Detection Of Tuberculosis Research Articles

Challenging case of disseminated tuberculosis in an immunocompetent adult.

Disseminated tuberculosis (TB) is a TB infection involving two or more noncontiguous sites. It is a challenging medical care due to its misleading presentation. Although it is associated with immunodepression, rare cases have been described in immunocompetent patients.We report the case of a 27-year-old immunocompetent man who presented with an altered general condition and dyspnea. Chest computed tomography (CT) revealed pulmonary masses and mediastinal lymphadenopathy. Sputum smear microscopy for acid-fast bacilli was negative. Scannoguided transparietal lung biopsy was performed twice with inflammatory tissue in histological study. A surgical lung biopsy was then made. The histopathology report confirmed TB involving lung, pleura and lymph nodes. A few days after antituberculosis therapy was started, the patient developed psychomotor slowing related to quadriportal hydrocephalus. He benefited from ventriculoperitoneal bypass. Polymerase chain reaction for detection of Mycobacterium TB was positive in cerebrospinal fluid.In conclusion: The diagnosis of disseminated TB is challenging owing to its non-specific clinical and radiological presentation and the frequent negativity of biological fluids for tubercle bacilli. Molecular biology should always be considered in front of lingering symptoms, particularly in endemic areas, given their speed and sensitivity, thus avoiding invasive diagnostic procedures, as was the case with our patient.

Bayesian accuracy estimates for diagnostic tests to detect tuberculosis in captive sun bears (Helarctos malayanus) and Asiatic black bears (Ursus thibetanus) in Cambodia and Vietnam.

Effective control of tuberculosis (TB) depends on early diagnosis of disease, yet available tests are unable to perfectly detect infected individuals. In novel hosts diagnostic testing methods for TB are extrapolated from other species, with unknown accuracy. The primary challenge to evaluating the accuracy of TB tests is the lack of a perfect reference test. Here we use a Bayesian latent class analysis approach to evaluate five tests available for ante-mortem detection of pulmonary TB in captive sun bears and Asiatic black bears in Southeast Asia. Using retrospective results from screening of 344 bears at three rescue centres, we estimate accuracy parameters for thoracic radiography, a serological assay (DPP VetTB), and three microbiological tests (microscopy, PCR (Xpert MTB/RIF, Xpert MTB/RIF Ultra), mycobacterial culture) performed on bronchoalveolar lavage samples. While confirming the high specificities (≥ 0.99) of the three microbiological tests, our model demonstrated their sub-optimal sensitivities (<0.7). Thoracic radiography was the only diagnostic method with sensitivity (0.95, 95% BCI: 0.76, 0.998) and specificity (0.95, 95% BCI: 0.91, 0.98) estimated above 0.9. We recommend caution when interpreting DPP VetTB results, with the increased sensitivity resulting from treatment of weakly visible reactions as positive accompanied by a drop in specificity, and we illustrate how the diagnostic value of weak DPP VetTB reactions is particularly reduced if disease prevalence and/or clinical suspicion is low. Conversely, the reduced utility of negative microbiological tests on bronchoalveolar lavage fluid samples when prevalence and/or clinical suspicion is high is demonstrated. Taken together our results suggest multiple tests should be applied and accompanied by consideration of the testing context, to minimise the consequences of misclassification of disease status of bears at risk of TB in sanctuary settings.

Enhanced Tuberculosis Detection in Chest X-Rays Using Optimal Feature Selection with Hybrid Binary Particle Swarm Optimization (HBPSO) and a Dual Classifier Framework Combining LSTM and Spiking Neural Networks

Tuberculosis (TB) remains a significant global health challenge, particularly in developing countries, where rapid and accurate diagnosis is crucial for effective treatment. Recent advancements in machine learning and image processing have shown promise in enhancing the detection of TB from chest X-ray images. This research introduces a novel approach that integrates a hybrid feature selection method with a dual classifier framework to improve the accuracy of tuberculosis detection. Feature extraction is conducted using Multi-Scale Local Binary Patterns (MSLBP), Speeded-Up Robust Features (SURF) with Bag of Words (BoW), and Cartoon Texture Features, combining them into a comprehensive feature vector. The Hybrid Binary Particle Swarm Optimization (HBPSO) is employed for optimal feature selection, reducing redundancy and improving classification efficiency. Classification is performed using a dual framework involving a Long Short-Term Memory (LSTM) network and a Spiking Neural Network (SNN), which work in tandem to provide robust predictions. Majority voting is utilized to finalize predictions, ensuring high accuracy and adaptability across varying TB presentations. Extensive evaluation on publicly available datasets shows that this hybrid approach significantly outperforms existing methods, achieving a highest accuracy of 99.73%, along with superior precision, recall, and F1 scores.

NOVEL DEVICE FOR ENHANCING TUBERCULOSIS DIAGNOSIS FOR FASTER, MORE ACCURATE SCREENING RESULTS

Tuberculosis (TB) continues to be a leading global cause of illness and death, highlighting an urgent need for rapid, precise diagnostic solutions. Conventional methods, such as chest X-rays and sputum smear microscopy, often lack adequate sensitivity and specificity, resulting in treatment delays and increased transmission. This research introduces a novel device leveraging deep learning to enhance TB diagnosis, utilizing a convolutional neural network (CNN) model designed to automatically detect TB-related abnormalities in chest X-ray images. The device, powered by a large annotated dataset, incorporates transfer learning to fine-tune pre-trained models, maximizing diagnostic performance. Key evaluation metrics—accuracy, precision, recall, and F1-score—demonstrate the model’s efficacy compared to traditional diagnostics. Preliminary findings show that the AI-driven approach significantly enhances diagnostic accuracy, reducing false negatives and facilitating faster screenings. These results suggest that embedding AI technology in clinical workflows can enable earlier TB detection, optimize healthcare resources, and improve patient outcomes. Future directions include testing the device across diverse clinical environments and exploring integration with mobile health technologies to broaden access to rapid TB diagnostics.

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

ObjectiveTo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.DesignWe searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.ResultsSeveral studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.ConclusionThe literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

Detection of tuberculosis using cough audio analysis: a deep learning approach with capsule networks

PurposeTuberculosis (TB) is a widespread infectious disease that requires early detection for effective treatment and control. This study aims to improve TB detection using cough audio analysis, comparing the performance of capsule networks to other deep learning models.MethodsWe used cough audio recordings from 1105 individuals with a new or worsening cough for at least two weeks, totaling 9772 recordings. These recordings were processed into spectral images, and HOG features were extracted. Various models, including Capsule Networks + FCNN, CNN, VGG16, and ResNet50 were trained and evaluated.ResultsCapsule Networks + FCNN achieved the best performance with an accuracy of 0.97, sensitivity of 0.98, specificity of 0.96, F1 score of 0.97, and precision of 0.97, outperforming other models. This attribute is due to the model’s ability to learn complex features from spectral images.ConclusionsThis study concludes that Capsule Networks are more effective than typical CNN-based models in diagnosing TB from cough audio. This suggests that advanced deep learning frameworks could significantly enhance TB screening accuracy, especially in resource-limited areas.

Evaluation of feasibility and utility of urine based Lipoarabinomannan assay against conventional sputum microscopy in the detection of Tuberculosis among people with and without HIV infection.

Evaluation of feasibility and utility of urine based Lipoarabinomannan assay against conventional sputum microscopy in the detection of Tuberculosis among people with and without HIV infection.

Improving Tuberculosis Detection: A Comprehensive Evaluation of Contact Investigation Implementation

Improving Tuberculosis Detection: A Comprehensive Evaluation of Contact Investigation Implementation

Optimized CNN-Based Approach for Tuberculosis Detection from Chest X-rays Under Limited Data Constraints

Optimized CNN-Based Approach for Tuberculosis Detection from Chest X-rays Under Limited Data Constraints

Development and preliminary assessment of the iFIND TBR: all-in- one molecular diagnostic assay for rapid detection of Mycobacterium tuberculosis and rifampicin resistance.

Early and accurate diagnosis of tuberculosis (TB) is crucial for initiating timely treatment and preventing new infections. In this study, we introduced the iFIND TBR assay, an automated all-in-one tuberculosis detection approach that simultaneously detect Mycobacterium tuberculosis (MTB) and rifampicin (RIF) resistance. The limits of detection (LOD), sensitivity, specificity, and RIF-R rpoB mutation detection of the iFIND TBR were tested on Mycobacterium tuberculosis DNA or sputum samples spiked with known numbers of M.tuberculosis H37Rv. Frozen clinical samples from patients suspected of having TB were also tested. The LOD of the iFIND TBR for MTB detection were 13.34 CFU/ml (95% CI, 11.71-16.47), and for RIF resistance was 109.79CFU/mL (95% CI, 95-138.19). The iFIND TBR assay accurately distinguish MTB strains from non-tuberculous mycobacteria (NTM) without any cross reactivity. Testing on 157 clinical sputum samples, compared with the bacteriologically TB standard, the overall sensitivity and specificity of the iFIND TBR was 100% (95%CI, 94.64, 100) and 85.29% (95% CI, 74.61, 92.72), respectively. When assessing RIF susceptibility, the iFIND TBR achieved a sensitivity of 98.15% (95% CI, 90.11-99.95) and a specificity of 85.71% (95% CI, 67.33-95.97), compared with phenotypic drug susceptibility testing. Discordant RIF susceptibility results were more frequently observed in samples exhibiting heteroresistance. These findings demonstrate that iFIND TBR assay performs well in detecting TB and RIF resistance, and shows promise as a point-of-care tool in resource-limited areas.

Cost-effective chemiresistive biosensor with MWCNT-ZnO nanofibers for early detection of tuberculosis (TB) lipoarabinomannan (LAM) antigen.

Thedevelopment of an affordable chemiresistive biosensor enhanced with a multi-walled carbon nanotube-zinc oxide (MWCNT-ZnO) nanofiber compositeis presented. The sensor leverages the precise interaction between lipoarabinomannan (LAM) tuberculosis (TB) antigens and antibodies to achieve high sensitivity and specificity. The MWCNT-ZnO nanofibers have a larger surface area and better electrical conductivity, which makes it easier for TB antibodies to stick to them. The binding of LAM TB antigens to the fixed Monoclonal Antibody-MBS320597 induces significant resistance changes in the chemiresistive sensor, enabling accurate TB detection. Performance evaluation reveals a linear detection range from 1.0 to 100.0pg/mL in the lower concentration range and up to 6.0ng/mL in the higher concentration range, with a sensitivity of 79.750mApgmL-1cm-2 and a lower limit of detection of 40.54fg/mL. The sensor exhibits a response time of 102s. Featuring rapid response time and high sensitivity, this biosensor is ideally suited for point-of-care (PoC) applications. The incorporation of MWCNT-ZnO nanofibers shows great potential for enhancing the development of sensitive and cost-effective TB diagnostic tools, which could play a crucial role in advancing global TB control and management efforts.

Rapid and sensitive detection of Mycobacterium tuberculosis using nested multi-enzyme isothermal rapid amplification in a single reaction.

In this study, we have successfully developed a method called nested multi-enzyme isothermal rapid amplification (nestMIRA) for the detection of Mycobacterium tuberculosis (Mtb). This method involves a two-step thermostatic amplification process in the same tube and can be read using fluorescence and lateral flow dipstick (LFD) assays. It is known to be rapid, specific, and highly sensitive. Our method has shown promising results in the detection of clinical specimens, and we believe that it can be a valuable tool for the rapid detection of Mtb in a clinical setting.

Factors Affecting the Early Diagnosis of Smear-positive Pulmonary Tuberculosis in Patients in Qom Province, Iran

Background and Objectives: According to the report of the World Health Organization (WHO), almost one-third of the world’s population is infected with tuberculosis (TB). The delay in TB diagnosis, in addition to increasing mortality, can increase the duration of disease transmission. This study aims to identify the factors affecting the early detection of smear-positive pulmonary TB in Patients in Qom Province, Iran. Methods: In this case-control study, 120 patients with smear-positive pulmonary TB (60 with a diagnosis delayed for more than three months and 60 with a timely diagnosis) diagnosed from 2018 to 2021 in Qom Province participated. To determine the factors effective in early diagnosis of pulmonary TB, the odds ratio of each factor was calculated, and logistic regression analysis was used for data analysis. Results: The prescription of the sputum test at the first visit had the most significant effect on the early diagnosis of pulmonary TB (odds ratio=161, P&lt;0.001). The TB in patients who visited the clinics and those visited by general practitioners was diagnosed with a higher delay. The lack of knowledge of existing TB diagnosis and treatment services in health centers had increased the diagnosis delay by 2.3. Occupation, gender, and place of residence were not found to be effective factors. Conclusion: It seems that most doctors in Iran neglect to prescribe a sputum test for patients suspected of pulmonary TB and only prescribe chest x-rays. Training and informing doctors, private medical centers, and pharmacies in referring patients suspected of pulmonary TB to the health centers where the patients live can cause timely diagnosis of pulmonary TB.

Diagnostic values of adenosine deaminase cytokines in fluid contributed to the detection of extrapulmonary tuberculosis

Tuberculosis (TB) is a serious global public health problem. Detecting the changes of adenosine deaminase (ADA), interleukin (IL)-1β, IL-2, tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ) released from macrophages and monocytes is greatly valuable in the diagnosis of TB. Hence, this study was designed to determine the optimal cut-off points of ADA, IL-1β, IL-2, TNF-α, and IFN-γ, allowing to differentiate pleural TB (PLTB), peritoneal TB (PTB), and meningeal TB (MTB). The study included 386 patients with pleural effusion, 47 patients with peritoneal effusion, and 134 patients with encephalitis and meningoencephalitis suspect of TB. ADA was determined by an enzyme kinetics method; cytokine concentration was measured by a test based on the competitive enzyme-linked immunosorbent assay principle. Sensitivity, specificity, and positive and negative predictive value were calculated and described. In the aspiration fluid, ADA activity was greatly different between PLTB, PTB, and MTB groups with values of 37.5 U/L, 30.5 U/L, and 8.1 U/L and sensitivity and specificity of 87.7% and 83.4%; 100% and 88.9%; 97.3% and 98.6%, respectively. Similar, IFN-γ concentrations were distinctively different between PLTB, PTB, and MTB patients (with values of 120 pg/mL, 200 pg/mL, and 30 pg/mL with sensitivity and specificity of 80.3% and 80.9%; 80.0% and 92.6%; 98.6% and 61.7%, respectively). The determination of ADA and IFN-γ levels in the aspiration fluid is potentially considered a highly sensitive and specific test for differentiating PLTB, PTB, and MTB.

Targeted next-generation sequencing - a promising approach in the diagnosis of Mycobacterium tuberculosis and drug resistance.

Targeted next-generation sequencing (tNGS) offers a high-throughput, culture-independent approach that delivers a comprehensive resistance profile in a significantly shorter turn-around time, making it promising in enhancing tuberculosis (TB) diagnosis and informing treatment decisions. This study aims to evaluate the performance of tNGS in the TB diagnosis and drug resistance detection of Mycobacterium tuberculosis (MTB) using MTB clinical isolates and bronchoalveolar lavage fluid (BALF) samples. A total of 143 MTB clinical isolates were assessed, tNGS, phenotypic antimicrobial susceptibility testing (AST), and AST based on whole genome sequencing (WGS) exhibited high concordance rates, averaging 95.10% and 97.05%. Among 158 BALF samples, culture, Xpert MTB/RIF, and tNGS reported 29, 70 and 111 positives, respectively. In the confirmed cases with etiological evidence (smears, cultures, or molecular test), the positive rate of tNGS (73/83, 87.95%) was higher than that of Xpert MTB (67/83, 80.72%). Additionally, 45% (27/60) of clinically diagnosed cases (with imaging or immunological evidence) were positive for tNGS. Further validation on the discrepant results between tNGS and Xpert MTB/RIF with droplet digital PCR (ddPCR) yielded 35 positives, tNGS detected all, and Xpert MTB/RIF only identified 6 positives. In conclusion, tNGS demonstrates robust and rapid performance in the identification of MTB and its associated drug resistance, and can be directly applied to clinical samples, positioning it as a promising approach for laboratory testing of tuberculosis.

Individual, community and health systems factors influencing time to notification of tuberculosis: situating software and hardware bottlenecks in local health systems

BackgroundDespite several global interventions, tuberculosis (TB) remains a leading cause of death affecting millions of people globally. Many TB patients either have no access to quality care or go undetected by national health systems. Several multilevel factors account for under-detection of persons with TB. This study sought to explore patient-related software, community and health systems software and hardware factors influencing time to notification of TB in Lusaka District, Zambia.MethodsThis was an exploratory qualitative case study that adopted a software and hardware lens of conceptualizing health systems. Data were collected from across three sites – urban and peri-urban areas: Chongwe, Kafue, and Lusaka – within Lusaka Province, Zambia. Sixteen key informants - TB corner nurses, community TB treatment supporters, and TB program managers - were interviewed. Six focus groups were held with TB patients. Data were analyzed using thematic analysis.ResultsThe study identified factors influencing timely TB notification, categorized into software and hardware elements.Patient-related software elements, including TB knowledge and awareness, and health-seeking behavior, are crucial for prompt notification among TB patients.In the community health system, software elements like social stigma and undesirable community attitudes towards contact tracing, and hardware elements such as unbalanced schedules, excessive workload and limited capacity of community TB treatment supporters contribute to delayed TB notification.In the formal health system, software elements like negative attitudes of health providers towards TB patients and demotivation of TB staff, and hardware elements such as high diagnostics and transportation costs, outdated diagnostics in primary care facilities, and slow referral mechanisms, can also delay TB notification.ConclusionDelays in time to TB notification are influenced by a combination of software (attitudinal and behavioral) and hardware (resource-related) elements across TB patients, community health systems, community TB treatment supporters, health providers, and TB staff. Addressing these factors, particularly social stigma, negative attitudes, and resource constraints, is crucial to improving timely TB detection and treatment.

Comprehensive Approach to TB Diagnosis Powered by Machine Learning in Endemic Countries

Abstract Introduction/Objective Tuberculosis (TB), a leading cause of mortality worldwide, is primarily diagnosed through methods that face numerous challenges, including lengthy result times and diagnostic inaccuracy, particularly in the context of drug-resistant strains and latent infections. This study proposes a novel approach that integrates multiple diagnostic modalities with machine learning to enhance TB diagnosis accuracy. Methods/Case Report The Machine learning model was tailored for the classification of radiological images, specifically CT scans, using original DICOM images to preserve valuable voxel information. The study involved a curated dataset of CT scans from Pakistan, TB-positive (smear+/Culture+ n=72; smear+/culture- n= 3; smear-/Culture+ n=16; smear-/Culture- n=14; clinical and radiological diagnosis only n= 12) and healthy (n=103) scans from COVID-CTSet were included in the training to assess the specificity of the model. Data preprocessing included noise reduction, histogram matching, and data augmentation techniques to ensure a robust training dataset. The methodology also incorporated using k-fold cross validation and a custom learning rate scheduling strategy to optimize the model’s training process. To further test its robustness, the model was evaluated by swapping random patches and analyzing 60 mm patches from the entire CT scan for local feature categorization. Results (if a Case Study enter NA) The model has demonstrated outstanding performance, with sensitivity and specificity exceeding 95% on our internal dataset (n=117) and aligning well with clinical, smear, culture, and anti-TB antibody findings. It also showed promising results on the external NIH TB Portal’s dataset (n= 1,460). The model is robust to variations in CT scanner image quality and noise profiles, maintaining strong generalization across datasets. Even when analyzing random 60mm patches, effectively classifying all local features Conclusion Our study enhances tuberculosis diagnostic methods in endemic regions by blending machine learning with conventional diagnostic techniques. This approach improves TB detection, particularly the early and accurate identification in areas impacted by drug-resistant strains and asymptomatic latent infections. By integrating radiological and clinico-pathological data, we aim to develop a comprehensive diagnostic score. Moving forward, we plan to further integrate this method into clinical decision support systems and adapt the approach to other infectious diseases.

Pattern of situation of tuberculosis in the Islamic republic of Afghanistan

Purpose of the study: to study the incidence of tuberculosis depending on gender, age and regional characteristics over the past 6 years (2017-2022) in the Islamic Republic of Afghanistan. Material and methods: Data from the official statistics of the National Tuberculosis Program of Afghanistan, as well as the WHO assessment of the incidence of tuberculosis in the Islamic Republic of Afghanistan over the past 6 years (2017-2022) were analyzed. Results: In the Islamic Republic of Afghanistan, the situation with tuberculosis remains a cause for concern. Over the past 6 years, the difference in the frequency of detection of new cases of tuberculosis between the WHO estimate and official statistics ranges from 26.7% to 36.9%. It should be noted that in 2017-2019, the detection of tuberculosis improved slightly; in 2019-2020, when the COVID-19 pandemic occurred, the detection of new cases of tuberculosis decreased, and in 2020-2022 it increased again.. Over the past 6 years More women than men suffer from tuberculosis. The difference in incidence is 4490 cases in 2017, 7936 in 2018, 10990 in 2019, 4230 in 2020, 12453 in 2021 and 5690 cases in 2022 and is significant. In 2022, as in other years, more (40%) were diagnosed with tuberculosis in young people aged 15-34 years. Moreover, about 11% of new cases (5562 out of 51654 cases) were registered in the age group 0-4 years, which should be paid special attention to. The most serious situation regarding the incidence of tuberculosis is in the Khost, Nangarhar and Nimroz regions, which are 350, 324 and 315 per 100 thousand population, respectively. The best situation for tuberculosis incidence is in the Panjshir, Maidan-Wardak and Bamiyan regions, which are 47, 72 and 83 per 100,000 population, respectively. Conclusions: The difference in the incidence of new cases of tuberculosis between the WHO estimate and official statistics ranges from 26.7% to 36.9%. In 2019-2020, when the COVID-19 pandemic occurred, the number of new cases of tuberculosis decreased, and in 2020-2022 it increased again. Women are more prone to tuberculosis than men. 40% of new cases of tuberculosis are registered among young people aged 15-34 years, about 11% of new cases are registered in the age group 0-4 years. Among the 34 provinces of Afghanistan, the worst situation is in the provinces of Khost, Nangarhar and Nimroz, and the best situation for tuberculosis is in the provinces of Panjshir, Maidan Wardak and Bamiyan.

Comparative assessment of the epidemic state of tuberculosis before and during the war in Ukraine

BACKGROUND. The war has a negative impact on patient care and the efficiency of the health care system; access to medical care is limited, coverage of preventive examinations and detection of tuberculosis (TB) are deteriorating. There is a threat of exacerbation of the epidemic situation with TB in Ukraine. OBJECTIVE. To assess the epidemic situation with TB during the war in Ukraine. MATERIALS AND METHODS. Comparison of data from the Ministry of health on TB (epidemiological indicators and indicators on detection, diagnosis and prevention) in the pre-war (2018-2021) and war (2022-2023) periods. RESULTS. The following were revealed: 1) a positive trend towards a decrease in epidemiological indicators of TB in 2018-2020, which slowed down in 2021 due to the COVID-19 pandemic, and a negative trend towards an increase in almost all indicators of TB incidence, separate indicators of mortality from TB and its prevalence in 2022-2023; 2) significant increase in the incidence of TB among contacts and children; 3) increase by ⅓-. of mortality from TB in certain regions; 4) correlation between regional rates of morbidity, mortality and prevalence of TB. In 2018-2023, the following decreased: fluorography examination, tuberculin diagnostics, detection of TB during preventive examinations (by 2-4 times), preventive vaccinations for children of the first year of life, detection of newly diagnosed and recurrent TB by smear, preventive treatment in contacts. CONCLUSIONS. Incidence is a marker of response to negative external factors and the main criterion for assessing the epidemic state of TB during the war. Fluorography examination, tuberculin diagnostics, detection of TB during professional examinations, preventive vaccinations for children of the first year of life, detection of newly diagnosed and recurrent TB by smear, preventive treatment of contacts are the main criteria for evaluating the activity of the anti-TB service during the wartime, since they are the first to significantly respond to the challenges of war.

Peculiarities of the dynamics of tuberculosis incidence in children in Ukraine

OBJECTIVE. To analyze the dynamics of the epidemiological situation with tuberculosis (TB) in children in Ukraine, taking into account the adverse effects of the coronavirus disease (COVID-19) pandemic and large-scale war. MATERIALS AND METHODS. The epidemiological and statistical indicators of TB incidence in Ukraine were analyzed. RESULTS. During 2022-2023, there was a negative trend in the incidence of TB – an increase in the incidence among all age groups. Among the children with TB, an accretion in the percentage of the most vulnerable contingents – children younger than 1 year old and adolescents (15-17 years old) – has been observed. Simultaneously there were a decrease in the number of children with small, limited forms of TB and an increase in the percentage of patients with bacterial excretion in all age groups. CONCLUSIONS. During the previous 2 years, for obvious reasons, the situation with TB detection, treatment and follow-up has deteriorated significantly. During the war, all children in Ukraine belong to the high-risk group for infection with Mycobacterium tuberculosis and TB disease and need (at the earliest opportunity) a specific screening examination for TB.