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Background: 99DOTS is a cellphone-based digital adherence technology. The state of Himachal Pradesh, India, made 99DOTS available to all adults being treated for drug-sensitive tuberculosis (TB) in the public sector in May 2018. While 99DOTS has engaged over 500,000 people across India, few studies have evaluated its effectiveness in improving TB treatment outcomes. Methods: We compared treatment outcomes of adults with drug-sensitive TB before and after Himachal Pradesh's 99DOTS launch using data from India's national TB database. The pre-intervention group initiated treatment between February and October 2017 (N = 7722), and the post-intervention group between July 2018 and March 2019 (N = 8322). We analyzed engagement with 99DOTS and used multivariable logistic regression to estimate impact on favorable treatment outcomes (those marked as cured or treatment complete). Results: In the post-intervention group, 2746 (33.0%) people called 99DOTS at least once. Those who called did so with a wide variation in frequency (< 25% of treatment days: 24.6% of callers; 25–50% of days: 15.1% of callers, 50–75% of days: 15.7% of callers; 75–100% of days: 44.6% of callers). In the pre-intervention group, 7186 (93.1%) had favorable treatment outcomes, compared to 7734 (92.9%) in the post-intervention group. This difference was not statistically significant (OR = 0.981, 95% CI [0.869, 1.108], p = 0.758), including after controlling for individual characteristics (adjusted OR = 0.970, 95% CI [0.854, 1.102]). Conclusions: We found no statistically significant difference in treatment outcomes before and after a large-scale implementation of 99DOTS. Additional work could help to elucidate factors mediating site-wise variations in uptake of the intervention. [ABSTRACT FROM AUTHOR]

Background: The private sector is an important yet underregulated component of the TB treatment infrastructure in India. The Joint Effort for Elimination of Tuberculosis (Project JEET) aims to link private sector TB care with the constellation of social support mechanisms available through the Indian National TB Elimination Programme (NTEP), including the provision of free fixed-dose combination (FDCs) drugs to patients. This quasi-experimental study analysed routinely collected data to determine the impact of free drugs on patient follow-ups and treatment outcomes. Methods: We used data for private sector patients enrolled with Project JEET who were diagnosed with pulmonary and extrapulmonary TB between 1 and 2019 and 31 March 2020, and completed treatment by 31 December 2021. Propensity score matching was used to create a dataset to compare the number of follow-ups and proportion of successful treatment outcomes for patients on free drugs to a control group who paid out-of-pocket. 11,621 matched pairs were included in the analysis. Logistic regression and ordinary least squares regression models were used to estimate the impact of free drugs on number of follow-ups and treatment success, where latter is defined as treatment completion or cure. Results: After controlling for potential confounders, patients on free drugs received on average 2.522 (95% C.I.: 2.325 to 2.719) additional follow-ups compared to patients who paid out of pocket. This equates to a 25% mean and 32% median increase in follow-ups for patients availing free drugs. For treatment success, patients receiving free drugs had 45% higher odds of a successful treatment (Odds Ratio: 1.452, 95% C.I.: 1.288 to 1.637). Conclusions: Patients receiving free drugs were found to follow up with their treatment coordinator more frequently, in part likely to enable drug refilling, compared to patients who were paying out of pocket. These additional contacts would have offered opportunities to address concerns regarding side effects, provide additional treatment information, and connect with social support services, all of which subsequently contributed to patients' continual engagement with their treatment. This potentially represents the unmeasured effect of free drugs on continual social support, which translates into a higher odds of treatment success for patients. [ABSTRACT FROM AUTHOR]

Background: In this phase 2 randomised placebo-controlled clinical trial in patients with COVID-19, we hypothesised that blocking mineralocorticoid receptors using a combination of dexamethasone to suppress cortisol secretion and spironolactone is safe and may reduce illness severity. Methods: Hospitalised patients with confirmed COVID-19 were randomly allocated to low dose oral spironolactone (50 mg day 1, then 25 mg once daily for 21 days) or standard of care in a 2:1 ratio. Both groups received dexamethasone 6 mg daily for 10 days. Group allocation was blinded to the patient and research team. Primary outcomes were time to recovery, defined as the number of days until patients achieved WHO Ordinal Scale (OS) category ≤ 3, and the effect of spironolactone on aldosterone, D-dimer, angiotensin II and Von Willebrand Factor (VWF). Results: One hundred twenty patients with PCR confirmed COVID were recruited in Delhi from 01 February to 30 April 2021. 74 were randomly assigned to spironolactone and dexamethasone (SpiroDex), and 46 to dexamethasone alone (Dex). There was no significant difference in the time to recovery between SpiroDex and Dex groups (SpiroDex median 4.5 days, Dex median 5.5 days, p = 0.055). SpiroDex patients had significantly lower D-dimer levels on days 4 and 7 (day 7 mean D-dimer: SpiroDex 1.15 µg/mL, Dex 3.15 µg/mL, p = 0.0004) and aldosterone at day 7 (SpiroDex 6.8 ng/dL, Dex 14.52 ng/dL, p = 0.0075). There was no difference in VWF or angiotensin II levels between groups. For secondary outcomes, SpiroDex patients had a significantly greater number of oxygen free days and reached oxygen freedom sooner than the Dex group. Cough scores were no different during the acute illness, however the SpiroDex group had lower scores at day 28. There was no difference in corticosteroid levels between groups. There was no increase in adverse events in patients receiving SpiroDex. Conclusion: Low dose oral spironolactone in addition to dexamethasone was safe and reduced D-dimer and aldosterone. Time to recovery was not significantly reduced. Phase 3 randomised controlled trials with spironolactone and dexamethasone should be considered. Trial registration: The trial was registered on the Clinical Trials Registry of India TRI: CTRI/2021/03/031721, reference: REF/2021/03/041472. Registered on 04/03/2021. [ABSTRACT FROM AUTHOR]

Background: The number of people receiving second-line antiretroviral therapy (ART) has increased as global access to ART has expanded. Data on the burden and factors associated with second-line ART virologic failure (VF) from India remain limited. Methods: We conducted cross-sectional viral load (VL) testing among adults (≥ 18 years) who were registered at a publicly funded ART center in western India between 2014 and 2015 and had received second-line ART for at least 6 months. Sociodemographic and clinical characteristics were abstracted from routinely collected programmatic data. Logistic regression evaluated factors associated with VF (defined as VL > 1000 copies/mL). Results: Among 400 participants, median age was 40 years (IQR 34–44), 71% (285/400) were male, and 15% (59/400) had VF. Relative to participants without VF, those with VF had lower median CD4 counts (230 vs 406 cells/mm3, p < 0.0001), lower weight at first-line failure (49 vs 52 kg, p = 0.003), were more likely to have an opportunistic infection (17% vs 3%, p < 0.0001) and less likely to have optimal ART adherence (71% vs 87%, p = 0.005). In multivariable analysis, VF was associated with opportunistic infection (aOR, 4.84; 95% CI, 1.77–13.24), lower CD4 count (aOR 4.15; 95% CI, 1.98–8.71) and lower weight at first-line failure (aOR, 2.67; 95% CI, 1.33–5.34). Conclusions: We found second-line VF in about a sixth of participants in our setting, which was associated with nearly fivefold increased odds in the context of opportunistic infection. Weight could be a useful clinical indicator for second-line VF. [ABSTRACT FROM AUTHOR]

Background: Increased occurrence of mucormycosis during the second wave of COVID-19 pandemic in early 2021 in India prompted us to undertake a multi-site case-control investigation. The objectives were to examine the monthly trend of COVID-19 Associated Mucormycosis (CAM) cases among in-patients and to identify factors associated with development of CAM.Methods: Eleven study sites were involved across India; archived records since 1st January 2021 till 30th September 2021 were used for trend analysis. The cases and controls were enrolled during 15th June 2021 to 30th September 2021. Data were collected using a semi-structured questionnaire. Among 1211 enrolled participants, 336 were CAM cases and 875 were COVID-19 positive non-mucormycosis controls.Results: CAM-case admissions reached their peak in May 2021 like a satellite epidemic after a month of in-patient admission peak recorded due to COVID-19. The odds of developing CAM increased with the history of working in a dusty environment (adjusted odds ratio; aOR 3.24, 95% CI 1.34, 7.82), diabetes mellitus (aOR: 31.83, 95% CI 13.96, 72.63), longer duration of hospital stay (aOR: 1.06, 95% CI 1.02, 1.11) and use of methylprednisolone (aOR: 2.71, 95% CI 1.37, 5.37) following adjustment for age, gender, occupation, education, type of houses used for living, requirement of ventilatory support and route of steroid administration. Higher proportion of CAM cases required supplemental oxygen compared to the controls; use of non-rebreather mask (NRBM) was associated as a protective factor against mucormycosis compared to face masks (aOR: 0.18, 95% CI 0.08, 0.41). Genomic sequencing of archived respiratory samples revealed similar occurrences of Delta and Delta derivates of SARS-CoV-2 infection in both cases and controls.Conclusions: Appropriate management of hyperglycemia, judicious use of steroids and use of NRBM during oxygen supplementation among COVID-19 patients have the potential to reduce the risk of occurrence of mucormycosis. Avoiding exposure to dusty environment would add to such prevention efforts. [ABSTRACT FROM AUTHOR]

Background: COVID-19 poses a severe threat to global human health, especially the USA, Brazil, and India cases continue to increase dynamically, which has a far-reaching impact on people's health, social activities, and the local economic situation.Methods: The study proposed the ARIMA, SARIMA and Prophet models to predict daily new cases and cumulative confirmed cases in the USA, Brazil and India over the next 30 days based on the COVID-19 new confirmed cases and cumulative confirmed cases data set(May 1, 2020, and November 30, 2021) published by the official WHO, Three models were implemented in the R 4.1.1 software with forecast and prophet package. The performance of different models was evaluated by using root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).Results: Through the fitting and prediction of daily new case data, we reveal that the Prophet model has more advantages in the prediction of the COVID-19 of the USA, which could compose data components and capture periodic characteristics when the data changes significantly, while SARIMA is more likely to appear over-fitting in the USA. And the SARIMA model captured a seven-day period hidden in daily COVID-19 new cases from 3 countries. While in the prediction of new cumulative cases, the ARIMA model has a better ability to fit and predict the data with a positive growth trend in different countries(Brazil and India).Conclusions: This study can shed light on understanding the outbreak trends and give an insight into the epidemiological control of these regions. Further, the prediction of the Prophet model showed sufficient accuracy in the daily COVID-19 new cases of the USA. The ARIMA model is suitable for predicting Brazil and India, which can help take precautions and policy formulation for this epidemic in other countries. [ABSTRACT FROM AUTHOR]

Background: There have been over 30 million cases of COVID-19 in India and over 430,000 deaths. Transmission rates vary from region to region, and are influenced by many factors including population susceptibility, travel and uptake of preventive measures. To date there have been relatively few studies examining the impact of the pandemic in lower income, rural regions of India. We report on a study examining COVID-19 burden in a rural community in Tamil Nadu.Methods: The study was undertaken in a population of approximately 130,000 people, served by the Rural Unit of Health and Social Affairs (RUHSA), a community health center of CMC, Vellore. We established and evaluated a COVID-19 PCR-testing programme for symptomatic patients-testing was offered to 350 individuals, and household members of test-positive cases were offered antibody testing. We also undertook two COVID-19 seroprevalence surveys in the same community, amongst 701 randomly-selected individuals.Results: There were 182 positive tests in the symptomatic population (52.0%). Factors associated with test-positivity were older age, male gender, higher socioeconomic status (SES, as determined by occupation, education and housing), a history of diabetes, contact with a confirmed/suspected case and attending a gathering (such as a religious ceremony, festival or extended family gathering). Amongst test-positive cases, 3 (1.6%) died and 16 (8.8%) suffered a severe illness. Amongst 129 household contacts 40 (31.0%) tested positive. The two seroprevalence surveys showed positivity rates of 2.2% (July/Aug 2020) and 22.0% (Nov 2020). 40 tested positive (31.0%, 95% CI: 23.02 - 38.98). Our estimated infection-to-case ratio was 31.7.Conclusions: A simple approach using community health workers and a community-based testing clinic can readily identify significant numbers of COVID-19 infections in Indian rural population. There appear, however, to be low rates of death and severe illness, although vulnerable groups may be under-represented in our sample. It's vital these lower income, rural populations aren't overlooked in ongoing pandemic monitoring and vaccine roll-out in India. [ABSTRACT FROM AUTHOR]

Background: Comorbidities such as undernutrition and parasitic infections are widespread in India and other tuberculosis (TB)-endemic countries. This study examines how these conditions as well as food supplementation and parasite treatment might alter immune responses to Mycobacterium tuberculosis (Mtb) infection and risk of progression to TB disease.Methods: This is a 5-year prospective clinical trial at Jawaharlal Institute of Post Graduate Medical Education and Research in Puducherry, Tamil Nadu, India. We aim to enroll 760 household contacts (HHC) of adults with active TB in order to identify 120 who are followed prospectively for 2 years: Thirty QuantiFERON-TB Gold Plus (QFT-Plus) positive HHCs ≥ 18 years of age in four proposed groups: (1) undernourished (body mass index [BMI] < 18.5 kg/m2); (2) participants with a BMI ≥ 18.5 kg/m2 who have a parasitic infection (3) undernourished participants with a parasitic infection and (4) controls-participants with BMI ≥ 18.5 kg/m2 and without parasitic infection. We assess immune response at baseline and after food supplementation (for participants with BMI < 18.5 kg/m2) and parasite treatment (for participants with parasites). Detailed nutritional assessments, anthropometry, and parasite testing through polymerase chain reaction (PCR) and microscopy are performed. In addition, at serial time points, these samples will be further analyzed using flow cytometry and whole blood transcriptomics to elucidate the immune mechanisms involved in disease progression.Conclusions: This study will help determine whether undernutrition and parasite infection are associated with gene signatures that predict risk of TB and whether providing nutritional supplementation and/or treating parasitic infections improves immune response towards this infection. This study transcends individual level care and presents the opportunity to benefit the population at large by analyzing factors that affect disease progression potentially reducing the overall burden of people who progress to TB disease. Trial registration ClinicalTrials.gov; NCT03598842; Registered on July 26, 2018; https://clinicaltrials.gov/ct2/show/NCT03598842. [ABSTRACT FROM AUTHOR]

In this paper, we propose a numerical algorithm to obtain the optimal epidemic parameters for a time-dependent Susceptible-Unidentified infected-Confirmed (tSUC) model. The tSUC model was developed to investigate the epidemiology of unconfirmed infection cases over an extended period. Among the epidemic parameters, the transmission rate can fluctuate significantly or remain stable due to various factors. For instance, if early intervention in an epidemic fails, the transmission rate may increase, whereas appropriate policies, including strict public health measures, can reduce the transmission rate. Therefore, we adaptively estimate the transmission rate to the given data using the linear change points of the number of new confirmed cases by the given cumulative confirmed data set, and the time-dependent transmission rate is interpolated based on the estimated transmission rates at linear change points. The proposed numerical algorithm preprocesses actual cumulative confirmed cases in India to smooth it and uses the preprocessed data to identify linear change points. Using these linear change points and the tSUC model, it finds the optimal time-dependent parameters that minimize the difference between the actual cumulative confirmed cases and the computed numerical solution in the least-squares sense. Numerical experiments demonstrate the numerical solution of the tSUC model using the optimal time-dependent parameters found by the proposed algorithm, validating the performance of the algorithm. Consequently, the proposed numerical algorithm calculates the time-dependent transmission rate for the actual cumulative confirmed cases in India, which can serve as a basis for analyzing the COVID-19 pandemic in India., (© 2024. The Author(s).)