Comparison of the novel ResPlex III assay and existing techniques for the detection and subtyping of influenza virus during the influenza season 2006–2007

Influenza virus is a major cause of disease worldwide. The accurate detection and further subtyping of influenza A viruses are important for epidemiologic surveillance, and subsequent comprehensive characterization of circulating influenza viruses is essential for the selection of an optimal vaccine composition. ResPlex III is a new multiplex reverse transcriptase polymerase chain reaction (RT-PCR)-based method for detecting, typing, and subtyping influenza virus in clinical specimens. The ResPlex III assay was compared with other methods with respect to sensitivity and accuracy, using 450 clinical specimens obtained from subjects throughout Germany during the 2006–2007 influenza season. Samples were analyzed for the presence of influenza virus in Madin-Darby canine kidney (MDCK) cells by rapid cell culture using peroxidase staining and conventional cell culture confirmed by hemagglutination inhibition assay, a rapid diagnostic assay (Directigen Flu A+B test; BD Diagnostic Systems, Heidelberg, Germany), in-house real-time RT-PCR (RRT-PCR), and ResPlex III (Qiagen, Hilden, Germany). ResPlex III had the highest sensitivity for detecting influenza virus in clinical specimens, followed by in-house RRT-PCR (96% compared with ResPlex III). Conventional cell culture in MDCK cells, rapid culture, and quick test assays were substantially less sensitive (55%, 72%, and 39%, respectively). Virus subtyping results were identical using ResPlex III and the standard virological subtyping method, hemagglutination inhibition. ResPlex III is a quick, accurate, and sensitive assay for detecting and typing influenza A and B viruses and subtyping influenza A viruses in clinical specimens, and might be considered for a supplemental role in worldwide seasonal and pandemic influenza surveillance

Revised estimates of influenza-associated excess mortality, United States, 1995 through 2005

<p>Abstract</p> <p>Background</p> <p>Excess mortality due to seasonal influenza is thought to be substantial. However, influenza may often not be recognized as cause of death. Imputation methods are therefore required to assess the public health impact of influenza. The purpose of this study was to obtain estimates of monthly excess mortality due to influenza that are based on an epidemiologically meaningful model.</p> <p>Methods and Results</p> <p>U.S. monthly all-cause mortality, 1995 through 2005, was hierarchically modeled as Poisson variable with a mean that linearly depends both on seasonal covariates and on influenza-certified mortality. It also allowed for overdispersion to account for extra variation that is not captured by the Poisson error. The coefficient associated with influenza-certified mortality was interpreted as ratio of total influenza mortality to influenza-certified mortality. Separate models were fitted for four age categories (<18, 18–49, 50–64, 65+). Bayesian parameter estimation was performed using Markov Chain Monte Carlo methods. For the eleven year study period, a total of 260,814 (95% CI: 201,011–290,556) deaths was attributed to influenza, corresponding to an annual average of 23,710, or 0.91% of all deaths.</p> <p>Conclusion</p> <p>Annual estimates for influenza mortality were highly variable from year to year, but they were systematically lower than previously published estimates. The excellent fit of our model with the data suggest validity of our estimates.</p

Online detection and quantification of epidemics

<p>Abstract</p> <p>Background</p> <p>Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses.</p> <p>Results</p> <p>We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at <url>http://www.u707.jussieu.fr/periodic_regression/</url>. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea).</p> <p>Conclusion</p> <p>The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners.</p

Initial surveillance of 2009 influenza A(H1N1) pandemic in the European Union and European Economic Area, April – September 2009

European Union (EU) and European Economic Area (EEA) countries reported surveillance data on 2009 pandemic influenza A(H1N1) cases to the European Centre for Disease Prevention and Control (ECDC) through the Early Warning and Response System (EWRS) during the early phase of the 2009 pandemic. We describe the main epidemiological findings and their implications in respect to the second wave of the 2009 influenza pandemic. Two reporting systems were in place (aggregate and case-based) from June to September 2009 to monitor the evolution of the pandemic. The notification rate was assessed through aggregate reports. Individual data were analysed retrospectively to describe the population affected. The reporting peak of the first wave of the 2009 pandemic influenza was reached in the first week of August. Transmission was travel-related in the early stage and community transmission within EU/EEA countries was reported from June 2009. Seventy eight per cent of affected individuals were less than 30 years old. The proportions of cases with complications and underlying conditions were 3% and 7%, respectively. The most frequent underlying medical conditions were chronic lung (37%) and cardio-vascular diseases (15%). Complication and hospitalisation were both associated with underlying conditions regardless of age. The information from the first wave of the pandemic produced a basis to determine risk groups and vaccination strategies before the start of the winter wave. Public health recommendations should be guided by early capture of profiles of affected populations through monitoring of infectious diseases

Surveillance trends of the 2009 influenza A(H1N1) pandemic in Europe

We describe the epidemiology and virology of the official length of the 2009 pandemic (68 weeks from April 2009 to August 2010) in the 27 European Union Member States plus Norway and Iceland. The main trends are derived from published literature as well as the analysis and interpretation of data provided to the European Centre for Disease Prevention and Control (ECDC) through the European Influenza Surveillance Network (EISN) and data collected by the ECDC itself. The 2009 influenza A(H1N1) pandemic started in Europe around week 16 of 2009 (although the World Health Organization (WHO) declared only in week 18). It progressed into an initial spring/summer wave of transmission that occurred in most countries, but was striking only in a few, notably the United Kingdom. During the summer, transmission briefly subsided but then escalated again in early autumn, just after the re-opening of the schools. This wave affected all countries, and was brief but intense in most, lasting about 14 weeks. It was accompanied by a similar but slightly delayed wave of hospitalisations and deaths. By the time the WHO declared the pandemic officially over in August 2010 (week 32), Europe had experienced transmission at low level for about 34 weeks