Acceptance of treatment for latent tuberculosis infection: prospective cohort study in the United States and Canada
Bibliographic record
Abstract
SETTING: An estimated 300 000 individuals are treated for latent tuberculosis infection (LTBI) in the United States and Canada annually. Little is known about the proportion or characteristics of those who decline treatment. OBJECTIVE: To define the proportion of individuals in various groups who accept LTBI treatment and to identify factors associated with non-acceptance of treatment. DESIGN: Persons offered LTBI treatment at 30 clinics in 12 Tuberculosis Epidemiologic Studies Consortium sites were prospectively enrolled. Multivariate regression models were constructed based on manual stepwise assessment of potential predictors. RESULTS: Of 1692 participants enrolled from March 2007 to September 2008, 1515 (89.5%) accepted treatment and 177 (10.5%) declined. Predictors of acceptance included believing one could personally spread TB germs, having greater TB knowledge, finding clinic schedules convenient and having low acculturation. Predictors of non-acceptance included being a health care worker, being previously recommended for treatment and believing that taking medicines would be problematic. CONCLUSION: This is the first prospective multisite study to examine predictors of LTBI treatment acceptance in general clinic populations. Greater efforts should be made to increase acceptance among health care workers, those previously recommended for treatment and those who expect problems with LTBI medicines. Ensuring convenient clinic schedules and TB education to increase knowledge could be important for ensuring acceptance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".