Influenza epidemiology and influenza vaccine effectiveness during the 2016–2017 season in the Global Influenza Hospital Surveillance Network (GIHSN)
Bibliographic record
Abstract
BACKGROUND: The Global Influenza Hospital Surveillance Network (GIHSN) aims to determine the burden of severe influenza disease and Influenza Vaccine Effectiveness (IVE). This is a prospective, active surveillance and hospital-based epidemiological study to collect epidemiological data in the GIHSN. In the 2016-2017 influenza season, 15 sites in 14 countries participated in the GIHSN, although the analyses could not be performed in 2 sites. A common core protocol was used in order to make results comparable. Here we present the results of the GIHSN 2016-2017 influenza season. METHODS: A RT-PCR test was performed to all patients that accomplished the requirements detailed on a common core protocol. Patients admitted were included in the study after signing the informed consent, if they were residents, not institutionalised, not discharged in the previous 30 days from other hospitalisation with symptoms onset within the 7 days prior to admission. Patients 5 years old or more must also complied the Influenza-Like Illness definition. A test negative-design was implemented to perform IVE analysis. IVE was estimated using a logistic regression model, with the formula IVE = (1-aOR) × 100, where aOR is the adjusted Odds Ratio comparing cases and controls. RESULTS: Among 21,967 screened patients, 10,140 (46.16%) were included, as they accomplished the inclusion criteria, and tested, and therefore 11,827 (53.84%) patients were excluded. Around 60% of all patients included with laboratory results were recruited at 3 sites. The predominant strain was A(H3N2), detected in 63.6% of the cases (1840 patients), followed by B/Victoria, in 21.3% of the cases (618 patients). There were 2895 influenza positive patients (28.6% of the included patients). A(H1N1)pdm09 strain was mainly found in Mexico. IVE could only be performed in 6 sites separately. Overall IVE was 27.24 (95% CI 15.62-37.27. Vaccination seemed to confer better protection against influenza B and in people 2-4 years, or 85 years old or older. The aOR for hospitalized and testing positive for influenza was 3.02 (95% CI 1.59-5.76) comparing pregnant with non-pregnant women. CONCLUSIONS: Vaccination prevented around 1 in 4 hospitalisations with influenza. Sparse numbers didn't allow estimating IVE in all sites separately. Pregnancy was found a risk factor for influenza, having 3 times more risk of being admitted with influenza for pregnant women.
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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 | 0.019 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".