Modeling ROI in Chronic Disease Management: A Simulation-Based Framework Integrating Patient Adherence and Policy Timing

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This paper is entirely focused on health economics and chronic disease policy simulation. While it uses stochastic modeling, Monte Carlo simulation, and continuous-time formulations that share mathematical foundations with quantitative finance, the domain, objectives, and applications are unrelated to trading, portfolio management, or financial markets. The ROI concept here refers to healthcare expenditure savings, not investment returns in a financial market sense.

The core model is a relatively straightforward continuous-time cost function with logistic disease progression, quadratic adherence terms, and policy cost functions. Monte Carlo simulation with 10,000 draws is computationally modest. Implementation in Python with NumPy/SciPy is accessible. However, the full framework with six scenarios, stochastic adherence distributions, income-stratified calibration, sensitivity analyses, and 3D ROI surface visualization adds moderate complexity. The mathematical formulation is tractable but requires careful parameter calibration to MEPS/NHANES data.

Simulation code and generated datasets are publicly available on Kaggle. Parameters are calibrated to publicly available MEPS (2015-2023) and NHANES (2015-2022) data. Python implementation using NumPy/SciPy is described. Supplementary Appendices provide detailed derivations, parameter tables, and robustness tests. However, some parameter values are described as 'illustrative' and the exact calibration procedure for some parameters (e.g., β, α) could be more transparent.