Evidence map›Paper›PMID 21674033›Full record

SynthesisPloS one2011

Prevalence of drug-resistant tuberculosis in mainland China: systematic review and meta-analysis.

Yu Yang, Xiangwei Li, Feng Zhou, Qi Jin, Lei Gao

Abstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 10 pooled it
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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, 10 syntheses or guidelines pooled it.

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  2. Incentives and enablers to improve adherence in tuberculosis.The Cochrane database of systematic reviews · 2015
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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

5 authors.

Yu YangState Key Laboratory for Molecular Virology and Genetic Engineering, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiangwei Li
Feng Zhou
Qi Jin
Lei Gao

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe spread of drug-resistant tuberculosis (TB) is one of the major public health problems in the world. Surveillance of anti-TB drug resistance is important for monitoring TB control strategies. However, the status of drug-resistant TB in China has been reported inconsistently.

methodsWe systematically reviewed published studies on drug-resistant TB in China until March 31, 2011, and quantitatively summarized prevalence and patterns of anti-TB drug resistance among new cases and previously treated cases, respectively.

resultsNinety-five eligible articles, published during 1993-2011, were included in this review. The meta-analyses showed that the prevalence of drug-resistant TB in new cases was 27.9% (95% CI, 25.6%-30.2%) (n/N = 27,360/104,356) and in previously treated cases was 60.3% (95% CI, 56.2%-64.2%) (n/N = 30,350/45,858). Furthermore, in these two study populations, the prevalence of multiple drug resistance was found to be 5.3% (95% CI, 4.4%-6.4%) (n/N = 8810/101,718) and 27.4% (95% CI, 24.1%-30.9%) (n/N = 10,486/44,530) respectively. However, the results were found to be frequently heterogeneous (p for Q tests <0.001). The most common resistance was observed for isoniazid among both study populations. Different patterns of drug resistance were observed in the subgroup analysis with respect to geographic areas, drug susceptibility testing methods and subject enrollment time.

conclusionsResults of meta-analyses indicated a severe status of drug-resistant TB in China, which attaches an importance to strength TB prevention and control.

Indexed as

Drug Resistance, BacterialChinaHumansTuberculosis

Identifiers

PMID21674033
PMCPMC3108589

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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.