Primary dental health aide (PDHA) service-delivery models (PDHA program; PDHA + intensive staffing; PDHA + robust prevention) vs Baseline pre-intervention service-delivery scenario: Projected dental care utilisation
- Population
- Alaska Native children (below age 18)
- Intervention
- Primary dental health aide (PDHA) service-delivery models (PDHA program; PDHA + intensive staffing; PDHA + robust prevention)
- Comparison
- Baseline pre-intervention service-delivery scenario
- Outcome
- Projected dental care utilisation
A simulation projected that the PDHA program would raise dental care utilisation from 75% to 90% (certainty not graded).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Primary dental health aide (PDHA) service-delivery models (PDHA program; PDHA + intensive staffing; PDHA + robust prevention) vs Baseline pre-intervention service-delivery scenario Projected dental care utilisation · Alaska Native children (below age 18) · 20-year projection, Alaska Native communities (simulation of the Alaska Dental Health Aide program) A simulation projected that the PDHA program would raise dental care utilisation from 75% to 90% (certainty not graded). Reducing Alaska Native paediatric oral health disparities: a systematic review of oral health interventions… (2013) All outcomes | Projected dental care utilisation (%) 90 | 1 study | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Reducing Alaska Native paediatric oral health disparities: a systematic review of oral health interventions and a case study on multilevel… 2013 All outcomes | October 5, 2012 | 11 | Critically Low / Critically Low |
Studies behind the findings
The primary studies the full-text reviews tie to this outcome. Reviews with headline outcomes only do not list them here.
- Fiset 2005 · included by 1 of these review
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.