Cost effectiveness of newborn screening for cystic fibrosis: a simulation study.
BACKGROUND
Early detection of cystic fibrosis (CF) by newborn screening (NBS) reduces the rate of avoidable complications. NBS protocols vary by jurisdiction and the cost effectiveness of these different protocols is debated.


OBJECTIVE
To compare the cost effectiveness of various CF NBS options.


METHODS
A Markov model was built to simulate the cost effectiveness of various CF-NBS options for a hypothetical CF-NBS program over a 5-year time horizon assuming its integration into an existing universal NBS program. NBS simulated options were based on a combination of tests between the two commonly used immunoreactive trypsinogen (IRT) cutoffs (96th percentile and 99.5th percentile) as first tier tests, and, as a second tier test, either a second IRT, pancreatic-associated protein (PAP) or CFTR mutation panels. CFTR mutation panels were also considered as an eventual third tier test. Data input parameters used were retrieved from a thorough literature search. Outcomes considered were the direct costs borne by the Quebec public health care system and the number of cases of CF detected through each strategy, including the absence of screening option.


RESULTS
IRT-PAP with an IRT cutoff at the 96th percentile is the most favorable option with a ratio of CAD$28,432 per CF case detected. The next most favorable alternative is the IRT1-IRT2 option with an IRT1 cutoff at the 96th percentile. The no-screening option is dominated by all NBS screening protocols considered. Results were robust in sensitivity analyses.


CONCLUSIONS
This study suggests that NBS for cystic fibrosis is a cost-effective strategy compared to the absence of NBS. The IRT-PAP newborn screening algorithm with an IRT cutoff at the 96th percentile is the most cost effective NBS approach for Quebec.
Département de médecine sociale et préventive, Faculté de Médecine, Université Laval, Québec, Québec, Canada.
L
L
Nshimyumukiza
Département de génie électrique, Faculté des Sciences et de génie, Université Laval, Québec, Québec, Canada.
A
A
Bois
Département de pédiatrie, Centre hospitalier universitaire(CHU) de Québec, Québec, Québec, Canada.
P
P
Daigneault
Department of Medicine, Faculty of Medicine, McGill University, Montréal, Quebec, Canada.
L
L
Lands
Département de pédiatrie, Centre hospitalier universitaire Ste Justine, Montréal, Québec, Canada.
A-M
AM
Laberge
Département de génie électrique, Faculté des Sciences et de génie, Université Laval, Québec, Québec, Canada.
D
D
Fournier
Département de médecine sociale et préventive, Faculté de Médecine, Université Laval, Québec, Québec, Canada.
J
J
Duplantie
Département de biologie moléculaire, biochimie médicale et pathologie, Université Laval, Québec, Québec, Canada.
Y
Y
Giguère
Département de pédiatrie, Centre hospitalier universitaire(CHU) de Québec, Québec, Québec, Canada.
J
J
Gekas
Département de génie électrique, Faculté des Sciences et de génie, Université Laval, Québec, Québec, Canada.
C
C
Gagné
Département de biologie moléculaire, biochimie médicale et pathologie, Université Laval, Québec, Québec, Canada.
F
F
Rousseau
Département de médecine sociale et préventive, Faculté de Médecine, Université Laval, Québec, Québec, Canada.
D
D
Reinharz
D000465:Algorithms; D000951:Antigens, Neoplasm / Q000378:metabolism; D015415:Biomarkers / Q000378:metabolism; D014408:Biomarkers, Tumor / Q000378:metabolism; D002675:Child, Preschool; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D003550:Cystic Fibrosis / Q000175:diagnosis* / Q000191:economics* / Q000378:metabolism; D019005:Cystic Fibrosis Transmembrane Conductance Regulator / Q000235:genetics*; D005820:Genetic Testing / Q000191:economics / Q000379:methods; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D037181:Lectins, C-Type / Q000378:metabolism; D008390:Markov Chains; D015997:Neonatal Screening / Q000191:economics* / Q000379:methods*; D000073718:Pancreatitis-Associated Proteins; D012680:Sensitivity and Specificity; D014362:Trypsinogen / Q000378:metabolism
D000951:Antigens, Neoplasm; D015415:Biomarkers; D014408:Biomarkers, Tumor; C505032:CFTR protein, human; D037181:Lectins, C-Type; D000073718:Pancreatitis-Associated Proteins; C000612141:REG3A protein, human; D019005:Cystic Fibrosis Transmembrane Conductance Regulator; D014362:Trypsinogen
CFTR; Cost effectiveness; Cystic fibrosis; Immune-reactive trypsinogen (IRT); Newborn screening; Pancreatic-associated protein (PAP); Simulation