Evidence map›Paper›PMID 26859846›Full record

SynthesisPloS one2016

The Prevalence of Drug-Resistant Tuberculosis in Mainland China: An Updated Systematic Review and Meta-Analysis.

Qionghong Duan, Zi Chen, Cong Chen, Zhengbin Zhang, Zhouqin Lu, Yalong Yang, Lin Zhang

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Review
  6. Article
  7. Article
  8. Observational
  9. Article
  10. Review
  11. Article
  12. Whole-genome sequencing reveals transmission pattern and drug resistance ofFrontiers in cellular and infection microbiology · 2024
    Article
  13. Article
  14. Article
  15. Tropical medicine and infectious disease · 2022
    Review
  16. Drug resistance characteristics ofEpidemiology and infection · 2022
    Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Qionghong DuanDepartment of Tuberculosis Prevention, Wuhan Tuberculosis Institution, Wuhan, Hubei, China.
Zi ChenDepartment of Tuberculosis Prevention, Wuhan Tuberculosis Institution, Wuhan, Hubei, China.
Cong ChenDepartment of Tuberculosis Prevention, Wuhan Tuberculosis Institution, Wuhan, Hubei, China.
Zhengbin ZhangDepartment of Tuberculosis Prevention, Wuhan Tuberculosis Institution, Wuhan, Hubei, China.
Zhouqin LuDepartment of Tuberculosis Prevention, Wuhan Tuberculosis Institution, Wuhan, Hubei, China.
Yalong YangDepartment of Thyroid-Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Lin ZhangDepartment of Thyroid-Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn recent years, drug resistant tuberculosis (DR-TB) particularly the emergence of multi-drug-resistant tuberculosis (MDR-TB) has become a major public health issue. The most recent study regarding the prevalence of drug-resistant tuberculosis in mainland China was a meta-analysis published in 2011, and the subjects from the included studies were mostly enrolled before 2008, thus making it now obsolete. Current data on the national prevalence of DR-TB is needed. This review aims to provide a comprehensive and up-to-date assessment of the status of DR-TB epidemic in mainland China.

methodsA systematic review and meta-analysis of studies regarding the prevalence of drug-resistant tuberculosis in mainland China was performed. Pubmed/MEDLINE, EMBASE, the Cochrane central database, the Chinese Biomedical Literature Database and the China National Knowledge Infrastructure Database were searched for studies relevant to drug-resistant tuberculosis that were published between January 1, 2012 and May 18, 2015. Comprehensive Meta-Analysis (V2.2, Biostat) software was used to analyse the data.

resultsA total of fifty-nine articles, published from 2012 to 2015, were included in our review. The result of this meta-analysis demonstrated that among new cases, the rate of resistance to any drug was 20.1% (18.0%-22.3%; n/N = 7203/34314) and among retreatment cases, the rate was 49.8% (46.0%-53.6%; n/N = 4155/8291). Multi-drug resistance among new and retreatment cases was 4.8% (4.0%-5.7%; n/N = 2300/42946) and 26.3% (23.1%-29.7%; n/N = 3125/11589) respectively. The results were significantly heterogeneous (p<0.001, I2 tests). Resistance to isoniazid was the most common resistance observed, and HRSE (H: isoniazid; R: rifampicin; S: streptomycin; E: ethambutol) was the most common form for MDR among both new and retreatment cases. Different drug resistance patterns were found by subgroup analysis according to geographic areas, subject enrolment time, and methods of drug susceptibility test (DST).

conclusionsThe prevalence of resistance to any drug evidently dropped for both new and retreatment cases, and multi-drug resistance declined among new cases but became more prevalent among retreatment cases compared to the data before 2008. Therefore, drug-resistant tuberculosis, particularly multi-drug-resistant tuberculosis among retreatment TB cases is a public health issue in China that requires a constant attention in order to prevent increase in MDR-TB cases.

Indexed as

ChinaHumansPrevalenceTuberculosis, Multidrug-Resistant

Identifiers

PMID26859846
PMCPMC4747587

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.