Question · asked by 1 review

Dental erosion assessment vs None (descriptive prevalence review; subgroup analyses by urban/rural status, country and year): Dental erosion prevalence

Population
Five-year-old preschool children in Sharjah
Intervention
Dental erosion assessment
Comparison
None (descriptive prevalence review; subgroup analyses by urban/rural status, country and year)
Outcome
Dental erosion prevalence

One study found a high prevalence of dental erosion (58.8%) among 5-year-olds in Sharjah, associated with non-Emirati nationality, caries experience and sugary/carbonated beverage consumption (narrative synthesis; not GRADE-rated).

What each review found

Current versions only
FindingEffectStudiesGRADE certainty
Dental erosion assessment vs None (descriptive prevalence review)
Dental erosion prevalence · Preschool children (5-year-olds) in the United Arab Emirates (Sharjah) · Sharjah, UAE; kindergarten
One study found a high prevalence of dental erosion (58.8%) among 5-year-olds in Sharjah, associated with non-Emirati nationality, caries experience and sugary/carbonated beverage consumption (narrative synthesis; not GRADE-rated).
Oral Health of Children and Adolescents in the United Arab Emirates: A Systematic Review of the Past Decade. (2021) All outcomes
Among 5-year-olds in Sharjah (n=403), Gopinath reported dental erosion in 58.8% and dissolution of enamel in 55.0% (exposed dentin 3.7%). Predictors of erosion included Arab non-Emirati nationality (OR 0.27, 95% CI…1 study · 403 participantsGRADE not reported

Findings are shown side by side, never pooled across reviews.

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Oral Health of Children and Adolescents in the United Arab Emirates: A Systematic Review of the Past Decade.
2021 All outcomes
May 4, 202142Not applicable

Studies behind the findings

The primary studies the full-text reviews tie to this outcome. Reviews with headline outcomes only do not list them here.

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.