As populations age, the rise of multimorbidity poses a significant healthcare challenge. However, our ability to quantitatively forecast the progression of hospital-recorded multimorbidity remains limited. Leveraging a nationwide dataset comprising approximately 45 million inpatient hospital stays spanning 17 years in Austria, we develop a compartmental model for chronic disease trajectories across 132 distinct multimorbidity patterns (compartments).

Each compartment represents a constellation of co-occurring chronic conditions, with transitions modeled as age- and sex-dependent probabilities. We use the compartmental disease trajectory model (CDTM) to simulate disease trajectories to 2030, estimating the frequency of all observed co-occurrence patterns in hospitals among more than 100 diagnosis groups.

We demonstrate the model’s utility in identifying high-impact prevention targets. A 5% reduction in new cases of hypertensive diseases (I10–I15) leads to a 0.57 (SD 0.06)% reduction in all-cause mortality over the 2015–2030 projection period, and a 0.57 (SD 0.07)% reduction in mortality for malignant neoplasms (C00–C97).

We also evaluate the potential long-term impacts of SARS-CoV-2 sequelae using stress-test scenarios, projecting earlier and more frequent hospitalizations across a range of diagnoses. Our data-driven modeling approach identifies leverage points for proactive preparation by physicians and policymakers within hospital morbidity dynamics, emphasizing patient-centered healthcare planning in aging societies.

K. Ledebur, A. Kautzky-Willer, S. Thurner, P. Klimek, Projecting multimorbidity and mortality under demographic change and preventive interventions, npj Complexity (2026).