Can we eliminate tuberculosis?: Addressing tuberculosis among Inuit in Canada
Bibliographic record
Abstract
The average annual rate of tuberculosis (TB) among Inuit in Canada is now more than 290 times higher than Canadian born non-Indigenous people. How did this happen? Using the Territory of Nunavut as a case example, the roots of this situation can largely be traced back to social determinants of health and challenges in access to health care. Half (52%) of all Nunavut residents live in social housing, often under overcrowded conditions. Many experience food insecurity, with food prices in Nunavut that are twice those in southern Canada. Sixty percent of Nunavut residents smoke. Challenges in health care delivery include the small isolated communities, with few roads and difficult weather conditions during the long winters, which impede the ability to reach or provide healthcare, staff that arrive with little TB experience or cultural knowledge, multiple competing health care demands, limited resources and high staff turnover. The housing shortage is not only a social determinant of health, it also impacts the ability to hire new staff or mount an effective response in the event of an outbreak. Yet despite these challenges, progress has been made. Tuberculosis care in Nunavut includes active case finding, contact tracing for all cases of infectious TB, and screening of school age children. Rapid testing with the GeneXpert© platform has resulted in a quicker diagnosis of active TB, earlier treatment (preventing progression of disease) and less transmission. Progressively, there has been a switch from plain film to digital x-rays reducing x-ray turnaround time from as long as two to three weeks to one or two days. Standard treatment protocols include quadruple therapy until sensitivities are known, the use of home isolation for active cases and directly observed treatment (DOT) for both latent and active TB. Special access to rifapentine (Priftin), and its use in combination therapy (3HP), requires only once weekly treatments that can be completed in 12 visits instead of 78 visits for isoniazid (INH) or 120 visits for rifampin, which increases adherence and greatly reduces the health care resources needed to treat TB. In October 2017, the Honourable Jane Philpott, then Minister of Health and now Minister of Indigenous Services, and Natan Obed, president of Inuit Tapiriit Kanatami (ITK) announced the establishment of a Task Force to develop an Inuit TB Elimination Action Framework, accompanied by regional action plans. It is hoped that the task force, and current efforts in Nunavut, will lead to the long term changes needed to ultimately eliminate TB among Inuit in Canada.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".