Chronic Multimorbidity Patterns in Relation to COVID-19 Severe Infection

Overview

The aim of the study was to analyze the patterns of chronic multimorbidity of a cohort of Covid-19 patients, and to assess the relation between the patterns and the development of severe infection or mortality.

This is a Big Data study that aims to establish the multimorbidity (MM) clusters of a cohort of Covid-19 patients, and assess their potential relation with severe infection or mortality. Databases of demographic information and complete medical records from the cohort will be provided by the Agency for Health Quality and Assessment of Catalonia (AQuAS). Data will be collected and integrated in a complete database in order to define the study variables: multimorbidity patterns, COVID-19 severe infection and COVID-19 associated mortality. The population will be stratified by sex and age (21-45, 46-65, 66-80 and 81-95 years). Diagnoses from primary care and hospitals will be filtered and classified using the Chronic Condition Indicator v.2021 and the Clinical Classification Software v.2021 \[1\], in order to identify the Chronic Conditions (CC) of the patients. CC with prevalence \>2% in each age-sex strata will be subjected to a fuzzy c-means clustering analysis. The final number of clusters in each age-sex group will be determined under the clinical criteria of the research group members. Percentages of patients that suffered severe Covid-19 infection or death in each cluster will be calculated. Bivariate statistical analysis comparing the demographic data and the cluster distribution with severe infection and mortality will be performed.

Conditions

Eligibility

Locations (1)

Outcomes