Evidence map›Paper›PMID 32513262›Full record

ArticleInfectious diseases of poverty2020

Rising challenge of multidrug-resistant tuberculosis in China: a predictive study using Markov modeling.

Bing-Ying Li, Wen-Pei Shi, Chang-Ming Zhou, Qi Zhao, Vinod K Diwan, Xu-Bin Zheng, Yang Li, Sven Hoffner, Biao Xu

Abstract read
In one paragraph

Article in Infectious diseases of poverty, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–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

16 citing papers in PubMed.

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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

9 authors.

Bing-Ying LiSchool of Public Health, Fudan University, Shanghai, China.
Wen-Pei ShiSchool of Public Health, Fudan University, Shanghai, China.
Chang-Ming ZhouSchool of Public Health, Fudan University, Shanghai, China.
Qi ZhaoSchool of Public Health, Fudan University, Shanghai, China.
Vinod K DiwanDepartment of Public Health Sciences (Global Health/IHCAR), Karolinska Institutet, Stockholm, Sweden.
Xu-Bin ZhengSchool of Public Health, Fudan University, Shanghai, China.
Yang LiSchool of Public Health, Fudan University, Shanghai, China.
Sven HoffnerDepartment of Public Health Sciences (Global Health/IHCAR), Karolinska Institutet, Stockholm, Sweden.
Biao XuSchool of Public Health, Fudan University, Shanghai, China. bxu@shmu.edu.cn.ORCID http://orcid.org/0000-0001-8695-7396

Funding

the National Science and Technology Major Project of China 2018ZX10715012-4the Swedish Research Council and the National Natural Science Foundation of China joint project 540-2013-8797 and 81361138019the Swedish Research Council and the National Natural Science Foundation of China joint project 81361138019 and 540-2013-8797
6 · The paper itself

Abstract

backgroundMultidrug-resistant tuberculosis (MDR-TB) is on the rise in China. This study used a dynamic Markov model to predict the longitudinal trends of MDR-TB in China by 2050 and to assess the effects of alternative control measures.

methodsEight states of tuberculosis transmission were set up in the Markov model using a hypothetical cohort of 100 000 people. The prevalence of MDR-TB and bacteriologically confirmed drug-susceptible tuberculosis (DS-TB

resultsWithout any intervention changes to current conditions, the prevalence of DS-TB

conclusionsMDR-TB, especially untreated MDR-TB, would rise rapidly under China's current MDR-TB control strategies. Interventions designed to promote effective detection and treatment of MDR-TB are imperative in the fights against MDR-TB epidemics.

Indexed as

ChinaCohort StudiesHumansMarkov ChainsPrevalenceTuberculosis, Multidrug-ResistantMarkov chainsMultidrug-resistant tuberculosisPrevalencePrevention and control

Identifiers

PMID32513262
PMCPMC7281937

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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.