Mycobacterium Tuberculosis Genotype Variation and Household Transmissibility in Different Tuberculosis Epidemic Areas of Guangxi, China
Doctor of Philosophy ( Epidemiology (International Program)), 2019
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Prince of Songkla University
2024
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Tuberculosis China Guangxi Province |
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Tuberculosis China Guangxi Province Zhezhe Cui Mycobacterium Tuberculosis Genotype Variation and Household Transmissibility in Different Tuberculosis Epidemic Areas of Guangxi, China |
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Doctor of Philosophy ( Epidemiology (International Program)), 2019 |
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Virasakdi Chongsuvivatwong |
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Virasakdi Chongsuvivatwong Zhezhe Cui |
| format |
Theses and Dissertations |
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Zhezhe Cui |
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Zhezhe Cui |
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Mycobacterium Tuberculosis Genotype Variation and Household Transmissibility in Different Tuberculosis Epidemic Areas of Guangxi, China |
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Mycobacterium Tuberculosis Genotype Variation and Household Transmissibility in Different Tuberculosis Epidemic Areas of Guangxi, China |
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Mycobacterium Tuberculosis Genotype Variation and Household Transmissibility in Different Tuberculosis Epidemic Areas of Guangxi, China |
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Mycobacterium Tuberculosis Genotype Variation and Household Transmissibility in Different Tuberculosis Epidemic Areas of Guangxi, China |
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Mycobacterium Tuberculosis Genotype Variation and Household Transmissibility in Different Tuberculosis Epidemic Areas of Guangxi, China |
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mycobacterium tuberculosis genotype variation and household transmissibility in different tuberculosis epidemic areas of guangxi, china |
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Prince of Songkla University |
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2024 |
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http://kb.psu.ac.th/psukb/handle/2016/19465 |
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th-psu.2016-194652024-06-07T07:08:40Z Mycobacterium Tuberculosis Genotype Variation and Household Transmissibility in Different Tuberculosis Epidemic Areas of Guangxi, China Zhezhe Cui Virasakdi Chongsuvivatwong Faculty of Medicine (Epidemiology) คณะแพทยศาสตร์ สาขาวิชาระบาดวิทยา Tuberculosis China Guangxi Province Doctor of Philosophy ( Epidemiology (International Program)), 2019 Background: Not only Guangxi is one of the poorest provinces in China, but it is also regarded as a high TB incidence region with about 50,000 new TB cases reported by National Notifiable Disease Reported System (NNDRS) and ranks third highest TB burden province in China. The patterns of TB transmission in Guangxi is still unclear. Through spatial analysis, high incidence spatial clusters are mainly concentrated in low economic level counties compared to the other counties. Furthermore, little is known about the epidemic strains of MTB and their evolution process in Guangxi as well as their virulence and transmissibility. Objective 1: To identify and describe high and low TB reported incidence clusters in Guangxi, China. Methods 1: We performed a spatiotemporal analysis with prediction using time series analysis, Moran's I global and local spatial autocorrelation statistics, and space-time scan statistics to detect temporal and spatial clusters of tuberculosis notifications in Guangxi between 2010 and 2016. Spatial panel models were employed to identify potential associating factors. Results 1: The number of reported cases peaked in spring and summer and decreased in autumn and winter. The predicted number of reported cases was 49,946 in 2017. Moran's I global statistics were greater than 0 (0.363 – 0.536) during the study period. The most significant hot spots were mainly located in the central area. The eastern area exhibited a low-low relation. By the space-time scanning, the clusters identified were similar to those of the local autocorrelation statistics and were clustered toward the early part of 2016. Duration of sunshine, per capita gross domestic product, the treatment success rate of tuberculosis and participation rate of the new cooperative medical care insurance scheme in rural areas had a significant negative association with tuberculosis notification rates. Conclusion 1: The notification rate of tuberculosis in Guangxi remains high, with the highest notification cluster located in the central region. The notification rate is associated with an economic level, treatment success rate and participation in the new cooperative medical care insurance scheme. Objective 2: To compare MTB genotype patterns and variations within counties having the highest and lowest TB reported incidence. Methods 2: A case-only study in the TB notification hot and cold spot areas of Guangxi was conducted from January to June 2018. The population genomic structure comparison was performed. Results 2: The population genomic structure comparison was performed. The predominate Mtb strain is lineage 2.2 (Beijing family strain) accounting for 57.7% followed by lineage 4 (39.3%), and it was significantly higher in TB notification hot spots. Through the population genetic structure analysis (Fst) and the multidimensional scaling modeling, two spot areas had a genetic differentiation, and the spatial internal consistency was relatively high. Specific SNPs sites between the cold and hot spots with high Fst estimation may map to the pathogenicity differences in Mtb. In addition, living in the hot spot area (adjusted OR= 1.62) and having no outside history of traveling recently (adjusted OR = 2.66) were predictors of Beijing family strain infection. Conclusion 2: The findings of this study demonstrate that the promotion of the genetic diagnosis in tuberculosis clinics and early identification of Beijing family strain should be considered in tracking and stopping TB transmission. Objective 3: To test the hypothesis of whether TB household transmission is influenced by the genotype of Mtb and socioeconomic status in cold and hot spot areas. Methods 3: Parallel case-contact studies were conducted in two geographic areas classified as "cold" and "hot" spots based on TB notification and spatial clustering between January and June 2018 in Guangxi, China, using data from field contact investigations, whole genome sequencing, tuberculin skin tests (TSTs) and chest radiographs. Results 3: Beijing family strains accounted for 64.6% of Mycobacterium tuberculosis (Mtb) strains transmitted in hot spots, and 50.7% in cold spots (p-value = 0.02). The positive TST rate in hot spot areas was significantly higher than that observed in cold spot areas (p-value <0.01). Living in hot spots [adjusted odds ratio (aOR) = 1.75, 95%, confidence interval (CI): 1.22, 2.50], Beijing family genotype (aOR = 1.83, 95% CI: 1.19, 2.81), living in the same room with an index case (aOR = 2.29, 95% CI: 1.5, 3.49), travelling time from home to a medical facility (aOR = 4.78, 95% CI: 2.96, 7.72), history of Bacillus Calmette-Guérin vaccination (aOR = 2.02, 95% CI: 1.13 3.62), and delay in diagnosis (aOR = 2.56, 95% CI: 1.13, 5.80) were significantly associated with positive TST results among household contacts of TB cases. Conclusion 3: The findings of this study confirmed the strong transmissibility of the Beijing genotype family strains and this genotype's important role in household transmission. We found that an extended traveling time from home to the medical facility was an important socioeconomic factor for Mtb transmission in the family. It is still necessary to improve the medical facility infrastructure and management, especially in areas with a high TB prevalence. Keywords: Tuberculosis; notification; spatiotemporal; ecological; spatial panel; genotypes; socioeconomic; transmission; household; contact; polymorphisms; influence Received Scholarship of Thailand's Education Hub for Southern Region of ASEAN countries. 2024-06-07T07:08:40Z 2024-06-07T07:08:40Z 2019 Thesis http://kb.psu.ac.th/psukb/handle/2016/19465 en Attribution-NonCommercial-NoDerivs 3.0 Thailand http://creativecommons.org/licenses/by-nc-nd/3.0/th/ application/pdf Prince of Songkla University |