Oncological Prevention:
Strategies and Impacts on the Healthcare System (2024)

Executive Summary

 

Title: A Strategic Model for Cancer Prevention and Fiscal Efficiency in Healthcare Systems: Evidence from Italy

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Overview: This report proposes an innovative hybrid model aimed at enhancing public healthcare sustainability through proactive cancer prevention. Based on advanced epidemiological and economic simulations, the model integrates personalized screening, genetic risk profiling, and cost-efficient financing tools such as performance-linked budgeting and tax-based redistribution. Initially designed within the Italian context, it has potential applications across decentralized health systems globally.

 

Core Proposition: Preventive oncology must be positioned as a strategic priority. Through simulation tools like multivariate logistic regression, Cox survival models, and Monte Carlo methods, this model evaluates not only health outcomes but also return on investment (ROI) across various scenarios. The model includes:

• Genetic screening (e.g., BRCA1/2)

• Risk-reducing surgery (e.g., prophylactic mastectomy)

• Pharmacoprevention (e.g., Tamoxifen use)

• Personalized screening intervals

 

Fiscal Innovation: We recommend a hybrid Performance-Linked Financing (PLF) model integrating:

• Weighted fund distribution based on regional performance indicators

• Risk-adjusted allocations for high-risk populations

• Incentivized coverage increases (e.g., D99 exemption usage)

 

Key Economic Outcomes:

• ROI up to 480% for targeted screening programs

• €312M investment in genetic testing could save €1.5B+ in treatment costs

• Savings of €5-6M per 100 patients via early treatment vs. late-stage oncology

 

Modeling Tools & Indicators:

• AUC: 0.85 for mixed-effects risk prediction models

• Hazard Ratios: 0.6 (genetic testing), 0.45 (surgery), 0.5 (pharmacoprevention)

• Monte Carlo scenarios: simulate 10-year ROI curves with varying adherence

 

Strategic Objectives:

• Democratize access to advanced preventive care, especially in Southern Italy

• Promote early detection to reduce advanced treatment costs

• Improve health equity by removing financial and geographic barriers

 

Why It Matters: The Italian healthcare system spends over €2B annually on late-stage cancer treatment. Shifting the balance toward prevention offers dual returns: better patient outcomes and reduced fiscal pressure. By combining data science, policy reform, and local autonomy, this model makes the case for reshaping national strategies through regional piloting.

 

Scalability and Applicability: This model can be applied in other EU member states facing regional disparities. It aligns with WHO and EU public health strategies and introduces a replicable decision-making framework for resource allocation based on projected impact, adherence, and risk-adjusted efficiency.

 

Recommendation: Pilot in regions with low screening adherence (e.g., Sicily, Calabria), accompanied by digital registries, AI-enhanced triage, and public awareness campaigns. Success metrics should include QALYs, DALYs, ROI, and avoided treatment costs over 10 years.

 

A full annex includes:

• Cost tables (genetic panels, treatment stages)

• Screening ROI projections by cancer type

• Risk maps and resource reallocation simulations

• Policy brief for national and EU-level funding agencies

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Dr. Giancarlo Pregadio

December 2024

 

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