Adherence to treatment for latent tuberculosis infection: systematic review of studies in the US and Canada.
Bibliographic record
Abstract
BACKGROUND: There is renewed attention to the critical role of successfully treating latent tuberculosis infection (LTBI) in reducing the overall impact of tuberculosis (TB). However, levels of treatment adherence are consistently low in industrialized countries such as the United States and Canada. OBJECTIVE: A systematic review of studies in the US and Canada was undertaken to analyze measurement of adherence to treatment of LTBI (TLTBI), TLTBI completion rates, predictors of TLTBI adherence and TLTBI adherence interventions. METHODS: PUBMED, MEDLINE and PsycINFO electronic databases were searched for quantitative studies published between 1997 and 2007. Full texts of articles were reviewed for data abstraction and studies were critically examined for their methodology and rigor. The present review presents outcomes from 78 studies. RESULTS: Adherence and completion rates of TLTBI are suboptimal across high-risk groups, regardless of regimen. Associations between adherence and patient factors, clinic facilities or treatment characteristics were found to be inconsistent across studies. Several adherence interventions have been developed to improve TLTBI adherence in the US and Canada; however, no single intervention has shown consistent effectiveness. CONCLUSION: LTBI must be effectively treated if the goal of TB elimination is to be realized. Consistently employing tools for measuring and improving adherence is fundamental. Identifying barriers to adherence and treatment completion will facilitate the development of effective, appropriate interventions. A 'one-size-fits-all' approach to treatment for TLTBI adherence is not likely to succeed across all settings. Innovative approaches can inspire future interventions and suggest solutions for the current problems facing LTBI programs and their patients.
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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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.016 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".