Case fatality risk of influenza A (H1N1pdm09): a systematic review

Using Clinical Research Networks to Assess Severity of an Emerging Influenza Pandemic

Background

Early clinical severity assessments during the 2009 influenza A H1N1 pandemic (pH1N1) overestimated clinical severity due to selection bias and other factors. We retrospectively investigated how to use data from the International Network for Strategic Initiatives in Global HIV Trials, a global clinical influenza research network, to make more accurate case fatality ratio (CFR) estimates early in a future pandemic, an essential part of pandemic response.

Methods

We estimated the CFR of medically attended influenza (CFR_MA) as the product of probability of hospitalization given confirmed outpatient influenza and the probability of death given hospitalization with confirmed influenza for the pandemic (2009–2011) and post-pandemic (2012–2015) periods. We used literature survey results on health-seeking behavior to convert that estimate to CFR among all infected persons (CFR_AR).

Results

During the pandemic period, 5.0% (3.1%–6.9%) of 561 pH1N1-positive outpatients were hospitalized. Of 282 pH1N1-positive inpatients, 8.5% (5.7%–12.6%) died. CFR_MA for pH1N1 was 0.4% (0.2%–0.6%) in the pandemic period 2009–2011 but declined 5-fold in young adults during the post-pandemic period compared to the level of seasonal influenza in the post-pandemic period 2012–2015. CFR for influenza-negative patients did not change over time. We estimated the 2009 pandemic CFR_AR to be 0.025%, 16-fold lower than CFR_MA.

Conclusions

Data from a clinical research network yielded accurate pandemic severity estimates, including increased severity among younger people. Going forward, clinical research networks with a global presence and standardized protocols would substantially aid rapid assessment of clinical severity.

Clinical Trials Registration

NCT01056354 and NCT010561.

Phylodynamics of Influenza A/H1N1pdm09 in India Reveals Circulation Patterns and Increased Selection for Clade 6b Residues and Other High Mortality Mutants

The clinical severity and observed case fatality ratio of influenza A/H1N1pdm09 in India, particularly in 2015 and 2017 far exceeds current global estimates. Reasons for these frequent and severe epidemic waves remain unclear. We used Bayesian phylodynamic methods to uncover possible genetic explanations for this, while also identifying the transmission dynamics of A/H1N1pdm09 between 2009 and 2017 to inform future public health interventions. We reveal a disproportionate selection at haemagglutinin residue positions associated with increased morbidity and mortality in India such as position 222 and clade 6B characteristic residues, relative to equivalent isolates circulating globally. We also identify for the first time, increased selection at position 186 as potentially explaining the severity of recent A/H1N1pdm09 epidemics in India. We reveal national routes of A/H1N1pdm09 transmission, identifying Maharashtra as the most important state for the spread throughout India, while quantifying climactic, ecological, and transport factors as drivers of within-country transmission. Together these results have important implications for future A/H1N1pdm09 surveillance and control within India, but also for epidemic and pandemic risk prediction around the world.

Inpatient hospital fatality related to coding (ICD-9-CM) of the influenza diagnosis in Spain (2009-2015)

Background

To analyze hospitalization episodes with an ICD-9 diagnosis code of influenza (codes 487 and 488) in any diagnostic position from 2009 to 2015 in the Spanish hospital surveillance system.

Methods

Information about age, length of stay in hospital, mortality, comorbidity with an influenza diagnosis code between 1 October 2009 and 30 September 2015 was obtained from the National Surveillance System for Hospital Data (Conjunto Mínimo Básico de Datos, CMBD).

Results

52,884 hospital admissions were obtained. A total of 24,527 admissions corresponded to diagnoses ICD-9 code 487 (46.4%), and 28,357 (53.6%) corresponded to ICD-9 code 488. The global hospitalization rates were 8.7 and 10.6 per 100,000 people, respectively. Differences between the two diagnostic groups were found for each of the six analyzed seasons. The diagnostic ICD-9-CM 488, male gender, and high-risk patients classified by risk vaccination groups showed direct relationship with inpatient hospital death.

Conclusions

Influenza diagnosis was present in a significant number of hospital admissions. The code used for diagnosis (ICD-9-CM 488), male sex, age groups and associated risk clinical conditions showed a direct relationship with inpatient hospital fatality.

Humoral response to natural influenza infection in solid organ transplant recipients

The humoral immune response of transplant recipients to influenza vaccination has been studied in detail. In contrast, the hemagglutinin inhibiting (HI) antibody response evoked by natural influenza infection and its impact on viral kinetics is unknown. In this prospective, multicenter, cohort study of natural influenza infection in transplant recipients, we measured HI antibody titers at presentation and 4 weeks later. Serial nasopharyngeal viral loads were determined using a quantitative influenza A polymerase chain reaction (PCR). We analyzed 196 transplant recipients with influenza infection. In the cohort of organ transplant patients with influenza A (n = 116), seropositivity rates for strain-specific antibodies were 44.0% (95% confidence interval [CI] 31.5-53.2%) at diagnosis and 64.7% (95% CI 55.4-72.9%) 4 weeks postinfection. Seroconversion was observed in 32.8% (95% CI 24.7-41.9%) of the cases. Lung transplant recipients were more likely to seroconvert (P = .002) and vaccine recipients were less likely to seroconvert (P = .024). A subset of patients (n = 30) who were unresponsive to prior vaccination were also unresponsive to natural infection. There was no correlation between viral kinetics and antibody response. This study provides novel data on the seroresponse to influenza infection in transplant patients and its relationship to a number of parameters including a prior vaccination status, virologic measures, and clinical variables.

Neonatal influenza virus infection affects myelination in influenza-recovered mouse brain

Influenza virus infection is a zoonosis that has great socioeconomic effects worldwide. Influenza infection induces respiratory symptoms, while the influenza virus can infect brain and leave central nervous system sequelae. As children are more vulnerable to infection, they are at risk of long-term neurological effects once their brains are infected. We previously demonstrated that functional changes in hippocampal neurons were observed in mice recovered from neonatal influenza infection. In this study, we investigated changes in myelination properties that could affect neural dysfunction. Mice were infected with the influenza virus on postnatal day 5. Tissues were harvested from recovered mice 21-days post-infection. The expression levels for myelin basic protein (MBP) were determined, and immunohistochemical staining and transmission electron microscopy were performed. Real-time polymerase chain reaction and Western blot analyses showed that mRNA and protein expressions increased in the hippocampus and cerebellum of recovered mice. Increased MBP-staining signal was observed in the recovered mouse brain. By calculating the relative thickness of myelin sheath in relation to nerve fiber diameter (G-ratio) from electron photomicrographs, an increased G-ratio was observed in both the hippocampus and cerebellum of recovered mice. Influenza infection in oligodendrocyte-enriched primary brain cell cultures showed that proinflammatory cytokines may induce MBP upregulation. These results suggested that increased MBP expression could be a compensatory change related to hypomyelination, which may underlie neural dysfunction in recovered mice. In summary, the present results demonstrate that influenza infection during the neonatal period affects myelination and further induces functional changes in influenza-recovered mouse brain.

Transmissibility and severity of influenza virus by subtype

The characteristics of influenza might vary depending on the disease subtype. This review includes previous studies on the transmissibility and severity of influenza and summarizes them by subtype. The attack rate and incubation period of influenza A were 2.3-12.3% and 1.4 days, respectively, and those of influenza B were 0.6-5.5% and 0.6 days, respectively. The five subtypes of influenza A virus, namely, H1N1, H2N2, H3N3, H5N1, and H7N9, are reviewed. The indexes related to transmissibility (reproduction number, attack rate, serial interval, latent period, incubation period, infectious period) and severity (hospitalization rate, case fatality rate) differed by influenza subtype. Generally, H3N2 showed a higher attack rate than H1N1 and H2N2. Additionally, H5N1 and H7N9 showed higher mortality rates than the other subtypes, including H1N1, H2N2, H3N2.

Effectiveness of Masks and Respirators Against Respiratory Infections in Healthcare Workers: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis quantified the protective effect of facemasks and respirators against respiratory infections among healthcare workers. Relevant articles were retrieved from Pubmed, EMBASE, and Web of Science. Meta-analyses were conducted to calculate pooled estimates. Meta-analysis of randomized controlled trials (RCTs) indicated a protective effect of masks and respirators against clinical respiratory illness (CRI) (risk ratio [RR] = 0.59; 95% confidence interval [CI]:0.46-0.77) and influenza-like illness (ILI) (RR = 0.34; 95% CI:0.14-0.82). Compared to masks, N95 respirators conferred superior protection against CRI (RR = 0.47; 95% CI: 0.36-0.62) and laboratory-confirmed bacterial (RR = 0.46; 95% CI: 0.34-0.62), but not viral infections or ILI. Meta-analysis of observational studies provided evidence of a protective effect of masks (OR = 0.13; 95% CI: 0.03-0.62) and respirators (OR = 0.12; 95% CI: 0.06-0.26) against severe acute respiratory syndrome (SARS). This systematic review and meta-analysis supports the use of respiratory protection. However, the existing evidence is sparse and findings are inconsistent within and across studies. Multicentre RCTs with standardized protocols conducted outside epidemic periods would help to clarify the circumstances under which the use of masks or respirators is most warranted.

Incidence of influenza A(H3N2) virus infections in Hong Kong in a longitudinal sero-epidemiological study, 2009-2015

Background

Many serologic studies were done during and after the 2009 influenza pandemic, to estimate the cumulative incidence of influenza A(H1N1)pdm09 virus infections, but there are few comparative estimates of the incidence of influenza A(H3N2) virus infections during epidemics.

Methods

We conducted a longitudinal serologic study in Hong Kong. We collected sera annually and tested samples from 2009–13 by HAI against the A/Perth/16/2009(H3N2) virus, and samples from 2013–15 against the A/Victoria/361/2011(H3N2) virus using the hemagglutination inhibition (HAI) assay. We estimated the cumulative incidence of infections based on 4-fold or greater rises in HAI titers in consecutive sera.

Results

There were four major H3N2 epidemics: (1) Aug-Oct 2010; (2) Mar-Jun 2012; (3) Jul-Oct 2013; and (4) Jun-Jul 2014. Between 516 and 619 relevant pairs of sera were available for each epidemic. We estimated that 9%, 19%, 7% and 7% of the population were infected in each epidemic, respectively, with higher incidence in children in epidemics 1 and 4.

Conclusions

We found that re-infections in each of the four H3N2 epidemics that occurred from 2010 through 2014 were rare. The largest H3N2 epidemic occurred with the lowest level of pre-epidemic immunity.

Severity and burden of hand, foot and mouth disease in Asia: a modelling study

Background

Hand, foot and mouth disease (HFMD) affects millions of children across Asia annually, leading to an increase in implemented control policies such as surveillance, isolation and social distancing in affected jurisdictions. However, limited knowledge of disease burden and severity causes difficulty in policy optimisation as the associated economic cost cannot be easily estimated. We use a data synthesis approach to provide a comprehensive picture of HFMD disease burden, estimating infection risk, symptomatic rates, the risk of complications and death, and overall disability-adjusted life-year (DALY) losses, along with associated uncertainties.

Methods

Complementary data from a variety of sources were synthesised with mathematical models to obtain estimates of severity of HFMD. This includes serological and other data extracted through a systematic review of HFMD epidemiology previously published by the authors, and laboratory investigations and sentinel reports from Singapore's surveillance system.

Results

HFMD is estimated to cause 96 900 (95% CI 40 600 to 259 000) age-weighted DALYs per annum in eight high-burden countries in East and Southeast Asia, with the majority of DALYs attributed to years of life lost. The symptomatic case hospitalisation rate of HFMD is 6% (2.8%-14.9%), of which 18.7% (6.7%-31.5%) are expected to develop complications. 5% (2.9%-7.4%) of such cases are fatal, bringing the overall case fatality ratio to be 52.3 (24.4-92.7) per 100 000 symptomatic infections. In contrast, the EV-A71 case fatality ratio is estimated to be at least 229.7 (75.4-672.1) per 100 000 symptomatic cases. Asymptomatic rate for EV-A71 is 71.4% (68.3%-74.3%) for ages 1-4, the years of greatest incidence.

Conclusion

Despite the high incidence rate of HFMD, total DALY due to HFMD is limited in comparison to other endemic diseases in the region, such as dengue and upper respiratory tract infection. With the majority of DALY caused by years of life lost, it is possible to mitigate most with increased EV-A71 vaccine coverage.

JOINT ESTIMATION OF THE TRANSMISSIBILITY AND SEVERITY OF EBOLA VIRUS DISEASE IN REAL TIME

The effective reproduction number [Formula: see text], the average number of secondary cases that are generated by a single primary case at calendar time [Formula: see text], plays a critical role in interpreting the temporal transmission dynamics of an infectious disease epidemic, while the case fatality risk (CFR) is an indispensable measure of the severity of disease. In many instances, [Formula: see text] is estimated using the reported number of cases (i.e., the incidence data), but such report often does not arrive on time, and moreover, the rate of diagnosis could change as a function of time, especially if we handle diseases that involve substantial number of asymptomatic and mild infections and large outbreaks that go beyond the local capacity of reporting. In addition, CFR is well known to be prone to ascertainment bias, often erroneously overestimated. In this paper, we propose a joint estimation method of [Formula: see text] and CFR of Ebola virus disease (EVD), analyzing the early epidemic data of EVD from March to October 2014 and addressing the ascertainment bias in real time. To assess the reliability of the proposed method, coverage probabilities were computed. When ascertainment effort plays a role in interpreting the epidemiological dynamics, it is useful to analyze not only reported (confirmed or suspected) cases, but also the temporal distribution of deceased individuals to avoid any strong impact of time dependent changes in diagnosis and reporting.

Relative incidence and individual-level severity of seasonal influenza A H3N2 compared with 2009 pandemic H1N1

Background

Two subtypes of influenza A currently circulate in humans: seasonal H3N2 (sH3N2, emerged in 1968) and pandemic H1N1 (pH1N1, emerged in 2009). While the epidemiological characteristics of the initial wave of pH1N1 have been studied in detail, less is known about its infection dynamics during subsequent waves or its severity relative to sH3N2. Even prior to 2009, few data was available to estimate the risk of severe outcomes following infection with one circulating influenza strain relative to another.

Methods

We analyzed antibodies in quadruples of sera from individuals in Hong Kong collected between July 2009 and December 2011, a period that included three distinct influenza virus epidemics. We estimated infection incidence using these assay data and then estimated rates of severe outcomes per infection using population-wide clinical data.

Results

Cumulative incidence of infection was high among children in the first epidemic of pH1N1. There was a change towards the older age group in the age distribution of infections for pH1N1 from the first to the second epidemic, with the age distribution of the second epidemic of pH1N1 more similar to that of sH3N2. We found no serological evidence that individuals were infected in both waves of pH1N1. The risks of excess mortality conditional on infection were higher for sH3N2 than for pH1N1, with age-standardized risk ratios of 2.6 [95% CI: 1.8, 3.7] for all causes and 1.5 [95% CI: 1.0, 2.1] for respiratory causes throughout the study period.

Conclusions

Overall increase in clinical incidence of pH1N1 and higher rates of severity in older adults in post pandemic waves were in line with an age-shift in infection towards the older age groups. The absence of repeated infection is good evidence that waning immunity did not cause the second wave. Despite circulating in humans since 1968, sH3N2 is substantially more severe per infection than the pH1N1 strain. Infection-based estimates of individual-level severity have a role in assessing emerging strains; updating seasonal vaccine components; and optimizing of vaccination programs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-017-2432-7) contains supplementary material, which is available to authorized users.

CONSISE statement on the reporting of Seroepidemiologic Studies for influenza (ROSES-I statement): an extension of the STROBE statement

BACKGROUND

Population-based serologic studies are a vital tool for understanding the epidemiology of influenza and other respiratory viruses, including the early assessment of the transmissibility and severity of the 2009 influenza pandemic, and Middle East respiratory syndrome coronavirus. However, interpretation of the results of serologic studies has been hampered by the diversity of approaches and the lack of standardized methods and reporting.

OBJECTIVE

The objective of the CONSISE ROSES-I statement was to improve the quality and transparency of reporting of influenza seroepidemiologic studies and facilitate the assessment of the validity and generalizability of published results.

METHODS

The ROSES-I statement was developed as an expert consensus of the CONSISE epidemiology and laboratory working groups. The recommendations are presented in the familiar format of a reporting guideline. Because seroepidemiologic studies are a specific type of observational epidemiology study, the ROSES-I statement is built upon the STROBE guidelines. As such, the ROSES-I statement should be seen as an extension of the STROBE guidelines.

RESULTS

The ROSES-I statement presents 42 items that can be used as a checklist of the information that should be included in the results of published seroepidemiologic studies, and which can also serve as a guide to the items that need to be considered during study design and implementation.

CONCLUSIONS

We hope that the ROSES-I statement will contribute to improving the quality of reporting of seroepidemiologic studies.

Population seroprevalence of antibody to influenza A(H7N9) virus, Guangzhou, China

Background

Since the identification in early 2013 of severe disease caused by influenza A(H7N9) virus infection, there have been few attempts to characterize the full severity profile of human infections. Our objective was to estimate the number and severity of H7N9 infections in Guangzhou, using a serological study.

Methods

We collected residual sera from patients of all ages admitted to a hospital in the city of Guangzhou in southern China in 2013 and 2014. We screened the sera using a haemagglutination inhibition assay against a pseudovirus containing the H7 and N9 of A/Anhui/1/2013(H7N9), and samples with a screening titer ≥10 were further tested by standard hemagglutination-inhibition and virus neutralization assays for influenza A(H7N9). We used a statistical model to interpret the information on antibody titers in the residual sera, assuming that the residual sera provided a representative picture of A(H7N9) infections in the general population, accounting for potential cross-reactions.

Results

We collected a total of 5360 residual sera from December 2013 to April 2014 and from October 2014 to December 2014, and found two specimens that tested positive for H7N9 antibody at haemagglutination inhibition titer ≥40 and a neutralization titer ≥40. Based on this, we estimated that 64,000 (95 % credibility interval: 7300, 190,000) human infections with influenza A(H7N9) virus occurred in Guangzhou in early 2014, with an infection-fatality risk of 3.6 deaths (95 % credibility interval: 0.47, 15) per 10,000 infections.

Conclusions

Our study suggested that the number of influenza A(H7N9) virus infections in Guangzhou substantially exceeded the number of laboratory-confirmed cases there, albeit with considerable imprecision. Our study was limited by the small number of positive specimens identified, and larger serologic studies would be valuable. Our analytic framework would be useful if larger serologic studies are done.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-1983-3) contains supplementary material, which is available to authorized users.

Severe influenza in children: incidence and risk factors

INTRODUCTION

The identification of factors that can predispose to the development of severe influenza is essential to enable the implementation of optimal prevention and control measures for vulnerable populations.

AREAS COVERED

Unfortunately, data in the pediatric age group remain difficult to interpret. However, epidemiological data seem to suggest that the most severe influenza cases, those who are hospitalized, those who are admitted to the intensive care unit, and those who died, occur in children in the first 2 years of life and in school age patients. Expert commentary: Immaturity of the immune system, and in particular of the mechanisms that usually recognize influenza viruses and activate cytokine and chemokine responses to reduce viral replication, might explain the high hospitalization rate observed in the youngest patients. Some underlying chronic conditions favour the development of the severe cases, sometime leading to death, although both admission to the intensive care unit and death can occur in otherwise healthy subjects.

Review Article: The Fraction of Influenza Virus Infections That Are Asymptomatic: A Systematic Review and Meta-analysis

BACKGROUND

The fraction of persons with influenza virus infection, who do not report any signs or symptoms throughout the course of infection is referred to as the asymptomatic fraction.

METHODS

We conducted a systematic review and meta-analysis of published estimates of the asymptomatic fraction of influenza virus infections. We found that estimates of the asymptomatic fraction were reported from two different types of studies: first, outbreak investigations with short-term follow-up of potentially exposed persons and virologic confirmation of infections; second, studies conducted across epidemics typically evaluating rates of acute respiratory illness among persons with serologic evidence of infection, in some cases adjusting for background rates of illness from other causes.

RESULTS

Most point estimates from studies of outbreak investigations fell in the range 4%-28% with low heterogeneity (I = 0%) with a pooled mean of 16% (95% confidence interval = 13%, 19%). Estimates from the studies conducted across epidemics without adjustment were very heterogeneous (point estimates 0%-100%; I = 97%), while estimates from studies that adjusted for background illnesses were more consistent with point estimates in the range 65%-85% and moderate heterogeneity (I = 58%). Variation in estimates could be partially explained by differences in study design and analysis, and inclusion of mild symptomatic illnesses as asymptomatic in some studies.

CONCLUSIONS

Estimates of the asymptomatic fraction are affected by the study design, and the definitions of infection and symptomatic illness. Considerable differences between the asymptomatic fraction of infections confirmed by virologic versus serologic testing may indicate fundamental differences in the interpretation of these two indicators.

The new pandemic influenza A/(H1N1)pdm09 virus: is it really "new"?

In June 2009, the World Health Organization (WHO) issued a pandemic alert concerning the spread of an influenza A (H1N1) virus that showed distinctive genetic characteristics vis-à-vis both seasonal influenza strains and vaccine strains. The main mutation occurred in the gene coding for hemagglutinin (HA). Mathematical models were developed to calculate the transmissibility of the virus; the results indicated a significant overlap with the transmissibility of previous pandemic strains and seasonal strains. The remarkable feature of A/(H1N1)pdm09, compared with seasonal strains, is its high fatality rate and its higher incidence among younger people. Data provided by the WHO on the number of deaths caused by A/(H1N1)pdm09 only include laboratory-confirmed cases. Some authors suggest that these data could underestimate the magnitude of the event, as laboratory confirmation is not obtained in all cases. It is important to bear in mind that the A/(H1N1)pdm09 virus is still circulating in the population. It is therefore essential to maintain its epidemiological and virological surveillance.

Real-time characterization of risks of death associated with the Middle East respiratory syndrome (MERS) in the Republic of Korea, 2015

BACKGROUND

An outbreak of the Middle East respiratory syndrome (MERS), comprising 185 cases linked to healthcare facilities, occurred in the Republic of Korea from May to July 2015. Owing to the nosocomial nature of the outbreak, it is particularly important to gain a better understanding of the epidemiological determinants characterizing the risk of MERS death in order to predict the heterogeneous risk of death in medical settings.

METHODS

We have devised a novel statistical model that identifies the risk of MERS death during the outbreak in real time. While accounting for the time delay from illness onset to death, risk factors for death were identified using a linear predictor tied to a logit model. We employ this approach to (1) quantify the risks of death and (2) characterize the temporal evolution of the case fatality ratio (CFR) as case ascertainment greatly improved during the course of the outbreak.

RESULTS

Senior persons aged 60 years or over were found to be 9.3 times (95% confidence interval (CI), 5.3-16.9) more likely to die compared to younger MERS cases. Patients under treatment were at a 7.8-fold (95% CI, 4.0-16.7) significantly higher risk of death compared to other MERS cases. The CFR among patients aged 60 years or older under treatment was estimated at 48.2% (95% CI, 35.2-61.3) as of July 31, 2015, while the CFR among other cases was estimated to lie below 15%. From June 6, 2015, onwards, the CFR declined 0.3-fold (95% CI, 0.1-1.1) compared to the earlier epidemic period, which may perhaps reflect enhanced case ascertainment following major contact tracing efforts.

CONCLUSIONS

The risk of MERS death was significantly associated with older age as well as treatment for underlying diseases after explicitly adjusting for the delay between illness onset and death. Because MERS outbreaks are greatly amplified in the healthcare setting, enhanced infection control practices in medical facilities should strive to shield risk groups from MERS exposure.

Severe Acute Respiratory Illness Deaths in Sub-Saharan Africa and the Role of Influenza: A Case Series From 8 Countries

BACKGROUND

Data on causes of death due to respiratory illness in Africa are limited.

METHODS

From January to April 2013, 28 African countries were invited to participate in a review of severe acute respiratory illness (SARI)-associated deaths identified from influenza surveillance during 2009-2012.

RESULTS

Twenty-three countries (82%) responded, 11 (48%) collect mortality data, and 8 provided data. Data were collected from 37 714 SARI cases, and 3091 (8.2%; range by country, 5.1%-25.9%) tested positive for influenza virus. There were 1073 deaths (2.8%; range by country, 0.1%-5.3%) reported, among which influenza virus was detected in 57 (5.3%). Case-fatality proportion (CFP) was higher among countries with systematic death reporting than among those with sporadic reporting. The influenza-associated CFP was 1.8% (57 of 3091), compared with 2.9% (1016 of 34 623) for influenza virus-negative cases (P < .001). Among 834 deaths (77.7%) tested for other respiratory pathogens, rhinovirus (107 [12.8%]), adenovirus (64 [6.0%]), respiratory syncytial virus (60 [5.6%]), and Streptococcus pneumoniae (57 [5.3%]) were most commonly identified. Among 1073 deaths, 402 (37.5%) involved people aged 0-4 years, 462 (43.1%) involved people aged 5-49 years, and 209 (19.5%) involved people aged ≥50 years.

CONCLUSIONS

Few African countries systematically collect data on outcomes of people hospitalized with respiratory illness. Stronger surveillance for deaths due to respiratory illness may identify risk groups for targeted vaccine use and other prevention strategies.

Hospitalization Fatality Risk of Influenza A(H1N1)pdm09: A Systematic Review and Meta-Analysis

During the 2009 influenza pandemic, uncertainty surrounding the severity of human infections with the influenza A(H1N1)pdm09 virus hindered the calibration of the early public health response. The case fatality risk was widely used to assess severity, but another underexplored and potentially more immediate measure is the hospitalization fatality risk (HFR), defined as the probability of death among H1N1pdm09 cases who required hospitalization for medical reasons. In this review, we searched for relevant studies published in MEDLINE (PubMed) and EMBASE between April 1, 2009, and January 9, 2014. Crude estimates of the HFR ranged from 0% to 52%, with higher estimates from tertiary-care referral hospitals in countries with a lower gross domestic product, but in wealthy countries the estimate was 1%-3% in all settings. Point estimates increased substantially with age and with lower gross domestic product. Early in the next pandemic, estimation of a standardized HFR may provide a picture of the severity of infection, particularly if it is presented in comparison with a similarly standardized HFR for seasonal influenza in the same setting.

Biosurveillance capability requirements for the global health security agenda: lessons from the 2009 H1N1 pandemic

The biosurveillance capabilities needed to rapidly detect and characterize emerging biological threats are an essential part of the Global Health Security Agenda (GHSA). The analyses of the global public health system's functioning during the 2009 H1N1 pandemic suggest that while capacities such as those identified in the GHSA are essential building blocks, the global biosurveillance system must possess 3 critical capabilities: (1) the ability to detect outbreaks and determine whether they are of significant global concern, (2) the ability to describe the epidemiologic characteristics of the pathogen responsible, and (3) the ability to track the pathogen's spread through national populations and around the world and to measure the impact of control strategies. The GHSA capacities-laboratory and diagnostic capacity, reporting networks, and so on-were essential in 2009 and surely will be in future events. But the 2009 H1N1 experience reminds us that it is not just detection but epidemiologic characterization that is necessary. Similarly, real-time biosurveillance systems are important, but as the 2009 H1N1 experience shows, they may contain inaccurate information about epidemiologic risks. Rather, the ability of scientists in Mexico, the United States, and other countries to make sense of the emerging laboratory and epidemiologic information that was critical-an example of global social capital-enabled an effective global response. Thus, to ensure that it is meeting its goals, the GHSA must track capabilities as well as capacities.

Viral Respiratory Infections of Adults in the Intensive Care Unit

Viral lower respiratory tract infections (LRTIs) are an underappreciated cause of critical illness in adults. Recent advances in viral detection techniques over the past decade have demonstrated viral LRTIs are associated with rates of morbidity, mortality, and health care utilization comparable to those of seen with bacterial community acquired and nosocomial pneumonias. In this review, we describe the relationship between viral LRTIs and critical illness, as well as discuss relevant clinical features and management strategies for the more prevalent respiratory viral pathogens.

Surveillance for seasonal influenza virus prevalence in hospitalized children with lower respiratory tract infection in Guangzhou, China during the post-pandemic era

Background

Influenza A(H1N1)pdm09, A(H3N2) and B viruses have co-circulated in the human population since the swine-origin human H1N1 pandemic in 2009. While infections of these subtypes generally cause mild illnesses, lower respiratory tract infection (LRTI) occurs in a portion of children and required hospitalization. The aim of our study was to estimate the prevalence of these three subtypes and compare the clinical manifestations in hospitalized children with LRTI in Guangzhou, China during the post-pandemic period.

Methods

Children hospitalized with LRTI from January 2010 to December 2012 were tested for influenza A/B virus infection from their throat swab specimens using real-time PCR and the clinical features of the positive cases were analyzed.

Results

Of 3637 hospitalized children, 216 (5.9%) were identified as influenza A or B positive. Infection of influenza virus peaked around March in Guangzhou each year from 2010 to 2012, and there were distinct epidemics of each subtype. Influenza A(H3N2) infection was more frequently detected than A(H1N1)pdm09 and B, overall. The mean age of children with influenza A virus (H1N1/H3N2) infection was younger than those with influenza B (34.4 months/32.5 months versus 45 months old; p<0.005). Co-infections of influenza A/ B with mycoplasma pneumoniae were found in 44/216 (20.3%) children.

Conclusions

This study contributes the understanding to the prevalence of seasonal influenza viruses in hospitalized children with LRTI in Guangzhou, China during the post pandemic period. High rate of mycoplasma pneumoniae co-infection with influenza viruses might contribute to severe disease in the hospitalized children.

Clinical severity of human infections with avian influenza A(H7N9) virus, China, 2013/14

Assessing the severity of emerging infections is challenging because of potential biases in case ascertainment. The first human case of infection with influenza A(H7N9) virus was identified in China in March 2013; since then, the virus has caused two epidemic waves in the country. There were 134 laboratory-confirmed cases detected in the first epidemic wave from January to September 2013. In the second epidemic wave of human infections with avian influenza A(H7N9) virus in China from October 2013 to October 2014, we estimated that the risk of death among hospitalised cases of infection with influenza A(H7N9) virus was 48% (95% credibility interval: 42-54%), slightly higher than the corresponding risk in the first wave. Age-specific risks of death among hospitalised cases were also significantly higher in the second wave. Using data on symptomatic cases identified through national sentinel influenza-like illness surveillance, we estimated that the risk of death among symptomatic cases of infection with influenza A(H7N9) virus was 0.10% (95% credibility interval: 0.029-3.6%), which was similar to previous estimates for the first epidemic wave of human infections with influenza A(H7N9) virus in 2013. An increase in the risk of death among hospitalised cases in the second wave could be real because of changes in the virus, because of seasonal changes in host susceptibility to severe infection, or because of variation in treatment practices between hospitals, while the increase could be artefactual because of changes in ascertainment of cases in different areas at different times.

Are influenza-associated morbidity and mortality estimates for those ≥ 65 in statistical databases accurate, and an appropriate test of influenza vaccine effectiveness?

PURPOSES

To assess the accuracy of estimates using statistical databases of influenza-associated morbidity and mortality, and precisely measure influenza vaccine effectiveness.

PRINCIPAL RESULTS

Laboratory testing of influenza is incomplete. Death certificates under-report influenza. Statistical database models are used as an alternative to randomised controlled trials (RCTs) to assess influenza vaccine effectiveness. Evidence of the accuracy of influenza morbidity and mortality estimates was sought from: (1) Studies comparing statistical models. For four studies Poisson and ARIMA models produced higher estimates than Serfling, and Serfling higher than GLM. Which model is more accurate is unknown. (2) Studies controlling confounders. Fourteen studies mostly controlled one confounder (one controlled comorbidities), and limited control of confounders limits accuracy.

EVIDENCE FOR VACCINE EFFECTIVENESS WAS SOUGHT FROM

(1) Studies of regions with increasing vaccination rates. Of five studies two controlled for confounders and one found a positive vaccination effect. Three studies did not control confounders and two found no effect of vaccination. (2) Studies controlling multiple confounders. Of thirteen studies only two found a positive vaccine effect and no mortality differences between vaccinees and non-vaccinees in non-influenza seasons, showing confounders were controlled. Key problems are insufficient testing for influenza, using influenza-like illness, heterogeneity of seasonal and pandemic influenza, population aging, and incomplete confounder control (co-morbidities, frailty, vaccination history) and failure to demonstrate control of confounders by proving no mortality differences between vaccinees and non-vaccinees in non-influenza seasons.

MAJOR CONCLUSIONS

Improving model accuracy requires proof of no mortality differences in pre-influenza periods between the vaccinated and non-vaccinated groups, and reduction in influenza morbidity and mortality in seasons with a good vaccine match, more virulent strains, in the younger elderly with less immune senescence, and specific outcomes (laboratory-confirmed outcomes, pneumonia deaths). Proving influenza vaccine effectiveness requires appropriately powered RCTs, testing participants with RT-PCR tests, and comprehensively monitoring morbidity and mortality.

Effectiveness of neuraminidase inhibitors in reducing mortality in patients admitted to hospital with influenza A H1N1pdm09 virus infection: a meta-analysis of individual participant data

BACKGROUND

Neuraminidase inhibitors were widely used during the 2009-10 influenza A H1N1 pandemic, but evidence for their effectiveness in reducing mortality is uncertain. We did a meta-analysis of individual participant data to investigate the association between use of neuraminidase inhibitors and mortality in patients admitted to hospital with pandemic influenza A H1N1pdm09 virus infection.

METHODS

We assembled data for patients (all ages) admitted to hospital worldwide with laboratory confirmed or clinically diagnosed pandemic influenza A H1N1pdm09 virus infection. We identified potential data contributors from an earlier systematic review of reported studies addressing the same research question. In our systematic review, eligible studies were done between March 1, 2009 (Mexico), or April 1, 2009 (rest of the world), until the WHO declaration of the end of the pandemic (Aug 10, 2010); however, we continued to receive data up to March 14, 2011, from ongoing studies. We did a meta-analysis of individual participant data to assess the association between neuraminidase inhibitor treatment and mortality (primary outcome), adjusting for both treatment propensity and potential confounders, using generalised linear mixed modelling. We assessed the association with time to treatment using time-dependent Cox regression shared frailty modelling.

FINDINGS

We included data for 29,234 patients from 78 studies of patients admitted to hospital between Jan 2, 2009, and March 14, 2011. Compared with no treatment, neuraminidase inhibitor treatment (irrespective of timing) was associated with a reduction in mortality risk (adjusted odds ratio [OR] 0·81; 95% CI 0·70-0·93; p=0·0024). Compared with later treatment, early treatment (within 2 days of symptom onset) was associated with a reduction in mortality risk (adjusted OR 0·48; 95% CI 0·41-0·56; p<0·0001). Early treatment versus no treatment was also associated with a reduction in mortality (adjusted OR 0·50; 95% CI 0·37-0·67; p<0·0001). These associations with reduced mortality risk were less pronounced and not significant in children. There was an increase in the mortality hazard rate with each day's delay in initiation of treatment up to day 5 as compared with treatment initiated within 2 days of symptom onset (adjusted hazard ratio [HR 1·23] [95% CI 1·18-1·28]; p<0·0001 for the increasing HR with each day's delay).

INTERPRETATION

We advocate early instigation of neuraminidase inhibitor treatment in adults admitted to hospital with suspected or proven influenza infection.

FUNDING

F Hoffmann-La Roche.

Human infection with avian influenza A H7N9 virus: an assessment of clinical severity

BACKGROUND

Characterisation of the severity profile of human infections with influenza viruses of animal origin is a part of pandemic risk assessment, and an important part of the assessment of disease epidemiology. Our objective was to assess the clinical severity of human infections with avian influenza A H7N9 virus, which emerged in China in early 2013.

METHODS

We obtained information about laboratory-confirmed cases of avian influenza A H7N9 virus infection reported as of May 28, 2013, from an integrated database built by the Chinese Center for Disease Control and Prevention. We estimated the risk of fatality, mechanical ventilation, and admission to the intensive care unit for patients who required hospital admission for medical reasons. We also used information about laboratory-confirmed cases detected through sentinel influenza-like illness surveillance to estimate the symptomatic case fatality risk.

FINDINGS

Of 123 patients with laboratory-confirmed avian influenza A H7N9 virus infection who were admitted to hospital, 37 (30%) had died and 69 (56%) had recovered by May 28, 2013. After we accounted for incomplete data for 17 patients who were still in hospital, we estimated the fatality risk for all ages to be 36% (95% CI 26-45) on admission to hospital. Risks of mechanical ventilation or fatality (69%, 95% CI 60-77) and of admission to an intensive care unit, mechanical ventilation, or fatality (83%, 76-90) were high. With assumptions about coverage of the sentinel surveillance network and health-care-seeking behaviour for patients with influenza-like illness associated with influenza A H7N9 virus infection, and pro-rata extrapolation, we estimated that the symptomatic case fatality risk could be between 160 (63-460) and 2800 (1000-9400) per 100,000 symptomatic cases.

INTERPRETATION

Human infections with avian influenza A H7N9 virus seem to be less serious than has been previously reported. Many mild cases might already have occurred. Continued vigilance and sustained intensive control efforts are needed to minimise the risk of human infection.

FUNDING

Chinese Ministry of Science and Technology; Research Fund for the Control of Infectious Disease; Hong Kong University Grants Committee; China-US Collaborative Program on Emerging and Re-emerging Infectious Diseases; Harvard Center for Communicable Disease Dynamics; US National Institute of Allergy and Infectious Disease; and the US National Institutes of Health.