The Epidemiological Characteristics of Pulmonary Tuberculosis - Kashgar Prefecture, Xinjiang Uygur Autonomous Region, China, 2011-2020

Effectiveness and Determinants of Implementing the "Xinjiang Model" for Tuberculosis Prevention and Control: A Quantitative Study

Objective

To analyze the effectiveness of the "Xinjiang Model" for tuberculosis prevention and control in Kashgar Prefecture, Xinjiang, and to explore the determinants of the policy implementation effect.

Methods

The registration data of pulmonary tuberculosis (PTB) patients in Kashgar Prefecture from 2012 to 2021 were collected to describe the temporal trend of registered incidence. A questionnaire survey was conducted among PTB patients registered and treated in the tuberculosis management information system in Zepu and Shache Counties from January 2022 to July 2023 to collect and analyze "Xinjiang model" determinants of effectiveness.

Results

The PTB registered incidence in Kashgar Prefecture showed a significant increasing trend from 2012 to 2018 (APC=18.7%) and a significant decreasing trend from 2018-2021 (APC=-28.8%). Among the Kashgar Prefecture, compared with average registered incidence in 2012-2017, registered incidence in 2021 in Shufu, Maigaiti, and Zepu Counties had a greater decline rate of 58.68%, 57.16%, and 54.02%, respectively, while the registered incidence in 2021 in Shache County increased by 6.32%. According to the comprehensive analysis of the factors affecting the effect of policy implementation, the proportion of PTB patients in Zepu County whose health status has now significantly improved compared with that before treatment was significantly greater than that in Shache County (P<0.05); patients in Shache County were significantly less aware than those in Zepu County of how to take tuberculosis drugs, precautions, adverse reactions, and regular reviews during treatment; the factors that accounted for the greater proportion of heavy treatment burden in both Shache and Zepu Counties were discomfort caused by taking or injecting drugs, accounting for 12.8% and 8.7%, respectively.

Conclusion

The "Xinjiang model" can effectively control the epidemic situation of tuberculosis in Kashgar, and the knowledge of tuberculosis treatment, adverse reactions to tuberculosis drugs, and treatment costs were the determinants of the effectiveness of policy implementation.

Crucial computed tomography and magnetic resonance imaging findings of fallopian tubal tuberculosis for diagnosis: a retrospective study of 26 cases

Background

Fallopian tubal tuberculosis (FTTB), which typically presents with non-specific clinical symptoms and mimics ovarian malignancies clinically and radiologically, often affects young reproductive females and can lead to infertility if not promptly managed. Early diagnosis by imaging modalities is crucial for initiating timely anti-tuberculosis (anti-TB) treatment. Currently, comprehensive radiological descriptions of this relatively rare disease are limited. We aimed to comprehensively investigate the computed tomography (CT) and magnetic resonance imaging (MRI) characteristics of FTTB in patients from the Kashi area, which has the highest incidence of TB in China, to extend radiologists' understanding of this disease.

Methods

We conducted a retrospective cross-sectional study of 26 patients diagnosed with FTTB at the First People's Hospital of Kashi Area. All the patients underwent abdominal and pelvic contrast-enhanced CT examinations and/or pelvic contrast-enhanced MRI from January 2017 to June 2022. The imaging findings were evaluated in consensus by two experienced radiologists specialized in abdominal and pelvic imaging. The evaluated sites included the fallopian tubes, ovaries, peritoneum, mesentery, retroperitoneal nodes, and parailiac nodes. The patient characteristics are reported using descriptive statistics. The patient imaging results are presented as percentages. The normally distributed continuous variables are reported as the mean ± standard deviation (SD), and otherwise as the median with the interquartile range (IQR).

Results

The median age of the patients was 27 years (IQR: 25-34 years). Bilateral involvement of the fallopian tubes was observed in all patients. The tubal wall appeared coarse with tiny intraductal nodules in 96% (25 of 26) of the patients. The mean CT value of the tubal contents was 34 Hounsfield units (HUs; SD: 3.3 HUs). Ascites was present in 92% (24 of 26) of the patients, with 20 patients showing encapsulated effusion. Among these patients, 20 exhibited the highest CT values of ascites (>20 HUs). Linear enhancement of the parietal peritoneum was observed in 88% (23 of 26) of the patients, of whom 22 had peritoneal nodules measuring a median diameter of 0.4 cm (IQR: 0.3-0.6 cm). Eight patients had retroperitoneal and parailiac nodal enlargement, of whom two showed nodal necrosis, and none displayed nodal calcification.

Conclusions

FTTB is consistently accompanied by tuberculous peritonitis. FTTB typically presents with tubal dilation, and coarseness and nodules in the lumen, as well as intraductal caseous material and calcification. Tuberculous peritonitis exhibits high-density ascites, peritoneal adhesion, linear enhancement of the parietal peritoneum, and tiny peritoneal nodules. The co-occurrence of these features strongly suggests a diagnosis of FTTB.

Outdoor environmental exposome and the burden of tuberculosis: Findings from nearly two million adults in northwestern China

We simultaneously assessed the associations for a range of outdoor environmental exposures with prevalent tuberculosis (TB) cases in a population-based health program with 1940,622 participants ≥ 15 years of age. TB status was confirmed through bacteriological and clinical assessment. We measured 14 outdoor environmental exposures at residential addresses. An exposome-wide association study (ExWAS) approach was used to estimate cross-sectional associations between environmental exposures and prevalent TB, an adaptive elastic net model (AENET) was implemented to select important exposure(s), and the Extreme Gradient Boosting algorithm was subsequently applied to assess their relative importance. In ExWAS analysis, 12 exposures were significantly associated with prevalent TB. Eight of the exposures were selected as predictors by the AENET model: particulate matter ≤ 2.5 µm (odds ratio [OR]=1.01, p = 0.3295), nitrogen dioxide (OR=1.09, p < 0.0001), carbon monoxide (OR=1.19, p < 0.0001), and wind speed (OR=1.08, p < 0.0001) were positively associated with the odds of prevalent TB while sulfur dioxide (OR=0.95, p = 0.0017), altitude (OR=0.97, p < 0.0001), artificial light at night (OR=0.98, p = 0.0001), and proportion of forests, shrublands, and grasslands (OR=0.95, p < 0.0001) were negatively associated with the odds of prevalent TB. Air pollutants had higher relative importance than meteorological and geographical factors, and the outdoor environment collectively explained 11% of TB prevalence.

Analysis of spatial characteristics and geographic weighted regression of tuberculosis prevalence in Kashgar, China

Number of cases of tuberculosis (TB) was higher than that of the national level in Kashgar, China. This study aimed to analyze the spatial and temporal distribution of TB and the relationship between TB and social factors, which can provide a reference for the prevention and control of TB. We applied spatial autocorrelation analysis to study the distribution of tuberculosis in Kashgar. We used a geographically weighted regression (GWR) model to analyze the relationship between TB and social factors. A total of 100,330 cases of TB in Kashgar from 2016 to 2021 were analyzed. The number of TB cases in Kashgar was higher in the east, lower in the west, and most elevated in the center. The highest cumulative number of cases was found in Shache county. Global Moran's I ranged from -0.212 to -0.549, and local spatial autocorrelation analysis identified four clusters. According to our analysis, the incidence of tuberculosis was negatively correlated among the regions of Kashgar, and the related causes need to be analyzed in depth in future studies. Per capita gross domestic product (GDP), number of medical institutions per capita, and total population influenced the incidence of tuberculosis in Kashgar. Based on our findings, we suggest some effective measures to reduce the risk of TB infection, such as improving the living standard, developing the regional economy, and distributing health resources rationally.

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis

Background

Tuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014–2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients.

Methods

Untargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, “caret” R package, “e1071” R package and “Tensorflow” Python package, respectively.

Results

Metabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83–93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent.

Conclusions

Our findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07694-8.

Artificial Intelligence Assisting the Early Detection of Active Pulmonary Tuberculosis From Chest X-Rays: A Population-Based Study

As a major infectious disease, tuberculosis (TB) still poses a threat to people’s health in China. As a triage test for TB, reading chest radiography with traditional approach ends up with high inter-radiologist and intra-radiologist variability, moderate specificity and a waste of time and medical resources. Thus, this study established a deep convolutional neural network (DCNN) based artificial intelligence (AI) algorithm, aiming at diagnosing TB on posteroanterior chest X-ray photographs in an effective and accurate way. Altogether, 5,000 patients with TB and 4,628 patients without TB were included in the study, totaling to 9,628 chest X-ray photographs analyzed. Splitting the radiographs into a training set (80.4%) and a testing set (19.6%), three different DCNN algorithms, including ResNet, VGG, and AlexNet, were trained to classify the chest radiographs as images of pulmonary TB or without TB. Both the diagnostic accuracy and the area under the receiver operating characteristic curve were used to evaluate the performance of the three AI diagnosis models. Reaching an accuracy of 96.73% and marking the precise TB regions on the radiographs, ResNet algorithm-based AI outperformed the rest models and showed excellent diagnostic ability in different clinical subgroups in the stratification analysis. In summary, the ResNet algorithm-based AI diagnosis system provided accurate TB diagnosis, which could have broad prospects in clinical application for TB diagnosis, especially in poor regions with high TB incidence.