The problem with defining foreign birth as a risk factor in tuberculosis epidemiology studies
Bibliographic record
Abstract
OBJECTIVE: To examine how stratifying persons born outside Canada according to tuberculosis (TB) incidence in their birth country and other demographic factors refines our understanding of TB epidemiology and local TB transmission. BACKGROUND: Population-level TB surveillance programs and research studies in low incidence settings often report all persons born outside the country in which the study is conducted as "foreign-born"-a single label for a highly diverse population with variable TB risks. This may mask important TB epidemiologic trends and not accurately reflect local transmission patterns. METHODS: We used population-level data from two large cohorts in British Columbia (BC), Canada: an immigration cohort (n = 337,492 permanent residents to BC) and a genotyping cohort (n = 2290 culture-confirmed active TB cases). We stratified active TB case counts, incidence rates, and genotypic clustering (an indicator of TB transmission) in BC by birth country TB incidence, age at immigration, and years since arrival. RESULTS: Persons from high-incidence countries had a 12-fold higher TB incidence than those emigrating from low-incidence settings. Estimates of local transmission, as captured by genotyping, versus reactivation of latent TB infection acquired outside Canada varied when data were stratified by birthplace TB incidence, as did patient-level characteristics of individuals in each group, such as age and years between immigration and diagnosis. CONCLUSION: Categorizing persons beyond simply "foreign-born", particularly in the context of TB epidemiologic and molecular data, is needed for a more accurate understanding of TB rates and patterns of transmission.
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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 | 0.670 | 0.753 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".