Question · asked by 1 review
Higher drinking-water fluoride exposure vs Null / lowest drinking-water fluoride exposure (reference): Hip Fracture
- Population
- general population
- Intervention
- Higher drinking-water fluoride exposure
- Comparison
- Null / lowest drinking-water fluoride exposure (reference)
- Outcome
- Hip Fracture
Overall hip-fracture risk showed little association with drinking-water fluoride and a nearly flat dose-response curve (narrow CI); no numeric RR reported in text; ungraded.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Higher drinking-water fluoride exposure vs Null / lowest drinking-water fluoride exposure (reference) Risk of hip fractures · General population (both sexes, all ages) · Long-term / lifetime fluoride exposure, General population; 37 observational studies (1944-2024), predominantly North America (USA, Canada) and Northern Europe (Finland, Sweden), plus UK, Poland, Italy, Germany, China, Taiwan, South Korea, Ethiopia; exposure mainly community drinking-water fluoride, Overall dose-response (0 to highest exposure) Overall hip-fracture risk showed little association with drinking-water fluoride and a nearly flat dose-response curve (narrow CI); no numeric RR reported in text; ungraded. The association of fluoride exposure with bone density and fracture risk: a dose-response meta-analysis. (2025) All outcomes | Curve indicated little association and was nearly flat, with a comparatively narrow 95% confidence band (no numeric RR reported in text) | Not reported | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| The association of fluoride exposure with bone density and fracture risk: a dose-response meta-analysis. 2025 All outcomes | Inception to August 26, 2025 | 74 | 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.