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Guangxi is one of the provinces having the highest notification rate of tuberculosis in China. However, spatial and temporal patterns and the association between environmental diversity and tuberculosis notification are still unclear. To detect the spatiotemporal pattern of tuberculosis notification rates from 2010 to 2016 and its potential association with ecological environmental factors in Guangxi Zhuang autonomous region, China. 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. 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. 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 economic level, treatment success rate and participation in the new cooperative medical care insurance scheme.

Background Tuberculosis (TB) is caused by a bacterium called Mycobacterium tuberculosis (Mtb). China is the third in top 8 high TB burden countries and Guangxi is one of the high incidence areas in South China. Determine bacterial factors that affected TB incidence rate is a step toward Ending the TB epidemic. Results Genomes of M. tuberculosis cultures from a relatively high and low incidence region in Guangxi have been sequenced. 347 of 358(96.9%) were identified as M. tuberculosis . All the strains belong to Lineage 2 and Lineage 4, except for one in Lineage 1. We found that the genetic structure of the M. tuberculosis population in each county varies enormously. Low incidence rate regions have a lower prevalence of Beijing genotypes than other regions. Four isolates which harbored mutT4 -48 also had mutT2 -58 mutations. It is suggested that strains from the ancestors of modern Beijing lineage is circulating in Guangxi. Strains of modern Beijing lineage (OR=2.04) were more likely to acquire drug resistances than Lineage 4. Most of the lineage differentiation SNPs are related to cell wall biosynthetic pathways. Conclusions These results provided a higher resolution to better understand the history of transmission of M. tuberculosis from/to South China. And the incidence rate of tuberculosis might be affected by bacterial population structure shaped by demographic history. Our findings also support the hypothesis that Modern Beijing lineage originated in South China.

Background The objectives of this study were to examine nutrient intakes of tuberculosis (TB) patients and to identify their associated factors. Methods In this cross-sectional study, 300 adult TB patients were surveyed in two impoverished counties in China. Nutrient intakes were evaluated through two consecutive 24-h dietary recalls and compared with the Chinese Dietary Reference Intakes (DRIs) 2013. The potential socio-demographic and behavioral factors were analyzed using multivariate logistic model to identify strong influential factors. Results We found that mean daily energy intake was 1655.0 kcal (SD: 619.3 kcal) and 1360.3 kcal (SD: 552.1 kcal) for male and female patients, respectively. The mean daily energy intake was significantly lower than that has been recommended by DRI (i.e., 2250 and 1800 kcal for males and females, respectively), with 87.4% of the male patients and 59.9% of female patients failed to consume adequate energy. The protein intakes were 44.6 g (SD: 18.2 g) and 35.9 g (SD: 12.3 g) for male and female patients, respectively, which were lower than the recommended values by DRI (i.e., 65 and 55 g for males and females, respectively). Most male (90.8%) and female (58.4%) TB patients had insufficient daily protein intake. Further analyses suggested that mean daily intakes of many micronutrients, were insufficient, while for most of patients, intakes of vitamin E and sodium were sufficient. We identified that unemployment was a risk factor for low energy intake ( p  < 0.05) and out-home-eating was a protective factor for low protein intake ( p  < 0.01). Conclusions In impoverished areas in China, intakes of macronutrients and most micronutrients in TB patients were inadequate compared with DRIs, especially for unemployed patients and patients eating at home. These findings suggested that public health actions are needed to promote education on TB patients about significance of nutritional support, and, further interventions in TB patients’ nutritional intakes are also required.

Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p  = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p  < 0.001). Three genomic clusters, each with genomic distance ≤12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots. Conclusion Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

Background Part of tuberculosis (TB) patients were missed if symptomatic screening was based on the main TB likely symptoms. This study conducted to compare the yield and relative costs of different TB screening algorithms in active case-finding in the whole population in China. Methods The study population was screened based on the TB likely symptoms through a face-to-face interview in selected 27 communities from 10 counties of 10 provinces in China. If the individuals had any of the enhanced TB likely symptoms, both chest X-ray and sputum tests were carried out for them furtherly. We used the McNemar test to analyze the difference in TB detection among four algorithms in active case-finding. Of four algorithms, two were from WHO recommendations including 1a/1c, one from China National Tuberculosis Program, and one from this study with the enhanced TB likely symptoms. Furthermore, a two-way ANOVA analysis was performed to analyze the cost difference in the performance of active case-finding adjusted by different demographic and health characteristics among different algorithms. Results Algorithm with the enhanced TB likely symptoms defined in this study could increase the yield of TB detection in active case-finding, compared with algorithms recommended by WHO (p < 0.01, Kappa 95% CI: 0. 93–0.99) and China NTP (p = 0.03, Kappa 95% CI: 0.96–1.00). There was a significant difference in the total costs among different three algorithms WHO 1c/2/3 (F = 59.13, p < 0.01). No significant difference in the average costs for one active TB case screened and diagnosed through the process among Algorithms 1c/2/3 was evident (F = 2.78, p = 0.07). The average costs for one bacteriological positive case through algorithm WHO 1a was about two times as much as the costs for one active TB case through algorithms WHO 1c/2/3. Conclusions Active case-finding based on the enhanced symptom screening is meaningful for TB case-finding and it could identify more active TB cases in time. The findings indicated that this enhanced screening approach cost more compared to algorithms recommend by WHO and China NTP, but the increased yield resulted in comparative costs per patient. And it cost much more that only smear/bacteriological-positive TB cases are screened in active case-finding.

The aims of the study were: (1) compare sociodemographic characteristics among active tuberculosis (TB) cases and their household contacts in cold and hot spot transmission areas, and (2) quantify the influence of locality, genotype and potential determinants on the rates of latent tuberculosis infection (LTBI) among household contacts of index TB cases. 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. 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. 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.

The aim of this paper is to evaluate the link between the history of exposure to tuberculosis (TB) in the household and diagnosed TB cases at school, and to compare the detection rate of active TB among household contacts and classroom contacts of adolescent TB cases with the rates among contacts of healthy controls. From November 2016 to December 2017, a prospective matched case-control study was conducted using passively identified index adolescent student cases from the TB surveillance system and healthy controls (matched by county, school type, sex, age and ethnicity). Contacts in households and classrooms of index cases and of controls were investigated. Matched tabulation of 117 case-control pairs revealed exposure to TB in the household as a strong risk factor (odds ratio (OR) = 21.0, 95% confidence interval (CI): 3.4, 868.6). Forty-five (case detection rate 0.69%) and two (case detection rate 0.03%) new active TB cases were detected among 6512 and 6480 classroom contacts of the index cases and controls, respectively. Having an index case in the classroom significantly increased the risk of classmates contracting active TB (OR = 22.5, 95% CI: 5.9, 191.4). Our findings suggested that previous exposure to TB in the household could lead a child to catch TB at school, then spread TB to classmates.

Objective. To evaluate the infectivity of Mycobacterium tuberculosis (M.tb) genotypes of index cases in the classroom of adolescent schools in Guangxi, China. Methods. Adolescent school tuberculosis (TB) contact investigations were conducted for all reported index TB cases from November 2016 to December 2017 in Guangxi, China. Genotypes of index cases and contact cases were identified by 15-loci mycobacterial interspersed repetitive units–variable number tandem repeat and spoligotyping. Outcome variable was 5 levels’ order of tuberculin skin test (TST) results to new active TB [0-5 mm, 6-9 mm, 10-14 mm, ≥ 15 mm (without TB), and ≥15 mm (with TB)]. Multivariate ordered logistic regression analysis was performed to evaluate the independent effect of genotypes of index case on contact screening outcome. Results. Beijing genotype occurred more commonly in female index patients. One genotypic cluster of two index cases and one cluster of two contact cases were detected. The association between infectivity of Beijing genotype of index cases and outcome of contact investigation was statistically significant in univariate analysis but no so after adjustment for characteristics of contacts and sex of index cases (P value=0.057). Female index cases increased the chance for TB infection/being active TB among contacts (ordinal odds ratio = 1.39, 95% confidence interval: 1.21, 1.60). Contacts who studied in the middle school, who with non-Han ethnicity and who without BCG scar had increased risk for TB infection/being active TB. Conclusion. There was not enough evidence from our data to support that Beijing strains were more infective than non-Beijing strains in TB transmission in school setting.

Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤ 12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots. Conclusion Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.