First-year results of the Global Influenza Hospital Surveillance Network: 2012–2013 Northern hemisphere influenza season

BACKGROUND: The Global Influenza Hospital Surveillance Network (GIHSN) was developed to improve understanding of severe influenza infection, as represented by hospitalized cases. The GIHSN is composed of coordinating sites, mainly affiliated with health authorities, each of which supervises and compiles data from one to seven hospitals. This report describes the distribution of influenza viruses A(H1N1), A(H3N2), B/Victoria, and B/Yamagata resulting in hospitalization during 2012–2013, the network’s first year. METHODS: In 2012–2013, the GIHSN included 21 hospitals (five in Spain, five in France, four in the Russian Federation, and seven in Turkey). All hospitals used a reference protocol and core questionnaire to collect data, and data were consolidated at five coordinating sites. Influenza infection was confirmed by reverse-transcription polymerase chain reaction. Hospitalized patients admitted within 7 days of onset of influenza-like illness were included in the analysis. RESULTS: Of 5034 patients included with polymerase chain reaction results, 1545 (30.7%) were positive for influenza. Influenza A(H1N1), A(H3N2), and both B lineages co-circulated, although distributions varied greatly between coordinating sites and over time. All age groups were affected. A(H1N1) was the most common influenza strain isolated among hospitalized adults 18–64 years of age at four of five coordinating sites, whereas A(H3N2) and B viruses were isolated more often than A(H1N1) in adults ≥65 years of age at all five coordinating sites. A total of 16 deaths and 20 intensive care unit admissions were recorded among patients with influenza. CONCLUSIONS: Influenza strains resulting in hospitalization varied greatly between coordinating sites and over time. These first-year results of the GIHSN are relevant, useful, and timely. Due to its broad regional representativeness and sustainable framework, this growing network should contribute substantially to understanding the epidemiology of influenza, particularly for more severe disease.