Burden of influenza-associated respiratory hospitalizations in the Americas, 2010–2015
Bibliographic record
Abstract
BACKGROUND: Despite having influenza vaccination policies and programs, countries in the Americas underutilize seasonal influenza vaccine, in part because of insufficient evidence about severe influenza burden. We aimed to estimate the annual burden of influenza-associated respiratory hospitalizations in the Americas. METHODS: Thirty-five countries in the Americas with national influenza surveillance were invited to provide monthly laboratory data and hospital discharges for respiratory illness (International Classification of Diseases 10th edition J codes 0-99) during 2010-2015. In three age-strata (<5, 5-64, and ≥65 years), we estimated the influenza-associated hospitalizations rate by multiplying the monthly number of respiratory hospitalizations by the monthly proportion of influenza-positive samples and dividing by the census population. We used random effects meta-analyses to pool age-group specific rates and extrapolated to countries that did not contribute data, using pooled rates stratified by age group and country characteristics found to be associated with rates. RESULTS: Sixteen of 35 countries (46%) contributed primary data to the analyses, representing 79% of the America's population. The average pooled rate of influenza-associated respiratory hospitalization was 90/100,000 population (95% confidence interval 61-132) among children aged <5 years, 21/100,000 population (13-32) among persons aged 5-64 years, and 141/100,000 population (95-211) among persons aged ≥65 years. We estimated the average annual number of influenza-associated respiratory hospitalizations in the Americas to be 772,000 (95% credible interval 716,000-829,000). CONCLUSIONS: Influenza-associated respiratory hospitalizations impose a heavy burden on health systems in the Americas. Countries in the Americas should use this information to justify investments in seasonal influenza vaccination-especially among young children and the elderly.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.012 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".