TRA-4 PROBABILISTIC SENSITIVITY ANALYSIS OF COST-EFFECTIVENESS ANALYSIS OF THE BREAST CANCER SCREENING PROGRAMME IN THE BASQUE COUNTRY: A MULTI-COHORT DISCRETE-EVENT SIMULATION MODEL

Monday, June 13, 2016: 10:30
Auditorium (30 Euston Square)

Arantzazu Arrospide1, Montserrat Rue2, Nicolien T. van Ravesteyn, PhD3, Merce Comas4, Myriam Soto-Gordoa1, Garbiñe Sarriugarte5, Luis Carlos Abecia6 and Javier Mar, MD, PhD7, (1)Integrated Health Organization Alto Deba, Osakidetza., Arrasate, Spain, (2)Biomedical Research Institute of Lleida, University of Lleida., Lleida, Spain, (3)Erasmus MC, University Medical Center, Department of Public Health, Rotterdam, Netherlands, (4)Hospital del Mar – IMIM (Hospital del Mar Medical Research Institute)., Barcelona, Spain, (5)Public Health Division of Bizkaia, Basque Government., Bilbao, Spain, (6)University of the Basque Country (UPV-EHU), Vitoria-Gasteiz, Spain, (7)Alto Deba Hospital, Mondragón, Spain

TITLE: Probabilistic sensitivity analysis of cost-effectiveness analysis of the Breast cancer screening programme in the Basque country: A multi-cohort discrete-event simulation model.

Purpose:   The aim of this study was the evaluation of the breast cancer early detection programme in the Basque Country from 1996 to 2011 in terms of probabilistic cost-effectiveness analysis and the probabilistic sensitivity analysis.

Method(s):   A discrete event simulation model was built to reproduce the natural history of breast cancer (BC). We estimated for lifetime follow-up the total cost of BC (screening, diagnosis and treatment), as well as quality-adjusted life years (QALY), for women invited to participate in the evaluated programme during the 15-year period in the actual screening scenario and in a hypothetical unscreened scenario. The probabilistic feature of the model was based on varying the main variables randomly at the same time. Uniform distributions were adopted to vary time between successive invitations and the mean duration of the pre-clinical phase, Beta distributions for sensitivity and specificity of the programme and Dirichlet was the distribution selected for the detection stages classification in screen-detected cancers were the variables we varied in the probabilistic sensitivity analysis. Therefore, we were able to examine the effect of joint uncertainty in these variables through cost-effectiveness plane and calculate the expected value of perfect information.

Result(s):   The actual screening programme involved a mean cost of 1,123 million euros and provided 6.7 million QALYs over the lifetime of the target population, resulting in a gain of 10,110 QALYs for an additional cost of 22.3 million euros, compared with the unscreened scenario. Thus, the incremental cost-effectiveness ratio was 2,209€/QALY. All the model runs in the probabilistic sensitivity analysis resulted in an incremental cost-effectiveness ratio lower than 10,000€/QALY. The expected value of perfect information associated to a 5,000€/QALY threshold was a population opportunity loss of 163,620€. Cancer stage distribution in screen-detected cancers was the variable with greater impact on the final incremental cost-effectiveness ratio.

Conclusion(s):    The BC screening programme in the Basque Country proved to be cost-effective during the evaluated period. No addition research on the main parameters was necessary. These results confirm the epidemiological benefits related to the centralised screening system and support the continuation of the programme.

Figure 1:   Cost-effectiveness plane for the period from 1996 through 2011

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