Clinical symptoms cannot predict influenza infection during the 2013 influenza season in Bavaria, Germany

What 'case definition' for respiratory syncytial virus infection? Results of a systematic literature review to improve surveillance among the adults

BACKGROUND

Human respiratory syncytial virus (hRSV) is a leading cause of acute lower respiratory tract infection in frail individuals, including children, the elderly and immunocompromised people, with mild to severe symptoms. World Health Organization claims hRSV causes most elderly influenza-like illnesses (ILI) and severe acute respiratory infections (SARI). In this study, different case definitions for hRSV surveillance were examined for accuracy.

METHODS

The following search query ('Respiratory Syncytial Virus' OR 'RSV' OR 'hRSV' AND 'case definition') was used on PubMed/MEDLINE and Scopus with a 15-year-old baseline age restriction to conduct a systematic literature review.

RESULTS

Of 12 records, 58% employed the SARI definition, 50% the ILI definition and 42% the acute respiratory infection (ARI) definition, with some overlap. In young adults (18-64 years old), most studies show RSV prevalence between 6.25 and 72.54 cases per 1000 per year, and 19.23 to 98.5 in older adults. The outpatient ARI and hospitalized SARI criteria are particularly sensitive and specific.

CONCLUSIONS

Disease burden measurement requires a clear case definition; however, current literature is questionable. Currently, hRSV surveillance uses numerous case definitions with debatable accuracy. The epidemiology, clinical characteristics, and disease burden of hRSV are difficult to characterize without a standard surveillance case definition.

Developing and validating clinical features-based machine learning algorithms to predict influenza infection in influenza-like illness patients

BACKGROUND

Seasonal influenza poses a significant risk, and patients can benefit from early diagnosis and treatment. However, underdiagnosis and undertreatment remain widespread. We developed and compared clinical feature-based machine learning (ML) algorithms that can accurately predict influenza infection in emergency departments (EDs) among patients with influenza-like illness (ILI).

MATERIAL AND METHODS

We conducted a prospective cohort study in five EDs in the US and Taiwan from 2015 to 2020. Adult patients visiting the EDs with symptoms of ILI were recruited and tested by real-time RT-PCR for influenza. We evaluated seven ML algorithms and compared their results with previously developed clinical prediction models.

RESULTS

Out of the 2189 enrolled patients, 1104 tested positive for influenza. The eXtreme Gradient Boosting achieved superior performance with an area under the receiver operating characteristic curve of 0.82 (95% confidence interval [CI] = 0.79-0.85), with a sensitivity of 0.92 (95% CI = 0.88-0.95), specificity of 0.89 (95% CI = 0.86-0.92), and accuracy of 0.72 (95% CI = 0.69-0.76) in the testing set over cut-offs of 0.4, 0.6 and 0.5, respectively. These results were superior to those of previously proposed clinical prediction models. The model interpretation revealed that body temperature, cough, rhinorrhea, and exposure history were positively associated with and the days of illness and influenza vaccine were negatively associated with influenza infection. We also found the week of the influenza season, pulse rate, and oxygen saturation to be associated with influenza infection.

CONCLUSIONS

The clinical feature-based ML model outperformed conventional models for predicting influenza infection.

Association of household fuel with acute respiratory infection (ARI) under-five years children in Bangladesh

In developing countries, acute respiratory infections (ARIs) cause a significant number of deaths among children. According to Bangladesh Demographic and Health Survey (BDHS), about 25% of the deaths in children under-five years are caused by ARI in Bangladesh every year. Low-income families frequently rely on wood, coal, and animal excrement for cooking. However, it is unclear whether using alternative fuels offers a health benefit over solid fuels. To clear this doubt, we conducted a study to investigate the effects of fuel usage on ARI in children. In this study, we used the latest BDHS 2017-18 survey data collected by the Government of Bangladesh (GoB) and estimated the effects of fuel use on ARI by constructing multivariable logistic regression models. From the analysis, we found that the crude (the only type of fuel in the model) odds ratio (OR) for ARI is 1.69 [95% confidence interval (CI): 1.06-2.71]. This suggests that children in families using contaminated fuels are 69.3% more likely to experience an ARI episode than children in households using clean fuels. After adjusting for cooking fuel, type of roof material, child's age (months), and sex of the child-the effect of solid fuels is similar to the adjusted odds ratio (AOR) for ARI (OR: 1.69, 95% CI: 1.05-2.72). This implies that an ARI occurrence is 69.2% more likely when compared to the effect of clean fuel. This study found a statistically significant association between solid fuel consumption and the occurrence of ARI in children in households. The correlation between indoor air pollution and clinical parameters of ARI requires further investigation. Our findings will also help other researchers and policymakers to take comprehensive actions by considering fuel type as a risk factor as well as taking proper steps to solve this issue.

Does knowing the influenza epidemic threshold has been reached influence the performance of influenza case definitions?

Background

Disease surveillance using adequate case definitions is very important. The objective of the study was to compare the performance of influenza case definitions and influenza symptoms in the first two epidemic weeks with respect to other epidemic weeks.

Methods

We analysed cases of acute respiratory infection detected by the network of sentinel primary care physicians of Catalonia for 10 seasons. We calculated the diagnostic odds ratio (DOR) and 95% confidence intervals (CI) for the first two epidemic weeks and for other epidemic weeks.

Results

A total of 4,338 samples were collected in the epidemic weeks, of which 2,446 (56.4%) were positive for influenza. The most predictive case definition for laboratory-confirmed influenza was the WHO case definition for influenza-like illness (ILI) in the first two epidemic weeks (DOR 2.10; 95% CI 1.57–2.81) and in other epidemic weeks (DOR 2.31; 95% CI 1.96–2.72). The most predictive symptom was fever. After knowing that epidemic threshold had been reached, the DOR of the ILI WHO case definition in children aged <5 years and cough and fever in this group increased (190%, 170% and 213%, respectively).

Conclusions

During influenza epidemics, differences in the performance of the case definition and the discriminative ability of symptoms were found according to whether it was known that the epidemic threshold had been reached or not. This suggests that sentinel physicians are stricter in selecting samples to send to the laboratory from patients who present symptoms more specific to influenza after rather than before an influenza epidemic has been declared.

Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier: A Case Study of Seasonal Influenza in Hong Kong

OBJECTIVE

The annual influenza epidemic is a heavy burden on the health care system, and has increasingly become a major public health problem in some areas, such as Hong Kong (China). Therefore, based on a variety of machine learning methods, and considering the seasonal influenza in Hong Kong, the study aims to establish a Combinatorial Judgment Classifier (CJC) model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.

METHODS

The characteristic variables were selected using the single-factor statistical method to establish the influencing factor system of an influenza outbreak. On this basis, the CJC model was proposed to provide an early warning for an influenza outbreak. The characteristic variables in the final model included atmospheric pressure, absolute maximum temperature, mean temperature, absolute minimum temperature, mean dew point temperature, the number of positive detections of seasonal influenza viruses, the positive percentage among all respiratory specimens, and the admission rates in public hospitals with a principal diagnosis of influenza.

RESULTS

The accuracy of the CJC model for the influenza outbreak trend reached 96.47%, the sensitivity and specificity change rates of this model were lower than those of other models. Hence, the CJC model has a more stable prediction performance. In the present study, the epidemic situation and meteorological data of Hong Kong in recent years were used as the research objects for the construction of the model index system, and a lag correlation was found between the influencing factors and influenza outbreak. However, some potential risk factors, such as geographical nature and human factors, were not incorporated, which ideally affected the prediction performance to some extent.

CONCLUSION

In general, the CJC model exhibits a statistically better performance, when compared to some classical early warning algorithms, such as Support Vector Machine, Discriminant Analysis, and Ensemble Classfiers, which improves the performance of the early warning of seasonal influenza.

Reciprocal circulation pattern of SARS-CoV-2 and influenza viruses during the influenza seasons 2019/2020 and 2020/2021 in the Bavarian Influenza Sentinel (Germany)

The corona virus disease-2019 (COVID-19) pandemic began in Wuhan, China, and quickly spread around the world. The pandemic overlapped with two consecutive influenza seasons (2019/2020 and 2020/2021). This provided the opportunity to study community circulation of influenza viruses and severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) in outpatients with acute respiratory infections during these two seasons within the Bavarian Influenza Sentinel (BIS) in Bavaria, Germany. From September to March, oropharyngeal swabs collected at BIS were analysed for influenza viruses and SARS-CoV-2 by real-time polymerase chain reaction. In BIS 2019/2020, 1376 swabs were tested for influenza viruses. The average positive rate was 37.6%, with a maximum of over 60% (in January). The predominant influenza viruses were Influenza A(H1N1)pdm09 (n = 202), Influenza A(H3N2) (n = 144) and Influenza B Victoria lineage (n = 129). In all, 610 of these BIS swabs contained sufficient material to retrospectively test for SARS-CoV-2. SARS-CoV-2 RNA was not detectable in any of these swabs. In BIS 2020/2021, 470 swabs were tested for influenza viruses and 457 for SARS-CoV-2. Only three swabs (0.6%) were positive for Influenza, while SARS-CoV-2 was found in 30 swabs (6.6%). We showed that no circulation of SARS-CoV-2 was detectable in BIS during the 2019/2020 influenza season, while virtually no influenza viruses were found in BIS 2020/2021 during the COVID-19 pandemic.

Relative Effectiveness of the Cell-Cultured Quadrivalent Influenza Vaccine Compared to Standard, Egg-derived Quadrivalent Influenza Vaccines in Preventing Influenza-like Illness in 2017-2018

Background

Influenza antigens may undergo adaptive mutations during egg-based vaccine production. In the 2017–2018 influenza season, quadrivalent, inactivated cell-derived influenza vaccine (ccIIV4) vaccine was produced using A(H3N2) seed virus propagated exclusively in cell culture, thus lacking egg adaptive changes. This United States study estimated relative vaccine effectiveness (rVE) of ccIIV4 vs egg-derived quadrivalent vaccines (egg-derived IIV4) for that season.

Methods

Vaccination, outcome, and covariate data were ascertained retrospectively from a electronic medical record (EMR) dataset and analyzed. The study cohort included patients ≥ 4 years of age. rVE was estimated against influenza-like illness (ILI) using diagnostic International Classification of Diseases, Ninth or Tenth Revision codes. The adjusted odds ratios used to derive rVE estimates were estimated from multivariable logistic regression models adjusted for age, sex, race/ethnicity, geographic region, and health status.

Results

Overall, 92 187 individuals had a primary care EMR record of ccIIV4 and 1 261 675 had a record of egg-derived IIV4. In the ccIIV4 group, 1705 narrowly defined ILI events occurred, and 25 645 occurred in the standard egg-derived IIV4 group. Crude rVE was 9.2% (95% confidence interval [CI], 4.6%–13.6%). When adjusted for age, sex, health status, comorbidities, and geographic region, the estimated rVE changed to 36.2% (95% CI, 26.1%–44.9%).

Conclusions

ccIIV4, derived from A(H3N2) seed virus propagated exclusively in cell culture, was more effective than egg-derived IIV4 in preventing ILI during the 2017–2018 influenza season. This result suggests that cell-derived influenza vaccines may have greater effectiveness than standard egg-derived vaccines.

Virological COVID-19 surveillance in Bavaria, Germany suggests no SARS-CoV-2 spread prior to the first German case in January 2020

The Bavarian Influenza Sentinel (BIS) monitors the annual influenza season by combining virological and epidemiological data. The 2019/2020 influenza season overlapped with the beginning COVID-19 pandemic thus allowing to investigate whether there was an unnoticed spread of SARS-CoV-2 among outpatients with acute respiratory infections in the community prior to the first COVID-19 cluster in Bavaria. Therefore, we retrospectively analysed oropharyngeal swabs obtained in BIS between calendar week (CW) 39 in 2019 and CW 14 in 2020 for the presence of SARS-CoV-2 RNA by RT-PCR. 610 of all 1376 BIS swabs-contained sufficient material to test for SARS-CoV-2, among them 260 oropharyngeal swabs which were collected prior to the first notified German COVID-19 case in CW 04/2020. In none of these swabs SARS-CoV-2 RNA was detected suggesting no SARS-CoV-2 spread prior to late January 2020 in Bavaria.

Clinical evaluation of fully automated molecular diagnostic system "Simprova" for influenza virus, respiratory syncytial virus, and human metapneumovirus

Influenza virus, respiratory syncytial virus, and human metapneumovirus commonly cause acute upper and lower respiratory tract infections, especially in children and the elderly. Although rapid antigen detection tests for detecting these infections have been introduced recently, these are less sensitive than nucleic acid amplification tests. More recently, highly sensitive point-of-care testings (POCTs) have been developed based on nucleic acid amplification tests, which are easy to use in clinical settings. In this study, loop-mediated isothermal amplification (LAMP)-based POCT “Simprova” to detect influenza A and B viruses, respiratory syncytial virus, and human metapneumovirus was developed. Simprova system is fully automated and does not require skilled personnel. In addition, positive results can be achieved faster than with PCR. In this study, the accuracy of the POCT was retrospectively analyzed using 241 frozen stocked specimens. Additionally, the usability of the Simprova at clinical sites was assessed in a prospective clinical study using 380 clinical specimens and compared to those of real-time PCR and rapid antigen detection test. The novel LAMP-based POCT demonstrated high sensitivity and specificity in characterizing clinical specimens from patients with influenza-like illnesses. The Simprova is a powerful tool for early diagnosis of respiratory viral infections in point-of-care settings.

Analysis of influenza data generated by four epidemiological surveillance laboratories in Mexico, 2010-2016

The disease caused by the influenza virus is a global public health problem due to its high rates of morbidity and mortality. Thus, analysis of the information generated by epidemiological surveillance systems has vital importance for health decision making. A retrospective analysis was performed using data generated by the four molecular diagnostic laboratories of the Mexican Social Security Institute between 2010 and 2016. Demographics, influenza positivity, seasonality, treatment choices and vaccination status analyses were performed for the vaccine according to its composition for each season. In all cases, both the different influenza subtypes and different age groups were considered separately. The circulation of A/H1N1pdm09 (48.7%), influenza A/H3N2 (21.1%), influenza B (12.6%), influenza A not subtyped (11%) and influenza A/H1N1 (6.6%) exhibited well-defined annual seasonality between November and March, and there were significant increases in the number of cases every 2 years. An inadequate use of oseltamivir was determined in 38% of cases, and the vaccination status in general varied between 12.1 and 18.5% depending on the season. Our results provide current information about influenza in Mexico and demonstrate the need to update both operational case definitions and medical practice guidelines to reduce the inappropriate use of antibiotics and antivirals.

Epidemiological features and time-series analysis of influenza incidence in urban and rural areas of Shenyang, China, 2010-2018

In recent years, there have been a significant influenza activity and emerging influenza strains in China, resulting in an increasing number of influenza virus infections and leading to public health concerns. The aims of this study were to identify the epidemiological and aetiological characteristics of influenza and establish seasonal autoregressive integrated moving average (SARIMA) models for forecasting the percentage of visits for influenza-like illness (ILI%) in urban and rural areas of Shenyang. Influenza surveillance data were obtained for ILI cases and influenza virus positivity from 18 sentinel hospitals. The SARIMA models were constructed to predict ILI% for January–December 2019. During 2010–2018, the influenza activity was higher in urban than in rural areas. The age distribution of ILI cases showed the highest rate in young children aged 0–4 years. Seasonal A/H3N2, influenza B virus and pandemic A/H1N1 continuously co-circulated in winter and spring seasons. In addition, the SARIMA (0, 1, 0) (0, 1, 2)₁₂ model for the urban area and the SARIMA (1, 1, 1) (1, 1, 0)₁₂ model for the rural area were appropriate for predicting influenza incidence. Our findings suggested that there were regional and seasonal distinctions of ILI activity in Shenyang. A co-epidemic pattern of influenza strains was evident in terms of seasonal influenza activity. Young children were more susceptible to influenza virus infection than adults. These results provide a reference for future influenza prevention and control strategies in the study area.

Gastroenteritis and respiratory infection outbreaks in French nursing homes from 2007 to 2018: Morbidity and all-cause lethality according to the individual characteristics of residents

Background

Gastroenteritis (GE) and respiratory tract infection (RTI) outbreaks are a significant issue in nursing homes. This study aimed to describe GE and RTI outbreaks with infection and all-cause lethality rates according to the individual characteristics of nursing home residents.

Methods

Clinical and virological surveillance were conducted (2007 to 2018). Virus stratifications for the analysis were: outbreaks with positive norovirus or influenza identifications (respectively NoV+ or Flu+), episodes with no NoV or influenza identification or testing (respectively NoV- or Flu-). Associations between individual variables (sex, age, length of stay (LOS), autonomy status) and infection and lethality rates were tested with univariate and Mantel-Haenszel (MH) methods.

Results

61 GE outbreaks and 76 RTI oubreaks (total 137 outbreaks) were recorded involving respectively 4309 and 5862 residents. In univariate analysis, higher infection rates and age were associated in NoV+, NoV-, and Flu+ contexts, and lower infection rates were associated with longer stays (NoV+ and NoV-). In MH stratified analysis (virus, sex (female/male)) adjusted for LOS (<4 or ≥4 years), the odds of being infected remained significant among older residents (≥86 years): NoV+/male (Odds ratio (OR_MH): 1.64, 95% confidence interval (CI): 1.16–2.30) and Flu+/female and male (respectively OR_MH: 1.50, CI: 1.27–1.79 and 1.73, CI: 1.28–2.33). In univariate analysis, lower autonomy status (NoV+, Flu+ and Flu-) and increased age (Flu+) were associated with higher lethality. In MH adjusted analysis, significant OR_age adjusted for autonomy was: Flu+/ ≥86 years compared with <86 years, 1.97 (1.19–3.25) and OR_autonomy adjusted for age for the more autonomous group (compared with the less autonomous group) was: Flu+, 0.41 (0.24–0.69); Flu-, 0.42 (0.20, 0.90).

Conclusion

The residents of nursing homes are increasingly elderly and dependent. The specific infection and lethality risks according to these two factors indicate that surveillance and infection control measures are essential and of high priority.

Estimating the burden on general practitioner services in England from increases in respiratory disease associated with seasonal respiratory pathogen activity

Understanding the burden of respiratory pathogens on health care is key to improving public health emergency response and interventions. In temperate regions, there is a large seasonal rise in influenza and other respiratory pathogens. We have examined the associations between individual pathogens and reported respiratory tract infections to estimate attributable burden. We used multiple linear regression to model the relationship between doctor consultation data and laboratory samples from week 3 2011 until week 37 2015. We fitted separate models for consultation data with in-hours and out-of-hours doctor services, stratified by different age bands. The best fitting all ages models (R2 > 80%) for consultation data resulted in the greatest burden being associated with influenza followed by respiratory syncytial virus (RSV). For models of adult age bands, there were significant associations between consultation data and invasive Streptococcus pneumoniae. There were also smaller numbers of consultations significantly associated with rhinovirus, parainfluenza, and human metapneumovirus. We estimate that a general practice with 10 000 patients would have seen an additional 18 respiratory tract infection consultations per winter week of which six had influenza and four had RSV. Our results are important for the planning of health care services to minimise the impact of winter pressures. •Respiratory pathogen incidence explains over 80% of seasonal variation in respiratory consultation data.•Influenza and RSV are associated with the biggest seasonal rises in respiratory consultation counts.•A third of consultation counts associated with respiratory pathogens were due to influenza.

Pathogen Detection Using Frequency Domain Fluorescent Lifetime Measurements

OBJECTIVE

Inflammation of the meninges is a source of severe morbidity and therefore is an important health concerns worldwide. The conventional clinical microbiology approaches used today to identify pathogens suffer from several drawbacks and frequently provide false results. This research describes a fast method to detect the presence of pathogens using the frequency domain (FD) fluorescence lifetime (FLT) imaging microscopy (FLIM) system.

METHODS

The study included 43 individuals divided into 4 groups: 9 diagnosed with different types of bacteria; 16 diagnosed with different types of viruses; 5 healthy samples served as a control; and 12 samples were negative to any pathogen, although presenting related symptoms. All samples contained leukocytes that were extracted from the cerebrospinal fluid (CSF) and were subjected to nuclear staining by 4', 6-diamidino-2-phenylindole (DAPI) and FLT analyses based on phase and amplitude crossing point (CRPO).

RESULTS

Using notched boxplots, we found differences in 95% probability between the first three groups through different notch ranges (NR). Pathogen samples presented a longer median FLT (3.28 ns with NR of 3.24-3.32 ns in bacteria and 3.18 ns with NR of 3.16-3.21 ns in viruses) compared to the control median FLT (2.65 ns with NR of 2.63-2.67 ns). Furthermore, we found that the undetected forth group was divided into two types: a relatively normal median FLT (2.72 ns with NR of 2.68-2.76 ns) and a prolonged FLT (3.22 ns with NR of 3.17-3.27 ns).

CONCLUSION

FLT measurements can differentiate between control and pathogen by the CRPO method.

SIGNIFICANCE

The FD-FLIM system can provide a high throughput diagnostic technique that does not require a physician.

Clinical and laboratory predictors of influenza infection among individuals with influenza-like illness presenting to an urban Thai hospital over a five-year period

Early diagnosis of influenza infection maximizes the effectiveness of antiviral medicines. Here, we assess the ability for clinical characteristics and rapid influenza tests to predict PCR-confirmed influenza infection in a sentinel, cross-sectional study for influenza-like illness (ILI) in Thailand. Participants meeting criteria for acute ILI (fever > 38°C and cough or sore throat) were recruited from inpatient and outpatient departments in Bangkok, Thailand, from 2009-2014. The primary endpoint for the study was the occurrence of virologically-confirmed influenza infection (based upon detection of viral RNA by RT-PCR) among individuals presenting for care with ILI. Nasal and throat swabs were tested by rapid influenza test (QuickVue) and by RT-PCR. Vaccine effectiveness (VE) was calculated using the case test-negative method. Classification and Regression Tree (CART) analysis was used to predict influenza RT-PCR positivity based upon symptoms reported. We enrolled 4572 individuals with ILI; 32.7% had detectable influenza RNA by RT-PCR. Influenza cases were attributable to influenza B (38.6%), A(H1N1)pdm09 (35.1%), and A(H3N2) (26.3%) viruses. VE was highest against influenza A(H1N1)pdm09 virus and among adults. The most important symptoms for predicting influenza PCR-positivity among patients with ILI were cough, runny nose, chills, and body aches. The accuracy of the CART predictive model was 72.8%, with an NPV of 78.1% and a PPV of 59.7%. During epidemic periods, PPV improved to 68.5%. The PPV of the QuickVue assay relative to RT-PCR was 93.0% overall, with peak performance during epidemic periods and in the absence of oseltamivir treatment. Clinical criteria demonstrated poor predictive capability outside of epidemic periods while rapid tests were reasonably accurate and may provide an acceptable alternative to RT-PCR testing in resource-limited areas.

Whole genome sequencing identifies influenza A H3N2 transmission and offers superior resolution to classical typing methods

OBJECTIVES

Influenza with its annual epidemic waves is a major cause of morbidity and mortality worldwide. However, only little whole genome data are available regarding the molecular epidemiology promoting our understanding of viral spread in human populations.

METHODS

We implemented a RT-PCR strategy starting from patient material to generate influenza A whole genome sequences for molecular epidemiological surveillance. Samples were obtained within the Bavarian Influenza Sentinel. The complete influenza virus genome was amplified by a one-tube multiplex RT-PCR and sequenced on an Illumina MiSeq.

RESULTS

We report whole genomic sequences for 50 influenza A H3N2 viruses, which was the predominating virus in the season 2014/15, directly from patient specimens. The dataset included random samples from Bavaria (Germany) throughout the influenza season and samples from three suspected transmission clusters. We identified the outbreak samples based on sequence identity. Whole genome sequencing (WGS) was superior in resolution compared to analysis of single segments or partial segment analysis. Additionally, we detected manifestation of substantial amounts of viral quasispecies in several patients, carrying mutations varying from the dominant virus in each patient.

CONCLUSION

Our rapid whole genome sequencing approach for influenza A virus shows that WGS can effectively be used to detect and understand outbreaks in large communities. Additionally, the genomic data provide in-depth details about the circulating virus within one season.

Detection of viral respiratory pathogens in mild and severe acute respiratory infections in Singapore

To investigate the performance of laboratory methods and clinical case definitions in detecting the viral pathogens for acute respiratory infections (ARIs) from a prospective community cohort and hospital inpatients, nasopharyngeal swabs from cohort members reporting ARIs (community-ARI) and inpatients admitted with ARIs (inpatient-ARI) were tested by Singleplex Real Time-Polymerase Chain Reaction (SRT-PCR), multiplex RT-PCR (MRT-PCR) and pathogen-chip system (PathChip) between April 2012 and December 2013. Community-ARI and inpatient-ARI was also combined with mild and severe cases of influenza from a historical prospective study as mild-ARI and severe-ARI respectively to evaluate the performance of clinical case definitions. We analysed 130 community-ARI and 140 inpatient-ARI episodes (5 inpatient-ARI excluded because multiple pathogens were detected), involving 138 and 207 samples respectively. Detection by PCR declined with days post-onset for influenza virus; decrease was faster for community-ARI than for inpatient-ARI. No such patterns were observed for non-influenza respiratory virus infections. PathChip added substantially to viruses detected for community-ARI only. Clinical case definitions discriminated influenza from other mild-ARI but performed poorly for severe-ARI and for older participants. Rational strategies for diagnosis and surveillance of influenza and other respiratory virus must acknowledge the differences between ARIs presenting in community and hospital settings.

Underdiagnosing of Mycoplasma pneumoniae infections as revealed by use of a respiratory multiplex PCR panel

We compared a multiplex PCR diagnostic approach against specific PCR diagnosis for detection of Mycoplasma pneumoniae infection. Seventy-five percent of all M. pneumoniae infections were only detected "unintentionally" by the use of the multiplex PCR indicating underdiagnosing of M. pneumoniae due to absence of clinical suspicion.