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Background: Cancer survivors-people living with and beyond cancer-are a growing population with different health needs depending on prognosis and time since diagnosis. Despite being increasingly necessary, complete information on cancer prevalence is not systematically available in all European countries. We aimed to fill this gap by analysing population-based cancer registry data from the EUROCARE-6 study., Methods: In this population-based study, using incidence and follow-up data up to Jan 1, 2013, from 61 cancer registries, complete and limited-duration prevalence by cancer type, sex, and age were estimated for 29 European countries and the 27 countries in the EU (EU27; represented by 22 member states that contributed registry data) using the completeness index method. We focused on 32 malignant cancers defined according to the third edition of the International Classification of Diseases for Oncology, and only the first primary tumour was considered when estimating the prevalence. Prevalence measures are expressed in terms of absolute number of prevalent cases, crude prevalence proportion (reported as percentage or cases per 100 000 resident people), and age-standardised prevalence proportion based on the European Standard Population 2013. We made projections of cancer prevalence proportions up to Jan 1, 2020, using linear regression., Findings: In 2020, 23 711 thousand (95% CI 23 565-23 857) people (5·0% of the population) were estimated to be alive after a cancer diagnosis in Europe, and 22 347 thousand (95% CI 22 210-22 483) in EU27. Cancer survivors were more frequently female (12 818 thousand [95% CI 12 720-12 917]) than male (10 892 thousand [10 785-11 000]). The five leading tumours in female survivors were breast cancer, colorectal cancer, corpus uterine cancer, skin melanoma, and thyroid cancer (crude prevalence proportion from 2270 [95%CI 2248-2292] per 100 000 to 301 [297-305] per 100 000). Prostate cancer, colorectal cancer, urinary bladder cancer, skin melanoma, and kidney cancer were the most common tumours in male survivors (from 1714 [95% CI 1686-1741] per 100 000 to 255 [249-260] per 100 000). The differences in prevalence between countries were large (from 2 to 10 times depending on cancer type), in line with the demographic structure, incidence, and survival patterns. Between 2010 and 2020, the number of prevalent cases increased by 3·5% per year (41% overall), partly due to an ageing population. In 2020, 14 850 thousand (95% CI 14 681-15 018) people were estimated to be alive more than 5 years after diagnosis and 9099 thousand (8909-9288) people were estimated to be alive more than 10 years after diagnosis, representing an increasing proportion of the cancer survivor population., Interpretation: Our findings are useful at the country level in Europe to support evidence-based policies to improve the quality of life, care, and rehabilitation of patients with cancer throughout the disease pathway. Future work includes estimating time to cure by stage at diagnosis in prevalent cases., Funding: European Commission., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Environmental factors have an impact on the effectiveness of treatments for chronic lymphocytic leukemia: vulnerability, organization of the supply of care and proximity of the patient to health professionals. The disparity of care was assessed; vulnerability by the European index of deprivation, the provision of care by values of localized potential accessibility to general practitioners, nurses and pharmacists and hospital supply by the density of hematologists and time access to the center. The data, extracted from the public databases for each grouped island for statistical information, were cross-referenced to apply a principal component analysis and group them into 4 clusters. Cluster 1 has an average EDI, easy access to city professionals, remote access to the referral center, and a good density of hematologists. Cluster 2 has low EDI, satisfactory access to professionals, satisfactory proximity to the referral center and average density of hematologists. Cluster 3 has good EDI, access to professionals is difficult, access to the reference center is long, and the density of hematologists remains average. Cluster 4 has a good EDI, with access to professionals easier than in Cluster 3 but still difficult. The access time to the reference center is better than that of cluster 3 but remains elongated, the density of hematologists remaining average. Mapping is a tool for hospitals and institutions to evaluate care and compare it to other territories., (Copyright © 2018 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.)