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Population-based screening of newborns: Findings from the newborn screening expansion study (part two)
Kee Chan1, Amy Brower1, Marc S Williams2
1American College of Medical Genetics and Genomics, Bethesda, MD, United States.
Insights
Genomic advancements will expand newborn screening (NBS), but challenges like inconsistent panels and limited pilot data hinder progress. This study proposes using modeling to overcome these hurdles and improve NBS expansion.
Area of Science:
- Genomics
- Public Health
- Systems Science
Background:
- Genomic technologies are rapidly advancing newborn screening (NBS).
- Four key factors impede NBS expansion: screening panel variability, short pilot durations, evolving definitions of NBS, and capacity constraints in the RUSP process.
- These factors complicate the integration of new genomic tests into newborn healthcare.
Purpose of the Study:
- To identify and propose solutions for challenges hindering newborn screening (NBS) expansion.
- To explore the application of modeling to address specific NBS expansion barriers.
- To review existing models from diverse disciplines applicable to NBS.
Main Methods:
- Developed use cases for each identified factor delaying NBS expansion.
- Conducted a literature review of models from systems science, management, AI, and machine learning.
- Assessed the applicability of identified models to address NBS challenges.
Main Results:
- At least one suitable model was identified for each of the four major challenges in NBS expansion.
- Modeling approaches can effectively evaluate changes and improvements in NBS processes.
- Diverse modeling techniques offer potential solutions for variability, pilot limitations, definition expansion, and capacity constraints.
Conclusions:
- Modeling provides a viable strategy to overcome the identified challenges in expanding newborn screening (NBS).
- The application of systems science, AI, and machine learning models can enhance NBS efficiency and effectiveness.
- Adopting modeling approaches is crucial for the successful and sustainable expansion of NBS programs.
Abstract:
Rapid advances in genomic technologies to screen, diagnose, and treat newborns will significantly increase the number of conditions in newborn screening (NBS). We previously identified four factors that delay and/or complicate NBS expansion: 1) variability in screening panels persists; 2) the short duration of pilots limits information about interventions and health outcomes; 3) recent recommended uniform screening panel (RUSP) additions are expanding the definition of NBS; and 4) the RUSP nomination and evidence review process has capacity constraints. In this paper, we developed a use case for each factor and suggested how model(s) could be used to evaluate changes and improvements. The literature on models was reviewed from a range of disciplines including system sciences, management, artificial intelligence, and machine learning. The results from our analysis highlighted that there is at least one model which could be applied to each of the four factors that has delayed and/or complicate NBS expansion. In conclusion, our paper supports the use of modeling to address the four challenges in the expansion of NBS.
