Respiratory Virus Infection and Risk of Invasive Meningococcal Disease in Central Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: In temperate climates, invasive meningococcal disease (IMD) incidence tends to coincide with or closely follow peak incidence of influenza virus infection; at a seasonal level, increased influenza activity frequently correlates with increased seasonal risk of IMD. METHODS: We evaluated 240 cases of IMD reported in central Ontario, Canada, from 2000 to 2006. Associations between environmental and virological (influenza A, influenza B and respiratory syncytial virus (RSV)) exposures and IMD incidence were evaluated using negative binomial regression models controlling for seasonal oscillation. Acute effects of weekly respiratory virus activity on IMD risk were evaluated using a matched-period case-crossover design with random directionality of control selection. Effects were estimated using conditional logistic regression. RESULTS: Multivariable negative binomial regression identified elevated IMD risk with increasing influenza A activity (per 100 case increase, incidence rate ratio = 1.18, 95% confidence interval (CI): 1.06, 1.31). In case-crossover models, increasing weekly influenza A activity was associated with an acute increase in the risk of IMD (per 100 case increase, odds ratio (OR) = 2.03, 95% CI: 1.28 to 3.23). Increasing weekly RSV activity was associated with increased risk of IMD after adjusting for RSV activity in the previous 3 weeks (per 100 case increase, OR = 4.31, 95% CI: 1.14, 16.32). No change in disease risk was seen with increasing influenza B activity. CONCLUSIONS: We have identified an acute effect of influenza A and RSV activity on IMD risk. If confirmed, these finding suggest that influenza vaccination may have the indirect benefit of reducing IMD risk.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".