A Two-Step Markov Processes Approach for Parameterization of Cancer State-Transition Models for Low- and...

Area of Science:

• Oncology
• Mathematical Modeling
• Public Health

Background:

• Organized cancer screening significantly reduces incidence and mortality in high-income countries (HICs).
• Low- and middle-income countries (LMICs) lack screening programs, leading to late-stage diagnoses and poor survival rates.
• Economic analyses for cancer control in LMICs are hindered by the absence of longitudinal data for population-based models.

Purpose of the Study:

• To present a novel mathematical methodology for parameterizing natural cancer onset and progression models in LMICs lacking longitudinal data.
• To enable comprehensive economic analyses of cancer control programs, including screening, in resource-limited settings.
• To account for both benefits and adverse effects of screening, such as over-diagnosis and false positives.

Main Methods:

• Developed a mathematical methodology for cancer onset and progression modeling tailored for LMICs without longitudinal data.
• Applied the methodology to breast, cervical, and colorectal cancers in Eastern Sub-Saharan Africa (AFRE) and Southeast Asia (SEARB).
• Integrated cancer models into Spectrum software, interfaced with DemProj and the OneHealth tool for demographic and costing data.

Main Results:

• The methodology successfully parameterized cancer onset and progression models for selected cancers in LMIC regions.
• The integrated software allows for country-specific analysis of cancer screening strategies.
• Models can now assess the full impact of screening interventions, including potential harms.

Conclusions:

• This new modeling approach provides a vital tool for cancer control planning in LMICs.
• It facilitates cost-effectiveness analyses and resource prioritization for cancer screening programs.
• The open-access software empowers stakeholders to develop tailored, evidence-based cancer screening strategies.