Prevalence of smoking and smokeless tobacco use during breastfeeding: A cross-sectional secondary data analysis based on 0.32 million sample women in 78 low-income and middle-income countries
Bibliographic record
Abstract
Background: Smoking and smokeless tobacco use during the postpartum period is well studied in high-income countries, whereas low-income and middle-income countries (LMICs) lack evidence. Methods: In this cross-sectional study we used data from the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) conducted in 78 LMICs between January 2010 and December 2019 to study tobacco use among 0.32 million sample lactating women. Age-standardized prevalence of smoking and smokeless tobacco use was estimated and presented with a 95% Confidence Interval (CI) for 78 LMICs. Pooled estimates overall and by WHO regions were obtained using random-effects meta-analyses. Country-level and community-level variance to understand contextual factors was also quantified using multilevel modelling. Findings: Pooled prevalence of any tobacco use among breastfeeding women in LMICs was 3.61% (95% CI 3.53-3.70); with the lowest prevalence in regions of the Americas (1.44%, 1.26-1.63) and the highest in the Southeast Asia region (6.13%, 6.0-6.27). The pooled prevalence of tobacco smoking was reported to be 1.16% (1.11-1.21), with the highest prevalence in the Eastern Mediterranean region (4.27%, 3.88-4.67) and the lowest in the African region (0.81%, 0.76-0.86). The pooled prevalence of smokeless tobacco use was reported to be 2.56% (2.49-2.63), with the highest prevalence in the Southeast Asia region (4.92%, 4.80-5.04). Illiterate and poor women in LMICs bore the enormous burden of tobacco use. Interpretation: The prevalence of smoking and smokeless tobacco use among lactating women in LMICs varied considerably across different WHO regions. Considering the cross-sectional design of the study, caution is required while interpreting the results. To improve mothers' and children's health and nutrition outcomes and reduce health inequalities in LMICs, reducing tobacco use through evidence-based interventions is critical. Funding: None.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".