Burden of Respiratory syncytial virus (RSV) infection among adults in nursing and care homes: a systematic review
Bibliographic record
Abstract
Background Older adults in nursing and care homes (NCHs) are vulnerable to severe respiratory syncytial virus (RSV) infection, hospitalisation, and death. This study aimed to gather data on RSV disease among older adults in NCHs and identify reported risk factors for RSV hospitalisation and case fatality. Methods The study protocol was registered in PROSPERO (CRD42022371908). We searched MEDLINE, EMBASE and Global Health databases to identify articles published between 2000 and 2023. Observational and experimental studies conducted among older adults in NCHs requiring assistive care and reporting RSV illness were included and relevant data were extracted. Results Of 18,690 studies screened, 32 were selected for full-text review and 20 were included. Overall, the number of NCH residents ranged from 42 to 1,459 with a mean age between 67.6 and 85 years. Attack rates ranged from 6.7 – 47.6% and annual incidence ranged from 0.5 – 14%. Case fatality rates ranged from 7.7 – 23.1%. We found similar annual incidence rates of RSV-positive acute respiratory infection (ARI) of 4,582 (95% CI: 3,259 – 6,264) and 4,785 (95% CI: 2,258 – 10,141) per 100,000 reported in two studies. Annual incidence rate of RSV-positive lower respiratory tract infection was 3,040 (95% CI: 1,986 – 4,454) cases per 100,000 adults. Annual RSV-ARI hospital admission rates were between 600 (95% CI: 190 -10,000) and 1,104 (95% CI: 350 – 1,930) per 100,000 person-years. Among all RSV disease cases, commonly reported chronic medical conditions included chronic obstructive pulmonary disease (COPD), heart failure, ischemic heart disease, coronary artery disease, hypertension, diabetes, kidney dysfunction, cerebrovascular accident, malignancies, dementia, and those with a Charlson comorbidity score > 6.5. Conclusion Data on RSV infection among NCH residents are limited and largely heterogeneous but document a high risk of illness, frequent hospitalisation, and high mortality. Preventive interventions, such as vaccination should be considered for this high-risk population. Nationally representative epidemiologic studies and NCH-based viral pathogen surveillance could more precisely assess the burden on NCH residents.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".