SUPPORTING COVERAGE DECISION-MAKING THROUGH A COLLABORATIVE EPIDEMIOLOGICAL CALCULATOR: A REAL-WORLD CASE STUDY IN HR+ HER2- METASTATIC BREAST CANCER IN ARGENTINA
Author(s)
Francisco Pinto1, LUIS ANGEL DI GIUSEPPE, Sr., PhD2, Gabriela Buela, .2, NELDA GIORGETTI, PharmD3, Marcelo Manzi, .4, Marina Fiorella Tabares, MASc5.
1Health Economist, AstraZeneca, Santiago, Chile, 2Hospital Italiano de Buenos Aires, Buenos Aires, Argentina, 3AstraZeneca, BUENOS AIRES, Argentina, 4AstraZeneca, Buenos Aires, Argentina, 5ASTRA ZENECA ARGENTINA, Ciudad Autonoma de Buenos Aires, Argentina.
1Health Economist, AstraZeneca, Santiago, Chile, 2Hospital Italiano de Buenos Aires, Buenos Aires, Argentina, 3AstraZeneca, BUENOS AIRES, Argentina, 4AstraZeneca, Buenos Aires, Argentina, 5ASTRA ZENECA ARGENTINA, Ciudad Autonoma de Buenos Aires, Argentina.
OBJECTIVES: coverage negotiations in Argentina face significant uncertainty due to the lack of local epidemiological data, limited patient registries, and a highly fragmented healthcare system. These challenges make it difficult to estimate the eligible patient population from the payer perspective. The objective of this real-world experience was to develop a tool to support coverage negotiations by improving the predictability of healthcare expenditure.
METHODS: an epidemiological calculator was co-developed as a tool to support coverage negotiation. The tool integrated clinical and epidemiological assumptions from local sources also alignment between the payer and manufacturer to estimate the number of eligible patients and then support the negotiations.
RESULTS: based on data from GLOBOCAN (2020), an annual breast cancer incidence of 22,024 cases in Argentina was used. Epidemiological inputs were informed by available local evidence and refined through iterative discussions between the manufacturer and payer. Key assumptions included HER2 testing (92%), HR+/HER2− tumors (65%), and stage distribution at diagnosis of 82% for stages I-IIIa, 7% for stages IIIb-IIIc, and 11% for stage IV disease. Progression rates to first-line metastatic disease were estimated at 5%, 10%, and 100% for these stage groups, respectively. A 60% progression rate was assumed for patients transitioning from second-line (2L) to third-line (3L) metastatic treatment. Based on these assumptions and access alignment / agreement between the manufacturers and payer, the estimated eligible population for the coverage negotiation was 142 patients in Argentina, comprising 67 patients in (2L setting and 75 patients in 3L).
CONCLUSIONS: The epidemiological calculator facilitated alignment of assumptions between negotiating parties and enabled a transparent and consensual estimate of eligible patients, supporting more informed negotiations. By improving predictability of the expenditure, sustainable coverage decisions while helping to ensure patient access. This approach could serve as practical and easy example to tackle patient uncertainties during reimbursement negotiations.
METHODS: an epidemiological calculator was co-developed as a tool to support coverage negotiation. The tool integrated clinical and epidemiological assumptions from local sources also alignment between the payer and manufacturer to estimate the number of eligible patients and then support the negotiations.
RESULTS: based on data from GLOBOCAN (2020), an annual breast cancer incidence of 22,024 cases in Argentina was used. Epidemiological inputs were informed by available local evidence and refined through iterative discussions between the manufacturer and payer. Key assumptions included HER2 testing (92%), HR+/HER2− tumors (65%), and stage distribution at diagnosis of 82% for stages I-IIIa, 7% for stages IIIb-IIIc, and 11% for stage IV disease. Progression rates to first-line metastatic disease were estimated at 5%, 10%, and 100% for these stage groups, respectively. A 60% progression rate was assumed for patients transitioning from second-line (2L) to third-line (3L) metastatic treatment. Based on these assumptions and access alignment / agreement between the manufacturers and payer, the estimated eligible population for the coverage negotiation was 142 patients in Argentina, comprising 67 patients in (2L setting and 75 patients in 3L).
CONCLUSIONS: The epidemiological calculator facilitated alignment of assumptions between negotiating parties and enabled a transparent and consensual estimate of eligible patients, supporting more informed negotiations. By improving predictability of the expenditure, sustainable coverage decisions while helping to ensure patient access. This approach could serve as practical and easy example to tackle patient uncertainties during reimbursement negotiations.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
EPH24
Topic
Epidemiology & Public Health, Health Technology Assessment, Real World Data & Information Systems
Disease
Oncology