The Challenge of Multisite Epidemiologic Studies in Diverse Populations: Design and Implementation of a 22-Site Study of Tuberculosis in Foreign-Born People
Bibliographic record
Abstract
OBJECTIVES: We designed a population-based study of the epidemiology of tuberculosis among foreign-born people in the U.S. and Canada. Challenges included standardizing recruitment and data entry at 22 sites, enrolling individuals who did not speak English and may be undocumented, and obtaining clearance from 36 institutional review boards (IRBs). METHODS: We used stratified sampling to recruit patients through the Tuberculosis Epidemiologic Studies Consortium, a research consortium funded by the Centers for Disease Control and Prevention. Because recruitment sites were overseen by more than 30 local IRBs, we developed a simple process to designate a central IRB. We translated instruments into 10 main languages, arranged for fast translation of consent "short forms" into other languages, used one telephone interpretation service at all sites, and provided extensive interviewer training including mock interviews with simulated patients. RESULTS: We interviewed 1,696 participants in 19 states and provinces. Participants from 99 countries were interviewed in 40 languages. Twenty-three percent did not speak English at all; 64% needed an interpreter. More than 20% of participants reported they were undocumented. Participants' age, gender, and birthplaces were broadly similar to the target populations. One-third of local IRBs used the central IRB. CONCLUSIONS: Special confidentiality protections, substantial resources for translation and interpretation, and a centralized IRB made possible the recruitment of a representative sample of foreign-born people. The approaches may be applicable to studies of other diseases in multinational populations in the U.S. and Canada.
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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 | 0.411 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".