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Introduction: Tuberculosis (TB) is the most common infectious disease caused by mycobacterium tuberculosis. Apart from the lungs, tuberculosis also affects the bone marrow. There are significant hematological abnormalities that occur in association with tuberculosis. So we can use these hematological parameters as a marker for the diagnosis, prognosis, and response to therapy. Aims: To evaluate the hematological parameters in pulmonary tuberculosis patients. Methods and Materials: A total of 70 diagnosed tuberculosis patients and 70 healthy controls were selected by purposive sampling in this study. About 4 ml of venous blood was collected with proper aseptic precaution. 2 ml ethylenediamine tetra acetic acid (EDTA) tube blood was used for hematological analysis by using Siemens Advia 2120i 5-part hematology analyzer. Rest 2 ml blood was used for measurement of erythrocyte sedimentation rate (ESR) by Wintrobe's method. Results: The hemoglobin, packed cell volume (PCV), and blood indices values were significantly lower compared to healthy controls in both sexes. White blood cell (WBC) count, absolute neutrophil count, platelet count, and ESR values were significantly increased in tuberculosis patients as compared to healthy controls and were found to be statistically significant (P-value < 0.05). Conclusions: To measure hematological parameters in tuberculosis is a simple and cost-effective method to predict the course of the disease and monitor complications in developing countries like India. [ABSTRACT FROM AUTHOR]

Objective To assess treatment outcomes in tuberculosis patients participating in support group meetings in five districts of Karnataka and Telangana states in southern India. Methods Tuberculosis patients from five selected districts who began treatment in 2019 were offered regular monthly support group meetings, with a focus on patients in urban slum areas with risk factors for adverse outcomes. We tracked the patients' participation in these meetings and extracted treatment outcomes from the Nikshay national tuberculosis database for the same patients in 2021. We compared treatment outcomes based on attendance of the support groups meetings. Findings Of 30 706 tuberculosis patients who started treatment in 2019, 3651 (11.9%) attended support groups meetings. Of patients who attended at least one support meeting, 94.1% (3426/3639) had successful treatment outcomes versus 88.2% (23 745/26 922) of patients who did not attend meetings (adjusted odds ratio, aOR: 2.44; 95% confidence interval, CI: 2.10-2.82). The odds of successful treatment outcomes were higher in meeting participants than non-participants for all variables examined including: age ≥ 60 years (aOR: 3.19; 95% CI: 2.26-4.51); female sex (aOR: 3.33; 95% CI: 2.46-4.50); diabetes comorbidity (aOR: 3.03; 95% CI: 1.91-4.81); human immunodeficiency virus infection (aOR: 3.73; 95% CI: 1.76-7.93); tuberculosis retreatment (aOR: 1.69; 1.22-2.33); and drug-resistant tuberculosis (aOR: 1.93; 95% CI: 1.21-3.09). Conclusion Participation in support groups for tuberculosis patients was significantly associated with successful tuberculosis treatment outcomes, especially among high-risk groups. Expanding access to support groups could improve tuberculosis treatment outcomes at the population level. [ABSTRACT FROM AUTHOR]

India has been implementing active case-finding (ACF) for TB among marginalised and vulnerable (high-risk) populations since 2017. The effectiveness of ACF cycle(s) is dependent on the use of appropriate screening and diagnostic tools and meeting quality indicators. To determine the number of ACF cycles implemented in 2021 at national, state (n = 36) and district (n = 768) level and quality indicators for the first ACF cycle. In this descriptive study, aggregate TB program data for each ACF activity that was extracted was further aggregated against each ACF cycle at the district level in 2021. One ACF cycle was the period identified to cover all the high-risk populations in the district. Three TB ACF quality indicators were calculated: percentage population screened (≥10%), percentage tested among screened (≥4.8%) and percentage diagnosed among tested (≥5%). We also calculated the number needed to screen (NNS) for diagnosing one person with TB (≤1538). Of 768 TB districts, ACF data for 111 were not available. Of the remaining 657 districts, 642 (98%) implemented one, and 15 implemented two to three ACF cycles. None of the districts or states met all three TB ACF quality indicators' cut-offs. At the national level, for the first ACF cycle, 9.3% of the population were screened, 1% of the screened were tested and 3.7% of the tested were diagnosed. The NNS was 2824: acceptable (≤1538) in institutional facilities and poor for population-based groups. Data were not consistently available to calculate the percentage of i) high-risk population covered, ii) presumptive TB among screened and iii) tested among presumptive. In 2021, India implemented one ACF cycle with sub-optimal ACF quality indicators. Reducing the losses between screening and testing, improving data quality and sensitising stakeholders regarding the importance of meeting all ACF quality indicators are recommended. [ABSTRACT FROM AUTHOR]

Background: TB is a preventable and treatable disease. Yet, successful treatment outcomes at desired levels are elusive in many national TB programs, including India. We aim to identify risk factors for unfavourable outcomes to TB treatment, in order to subsequently design a care model that would improve treatment outcomes among these at-risk patients.Methods: We conducted a cohort analysis among TB patients who had been recently initiated on treatment. The study was part of the internal program evaluation of a USAID-THALI project, implemented in select towns/cities of Karnataka and Telangana, south India. Community Health Workers (CHWs) under the project, used a pre-designed tool to assess TB patients for potential risks of an unfavourable outcome. CHWs followed up this cohort of patients until treatment outcomes were declared. We extracted treatment outcomes from patient's follow-up data and from the Nikshay portal. The specific cohort of patients included in our study were those whose risk was assessed during July and September, 2018, subsequent to conceptualisation, tool finalisation and CHW training. We used bivariate and multivariate logistic regression to assess each of the individual and combined risks against unfavourable outcomes; death alone, or death, lost to follow up and treatment failure, combined as 'unfavourable outcome'.Results: A significantly higher likelihood of death and experiencing unfavourable outcome was observed for individuals having more than one risk (AOR: 4.19; 95% CI: 2.47-7.11 for death; AOR 2.21; 95% CI: 1.56-3.12 for unfavourable outcome) or only one risk (AOR: 3.28; 95% CI: 2.11-5.10 for death; AOR 1.71; 95% CI: 1.29-2.26 for unfavourable outcome) as compared to TB patients with no identified risk. Male, a lower education status, an initial weight below the national median weight, co-existing HIV, previous history of treatment, drug-resistant TB, and regular alcohol use had significantly higher odds of death and unfavourable outcome, while age > 60 was only associated with higher odds of death.Conclusion: A rapid risk assessment at treatment initiation can identify factors that are associated with unfavourable outcomes. TB programs could intensify care and support to these patients, in order to optimise treatment outcomes among TB patients. [ABSTRACT FROM AUTHOR]