Question · asked by 1 review
HAART (highly active antiretroviral therapy, >=3 drugs) vs HIV-positive not receiving HAART (standard ART or HAART-naive): Caries in primary teeth: dmft (3 years)
- Population
- Children and adolescents (<18 years) living with HIV (CLWH)
- Intervention
- HAART (highly active antiretroviral therapy, >=3 drugs)
- Comparison
- HIV-positive not receiving HAART (standard ART or HAART-naive)
- Outcome
- Caries in primary teeth: dmft (3 years)
Among HIV+ children, HAART versus no HAART showed no significant difference in the dmft index in case-control studies (MD 0.66, 95% CI -0.26 to 1.58; ungraded).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
HAART (highly active antiretroviral therapy, >=3 drugs) vs HIV-positive not receiving HAART (standard ART or HAART-naive) Dental caries severity (dmft index, primary dentition) · Children and adolescents under 18 years with HIV · Not specified (case-control), Dental examination of children/adolescents with HIV in clinic and community settings; low- and middle-income countries (Brazil, India, Nigeria, Cambodia, Uganda, West Africa), Case-control studies; dmft index Among HIV+ children, HAART versus no HAART showed no significant difference in the dmft index in case-control studies (MD 0.66, 95% CI -0.26 to 1.58; ungraded). Association between antiretroviral therapy and dental caries in children and adolescents with HIV: a… (2025) All outcomes | MD 0.66 [-0.26, 1.58] | 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 |
|---|---|---|---|
| Association between antiretroviral therapy and dental caries in children and adolescents with HIV: a systematic review and meta-analysis. 2025 All outcomes | Inception to October 2024 (LILACS expanded search updated February 2025) | 17 | 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.