title: Modeling the impact of genetic screening technologies on healthcare: theoretical model for asthma in children.
abstract: OBJECTIVE
  This study focuses on the potential impact of genetic screening technologies on healthcare. Genetic screening for asthma in children was chosen as a case study to explore the cost effectiveness of applying early genetic screening to infants, and preventive treatment to the population at risk. Early intervention could prevent progression and facilitate clinical management of the disease. From the elite group of genetic markers that have been associated with asthma-related phenotypes, ADAM33 was the first published candidate gene detected by a positional cloning approach, marking the entry of asthma research into the genomic era. The model was, therefore, initially set for an ex ante analysis of the cost effectiveness of applying the preventive program to an infant population at risk, i.e. infants presenting wheezing episodes during the first year of life, and the ADAM33 ST+7 genetic marker, with the idea of expanding to further markers and their combinations lat a later date.
  
  
  METHODS
  In accordance with the US National Heart, Lung, and Blood Institute, four categories of asthma were considered. A Markov model was constructed, consisting of six mutually exclusive disease states (including healthy and dead states) with a simulation horizon of 100 years and a cycle length of 1 year. We define a scenario where early genetic screening was applied to infants presenting wheezing episodes during the first year of life and a preventive treatment to those children within this group who tested positive for selected ADAM33 polymorphism (ST+7). The cost-effectiveness analysis was performed from the third-party payer and patient perspective after year 6. We applied our model to a hypothetical cohort of 100 European infants.
  
  
  RESULTS
  The number of quality-adjusted life-years (QALYs) gained during the 6 years was 1.483, and the incremental cost-effectiveness ratio per QALY gained was euro 10,100/QALY. A sensitivity analysis was carried out that varied the discount rate and cost of genetic testing, and considered two different transition matrices for the preventive program. Three main conclusions were drawn from the sensitivity analysis. Firstly, if the discount rate for both cost and health outcomes is increased by 2%, the cost effectiveness of the preventive program does not vary significantly. Discounting costs and benefits at 5%, the preventive program appears cost effective (euro 11,100/QALY). Secondly, if the cost of genetic testing is increased to euro 100, the cost effectiveness of the preventive program remains within the limits of cost effectiveness. Thirdly, the cost of genetic screening, together with transition probabilities between health states, will determine the cost effectiveness of applying a preventive program based on genetic information.
  
  
  CONCLUSIONS
  Preventive treatment based on an early genetic screening of those children who present wheezing episodes during the first year of life, with treatment applied to those who test positive for the asthma-associated genetic marker ADAM33 ST+7, is theoretically cost effective. The model is a valuable tool for the ex ante assessment of the cost effectiveness of preventive schemes based on genetic screening. The value of modeling prior to clinical trials lies in informing study design and setting priorities for future research.
authors.affiliation: Institute for Prospective Technological Studies (Joint Research Centre - European Commission), Seville, Spain. Emma.GUTIERREZ-DE-MESA@ec.europa.eu
authors.forename: Emma
authors.initials: E
authors.lastname: Gutiérrez de Mesa
authors.forename: Ignacio
authors.initials: I
authors.lastname: Hidalgo
authors.forename: Panayotis
authors.initials: P
authors.lastname: Christidis
authors.forename: Juan Carlos
authors.initials: JC
authors.lastname: Ciscar
authors.forename: Eva
authors.initials: E
authors.lastname: Vegas
authors.forename: Dolores
authors.initials: D
authors.lastname: Ibarreta
mesh_terms: D051722:ADAM Proteins / Q000235:genetics; D001249:Asthma / Q000175:diagnosis* / Q000235:genetics; D002648:Child; D005820:Genetic Testing / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D019057:Quality-Adjusted Life Years
chemical_list: D051722:ADAM Proteins; C450441:ADAM33 protein, human