Risk of hospitalization of diagnosed COVID-19 cases during the pandemic: A time-series analysis to unveil short- and long-run dynamics

Abstract

<p><strong>Introduction: </strong>The dynamics of the COVID-19 pandemic alternated periods of high incidence (waves) with others of low incidence, making it difficult to separate short- and long-run relationship between the number of COVID-19 cases diagnosed and the demand for hospital beds. The aim of this study was to model the risk of hospitalization of diagnosed cases during all the periods of the COVID-19 pandemic. </p><p><strong>Methods:</strong> Time series techniques were applied to evaluate the short- and long-run relationship between daily number of COVID-19 cases diagnosed and daily number hospital admissions. Drawing on daily Spanish data from 11 May 2020 to 20 March 2022, an error correction model that introduces a short-run mechanism was applied to adjust transitory disequilibrium in the long term. The impact of vaccination on the need for in-patient care were assessed. To examine changes during different life stages, the same analysis was performed by age group. </p><p><strong>Results:</strong> Dynamics between the number of positive cases and demand for hospital beds tended to the equilibrium in the long run, with 9% of any deviation being corrected after one period. Individuals aged between 50 and 69 benefited most from the mass vaccination policy, while vaccination proved to be less effective for people aged over 80. </p><p><strong>Discussion:</strong> Models discriminating between the short- and long-run dynamics provide health planners with a valuable demand forecasting tool which should be useful for developing both structural programs and emergency interventions.</p>