Impact of the COVID-19 pandemic on some modifiable risk factors of dementia in an aging, rural Indian population.

Introduction: The impact of the COVID-19 pandemic and associated lockdowns is likely to have caused adverse changes in lifestyle-related/cardiovascular risk factors and other such modifiable risk factors of dementia. We aimed to examine the pandemic's impact on some modifiable risk factors of dementia among rural Indians belonging to a large, prospective aging cohort-Srinivaspura Aging, NeuoSenescence, and COGnition (SANSCOG). Methods: This was a cross-sectional study among adults aged ≥ 45 years (n = 3,148; 1,492 males and 1,656 females) residing in the villages of Srinivaspura in Karnataka state, India. SANSCOG study data (clinical and biochemical assessments) of these participants were obtained from three distinct periods: (i) the "pre-COVID period"-before India's nationwide lockdown on 24 March 2020, (ii) the "COVID period"-during the first and second waves of the pandemic, wherein the social restrictions were prominent (25 March 2020 to 30 September 2021), and (iii) the "post-COVID period"-after easing of restrictions (from 1 October 2021 onward). Proportions of participants with diabetes, hypertension, obesity, dyslipidemia (diagnosed using standard criteria), and depression (diagnosed using the Geriatric Depression Scale) were compared between the above three periods. Results: The odds of having obesity, abnormal triglycerides, and depression among individuals in the COVID period were 1.42 times, 1.38 times, and 2.65 times more than the odds in the pre-COVID period, respectively. The odds of having hypertension, obesity, abnormal total cholesterol, abnormal triglycerides, abnormal LDL, and depression among individuals in the post-COVID period were 1.27 times, 1.32 times, 1.58 times, 1.95, 1.23, and 3.05 times more than the odds in the pre-COVID period, respectively. The odds of diabetes did not differ between any of the three periods. Discussion: We found significantly higher odds of some of the studied risk factors in the COVID and post-COVID periods compared to the pre-COVID period, suggesting that the pandemic adversely impacted the physical and psychological health of this marginalized, rural Indian population. We call for urgent public health measures, such as multimodal, lifestyle-based, and psychosocial interventions, to mitigate this negative impact and reduce the future risk of dementia.

Approaches to engage an aging, rural cohort in southern India during the COVID-19 crisis and the psychological impact of COVID-19 in this cohort.

INTRODUCTION: The COVID-19 pandemic produced an unprecedented crisis across the world. Long-term cohort studies were stalled, including our longitudinal aging cohort study in rural India. METHODS: We describe approaches undertaken to engage with our cohort (n = 1830) through multiple rounds of calls and how we provided useful services to our subjects during the lockdown period. Consenting subjects also underwent telephonic assessments for depression and anxiety using validated, self-report questionnaires. RESULTS: Subjects reported benefitting from our telephonic engagement strategies, including the COVID-related safety awareness and counselling service. The proportion of subjects with depression increased from 7.42% pre-COVID to 28.97% post-COVID. DISCUSSION: We envisage that such engagement strategies would improve subject rapport and cohort retention, and thus, could be adopted by similar cohort studies across the world. This marginalized, rural Indian community had severe, adverse psychological impact in this pandemic. Urgent public health measures are needed to mitigate this impact and develop appropriate preventive strategies. This article is protected by copyright. All rights reserved.

EEG Findings in Coronavirus Disease.

PURPOSE: Neurologic manifestations of coronavirus disease (COVID-19) such as encephalopathy and seizures have been described. To our knowledge, detailed EEG findings in COVID-19 have not yet been reported. This report adds to the scarce body of evidence. METHODS: We identified eight COVID-19 positive patients who underwent EEG monitoring in our hospital system. RESULTS: EEGs were most commonly ordered for an altered level of consciousness, a nonspecific neurologic manifestation. We observed generalized background slowing in all patients and generalized epileptiform discharges with triphasic morphology in three patients. Focal electrographic seizures were observed in one patient with a history of focal epilepsy and in another patient with no such history. Five of eight patients had a previous diagnosis of epilepsy, suggesting that pre-existing epilepsy can be a potential risk factor for COVID-19-associated neurological manifestations. Five of eight patients who underwent EEG experienced a fatal outcome of infection. CONCLUSIONS: Our findings underscore previous observations that neurologic manifestations are common in severe cases. COVID-19 patients with epilepsy may have an increased risk of neurological manifestations and abnormal EEG.

FDA Approved Drugs and Herbal Based Inhibitors Target SARS CoV-2 RNA Dependent RNA Polymerase

The recent outburst of COVID-19 started as an epidemic in Wuhan city, China, in December 2019. It was declared a pandemic by World Health Organization on 30 January 2020. The rapid spread of the novel coronavirus leads to more deaths worldwide. Also, it has spared many lives in its second wave of disease in many countries. Although scientists had produced vaccines, it does not suit every human being, and they are getting infected again, which is due to a lack of extensive clinical trials. Also, drug repurposing is ineffective. There is a need for more research;using in silico methods may be the better option in the current situation to save the lives of virus-affected individuals. The drugs used for other diseases and herbal compounds might help target the coronavirus. In this study, a protein, RNA-dependent RNA polymerase (RdRp), was chosen as a target from the virus for molecular docking. It was docked against several drugs on the market and also several herbal compounds. This study will help further in vitro and in vivo studies with new lead compounds, new horizons for drugs in trials, and a new approach for Insilco analysis to treat COVID-19. © 2021 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Measuring COVID-19 Opinion in the Online Debate using an Unsupervised Model

A large quantity of potentially threatening COVID-19 false information is available online. In this article, machine learning approach is adopted to assess COVID-19 materials in online health advice adversaries, particularly those who oppose immunizations like (anti-vaccine). Pro-vaccination (pro-vaccine) group is emerging a more attentive conversation regarding COVID-19 above its corresponding portion, the anti-vaccine group. However, the anti-vaccine group presents a wide series of flavors of COVID-19-relatedtopics, andas a result, can demandto a wider cross-section of entities searching for COVID-19 assistance online, such as those who may be wary of receiving a COVID-19 vaccine as a condition of employment or those looking for alternative medications. Later, the anti-vaccine group appears to be better positioned than the pro-vaccine side to obtain complete support moving forward. This is important because if the COVID-19 vaccine is not widely used, the world will not be able to produce herd immunity, parting countries exposed to a COVID-19 comeback in the future. An automatic supervision machine learning model is provided that clarifies these results andcan be used to evaluate the efficacy of intervention efforts. Our method is adaptable and capable of addressing the crucial problem that social media platforms face when analyzing the vast amounts of online health misinformation. © 2022 IEEE

COVID-19 Detection in the Images of Chest CT Scan Using K-NN in Comparison with NB Classifier to Improve the Accuracy

This chapter is about the improvisation in the accuracy in COVID-19 detection using chest CT-scan images through K-Nearest Neighbour (K-NN) compared with Naive-Bayes (NB) classifier. The sample size considered for this detection is 20, for group 1 and 2, where G-power is 0.8. The value of alpha and beta was 0.05 and 0.2 along with a confidence interval at 95%. The K-NN classifier has achieved 95.297% of higher accuracy rate when compared with Naive Bayes classifier 92.087%. The results obtained were considered to be error-free since it was having the significance value of 0.036 (p < 0.05). Therefore, in this work K-Nearest Neighbor has performed significantly better than Naive Bayes algorithm in detection of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Molecular Docking Studies of Ocimum Americanum Secondary Metabolites Against SARS-Cov-2 Spike Protein

Introduction and Aim: The outbreak of Covid-19 pandemic since December 2019 has raised serious global health concern. Because of rapid human to human transmission and non-availability of clinically proven drugs or vaccines, this Covid-19 pandemic has created a great threat to mankind. Many naturally derived molecules are being investigated for the treatment of Covid-19. Ocimum americanum is one such significant medicinal plant possessing a variety of biological activities. Material(s) and Method(s): In the present study, seven phytochemicals were selected from O. americanum and were docked against SARS-CoV-2 spike protein which is an important site for virus to enter the host cell. Docking was performed using Autodock Vina and the ADME properties of all these seven ligands were predicted using the Swiss-ADME tool. The bioactivity score was also predicted using the Molinspiration tool. Besides the secondary metabolites, all these analyses were also performed for well-known antiviral drugs namely lopinavir and ritonavir. Result(s): The binding energy obtained from the docking studies of SARS-CoV-2 spike protein with Lopinavir, Ritonavir, Alpha-farnesene, Beta-farnesene, Eugenol, Linalool, Estragole, Limonene and 1,8-Cineole was found to be-5.2,-5.1,-4.7,-4.5,-4.3,-4.1,-4,-3.9 and-3.8 Kcal/Mol respectively. Swiss-ADME results also suggest that all the selected ligands follow the drug likeness properties and hence they could be taken for further drug discovery process. Conclusion(s): From the present in silico study, it can be concluded that secondary metabolites of O. americanum have potential inhibiting activity against spike protein of SARS-CoV-2. Isolation and efficacy studies in vitro may provide an insight into the drug discovery to fight Covid-19.Copyright © 2023, Indian Association of Biomedical Scientists. All rights reserved.

Deep CNN based Multi Classification of Respiratory Disease using X-Ray Images

COVID-19 debuted in Wuhan, China on December 19, 2019. In a brief period, deadly virus now migrated to practically every country. To avoid the causative agent COVID-19 disease, governments implement a number of strict restrictions, notably prohibiting people from leaving their homes. This paper focused on detecting and classifying disease such as viral pneu-monia, covidand normal from x-ray images using deep learning methods along with pre-trained models. Moreover, validation accuracy of CNN model attained around 91 % while performing layers in neural network. Several investigations examined that identifying disease of covid reached more accuracy around 98% with hybrid and other algorithms without removing noise from particular images. But this work mainly focused on normalizing images to make the computation very efficient, convergence faster too. © 2022 IEEE.

Using a Layered Ensemble of Physics-Guided Graph Attention Networks to Predict COVID-19 Trends

The COVID-19 pandemic has spread rapidly and significantly impacted most countries in the world. Providing an accurate forecast of COVID-19 at multiple scales would help inform public health decisions, but recent forecasting models are typically used at the state or country level. Furthermore, traditional mathematical models are limited by simplifying assumptions, while machine learning algorithms struggle to generalize to unseen trends. This motivates the need for hybrid machine learning models that integrate domain knowledge for accurate long-term prediction. We propose a three-layer, geographically informed ensemble, an extensive peer-learning framework, for predicting COVID-19 trends at the country, continent, and global levels. As the base layer, we develop a country-level predictor using a hybrid Graph Attention Network that incorporates a modified SIR model, adaptive loss function, and edge weights informed by mobility data. We aggregated 163 country GATs to train the continent and world MLP layers of the ensemble. Our results indicate that incorporating quantitatively accurate equations and real-world data to model inter-community interactions improves the performance of spatio-temporal machine learning algorithms. Additionally, we demonstrate that integrating geographic information (continent composition) improves the performance of the world predictor in our layered architecture. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Predictors of mortality in COVID-19 disease

Background: COVID-19, an acute viral respiratory illness, was first noted in 2019, soon turned into pandemic with considerable mortality. With the objective of studying effect of comorbidities on COVID-19 disease severity and to identify laboratory markers associated with severe COVID-19 disease, we did a retrospective observational study in a tertiary care centre. Aims and Objectives: The objectives of this study were as follows: 1. To study effect of comorbidity on COVID-19 disease severity and 2. to identify laboratory markers associated with severe COVID-19 infection and mortality. Materials and Methods: This is an retrospective observational study conducted at SDMCMS&H, Dharwad from July 2020 to September 2020. A total of 402 cases who fall in the age group of 18 years and above were collected from medical record department. Statistical analysis used: The data were recorded in the Microsoft Excel sheet and analysis is done using Chi-square analysis and Cox linear regression method. Results: There were 402 patients whose data were collected. Out of 402 patients, 64 patients (15.92%) were in the age group of 18-39 years, 183 patients (45.52%) seen were in the age group of 40-60 years, 155 patients (38.56%) above 60 years, and consisting 291 male patients (72.39%) and 111 female patients (27.9%). Most common comorbidities seen were diabetes mellitus in 194 patients (48.26%) and hypertension in 182 patients (45.27%), followed by chronic kidney disease in 32 patients (7.96%) and ischemic heart disease in 24 patients (5.97%). Out 402 patients, 141 patients (35.07%) were on supplemental oxygen, which included 68 patients (48.23%) on low flow oxygen by face mask, seven patients (4.96%) were on non-rebreathing mask, 3 (2.13%) patients required NIV, and 63 patients (44.68%) required intubation and mechanical ventilation. It was found that uncontrolled diabetes rather than just presence of diabetes had significant impact on mortality with P=-0.0001 (95% CI OR 1.5-4.38). Patients with increased laboratory markers of inflammation such as Ferritin (95% CI OR 1.84-6.81) and LDH (95% CI OR 1.86-31.26) had strong association with mortality. The presence of thrombocytopenia showed significant association with mortality (95% CI OR 1.03-3.63). Conclusion: The presence of preceding uncontrolled hyperglycemia has significant effect on mortality. A state of hyperinflammation is directly associated with poor outcome.

Efficacy of Virtual Cognitive Behavioral Therapy on COVID-19 Pandemic Related Anxiety in a Middle Eastern Country: An Interventional Study

Introduction Emotional stress and anxiety during COVID-19 pandemic has gained a lot of attention. The capacity to withstand from the manipulated thinking and COVID-19 related stress and anxiety depends on the resilience level of an individual. Cognitive behavioral therapy (CBT) has patronizing benefits for people affected with altered mental health. Relieving COVID-19 related anxiety using CBT has beneficial impact on health and improves quality of life of people. Objective Aimed to relieve the anxiety of Omani population during COVID-19 pandemic using CBT. Methods This research utilized a pre-experimental one group pre-test post-test design. A non-probability convenient sampling technique was used to select 96 Omani people who fulfilled the inclusion criteria. The pre-anxiety level was assessed using CAS (Corona virus Anxiety Scale). The participants who scored above nine in the scale were given three sessions of CBT. Post-anxiety level was assessed using CAS after three CBT sessions. Results The study revealed that the level of anxiety reduced during post-test (6.35) after intervention when compared to pre-test (13.22). The CBT intervention was effective in reducing the anxiety in the post-test at p ≤ .000. Conclusion CBT is effective in reducing COVID-19 related anxiety among the Omani population. Therefore, this strategy is highly recommended in people having mental health issues.

MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images.

Coronavirus disease (COVID-19) has infected over 603 million confirmed cases as of September 2022, and its rapid spread has raised concerns worldwide. More than 6.4 million fatalities in confirmed patients have been reported. According to reports, the COVID-19 virus causes lung damage and rapidly mutates before the patient receives any diagnosis-specific medicine. Daily increasing COVID-19 cases and the limited number of diagnosis tool kits encourage the use of deep learning (DL) models to assist health care practitioners using chest X-ray (CXR) images. The CXR is a low radiation radiography tool available in hospitals to diagnose COVID-19 and combat this spread. We propose a Multi-Textural Multi-Class (MTMC) UNet-based Recurrent Residual Convolutional Neural Network (MTMC-UR2CNet) and MTMC-UR2CNet with attention mechanism (MTMC-AUR2CNet) for multi-class lung lobe segmentation of CXR images. The lung lobe segmentation output of MTMC-UR2CNet and MTMC-AUR2CNet are mapped individually with their input CXRs to generate the region of interest (ROI). The multi-textural features are extracted from the ROI of each proposed MTMC network. The extracted multi-textural features from ROI are fused and are trained to the Whale optimization algorithm (WOA) based DeepCNN classifier on classifying the CXR images into normal (healthy), COVID-19, viral pneumonia, and lung opacity. The experimental result shows that the MTMC-AUR2CNet has superior performance in multi-class lung lobe segmentation of CXR images with an accuracy of 99.47%, followed by MTMC-UR2CNet with an accuracy of 98.39%. Also, MTMC-AUR2CNet improves the multi-textural multi-class classification accuracy of the WOA-based DeepCNN classifier to 97.60% compared to MTMC-UR2CNet.

Efficacy of Virtual Cognitive Behavioral Therapy on COVID-19 Pandemic Related Anxiety in a Middle Eastern Country: An Interventional Study.

Introduction: Emotional stress and anxiety during COVID-19 pandemic has gained a lot of attention. The capacity to withstand from the manipulated thinking and COVID-19 related stress and anxiety depends on the resilience level of an individual. Cognitive behavioral therapy (CBT) has patronizing benefits for people affected with altered mental health. Relieving COVID-19 related anxiety using CBT has beneficial impact on health and improves quality of life of people. Objective: Aimed to relieve the anxiety of Omani population during COVID-19 pandemic using CBT. Methods: This research utilized a pre-experimental one group pre-test post-test design. A non-probability convenient sampling technique was used to select 96 Omani people who fulfilled the inclusion criteria. The pre-anxiety level was assessed using CAS (Corona virus Anxiety Scale). The participants who scored above nine in the scale were given three sessions of CBT. Post-anxiety level was assessed using CAS after three CBT sessions. Results: The study revealed that the level of anxiety reduced during post-test (6.35) after intervention when compared to pre-test (13.22). The CBT intervention was effective in reducing the anxiety in the post-test at p ≤ .000. Conclusion: CBT is effective in reducing COVID-19 related anxiety among the Omani population. Therefore, this strategy is highly recommended in people having mental health issues.

Polyclonal Antibody Generation against PvTRAg for the Development of a Diagnostic Assay for Plasmodium vivax.

The World Health Organization (WHO) has set forth a global call for eradicating malaria, caused majorly by the protozoan parasites Plasmodium falciparum and Plasmodium vivax. The lack of diagnostic biomarkers for P. vivax, especially those that differentiate the parasite from P. falciparum, significantly hinders P. vivax elimination. Here, we show that P. vivax tryptophan-rich antigen (PvTRAg) can be a diagnostic biomarker for diagnosing P. vivax in malaria patients. We report that polyclonal antibodies against purified PvTRAg protein show interactions with purified PvTRAg and native PvTRAg using Western blots and indirect enzyme-linked immunosorbent assay (ELISA). We also developed an antibody-antigen-based qualitative assay using biolayer interferometry (BLI) to detect vivax infection using plasma samples from patients with different febrile diseases and healthy controls. The polyclonal anti-PvTRAg antibodies were used to capture free native PvTRAg from the patient plasma samples using BLI, providing a new expansion range to make the assay quick, accurate, sensitive, and high-throughput. The data presented in this report provides a proof of concept for PvTRAg, a new antigen, for developing a diagnostic assay for P. vivax identification and differentiation from the rest of the Plasmodium species and, at a later stage, translating the BLI assay into affordable, point-of-care formats to make it more accessible.

Contribution of infection and vaccination to population-level seroprevalence through two COVID waves in Tamil Nadu, India (preprint)

Four rounds of serological surveys were conducted, spanning two COVID waves (October 2020 and April-May 2021), in Tamil Nadu (population 72 million) state in India. Each round included representative populations in each district of the state, totaling  ≥20,000 persons per round. State-level seroprevalence was 31.5% in round 1 (October-November 2020), after India’s first COVID wave. Seroprevalence fell to 22.9% in 2 (April 2021), consistent with waning of SARS-Cov-2 antibodies from natural infection. Seroprevalence rose to 67.1% by round 3 (June-July 2021), reflecting infections from the Delta-variant induced second COVID wave. Seroprevalence rose to 93.1% by round 4 (December 2021-January 2022), reflecting higher vaccination rates. Antibodies also appear to wane after vaccination. Seroprevalence in urban areas was higher than in rural areas, but the gap shrunk over time (35.7 v. 25.7% in round 1, 89.8% v. 91.4% in round 4) as the epidemic spread even in low-density rural areas. The study documents substantial waning of SARS-CoV-2 antibodies at the population level and demonstrates how to calculate the extent to which infection and vaccination separately contribute to seroprevalence estimates.

Recent advances in critical care: Part II.

With the increasing number of critically ill patients being admitted to intensive care units (ICUs), newer techniques and treatment modalities continue to evolve for their adequate management. Thus, it has become imperative to understand existing tools and resources, and utilise or repurpose them to achieve better results that can decrease morbidity and mortality. In this writeup, we chose five areas of interest, including analgosedation, role of colloids, recent advancements in the management of respiratory failure, the role of extracorporeal membrane oxygenation, and newer antimicrobials. The role of analgosedation in the critically ill has gained importance with focus on post-ICU syndromes, and albumin has re-entered the fray as a possible repairer of the injured glycocalyx. The coronavirus disease 2019 (COVID-19) pandemic forced us to relook at various ventilator strategies and mechanical support for the failing circulation has now become more common with clear end-points. Rising microbial antibiotic resistance has opened up the research on newer antibiotics.

Vaccination control measures of an epidemic model with long-term memristive effect.

COVID-19 is a drastic air-way tract infection that set off a global pandemic recently. Most infected people with mild and moderate symptoms have recovered with naturally acquired immunity. In the interim, the defensive mechanism of vaccines helps to suppress the viral complications of the pathogenic spread. Besides effective vaccination, vaccine breakthrough infections occurred rapidly due to noxious exposure to contagions. This paper proposes a new epidemiological control model in terms of Atangana Baleanu Caputo (ABC) type fractional order differ integrals for the reported cases of COVID-19 outburst. The qualitative theoretical and numerical analysis of the aforesaid mathematical model in terms of three compartments namely susceptible, vaccinated, and infected population are exhibited through non-linear functional analysis. The hysteresis kernel involved in AB integral inherits the long-term memory of the dynamical trajectory of the epidemics. Hyer-Ulam's stability of the system is studied by the dichotomy operator. The most effective approximate solution is derived by numerical interpolation to our proposed model. An extensive analysis of the vigorous vaccination and the proportion of vaccinated individuals are explored through graphical simulations. The efficacious enforcement of this vaccination control mechanism will mitigate the contagious spread and severity.

Is Multi System Inflammatory Syndrome Secondary to Covid (Mis-C) Same as Sepsis Associated Secondary Hlh?

Purpose: COVID 19 infection in children is generally mild,however some of them develop an unique immunological phenomenon called MIS-C(multi-system inflammatory syndrome, which is a hyperimmune state resulting in vasculitis,mycarditis and end organ damage.We compared immune status of MIS-C with another viral infection triggered hyperinflammtory state;sepsis hemophagocyticlymphohistiocytosis (SHLH) to understand the pathogenses of this novel clinical syndrome Methods: We included patients with MIS-C, SHLH and viral sepsis(S) Blood samples were collected after written informed consent, utilizing protocols approved by our institution. We evaluated differential leukocyte counts, soluble markers of T cell and macrophage activation (sIL-2R, sCD163 and Ferritin) in plasma and did immunophenotyping of T cells and monocytes on cryopreserved peripheral blood mononuclear cells Results: Total of 62 children (MIS-C 27, Sepsis 27 &SHLH 8) were included with age ranging from 1 to 16 years. Total leukocyte counts did not differ across the groups. MISC had higher neutrophil counts as compared to SHLH and sepsis.(Median cu/mm : MIS-C -10062 SHLH-4434, S- 3138). Monocyte(M)and lymphocyte (L)numbers were comparable with SHLH but lesser than sepsis(Median M/L cummMISC- 390/1488, SHLH-252/1565, S-795/2841). Plasma levels of sIL-2R in MIS-C and SHLH were similarly elevated as opposed to sepsis(Median pg/ml MIS-C- 17824, SHLH- 25702, S - 3653). sCD163 levels was elevated highest in SHLH, followed by MIS-C and Sepsis (Median ng/ml SHLH- 2.18, MIS-C 0-96,S- 0.25). Similar trend was seen in proportions of activated T cells (HLADR+CD38+) across the groups (Median % SHLH 32.5, MIS-C- 4.31, S 1.14). Median CD4:CD8 in MIS-C (2.5) is comparable to sepsis (1.2) but significantly higher than SHLH (0.75) There was no difference inmonocyte activation Conclusion(s):MIS-C is a hyperimmune state but the immune profile has features overlapping with SHLH and sepsis. It is a different hyperimmune syndrome as compared to SHLH and needs more mechanistic studies.

Depression and anxiety during the two waves of the COVID‐19 pandemic in two large, prospective, aging cohorts in rural and urban India

Background India was severely impacted by both the first and second waves of COVID‐19 in 2020 and 2021. We aimed to estimate proportions of depression and anxiety disorder during both waves, among rural and urban Indians, and to compare depression trends during four distinct periods before and after the pandemic. Method Aging adults (≥45 years) from two, parallel, harmonized cohorts in rural (SANSCOG) and urban (TLSA) sites in India, underwent telephonic assessments for depression (GDS‐7) and anxiety (GAD‐7) during both waves of COVID‐19. Utilizing prior data from depression assessments (GDS‐30) during regular evaluations pre‐ pandemic and during an interim period between the two waves, we compared trends in overall proportions of depression between the two cohorts during four periods: pre‐COVID (684 rural, 317 urban);post‐COVID first wave (733 rural, 297 urban);post‐COVID interim period (458 rural, 204 urban);post‐COVID second wave (611 rural, 305 urban). Result Overall proportion of depression among rural subjects during both waves was 28.8%. Corresponding numbers for anxiety disorder were 5.5% and 3.9%. Among urban subjects, 6.5% and 15.1% were depressed, whereas 1.7% and 0.66% had anxiety disorder, during the first and second waves, respectively. Depression trends during the four periods mentioned above were starkly different between rural and urban Indians. Sub‐analysis of the same subset of rural subjects with pre‐COVID and post‐COVID (both waves) data, revealed that subjects ≥ 65 years and those with comorbidities had significantly higher depression (36.8% vs. 25.3% and 32% vs. 15.7%, respectively) during the first wave only. Conclusion Multi‐fold increase in depression among aging, rural Indians during both waves, with alarmingly high depression among subjects ≥ 65 years and those with comorbidities during first wave, is seriously concerning. Urgent public health measures are needed to address this added mental health burden and prevent further potential adverse consequences.