Evidence map›Paper›PMID 42014551›Full record

SynthesisPediatric research2026

A scoping review of decision analytic model-based economic evaluations in neonatal intensive care.

Brian C King, Suzanne Hay, John A F Zupancic

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Pediatric research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Brian C KingDivision of Neonatology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA. bking6@bidmc.harvard.edu.ORCID http://orcid.org/0009-0000-6959-8969
Suzanne HayDivision of Neonatology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
John A F ZupancicDivision of Neonatology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFew 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.

methodsA 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.

resultsOf 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%).

conclusionsThe 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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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.