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Published on: April 11, 2018
Facilitators and Barriers to Increasing Equity in Cystic Fibrosis Newborn Screening Algorithms
Kellyn Madden1,2, Rebecca Mueller2, Camille Brown2
1Master of Science in Genetic Counseling Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Newborn screening for cystic fibrosis (CF) faces challenges in reducing health disparities for minority populations due to outdated CFTR panels. States encounter funding and logistical barriers when updating screening algorithms to improve equity.
Area of Science:
- Public Health
- Genetics
- Medical Screening
Background:
- Universal newborn screening (NBS) for cystic fibrosis (CF) was implemented in the US in 2010.
- Disparities in CF outcomes persist among racial and ethnic minority groups compared to European ancestry.
- Current CFTR panels for NBS often miss variants prevalent in minoritized populations, leading to false negatives.
Purpose of the Study:
- To investigate how states evaluate and update their CF NBS algorithms.
- To identify facilitators, barriers, and motivations influencing these updates.
- To understand the process of implementing more equitable CF NBS panels.
Main Methods:
- Semi-structured interviews were conducted with professionals from four diverse states and one national consultant.
- Interviews were transcribed verbatim.
- Inductive thematic analysis was used to analyze the interview data.
Main Results:
- Five themes emerged regarding the evaluation and updating of CF NBS algorithms.
- Effective communication with CF clinical centers and strong support for CF care were identified as facilitators.
- Barriers included limited data on false negatives and challenges integrating next-generation sequencing, despite awareness of disparities.
Conclusions:
- States face significant barriers, including funding and logistics, when attempting to update CF NBS panels for greater equity.
- Facilitators for updating algorithms involve strong clinical partnerships and institutional support.
- Understanding these experiences is crucial for developing more equitable CF NBS algorithms.
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