Modeling ROI in chronic disease management: a simulation-based framework integrating patient adherence and policy timing.

backgroundChronic diseases impose a sustained burden on healthcare systems through progressive deterioration and long-term costs. Although adherence-enhancing interventions are widely promoted, their return on investment (ROI) remains uncertain, particularly under heterogeneous patient behavior and socioeconomic variation.

methodsWe developed a simulation-based framework integrating disease progression, time-varying adherence, and policy timing. Cumulative healthcare costs were modeled over a 10-year horizon using continuous-time stochastic formulations calibrated with Medical Expenditure Panel Survey (MEPS) data stratified by income. ROI was estimated across adherence gains (δ) and policy costs (γ).

resultsEarly and adaptive interventions yielded the highest ROI by sustaining adherence and slowing progression. ROI exceeded 20% when [Formula: see text] and [Formula: see text], whereas low-impact or high-cost policies failed to break even. Subgroup analyses showed a 32% ROI gap between the lowest and highest income strata, with projected savings of $312 per patient versus baseline. Sensitivity tests confirmed robustness under stochastic adherence and inflation variability.

conclusionsThe framework offers a transparent, adaptable tool for evaluating cost-effective adherence strategies. By linking behavioral effectiveness with fiscal feasibility, it supports design of robust and equitable chronic disease policies. Reported ROI values represent conservative lower bounds, and extensions incorporating DALYs and QALYs illustrate scalability toward health outcome integration.