Background
Each year, seasonal influenza epidemics cause an estimated 3 to 5 million severe illnesses and 290,000 to 650,000 deaths worldwide [
1,
2]. Young children, older adults, pregnant women, immunocompromised individuals, and patients of any age with cardiopulmonary conditions or other chronic diseases are considered to have the highest risk of severe influenza illness [
3,
4]. Hospitalization and death due to influenza, however, can also occur in individuals who were previously healthy [
5].
Several countries collect substantial data on severe cases of influenza. In the United States, for example, the Centers for Disease Control and Prevention collect data on hospitalizations with laboratory-confirmed influenza illness through a network covering approximately 9% of the US population [
6]. These data are used to make decisions about US prevention strategies, diagnosis, and treatment. However, more detailed data are needed from geographically diverse settings over several influenza seasons to assess how the different influenza viruses affect clinically meaningful outcomes. Recognizing this need, the Global Influenza Hospital Surveillance Network (GIHSN) was established in 2011 [
7]. The network includes geographically dispersed and diverse sites linked with local health authorities, each of which coordinates influenza surveillance at participating hospitals according to a common core protocol. Influenza virus infection is confirmed by reverse transcription-polymerase chain reaction (RT-PCR), and to facilitate pooling between sites, the network takes several steps to improve data quality and comparability. Data collected by the GIHSN provide an opportunity to investigate the associations between severe illness, patient characteristics, and influenza virology. In the 2017–2018 season, the GIHSN included 20 coordinating sites in 19 countries on five continents.
The GIHSN has reported epidemiological findings from 2012–2013 [
5], 2013–2014 [
8], 2014–2015 [
9], and 2016–2017 influenza seasons [
10]. The studies have shown that all strains of influenza can result in hospitalization, intensive care unit (ICU) admission, or death and that the strains responsible for severe cases vary substantially between seasons, sites, and even within single regions or countries. In addition, these studies have confirmed that influenza can cause serious outcomes not only in older adults, individuals with comorbidities, and pregnant women but also in the wider population, irrespective of age, sex, or comorbidities [
5,
8,
9].
Herein we report the results from the GIHSN for the 2017–2018 influenza season. In addition to describing the epidemiology of hospitalized cases of influenza, we focused on identifying factors associated with complicated hospitalization in these patients.
Methods
Overall methodology of the GIHSN
The GIHSN uses prospective active surveillance to collect epidemiological and virological data from patients hospitalized with acute respiratory symptoms during the influenza season [
11]. All sites share the same core protocol and use RT-PCR to confirm influenza virus infection. The core protocol was approved by the institutional review board or ethics committee of each participating site.
To be eligible, patients had to reside in the predefined catchment area of a participating hospital, be hospitalized in the previous 48 h with acute respiratory symptoms, and not live in an institutionalized setting. Healthcare professionals trained to follow the GIHSN study protocol approached eligible patients. Patients aged > 5 years had to have at least one systemic symptom (fever/feverishness, malaise, headache, or myalgia) and at least one respiratory symptom (cough, sore throat, or shortness of breath) consistent with influenza and had to have been hospitalized ≤7 days after the onset of the symptoms (see Additional file
1 for diagnosis codes). Patients aged ≤5 years had to be hospitalized ≤7 days after the appearance of symptoms associated with influenza (see Additional file
2 for diagnosis codes). Patients were excluded if they had been discharged from a hospital < 30 days before the current episode.
After patients or their legal representatives provided informed consent, nasopharyngeal, nasal, oral, or oropharyngeal samples are obtained from each patient (see Additional file
3 for sample collection). Samples are placed in a single viral transport media tube and stored at ≤−20 °C at the study site or sent directly to the coordinating site’s reference laboratory for testing. Samples are collected within 48 h of hospital admission. Influenza virus infection is confirmed by RT-PCR, and positive samples are subtyped by RT-PCR to identify A(H1N1)pdm09, A(H3N2), B/Yamagata-lineage, and B/Victoria-lineage strains. Core questionnaires (one for patients aged < 5 years and one for patients aged ≥5 years), translated into the local language, are used to collect patient demographics, comorbidities, and influenza vaccination status through face-to-face interviews of patients or legal representatives, interviews of attending physicians, and a review of clinical records. Obesity is assessed only in adults (≥18 years). Direct exposure to smoking is assessed in participants ≥14 years of age and passive exposure to smoking in participants < 14 years of age. Obesity was defined as a body mass index > 30 kg/m
2. Functional status is assessed by Barthel Index [
12] in patients aged ≥65 years. Patients were considered vaccinated if they had received at least one dose of a 2017–2018 seasonal influenza vaccine ≥14 days before the onset of symptoms. Physicians involved in clinical care of patients may be involved in patient recruitment but are not involved in assessing eligibility for inclusion.
Statistical analysis
Only patients with respiratory specimens collected within 7 days of symptom onset were included to reduce false negatives due to decreasing viral RNA levels over time [
8]. Sites including fewer than 40 hospitalized patients were excluded from the current analysis to reduce variability. For each site, the inclusion period was defined by the weeks with positive specimens for influenza. Patients enrolled outside the inclusion period at each site were excluded from analysis. The co-primary endpoints were (a) the proportion of influenza-positive patients with “complicated hospitalization”, as defined by a need for mechanical ventilation support, admission to an ICU, or death during hospitalization; and (b) length of hospital stay in days. Age categories were as recommended by the World Health Organization for analysis of severe acute respiratory illness/influenza-like illness [
13].
Strain circulation was analyzed by site and grouped by World Health Organization transmission zone [
14]. The number of positive patients for each strain (A/H1N1pdm09, A/H3N2, A subtype unknown, B/Yamagata, B/Victoria, and B lineage unknown) was calculated separately; therefore, patients could have been infected with more than one strain.
Mixed effects logistic regression was used to identify factors associated with complicated hospitalization, with 95% confidence intervals (CIs) calculated by the Wald method. A linear mixed-effects regression model was used to identify factors associated with length of hospital stay (as a continuous variable), with 95% CIs calculated by profile-likelihood method. These analyses were conducted in four age strata (< 15, 15–< 50, 50–< 65, and ≥65 years). For admissions aged ≥15 years, covariates included cardiovascular disease, chronic obstructive pulmonary disease (COPD), diabetes, obesity, other chronic conditions, sex, current smoking, influenza type (A vs B), antiviral prescription during the current episode, hospitalization during the previous 12 months, site (as random effect), influenza vaccination status, and age. For admissions aged < 15 years, covariates included any chronic condition, sex, age, influenza type (A vs. B), antiviral prescription during the current episode, hospitalization during the previous 12 months, site (as random effect), influenza vaccination status, and age. Factors tested in the mixed effects logistic regression model were considered associated with complications if the 95% CI of the odds ratio (OR) did not cross 1. In the linear mixed-effects regression model, the coefficient indicated the change in length of hospital stay in days when the indicated factor was changed by one unit (i.e. from yes to no), and tested factors were considered associated with a longer hospital stay if the 95% CI of the coefficient did not cross 0.
Statistical analysis was performed using R software version 3.4.4 (R Core Team, 1993) or Excel version 1808 (Microsoft, Redmond, WA). Missing data were not replaced.
Discussion
The current study showed that during the 2017–2018 influenza season, approximately 10% of all hospitalized cases of influenza virus infection were complicated, as defined by admission to an ICU, need for mechanical ventilation, or death. Factors associated with complicated hospitalization in patients with influenza virus infection included COPD, diabetes, and hospitalization during the previous 12 months, although male sex, cardiovascular disease, and some lifestyle factors (smoking and obesity) were also identified in certain subgroups. Age was also an important factor associated with complicated hospitalization and a longer hospital stay in patients with influenza virus infection. Most of these are known risk factors for severe influenza illness [
4]. In addition to these factors, prescription of antiviral medication during the current episode was associated with an increased frequency of complications, probably because antivirals were mostly prescribed for influenza cases with a high likelihood of developing complications.
Influenza A infections are often thought to result in more severe illness than influenza B infections [
15]. The current study, however, did not find a difference between influenza A and B or between A/H1N1pdm09 and A/H3N2 in the risk for complicated hospitalization. It also did not find a consistent difference between influenza A and B in the risk for individual components of complicated hospitalization or in the length of hospital stay: influenza B was associated with shorter hospital stay in influenza-positive admissions 50 to < 65 years of age but a higher risk for mechanical ventilation in those under 15 years of age. These findings support a systematic literature review concluding that clinical presentation and severity of influenza illness do not appear to differ between influenza strains [
16].
During the 2017–2018 influenza season, influenza accounted for approximately one-third of hospital admissions with influenza-like symptoms, which agrees with previous influenza seasons in the GISHN, where proportions were between 21 and 31% [
8‐
10,
17]. As in previous studies, all strains of influenza were detected, with widely varying strain circulation between sites and even neighboring regions. Influenza B/Yamagata was the most frequently detected influenza strain, although A/H3N2 and A/H1N1pdm09 were nearly as common. There were some minor differences in timing, although these three strains essentially co-circulated globally. Furthermore, although a B/Victoria-lineage strain was included in the 2017–2018 Northern Hemisphere and 2018 Southern Hemisphere trivalent influenza vaccines, Yamagata was the dominant lineage of influenza B [
18,
19]. Finally, as in previous studies, more than half of the patients hospitalized with influenza virus infection did not have known chronic conditions, highlighting that influenza can cause severe illness even in individuals without high-risk conditions.
By using a shared protocol combined with active, prospective surveillance across many countries and continents, the GIHSN provides a combination of extensive global virological data and clinical data on severe seasonal influenza illness. Data from the GIHSN can be used to identify associations between influenza virology/epidemiology, patient characteristics, and severe cases of influenza illness. In the current study, we pooled data from more than 4000 influenza-positive patients at 14 coordinating sites in 13 countries on four continents. Although this provided a large enough dataset to assess many factors potentially associated with complications, the influence of some factors known to be associated with severe influenza, such as human immunodeficiency virus infection and neurological conditions [
4], could not be assessed because related information was not collected.
Analyzing pooled surveillance data can be complicated by differences in clinical practice, case definitions, and procedures between sites, as well as patients’ health-seeking behaviors and access to care and vaccination. The GIHSN improves comparability between sites by using active surveillance, validated RT-PCR, and a common core protocol for identifying hospitalized cases of influenza illness. In addition, to reduce bias caused by false-negative tests due to decreasing viral shedding over time [
8], only patients who had been hospitalized within 7 days of symptom onset were eligible. To further reduce the effects of bias from differences between sites, factors associated with complicated influenza-related hospitalization were identified using a mixed effects model with a random effect per site [
20,
21]. These steps represent improvements over many surveillance efforts, where interpretability may be limited by non-systematic sampling, incomplete case ascertainment, lack of adjustment for confounders, sparse numbers, lack of consensus about case definitions and risk factors, and a lack of comparison groups. Despite the efforts to reduce heterogeneity, CIs were wide, which limited the ability to detect certain factors and interpret the size of the effects.
We did not examine the effect of influenza vaccination on complications or length of hospital stay because of low vaccination rates; the overall influenza vaccination rate across all sites and all included patients was below 15%, and, for more than half of the sites, the rate of influenza vaccination was below 10%, although a few sites had rates above 30%. This highlights differences in clinical practice, access to care, and included populations across the different sites.
Our results should be interpreted in the context of the definition for complicated hospitalization, as well as the case definition for influenza. There is currently no consensus of how to define complicated hospitalization, although some studies have reported on ICU admission, mechanical ventilation, and in-hospital death [
4,
16,
22,
23]. In the current study, complicated hospitalization was a composite of three outcomes, one of which is a treatment or support modality (mechanical ventilation), one mostly a metric for health care utilization (ICU admission), and one an indicator of severity (death during hospitalization). These, as well as length of hospitalization, capture different underlying aspects of severity, and each is influenced by a variety of patient, cultural, and healthcare practice factors, which may complicate interpreting results for individual factors. Nonetheless, this study was able to identify some common factors associated with complicated hospitalization and a longer hospital stay in influenza-positive patients. Given the limitations of influenza-like-illness case definitions for predicting influenza in individuals, particularly in older adults, to identify the maximum number of hospitalized influenza cases, potential cases were identified based on the presence of any acute respiratory symptoms possibly associated with influenza rather than based on a specific influenza-like illness case definition. Although this would reduce specificity of the inclusion criteria, specificity was ensured by confirming influenza virus infection by RT-PCR.
Care should be taken when generalizing the findings of this study. Although substantial efforts were made to improve comparability of data, the current results represent a single season and depend on the strains, sites, and populations included.
Acknowledgments
Medical writing was provided by Dr. Phillip Leventhal (Evidera).
The GIHSN 2017–2018 collaborators include: Sélilah Amour (Hôpital Edouard Herriot, Lyon, France), Coulibaly Anderson N’Gattia (Institut National d’Hygiène Publique, Treichville, Ivory Coast), Victor Baselga Moreno (Foundation for the Promotion of Health and Biomedical Research, Valencia, Spain), Elsa Baumeister (National Reference Laboratory for Viral Respiratory Diseases, Buenos Aires, Argentina), Jalila Ben Khelil (Institut Pasteur de Tunis, Tunis, Tunisia), Daria Danilenko (Research Institute of Influenza of the Ministry of Healthcare, Saint Petersburg, Russian Federation), Javier Diez-Domingo (Foundation for the Promotion of Health and Biomedical Research, Valencia, Spain), Anca Cristina Drăgănescu (National Institute for Infectious Diseases “Prof. Dr. Matei Bals”, Bucharest, Romania), Gideon O. Emukule (Kenya Medical Research Institute, Nairobi, Kenya), Zhetpisbayeva Gauhar (National Influenza Center, Almaty City, Republic of Kazakhstan), M. Lourdes Guerrero (National Institutes of Health, Mexico, Mexico City, Mexico), Ainara Mira-Iglesias (FISABIO, Valencia, Spain), Lidija Kisteneva (N.F. Gamaleya NRCEM, Ministry of Health of the Russian Federation, Moscow, Russia), Parvaiz A. Koul (Sher-i-Kashmir Institute, Srinagar, India), Ainagul Kuatbaeva (National Influenza Center, Almaty City, Republic of Kazakhstan), Victor Alberto Laguna Torres (Instituto de Medicina Tropical Universidad Nacional Mayor de San Marcos, Bellavista Callao, Peru), Odile Launay (Hôpital Cochin, Paris, France), Nezha Lenzi (Hôpital Cochin, Paris, France), Shabir Madhi (University of the Witwatersrand, Johannesburg, South Africa), Zdenka Mandakova (National Institute of Public Health, Prague, Czech Republic), Snežana Medić (Institute of Public Health of Vojvodina, Novi Sad, Serbia), Mioljub Ristić (Institute of Public Health of Vojvodina, Novi Sad, Serbia), Hyder Mir (Sher-i-Kashmir Institute, Srinagar, India), Aneta Nitsch-Osuch (Medical University of Warsaw, Warsaw, Poland), Nancy Otieno (Kenya Medical Research Institute, Nairobi, Kenya), Daniela Pițigoi (National Institute for Infectious Diseases “Prof. Dr. Matei Balş”, Bucharest, Romania) Andrea Pontoriero (National Reference Laboratory for Viral Respiratory Diseases, Buenos Aires, Argentina), Estela Ramirez (Instituto de Medicina Tropical Universidad Nacional Mayor de San Marcos, Bellavista Callao, Peru), Ben Salah (Institut Pasteur de Tunis, Tunis, Tunisia), Oana Sandulescu (National Institute for Infectious Diseases “Prof. Dr. Matei Bals”, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania), Natali Serafin (University of the Witwatersrand, Johannesburg, South Africa), Wei Shan (Fudan University, Shanghai, China), Anna Sominina (Research Institute of Influenza of the Ministry of Healthcare, Saint Petersburg, Russian Federation), Svetlana Trushakova (N.F.Gamaleya NRCEM, Ministry of Health of the Russian Federation, Moscow, Russia), Andrzej Zalewski (Medical University of Warsaw, Warsaw, Poland), and Tao Zhang (Fudan University, Shanghai, China).
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