Inflammatory bowel disease (IBD) vs Healthy controls without IBD: DMFT/DMFS index (dental caries experience)
- Population
- Patients with IBD (Crohn's disease and ulcerative colitis) and controls without IBD
- Intervention
- Inflammatory bowel disease (IBD)
- Comparison
- Healthy controls without IBD
- Outcome
- DMFT/DMFS index (dental caries experience)
Most included studies reported higher dental caries indices (DMF-t/DMF-s) in IBD patients (especially ulcerative colitis), but findings were inconsistent across studies and were not meta-analyzed or GRADE-rated.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Inflammatory bowel disease (IBD) vs Healthy controls without IBD Dental caries (DMF-t/DMF-s and related caries indices) · Patients with IBD (Crohn's disease and ulcerative colitis) and controls without IBD · Hospital/clinic-based case-control and cross-sectional studies in 11 countries; 15 studies total (1748 participants: 1104 Crohn's disease, 534 ulcerative colitis, 110 IBD-form unspecified) Most included studies reported higher dental caries indices (DMF-t/DMF-s) in IBD patients (especially ulcerative colitis), but findings were inconsistent across studies and were not meta-analyzed or GRADE-rated. Oral Health Status in Patients with Inflammatory Bowel Diseases: A Systematic Review. (2021) All outcomes | Not meta-analyzed (heterogeneity of caries indices). Abstract: "in most studies, patients with IBD were characterized by higher values of caries indices." Table 3 examples (IBD/subtype vs control): Zhang 2020 DMF-t… | 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 |
|---|---|---|---|
| Oral Health Status in Patients with Inflammatory Bowel Diseases: A Systematic Review. 2021 All outcomes | Up to 1 October 2021 | 16 | 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.