Modeling the impact of tuberculosis interventions on epidemiologic outcomes and health system costs
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) programs must invest in a variety of TB specific activities in order to reach ambitious global targets. Uncertainty exists surrounding the potential impact of each of these activities. The objective of our study was to model different interventions and quantify their impact on epidemiologic outcomes and costs from the health system perspective. METHODS: Decision analysis was used to define the TB patient trajectory within the health system of three different countries. We considered up to seven different interventions that could affect either the natural history of TB, or patient trajectories within the health system. The expected impact of interventions were derived from published studies where possible. Epidemiologic outcomes and associated health system costs were projected for each scenario. RESULTS: With no specific intervention, TB related death rates are high and less than 10% of the population starts on correct treatment. Interventions that either prevent cases or affect all patients with TB disease early in their trajectory are expected to have the biggest impact, regardless of underlying epidemiologic characteristics of the setting. In settings with a private sector, improving diagnosis and appropriate treatment across all sectors is expected to have a major impact on outcomes. CONCLUSION: In all settings, the greatest benefit will come from early diagnosis of all forms of TB. Once this has been achieved more specific interventions, such as those targeting HIV, drug resistance or the private sector can be integrated to increase impact.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".