Question · asked by 1 review
Higher national income (GNI per capita) vs Lower national income (GNI categories: middle <=$15,000 / high $15,001-25,000 / very high >$25,000): Odds of higher caries (DMFT) category
- Population
- 12-year-old children across European countries (national/regional epidemiological surveys, 2011-2022)
- Intervention
- Higher national income (GNI per capita)
- Comparison
- Lower national income (GNI categories: middle <=$15,000 / high $15,001-25,000 / very high >$25,000)
- Outcome
- Odds of higher caries (DMFT) category
Higher national income was strongly associated with lower odds of poor oral health (OR 0.10, 95% CI 0.03 to 0.37; about 90% lower odds); not GRADE-rated.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Higher national income (GNI per capita) vs Lower national income (GNI categories: middle <=$15,000 / high $15,001-25,000 / very high >$25,000) Odds of higher caries (DMFT) category · 12-year-old children across European countries (national/regional epidemiological surveys, 2011-2022) · Cross-sectional (survey data 2011-2022), European countries; ordinal logistic regression of DMFT category on socioeconomic indicators (adjusted), Higher vs lower income (ordinal logistic regression, adjusted) Higher national income was strongly associated with lower odds of poor oral health (OR 0.10, 95% CI 0.03 to 0.37; about 90% lower odds); not GRADE-rated. Caries status in 12-year-old children, geographical location and socioeconomic conditions across European… (2025) All outcomes | OR 0.10 [0.03, 0.37] | 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 |
|---|---|---|---|
| Caries status in 12-year-old children, geographical location and socioeconomic conditions across European countries: A systematic review… 2025 All outcomes | June 2023 | 53 | 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.