Interventions to reduce losses in the cascade of care for latent tuberculosis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Losses can occur throughout the latent tuberculosis infection (LTBI) cascade of care. This can result in suboptimal rates of effective treatment for LTBI. We conducted a systematic review and meta-analysis to estimate the effect of different interventions to reduce losses in the LTBI cascade before treatment completion. METHODS: We searched several databases for articles reporting outcomes for interventions designed to strengthen the LTBI cascade. We included papers published in English from January 1990 until February 2018. Where possible, estimates were pooled using random-effects meta-analysis. RESULTS: We identified 30 studies that evaluated 32 different interventions aimed at reducing losses in the LTBI cascade. In pooled analysis, interventions that improved completion of cascade steps included patient incentives (respectively 42 [95% CI 34–51] and 48 [95% CI 15–81] additional patients completing initial assessment and medical evaluation per 100 starting); health care worker education (28 [95% CI 4–52] additional patients initiating initial assessment per 100 identified; home visits (additional 13 [95% CI 4–21] patients completing initial assessment per 100 starting); digital solutions (additional 11 [95% CI 4–21] patients initiating initial assessment per 100 identified); and patient reminders (additional 7 [95% CI 0.3–13] patients completing initial assessment per 100 starting). Several other interventions reduced losses at specific cascade steps, but evidence for these interventions came from single studies and could not be pooled. CONCLUSIONS: Although there is limited evidence that any single intervention significantly improves the LTBI cascade, many studies provide information about effective ways to strengthen it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".