Skip to main content
SpringerLink
Log in

Modeling the Impact of Genetic Screening Technologies on Healthcare

Theoretical Model for Asthma in Children

  • Short Communication
  • Published:
Molecular Diagnosis & Therapy Aims and scope Submit manuscript

Abstract

Background and 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 €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 (€11 100/QALY). Secondly, if the cost of genetic testing is increased to €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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Table I
Table II
Table III
Table IV
Table V
Table VI

References

  1. Arenas-Guzman R, Tosti A, Hay R, et al. Pharmacoeconomics: an aid to better decision-making. J Eur Acad Dermatol Venereol 2005; 19Suppl. 1: 34–9

    Article  PubMed  Google Scholar 

  2. Collins FS, Green ED, Guttmacher AE, et al. A vision for the future of genomics research. Nature, 2003; 422(6934): 835–47

    Article  PubMed  CAS  Google Scholar 

  3. Eurostat, Health statistics. Key data on health 2002. Data 1970–2001, 2004 [online]. Available from URL: http://ec.europa.eu/health/index_en.htm [Accessed 2007 Oct 15]

  4. European Respiratory Society. European lung white book. Lausanne: European Respiratory Society Publication Department, 2003

    Google Scholar 

  5. European Commission. The social situation in the European Union in 2003. Luxembourg: European Commission Publications, 2004

    Google Scholar 

  6. van den Akker-van Marie ME, Bruil J, Detmar SB. Evaluation of cost of disease: assessing the burden to society of asthma in children in the European Union [report 21]. Delft: TOO, 2004

    Google Scholar 

  7. The International Study of Asthma and Allergies in Childhood (ISAAC) Steering Committee. Worldwide variation in prevalence of symptoms of asthma, allergic rhinoconjunctivitis and atopic eczema. Lancet 1998; 351: 1225–32

    Article  Google Scholar 

  8. European Community Respiratory Health Survey (ECRHS). Variations in the prevalence of respiratory symptoms, self reported asthma attacks, and use of asthma medication in the European Community. Respiratory Health Survey. Eur Respir J 1996; 9: 687–95

    Article  Google Scholar 

  9. Aubier M, Neukirch F, Annesi-Maesano I. Epidemiology of asthma and allergies: the prevalence of allergies increases worldwide, and asthma has reached his highest-ever prevalence in Europe: why? Bull Acad Natl Med 2005; 189(7): 1419–34; discussion 1434

    PubMed  Google Scholar 

  10. NIH. Global strategy for asthma management and prevention. Bethesda (MD): National Heart, Lung, and Blood Institute, 2002: 176

    Google Scholar 

  11. Gerth van Wijk R. Allergy: a global problem: quality of life. Allergy 2002; 57(12): 1097–110

    Article  PubMed  CAS  Google Scholar 

  12. European Environment Agency and the Joint Research Centre (JRC) of the European Commission. Environment and health. EEA Report No 10/2005. Luxembourg: European Commission Publications, 2005

    Google Scholar 

  13. Sennhauser FH, Braun-Fahrlander C, Wildhaber JH. The burden of asthma in children: a European perspective. Paediatr Respir Rev 2005; 6(1): 2–7

    Article  PubMed  Google Scholar 

  14. Blumenthal J, Blumenthal M. Genetics of asthma. Med Clin North Am 2002; 86(50): 937–50

    Article  PubMed  CAS  Google Scholar 

  15. King ME, Mannino DM, Holguin F. Risk factors for asthma incidence: a review of recent prospective evidence. Panminerva Med 2004; 46(2): 97–110

    PubMed  CAS  Google Scholar 

  16. Melen E, Wickman M, Nordvall SL, et al. Influence of early and current environmental exposure factors on sensitization and outcome of asthma in pre-school children. Allergy 2001; 56(7): 646–52

    Article  PubMed  CAS  Google Scholar 

  17. Brussee JE, Smit HA, van Strien RT, et al. Allergen exposure in infancy and the development of sensitization, wheeze, and asthma at 4 years. J Allergy Clin Immunol 2005; 115(5): 946–52

    Article  PubMed  Google Scholar 

  18. Lewis TC, Robins TG, Dvonch JT, et al. Air pollution-associated changes in lung function among asthmatic children in Detroit. Environ Health Perspect 2005; 113(8): 1068–75

    Article  PubMed  Google Scholar 

  19. Willers SM, Brunekreef B, Oldenwening M, et al. Gas cooking, kitchen ventilation, and asthma, allergic symptoms and sensitization in young children: the PIAMA study. Allergy 2006; 61(5): 563–8

    Article  PubMed  CAS  Google Scholar 

  20. Carroll W. Asthma genetics: pitfalls and triumphs. Paediatr Respir Rev 2005; 6(1): 68–74

    Article  PubMed  Google Scholar 

  21. Holgate ST, Yang Y, Haitchi HM, et al. The genetics of asthma: ADAM33 as an example of a susceptibility gene. Proc Am Thorac Soc 2006; 3(5): 440–3

    Article  PubMed  CAS  Google Scholar 

  22. Ober C, Hoffjan S. Asthma genetics 2006: the long and winding road to gene discovery. Genes Immun 2006; 7(2): 95–100

    Article  PubMed  CAS  Google Scholar 

  23. Martinez FD, Wright AL, Taussig LM, et al. Asthma and wheezing in the first six years of life. The Group Health Medical Associates. N Engl J Med 1995; 332(3): 133–8

    Article  PubMed  CAS  Google Scholar 

  24. Rhodes HL, Sporik R, Thomas P, et al. Early life risk factors for adult asthma: a birth cohort study of subjects at risk. J Allergy Clin Immunol 2001; 108(5): 720–5

    Article  PubMed  CAS  Google Scholar 

  25. Holgate ST. A need for circulating biomarkers of severe persistent asthma and its treatment. Clin Exp Allergy 2006; 36(11): 1355–6

    Article  PubMed  CAS  Google Scholar 

  26. Jongepier H, Boezen HM, Dijkastra A, et al. Polymorphisms of the ADAM33 gene are associated with accelerated lung function decline in asthma. Clin Exp Allergy 2004; 34(5): 757–60

    Article  PubMed  CAS  Google Scholar 

  27. Noguchi E, Ohtsuki Y, Tokunaga K, et al. ADAM33 polymorphisms are associated with asthma susceptibility in a Japanese population. Clin Exp Allergy 2006; 36(5): 602–8

    Article  PubMed  CAS  Google Scholar 

  28. Kedda MA, Duffy DL, Bradley B, et al. ADAM33 haplotypes are associated with asthma in a large Australian population. Eur J Hum Genet 2006; 14(9): 1027–36

    Article  PubMed  CAS  Google Scholar 

  29. van Diemen CC, Postma DS, Vonk JM, et al. A disintegrin and metalloprotease 33 polymorphisms and lung function decline in the general population. Am J Respir Crit Care Med 2005; 172(3): 329–33

    Article  PubMed  Google Scholar 

  30. Simpson A, Maniatis N, Jury F, et al. Polymorphisms in a disintegrin and metalloprotease 33 (ADAM33) predict impaired early-life lung function. Am J Respir Crit Care Med 2005; 172(1): 55–60

    Article  PubMed  Google Scholar 

  31. Liu AH. Consider the child: how early should we treat? J Allergy Clin Immunol 2004; 113(1 Suppl.): S19–24

    Article  PubMed  Google Scholar 

  32. Larsen GL, Kang JK, Guilbert T, et al. Assessing respiratory function in young children: developmental considerations. J Allergy Clin Immunol 2005; 115(4): 657–66

    Article  PubMed  Google Scholar 

  33. Van Eerdewegh P, Little RD, Duguis J, et al. Association of the ADAM33 gene with asthma and bronchial hyperresponsiveness. Nature 2002; 418(6896): 426–30

    Article  PubMed  Google Scholar 

  34. Hirota T, Hasegawa K, Obara K, et al. Association between ADAM33 polymorphisms and adult asthma in the Japanese population. Clin Exp Allergy 2006; 36(7): 884–91

    Article  PubMed  CAS  Google Scholar 

  35. Howard TD, Postma DS, Jongepier H, et al. Association of a disintegrin and metalloprotease 33 (ADAM33) gene with asthma in ethnically diverse populations. J Allergy Clin Immunol 2003; 112(4): 717–22

    Article  PubMed  CAS  Google Scholar 

  36. Lee JH, Park HS, Park SW, et al. ADAM33 polymorphism: association with bronchial hyper-responsiveness in Korean asthmatics. Clin Exp Allergy 2004; 34(6): 860–5

    Article  PubMed  CAS  Google Scholar 

  37. Werner M, Herbon N, Gohlke H, et al. Asthma is associated with single-nucleotide polymorphisms in ADAM33. Clin Exp Allergy 2004; 34(1): 26–31

    Article  PubMed  CAS  Google Scholar 

  38. Raby BA, Silverman EK, Kwiatkowski AS, et al. ADAM33 polymorphisms and phenotype associations in childhood asthma. J Allergy Clin Immunol 2004; 113(6): 1071–8

    Article  PubMed  CAS  Google Scholar 

  39. Schedel M, Depner M, Schoen C, et al. The role of polymorphisms in ADAM33, a disintegrin and metalloprotease 33, in childhood asthma and lung function in two German populations. Respir Res 2006; 7: 91

    Article  PubMed  Google Scholar 

  40. Wang P, Liu QJ, Li JS, et al. Lack of association between ADAM33 gene and asthma in a Chinese population. Int J Immunogenet 2006; 33(4): 303–6

    Article  PubMed  CAS  Google Scholar 

  41. Blakey J, Halapi E, Bjornsdottir US, et al. Contribution of ADAM33 polimorphisms to the population risk of asthma. Thorax 2005; 60(4): 274–6

    Article  PubMed  CAS  Google Scholar 

  42. Munakata M, Harada Y, Ishida T, et al. Molecular-based haplotype analysis of the beta 2-adrenergic receptor gene (ADRB2) in Japanese asthmatic and non-asthmatic subjects. Allergol Int 2006; 55(2): 191–8

    Article  PubMed  CAS  Google Scholar 

  43. Janssens AC, Aulchenko YS, Elefante S, et al. Predictive testing for complex diseases using multiple genes: fact or fiction? Genet Med 2006; 8(7): 395–400

    Article  PubMed  Google Scholar 

  44. Price MJ, Briggs AH. Development of an economic model to assess the cost effectiveness of asthma management strategies. Pharmacoeconomics 2002; 20(3): 183–94

    Article  PubMed  Google Scholar 

  45. Briggs A, Sculpher M. An introduction to Markov modelling for economic evaluation. Pharmacoeconomics 1998; 13(4): 397–409

    Article  PubMed  CAS  Google Scholar 

  46. O’Hagan A, Stevens JW. The probability of cost-effectiveness. BMC Med Res Methodol 2002; 2: 5

    Article  PubMed  Google Scholar 

  47. Salomon JA, Weinstein MC, Goldie SJ. Taking account of future technology in cost effectiveness analysis. BMJ 2004; 329(7468): 733–6

    Article  PubMed  Google Scholar 

  48. Dong H, Buxton M. Early assessment of the likely cost-effectiveness of a new technology: a Markov model with probabilistic sensitivity analysis of computer-assisted total knee replacement. Int J Technol Assess Health Care 2006; 22(2): 191–202

    Article  PubMed  Google Scholar 

  49. Combescure C, Chanez P, Saint Pierre P, et al. Assessment of variations in control of asthma over time. Eur Respir J 2003; 22(2): 298–304

    Article  PubMed  CAS  Google Scholar 

  50. Evans C, Tavakoli M, Crawford B. Use of quality adjusted life years and life years gained as benchmarks in economic evaluations: a critical appraisal. Health Care Manag Sci 2004; 7(1): 43–9

    Article  PubMed  Google Scholar 

  51. Cooper NJ, Sutton AJ, Abrams KR, et al. Comprehensive decision analytical modelling in economic evaluation: a Bayesian approach. Health Econ 2004; 13(3): 203–26

    Article  PubMed  Google Scholar 

  52. Buxton MJ. How much are health-care systems prepared to pay to produce a QALY? Eur J Health Econ 2005; 6(4): 285–7

    Article  PubMed  Google Scholar 

  53. Coughlin MT, Angus DC. Economic evaluation of new therapies in critical illness. Crit Care Med 2003; 31(1 Suppl.): S7–16

    Article  PubMed  Google Scholar 

  54. Drummond M, Sculpher MJ, Torrance GW, et al. Methods for the economic evaluation of health care programmes. 3rd ed. Oxford: Oxford University Press, 2005

    Google Scholar 

  55. Gold M, Siegel J, Russell L, et al. Cost-effectiveness in health and medicine. New York: Oxford University Press, 1996

    Google Scholar 

  56. Europe in figures: EuroStat yearbook 2005 [online]. Available from URL: http://epp.eurostat.ec.europa.eu [Accessed 2007 Oct 1]

  57. Godard P, Chanez P, Siraudin L, et al. Costs of asthma are correlated with severity: a 1-yr prospective study. Eur Respir J 2002; 19(1): 61–7

    Article  PubMed  CAS  Google Scholar 

  58. Cisternas MG, Blanc PD, Yen IH, et al. A comprehensive study of the direct and indirect costs of adult asthma. J Allergy Clin Immunol 2003; 111(6): 1212–8

    Article  PubMed  Google Scholar 

  59. Schramm B, Ehlken B, Smala A, et al. Cost of illness of atopic asthma and seasonal allergic rhinitis in Germany: 1-yr retrospective study. Eur Respir J 2003; 21(1): 116–22

    Article  PubMed  CAS  Google Scholar 

  60. Lasserson T, Cates CK, Jones AB, et al. Fluticasone versus HFA-beclomethasone dipropionate for chronic asthma in adults and children. Cochrane Database Syst Rev 2006; (2): CD005309

  61. Zika E, Gurwitz D, Ibarreta D. Pharmacogenetics and pharmacogenomics: state of the art and potential socio-economic impacts in the EU. EU Report. Luxembourg: European Commission, 2006

    Google Scholar 

  62. Priest VG, Begg EJ, Gardiner SJ, et al. Pharmacoeconomic analyses of azathioprine, methotrexate and prospective pharmacogenetic testing for the management of inflammatory bowel disease. Pharmacoeconomic 2006; 24(8): 767–81

    Article  Google Scholar 

  63. Oh KT, Anis AH, Bae SC. Pharmacoeconomic analysis of thiopurine methyltransferase polymorphism screening by polymerase chain reaction for treatment with azathioprine in Korea. Rheumatology 2004; 43(2): 1156–63

    Google Scholar 

  64. Marra CA, Esdaile JM, Anis AH, et al. Practical pharmacogenetics: the cost-effectiveness of screening for thiopurine S-methyltransferase polymorphisms in patients with rheumatological conditions treated with azathioprine. J Rheumatol 2002; 29(12): 2507–12

    PubMed  Google Scholar 

  65. Winter J, Walker A, Shapiro D, et al. Cost-effectiveness of thiopurine methyltrans-ferase genotype screening in patients about to commence azathioprine therapy for treatment of inflammatory bowel disease. Aliment Pharmacol Ther 2004; 25(9): 1069–77

    Article  Google Scholar 

  66. Chen S. The cytochrome P4502D6 (CYP2D6) enzyme polymorphism: screening costs and influence on clinical outcomes in psychiatry. Clin Pharmacol Ther 1996; 60(5): 522–34

    Article  PubMed  CAS  Google Scholar 

  67. Steimer W, Potter JM. Pharmacogenetic screening and therapeutic drugs. Clin Chim Acta 2002; 315: 137–55

    Article  PubMed  CAS  Google Scholar 

  68. Sin DD, Golmohammadi K, Jacobs P. Cost-effectiveness of inhaled corticosteroids for chronic obstructive pulmonary disease according to disease severity. Am J Med 2004; 116(5): 325–31

    Article  PubMed  CAS  Google Scholar 

  69. Sullivan SD, Buxton M, Andersson LF, et al. Cost-effectiveness analysis of early intervention with budesonide in mild persistent asthma. J Allergy Clin Immunol 2003; 112(6): 1229–36

    Article  PubMed  Google Scholar 

  70. Spencer M, Briggs AH, Grossman RF, et al. Development of an economic model to assess the cost effectiveness of treatment interventions for chronic obstructive pulmonary disease. Pharmacoeconomics 2005; 23(6): 619–37

    Article  PubMed  CAS  Google Scholar 

  71. Bos JM, Postma MJ, Annemans L. Discounting health effects in pharmacoeconomic evaluations: current controversies. Pharmacoeconomics 2005; 23(7): 639–49

    Article  PubMed  CAS  Google Scholar 

  72. Nootheti S, Bielory L. Risk of cataracts and glaucoma with inhaled steroid use in children. Compr Ophthalmol Update 2006; 7(1): 31–9

    PubMed  Google Scholar 

  73. Bell CM, et al. Bias in published cost effectiveness studies: systematic review. BMJ 2006; 332(7543): 699–703

    Article  PubMed  Google Scholar 

  74. Brennan A, Akehurst R. Modelling in health economic evaluation: what is its place? What is its value? Pharmacoeconomics 2000; 17(5): 445–59

    Article  PubMed  CAS  Google Scholar 

  75. Gold BD. Asthma and gastroesophageal reflux disease in children: exploring the relationship. J Pediatr 2005; 146 (3 Suppl. 1): S13–20

    Article  PubMed  Google Scholar 

  76. Tavadia SM, Mydlarski PR, Reis MD, et al. Screening for azathioprine toxicity: A pharmacoeconomic analysis based on a target case. J Am Acad Dematol 2000; 42(4): 628–32

    CAS  Google Scholar 

  77. Janssens AC, van Duijn CM. Towards predictive genetic testing of complex diseases. Eur J Epidemiol 2006; 21(12): 869–70

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The views expressed in this study do not necessarily reflect those of the European Commission. This project has been exclusively funded by the Institute for Prospective Technological Studies (IPTS). The team is indebted to Carmen Ruiz Leon from the Documentation Service (IPTS, Spain) and to the project’s advisory board: John S. Evans (Kuwait Public Health Project, Harvard Center for Risk Analysis USA), Erika von Mutius (University Children’s Hospital, Munich, Germany), Marco Martuzzi (WHO, European Centre for Environment and Health, Italy), Michael Spencer (GlaxoSmithKline, UK), and Ana Nieto Nuez (European Commission, DG RTD, Belgium) for their input and insights.

The authors have no conflicts of interest that are directly relevant to the content of this study.

Author information

Authors and Affiliations

  1. Agriculture and Life Science in the Economy (AGRILIFE) Unit, Research Centre, Institute for Prospective Technological Studies, European Commission, Edificio Expo, C/Inca Garcilaso, s/n, E-41092, Sevilla, Spain

    Emma Gutiérrez de Mesa, Ignacio Hidalgo, Panayotis Christidis, Juan Carlos Ciscar & Dolores Ibarreta

  2. Paediatric Primary Care, Servicio Madrileño de Salud — Madrid Health Services, Madrid, Spain

    Eva Vegas

Authors
  1. Emma Gutiérrez de Mesa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ignacio Hidalgo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Panayotis Christidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juan Carlos Ciscar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eva Vegas
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dolores Ibarreta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emma Gutiérrez de Mesa.

Rights and permissions

Reprints and permissions

About this article

Cite this article

de Mesa, E.G., Hidalgo, I., Christidis, P. et al. Modeling the Impact of Genetic Screening Technologies on Healthcare. Mol Diag Ther 11, 313–323 (2007). https://doi.org/10.1007/BF03256252

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03256252

Keywords

Search

Navigation