Relationships Between Community Virus Activity and Cardiorespiratory Rehospitalizations From Post-Acute Care
Bibliographic record
Abstract
OBJECTIVES: Quantify the relationship between increasing influenza and respiratory syncytial virus (RSV) community viral activity and cardiorespiratory rehospitalizations among older adults discharged to skilled nursing facilities (SNFs). DESIGN: Retrospective cohort. SETTING AND PARTICIPANTS: Adults aged ≥65 years who were hospitalized and then discharged to a US SNF between 2012 and 2015. METHODS: We linked Medicare Provider Analysis and Review claims to Minimum Data Set version 3.0 assessments, PRISM Climate Group data, and the Centers for Disease Control and Prevention viral testing data. All data were aggregated to US Department of Health and Human Services regions. Negative binomial regression models quantified the relationship between increasing viral activity for RSV and 3 influenza strains (H1N1pdm09, H3N2, and B) and cardiorespiratory rehospitalizations from SNFs. Incidence rate ratios described the relationship between a 5% increase in circulating virus and the rates of rehospitalization for cardiorespiratory outcomes. Analyses were repeated using the same model, but influenza and RSV were considered "in season" or "out of season" based on a 10% positive testing threshold. RESULTS: Cardiorespiratory rehospitalization rates increased by approximately 1% for every 5% increase in circulating influenza A(H3N2), influenza B, and RSV, but decreased by 1% for every 5% increase in circulating influenza A(H1N1pdm09). When respiratory viruses were in season (vs out of season), cardiorespiratory rehospitalization rates increased by approximately 6% for influenza A(H3N2), 3% for influenza B, and 5% for RSV, but decreased by 6% for influenza A(H1N1pdm09). CONCLUSIONS AND IMPLICATIONS: The respiratory season is a particularly important period to implement interventions that reduce cardiorespiratory hospitalizations among SNF residents. Decreasing viral transmission in SNFs through practices such as influenza vaccination for residents and staff, use of personal protective equipment, improved environmental cleaning measures, screening and testing of residents and staff, surveillance of viral activity, and quarantining infected individuals may be potential strategies to limit viral infections and associated cardiorespiratory rehospitalizations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".