Lessons Learned From a Peer-Supported Differentiated Care and Nutritional Supplementation for People With TB in a Southern Indian State.

Two critical components of patient support systems for people with TB are regular counseling and locally managed nutritional support. As part of an ongoing differentiated TB care initiative called Tamil Nadu Kasanoi Erappila Thittam (meaning TB death-free initiative in Tamil, TN-KET) to reduce TB deaths, adults with TB with very severe undernutrition, respiratory insufficiency, or poor performance status are identified at diagnosis (triage-positive) and prioritized for referral, comprehensive clinical assessment, and inpatient care. Between January and June 2023, in 6 districts, a pilot exercise was conducted in which trained TB survivors, known as TB champions, provided baseline counseling and additional counseling (if required) to triage-positive people with TB at diagnosis. Additionally, people with TB with severe undernutrition were prioritized for nutritional supplementation for at least 3 months. Among 652 people with TB who were triage-positive at diagnosis, the program staff shared details of 145 (22%), and all were counseled by TB champions (baseline counseling). Program staff identified 74 (11%) triage-positive people with TB who required additional counseling (i.e., those refusing referral or admission or continued admission), and 71 (96%) were counseled by TB champions. Among these, 54 (76%) were admitted or readmitted and successfully discharged. In addition, among 1,042 people with TB with severe undernutrition, program staff shared details of 390 (38%), of which 60% received nutritional supplementation through the efforts of TB champions. We conclude that TB champions were able to provide quality and timely peer support through direct counseling and by mobilizing local resources for nutritional support. The engagement of TB champions can be further strengthened by establishing robust coordination mechanisms with the TB program. Lessons from this pilot will contribute to the Tamil Nadu State TB Cell's plans to expand the role of TB champions and enhance community participation to end TB in India.

Quality of comprehensive assessment among severely ill TB patients referred after triaging in southern India.

To reduce TB deaths, Tamil Nadu, a southern Indian state, implemented the first state-wide differentiated TB care strategy starting April 2022. Triage-positive severely ill patients are prioritised for comprehensive assessment and inpatient care. Routine program data during October-December 2022 revealed that documentation of total score after comprehensive assessment was available in only 39%, possibly indicating poor quality of comprehensive assessment. We confirmed this using operational research. The case record form to record comprehensive assessment was used only in 26% and among these, the completeness and correctness in filling out the form were sub-optimal. There is a clear need to enhance the quality of comprehensive assessments.

Depuis avril 2022, le Tamil Nadu, un État du sud de l'Inde, a mis en œuvre la première stratégie de soins différenciés pour la TB à l'échelle de l'État afin de réduire le nombre de décès dus à la TB. Les personnes gravement malades ayant obtenu un résultat positif au triage sont prioritaires pour une évaluation complète et des soins hospitaliers. Les données du programme de routine entre octobre et décembre 2022 ont révélé que la documentation du score total après l'évaluation complète n'était disponible que dans 39% des cas, ce qui pourrait indiquer une mauvaise qualité de l'évaluation complète. Nous l'avons confirmé par le biais d'une recherche opérationnelle. Le formulaire de dossier pour enregistrer l'évaluation complète n'a été utilisé que dans 26% des cas et, parmi ceux-ci, l'exhaustivité et l'exactitude du remplissage du formulaire n'étaient pas optimales. Il est manifestement nécessaire d'améliorer la qualité de l'évaluation complète.

Transformer-Based Network for Accurate Classification of Lung Auscultation Sounds.

Respiratory diseases are a major cause of death worldwide, affecting a significant proportion of the population with lung function abnormalities that can lead to respiratory illnesses. Early detection and prevention are critical to effective management of these disorders. Deep learning algorithms offer a promising approach for analyzing complex medical data and aiding in early disease detection. While transformer-based models for sequence classification have proven effective for tasks like sentiment analysis, topic classification, etc., their potential for respiratory disease classification remains largely unexplored. This paper proposes a classifier utilizing the transformer-encoder block, which can capture complex patterns and dependencies in medical data. The proposed model is trained and evaluated on a large dataset from the International Conference on Biomedical Health Informatics 2017, achieving state-of-the-art results with a mean sensitivity of 70.53%, mean specificity of 84.10%, mean average score of 77.32%, and mean harmonic score of 76.10%. These results demonstrate the model's effectiveness in diagnosing respiratory diseases while taking up minimal computational resources.

Multivariate Parametric Study of Nailfold Capillary Images for Disease Detection.

Nailfold capillaroscopy is a tool which is non-invasive in nature and can be useful for diagnosis, research, therapeutic study and prognosis. Research shows that specific capillary morphology patterns are identified for diabetic subjects, hypertensive subjects and normal controls. In this study, we have proposed RATHEW approach of classifying these three classes of subjects. RATHEW approach employs a three step process for classifying nailfold images: one, identify six abnormality parameters from the image dataset; two, score these abnormality parameters based on the defined scoring rules; and three, combine them mathematically to segregate them into three classes. This technique can be further enhanced to grade the severity of disease and organ involvement. This can bring in a paradigm shift to the disease detection and therapeutic study mechanism.

The First Differentiated TB Care Model From India: Delays and Predictors of Losses in the Care Cascade.

To reduce TB deaths in resource-limited settings, a differentiated care strategy can be used to triage patients with high risk of severe illness (i.e., those with very severe undernutrition, respiratory insufficiency, or inability to stand without support) at diagnosis and refer them for comprehensive assessment and inpatient care. Globally, there are few examples of implementing this type of strategy in routine program settings. Beginning in April 2022, the Indian state of Tamil Nadu implemented a differentiated care strategy called Tamil Nadu-Kasanoi Erappila Thittam (TN-KET) for all adults aged 15 years and older with drug-susceptible TB notified by public facilities. Before evaluating the impact on TB deaths, we sought to understand the retention and delays in the care cascade as well as predictors of losses. During April-June 2022, 14,961 TB patients were notified and 11,599 (78%) were triaged. Of those triaged, 1,509 (13%) were at high risk of severe illness; of these, 1,128 (75%) were comprehensively assessed at a nodal inpatient care facility. Of 993 confirmed as severely ill, 909 (92%) were admitted, with 8% unfavorable admission outcomes (4% deaths). Median admission duration was 4 days. From diagnosis, the median delay in triaging and admission of severely ill patients was 1 day each. Likelihood of triaging decreased for people with extrapulmonary TB, those diagnosed in high-notification districts or teaching hospitals, and those transferred out of district. Predictors of not being comprehensively assessed included: aged 25-34 years, able to stand without support, and diagnosis at a primary or secondary-level facility. Inability to stand without support was a predictor of unfavorable admission outcomes. To conclude, the first quarter of implementation suggests that TN-KET was feasible to implement but could be improved by addressing predictors of losses in the care cascade and increasing admission duration.

--Eleven tips for operational researchers working with health programmes: our experience based on implementing differentiated tuberculosis care in south India.

Due to the workload and lack of a critical mass of trained operational researchers within their ranks, health systems and programmes may not be able to dedicate sufficient time to conducting operational research (OR). Hence, they may need the technical support of operational researchers from research/academic organisations. Additionally, there is a knowledge gap regarding implementing differentiated tuberculosis (TB) care in programme settings. In this 'how we did it' paper, we share our experience of implementing a differentiated TB care model along with an inbuilt OR component in Tamil Nadu, a southern state in India. This was a health system initiative through a collaboration of the State TB cell with the Indian Council of Medical Research institutes and the World Health Organisation country office in India. The learnings are in the form of eleven tips: four broad principles (OR on priority areas and make it a health system initiative, implement simple and holistic ideas, embed OR within routine programme settings, aim for long-term engagement), four related to strategic planning (big team of investigators, joint leadership, decentralised decision-making, working in advance) and three about implementation planning (conducting pilots, smart use of e-tools and operational research publications at frequent intervals). These may act as a guide for other Indian states, high TB burden countries that want to implement differentiated care, and for operational researchers in providing technical assistance for strengthening implementation and conducting OR in health systems and programmes (TB or other health programmes). Following these tips may increase the chances of i) an enriching engagement, ii) policy/practice change, and iii) sustainable implementation.

Electroencephalogram-Based Emotion Recognition Using a Particle Swarm Optimization-Derived Support Vector Machine Classifier.

We sort human emotions using Russell's circumplex model of emotion by classifying electroencephalogram (EEG) signals from 25 subjects into four discrete states, namely, happy, sad, angry, and relaxed. After acquiring signals, we use a standard database for emotion analysis using physiological EEG signals. Once raw signals are pre-processed in an EEGLAB, we perform feature extraction using Matrix Laboratory and apply discrete wavelet transform. Before classifying we optimize extracted features with particle swarm optimization. The acquired set of EEG signals are validated after finding average classification accuracy of 75.25%, average sensitivity of 76.8%, and average specificity of 91.06%.