Descriptive analysis of a tuberculosis outbreak from a northern Saskatchewan First Nations community—December 2018 to May 2019
Bibliographic record
Abstract
Background: The tuberculosis (TB) incidence rate for northern Saskatchewan First Nations on-reserve is 1.5 higher than the national average. In December 2018 a member of one of these communities was diagnosed with 4+ smear-positive TB, spurring an outbreak investigation. Objectives: To describe the public health response to TB outbreak investigation and highlight the risk factors associated with TB transmission in northern Saskatchewan; and to highlight the relevance of social network contact investigation tool in outbreak management. Methods: Descriptive analysis included active TB cases and latent TB infection (LTBI) cases linked by contact investigation to the index case. Data were collected from active TB case files. Statistical analyses were performed and social network analysis conducted using household locations as points of contact between cases. Results: A total of eight active TB cases and 41 LTBI cases were identified as part of the outbreak between December 2018 and May 2019. Half of the cases (4/8) were 25 to 34 years old, and five were smear negative. One-third of the people with LTBI were 15 to 24 years old, and about a half tested positive to the new tuberculin skin test (TST). The commonly reported risk factors for TB and LTBI cases were alcohol use, cigarette use, marijuana use, previous TB infection and homelessness. Social network analysis indicated a relationship between increased node centrality and becoming an active case. Conclusion: Real-time social network contact investigation used in active-case finding was very successful in identifying cases, and enhanced nursing support, mobile clinics and mobile X-ray worked well as a means of confirming cases and offering treatment. TB outbreaks in northern Saskatchewan First Nations on-reserve communities are facilitated by population-specific factors. Efforts to implement context-specific interventions are paramount in managing TB outbreaks and preventing future transmission.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".