Modeling the Impact of Alternative Strategies for Rapid Molecular Diagnosis of Tuberculosis in Southeast Asia
Bibliographic record
Abstract
Novel diagnostic tests hold promise for improving tuberculosis (TB) control, but their epidemiologic impact remains uncertain. Using data from the World Health Organization (2011-2012), we developed a transmission model to evaluate the deployment of 3 hypothetical TB diagnostic tests in Southeast Asia under idealized scenarios of implementation. We defined diagnostics by their sensitivity for smear-negative TB and proportion of patients testing positive who initiate therapy ("point-of-care amenability"), with tests of increasing point-of-care amenability having lower sensitivity. Implemented in the public sector (35% of care-seeking attempts), each novel test reduced TB incidence by 7%-9% (95% uncertainty range: 4%-13%) and mortality by 20%-22% (95% uncertainty range: 14%-27%) after 10 years. If also deployed in the private sector (65% of attempts), these tests reduced incidence by 13%-16%, whereas a perfect test (100% sensitivity and treatment initiation) reduced incidence by 20%. Annually detecting 20% of prevalent TB cases through targeted screening (70% smear-negative sensitivity, 85% treatment initiation) also reduced incidence by 19%. Sensitivity and point-of-care amenability are equally important considerations when developing novel diagnostic tests for TB. Novel diagnostics can substantially reduce TB incidence and mortality in Southeast Asia but are unlikely to transform TB control unless they are deployed actively and in the private sector.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".