Policy issues for expanding newborn screening programs: the cystic fibrosis newborn screening experience in the

Benjamin S Wilfond1, Sarah E Gollust

  • 1Social and Behavioral Research Branch, National Human Genome Research Institute and Department of Clinical Bioethics, Magnuson ClinicalCenter/NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA.

Insights

Newborn screening for cystic fibrosis (CF) varied widely across 11 states in 2002, with diverse mandates and testing strategies. This diversity offers valuable lessons for future CF screening program development.

Area of Science:

  • Public Health
  • Genetics
  • Neonatal Care

Background:

  • Cystic Fibrosis (CF) newborn screening programs aim to identify affected infants early for timely intervention.
  • Implementation strategies and screening approaches varied significantly among states in 2002.

Purpose of the Study:

  • To describe the different screening approaches and implementation strategies used in 12 cystic fibrosis newborn screening programs across 11 states in 2002.

Main Methods:

  • Telephone interviews were conducted with representatives from the 11 state programs in the spring of 2003.
  • Screening approaches were categorized into state-mandated, state-wide offering, hospital-based, and informed consent models.

Main Results:

  • Screening was state-mandated in seven states and routinely offered to most infants in nine.
  • Testing protocols varied, with some using immunoreactive trypsinogen alone and others incorporating a second-tier DNA test with distinct strategies.
  • Informed consent was explicitly documented in only two states, and results were routinely provided in person at a CF center in only two programs.

Conclusions:

  • The observed diversity in screening approaches provides valuable insights for future policy decisions regarding cystic fibrosis newborn screening.
  • Collecting and disseminating data on the clinical and psychosocial impact of these varied programs is crucial.
  • Future programs can be optimized for a better benefit/risk balance by learning from the successes and challenges of existing models.
Abstract

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