Impact of a mass immunization campaign to cotrol an outbreak of severe respiratory infections in Nunavik, northern Canada
Bibliographic record
Abstract
OBJECTIVES: In spring 2002, a mass immunization campaign using a 23-valent pneumococcal polysaccharide vaccine (23-PPV) was launched in order to control an outbreak of severe pneumonia caused by a virulent clone of serotype 1 Streptococcus pneumoniae in Nunavik, Quebec, Canada. The objective of the study was to evaluate the impact of this campaign on the incidence of invasive pneumococcal disease (IPD) and hospitalizations possibly associated with pneumococcal infections (HPAPI) in the mostly Inuit population aged 10 to 64 years. STUDY DESIGN: Retrospective analysis of surveillance and administrative data. METHODS: Multivariate Poisson model comparing the frequency rates of selected outcomes before the outbreak, during the outbreak, and after implementation of the mass immunization program. RESULTS: The reported incidence of serotype 1 IPD decreased markedly after the implementation of the vaccination campaign (rate ratio = 0.16; p < 0.002). The frequency of HPAPI and the mean duration of hospital stay also decreased. However, vaccine failures were documented and the HPAPI rate remained higher than in the period prior to the outbreak. CONCLUSIONS: Although 23-PPV contributed to control the outbreak, better vaccines are needed for the prevention of infections caused by serotype 1 S. pneumoniae.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".