Abstract / Summary
Chronic diseases present a formidable global health challenge, with burdens unevenly distributed across populations, disproportionately affecting vulnerable and marginalised groups. A lack of established methods limits policymakers’ ability to design effective and equitable care pathways for individuals with chronic conditions. This study addresses this gap by introducing Chronic-Sim, a simulation-based framework tailored to the complexities of healthcare delivery. Chronic-Sim captures individual patient characteristics, demographics, socio-economic status (SES), and comorbidities. The framework presents a patient flow that is customisable for any chronic disease and supports multimorbidity by simulating multiple chronic conditions simultaneously. A novel hierarchical labelling approach is introduced to classify patients by SES, enabling the generation of outputs by deprivation category. The framework’s adaptability is demonstrated step-by-step within a chronic obstructive pulmonary disease (COPD) service at a major healthcare provider in the United Kingdom, incorporating cardiovascular disease to model multimorbidity. Interventions targeting access barriers are evaluated across SES categories, with outputs showing cost-effective improvements in patient-related (mortality, quality of life) and service-related metrics (admissions, service usage). Chronic-Sim offers a modular, data-driven decision-support tool adaptable to diverse health systems. Its novel contribution lies in empowering policymakers to test interventions, project inequities, and design fair and effective chronic disease care pathways.