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ORIGINAL ARTICLE   

Giornale Italiano di Dermatologia e Venereologia 2020 December;155(6):764-71

DOI: 10.23736/S0392-0488.18.06106-0

Copyright © 2018 EDIZIONI MINERVA MEDICA

lingua: Inglese

Estimation of direct costs of melanoma in the Veneto Region: a budget assessment and cost-consequence analysis

Alessandra BUJA 1, Gino SARTOR 1, Manuela SCIONI 2 , Giovanni GIRARDI 3, Antonella VECCHIATO 4, Mario BOLZAN 2, Vincenzo REBBA 5, Vanna CHIARION SILENI 4, Angelo Claudio PALOZZO 4, Maria MONTESCO 4, Paolo DEL FIORE 4, Vincenzo BALDO 1, Carlo Riccardo ROSSI 4

1 Department of Cardiac, Thoracic and Vascular Sciences, University of Padua, Padua, Italy; 2 Department of Statistical Sciences, University of Padua, Padua, Italy; 3 School of Specialization in Hygiene, Preventive Medicine and Public Health, University of Padua, Padua, Italy; 4 Istituto Oncologico Veneto (IOV - IRCCS), Padua, Italy; 5 Marco Fanno Department of Economics and Management, University of Padua, Padua, Italy



BACKGROUND: While many evidence-based pathways have been introduced to drive quality improvements in cancer care, most of these do not include evidence about their affordability. The main aim of this study was to provide an estimation of the overall budget to cover all the needs of melanoma patients in Veneto Region, managed according to the clinical pathway defined by the Rete Oncologica Veneta. A second objective is to conduct a cost-consequence analysis, comparing two different treatments.
METHODS: A very detailed whole-disease model was developed describing the patient’s pathway from diagnosis through the first year of follow-up. Each procedure involved in the model was associated with a likelihood measure and a cost. The model can be used to estimate the expected direct costs associated with melanoma.
RESULTS: We can observe that 0 and I stage, despite accounting for a huge percentage of new melanoma cases are characterized by a small percentage of the total costs. Stage III can be considered as the most expensive stage accounting for 54% of the total costs with a 12% of patients. Finally, the stage IV patients, although very few accounts for almost the 7% of the total costs. Regarding the cost-consequence analysis, it was estimated that the therapies introduced in 2016 led to an approximately 14% increase in the total costs.
CONCLUSIONS: Modeling a clinical pathway with a high level of detail enables to identify the main sources of spending. The consequent analysis can thus help policymakers to plan the future resources allocation.


KEY WORDS: Melanoma; Costs and cost analysis; Incidence

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