Tuberculosis drug resistance in Canada: 2017
Bibliographic record
Abstract
BACKGROUND: Drug-resistant tuberculosis (TB) is a global public health issue. To monitor this in Canada, surveillance systems have been in place for the last 20 years. OBJECTIVE: To describe drug resistance patterns among TB isolates in Canada in 2017 by type of resistance as well as geographic location, demographic data and origin and to compare current data to those of the previous 10 years. METHODS: Data were derived and analyzed from two sources. The Canadian Tuberculosis Laboratory Surveillance System (CTBLSS) is an isolate-based laboratory surveillance system and was used to obtain information on the results of drug susceptibility testing (DST) as well as province or territory, sex and age of the individual from which the sample originated. The Canadian Tuberculosis Reporting System (CTBRS) is a case-based surveillance system with information on active and retreatment TB cases in Canada and was used to derive origin data, which is defined as either foreign-born, Canadian-born Indigenous or Canadian-born non-Indigenous. Analysis was descriptive and compared with data from these two sources for 2007-2016. RESULTS: In 2017, 1,515 TB isolates were tested for resistance to anti-TB drugs, with 123 (8.1%) demonstrating resistance to any first-line anti-TB drug. Of these, 103 were monoresistant, six were polyresistant and 14 were multidrug-resistant tuberculosis (MDR-TB). No extensively drug-resistant tuberculosis (XDR-TB) isolates were reported. Drug resistance was reported in seven provinces/territories (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec and New Brunswick). There were 63 isolates from females with drug resistance (9.5%) and 60 isolates from males with drug resistance (7.0%). Drug resistance was found in a greater percentage of isolates among those aged 25-34 (n=29, 23.6%). By origin, 1,072 (11%) foreign-born TB cases reported between 2005 and 2015 were drug-resistant. Among the Canadian-born non-Indigenous and Canadian-born Indigenous TB cases, 143 (9%) and 54 (2%) were drug-resistant, respectively. Compared with previous years, the number of isolates tested increased slightly (from 1,267 to 1,515); however, there was a decrease in the percentage of isolates with reported drug resistance (from 10.5% in 2007 to 8.1% in 2017). CONCLUSION: In 2017, TB drug resistance rates remained low 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".