Optimal prevention strategies for chronic diseases in a compartmental disease trajectory model (2403.14296v3)

Abstract: In countries with growing elderly populations, multimorbidity poses a significant healthcare challenge. Despite its pressing development, important questions remain on how to model the development of multimorbidity. Leveraging a comprehensive dataset from approximately 45 million hospital stays spanning 17 years in Austria, we propose a compartmental model, traditionally used in infectious diseases, describing chronic disease trajectories across 132 distinct multimorbidity patterns (compartments). Our compartmental disease trajectory model (CDTM) forecasts changes in the incidence of 131 diagnostic groups and their combinations until 2030, highlighting patterns involving hypertensive diseases with cardiovascular diseases and metabolic disorders. We additionally use the model to pinpoint specific diagnoses with the greatest potential for preventive interventions to promote healthy aging. According to our model, a 5% reduction in new cases of hypertensive disease (I10-I15) leads to a 0.57 (0.06)% reduction in all-cause mortality over a 15-year period, and a 0.57 (0.07)% reduction in mortality for malignant neoplasms (C00-C97). Furthermore, we use the model to assess the long-term consequences of the Covid-19 pandemic on hospitalizations, revealing earlier and more frequent hospitalizations across multiple diagnoses. Our fully data-driven approach identifies leverage points for proactive preparation by physicians and policymakers to reduce the overall disease burden in the population, emphasizing a shift toward patient-centered care.