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Ensuring Epidemiological Consistency in Risk-Stratified Cancer Screening Models: A Novel Approach Based on Flemish

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We developed a new dynamic breast cancer screening model that accurately maintains cancer prevalence across risk groups. This flexible Markov model ensures reliable epidemiological data for policymakers.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Policy

Background:

  • Existing Markov models for cancer screening often struggle to maintain accurate cancer prevalence when risk group definitions change.
  • This limitation hinders the development of flexible, interactive, and policy-relevant screening models.

Purpose of the Study:

  • To introduce a novel dynamic risk-stratified breast cancer screening Markov model.
  • To ensure robust and epidemiologically consistent cancer prevalences irrespective of risk-group composition.
  • To lower barriers for creating adaptable and policy-relevant screening models.

Main Methods:

  • The model integrates conditional transition probabilities with pre-stratified 'at-risk' states within a Markov cohort framework.
  • Lifetime risk is structurally defined, with onset timing determined by age-specific conditional probabilities.
  • Model components are derived from Flemish cancer registry data for epidemiological validation.

Main Results:

  • The model accurately reproduces empirical breast cancer incidence data from Flanders across age bands and cancer stages.
  • Observed incidence patterns are consistently replicated without requiring additional calibration.
  • Model stability is maintained even when risk group definitions or sizes are altered.

Conclusions:

  • The proposed method effectively achieves epidemiological consistency in risk-stratified and non-stratified cancer screening models.
  • This data-driven, transparent, and adaptable approach is suitable for various cancers and screening contexts.
  • The model is particularly valuable for interactive tools designed for policymakers.