Prevalence and patterns of drug-resistant Mycobacterium tuberculosis in newly diagnosed patients in China: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB), one of the deadliest infectious diseases globally, is increasingly exacerbated in China by the emergence of resistant Mycobacterium tuberculosis (MTB) strains. Drug-resistant TB, including mono-drug-resistant TB, multidrug-resistant TB (MDR-TB), and extensively drug-resistant TB (XDR-TB), presents significant public health challenges. METHODS: We conducted a systematic literature review from January 2010 to February 2024 using databases such as PubMed, Embase, Web of Science, and Google Scholar. Our focus was on empirical data related to drug resistance patterns in newly diagnosed TB cases. Non-empirical studies were excluded through meticulous filtering. For the meta-analysis, we used Review Manager (RevMan) 5.2 and assessed evidence quality using the Newcastle-Ottawa Scale (NOS). RESULTS: Our search strategy identified 40 studies that met the inclusion criteria, encompassing a total sample size of 87,667 participants. Among new TB cases, the estimated prevalence of MDR-TB in China was 6.9% (95% CI: 5.6-8.1%). Prevalence rates for mono-drug resistance to first-line anti-TB medications were as follows: isoniazid at 18.2% (95% CI: 16.4-20.6%), rifampicin at 10.5% (95% CI: 8.6-12.8%), and ethambutol at 5.7% (95% CI: 4.1-7.3%). The prevalence of streptomycin resistance, a former first-line anti-TB drug, was 17.1% (95% CI: 14.6-19.1%). The prevalence of other types of mono-drug resistance was 15.2% (95% CI: 13.9-17.3%), and for XDR-TB, it was 0.9% (95% CI: 0.6-1.4%). CONCLUSIONS: The high prevalence of drug-resistant TB in China poses a significant public health challenge. There is an urgent need for targeted interventions and continued surveillance to combat the spread of drug-resistant TB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".