The Epidemiology of Invasive Haemophilus influenzae Non-Serotype B Disease in Ontario, Canada from 2004 to 2013
Bibliographic record
Abstract
BACKGROUND: Since the widespread use of Haemophilus influenzae (Hi) type b (Hib) vaccines among children aged <5 years, an increase in invasive non-Hib disease incidence has been reported internationally. We sought to describe the epidemiology of invasive non-Hib disease in Ontario, Canada (population ~13.5 million). METHODS: Confirmed invasive non-Hib cases (non-typeable [NTHi] and serotypes a, c, d, e, and f) were obtained from the provincial laboratory data system from 2004-2013. Data were deterministically linked to the provincial reportable disease system to provide further case information. Antibiotic resistance data were analysed separately from 2010-2014. Descriptive analyses included incidence rates, age group, serotype, site of specimen collection and resistance patterns; ethnicity data were not available. Temporal trends were evaluated by Poisson regression and p-values <0.05 were considered significant. RESULTS: A total of 1307 cases of invasive non-Hib disease were included, increasing from 0.67 cases to 1.60 cases /100,000 from 2004 to 2013. Significant increases in the incidence of NTHi (0.50 to 1.28 cases/100 000 population), Hia (0.02 to 0.08 cases/100, 000) and Hif (0.13 to 0.18 cases/100, 000 population) were seen. Among persons aged 40-64 years, 3 Hi strains significantly increased over time; NTHi (0.22 to 0.99 cases/100, 000), Hia (0.00 to 0.06 cases/100, 000) and Hif (0.05 to 0.21 cases/100, 000). Among persons aged 65-84 years, there was a significant increase of NTHi (1.62 to 3.14 cases/100, 000) and Hia (0.00 to 0.34 cases/100, 000). Among persons aged 85+ years, only NTHi significantly increased from 4.89 to 10.28 cases/100, 000). Antimicrobial resistance (AMR) to ampicillin and clarithromycin was seen in greater than 25% of isolates but AMR did not increase over the duration of this study. CONCLUSIONS: The incidence of invasive non-Hib disease has increased over time; NTHi, Hif and Hia are emerging pathogens, and should be monitored.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".