Understanding the age spectrum of respiratory syncytial virus associated hospitalisation and mortality burden based on statistical modelling methods: a systematic analysis
Bibliographic record
Abstract
BACKGROUND: Statistical modelling studies based on excess morbidity and mortality are important for understanding RSV disease burden for age groups that are less frequently tested for RSV. We aimed to understand the full age spectrum of RSV morbidity and mortality burden based on statistical modelling studies, as well as the value of modelling studies in RSV disease burden estimation. METHODS: The databases Medline, Embase and Global Health were searched to identify studies published between January 1, 1995, and December 31, 2021, reporting RSV-associated excess hospitalisation or mortality rates of any case definitions using a modelling approach. All reported rates were summarised using median, IQR (Interquartile range) and range by age group, outcome and country income group; where applicable, a random-effects meta-analysis was conducted to combine the reported rates. We further estimated the proportion of RSV hospitalisations that could be captured in clinical databases. RESULTS: A total of 32 studies were included, with 26 studies from high-income countries. RSV-associated hospitalisation and mortality rates both showed a U-shape age pattern. Lowest and highest RSV acute respiratory infection (ARI) hospitalisation rates were found in 5-17 years (median: 1.6/100,000 population, IQR: 1.3-18.5) and < 1 year (2235.7/100,000 population, 1779.1-3552.5), respectively. Lowest and highest RSV mortality rates were found in 18-49 years (0.1/100,000 population, 0.06-0.2) and ≥ 75 years (80.0/100,000 population, 70.0-90.0) for high-income countries, respectively, and in 18-49 years (0.3/100,000 population, 0.1-2.4) and < 1 year (143.4/100,000 population, 143.4-143.4) for upper-middle income countries. More than 70% of RSV hospitalisations in children < 5 years could be captured in clinical databases whereas less than 10% of RSV hospitalisations could be captured in adults, especially for adults ≥ 50 years. Using pneumonia and influenza (P&I) mortality could potentially capture half of all RSV mortality in older adults but only 10-30% of RSV mortality in children. CONCLUSIONS: Our study provides insights into the age spectrum of RSV hospitalisation and mortality. RSV disease burden using laboratory records alone could be substantially severely underreported for age groups ≥ 5 years. Our findings confirm infants and older adults should be prioritised for RSV immunisation programmes. TRIAL REGISTRATION: PROSPERO CRD42020173430.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".