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Background <p>Chronic 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.</p> Methods <p>We 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 (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\delta\)</EquationSource> </InlineEquation>) and policy costs (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\gamma\)</EquationSource> </InlineEquation>).</p> Results <p>Early and adaptive interventions yielded the highest ROI by sustaining adherence and slowing progression. ROI exceeded 20% when <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\delta \ge 0.20\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\gamma \le 1.5\)</EquationSource> </InlineEquation>, 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.</p> Conclusions <p>The 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月19日

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  • Jinho Cha,
  • Eunchan Daniel Cha,
  • Emily Haneul Yoo,
  • Hyoshin Song

摘要

Background

Chronic 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.

Methods

We 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 ( \(\delta\) ) and policy costs ( \(\gamma\) ).

Results

Early and adaptive interventions yielded the highest ROI by sustaining adherence and slowing progression. ROI exceeded 20% when \(\delta \ge 0.20\) and \(\gamma \le 1.5\) , 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.

Conclusions

The 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.