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

China / epidemiology Cohort Studies Humans Markov Chains Prevalence Tuberculosis, Multidrug-Resistant / epidemiology
Markov chains Multidrug-resistant tuberculosis Prevalence Prevention and control

Journal

Infectious diseases of poverty
ISSN: 2049-9957
Titre abrégé: Infect Dis Poverty
Pays: England
ID NLM: 101606645

Informations de publication

Date de publication:
08 Jun 2020
Historique:
received: 02 03 2020
accepted: 28 05 2020
entrez: 10 6 2020
pubmed: 10 6 2020
medline: 7 10 2020
Statut: epublish

Résumé

Multidrug-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. Eight 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 Without any intervention changes to current conditions, the prevalence of DS-TB MDR-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.

Sections du résumé

BACKGROUND BACKGROUND
Multidrug-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.
METHODS METHODS
Eight 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
RESULTS RESULTS
Without any intervention changes to current conditions, the prevalence of DS-TB
CONCLUSIONS CONCLUSIONS
MDR-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.

Identifiants

DOI: 10.1186/s40249-020-00682-7 PMID: 32513262 PMC: PMC7281937
pubmed: 32513262
doi: 10.1186/s40249-020-00682-7
pii: 10.1186/s40249-020-00682-7
pmc: PMC7281937
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

65

Subventions

Organisme : the Swedish Research Council and the National Natural Science Foundation of China joint project
ID : 81361138019 and 540-2013-8797
Organisme : the Swedish Research Council and the National Natural Science Foundation of China joint project
ID : 540-2013-8797 and 81361138019
Organisme : the National Science and Technology Major Project of China
ID : 2018ZX10715012-4

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Auteurs

Bing-Ying Li (BY)

School of Public Health, Fudan University, Shanghai, China.
Key Lab of Health Technology Assessment (Fudan University), National Health Commission, Shanghai, China.

Wen-Pei Shi (WP)

School of Public Health, Fudan University, Shanghai, China.
Key Lab of Health Technology Assessment (Fudan University), National Health Commission, Shanghai, China.

Chang-Ming Zhou (CM)

School of Public Health, Fudan University, Shanghai, China.
Department of Cancer prevention, Fudan University Shanghai Cancer Center, Shanghai, China.

Qi Zhao (Q)

School of Public Health, Fudan University, Shanghai, China.
Key Lab of Health Technology Assessment (Fudan University), National Health Commission, Shanghai, China.

Vinod K Diwan (VK)

Department of Public Health Sciences (Global Health/IHCAR), Karolinska Institutet, Stockholm, Sweden.

Xu-Bin Zheng (XB)

School of Public Health, Fudan University, Shanghai, China.
Key Lab of Health Technology Assessment (Fudan University), National Health Commission, Shanghai, China.

Yang Li (Y)

School of Public Health, Fudan University, Shanghai, China.
Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai, China.

Sven Hoffner (S)

Department of Public Health Sciences (Global Health/IHCAR), Karolinska Institutet, Stockholm, Sweden.

Biao Xu (B)

School of Public Health, Fudan University, Shanghai, China. bxu@shmu.edu.cn.
Key Lab of Health Technology Assessment (Fudan University), National Health Commission, Shanghai, China. bxu@shmu.edu.cn.
Department of Public Health Sciences (Global Health/IHCAR), Karolinska Institutet, Stockholm, Sweden. bxu@shmu.edu.cn.
ORCID: 0000-0001-8695-7396

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Classifications MeSH

questionsmedicales.fr › Régions géographiques › Asie › Extrême-Orient › Chine › Rising challenge of multidrug-resistant...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Rising challenge of multidrug-resistant...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Rising challenge of multidrug-resistant...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Processus stochastiques › Chaines de Markov › Rising challenge of multidrug-resistant...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Collecte de données › Registre civil › Morbidité › Prévalence › Rising challenge of multidrug-resistant...
questionsmedicales.fr › Infection › Infections bactériennes et mycoses › Infections bactériennes › Infections bactériennes à Gram positif › Infections à Actinomycetales › Infections à Mycobacterium › Tuberculose › Tuberculose multirésistante › Rising challenge of multidrug-resistant...