1. High Resolution Italian studies Pamela Minicozzi Descriptive Studies and Health Planning Unit, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan

2. Rationale There are variations in survival for breast, prostate, colorectal cancer and melanoma and lymphoma, with higher survival in the north and lower in the south of Italy Reasons could be: treatments, early diagnosis or both Prognosis for lung cancer is uniformly low, but survival for patients with early stage cancers that can be operated is remarkably high, thus it is important to investigate patterns of care 2 Studies on breast, colorectal, prostate and lung cancers; lymphoma and melanoma have been carried out, analyses are in course

3. Tumour sites and no. of cases Breast: 500 / registry (in situ and malignant – 9 CRs-) Colorectum: 500 / registry (in situ and malignant – 9 CRs-) Lung: 300 / registry (- 5 CRs-) Melanoma: 100 / registry (in situ and malignant – 9 CRs-) Lymphoma: 100 / registry (excluding Chronic Lymphatic Leukaemia, Multiple Myeloma, Lymphocytic Lymphoma small cell – 4 CRs-) Randomly extracted from the Italian Association of Cancer Registry (AIRTum) database using a randomized procedure balanced for each participating registry and year of diagnosis Random selection included death certificate only and autopsy cases Period of diagnosis: 2003-2005 End of follow-up: December, 2008 3

4. Tumour sites and no. of cases Prostate: 300 / registry and period of diagnosis (– 8 CRs-) Randomly extracted from the EUROCARE database using a randomized procedure Random selection did not include death certificate only and autopsy cases Periods of diagnosis: 1996-1999 vs. 2005-2007 End of follow-up: December, 2010 4

5. Aims To explain the reasons of geographic variations in cancer survival across Italy To collect data regarding morphology and clinical-pathologic information To describe and compare the adhesion to selected standard of care across Italy (and, for prostate cancer, over time) For prostate cancer: to use mixed cure models to disentangle survival from lead-time bias and other artificial effects (overdiagnosis). 5