title: A decision-tree approach to cost comparison of newborn screening strategies for cystic fibrosis.
abstract: OBJECTIVE
  Because cystic fibrosis can be difficult to diagnose and treat early, newborn screening programs have rapidly developed nationwide but methods vary widely. We therefore investigated the costs and consequences or specific outcomes of the 2 most commonly used methods.
  
  
  METHODS
  With available data on screening and follow-up, we used a simulation approach with decision trees to compare immunoreactive trypsinogen (IRT) screening followed by a second IRT test against an IRT/DNA analysis. By using a Monte Carlo simulation program, variation in the model parameters for counts at various nodes of the decision trees, as well as for costs, are included and applied to fictional cohorts of 100 000 newborns. The outcome measures included the numbers of newborns given a diagnosis of cystic fibrosis and costs of screening strategy at each branch and cost per newborn.
  
  
  RESULTS
  Simulations revealed a substantial number of potential missed diagnoses for the IRT/IRT system versus IRT/DNA. Although the IRT/IRT strategy with commonly used cutoff values offers an average overall cost savings of $2.30 per newborn, a breakdown of costs by societal segments demonstrated higher out-of-pocket costs for families. Two potential system failures causing delayed diagnoses were identified relating to the screening protocols and the follow-up system.
  
  
  CONCLUSIONS
  The IRT/IRT screening algorithm reduces the costs to laboratories and insurance companies but has more system failures. IRT/DNA offers other advantages, including fewer delayed diagnoses and lower out-of-pocket costs to families.
authors.affiliation: Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI 53706, USA.
authors.forename: Janelle
authors.initials: J
authors.lastname: Wells
authors.forename: Marjorie
authors.initials: M
authors.lastname: Rosenberg
authors.forename: Gary
authors.initials: G
authors.lastname: Hoffman
authors.forename: Michael
authors.initials: M
authors.lastname: Anstead
authors.forename: Philip M
authors.initials: PM
authors.lastname: Farrell
mesh_terms: D000465:Algorithms; D003131:Combined Modality Therapy; D003362:Cost-Benefit Analysis; D003550:Cystic Fibrosis / Q000175:diagnosis* / Q000191:economics* / Q000235:genetics; D019005:Cystic Fibrosis Transmembrane Conductance Regulator / Q000235:genetics; D004252:DNA Mutational Analysis / Q000191:economics; D003663:Decision Trees; D003951:Diagnostic Errors; D005260:Female; D005500:Follow-Up Studies; D006580:Genetic Carrier Screening; D005817:Genetic Counseling / Q000191:economics; D017048:Health Care Costs / Q000706:statistics & numerical data; D005102:Health Expenditures / Q000706:statistics & numerical data; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D009010:Monte Carlo Method; D015997:Neonatal Screening / Q000191:economics* / Q000379:methods*; D015397:Program Evaluation; D014362:Trypsinogen / Q000097:blood
chemical_list: C505032:CFTR protein, human; D019005:Cystic Fibrosis Transmembrane Conductance Regulator; D014362:Trypsinogen