Fluoride varnish applied before 4 years of age vs Non-fluoride (placebo) varnish: Fluorosis (percentage prevalence in permanent teeth)
- Population
- Children exposed to topical fluoride when under 6 years old; fluorosis assessed in permanent teeth at 6 to 18 years
- Intervention
- Fluoride varnish applied before 4 years of age
- Comparison
- Non-fluoride (placebo) varnish
- Outcome
- Fluorosis (percentage prevalence in permanent teeth)
Fluoride varnish applied before 4 years of age (biannual) showed no clear difference in fluorosis (percentage prevalence in permanent teeth) compared with placebo varnish (low certainty).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Fluoride varnish applied before 4 years of age (biannual) vs Placebo varnish Fluorosis (percentage prevalence in permanent teeth) · Children exposed to topical fluoride when under 6 years old; fluorosis assessed in permanent teeth at 6 to 18 years · Fluorosis assessed when most permanent teeth of interest had erupted (6 to 18 years) Fluoride varnish applied before 4 years of age (biannual) showed no clear difference in fluorosis (percentage prevalence in permanent teeth) compared with placebo varnish (low certainty). Topical fluoride as a cause of dental fluorosis in children (2024) · Cochrane All outcomes | RR 0.77 [0.45, 1.31] | 1 study · 123 participants | Low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Topical fluoride as a cause of dental fluorosis in children 2024 · Cochrane All outcomes | 2022-07 | Not listed | Not assessed |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Fluoride varnish applied before 4 years of age vs No fluoride varnish application · Children exposed to topical fluoride when under 6 years old; fluorosis assessed in… · 1 review
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.