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A scoping review of decision analytic model-based economic evaluations in neonatal intensive care
Brian C King1, Suzanne Hay2, John A F Zupancic2
1Division of Neonatology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA. bking6@bidmc.harvard.edu.
Background:
Few prospective economic evaluations alongside clinical trials are being performed in neonatal intensive care. Decision analytical modeling is an alternate approach to economic evaluations that uses published data to model the impact of an intervention on costs and effects. There is little knowledge about how these models have been applied in neonatal intensive care to date.
Methods:
A scoping review was conducted to systematically assess the frequency and quality of decision analytical modeling for economic evaluations in neonatal intensive care from 1992 to 2024. A structured quality appraisal was performed to identify key areas of improvement.
Results:
Of the 81 included studies, 50% were published in the last six years. The majority of cost-effectiveness evaluations reported positive results below a desirable cost-effectiveness threshold in abstracts, with minimal uncertainty. Among all included studies, the average quality score was 65% (range 39% to 98%).
Conclusions:
The use of decision-analytical modeling to conduct economic evaluations in neonatal intensive care has grown rapidly over the past decade. Most studies report positive results in abstracts, without acknowledging uncertainty. Key areas identified for improvement include expanding the use of sensitivity analyses to explore structural and methodologic uncertainties, and formal evaluation of internal and external validity.
Impact:
This scoping review examines the current state of decision analytic modeling for economic evaluations in neonatal intensive care, assessing both content and quality. Highlights include: Significant growth in publications over the last decade using these techniques to conduct cost-effectiveness analyses The majority of published models report cost-effectiveness below a desirable threshold with minimal uncertainty in their abstract Structured quality assessment which identifies significant gaps in currently published models, identifying areas for improvement.
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