Evidence map›Paper›PMID 39852654›Full record

ArticleTropical medicine and infectious disease2024

Mapping Drug-Resistant Tuberculosis Treatment Outcomes in Hunan Province, China.

Temesgen Yihunie Akalu, Archie C A Clements, Zuhui Xu, Liqiong Bai, Kefyalew Addis Alene

Abstract read
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Article in Tropical medicine and infectious disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Temesgen Yihunie AkaluSchool of Population Health, Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia.ORCID 0000-0002-0340-0443
Archie C A ClementsGeospatial and Tuberculosis Research Team, Telethon Kids Institute, Perth, WA 6009, Australia.
Zuhui XuXiangya School of Public Health, Central South University, Changsha 410078, China.
Liqiong BaiTB Control Institute of Hunan Province, Changsha 410004, China.
Kefyalew Addis AleneSchool of Population Health, Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia.ORCID 0000-0002-1904-4682

Funding

Australian National Health and Medical Research Council (NHMRC) grant APP1196549
6 · The paper itself

Abstract

backgroundDrug-resistant tuberculosis (DR-TB) remains a major public health challenge in China, with varying treatment outcomes across different regions. Understanding the spatial distribution of DR-TB treatment outcomes is crucial for targeted interventions to improve treatment success in high-burden areas such as Hunan Province. This study aimed to map the spatial distribution of DR-TB treatment outcomes at a local level and identify sociodemographic and environmental factors associated with poor treatment outcomes in Hunan Province, China.

methodsA spatial analysis was conducted using DR-TB data from the Tuberculosis Control Institute of Hunan Province, covering the years 2013 to 2018. The outcome variable, the proportion of poor treatment outcomes, was defined as a composite measure of treatment failure, death, and loss to follow-up. Sociodemographic, economic, healthcare, and environmental variables were obtained from various sources, including the WorldClim database, the Malaria Atlas Project, and the Hunan Bureau of Statistics. These covariates were linked to a map of Hunan Province and DR-TB notification data using R software version 4.4.0. The spatial clustering of poor treatment outcomes was analyzed using the local Moran's I and Getis-Ord statistics. A Bayesian logistic regression model was fitted, with the posterior parameters estimated using integrated nested Laplace approximation (INLA).

resultsIn total, 1381 DR-TB patients were included in the analysis. An overall upward trend in poor DR-TB treatment outcomes was observed, peaking at 14.75% in 2018. Deaths and treatment failures fluctuated over the years, with a notable increase in deaths from 2016 to 2018, while the proportion of patients lost to follow-up significantly declined from 2014 to 2018. The overall proportion of poor treatment outcomes was 9.99% (95% credible interval (CI): 8.46% to 11.70%), with substantial spatial clustering, particularly in Anxiang (50%), Anren (50%), and Chaling (42.86%) counties. The proportion of city-level indicators was significantly associated with higher proportions of poor treatment outcomes (odds ratio (OR): 1.011; 95% CRI: 1.20 December 2024 001-1.035).

conclusionsThis study found a concerning increase in poor DR-TB treatment outcomes in Hunan Province, particularly in certain high-risk areas. Targeted public health interventions, including enhanced surveillance, focused healthcare initiatives, and treatment programs, are essential to improve treatment success.

Indexed as

drug-resistant tuberculosisHunan provincemapping

Identifiers

PMID39852654
PMCPMC11769319

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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.