Search

Objective: To assess the influence of the emergence of severe acute respiratory syndrome coronavirus 2 and the implementation of public health measures on the seasonality of outpatient antibiotic use and their possible association with the incidence of influenza., Methods: We performed a time-series ecological study in 1516 primary care centres of Andalusia, Spain, comparing the coronavirus disease 2019 period (April 2020 to March 2021) with the 6 previous years. We assessed the number of packs and defined daily doses per 1000 inhabitants of antibacterials and key antibiotics commonly used for acute respiratory tract infections and the number of influenza-positive cases per 100 000 inhabitants. We calculated the correlation between variables and analyzed the seasonal patterns and differences in quarterly antibiotic use., Results: For all quarters, a significant correlation was observed between influenza activity and antibiotic use (Spearman's r = 0.94; p < 0.001). Before the pandemic period, both variables presented similar seasonal patterns. After the start of the pandemic, influenza activity was suppressed and the pattern of antibiotic use flattened into a straight line (R 2  = 0.96; p = 0.022) with a quarterly change of 3.9% (p = 0.007). Total antibiotic use and antibiotics used for treating acute respiratory tract infections showed significant reductions in all quarters compared to the previous year (p < 0.01)., Discussion: The coronavirus disease 2019 pandemic has strongly influenced the seasonality of antibiotic use in primary care. The decline in respiratory viruses, among which the influenza virus is a major player that may act as a proxy for general prevalence, is proposed as a reason for the flattening of the seasonal fluctuations of outpatient antibiotic use in our region., (Copyright © 2022 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.)

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.