Vaping in Pregnancy: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Smoking in pregnancy increases the risk of negative health outcomes. Vaping can be effective for smoking cessation in nonpregnant populations. We conducted a systematic review of vaping in pregnancy, covering prevalence, patterns of use, reasons for use, smoking cessation, and health effects. METHODS: Five academic databases were searched on 17 February 2020. Studies reporting prevalence, patterns, reasons, cessation, or health effects of vaping in pregnancy were included; animal and in vitro studies were excluded. A narrative review was used, with risk of bias assessed using Hoy and colleague's tool, the Newcastle-Ottawa scale, and the Consolidated Criteria for reporting Qualitative Research. RESULTS: Twenty-three studies were identified: 11 survey, 7 qualitative, 3 cohort, and 2 secondary analyses of randomized clinical trials. Prevalence of vaping in pregnancy (four studies) was between 1.2% and 7.0% overall, and <1% among nonsmokers. Twelve studies reported patterns of use, but findings were inconsistent. Twelve of 14 studies asking why pregnant women vaped reported that most vaped to reduce or quit smoking. Mixed findings were reported from six studies on smoking cessation. Of three studies with health-related outcomes, two were underpowered and one reported similar birthweights for babies born to nonsmokers and women who vaped, with both higher (p < .0001) than the birthweight of babies born to smokers. CONCLUSIONS: There were insufficient data to draw conclusions about prevalence, patterns, and effects of vaping in pregnancy on smoking cessation. The limited literature suggests that vaping in pregnancy has little or no effect on birthweight. IMPLICATIONS: Smoking causes many negative health outcomes for pregnant women and to babies born to people who smoke. There remains a paucity of research on the effects of vaping in pregnancy. There is, however, the potential for vaping products to reduce the negative health outcomes associated with smoking. More research is needed to develop an evidence base in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".