DARWIN EU® – Prevalence of selected cancers

Study type

• Study topic:
  Disease /health condition
• Study topic, other:
  Cancers
• Study type:
  Non-interventional study
• Scope of the study:
  Disease epidemiology
• Data collection methods:
  Secondary use of data

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

No information provided.

Population studied

• Short description of the study population:
  All individuals in the database during the study period will be eligible for inclusion in the study. No prior history requirement will be applied in the primary analysis.
• Age groups:
  • In utero
  • Paediatric Population (< 18 years)

    • Neonate

      • Preterm newborn infants (0 – 27 days)
      • Term newborn infants (0 – 27 days)
    • Infants and toddlers (28 days – 23 months)
    • Children (2 to < 12 years)
    • Adolescents (12 to < 18 years)
  • Adult and elderly population (≥18 years)

    • Adults (18 to < 65 years)

      • Adults (18 to < 46 years)
      • Adults (46 to < 65 years)
    • Elderly (≥ 65 years)

      • Adults (65 to < 75 years)
      • Adults (75 to < 85 years)
      • Adults (85 years and over)

Study design details

• Study design:
  A cohort study will be conducted using routinely collected health data from 5 databases from 5 countries across Europe. This will consist of a descriptive epidemiological study providing point prevalence estimates of selected cancers in each participating database.
• Main study objective:
  The primary aim of this study is to estimate the point prevalence of rare blood cancers, pancreatic cancer, and soft tissue sarcoma across selected databases in Europe. ; The secondary aim of this study is to compare the prevalence of selected cancers included in the primary aim of the study, using denominator data available within the data source versus denominator data obtained from external sources.
• Setting:
  This study was conducted using routinely collected data from data sources in the DARWIN EU® network. The study was informed by 5 data sources in the DARWIN EU® network of data partners from 5 European countries, of which 4 European Union (EU) member states
• Outcomes:
  • Rare blood cancers: ; o Acute lymphocytic leukaemia (ALL) ; o Acute myeloid leukaemia (AML) ; o Chronic lymphocytic leukaemia (CLL) ; o Diffuse large B-Cell Lymphoma (DLBCL) ; o Follicular lymphoma ; o Multiple myeloma ; • Pancreatic cancer ; • Soft tissue sarcoma
• Summary results:
  CONCLUSION ; Across the included data sources, ALL and AML exhibited the lowest prevalence, with a 5-year partial point prevalence of less than 1.5 per 10,000. For other cancer types, estimated prevalence in the included data sources was below 5 per 10,000. Estimated prevalence in the included data sources also varied substantially across age groups, with older age groups obtaining the highest estimates across all cancer types, except for ALL. ; Prevalence estimates obtained from cancer registries, and for some cancer types in InGef RDB from 2020, were broadly consistent across data sources and aligned with the estimates reported in the prior DARWIN EU® study. NAJS and DK-DHR obtained the highest estimates, likely because of the inclusion of unconfirmed cancer diagnoses, which likely resulted in an overestimation of prevalence. In DK-DHR, restricting the analysis to cases originating from the national cancer registry produced estimates comparable to other data sources for most cancers studied. In NAJS, this restriction led to comparable estimates for ALL, AML, and pancreatic cancer, while other outcomes assessed obtained lower estimates than those reported for other data sources and warrant further investigation. Prevalence estimates based on denominators from Eurostat were generally consistent with those derived directly from data sources with population-level denominators and national coverage. In NAJS, the use of ; denominators derived from the data source resulted in marginally lower prevalence estimates across outcomes studied. This is likely attributed to an overestimation of the population in the data source, which exceeds the Croatian population reported in Eurostat by 400,000 individuals as of 2023. Small discrepancies in age-stratified results were also noted, likely reflecting the analytical choices applied in the analysis to handle time-varying stratifications. Findings from this study highlight the complementarity of different data sources to improve val