Seasonal Variations in Clostridium difficile Infections Are Associated with Influenza and Respiratory Syncytial Virus Activity Independently of Antibiotic Prescriptions: a Time Series Analysis in Québec, Canada
Bibliographic record
Abstract
Seasonal variations in Clostridium difficile-associated diarrhea (CDAD), with a higher incidence occurring during winter months, have been reported. Although winter epidemics of respiratory viruses may be temporally associated with an increase in CDAD morbidity, we hypothesized that this association is mainly due to increased antibiotic use for respiratory infections. The objective of this study was to evaluate the effect of the two most frequent respiratory viruses (influenza virus and respiratory syncytial virus [RSV]) and antibiotics prescribed for respiratory infections (fluoroquinolones and macrolides) on the CDAD incidence in hospitals in the province of Québec, Canada. A multivariable Box-Jenkins transfer function model was built to relate monthly CDAD incidence to the monthly percentage of positive tests for influenza virus and RSV and monthly fluoroquinolone and macrolide prescriptions over a 4-year period (January 2005 to December 2008). Analysis showed that temporal variations in CDAD incidence followed temporal variations for influenza virus (P = 0.043), RSV (P = 0.004), and macrolide prescription (P = 0.05) time series with an average delay of 1 month and fluoroquinolone prescription time series with an average delay of 2 months (P = 0.01). We conclude that influenza virus and RSV circulation is independently associated with CDAD incidence after controlling for fluoroquinolone and macrolide use. This association was observed at an aggregated level and may be indicative of other phenomena occurring during wintertime.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".