Question · asked by 1 review
Postnatal passive tobacco exposure (secondhand/household smoke in infancy) vs No passive tobacco exposure (non-exposed children): Caries presence in primary teeth
- Population
- Young children with primary (deciduous) dentition (aged 1 to 6 years at study start)
- Intervention
- Postnatal passive tobacco exposure (secondhand/household smoke in infancy)
- Comparison
- No passive tobacco exposure (non-exposed children)
- Outcome
- Caries presence in primary teeth
Postnatal (infancy) tobacco exposure is associated with higher odds of caries in primary dentition (pooled OR 1.72, 95% CI 1.45 to 2.05; 13 studies; high heterogeneity I2=83%); certainty not graded.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Postnatal passive tobacco exposure (secondhand/household smoke in infancy) vs No passive tobacco exposure (non-exposed children) Dental caries in primary dentition (presence) · Children (primary dentition) · Community/population-based samples of children and adolescents (households, schools and clinics) across multiple countries; mostly cross-sectional surveys plus some cohort and case-control studies, Postnatal exposure; primary dentition Postnatal (infancy) tobacco exposure is associated with higher odds of caries in primary dentition (pooled OR 1.72, 95% CI 1.45 to 2.05; 13 studies; high heterogeneity I2=83%); certainty not graded. Association between passive tobacco exposure and caries in children and adolescents. A systematic review and… (2018) All outcomes | OR 1.723 [1.450, 2.049] | 13 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Association between passive tobacco exposure and caries in children and adolescents. A systematic review and meta-analysis. 2018 All outcomes | January 2000 to 2 March 2018 | 29 | Not applicable |
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.