1506. Burden of Respiratory Syncytial Virus (RSV) and Other Lower Respiratory Tract Viral Infections During the First Two Years of Life: a Prospective Study
Bibliographic record
Abstract
Abstract Background Lower respiratory tract infections (LRTIs) are a leading cause of pediatric morbidity and mortality worldwide, with ~650,000 deaths recorded in < 5-year-olds in 2016. Cross-sectional studies on hospitalized LRTIs are available, but longitudinal studies on the total burden of viral LRTIs are scarce. This study (NCT01995175) prospectively collected incident RSV and other viral LRTIs in a multinational cohort. Methods From 2013 to 2017, infants in 8 countries were enrolled at birth and followed for LRTIs up to 2 years of age. Infants with suspected LRTIs were clinically examined and swabbed. Nasal swab samples were tested using quantitative real-time PCR for RSV and multiplex PCR panel for 16 other respiratory viruses/subtypes; bacterial culture was not performed. LRTI and severe LRTI episodes were defined per 2015 WHO LRTI case definitions. Viruses detected from nasal swabs collected from participants with WHO-defined LRTI and severe LRTI episodes are reported. Results The 2401 infants followed experienced 1012 LRTI episodes; 259 of these were severe LRTIs. At least 1 virus was detected from 909 (90%) and 235 (91%) LRTI and severe LRTI episodes, respectively. Enteroviruses/Rhinoviruses (EV/RV, 49%) were detected most frequently in samples collected from LRTI episodes, followed by RSV (22%), parainfluenza (PIV, 14%), human metapneumovirus (hMPV, 8%) and seasonal coronavirus (CoV, 6%). RSV was detected in 39% of samples from LRTI episodes in < 3-month-olds and in 18% of 1-year-olds (Table 1). In a similar trend, RSV was detected in 47% of samples from severe LRTI episodes in < 3-month-olds and in 21% of 1-year-olds (Table 2). Co-infection with another virus was common in CoV-positive samples (67%), while most samples positive for RSV (71%), hMPV (70%), EV/RV (67%) and PIV (58%) had no other virus detected. Table 1. Occurrence of laboratory confirmed respiratory viral infections by viral pathogens identified in nasal swab samples from WHO-defined LRTI episodes Table 2. Occurrence of laboratory confirmed respiratory viral infections by viral pathogens identified in nasal swab samples from WHO-defined severe LRTI episodes Conclusion Respiratory viruses are detected in the majority of LRTIs during the first 2 years of life. RSV likely accounts for much of this overall LRTI burden. Our results suggest that RSV most strongly impacted the very young; it was the most commonly detected virus in severe LRTIs in infants aged < 3 months. RSV was also persistently detected at high levels in samples from LRTIs (22%) and severe LRTIs (28%) in children up to 2 years old. Funding GlaxoSmithKline Biologicals SA Disclosures Ana Ceballos, MD, GSK group of companies (Scientific Research Study Investigator) Jo Ann Colas, MSc, GSK group of companies (Consultant) Luis Cousin, MD, Tecnología en Investigación (Scientific Research Study Investigator) Ilse Dieussaert, IR, GSK group of companies (Employee, Shareholder) Joseph B. Domachowske, MD, Astra Zeneca (Other Financial or Material Support, Grant/Research Support paid to my Institution on my behalf for sponsored human clinical trial activities)GSK group of companies (Other Financial or Material Support, Grant/Research Support paid to my Institution on my behalf for sponsored human clinical trial activities)Merck (Other Financial or Material Support, Grant/Research Support paid to my Institution on my behalf for sponsored human clinical trial activities) Janet A. Englund, MD, AstraZeneca (Scientific Research Study Investigator)GSK group of companies (Scientific Research Study Investigator)Meissa vaccines (Consultant)Merck (Scientific Research Study Investigator)Sanofi Pasteur (Consultant) Sanjay Gandhi, MD, GSK group of companies (Employee) Mélanie Hercor, PhD, GSK group of companies (Employee) Magali de Heusch, PhD, GSK group of companies (Employee) Joanne M. Langley, MD, GSK group of companies (Research Grant or Support)Immunivaccines Inc (Scientific Research Study Investigator, Research Grant or Support)Janssen (Research Grant or Support)Pfizer (Research Grant or Support)Symvivo (Scientific Research Study Investigator, Research Grant or Support)VBI Vaccines (Research Grant or Support) Amanda Leach, MRCPCH, GSK group of companies (Employee) Timo Vesikari, MD, PhD, Denka (Consultant) Sonia K. Stoszek, PhD, GSK group of companies (Employee, Shareholder)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".