The epidemiology of tuberculosis among foreign-born persons in Alberta, Canada, 1989-1998: identification of high risk groups.
Bibliographic record
Abstract
BACKGROUND: To date, there have been no comprehensive epidemiological studies in Canada of the incidence rates of tuberculosis among foreign-born persons. Accurate rate estimates are needed to develop cost-effective strategies for the prevention, control, and ultimate elimination of tuberculosis. METHODS: Most new immigrants to Canada settle in one of four provinces: Ontario, British Columbia, Quebec and Alberta. Data from the provincial reporting system for tuberculosis, from Statistics Canada censuses and population estimates of Treaty Status Indians provided by the Department of Indian and Northern Affairs Canada, were used to estimate the rate of tuberculosis in Alberta's Canadian-born and foreign-born population between 1989 and 1998. RESULTS: Age-adjusted tuberculosis case rates per 100,000 person-years were 19.4 and 61.9 in the foreign-born and Canadian-born Treaty Indians-approximately 10 times and 30 times, respectively, higher than the rate in the remainder of the Canadian-born population (2.1/100,000 person-years). Age-specific rates in male and female foreign-born persons were highest at ages 15-34 and > or = 65 years, whereas rates in Canadian-born non-Treaty and Treaty males and females increased incrementally, with a single peak at > or = 65 years. Tuberculosis case rates in the foreign-born varied significantly according to World Bank region and country of birth. By far the highest rates were seen in immigrants to Canada from Asia, in particular Vietnam, the Philippines, China, Hong Kong and India. Most (90%) tuberculosis patients from these countries had not been identified as requiring medical surveillance in Canada after arrival. CONCLUSION: Immigrants to Alberta from Asia and Treaty Status Indians are at high risk for tuberculosis. Physicians in Alberta who care for foreign-born persons should be aware that their patients are at increased risk for tuberculosis, even if they have been medically cleared during the legal immigration process.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".