Decision thresholds with genetic testing.
Decision thresholds
Diagnostic risk
Genetic risks
ICER
Journal
The European journal of health economics : HEPAC : health economics in prevention and care
ISSN: 1618-7601
Titre abrégé: Eur J Health Econ
Pays: Germany
ID NLM: 101134867
Informations de publication
Date de publication:
Aug 2022
Aug 2022
Historique:
received:
28
07
2021
accepted:
09
11
2021
pubmed:
3
12
2021
medline:
26
7
2022
entrez:
2
12
2021
Statut:
ppublish
Résumé
A genetic test is a test for the presence or absence of a genetic mutation. A positive test outcome that reveals a mutation associated with increased risk for a disease may lead a patient to seek preventive treatment provided that the penetrance (probability of developing the disease given the mutation) is sufficiently high. We derive the test threshold and the test-treatment threshold, which confine the mutation probability interval for the use of the genetic test. Test and treatment costs as well as a low penetrance rate of the mutation narrow this interval. We illustrate the model with parameters of the test for BRCA1 and BRCA2 genes as well as of preventive treatment options for breast cancer.
Identifiants
pubmed: 34855071
doi: 10.1007/s10198-021-01410-0
pii: 10.1007/s10198-021-01410-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1071-1078Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Levy, D., Byfield, S., Comstock, C., Garber, J., Syngal, S., Crown, W., Shields, A.: Underutilization of BRCA1/2 testing to guide breast cancer treatment: black and Hispanic women particularly at risk. Genet. Med. 13(4), 349–355 (2011)
doi: 10.1097/GIM.0b013e3182091ba4
Hoy, M., Peter, R., Richter, A.: Take-up for genetic tests and ambiguity. J. Risk Insur. 48, 111–133 (2014)
National Institute for Health and Care Excellence. Familial breast cancer: classify-cation, care and managing breast cancer and related risks in people with a family history of breast cancer: NICE guidelines. https://www.nice.org.uk/guidance/cg164 (2013)
Owens, D.K.: Risk Assessment, genetic counseling, and genetic testing for BRCA-related cancer: US Preventive Services Task Force Recommendation Statement. J. Am. Med. Assoc. 322(7), 652–665 (2019). https://doi.org/10.1001/jama.2019.10987
doi: 10.1001/jama.2019.10987
Pauker, S.G., Kassirer, J.P.: The threshold approach to clinical decision making. N. Engl. J. Med. 302(20), 1109–1117 (1980)
doi: 10.1056/NEJM198005153022003
Pauker, S.G., Kassirer, J.P.: Therapeutic decision making: a cost benefit analysis. N. Engl. J. Med. 293(5), 229–234 (1975)
doi: 10.1056/NEJM197507312930505
Courbage, C., Rey, B.: Decision thresholds and changes in risk for preventive treatment. Health Econ. 25, 111–124 (2016)
doi: 10.1002/hec.3127
Eeckhoudt, L.: Risk and medical decision making. Studies in Risk and Uncertainty. Boston, Kluwer Academic Publishers (2002)
doi: 10.1007/978-1-4615-0991-2
Felder, S., Mayrhofer, Th.: Risk preferences: consequences for test and treatment thresholds and optimal cutoffs. Med. Decis. Mak. 34, 33–41 (2014)
doi: 10.1177/0272989X13493969
Berger, L., Bleichrodt, H., Eeckhoudt, L.: Treatment decisions under ambiguity. J. Health Econ. 32, 559–569 (2013)
doi: 10.1016/j.jhealeco.2013.02.001
Nocetti, D.C.: Ambiguity and the value of information revisited. Geneva Risk Insur. Rev. 43(1), 25–38 (2018)
doi: 10.1057/s10713-018-0025-z
Felder, S.: The treatment decision under uncertainty: the effects of health, wealth and the probability of death. J. Health Econ. (2020). https://doi.org/10.1016/j.jhealeco.2019.102253
doi: 10.1016/j.jhealeco.2019.102253
pubmed: 31901575
Attema, A.E., Brouwer, W.B.F., l’Haridon, O.: Prospect theory in the health domain: a quantitative assessment. J. Health Econ. 32(6), 1057–1065 (2013)
doi: 10.1016/j.jhealeco.2013.08.006
Bleichrodt, H.: Probability weighting in choice under risk: an empirical test. J. Risk Uncertain. 23(2), 185–198 (2001)
doi: 10.1023/A:1011136203223
Starmer, C.: Developments in non-expected utility theory: the hunt for a descriptive theory of choice under risk. J. Econ. Lit. 38(2), 332–382 (2000)
doi: 10.1257/jel.38.2.332
Wakker, P., Stiggelbout, A.: Explaining distortions in utility elicitation through the Rank-dependent Model for risky choices. Med. Decis. Mak. 15(2), 180–186 (1995)
doi: 10.1177/0272989X9501500212
Wakker, P.P.: Lessons learned by (from?) an economist working in medical decision making. Med. Decis. Mak. 28(5), 690–698 (2008)
doi: 10.1177/0272989X08323916
Golman, R., Hagmann, D., Loewenstein, G.: Information avoidance. J. Econ. Lit. 55(1), 96–135 (2017)
doi: 10.1257/jel.20151245
Savage, L.J.: The Foundations of Statistics. Wiley, New York (1954)
Pinto-Prades, J.L., Rodriguez-Miguez, E.: The lead time tradeoff: the case of health states better than dead. Med. Decis. Mak. 35(3), 305–315 (2015)
doi: 10.1177/0272989X14541952
Barigozzi, F., Henriet, D.: Genetic information: comparing alternative regulatory approaches when prevention matters. J. Public Econ. Theory 13(1), 23–46 (2011)
doi: 10.1111/j.1467-9779.2009.01491.x
Peter, R., Richter, A., Thistle, P.: Endogenous information, adverse selection, and prevention: Implications for genetic testing policy. J. Health Econ. 55, 95–107 (2017)
doi: 10.1016/j.jhealeco.2017.06.010
Crainich, D.: Self-insurance with genetic testing tools. J. Risk Insur. 84(1), 73–94 (2017)
doi: 10.1111/jori.12085
Bardey, D., De Donder, P.: Genetic testing with primary prevention and moral hazard. J. Health Econ. 32(5), 768–779 (2013)
doi: 10.1016/j.jhealeco.2013.04.008
Hoy, M., Witt, J.: Welfare effects of banning genetic information in the life insurance market: the case of BRCA1/2 genes. J. Risk Insur. 74(3), 523–546 (2007)
doi: 10.1111/j.1539-6975.2007.00223.x
Anderson, K., Jacobson, J.S., Heitjan, D.F., Graff Zivin, J., Hershman, D., Neugut, A.I., Grann, V.R.: Cost-effectiveness of preventive strategies for women with a BRCA1or a BRCA2 Mutation. Ann. Intern. Med. 144, 397–406 (2006)
doi: 10.7326/0003-4819-144-6-200603210-00006
Guzauskas, G.F., Garbett, S., Zhou, Z., Spencer, S.J., Smith, H.S., Hao, J., et al.: Cost-effectiveness of population-wide genomic screening for hereditary breast and ovarian cancer in the United States. JAMA Netw. Open 3(10), e2022874 (2020). https://doi.org/10.1001/jamanetworkopen.2020.22874
doi: 10.1001/jamanetworkopen.2020.22874
pubmed: 33119106
pmcid: 7596578
Grann, V.R., Jacobson, J.S., Sundararajan, V., Albert, S.M., Troxel, A.B., Neugut, A.I.: The quality of life associated with prophylactic treatments for women with BRCA1/2 mutations. Cancer J. Sci. Am. 5, 283–292 (1999)
pubmed: 10526669
Toland, A.E., et al.: Clinical testing of BRCA1 and BRCA2: a worldwide snapshot of technological practices. Genomic Med. (2018). https://doi.org/10.1038/s41525-018-0046-7
doi: 10.1038/s41525-018-0046-7
Eccleston, A., Bentley, A., Dyer, M., Strydom, A., Vereecken, W., George, A., Rahman, N.: A cost-effectiveness evaluation of germline BRCA1 and BRCA2 testing in UK Women with ovarian cancer. Value Health 20(4), 567–576 (2017)
doi: 10.1016/j.jval.2017.01.004
Lawrence, W.F., Peshkin, B.N., Liang, W., Isaacs, C., Lerman, C., Mandelblatt, J.S.: Cost of genetic counseling and testing for BRCA1 and BRCA2 breast cancer susceptibility mutations. Cancer Epidemiol. Biomark. Prev. 10, 475–481 (2001)
Bleuler, E.: Das autistisch-undisziplinierte Denken in der Medizin und seine Überwindung [The Autistic-Undisciplined Thinking in Medicine and its Overcoming]. Springer, Berlin (1919)
doi: 10.1007/978-3-662-42333-2