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.

As one of the poorest provinces in China, Guangxi has a high HIV and TB prevalence, with the annual number of TB/HIV cases reported by health department among the highest in the country. However, studies on the burden of TB-HIV co-infection and risk factors for active TB among HIV-infected persons in Guangxi have rarely been reported. To investigate the risk factors for active TB among people living with HIV/AIDS in Guangxi Zhuang autonomous region, China. A surveillance survey was conducted of 1 019 HIV-infected patients receiving care at three AIDS prevention and control departments between 2013 and 2015. We investigated the cumulative prevalence of TB during 2 years. To analyze risk factors associated with active TB, we conducted a 1:1 pair-matched case-control study of newly reported active TB/HIV co-infected patients. Controls were patients with HIV without active TB, latent TB infection or other lung disease, who were matched with the case group based on sex and age (± 3 years). A total of 1 019 subjects were evaluated. 160 subjects (15.70%) were diagnosed with active TB, including 85 clinically diagnosed cases and 75 confirmed cases. We performed a 1:1 matched case-control study, with 82 TB/HIV patients and 82 people living with HIV/AIDS based on surveillance site, sex and age (±3) years. According to multivariate analysis, smoking (OR = 2.996, 0.992–9.053), lower CD 4⁺ T-cell count (OR = 3.288, 1.161–9.311), long duration of HIV-infection (OR = 5.946, 2.221–15.915) and non-use of ART (OR = 7.775, 2.618–23.094) were independent risk factors for TB in people living with HIV/AIDS. The prevalence of active TB among people living with HIV/AIDS in Guangxi was 173 times higher than general population in Guangxi. It is necessary for government to integrate control planning and resources for the two diseases. Medical and public health workers should strengthen health education for TB/HIV prevention and treatment and promote smoking cessation. Active TB case finding and early initiation of ART is necessary to minimize the burden of disease among patients with HIV, as is IPT and infection control in healthcare facilities.

Mycobacterium tuberculosis (Mtb) lineage 2 (L2) strains are present globally, contributing to a widespread tuberculosis (TB) burden, particularly in Asia where both prevalence of TB and numbers of drug resistant TB are highest. The increasing availability of whole-genome sequencing (WGS) data worldwide provides an opportunity to improve our understanding of the global genetic diversity of Mtb L2 and its association with the disease epidemiology and pathogenesis. However, existing L2 sublineage classification schemes leave >20 % of the Modern Beijing isolates unclassified. Here, we present a revised SNP-based classification scheme of L2 in a genomic framework based on phylogenetic analysis of >4000 L2 isolates from 34 countries in Asia, Eastern Europe, Oceania and Africa. Our scheme consists of over 30 genotypes, many of which have not been described before. In particular, we propose six main genotypes of Modern Beijing strains, denoted L2.2.M1–L2.2.M6. We also provide SNP markers for genotyping L2 strains from WGS data. This fine-scale genotyping scheme, which can classify >98 % of the studied isolates, serves as a basis for more effective monitoring and reporting of transmission and outbreaks, as well as improving genotype-phenotype associations such as disease severity and drug resistance. This article contains data hosted by Microreact.

Overuse and misuse of antibiotics are major factors in the development of antibiotic resistance in primary care institutions of rural China. In this study, the effectiveness of a Health Information System-based, automatic, and confidential antibiotic feedback intervention was evaluated. A randomized, cross-over, cluster-controlled trial was conducted in primary care institutions. All institutions were randomly divided into two groups and given either a three-month intervention followed by a three-month period without any intervention or vice versa. The intervention consisted of three feedback measures: a real-time pop-up warning message of inappropriate antibiotic prescriptions on the prescribing physician's computer screen, a 10-day antibiotic prescription summary, and distribution of educational manuals. The primary outcome was the 10-day inappropriate antibiotic prescription rate. There were no significant differences in inappropriate antibiotic prescription rates (69.1% vs. 72.0%) between two groups at baseline (P = 0.072). After three months (cross-over point), inappropriate antibiotic prescription rates decreased significantly faster in group A (12.3%, P < 0.001) compared to group B (4.4%, P < 0.001). At the end point, the inappropriate antibiotic prescription rates decreased in group B (15.1%, P < 0.001) while the rates increased in group A (7.2%, P < 0.001). The characteristics of physicians did not significantly affect the rate of antibiotic or inappropriate antibiotic prescription rates. A Health Information System-based, real-time pop-up warnings, a 10-day prescription summary, and the distribution of educational manuals, can effectively reduce the rates of antibiotic and inappropriate antibiotic prescriptions.

Purpose: To explore the prescription patterns and usage trends of antibiotics within primary care institutions located in underdeveloped regions of China from 2017 to 2022. Methods: A retrospective analysis of antibiotic prescriptions was conducted from 25 primary care institutions in Guizhou Province during the period of 2017– 2022. Antibiotic prescriptions were categorized into appropriate and inappropriate use. Appropriate use is further categorized into preferred medication, and antibiotics can be used or substituted. Inappropriate use is further categorized into unnecessary use, incorrect spectrum of antibiotics and combined use of antibiotics. Factors associated with inappropriate use were investigated using generalized estimation equations. Holt-Winters and SARIMA models were employed to predict the number of inappropriate antibiotic prescriptions as the alternative model. Results: A total of 941,924 prescriptions were included, revealing a decreasing trend in both the number and inappropriate rates of antibiotic prescriptions from 2017 to 2022. Diseases of the respiratory system (70.66%) was the most frequent target of antibiotic use, with acute upper respiratory infections of multiple and unspecified sites representing 52.04% of these cases. The most commonly used antibiotics were penicillins (64.44%). Among all prescriptions, inappropriate antibiotic prescriptions reached 66.19%. Physicians aged over 35, holding the title of associate chief physician and possessing more than 11 years of experience were more likely to prescribe antibiotics inappropriately. The phenomenon of inappropriate antibiotic use was commoner among children aged five or younger. By comparing model parameters, it was determined that the SARIMA model outperforms the Holt-Winters model in predicting the number of inappropriate antibiotic prescriptions among primary care institutions. Conclusion: The number and inappropriate rates of antibiotic prescriptions in southwest China exhibited a downward trend from 2017 to 2022, but inappropriate prescription remains a serious problem in primary care institutions. Therefore, future efforts should focus on strengthening physician education, training, and clinical practice. Additionally, physicians’ awareness of common misconceptions about inappropriate antibiotic use must be improved, and the prescribing behavior of physicians who fulfill patients’ expectations by prescribing antibiotics needs to be modified.

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.