Tuberculosis among Newly Arrived Immigrants and Refugees in the United States
Bibliographic record
Abstract
Abstract Rationale U.S. health departments routinely conduct post-arrival evaluation of immigrants and refugees at risk for tuberculosis (TB), but this important intervention has not been thoroughly studied. Objectives To assess outcomes of the post-arrival evaluation intervention. Methods We categorized at-risk immigrants and refugees as having had recent completion of treatment for pulmonary TB disease overseas (including in Mexico and Canada); as having suspected TB disease (chest radiograph/clinical symptoms suggestive of TB) but negative culture results overseas; or as having latent TB infection (LTBI) diagnosed overseas. Among 2.1 million U.S.-bound immigrants and refugees screened for TB overseas during 2013–2016, 90,737 were identified as at risk for TB. We analyzed a national data set of these at-risk immigrants and refugees and calculated rates of TB disease for those who completed post-arrival evaluation. Results Among 4,225 persons with recent completion of treatment for pulmonary TB disease overseas, 3,005 (71.1%) completed post-arrival evaluation within 1 year of arrival; of these, TB disease was diagnosed in 22 (732 cases/100,000 persons), including 4 sputum culture–positive cases (133 cases/100,000 persons), 13 sputum culture–negative cases (433 cases/100,000 persons), and 5 cases with no reported sputum-culture results (166 cases/100,000 persons). Among 55,938 with suspected TB disease but negative culture results overseas, 37,089 (66.3%) completed post-arrival evaluation; of these, TB disease was diagnosed in 597 (1,610 cases/100,000 persons), including 262 sputum culture–positive cases (706 cases/100,000 persons), 281 sputum culture–negative cases (758 cases/100,000 persons), and 54 cases with no reported sputum-culture results (146 cases/100,000 persons). Among 30,574 with LTBI diagnosed overseas, 18,466 (60.4%) completed post-arrival evaluation; of these, TB disease was diagnosed in 48 (260 cases/100,000 persons), including 11 sputum culture–positive cases (60 cases/100,000 persons), 22 sputum culture–negative cases (119 cases/100,000 persons), and 15 cases with no reported sputum-culture results (81 cases/100,000 persons). Of 21,714 persons for whom treatment for LTBI was recommended at post-arrival evaluation, 14,977 (69.0%) initiated treatment and 8,695 (40.0%) completed treatment. Conclusions Post-arrival evaluation of at-risk immigrants and refugees can be highly effective. To optimize the yield and impact of this intervention, strategies are needed to improve completion rates of post-arrival evaluation and treatment for LTBI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".