Cost-Effectiveness Analyses on Various Models of The Red Light, Purple Light Self-Regulation Intervention for Young Children
Early childhood interventions can improve self-regulation, but there are few economic evaluations of such interventions. This study analyzed the cost-effectiveness of an early childhood self-regulation intervention ( Red Light Purple Light! ; RLPL), comparing three different models of implementation...
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Veröffentlicht in: | Frontiers in psychology 2021-10, Vol.12, p.711578-711578 |
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Sprache: | eng |
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Zusammenfassung: | Early childhood interventions can improve self-regulation, but there are few economic evaluations of such interventions. This study analyzed the cost-effectiveness of an early childhood self-regulation intervention (
Red Light Purple Light!
; RLPL), comparing three different models of implementation across stages of intervention development: (Model 1) trained research assistants (RAs; graduate students) directly delivered the RLPL intervention to children; (Model 2) RAs trained trainers (e.g., program coaches), who then trained teachers to implement RLPL with children (e.g., train-the-trainer); and (Model 3) program faculty trained teachers to deliver the RLPL intervention to children. We implemented a cost-effectiveness analysis by calculating the incremental cost-effectiveness ratio. We also conducted a series of sensitivity analyses to adjust for parameter uncertainty. Our base-case analysis suggests that Model 2 was the most cost-effective strategy, in that a cost of $23 per child was associated with a one-unit increase of effect size on self-regulation scores. The “train-the-trainer” model remained the optimal strategy across scenarios in our sensitivity analysis. This study fills an important gap in cost-effectiveness analyses on early childhood self-regulation interventions. Our process and results can serve as a model for future cost-effectiveness analyses of early childhood intervention programs and may ultimately inform decisions related to intervention adoption that optimize resource allocation and improve program design. |
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ISSN: | 1664-1078 1664-1078 |
DOI: | 10.3389/fpsyg.2021.711578 |