Age Differences in Comorbidities, Presenting Symptoms, and Outcomes of Influenza Illness Requiring Hospitalization: A Worldwide Perspective From the Global Influenza Hospital Surveillance Network
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".