Severity and outcomes of influenza-related pneumonia in type A and B strains in China, 2013-2019

The effect of nonpharmaceutical interventions on influenza virus transmission

Background

The use of nonpharmaceutical interventions (NPIs) during severe acute respiratory syndrome 2019 (COVID-19) outbreaks may influence the spread of influenza viruses. This study aimed to evaluate the impact of NPIs against SARS-CoV-2 on the epidemiological features of the influenza season in China.

Methods

We conducted a retrospective observational study analyzing influenza monitoring data obtained from the China National Influenza Center between 2011 and 2023. We compared the changes in influenza-positive patients in the pre-COVID-19 epidemic, during the COVID-19 epidemic, and post-COVID-19 epidemic phases to evaluate the effect of NPIs on influenza virus transmission.

Results

NPIs targeting COVID-19 significantly suppressed influenza activity in China from 2019 to 2022. In the seventh week after the implementation of the NPIs, the number of influenza-positive patients decreased by 97.46% in southern regions of China and 90.31% in northern regions of China. However, the lifting of these policies in December 2022 led to an unprecedented surge in influenza-positive cases in autumn and winter from 2022 to 2023. The percentage of positive influenza cases increased by 206.41% (p < 0.001), with high positivity rates reported in both the northern and southern regions of China.

Conclusion

Our findings suggest that NPIs against SARS-CoV-2 are effective at controlling influenza epidemics but may compromise individuals' immunity to the virus.

Clinical characteristics of outpatients with influenza-B-associated pneumonia and molecular evolution of influenza B virus in Beijing, China, during the 2021-2022 influenza season

We analyzed the clinical characteristics of outpatients with influenza-B-associated pneumonia during the 2021-2022 influenza season and analyzed the molecular epidemiology and evolution of influenza B virus. The presence of influenza B virus was confirmed by reverse transcription polymerase chain reaction (RT-PCR). Electronic medical records were used to collect and analyze data of outpatients. The HA and NA genes were phylogenetically analyzed using ClustalW 2.10 and MEGA 11.0. Out of 1569 outpatients who tested positive for influenza B virus, 11.7% (184/1569) developed pneumonia, and of these, 19.0% (35/184) had underlying diseases. Fever, cough, and sore throat were the most common symptoms. Among the complications, acute respiratory distress syndrome (ARDS), acute kidney injury (AKI), and shock accounted for 2.7% (5/184), 4.9% (9/184), and 1.6% (3/184), respectively. Of the outpatients, 2.7% (5/184) were admitted to the hospital, and 0.5% (1/184) of them died. All of the strains from Beijing were identified as belonging to the B/Victoria lineage. The HA and NA gene sequences of 41 influenza B viruses showed high similarity to each other, and all of them belonged to clade 1A.3. Compared with the vaccine strain B/Washington/02/2019, all of the isolates contained N150K, G181E, and S194D mutations. S194D, E195K, and K200R mutations were detected in the 190 helix of the receptor binding region of HA. Co-mutations of H122Q, A127T, P144L, N150K, G181E, S194D, and K200R in HA and D53N, N59S, and G233E in NA were detected in 78.0% (32/41) of the isolates, and 56.3% (18/32) of these were from outpatients with influenza-B-associated pneumonia. Influenza outpatients with underlying diseases were more likely to develop pneumonia. No significant differences were observed in clinical symptoms or laboratory results between outpatients with and without pneumonia, so testing for influenza virus seems to be a good choice. The observed amino acid variations suggest that current vaccines might not provide effective protection.

Impact of influenza virus infection on lung microbiome in adults with severe pneumonia

BACKGROUND

Bacterial and viral infections are commonly implicated in the development of pneumonia. We aimed to compare the diversity and composition of lung bacteria among severe pneumonia patients who were influenza virus positive (IFVP) and influenza virus negative (IFVN).

METHODS

Bronchoalveolar lavage fluid specimens were procured from patients diagnosed with severe pneumonia to investigate the microbiome utilizing 16S-rDNA sequencing. The alpha diversity of the microbiome was evaluated employing Chao1, Shannon, and Simpson indexes, while the beta diversity was assessed using principal component analysis and principal coordinate analysis. Linear discriminant analysis effect size (LEfSe) was employed to determine the taxonomic differences between the IFVP and IFVN groups.

RESULTS

A total of 84 patients with 42 in the IFVP group and 42 in the IFVN group were enrolled. Slightly higher indexes of Shannon and Simpson were observed in the IFVP group without statistically significant difference. The dominant bacterial genera were Streptococcus, Klebsiella, Escherichia-Shigella in the IFVN group and Acinetobacter, Streptococcus, Staphylococcus in the IFVP group. Streptococcus pneumoniae and Acinetobacter baumannii were the most abundant species in the IFVN and IFVP groups, respectively. LEfSe analysis indicated a greater abundance of Klebsiella in the IFVN group.

CONCLUSIONS

Individuals with severe pneumonia infected with IFV exhibit heightened susceptibility to certain bacteria, especially Acinetobacter baumannii, and the underlying mechanism of the interaction between IFV and Acinetobacter baumannii in the progression of pneumonia needs further investigation.

Age Differences in Comorbidities, Presenting Symptoms, and Outcomes of Influenza Illness Requiring Hospitalization: A Worldwide Perspective From the Global Influenza Hospital Surveillance Network

Background

The Global Influenza Hospital Surveillance Network (GIHSN) was established in 2012 to conduct coordinated worldwide influenza surveillance. In this study, we describe underlying comorbidities, symptoms, and outcomes in patients hospitalized with influenza.

Methods

Between November 2018 and October 2019, GIHSN included 19 sites in 18 countries using a standardized surveillance protocol. Influenza infection was laboratory-confirmed with reverse-transcription polymerase chain reaction. A multivariate logistic regression model was utilized to analyze the extent to which various risk factors predict severe outcomes.

Results

Of 16 022 enrolled patients, 21.9% had laboratory-confirmed influenza; 49.2% of influenza cases were A/H1N1pdm09. Fever and cough were the most common symptoms, although they decreased with age (P < .001). Shortness of breath was uncommon among those <50 years but increased with age (P < .001). Middle and older age and history of underlying diabetes or chronic obstructive pulmonary disease were associated with increased odds of death and intensive care unit (ICU) admission, and male sex and influenza vaccination were associated with lower odds. The ICU admissions and mortality occurred across the age spectrum.

Conclusions

Both virus and host factors contributed to influenza burden. We identified age differences in comorbidities, presenting symptoms, and adverse clinical outcomes among those hospitalized with influenza and benefit from influenza vaccination in protecting against adverse clinical outcomes. The GIHSN provides an ongoing platform for global understanding of hospitalized influenza illness.

Clinical and Laboratory Findings of Viral Influenza among Children Hospitalized in Qazvin Pediatric Hospital in Iran (2015-2020)

BACKGROUND

Children are one of the most important groups at risk of catching an influenza infection. The consequences of influenza in some children, especially children with chronic and underlying diseases, can be very severe and lead to hospitalization.

OBJECTIVE

The purpose of this research was to determine children with influenza and their clinical and laboratory findings in Qazvin Children's Hospital between 2015 to 2020 years.

METHODS

In this descriptive cross-sectional study, epidemiological and clinical findings of children hospitalized due to confirmed influenza were considered. A total of 1468 children with a suspected diagnosis of influenza were included in this study. Then, based on the Real-time Polymerase Chain Reaction (PCR), a total of 229 were confirmed positive for influenza. Statistical analysis was done using software SPSS 23.0, Analysis Of Variance (ANOVA), and t-test (p≤0.05).

RESULTS

The results showed that most of the patients (53.7 %) were infected with influenza H1N1 type. Most comorbidities were observed with Central Nervous System (CNS) disease and febrile seizure (each one 3.10 %). The highest clinical feature was fever (83.4 %). Significant relationship was observed between the season (p=0.001), soreness in throat (p=0.001), febrile seizure (p=0.051), muscle and joint pain (p=0.059), rhinorrhea (p=0.006) and shiver (p=0.051), and occurrence of influenza. Also, 4 children had died from influenza during hospitalization.

CONCLUSION

Children with influenza were examined in this study. Influenza has some side effects on children's health. Due to the irreversible and dangerous effects of influenza, early diagnosis and appropriate treatment in children is important.

Acute respiratory infections in an adult refugee population: an observational study

The presence of acute infectious respiratory diseases (ARD) is one of the main reasons why recently arrived refugees seek medical help. This paper investigates the incidence rates of acute respiratory diseases in an adult refugee population as well as associated sociodemographic factors and drug treatments. We conducted a retrospective observational study of deidentified medical records. The data were collected between 2015 and 2019 in the health care centers of two large German initial reception centers for refugees. Multivariable analyses controlling for sociodemographics were carried out using generalized estimating equations. Out of 10,431 eligible residents, 6965 medical encounters of 2840 adult patients were recorded over 30 months. Of all the adult patients, 34.4% sought medical help for a respiratory symptom or diagnosis at least once. Older patients and patients from Sub-Saharan Africa sought help less often. The occurrence of ARD showed a typical distribution over the course of the year. Facility occupancy was not associated with ARD occurrence. Acute respiratory symptoms are a leading cause for adult refugee patients to seek medical care. The doctor contact rates due to ARD were consistently two to three times higher among refugees than among German residents.

Behavior of hospitalized severe influenza cases according to the outcome variable in Catalonia, Spain, during the 2017-2018 season

Influenza is an important cause of severe illness and death among patients with underlying medical conditions and in the elderly. The aim of this study was to investigate factors associated with ICU admission and death in patients hospitalized with severe laboratory-confirmed influenza during the 2017–2018 season in Catalonia. An observational epidemiological case-to-case study was carried out. Reported cases of severe laboratory-confirmed influenza requiring hospitalization in 2017–2018 influenza season were included. Mixed-effects regression analysis was used to estimate the factors associated with ICU admission and death. A total of 1306 cases of hospitalized severe influenza cases were included, of whom 175 (13.4%) died and 217 (16.6%) were ICU admitted. Age 65–74 years and ≥ 75 years and having ≥ 2 comorbidities were positively associated with death (aOR 3.19; 95%CI 1.19–8.50, aOR 6.95, 95%CI 2.76–1.80 and aOR 1.99; 95%CI 1.12–3.52, respectively). Neuraminidase inhibitor treatment and pneumonia were negatively associated with death. The 65–74 years and ≥ 75 years age groups were negatively associated with ICU admission (aOR 0.41; 95%CI 0.23–0.74 and aOR 0.30; 95%CI 0.17–0.53, respectively). A factor positively associated with ICU admission was neuraminidase inhibitor treatment. Our results support the need to investigate the worst outcomes of hospitalized severe cases, distinguishing between death and ICU admission.

Uncovering the pharmacological mechanisms of Xijiao Dihuang decoction combined with Yinqiao powder in treating influenza viral pneumonia by an integrative pharmacology strategy

Xijiao Dihuang decoction combined with Yinqiao powder (XDD-YQP) is a classical combination formula; however, its therapeutic effects in treating influenza viral pneumonia and the pharmacological mechanisms remain unclear. The therapeutic effect of XDD-YQP in influenza viral pneumonia was evaluated in mice. Subsequently, an everted gut sac model coupled with UPLC/Q-TOF MS were used to screen and identify the active compounds of XDD-YQP. Furthermore, network pharmacological analysis was adopted to probe the mechanisms of the active compounds. Lastly, we verified the targets predicted from network pharmacological analysis by differential bioinformatics analysis. Animal experiments showed that XDD-YQP has a therapeutic effect on influenza viral pneumonia. Moreover, 113 active compounds were identified from intestinal absorbed solutions of XDD-YQP. Using network pharmacological analysis, 90 major targets were selected as critical in the treatment of influenza viral pneumonia through 12 relevant pathways. Importantly, the MAPK signaling pathway was found to be closely associated with the other 11 pathways. Moreover, seven key targets, EGFR, FOS, MAPK1, MAP2K1, HRAS, NRAS, and RELA, which are common targets in the MAPK signaling pathway, were investigated. These seven key targets were identified as differentially expressed genes (DEGs) between influenza virus-infected and uninfected individuals. Hence, the seven key targets in the MAPK signaling pathway may play a vital role in the treatment of influenza viral pneumonia with XDD-YQP. This research may offer an integrative pharmacology strategy to clarify the pharmacological mechanisms of traditional Chinese medicines. The results provide a theoretical basis for a broader clinical application of XDD-YQP.

Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia

Background

Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP).

Methods

A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5–63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0–78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves.

Results

Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <−500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5–10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance.

Conclusions

Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.

Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia

BACKGROUND

Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP).

METHODS

A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5-63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0-78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves.

RESULTS

Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <-500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5-10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance.

CONCLUSIONS

Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.