Multi-Dimensional Scaling (MDS) of healthcare system profiles and pandemic outcomes in Cuba, Spain, Italy, and Germany

Objectives: This study examines how baseline health risks in Cuba, Spain, Italy, and Germany relate to COVID-19 mortality trajectories and to identify system features associated with better outcomes. While previous comparative studies have emphasized GDP levels or hospital capacity, few have systematically linked baseline health risks and health-system models to pandemic trajectories; this study addresses that gap.
Study Design: Cross-country observational study of four contrasting health system models using publicly available secondary data (Cuba: state-socialist; Spain/Italy: Mediterranean welfare states; Germany: corporatist Bismarckian).
Methods: We applied Multi-Dimensional Scaling (MDS) in two complementary stages: (i) A cross-sectional map of Baseline Health Indicators (BHI; eight pre-pandemic variables), and (ii) A trajectory-based map of Pandemic Trajectory Metrics (PTM; monthly reported indicators, 2020–2023) using correlation distance.
Results: The BHI stage revealed distinct pre-pandemic configurations: Cuba separated on higher cardiovascular mortality and male smoking; Spain on elevated female smoking; Italy on older age structure and higher population density; and Germany on demographic pressures with higher diabetes prevalence. In the PTM stage, Cuba recorded the lowest cumulative COVID-19 mortality among the four (776 deaths per million), whereas European countries reached 2070–3261 deaths per million.
Conclusions: The two-stage design clarifies how baseline risk profiles relate to pandemic trajectories. The Cuba–Europe separation is stable under the perturbations examined, while within-Europe distances are more variable; accordingly, we refrain from ranking Italy, Spain, and Germany. Reduced separability among the European cases is consistent with increasing financialization/marketisation and policy convergence in their health systems, which may compress structural differences in delivery and epidemic response and thus limits discrimination in the PTM space at our sample size and resolution.

Giuseppe Orlando currently is affiliated with the Department of Economics and Finance (DEF) at the University of Bari (Italy). His research interests include Economics, Finance, Actuarial Science, and Econometrics, for which he received the 'Bruno de Finetti' Award in Mathematics Applied to Economics. His current projects focus on Nonlinear Dynamics in Economics, Natural Catastrophe (NatCat) Modeling, and Interest Rate Forecasting. He has also served as Senior Risk Manager, Risk Consultant, Chief Risk Officer, and Head of Risk and Quantitative Research at financial institutions such as Allianz, ING, HSBC, and State Street.