Change in the Prevalence of Testing for Latent Tuberculosis Infection in the United States: 1999–2012
Bibliographic record
Abstract
Purpose. There is no information on the change in prevalence of latent tuberculosis infection (LTBI) testing in the United States (US) following the introduction of the interferon gamma release assay (IGRA), a new and alternative diagnostic method for LTBI. The purpose of this study was to evaluate potential changes in the prevalence of LTBI testing in the US following the introduction of IGRA. Methods. This was a multiyear cross-sectional study using nationally representative data from the 1999-2000 and 2011-2012 US National Health and Nutrition Examination Surveys. Self-reported prevalence of LTBI testing was estimated among groups known to have increased LTBI risk. Descriptive statistics were used. Results. Compared to 1999-2000, significantly fewer individuals self-reported being tested for LTBI in 2011-2012 among Hispanic Americans (68.0% versus 60.7%, p < 0.0001) and among those with comorbidities (74.7% versus 72.0%, p = 0.02). There were also nonsignificant trends towards less self-reported LTBI testing in 2011-2012 versus 1999-2000 among household contacts of active TB cases, foreign-born individuals, and African Americans. Conclusions. Despite the introduction of IGRA, LTBI testing occurs less frequently in the US among vulnerable groups. Possibly inadequate targeted LTBI testing could result in increased active TB in the US in the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".