BACKGROUND:Tuberculosis (TB) in adolescents is distinct from both childhood and adult TB, particularly in terms of risk factors; however, national-level data assessing these factors in adolescents remain limited despite growing attention to the issue. This study aims to identify factors associated with TB among individuals aged 10 to 18 years. METHODS:This study leverages data from the National Family Health Survey (NFHS-5) conducted in India during the year 2019-2021. A total of 479,674 adolescents were included. We employ a generalized linear mixed-effects logistic regression model to examine the association between household, environmental, demographic and behavioral factors and self-reported TB status among adolescents. RESULTS:A total of 363 adolescents reported having TB. The results show that adolescents who are male (aOR = 0.735, p < 0.001), living in a nuclear family (aOR = 0.782, p < 0.001), residing in a household without TB cases (aOR = 0.17, p < 0.001), using a traditional mud stove or chullah (aOR = 0.279, p < 0.001), do not have air conditioning or a cooler (aOR = 0.405, p < 0.001), do not use tobacco (aOR = 0.766, p < 0.001), and do not consume alcohol (aOR = 0.912, p < 0.001) have lower odds of TB. Conversely, older age (aOR = 1.136, p < 0.001), absence of a separate kitchen (aOR = 1.395, p < 0.001), belonging to poor (aOR = 2.787, p < 0.005) or middle-income households (aOR = 2.662, p < 0.001), and living in households without cattle (aOR = 1.489, p < 0.001) are associated with higher odds of TB. CONCLUSIONS:Using nationally representative NFHS data, this study identifies multiple household, socioeconomic, and behavioral factors associated with TB among adolescents in India. These findings highlight the need for targeted TB prevention strategies that address household conditions, socio-economic disparities, and adolescent health behaviors.

There is a paucity of studies applying Generalized Estimating Equations (GEE) for longitudinal analysis of smoking cessation outcomes within the framework of a cluster randomized trial, especially among tuberculosis (TB) patients. In this study, a GEE model which accounts for repeated measures and cluster-level effects was implemented to identify factors associated with smoking cessation among TB patients. The data included 375 TB patients who were smokers and given TB treatment during 2013-2016 in Kanchipuram and Villupuram districts under a cluster randomized trial. GEE modeling provided robust, population-averaged estimates while accounting for intra-cluster correlation, confirming the sustained impact of these interventions. The model demonstrated that smoking cessation interventions, when integrated with TB treatment, had an impact on cessation outcomes in these populations.

BACKGROUND:Smoking is a major public health concern in Tamil Nadu, as it is in many parts of the world. It is a leading cause of preventable diseases and deaths, with a significant economic burden on healthcare systems and society as a whole. Recognizing the need to address this issue, the implementation of smoking cessation strategies at primary health care (PHC) settings has gained attention. Conducting a cost-effectiveness analysis in this context can help policymakers and healthcare providers make informed decisions about the allocation of resources for such interventions. OBJECTIVES:To compare the cost-effectiveness of the smoking cessation of proposed strategies (PSs), PS1: enhanced counselling (EC) + nicotine replacement therapy (NRT) + bupropion tablet; PS2: behavioural intervention (BI) + NRT + promotion of bupropion sustained release (SR); PS3: EC + NRT + promotion of bupropion SR with the current strategy (BI +NRT+ Bupropion) in a population of smokers aged ≥15 years attending the PHC in Tamil Nadu. METHODS:In this hypothetical cohort of 100,000 individuals using the decision tree analysis, a cost-effectiveness assessment was conducted for both proposed and existing strategies. The results were evaluated in terms of incremental cost-effectiveness ratios (ICERs) per person quitting smoking. To assess the robustness of the findings, one-way sensitivity analysis and probabilistic sensitivity analysis were performed which aims to explore and address the uncertainties associated with the outcomes. RESULTS:The cost of the current strategy (CS) was higher (₹359 or $4.28 million) when compared with PS1 (₹327 or $3.90 million) and PS3 (₹327 or $3.90 million) strategies. The PS2 with BI + bupropion SR + NRT was found to be more cost (₹2,720,571 or $ 32,414.76) as compared to current strategy. ICER values indicates that compared to the current strategy, the PS1 and PS3 were found to be cost-saving, whereas the PS2 was found to be cost-effective. The cost-effectiveness acceptability curve demonstrated that the PS1 and PS3 indicates 100% probability of the intervention being cost-saving. After excluding dominated interventions (PS2 and CS), the remaining strategies (PS1 and PS3) were compared. The PS3, with an incremental cost of ₹462,497 ($5,510) for 131 additional quitters, resulted in an ICER of ₹3,531 ($42) per quitter, making it a cost-effective option compared to PS1. CONCLUSION:Our study findings indicate that the need for healthcare providers and policymakers to implement PS3 with EC, NRT, Bupropion SR, as which was found to be cost-saving compared to current practices.

Background:We aimed to estimat the economic burden of TB deaths in terms of gross domestic product (GDP) across Indian states, regions, and different demographic groups. Methodology:Using the Human Capital Approach, we estimated the non-health GDP losses due to TB deaths in India for 2021 at subnational level. The total monetary value for the years of life lost due to TB deaths was calculated. Results:In 2021, 0.393 million TB deaths occurred in India, which would reduce the non-health GDP by US$9.1 billion. North, West, South, and North Eastern states of India incurred 33.5%, 25.6%, 18.5%, and 9.3% of that economic loss respectively. Each TB death resulted in non-health GDP loss of US$23 161. The economic burden was highest among youngr males (20.5%) followed by males aged >75 years (17.3%). The economic cost was minimal among male adolescents and youth accounting for 3.4%. Conclusions:Finndings underscore the urgent need for concerted multisectoral efforts, sustained investments and strategies to reduce TB deaths, and mitigate the resulting economic losses at sub-national level.

Background: Globally, neonatal fungal sepsis (NFS) is a leading cause of neonatal mortality, particularly among vulnerable populations in neonatal intensive care units (NICU). The use of spatial frailty models with a Bayesian approach to identify hotspots and risk factors for neonatal deaths due to fungal sepsis has not been explored before. Methods: A cohort of 80 neonates admitted to the NICU at a Government Hospital in Tamil Nadu, India and diagnosed with fungal sepsis through blood cultures between 2018–2020 was considered for this study. Bayesian spatial frailty models using parametric distributions, such as Log-logistic, Log-normal, and Weibull proportional hazard (PH) models, were employed to identify associated risk factors for NFS deaths and hotspot areas using the R version 4.1.3 software and QGIS version 3.26 (Quantum Geographic Information System). Results: The spatial parametric frailty models were found to be good models for analyzing NFS data. Abnormal levels of activated thromboplastin carried a significantly higher risk of death in neonates across all PH models (Log-logistic, Hazard Ratio (HR), 95% Credible Interval (CI): 22.12, (5.40, 208.08); Log-normal: 20.87, (5.29, 123.23); Weibull: 18.49, (5.60, 93.41). The presence of hemorrhage also carried a risk of death for the Log-normal (1.65, (1.05, 2.75)) and Weibull models (1.75, (1.07, 3.12)). Villivakkam, Tiruvallur, and Poonamallee blocks were identified as high-risk areas. Conclusions: The spatial parametric frailty models proved their effectiveness in identifying these risk factors and quantifying their association with mortality. The findings from this study underline the importance of the early detection and management of risk factors to improve survival outcomes in neonates with fungal sepsis.

BACKGROUND:In India, there is no information on health related quality of life (HRQoL) of patients with drug sensitive tuberculosis (TB) using a longitudinal design that includes post- treatment period. This study is the first of its kind in India to assess HRQoL of TB patients from a longitudinal prospective and to identify the factors associated with changes in HRQoL. METHODS:The study participants were 180 newly diagnosed drug-sensitive smear-positive pulmonary TB patients who were initiated on treatment under the National TB Elimination Programme (NTEP) in Chennai and Tiruvallur districts of Tamil Nadu, South India. The patients were interviewed at four different time points between 2020 and 2023 using validated questionnaires assessing general health (European Quality of Life-5 Dimensions-5 Level (EQ-5D-5L), Short Form health survey (SF-20)), disease specific (St. George's Respiratory Questionnaire (SGRQ)) and mental health including depression and anxiety (Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder (GAD-7)). The Friedman test was used to identify changes in HRQoL scores over time and generalised estimating equation (GEE) were applied to identify factors associated with HRQoL. RESULTS:HRQoL scores of TB patients, as measured by different scales showed significant improvement from treatment initiation to treatment completion. The GEE analysis showed that the EQ-5D-5L scores over follow-up visits were significantly lower in females (-0.038, p < 0.005) and higher in those who did not skip their main meal in a day (0.077, p < 0.001). The PHQ-9 and GAD-7 scores were significantly higher among females (0.609, p < 0.05; 0.531, p < 0.05). Additionally, PHQ-9 scores were also higher among patients from rural district (0.392, p < 0.05). The SF-20 scores were significantly lower in patients aged >45 years (-1.675, p < 0.05), female (-3.809, p < 0.001) and unemployed (-2.277, p < 0.005). The SGRQ scores were higher in patients aged >45 years (3.043, p < 0.01), females (4.256, p < 0.05) and those from rural district (2.219, p < 0.05). The HRQoL scores were significantly higher in patients who did not skip their main meal and lower in females irrespective of the scales used. CONCLUSION:The HRQoL of TB patients improved significantly over a period of treatment. Gender, age, skipping main meals, region and employment status were the key factors influencing HRQoL. Focusing on HRQoL assessment in the care of TB patients could help to minimize physical, mental and social challenges and enable them to lead a normal life.

Despite advancements in detection and treatment, tuberculosis (TB), an infectious illness caused by the Mycobacterium TB bacteria, continues to pose a serious threat to world health. The TB diagnosis phase includes a patient’s medical history, physical examination, chest X-rays, and laboratory procedures, such as molecular testing and sputum culture. In artificial intelligence (AI), machine learning (ML) is an advanced study of statistical algorithms that can learn from historical data and generalize the results to unseen data. There are not many studies done on the ML algorithm that enables the prediction of treatment success for patients with pulmonary TB (PTB). The objective of this study is to identify an effective and predictive ML algorithm to evaluate the detection of treatment success in PTB patients and to compare the predictive performance of the ML models. In this retrospective study, a total of 1236 PTB patients who were given treatment under a randomized controlled clinical trial at the ICMR-National Institute for Research in Tuberculosis, Chennai, India were considered for data analysis. The multiple ML models were developed and tested to identify the best algorithm to predict the sputum culture conversion of TB patients during the treatment period. In this study, decision tree (DT), random forest (RF), support vector machine (SVM) and naïve bayes (NB) models were validated with high performance by achieving an area under the curve (AUC) of receiver operating characteristic (ROC) greater than 80%. The salient finding of the study is that the DT model was produced as a better algorithm with the highest accuracy (92.72%), an AUC (0.909), precision (95.90%), recall (95.60%) and F1-score (95.75%) among the ML models. This methodology may be used to study the precise ML model classification for predicting the treatment success of TB patients during the treatment period.

Background Fixed days and timings of service are challenges in the care of patients with tuberculosis (TB). We assessed whether provision of evening DOTS (directly observed treatment, short course) improves treatment outcomes in a city with a working population. Methods We enrolled new adult patients with TB from seven tuberculous units (TUs) in this prospective cohort study. Participants were offered the option of DOTS during the day (8 a.m. to 3:30 p.m.) or evening (4 p.m. to 8 p.m.) and assigned accordingly. Results Of 127 patients enrolled between April and July 2017, 19 (15%) opted for evening DOTS. The number varied between the seven TUs (p=0.002). On an average, antitubercular therapy (ATT) was taken at 9:41 a.m. in the routine and 5:14 p.m. in the evening DOTS centres. Patients who were employed, left residence and returned back at 9:05 a.m. and 6:40 p.m., respectively. Around 96% (104/108) opted for day-time DOTS due to closeness of the centre to their residence. Around 74% (14/19) chose evening DOTS because of time convenience. Around 15% of patients on routine DOTS (16) had unfavourable treatment outcomes. All had favourable outcomes in the evening DOTS. Men were less likely and those withut alcohol disorders were more likely to have treatment success. Conclusion Provision of time convenient services might improve adherence and treatment outcome.

Structural Equation Model (SEM) is an advanced multivariate statistical tool for modeling latent variables and to control measurement errors.Bayesian SEM (BSEM) gives better estimates of latent variable compared to classical SEM.To our knowledge the use of BSEM approach to identify the significant latent constructs influencing quit smoking in tuberculosis (TB) and human immunodeficiency virus (HIV) patients was not studied so far.The aim of this study is to identify latent variables influencing quitting smoking in TB and HIV patients using BSEM.The data used for the study consist of 160 patients (80 TB and 80 HIV) randomised to receive smoking cessation intervention under clinical trial.The smoking status was measured after one month of intervention.The latent variables 'reasons for smoking' (measured by the variables work tension, family tension and pleasure while smoking) and 'intensity of smoking' (measured by the variables Fagerstrom score, smoking type, number of times smoking per day and smoking duration) and the information on socio-economic characteristics were considered for analyses.This study elucidates the importance of applying BSEM to assess smoking cessation in TB and HIV patients.BSEM gave the estimates indicating the latent variable 'intensity of smoking' had negative effect on quit smoking.

Adherence to anti-Tuberculosis (TB) treatment is vital for curing TB patients and avoids drug resistance TB.Structural Equation Model (SEM) is a powerful tool for modeling latent variables and to control measurement errors.Bayesian SEM (BSEM) gives better estimates of the latent variable compared to conventional frequentist approaches to SEM.To our knowledge the use of BSEM to identify the significant latent constructs influencing the TB patients' adherence to anti-TB treatment was not studied so far.A total of 725 pulmonary TB patients who were registered with Directly Observed Treatment Short course (DOTS) at Government health facilities in Tiruvallur district, south India under National Tuberculosis Elimination Programme were used for this analysis.In this study, a model of adherence to anti-TB treatment of TB patients using BSEM was explored which used to identify the relationship between adherence to anti-TB treatment and the latent variables "socio-economic", "habits", "problems" (problems in taking treatment) and "DOT" (treatment related variables).The latent variables "DOT" (0.199, p<0.005) and "problems" (-0.202 p<0.01) were significantly associated with adherence variable.The latent variable "DOT" had a positive effect while "problems" had a negative effect on adherence to anti-TB treatment.Hence reducing the patient's specific problems might encourage treatment regularity under guided supervision.

BACKGROUND:The use of Bayesian Structural Equation Model (BSEM) to evaluate the impact of TB on self-reported health related quality of life (HRQoL) of TB patients has been not studied.OBJECTIVE:To identify the factors that contribute to the HRQoL of TB patients using BSEM.METHODS:This is a latent variable modeling with Bayesian approach using secondary data. HRQoL data collected after one year from newly diagnosed 436 TB patients who were registered and successfully completed treatment at Government health facilities in Tiruvallur district, south India under the National TB Elimination Programme (NTEP) were used for this analysis. In this study, the four independent latent variables such as physical well-being (PW = PW1-7), mental well-being (MW = MW1-7), social well-being (SW = SW1-4) and habits were considered. The BSEM was constructed using Markov Chain Monte Carlo algorithm for identifying the factors that contribute to the HRQoL of TB patients who completed treatment.RESULTS:Bayesian estimates were obtained using 46,300 observations after convergence and the standardized structural regression estimate of PW, MW, SW on HRQoL were 0.377 (p<0.001), 0.543 (p<0.001) and 0.208 (p<0.001) respectively. The latent variables PW, MW and SW were significantly associated with HRQoL of TB patients. The age was found to be significantly negatively associated with HRQoL of TB patients.CONCLUSIONS:The current study demonstrated the application of BSEM in evaluating HRQoL. This methodology may be used to study precise estimates of HRQoL of TB patients in different time points.

Background Shortening tuberculosis (TB) treatment duration is a research priority. We tested the efficacy and safety of 3- and 4-month regimens containing moxifloxacin in a randomised clinical trial in pulmonary TB (PTB) patients in South India. Methods New, sputum-positive, adult, HIV-negative, non-diabetic PTB patients were randomised to 3- or 4-month moxifloxacin regimens [moxifloxacin (M), isoniazid (H), rifampicin (R), pyrazinamide (Z) and ethambutol (E)] or to a control regimen (2H(3)R(3)Z(3)E(3)/4R(3)H(3)) [C]. The 4 test regimens were 3R(7)H(7)Z(7)E(7)M(7) [M3], 2R(7)H(7)Z(7)E(7)M(7)/2R(7)H(7)M(7) [M4], 2R(7)H(7)Z(7)E(7)M(7)/2R(3)H(3)M(3) [M4-I] or 2R(7)H(7)Z(7)E(7)M(7)/2R(3)H(3)E(3)M(3) [M4-IE]. Treatment was directly observed. Clinical and bacteriological assessments were done monthly during treatment and for 24 months post-treatment. The primary end point was TB recurrence post-treatment. Results Of 1371 patients, randomised, modified intention-to-treat (ITT) analysis was done in 1329 and per-protocol (PP) analysis in 1223 patients. Regimen M3 was terminated due to high TB recurrence rates. 'Favourable' response at end of treatment was 96-100% in the moxifloxacin regimens and 93% in the control regimen. Among these, the TB recurrence occurred in 4.1% in the M4 regimen and in 4.5% in the control regimen and demonstrated equivalence within a 5% margin (95% CI -3.68, 4.55). Similar findings were observed in modified ITT analysis. The TB recurrence rates in the M4-I and M4-IE regimens did not show equivalence with the control regimen. Sixteen (1.4%) of 1087 patients in the moxifloxacin regimens required treatment modification. Conclusion The 4-month daily moxifloxacin regimen [M4] was found to be equivalent and as safe as the 6-month thrice-weekly control regimen.

BACKGROUND:Tuberculosis burden is still high and smoking prevalence among males has increased in India. It is found that increased morbidity, mortality and relapse among TB smokers.METHOD:Setting: Patients from two Revised National Tuberculosis Control Program Centres of Tamilnadu form the study population.OBJECTIVE:To compare the effectiveness of Bupropion therapy along with standard counselling versus enhanced counseling versus standard counseling for smoking cessation among TB patients.STUDY DESIGN:Cluster randomized effectiveness trial.PROCEDURE:Patients from each of the thirty-six Designated Microscopic Centres were randomly allocated to receive one of the three interventions using cluster randomization. Smoking cessation was assessed by self-reporting and confirmed by Carbon monoxide(CO) monitors, done at three-time points and TB treatment outcome at the end of ATT.RESULTS:Out of 517 male patients enrolled to the study, the smoking status is available only to 381 subjects. The proportion of patients who have quit smoking in drug, enhanced and standard arms at the end of treatment was 67%, 83% and 52% (P= < 0.001). There was no statistical significance in response to TB treatment between those who quit and those who did not (Favourable response 99.2% vs 97.6%).CONCLUSION:Both enhanced counselling arm and drug arm are effective strategies for smoking cessation among TB patients and their implementation in the TB programs are recommended.

Tuberculosis still remains a major public health problem even though it is treatable and curable. Weight gain measurement during anti tuberculosis (TB) treatment period is an important component to assess the progress of TB patients. In this study, Latent Growth Models (LGMs) were implemented in a longitudinal design to predict the change in weight of TB patients who were given three different regimens under randomized controlled clinical trial for anti-TB treatment. Linear and Quadratic LGMs were fitted using Mplus software. The age, sex and treatment response of the TB patients were used as time invariant independent variables of the growth trajectories. The quadratic trend was found to be better in explaining the changes in weight without grouping than the quadratic model for three group comparisons. A significant increase in the change of weight over time was identified while a significant quadratic effect indicated that weights were sustained over time. The growth rate was similar in both the groups. The treament response had significant association with the growth rate of weight scores of the patients.

Heart failure (HF) is the major of cause of mortality and morbidity in the developed world. Gene expression profiles of animal model of heart failure have been used in number of studies to understand human cardiac disease. In this study, statistical methods of analysing microarray data on cardiac tissues from dogs with pacing induced HF were used to identify differentially expressed genes between normal and two abnormal tissues. The unsupervised techniques principal component analysis (PCA) and cluster analysis were explored to distinguish between three different groups of 12 arrays and to separate the genes which are up regulated in different conditions among 23912 genes in heart failure canines' microarray data. It was found that out of 23912 genes, 1802 genes were differentially expressed in the three groups at 5% level of significance and 496 genes were differentially expressed at 1% level of significance using one way analysis of variance (ANOVA). The genes clustered using PCA and clustering analysis were explored in the paper to understand HF and a small number of differentially expressed genes related to HF were identified.

Advancement in technology has helped to solve structures of several proteins including M. tuberculosis (MTB) proteins. Identifying similarity between protein structures could not only yield valuable clues to their function, but can also be employed for motif finding, protein docking and off-target identification. The current study has undertaken analysis of structures of all MTB gene products with available structures was analyzed. Majority of the MTB proteins belonged to the α/β class. 23 different protein folds are used in the MTB protein structures. Of these, the TIM barrel fold was found to be highly conserved even at very low sequence identity. We identified 21 paralogs and 27 analogs of MTB based on domains and EC classification. Our analysis revealed that many of the current drug targets share structural similarity with other proteins within the MTB genome, which could probably be off-targets. Results of this analysis have been made available in the Mycobacterium tuberculosis Structural Database (http://bmi.icmr.org.in/mtbsd/MtbSD.php/search.php) which is a useful resource for current and novel drug targets of MTB.

BACKGROUND:Nevirapine is an important component of paediatric combination HIV therapy. Adequate drug exposure is necessary in order to achieve long-lasting viral suppression.OBJECTIVES:To study the influence of age, drug dose and formulation type, nutritional status and CYP2B6 516G>T polymorphism on blood concentrations of nevirapine in children treated with generic antiretroviral drugs.METHODS:A multicentre study was conducted at four sites in India. HIV-infected children receiving generic nevirapine-based fixed-dose combinations were recruited. Trough and 2 h nevirapine plasma concentrations were determined by HPLC. Characterization of the CYP2B6 gene polymorphism was performed using direct sequencing. Clinical and nutritional status was recorded. Groups were compared using the Mann-Whitney U-test and multivariable logistic regression analysis was performed to identify factors contributing to low drug levels.RESULTS:Ninety-four children of median age 78 months were studied; 60% were undernourished or stunted. Stunted children had a significantly lower 2 h nevirapine concentration compared with non-stunted children (P < 0.05); there were no significant differences in trough concentrations between different nutritional groups. Nevirapine levels were significantly higher in children with TT compared with GG and GT CYP2B6 genotypes (P < 0.01). Children ≤ 3 years had a 3.2 (95% confidence interval 1.07-9.45) times higher risk of having sub-therapeutic nevirapine concentrations.CONCLUSIONS:Nevirapine blood concentrations are affected by many factors, most notably age ≤ 3 years; a combination of young age, stunting and CYP2B6 GG or GT genotype could potentially result in sub-therapeutic nevirapine concentrations. Dosing recommendations for children should be reviewed in the light of these findings.

Synonymous codon usage of protein coding genes of thirty two completely sequenced mycobacteriophage genomes was studied using multivariate statistical analysis. One of the major factors influencing codon usage is identified to be compositional bias. Codons ending with either C or G are preferred in highly expressed genes among which C ending codons are highly preferred over G ending codons. A strong negative correlation between effective number of codons (Nc) and GC3s content was also observed, showing that the codon usage was effected by gene nucleotide composition. Translational selection is also identified to play a role in shaping the codon usage operative at the level of translational accuracy. High level of heterogeneity is seen among and between the genomes. Length of genes is also identified to influence the codon usage in 11 out of 32 phage genomes. Mycobacteriophage Cooper is identified to be the highly biased genome with better translation efficiency comparing well with the host specific tRNA genes.