A Model of the Costs of Community and Nosocomial Pediatric Respiratory Syncytial Virus Infections in Canadian Hospitals
Bibliographic record
Abstract
BACKGROUND: Approximately one in 10 hospitalized patients will acquire a nosocomial infection (NI) after admission to hospital, of which 71% are due to respiratory viruses, including the respiratory syncytial virus (RSV). NIs are concerning and lead to prolonged hospitalizations. The economics of NIs are typically described in generalized terms and specific cost data are lacking. OBJECTIVE: To develop an evidence-based model for predicting the risk and cost of nosocomial RSV infection in pediatric settings. METHODS: A model was developed, from a Canadian perspective, to capture all costs related to an RSV infection hospitalization, including the risk and cost of an NI, diagnostic testing and infection control. All data inputs were derived from published literature. Deterministic sensitivity analyses were performed to evaluate the uncertainty associated with the estimates and to explore the impact of changes to key variables. A probabilistic sensitivity analysis was performed to estimate a confidence interval for the overall cost estimate. RESULTS: The estimated cost of nosocomial RSV infection adds approximately 30.5% to the hospitalization costs for the treatment of community-acquired severe RSV infection. The net benefits of the prevention activities were estimated to be equivalent to 9% of the total RSV-related costs. Changes in the estimated hospital infection transmission rates did not have a significant impact on the base-case estimate. CONCLUSIONS: The risk and cost of nosocomial RSV infection contributes to the overall burden of RSV. The present model, which was developed to estimate this burden, can be adapted to other countries with different disease epidemiology, costs and hospital infection transmission rates.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".