Tuberculosis drug resistance in Canada: 2018
Bibliographic record
Abstract
BACKGROUND: Drug-resistant tuberculosis (TB) is a public health issue of global importance that poses a threat to TB control efforts. Canada conducts nationwide surveillance to monitor emerging drug resistance trends and document progress towards reaching the goal of TB elimination. OBJECTIVE: To describe TB drug resistance trends across Canada from 2008-2018, with a focus on 2018, by drug resistance, geographic and demographic patterns. METHODS: TB drug resistance data are captured through two independent surveillance systems managed by the Public Health Agency of Canada: Canadian Tuberculosis Laboratory Surveillance System (CTBLSS) and the Canadian Tuberculosis Reporting System (CTBRS). Data from these systems were analyzed and descriptive statistics were reported by resistance profile, place of residence (province), age groups, sex and country of birth. RESULTS: In 2018, 1,459 TB isolates underwent drug susceptibility testing, a 4.3% decrease from 2017. Resistance to any first-line drug was reported in 148 isolates (10.1%), compared to 123 (8.1%) in 2017. Of these, 121 were monoresistant, five were polyresistant, 21 were multidrug-resistant tuberculosis (MDR-TB) and one was extensively drug-resistant TB (XDR-TB). Drug resistance was reported in all provinces and territories except Prince Edward Island, Northwest Territories and Yukon. Among individuals younger than 15 years, very little TB drug resistance was detected. Among individuals aged 15 years and older, the distribution of TB drug resistance varied with no discernable trends. The proportion of drug resistance was slightly higher in females than in males. By origin, 10.7% of foreign-born TB cases reported between 2006 and 2016 were drug-resistant. Among the Canadian-born non-Indigenous cases, 9.3% were drug resistant; among Canadian-born Indigenous, 2.4% were drug resistant. CONCLUSION: In 2018, the proportion of isolates with TB drug resistance in Canada remained low and below global averages, with stable drug resistance, both geographically and demographically.
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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 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".