Examining DNA fingerprinting as an epidemiology tool in the tuberculosis program in the Northwest Territories, Canada
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) is an important public health problem in the Northwest Territories (NWT), particularly among Canadian Aboriginal people. OBJECTIVE: To analyse the transmission patterns of tuberculosis among the population living in the NWT, a territorial jurisdiction located within Northern Canada. METHODS: This population-based retrospective study examined the DNA fingerprints of all laboratory confirmed cases of TB in the NWT, Canada, between 1990 and 2009. An isolate of each lab-confirmed case had genotyping done using IS6110 Restriction Fragment Length Polymorphism. DNA patterns were assigned to each DNA fingerprint, and indistinguishable fingerprints patterns were assigned a cluster. Social network analysis (SNA) was used to examine direct linkages among cases determined through conventional contact tracing (CCT), their DNA fingerprint and home community. RESULTS: Of the 225 lab-confirmed cases identified, the study was limited to 195 subjects due to DNA fingerprinting data availability. The mean age of the cases was 43.8 years (±22.6) and 120 (61.5%) males. The Dene (First Nations) encompassed 120 of the cases (87.7%), 8 cases (4.1%) were Inuit, 2 cases (1.0%) were Metis, 7 cases (3.6%) were Immigrants and 1 case had unknown ethnicity. One hundred and eighty six (95.4%) subjects were clustered, resulting in 8 clusters. Trend analysis showed significant relationships between with risk factors for unemployment (p=0.020), geographic location (p≤0.001) and homelessness (p≤0.001). Other significant risk factors included excessive alcohol consumption, prior infection with Mycobacterium tuberculosis and prior contact with a case of TB. CONCLUSIONS: This study demonstrates how DNA fingerprinting and SNA can be additional epidemiological tools, along with CCT method, to determine transmission patterns of TB.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".