The latent tuberculosis infection cascade of care in Iqaluit, Nunavut, 2012–2016
Bibliographic record
Abstract
BACKGROUND: A remote arctic region of Canada predominantly populated by Inuit with the country's highest incidence of tuberculosis. METHODS: The study was undertaken to describe the latent tuberculosis infection (LTBI) cascade of care and identify factors associated with non-initiation and non-completion of LTBI treatment. Data were extracted retrospectively from medical records for all patients with a tuberculin skin test (TST) implanted in Iqaluit, Nunavut between January 2012 and March 2016. Associations between demographic and clinical factors and both treatment non-initiation among and treatment non-completion were identified using log binomial regression models where convergence could be obtained and Poisson models with robust error variance where convergence was not obtained. RESULTS: Of 2303 patients tested, 439 (19.1%) were diagnosed with LTBI. Treatment was offered to 328 patients, was initiated by 246 (75.0% of those offered) and was completed by 186 (75.6% of initiators). In multivariable analysis, older age (adjust risk ratio [aRR] 1.17 per 5-year increase, 95%CI:1.09-1.26) and undergoing TST due to employment screening (aRR 1.63, 95%CI:1.00-2.65, compared to following tuberculosis exposure) were associated with increased non-initiation of treatment. Older age (aRR 1.13, 95%CI: 1.03-1.17, per 5-year increase) was associated with increased non-completion of treatment. CONCLUSIONS: A similar rate of treatment initiation and higher rate of treatment completion were found compared to previous North American studies. Interventions targeting older individuals and those identified via employment screening may be considered to help to address the largest losses in the cascade of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".