The Global Fund in China: Multidrug-resistant tuberculosis nationwide programmatic scale-up and challenges to transition to full country ownership
Bibliographic record
Abstract
China has the world's second largest burden of multidrug-resistant tuberculosis (MDR-TB; resistance to at least isoniazid and rifampicin), with an estimated 57,000 cases (range, 48,000-67,000) among notified pulmonary TB patients in 2015. During October 1, 2006-June 30, 2014, China expanded MDR-TB care through a partnership with the Global Fund to Fight AIDS, Tuberculosis, and Malaria (Global Fund). We analyzed data on site expansion, patient enrolment, treatment outcomes, cost per patient, and overall programme expenditure. China expanded MDR-TB diagnostic and treatment services from 2 prefectures in 2006 to 92 prefectures, covering 921 of the country's 3,000 counties by June 2014. A total of 130,910 patients were tested for MDR-TB, resulting in 13,744 laboratory-confirmed cases, and 9,183 patients started on MDR-TB treatment. Treatment success was 48.4% (2011 cohort). The partnership between China and the Global Fund resulted in enormous gains. However, changes to health system TB delivery and financing coincided with the completion of the Global Fund Programme, and could potentially impact TB and MDR-TB control. Transition to full country financial ownership is proving difficult, with a decline in enrollment and insufficient financial coverage. Given needed improvement to the current treatment success rates, these factors jeopardise investments made for MDR-TB control and care. China now has a chance to cement its status in TB control by strengthening future financing and ensuring ongoing commitment to quality service delivery.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".