The Changing Epidemiology of Invasive Pneumococcal Disease Among the Indigenous and Non-indigenous Population of Northwestern Ontario, Canada, from 2006 Through 2015 and Emergence of Non-vaccine Serotypes
Bibliographic record
Abstract
Rates of invasive pneumococcal disease (IPD) among indigenous populations remain substantially higher than their non-indigenous counterparts. The goal of this study was to analyze the epidemiology and demographic features of IPD in northwestern Ontario (NWO) among the indigenous and non-indigenous population in the context of recent changes in the provincial pneumococcal vaccination programs. Two databases were used to identify cases of IPD in NWO: Thunder Bay Regional Health Sciences Centre and the Thunder Bay District Health Unit. Adult patients with a diagnosis of IPD at the TBRHSC from January 1, 2006 to December 31, 2015 had their medical charts retrospectively reviewed; TBDHU data contained only serotype, age, and gender data. Number of IPD cases and case fatality rate by indigenous status at the TBRHSC, 2006–2015 53 of 182 (29.0%) of patients were indigenous. 35 of 53 (66.0%) of indigenous patients were immunocompromised, whereas 38 of 129 (29.5%) of non-indigenous patients were immunocompromised and the difference was statistically significant (P < 0.001, by chi square test). 35 of 73 (48.0%) of immunocompromised patients were indigenous. Serotype distribution of Streptococcus pneumoniae causing IPD in northwestern Ontario, Canada, 2006–2015 (includes both TBRHSC + TBDHU data). The proportion of non-vaccine serotypes has increased, on average, by 16% per year (P = 0.024, 95% CI: 1.02, 1.32). High rates of IPD were found to occur among immunocompromised indigenous adults in NWO. Our findings identify a vulnerable cohort of the population that would benefit from pneumococcal vaccination coverage. The proportion of non-vaccine serotypes causing IPD has increased during the 10-year observation period. M. Ulanova, Pfizer: Grant Investigator, Grant recipient
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".