Question · asked by 1 review

Primary dental health aide (PDHA) service-delivery models (PDHA program; PDHA + intensive staffing; PDHA + robust prevention) vs Baseline pre-intervention service-delivery scenario: Projected mean DMFT (children below 5 years)

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 mean DMFT (children below 5 years)

A simulation projected mean DMFT in children under 5 would fall from 7.4 to 4.9 under the PDHA model (certainty not graded).

What each review found

Current versions only
FindingEffectStudiesGRADE 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 mean DMFT (children below 5 years) · Alaska Native children (below age 18) · 20-year projection, Alaska Native communities (simulation of the Alaska Dental Health Aide program)
A simulation projected mean DMFT in children under 5 would fall from 7.4 to 4.9 under the PDHA model (certainty not graded).
Reducing Alaska Native paediatric oral health disparities: a systematic review of oral health interventions… (2013) All outcomes
Mean DMFT, children <5 y 4.91 studyGRADE not reported

Findings are shown side by side, never pooled across reviews.

The reviews

ReviewSearched up toIncluded studiesAMSTAR 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, 201211Critically 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.

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.