Changes in treatment for multidrug-resistant tuberculosis according to national income
Bibliographic record
Abstract
The aim of this study was to analyse temporal changes in treatments for and outcomes of multidrug-resistant (MDR)/rifampin-resistant (RR)-tuberculosis (TB) in the context of national economic status.We analysed data collected by the Collaborative Group for the Meta-Analysis of Individual Patient Data in MDR-TB Treatment on MDR/RR-TB patients from 37 countries. The data were stratified by three national income levels (low-/lower-middle, upper-middle and high) and grouped by time of treatment initiation (2001-2003, 2004-2006, 2007-2009, 2010-2012 and 2013-2015). Temporal trends over the study period were analysed. The probability of treatment success in different income groups over time was calculated using generalised linear mixed models with random effects.In total, 9036 patients were included in the analysis. Over the study period, use of group A drugs (levofloxacin/moxifloxacin, bedaquiline and linezolid) recommended by the World Health Organization increased and treatment outcomes improved in all income groups. Between 2001-2003 and 2013-2015, treatment success rates increased from 60% to 78% in low-/lower-middle-income countries, from 40% to 67% in upper-middle-income countries, and from 73% to 81% in high-income countries. In earlier years, the probability of treatment success in upper-middle-income countries was lower than that in low-/lower-middle-income countries, but no difference was observed after 2010. However, high-income countries had persistently higher probability of treatment success compared to upper-middle income countries.Improved treatment outcomes and greater uptake of group A drugs were observed over time for patients with MDR/RR-TB at all income levels. However, treatment outcomes are still unsatisfactory, especially in upper-middle-income countries.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".