Prevalence and Predictors of Concomitant Bacterial Infections in Patients With Respiratory Viruses in Ontario: A Cohort Study
Bibliographic record
Abstract
Abstract Background To investigate the prevalence of concomitant bacterial infection across common viral infections. Methods This population-based cohort study included patients infected with influenza A and B (FLUA, FLUB) and respiratory syncytial virus (RSV) in Ontario between 2017 and 2019 and patients with SARS-CoV-2 between 2020 and 2021. Specific bacteria present in concomitant infections were identified. Concomitant infections were further classified into different categories (eg, coinfection −2 to +2 days from viral infection and secondary infection >2 days after viral infection). We used logistic regression models to estimate the odds of bacterial infections for FLUA, FLUB, and RSV relative to SARS-CoV-2 while adjusting for confounders. Results A total of 4230 (0.5%, 885 004) viral cases had concomitant bacterial infections, encompassing 422 of FLUB (4.7%, 8891), 861 of FLUA (3.9%, 22 313), 428 of RSV (3.4%, 12 774), and 2519 of COVID-19 (0.3%, 841 026). The most prevalent species causing concomitant bacterial infection were Staphylococcus aureus, Streptococcus pyogenes, and Pseudomonas aeruginosa. When compared with SARS-CoV-2, the adjusted odds ratio for bacterial infection was 1.69 (95% CI, 1.48–1.93) for FLUA, 2.30 (95% CI, 1.97–2.69) for FLUB, and 1.56 (95% CI, 1.33–1.82) for RSV. The adjusted odds of coinfection in patients with SARS-CoV-2 were lower but higher for secondary infection as compared with the other viruses. Conclusions A higher prevalence and risk of concomitant bacterial infection were found in FLUA, FLUB, and RSV as compared with SARS-CoV-2, although this is largely driven by coinfections. Ongoing surveillance efforts are needed to compare the risk of concomitant infections during periods when these viruses are cocirculating.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".