
Abstract
BACKGROUND: Smoking during pregnancy (SDP) is an important source of preventable morbidity and mortality for both mother and child. OBJECTIVES: The aim of this study was to describe changes in the prevalence of SDP over the last 25 years in developed countries (Human Development Index >0.8 in 2020) and associated social inequalities. DATA SOURCES: A systematic review was conducted based on a search in PubMed, Embase and PsycInfo databases and government sources. STUDY SELECTION AND DATA EXTRACTION: Published studies between January 1995 and March 2020, for which the primary outcome was to assess the national prevalence of SDP and the secondary outcome was to describe related socio-economic data were included in the analysis. The selected articles had to be written in English, Spanish, French or Italian. SYNTHESIS: The articles were selected after successive reading of the titles, abstracts and full-length text. An independent double reading with intervention of a third reader in case of disagreement allowed including 35 articles from 14 countries in the analysis. RESULTS: The prevalence of SDP differed across the countries studied despite comparable levels of development. After 2015, the prevalence of SDP ranged between 4.2% in Sweden and 16.6% in France. It was associated with socio-economic factors. The prevalence of SDP slowly decreased over time, but this overall trend masked inequalities within populations. In Canada, France and the United States, the prevalence decreased more rapidly in women of higher socio-economic status, and inequalities in maternal smoking were more marked in these countries. In the other countries, inequalities tended to decrease but remained significant. CONCLUSIONS: During pregnancy, that is a period described as a window of opportunity, smoking and social vulnerability factors need to be detected to implement targeted prevention strategies aiming at reducing related social inequalities.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".