Residence in low-contamination area (approx. 5-15 Ci/km2) vs Residence in non-contaminated (non-exposed) area (<1 Ci/km2): Dental caries
- Population
- Children aged 5-7 years (early school-age)
- Intervention
- Residence in low-contamination area (approx. 5-15 Ci/km2)
- Comparison
- Residence in non-contaminated (non-exposed) area (<1 Ci/km2)
- Outcome
- Dental caries
In early school-age children (5-7 y) from a low-contamination area, one study reported higher DMFT/dmft in exposed than non-exposed children; the estimate was not pooled (ungraded).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Residence in low-contamination area (approx. 5-15 Ci/km2) vs Residence in non-contaminated (non-exposed) area (<1 Ci/km2) Dental caries experience (DMFT/dmft index) · Children aged 5-7 years (early school-age) · Chernobyl fallout areas, Eastern Europe (Ukraine, Belarus, Russian Federation), low contamination In early school-age children (5-7 y) from a low-contamination area, one study reported higher DMFT/dmft in exposed than non-exposed children; the estimate was not pooled (ungraded). A systematic review of the caries prevalence among children living in Chernobyl fallout countries. (2019) All outcomes | WMD 5.22 [4.72, 5.72] | 1 study · 36 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| A systematic review of the caries prevalence among children living in Chernobyl fallout countries. 2019 All outcomes | February 2017 to February 2018 | 9 | Not applicable |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Residence in low-contamination area (approx. 5-15 Ci/km2) vs Residence in non-contaminated (non-exposed) area (<1 Ci/km2) · Children aged 12-15 years (adolescents) · 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.