Tuberculosis situation among the pediatric population of Saratov Region (2015-2019)

Subjects and Methods. Incidence and prevalence of tuberculosis among children and adolescents in Saratov Region were retrospectively analyzed for 2015-2019.Results. The incidence of tuberculosis in children reflects general trends in the epidemic situation over the years in Saratov Region. In this region during 10 years of monitoring, tuberculosis incidence among children decreased 4 times by 2019 and it made 2.3 per 100,000 population, and among adolescents, it decreased 1.6 times (16.3 per 100,000 population). The number of children with post-tuberculosis changes detected for the first time increased 3 times in 2019 versus 2015, and the number of adolescents increased 4.5 times not only due to transition to tuberculosis screening in children aged 8-17 years old by skin test with tuberculosis recombinant allergen but also to the examination of children by computed tomography. The incidence in children followed up as Group IVA in 2019 was 15 times higher than this rate in the overall pediatric population. Analysis of the number of children and adolescents who developed tuberculosis and followed up as Groups IV and VI in 2015-2019 showed that the number of children ill with tuberculosis in Group IV was 3 times higher than the number of children in Group VI.

Repairing boundaries along pathways to tuberculosis case detection: a qualitative synthesis of intervention designs

Background Tuberculosis case-finding interventions often involve several activities to enhance patient pathways, and it is unclear which activity defines the type of case-finding intervention. When conducting studies to identify the most effective case-finding intervention it is important to have a clear understanding of these interventions for meaningful comparisons. This review aimed to construct a systems-based logic model of all pathways to tuberculosis case detection through a synthesis of intervention designs. Methods We identified an existing systematic review on the effectiveness of interventions to increase tuberculosis case detection and updated the search from December 2016 to October 2020. We included randomized controlled trials, as these designs encourage detailed description of interventions. Taking each study in turn, intervention descriptions were read in detail. The texts were analysed qualitatively by constantly comparing emerging codes to construct patient journeys, visualized as logical chains. Actions taken as part of interventions were positioned along patient journeys to theorize the sequence of outcomes. Patient journeys formed the basis of the model, which was refined through discussion. Results Based on intervention descriptions from 17 randomized controlled trials, our model distinguishes two care-seeking pathways and four screening pathways. An open invitation to people with tuberculosis symptoms creates care-seeking pathways. On care-seeking pathways, systematic screening can be conducted at general health services, but not at specific TB care services. People invited to tuberculosis services regardless of symptoms follow tuberculosis screening pathways and may be identified with presumptive tuberculosis even if they do not seek care for tuberculosis symptoms. Tuberculosis screening pathways include screening offered to all people accessing care at general health services, screening at a mobile clinic or health facility with open invitation to a whole population or tuberculosis contacts, screening personally offered to a whole population or tuberculosis contacts at home, work or school, and screening offered to people receiving care for human immunodeficiency virus or other clinical risk-group care. Conclusion This systems-based logic model of tuberculosis case-finding pathways may support standardized terminology, consistency, transparency and improved communication among researchers, policy-makers, health workers and community members when implementing and evaluating interventions to improve tuberculosis case detection.

Evaluation of chest X-Ray with automated interpretation algorithms for mass tuberculosis screening in prisons.

Rationale: The World Health Organization (WHO) recommends systematic tuberculosis (TB) screening in prisons. Evidence is lacking for accurate and scalable screening approaches in this setting. Objectives: To assess the diagnostic accuracy of artificial intelligence-based chest x-ray interpretation algorithms for TB screening in prisons. Methods: Prospective TB screening study in three prisons in Brazil from October 2017 to December 2019. We administered a standardized questionnaire, performed chest x-ray in a mobile unit, and collected sputum for confirmatory testing using Xpert MTB/RIF and culture. We evaluated x-ray images using three algorithms (CAD4TB version 6, LunitTB and qXR) and compared their diagnostic accuracy. We utilized multivariable logistic regression to assess the effect of demographic and clinical characteristics on algorithm accuracy. Finally, we investigated the relationship between abnormality scores and Xpert semi-quantitative results. Measurements and Main Results: Among 2,075 incarcerated individuals, 259 (12.5%) had confirmed TB. All three algorithms performed similarly overall with AUCs of 0.87-0.91. At 90% sensitivity, only LunitTB and qXR met the WHO Target Product Profile requirements for a triage test, with specificity of 84% and 74%, respectively. All algorithms had variable performance by age, prior TB, smoking, and presence of TB symptoms. LunitTB was the most robust to this heterogeneity, but nonetheless failed to meet the TPP for individuals with previous TB. Abnormality scores of all three algorithms were significantly correlated with sputum bacillary load. Conclusions: Automated x-ray interpretation algorithms can be an effective triage tool for TB screening in prisons. However, their specificity is insufficient in individuals with previous TB.

Improving rural health services: The case of a family physician’s contribution at Aber Hospital, Northern Uganda

Family physicians (FPs) provide quality comprehensive primary care services responsive to the needs of the people they are serving. In Uganda, FPs are still few with poor visibility hence difficult to demonstrate their impact. This short report describes the contribution of a FP guided by the principles of family medicine to improving health care services to meet the needs of a rural population in Northern Uganda. This was carried out through targeted capacity building for teams within various hospital departments and the provision of transformative leadership and management. Hospital laboratory and radiology departments were strengthened to provide the needed diagnostic services to the population and human immunodeficiency virus (HIV) care and tuberculosis screening were improved through the establishment of community service centres together with strengthening community outreaches. The transformative leadership of the multidisciplinary team provided by the FP significantly improved the quantity and quality of health care services.

Evaluation of an artificial intelligence (AI) system to detect tuberculosis on chest X-ray at a pilot active screening project in Guangdong, China in 2019

BACKGROUND: Although computer-aided detection (CAD) software employed with Artificial Intelligence (AI) system has been developed aiming to assist Tuberculosis (TB) triage, screening, and diagnosis, its clinical performance for tuberculosis screening remains unknown. OBJECTIVE: To evaluate performance of an CAD software for detecting TB on chest X-ray images at a pilot active TB screening project. METHODS: A CAD software scheme employed with AI was used to screen chest X ray images of participants and produce probability scores of cases being positive for TB. CAD-generated TB detection scores were compared with on-site and senior radiologists via several performance evaluation indices including area under the ROC curves (AUC), specificity, sensitive, and positive predict value. Pycharm CE and SPSS statistics software packages were used for data analysis. RESULTS: Among 2,543 participants, eight TB patients were identified from this screening pilot program. The AI-based CAD system outperformed the onsite (AUC = 0.740) and senior radiologists (AUC = 0.805) either using thresholds of 30% (AUC = 0.978) and 50% (AUC = 0.859) when taking the final diagnosis as the ground truth. CONCLUSIONS: The AI-based CAD software successfully detects all TB patients as identified from this study at a threshold of 30% . It demonstrates feasibility and easy accessibility to carry out large scale TB screening using this CAD software equipped in medical vans with chest X-ray imaging machine.

Latent tuberculosis screening and treatment in HIV: highly acceptable in a prospective cohort study

BackgroundPeople living with HIV (PLWH) are at increased risk of reactivation of latent TB infection (LTBI). Although UK and international guidelines identify this group as a priority for LTBI screening and treatment, data on attitudes of PLWH to this policy recommendation are lacking.MethodsA five-point, Likert-style questionnaire was administered to PLWH to assess views and intentions towards accepting LTBI screening and treatment. Subsequent Immune Gamma Release Assay (IGRA) testing was offered, and chemoprophylaxis if required. Influencing demographic and psychological associations with planned, and actual, testing and treatment uptake, were assessed using multivariable logistic regression.Results444/716 (62%) patients responded. 417/437 (95.4%) expressed intention to accept LTBI testing. The only significant association was the perceived importance of testing to the individual (aOR 8.98, 95% CI 2.55–31.67). 390/393 (99·2%) accepted appropriate IGRA screening; 41/390 (10·5%) were positive. 397/431 (92.1%) expressed intention to accept chemoprophylaxis, associated with perceived importance of treatment (aOR 3.52, 95% CI 1.46–8.51), a desire to have treatment for LTBI (aOR 1.77, 95% CI 0.99–3.15), and confidence in taking treatment (aOR 3.77, 95% CI 1.84–7.72). Of those offered chemoprophylaxis, 36/37 (97·3%) accepted and 34/36 (94·4%) completed treatment. There were no correlates with actual screening acceptance.ConclusionsLTBI is common amongst PLWH, highlighting the importance of robust screening and treatment programmes. This study shows that screening and treatment for LTBI is highly acceptable to PLWH and provides strong, objective, evidence for policy-makers developing guidelines in this cohort.

Tuberculosis infection under anti-TNF alpha treatment

Background: Anti-tumor necrosis factor-α (TNF-α) is a life-changing treatment leading to quality-of-life improvement. Nonetheless, this treatment is associated with a high risk of infection, especially tuberculosis. Objective: Our study aimed to determine the frequency of active tuberculosis in our patients with chronic rheumatic disease and treated with TNF-α. Methods: We conducted a retrospective study including patients with Rheumatoid Arthritis and Spondylarthritis diagnosed according to ACR/EULAR 2009 criteria and ASAS 2010, respectively, and treated with biological agents for at least 6 months. We collected data regarding tuberculosis screening and the occurrence of active tuberculosis during follow-up. Results: 82 patients were included (37 men and 45 women). The mean age was 42 ± 3.4 years. At inclusion, no patient had a medical history of tuberculosis. The diagnosis of latent tuberculosis infection was established in 17 patients (20.7%). Prophylactic treatment was prescribed in all these cases for three months. Two cases (2.4%) of active tuberculosis occurred under biologic (infliximab). It was two severe forms of tuberculosis. The first case had miliary tuberculosis associated with hepatic and peritoneal involvement. The second one had pleural tuberculosis. These two patients received anti-tuberculosis therapy, and the biological treatment was interrupted. Given the high disease activity, the anti-TNF-α was restarted after 3 and 4 months. There was no recurrence of tuberculosis after 7 years of follow-up. Conclusion: The use of TNF-α blockers is associated with a risk of disseminated forms of tuberculosis. Tuberculosis screening, which is recommended before the biological onset, is also necessary under this treatment. Restarting the anti-TNF-α after appropriate treatment of tuberculosis seemed to be safe.