How much do Indians pay for tuberculosis treatment? A cost analysis
Bibliographic record
Abstract
SETTING: India's National Tuberculosis Elimination Programme (NTEP) covers diagnostic and therapeutic costs of TB treatment. However, persons living with TB (PLWTB) continue to experience financial distress due to direct costs (payment for testing, treatment, travel, hospitalization, and nutritional supplements) and indirect costs (lost wages, loan interest, and cost of domestic helpers). OBJECTIVE: To analyze the magnitude and pattern of TB-related costs from the perspective of Indian PLWTB. DESIGN: We identified relevant articles using key search terms ('tuberculosis,' 'India,' 'cost,' 'expenditures,' 'financing,' 'catastrophic' and 'out of pocket') and calculated variance-weighted mean costs. RESULTS: Indian patients incur substantial direct costs (mean: US$46.8). Mean indirect costs (US$666.6) constitute 93.4% of the net costs. Mean direct costs before diagnosis can be up to four-fold that of costs during treatment. Treatment in the private sector can result in costs up to six-fold higher than in government facilities. As many as one in three PLWTB in India experience catastrophic costs. CONCLUSION: PLWTB in India face high direct and indirect costs. Priority interventions to realize India's goal of eliminating catastrophic costs from TB include decreasing diagnostic delays through active case finding, reducing the need for travel, improving awareness and perception of NTEP services, and ensuring sufficient reimbursement for inpatient TB care.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".