Emergence of drug resistance in patients with tuberculosis cared for by the Indian health-care system: a dynamic modelling study
Bibliographic record
Abstract
BACKGROUND: India has the highest number of patients with tuberculosis and multidrug-resistant tuberculosis in the world. We used a transmission model to project the emergence of drug resistance in India due to incorrect tuberculosis management practices in multiple sectors, including public and private providers, chemists, and non-allopathic practitioners. METHODS: We constructed a dynamic Markov model to represent India's tuberculosis epidemic, including a probabilistic framework reflecting complex treatment-seeking pathways. Underlying drug resistance and the acquisition of drug resistance during treatment were included. India-specific epidemiological data, including tuberculosis management practices, were obtained from published literature. Outcomes, which included annual risk of infection, incidence of new disease, prevalence of untreated tuberculosis, and tuberculosis-related mortality, were stratified by underlying drug resistance, as well as by health sector to understand how each sector contributes to the emergence of drug resistance. FINDINGS: If tuberculosis management practices across sectors in India remain unchanged over the next 20 years, we estimated a 47% increase in the incidence of isoniazid resistance, a 152% increase in multidrug-resistant tuberculosis incidence, a 242% increase in prevalent untreated multidrug-resistant tuberculosis, and a 275% increase in the risk of multidrug-resistant tuberculosis infection. By 2032, an estimated 85% of multidrug-resistant tuberculosis will be primary multidrug-resistant tuberculosis compared with only 15% in 2012. The public sector contributed 87% of acquired multidrug-resistant tuberculosis, related to irregular adherence; the remainder came from the private sector, related to treatment non-completion. Chemists and non-allopathic practitioners do not treat with rifampicin, but because of the high rates of inappropriate isoniazid-containing regimens, and treatment non-adherence, this would generate isoniazid resistance. INTERPRETATION: We predict a gradual transformation from the current epidemic of drug-susceptible tuberculosis to a drug-resistant epidemic. Evidence-based strategies to improve provider practices and patient adherence across health sectors are urgently needed to prevent this. FUNDING: United States Agency for International Development and the Canadian Institutes for Health Research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".