Local epidemic history as a predictor of tuberculosis incidence in Saskatchewan Aboriginal communities
Bibliographic record
Abstract
BACKGROUND: Average tuberculosis (TB) incidence rates are high in Canadian Aboriginal communities, but there is significant variability within this group. OBJECTIVE: To determine whether local history of post-contact TB epidemics is predictive of contemporary epidemiology among Aboriginal communities in Saskatchewan, Canada. METHODS: TB incidence, age-specific morbidity patterns and rates of clustering of TB genotypes from 1986 to 2004 were compared between two groups of communities: Group 1, in which post-contact epidemics of TB were established around 1870, and Group 2, in which they were delayed until after 1920. Concomitant effects of socio-economic and geographic variables were explored with multivariate models. RESULTS: Group 2 communities were characterized by higher annual incidence of TB (median 431 per 100,000 population vs. 38/100,000). In multivariate models that included socio-economic and geographic variables, historical grouping remained a significant independent predictor of community incidence of TB. Clustering of TB genotypes was associated with Group 2 (OR 8.7, 95%CI 3.3-22.7) and age 10-34 years (OR 2.5, 95%CI 1.1-5.7). CONCLUSIONS: TB transmission dynamics can vary significantly as a function of a population's historical experience with TB. Populations at different stages along the epidemic trajectory may be amenable to different types of interventions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".