Latent heterogeneity in COVID‐19 hospitalisations: a cluster‐weighted approach to analyse mortality.

Summary: The COVID‐19 pandemic caused an unprecedented excess mortality. Since 2020, many studies have focussed on the characteristics of COVID‐19 patients who did not survive. From the statistical point of view, what seems to dominate is the large heterogeneity of the populations affected by COVID‐19 and the extreme difficulty in identifying subpopulations who died affected by a plurality of contemporary characteristics. In this paper, we propose an extremely flexible approach based on a cluster‐weighted model, which allows us to identify latent groups of patients sharing similar characteristics at the moment of hospitalisation as well as a similar mortality. We focus on one of the hardest hit areas in Italy and study the heterogeneity in the population of patients affected by COVID‐19 using administrative data on hospitalisations in the first wave of the pandemic. Results highlighted that a model‐based clustering approach is essential to understand the complexity of the COVID‐19 patients treated by hospitals and who die during hospitalisation. [ABSTRACT FROM AUTHOR]