Question · asked by 1 review
Populations with a lower proportion of university-educated people (<33.78% with university level) vs Not applicable: Dental caries
- Population
- Adult participants in 25 meta-regression studies
- Intervention
- Populations with a lower proportion of university-educated people (<33.78% with university level)
- Comparison
- Not applicable
- Outcome
- Dental caries
The Model 1 adjusted meta-regression coefficient for lower university education was -4.132903.
What each review found
Current versions onlyNo studies found 0 · 0%GRADE not reported 2 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Lower university education (<33.78% of the population) vs Not applicable Mean DMFT · Adult participants in 25 meta-regression studies · Country-level study populations, Model 1 adjusted The Model 1 adjusted meta-regression coefficient for lower university education was -4.132903. Socioeconomic Factors and Caries in People between 19 and 60 Years of Age: An Update of a Systematic Review… (2018) All outcomes | Meta-regression coefficient -4.132903 | 6 studies | GRADE not reported |
Lower university education (<33.78% of the population) vs Not applicable Mean DMFT · Adult participants in 25 meta-regression studies · Country-level study populations, Unadjusted model The unadjusted meta-regression coefficient for lower university education was -0.3066788. Socioeconomic Factors and Caries in People between 19 and 60 Years of Age: An Update of a Systematic Review… (2018) All outcomes | Meta-regression coefficient -0.3066788 | 18 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Socioeconomic Factors and Caries in People between 19 and 60 Years of Age: An Update of a Systematic Review and Meta-Analysis of… 2018 All outcomes | March 2017 | 61 | 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.