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Development and Application of a Multidrug-Resistant Tuberculosis Case Management System — Yunnan Province, China, 2017–2020

INTRODUCTION: Treatment and case management of multidrug-resistant tuberculosis (MDR-TB) is a significant challenge in tuberculosis (TB) control and prevention. This pilot study aims to apply and test a new electronic information system in order to help bolster case management of MDR-TB. METHODS: Th...

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Autores principales: Chen, Jinou, Ruan, Yunzhou, Wang, Kai, Li, Ling, Yang, Yunbin, Yang, Rui, Xu, Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579969/
https://www.ncbi.nlm.nih.gov/pubmed/36284689
http://dx.doi.org/10.46234/ccdcw2022.177
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author Chen, Jinou
Ruan, Yunzhou
Wang, Kai
Li, Ling
Yang, Yunbin
Yang, Rui
Xu, Lin
author_facet Chen, Jinou
Ruan, Yunzhou
Wang, Kai
Li, Ling
Yang, Yunbin
Yang, Rui
Xu, Lin
author_sort Chen, Jinou
collection PubMed
description INTRODUCTION: Treatment and case management of multidrug-resistant tuberculosis (MDR-TB) is a significant challenge in tuberculosis (TB) control and prevention. This pilot study aims to apply and test a new electronic information system in order to help bolster case management of MDR-TB. METHODS: The MDR-TB Case Management System (CMS) was developed and piloted in the Yunnan Tuberculosis Clinical Center (TCC) in 2017. Next, 5 sites in Yunnan were randomly selected and sampled as pilots in 2018. The real-time regular follow-up rate (RFUR) was calculated for pilot sites. Loss to follow-up (LTFU) rates of MDR-TB treatment cohorts between pilot and non-pilot sites were compared by a chi-square test. LTFU for MDR-TB treatment cohorts was then assessed by univariate and multivariate binary logistic regression. RESULTS: The average regular follow-up rate was 90.7% in TCC and 73.7% in five other pilot sites of Yunnan Province respectively. The average LTFU rate for pilot sites (9.0%) was lower than non-pilot sites (20.6%, P<0.01). The risk of LTFU during MDR-TB treatment reduced 61.7% in CMS pilot cases (adjusted odds ratio: 0.38, 95% confidence interval: 0.23–0.60) compared with non-pilot cases. CONCLUSIONS: As a significant supplement to the Tuberculosis Information Management System, the CMS strengthened the collection, analysis, and utilization of strategic information for MDR-TB cases. The system improved case management by embedding it as a tool of the Comprehensive Supportive Care service model.
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spelling pubmed-95799692022-10-24 Development and Application of a Multidrug-Resistant Tuberculosis Case Management System — Yunnan Province, China, 2017–2020 Chen, Jinou Ruan, Yunzhou Wang, Kai Li, Ling Yang, Yunbin Yang, Rui Xu, Lin China CDC Wkly Methods and Applications INTRODUCTION: Treatment and case management of multidrug-resistant tuberculosis (MDR-TB) is a significant challenge in tuberculosis (TB) control and prevention. This pilot study aims to apply and test a new electronic information system in order to help bolster case management of MDR-TB. METHODS: The MDR-TB Case Management System (CMS) was developed and piloted in the Yunnan Tuberculosis Clinical Center (TCC) in 2017. Next, 5 sites in Yunnan were randomly selected and sampled as pilots in 2018. The real-time regular follow-up rate (RFUR) was calculated for pilot sites. Loss to follow-up (LTFU) rates of MDR-TB treatment cohorts between pilot and non-pilot sites were compared by a chi-square test. LTFU for MDR-TB treatment cohorts was then assessed by univariate and multivariate binary logistic regression. RESULTS: The average regular follow-up rate was 90.7% in TCC and 73.7% in five other pilot sites of Yunnan Province respectively. The average LTFU rate for pilot sites (9.0%) was lower than non-pilot sites (20.6%, P<0.01). The risk of LTFU during MDR-TB treatment reduced 61.7% in CMS pilot cases (adjusted odds ratio: 0.38, 95% confidence interval: 0.23–0.60) compared with non-pilot cases. CONCLUSIONS: As a significant supplement to the Tuberculosis Information Management System, the CMS strengthened the collection, analysis, and utilization of strategic information for MDR-TB cases. The system improved case management by embedding it as a tool of the Comprehensive Supportive Care service model. Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2022-09-23 /pmc/articles/PMC9579969/ /pubmed/36284689 http://dx.doi.org/10.46234/ccdcw2022.177 Text en Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2022 https://creativecommons.org/licenses/by-nc-sa/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ (https://creativecommons.org/licenses/by-nc-sa/4.0/)
spellingShingle Methods and Applications
Chen, Jinou
Ruan, Yunzhou
Wang, Kai
Li, Ling
Yang, Yunbin
Yang, Rui
Xu, Lin
Development and Application of a Multidrug-Resistant Tuberculosis Case Management System — Yunnan Province, China, 2017–2020
title Development and Application of a Multidrug-Resistant Tuberculosis Case Management System — Yunnan Province, China, 2017–2020
title_full Development and Application of a Multidrug-Resistant Tuberculosis Case Management System — Yunnan Province, China, 2017–2020
title_fullStr Development and Application of a Multidrug-Resistant Tuberculosis Case Management System — Yunnan Province, China, 2017–2020
title_full_unstemmed Development and Application of a Multidrug-Resistant Tuberculosis Case Management System — Yunnan Province, China, 2017–2020
title_short Development and Application of a Multidrug-Resistant Tuberculosis Case Management System — Yunnan Province, China, 2017–2020
title_sort development and application of a multidrug-resistant tuberculosis case management system — yunnan province, china, 2017–2020
topic Methods and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579969/
https://www.ncbi.nlm.nih.gov/pubmed/36284689
http://dx.doi.org/10.46234/ccdcw2022.177
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