Incidence of Human Metapneumovirus Among Older Adults in 10 High-Income Countries: A Systematic Literature Review, Meta-analysis, and Modeling Study
Bibliographic record
Abstract
Background: The epidemiologic landscape for human metapneumovirus (hMPV), a respiratory pathogen, is poorly characterized, particularly among older adults. Methods: Leveraging the latest estimates of lower respiratory infection (LRI) incidence from the Global Burden of Disease Study 2021 and meta-analyzed findings from a systematic literature review, we quantified the incidence of LRI with hMPV in 10 high-income locations among older adults in the most recent prepandemic year (2019): Canada, Chile, France, Germany, New Zealand, Netherlands, Italy, Spain, United Kingdom, and United States. Results: The systematic literature review identified 21 studies with data on the percentage of LRI episodes associated with hMPV in adults aged ≥60 years in the targeted locations. Combining the meta-analyzed percentage of LRI cases associated with hMPV from these studies (7.0%; 95% CI, 5.4%-9.1%) with age-, sex-, and location-specific estimates of LRI incidence from the Global Burden of Disease Study 2021, we estimated that hMPV incidence rates per 100 000 in 2019 among adults aged ≥60 ranged from 185.7 (95% uncertainty interval, 134.7-251.1) in Italy to 462.1 (333.1-628.2) in the United States. Conclusions: Overall, our literature review, meta-analysis, and modeling study confirm a significant burden of hMPV-associated LRI in older adults. This work fills a critical evidence gap in the epidemiologic landscape of hMPV and yields actionable estimates to inform vaccine development strategies and other strategic initiatives. Future inclusion of hMPV in routine surveillance would enable more comprehensive estimates of hMPV incidence and outcomes.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.046 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".