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Personalized Mammography Screening and Screening Adherence: A Simulation and Economic Evaluation

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JournalValue in Health
DateAccepted/In press - 13 Dec 2017
DateE-pub ahead of print - 21 Feb 2018
DatePublished (current) - Jul 2018
Issue number7
Volume21
Pages (from-to)799-808
Early online date21/02/18
Original languageEnglish

Abstract

Objective: Personalized breast cancer screening has so far been economically evaluated under the assumption of full screening adherence. This is the first study to evaluate the effects of nonadherence on the evaluation and selection of personalized screening strategies. Methods: Different adherence scenarios were established on the basis of findings from the literature. A Markov microsimulation model was adapted to evaluate the effects of these adherence scenarios on three different personalized strategies. Results: First, three adherence scenarios describing the relationship between risk and adherence were identified: 1) a positive association between risk and screening adherence, 2) a negative association, or 3) a curvilinear relationship. Second, these three adherence scenarios were evaluated in three personalized strategies. Our results show that it is more the absolute adherence rate than the nature of the risk-adherence relationship that is important to determine which strategy is the most cost-effective. Furthermore, probabilistic sensitivity analyses showed that there are risk-stratified screening strategies that are more cost-effective than routine screening if the willingness-to-pay threshold for screening is below US $60,000. Conclusions: Our results show that "nonadherence" affects the relative performance of screening strategies. Thus, it is necessary to include the true adherence level to evaluate personalized screening strategies and to select the best strategy.

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© 2018, International Society for Pharmacoeconomics andOutcomes Research (ISPOR). Published by Elsevier Inc. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

    Research areas

  • Adherence, Breast cancer screening, Decision analysis, Economic evaluation, Mammography, Markov model, Personalized medicine

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