Expanding the epidemiologic profile: risk factors for active tuberculosis in people immigrating to Ontario.
Bibliographic record
Abstract
BACKGROUND: Many people immigrating to Canada come from countries with a high burden of tuberculosis. The aim of this study was to develop a detailed epidemiologic profile of foreign-born people with tuberculosis living in Ontario. METHODS: In this population-based case-control study, cases of tuberculosis diagnosed in 1994-1995 were identified from the database of the Ontario Reportable Disease Information Service and were considered eligible for analysis if a record of landing (receipt of permission to establish residence in Canada) from the period 1986-1995 was found in the Citizenship and Immigration Canada (CIC) database, if the person was at least 11 years of age at the time their visa was issued, and if the person had not been diagnosed with tuberculosis before becoming legally landed in Canada. Control subjects, who met the same criteria as the case subjects but who did not have tuberculosis in 1994-1995, were identified from a CIC database for landed immigrants. RESULTS: A total of 1341 cases of tuberculosis in foreign-born people were reported in Ontario in 1994-1995. A record of landing was found in CIC databases for 1099 of these people, 224 of whom were not legally landed at the time of diagnosis. In total, 602 cases met the inclusion criteria. The 2 strongest determinants of risk among those who had become landed within the preceding 10 years were referral for medical surveillance by immigration officials (odds ratio [OR] 3.8, 95% confidence interval [CI] 2.6-6.0) and world region of origin (Somalia [OR 67.7, 95% CI 31.3-154.9], Vietnam [OR 25.0, 95% CI 12.5-50.0], the Philippines [OR 11.9, 95% CI 6.0-23.3], other sub-Saharan African countries [OR 11.6, 95% CI 5.7-23.2], India [OR 9.7, 95% CI 4.9-18.9], China [OR 6.1, 95% CI 3.1-12.1], other Asian countries [OR 4.7, 95% CI 2.4-9.1], the Middle East [OR 4.1, 95% CI 2.0-8.3], Latin America [OR 1.9, 95% CI 0.9-3.8), and the former socialist countries of Europe [OR 1.8, 95% CI 0.8-3.8]; the reference category was countries with established market economies). Low socioeconomic status was an independent risk factor. INTERPRETATION: The risk of tuberculosis in groups of people migrating to Ontario is highly variable and is influenced by several factors. Successful population-based tuberculosis prevention strategies will need to accommodate this variability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".