Multimorbidity Profiles and Infection Severity in COVID-19 Population Using Network Analysis in the Andalusian Health Population Database

Jonás Carmona-Pírez, Ignatios Ioakeim-Skoufa, Antonio Gimeno-Miguel, Beatriz Poblador-Plou, Francisca González-Rubio, Dolores Muñoyerro-Muñiz, Juliana Rodríguez-Herrera, Juan Antonio Goicoechea-Salazar, Alexandra Prados-Torres and Román Villegas-Portero
Additional contact information
Jonás Carmona-Pírez: EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, 50009 Zaragoza, Spain
Ignatios Ioakeim-Skoufa: EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, 50009 Zaragoza, Spain
Antonio Gimeno-Miguel: EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, 50009 Zaragoza, Spain
Beatriz Poblador-Plou: EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, 50009 Zaragoza, Spain
Francisca González-Rubio: EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, 50009 Zaragoza, Spain
Dolores Muñoyerro-Muñiz: Subdirección Técnica Asesora de Gestión de la Información, Servicio Andaluz de Salud (SAS), 41071 Seville, Spain
Juliana Rodríguez-Herrera: Subdirección Técnica Asesora de Gestión de la Información, Servicio Andaluz de Salud (SAS), 41071 Seville, Spain
Juan Antonio Goicoechea-Salazar: Subdirección Técnica Asesora de Gestión de la Información, Servicio Andaluz de Salud (SAS), 41071 Seville, Spain
Alexandra Prados-Torres: EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, 50009 Zaragoza, Spain
Román Villegas-Portero: Subdirección Técnica Asesora de Gestión de la Información, Servicio Andaluz de Salud (SAS), 41071 Seville, Spain

IJERPH, 2022, vol. 19, issue 7, 1-13

Abstract: Identifying the population at risk of COVID-19 infection severity is a priority for clinicians and health systems. Most studies to date have only focused on the effect of specific disorders on infection severity, without considering that patients usually present multiple chronic diseases and that these conditions tend to group together in the form of multimorbidity patterns. In this large-scale epidemiological study, including primary and hospital care information of 166,242 patients with confirmed COVID-19 infection from the Spanish region of Andalusia, we applied network analysis to identify multimorbidity profiles and analyze their impact on the risk of hospitalization and mortality. Our results showed that multimorbidity was a risk factor for COVID-19 severity and that this risk increased with the morbidity burden. Individuals with advanced cardio-metabolic profiles frequently presented the highest infection severity risk in both sexes. The pattern with the highest severity associated in men was present in almost 28.7% of those aged ≥ 80 years and included associations between cardiovascular, respiratory, and metabolic diseases; age-adjusted odds ratio (OR) 95% confidence interval (1.71 (1.44–2.02)). In women, similar patterns were also associated the most with infection severity, in 7% of 65–79-year-olds (1.44 (1.34–1.54)) and in 29% of ≥80-year-olds (1.35 (1.18–1.53)). Patients with mental health patterns also showed one of the highest risks of COVID-19 severity, especially in women. These findings strongly recommend the implementation of personalized approaches to patients with multimorbidity and SARS-CoV-2 infection, especially in the population with high morbidity burden.

Keywords: COVID-19; multimorbidity; chronic diseases; network analysis; severity; disease burden (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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