The Global Prevalence and Severity of Dental Caries among Racially Minoritized Children: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Racially minoritized children often bear a greater burden of dental caries, but the overall magnitude of racial gaps in oral health and their underlying factors are unknown. A systematic review and meta-analysis were conducted to fill these knowledge gaps. We compared racially minoritized (E) children aged 5-11 years (P) with same-age privileged groups (C) to determine the magnitude and correlates of racial inequities in dental caries (O) in observational studies (S). Using the PICOS selection criteria, a targeted search was performed from inception to December 1, 2021, in nine major electronic databases and an online web search for additional grey literature. The primary outcome measures were caries severity, as assessed by mean decayed, missing, and filled teeth (dmft) among children and untreated dental caries prevalence (d > 0%). The meta-analysis used the random-effects model to calculate standardized mean differences (SMD) and 95% confidence intervals (95% CI). Subgroup analysis, tests for heterogeneity (I2, Galbraith plot), leave-one-out sensitivity analysis, cumulative analysis, and publication bias (Egger's test and funnel plots) tests were carried out. The New Castle Ottawa scale was used to assess risk of bias. This review was registered with PROSPERO, CRD42021282771. A total of 75 publications were included in the descriptive analysis. The SMD of dmft score was higher by 2.30 (95% CI: 0.45, 4.15), and the prevalence of untreated dental caries was 23% (95% CI: 16, 31) higher among racially minoritized children, compared to privileged groups. Cumulative analysis showed worsening caries outcomes for racially marginalized children over time and larger inequities in dmft among high-income countries. Our study highlights the high caries burden among minoritized children globally by estimating overall trends and comparing against factors including time, country, and world income. The large magnitude of these inequities, combined with empirical evidence on the oral health impacts of racism and other forms of oppression, reinforce that oral health equity can only be achieved with social and political changes at a global level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".