Respiratory syncytial virus-attributable hospitalizations among adults in high- and middle-income countries: application of the Global Burden of Disease framework
Notice bibliographique
Résumé
Background Respiratory syncytial virus (RSV) in adults is typically underdiagnosed due to non-specific symptoms, infrequent routine testing, and low-test sensitivity; consequently, its impact is not well understood. To address this gap, we developed a novel approach to estimate adult RSV-related hospitalizations, leveraging methods from the Global Burden of Disease (GBD) study. Methods We collected aggregated clinical data from hospital statistics and insurance claims on respiratory and cardiorespiratory hospitalizations and RSV activity proxies for age groups 18–59 years, 60–74 years, ≥60 years, and ≥75 years in 15 countries (Argentina, Brazil, Canada, Chile, Georgia, Germany, Greece, Ireland, Italy, Japan, Mexico, New Zealand, Poland, Spain, and the United States) between 1992 and 2021. In addition, we collected RSV surveillance data, i.e., the percentage of samples tested positive for RSV from the WHO GISRS platform—the Global Influenza Surveillance and Response System and from country-specific reporting platforms for countries from North and South America, Europe and Asia, covering the years 2015–2023. Using the GBD comparative risk assessment framework, we estimated exposure-response relationships between RSV activity and hospitalizations using generalized additive models (GAMs), adjusting for trend, seasonality, meteorological influence and influenza activity, between the years 2015–2019, and calculated the population attributable fraction (PAF) and RSV-attributable hospitalizations. We evaluated the predictive power of surveillance-based versus hospital-based RSV proxies based on adjusted R 2 , and generalized cross-validation (GCV) score. Findings We identified significant relationships (p-value < 0.01) between RSV activity and increased respiratory and cardiorespiratory hospitalizations among adults. Generally, hospital-based RSV proxies predicted hospitalization better than surveillance-based proxies. RSV-attributable hospitalization rates and PAFs varied substantially by age and country. The highest annual RSV-attributable hospitalization rates were estimated for individuals 75 years and older, ranging from 110.9 (95% uncertainty interval [UI]: 66.9–156.1, median: 113.5, inter quartile range [IQR]: 10.4) per 100,000 population in Argentina for respiratory hospitalizations to 1199.8 (1087.0–1313.8, 1209.5, 88.9) per 100,000 in New Zealand for cardiorespiratory hospitalizations. The lowest RSV-attributable hospitalizations, for respiratory and cardiorespiratory diseases, were found for adults aged 18–59 years in Spain with 5.0 (95% UI: 0.8–9.3) hospitalizations per 100,000 for the hospital-based proxy. Interpretation Innovations introduced by this analysis include non-parametric modelling of the exposure-response relationship between RSV activity and hospitalizations and evaluating the predictive reliability of two RSV proxies. Our findings highlight the substantial adult RSV disease burden, provide estimates for countries with no prior data (particularly those in (sub)tropical climates such as Mexico and Brazil), and illustrate the considerable geographic variability in adult RSV incidence. These results can guide future research, interventions, and policy decisions, including those involving adult RSV vaccines. Funding This study was sponsored by Pfizer Inc.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».