Barriers to treatment adherence for individuals with latent tuberculosis infection: A systematic search and narrative synthesis of the literature
Bibliographic record
Abstract
OBJECTIVES: We investigated the rates of initiation and completion of treatment for latent TB infection (LTBI), factors explaining nonadherence and interventions to improve treatment adherence in countries with low TB incidence. DESIGN: A systematic search was performed in PubMed and Embase. All included articles were assessed for risk of bias. A narrative synthesis of the results was conducted. RESULTS: There were 54 studies included in this review. The proportion of people initiating treatment varied from 24% to 98% and the proportion of people completing treatment varied from 19% to 90%. The main barriers to adherence included the fear or experience of adverse effects, long duration of treatment, financial barriers, lack of transport to clinics (for patients), and insufficient resources for LTBI control. While interventions like peer counseling, incentives, and culturally specific case management have been used to improve adherence, the proportion of people who initiate and complete LTBI treatment still remains low. CONCLUSION: To further improve treatment and LTBI control and to fulfill the World Health Organization goal of eliminating TB in low-incidence countries, greater priority should be given to the use of treatment regimens involving shorter durations and fewer adverse effects, like the 3-month regimen of weekly rifapentine plus isoniazid, supported by innovative patient education and incentive strategies.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".