Question · asked by 1 review
Caregiver help with child tooth brushing vs Not applicable (single-group prevalence): Caregiver help with tooth brushing prevalence (among children with caries)
- Population
- Children with dental caries (deciduous and/or permanent dentition) in low- and middle-income countries
- Intervention
- Caregiver help with child tooth brushing
- Comparison
- Not applicable (single-group prevalence)
- Outcome
- Caregiver help with tooth brushing prevalence (among children with caries)
About 54% of children with caries in LMICs received caregiver help with tooth brushing (pooled prevalence 0.541, 95% CI 0.266 to 0.803; 3 studies; not GRADE-rated).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Caregiver help with child tooth brushing vs None (descriptive single-group pooled prevalence) Caregiver help with tooth brushing prevalence (among children with caries) · Children with dental caries (deciduous and/or permanent dentition) in low- and middle-income countries · Low- and middle-income countries (Brazil, Nigeria, Cambodia, Tanzania, Mexico, Ethiopia) About 54% of children with caries in LMICs received caregiver help with tooth brushing (pooled prevalence 0.541, 95% CI 0.266 to 0.803; 3 studies; not GRADE-rated). Individual, Family, and Socioeconomic Contributors to Dental Caries in Children from Low- and Middle-Income… (2022) All outcomes | Prevalence 0.5408 [0.2663, 0.8025] | 3 studies · 830 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Individual, Family, and Socioeconomic Contributors to Dental Caries in Children from Low- and Middle-Income Countries. 2022 All outcomes | March 2022 (all databases searched 11 March 2022; MEDLINE 1946 to 11 March 2022; CINAHL 1937 to 11 March 2022) | 11 | 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.