Articles related to Mortality due to Covid-19

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Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21

Authors: COVID-19 Excess Mortality Collaborators*


Background Mortality statistics are fundamental to public health decision making. Mortality varies by time and location, and its measurement is affected by well known biases that have been exacerbated during the COVID-19 pandemic. This paper aims to estimate excess mortality from the COVID-19 pandemic in 191 countries and territories, and 252 subnational units for selected countries, from Jan 1, 2020, to Dec 31, 2021. Methods All-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 2020 and 2021, and for up to 11 year previously. In addition, we obtained excess mortality data for 12 states in India. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality. Six models were used to estimate expected mortality; final estimates of expected mortality were based on an ensemble of these models. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. With the selected best model, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was done on the basis of our final model specification. Findings Although reported COVID-19 deaths between Jan 1, 2020, and Dec 31, 2021, totalled 5·94 million worldwide, we estimate that 18·2 million (95% uncertainty interval 17·1–19·6) people died worldwide because of the COVID-19 pandemic (as measured by excess mortality) over that period. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120·3 deaths (113·1–129·3) per 100000 of the population, and excess mortality rate exceeded 300 deaths per 100 000 of the population in 21 countries. The number of excess deaths due to COVID-19 was largest in the regions of south Asia, north Africa and the Middle East, and eastern Europe. At the country level, the highest numbers of cumulative excess deaths due to COVID-19 were estimated in India (4·07 million [3·71–4·36]), the USA (1·13 million [1·08–1·18]), Russia (1·07 million [1·06–1·08]), Mexico (798 000 [741000–867000]), Brazil (792 000 [730 000–847000]), Indonesia (736 000 [594000–955000]), and Pakistan (664 000 [498 000–847000]). Among these countries, the excess mortality rate was highest in Russia (374·6 deaths [369·7–378·4] per 100 000) and Mexico (325·1 [301·6–353·3] per 100000), and was similar in Brazil (186·9 [172·2–199·8] per 100000) and the USA (179·3 [170·7–187·5] per 100 000). Interpretation The full impact of the pandemic has been much greater than what is indicated by reported deaths due to COVID-19 alone. Strengthening death registration systems around the world, long understood to be crucial to global public health strategy, is necessary for improved monitoring of this pandemic and future pandemics. In addition, further research is warranted to help distinguish the proportion of excess mortality that was directly caused by SARS-CoV-2 infection and the changes in causes of death as an indirect consequence of the pandemic. Funding Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

2 mortality.pdf

All-cause mortality during the COVID-19 pandemic in Chennai, India: an observational study

Authors: Joseph A Lewnard, Ayesha Mahmud, Tejas Narayan, Brian Wahl, T S Selvavinayagam, Chandra Mohan B, Ramanan Laxminarayan


Background: India has been severely affected by the ongoing COVID-19 pandemic. However, due to shortcomings in disease surveillance, the burden of mortality associated with COVID-19 remains poorly understood. We aimed to assess changes in mortality during the pandemic in Chennai, Tamil Nadu, using data on all-cause mortality within the district. Methods: For this observational study, we analysed comprehensive death registrations in Chennai, from Jan 1, 2016, to June 30, 2021. We estimated expected mortality without the effects of the COVID-19 pandemic by fitting models to observed mortality time series during the pre-pandemic period, with stratification by age and sex. Additionally, we considered three periods of interest: the first 4 weeks of India’s first lockdown (March 24 to April 20, 2020), the 4-month period including the first wave of the pandemic in Chennai (May 1 to Aug 31, 2020), and the 4-month period including the second wave of the pandemic in Chennai (March 1 to June 30, 2021). We computed the difference between observed and expected mortality from March 1, 2020, to June 30, 2021, and compared pandemic-associated mortality across socioeconomically distinct communities (measured with use of 2011 census of India data) with regression analyses. Findings: Between March 1, 2020, and June 30, 2021, 87 870 deaths were registered in areas of Chennai district represented by the 2011 census, exceeding expected deaths by 25990 (95% uncertainty interval 25640–26360) or 5·18 (5·11–5·25) excess deaths per 1000 people. Stratified by age, excess deaths numbered 21·02 (20·54–21·49) excess deaths per 1000 people for individuals aged 60–69 years, 39·74 (38·73–40·69) for those aged 70–79 years, and 96·90 (93·35–100·16) for those aged 80 years or older. Neighbourhoods with lower socioeconomic status had 0·7% to 2·8% increases in pandemic-associated mortality per 1 SD increase in each measure of community disadvantage, due largely to a disproportionate increase in mortality within these neighbourhoods during the second wave. Conversely, differences in excess mortality across communities were not clearly associated with socioeconomic status measures during the first wave. For each increase by 1 SD in measures of community disadvantage, neighbourhoods had 3·6% to 8·6% lower pandemic-associated mortality during the first 4 weeks of India’s country-wide lockdown, before widespread SARS-CoV-2 circulation was underway in Chennai. The greatest reductions in mortality during this early lockdown period were observed among men aged 20–29 years, with 58% (54–62) fewer deaths than expected from pre-pandemic trends. Interpretation: Mortality in Chennai increased substantially but heterogeneously during the COVID-19 pandemic, with the greatest burden concentrated in disadvantaged communities. Reported COVID-19 deaths greatly underestimated pandemic-associated mortality. Funding: National Institute of General Medical Sciences, Bill & Melinda Gates Foundation, National Science Foundation. Copyright: © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

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COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits

Authors: Shanmukh Alle, Akshay KanakanID, Samreen Siddiqui, Akshit Garg, Akshaya Karthikeyan, Priyanka Mehta , Neha Mishra , Partha Chattopadhyay, Priti Devi, Swati Waghdhare, Akansha Tyagi, Bansidhar Tarai , Pranjal Pratim Hazarik , Poonam Das , Sandeep Budhiraja , Vivek Nangia , Arun Dewan , Ramanathan Sethuraman , C. Subramanian , Mashrin Srivastava , Avinash Chakravarthi , Johnny Jacob , Madhuri Namagiri , Varma Konala , Debasish Dash , Tavpritesh Sethi , Sujeet Jha, Anurag Agrawal, Rajesh PandeyID, P. K. Vinod, U. Deva PriyakumarI


The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.

mortality 1.pdf

Predictors of mortality among hospitalized COVID-19 patients and risk score formulation for prioritizing tertiary care—An experience from South India

Authors: Narendran GopalanID1‡ *, Sumathi Senthil2‡ , Narmadha Lakshmi Prabakar2‡, Thirumaran SenguttuvanID1‡, Adhin Bhaskar3 , Muthukumaran Jagannathan4 , Ravi Sivaraman5 , Jayalakshmi Ramasamy2 , Ponnuraja Chinnaiyan3 , Vijayalakshmi Arumugam6 , Banumathy Getrude7 , Gautham Sakthivel2 , Vignes Anand SrinivasaluID1 , Dhanalakshmi RajendranID1,8, Arunjith Nadukkandiyil2 , Vaishnavi Ravi2 , Sadiqa Nasreen Hifzour Rahamane2 , Nirmal Athur Paramasivam2 , Tamizhselvan Manoharan3 , Maheshwari Theyagarajan1 , Vineet Kumar Chadha9 , Mohan Natrajan1 , Baskaran Dhanaraj1 , Manoj Vasant Murhekar10,11‡, Shanthi Malar Ramalingam4,12‡, Padmapriyadarsini Chandrasekaran10‡



We retrospectively data-mined the case records of Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmed COVID-19 patients hospitalized to a tertiary care centre to derive mortality predictors and formulate a risk score, for prioritizing admission.

Methods and findings:

Data on clinical manifestations, comorbidities, vital signs, and basic lab investigations collected as part of routine medical management at admission to a COVID-19 tertiary care centre in Chengalpattu, South India between May and November 2020 were retrospectively analysed to ascertain predictors of mortality in the univariate analysis using their relative difference in distribution among ‘survivors’ and ‘non-survivors’. The regression coefficients of those factors remaining significant in the multivariable logistic regression were utilised for risk score formulation and validated in 1000 bootstrap datasets.

Among 746 COVID-19 patients hospitalised [487 “survivors” and 259 “non-survivors” (deaths)], there was a slight male predilection [62.5%, (466/746)], with a higher mortality rate observed among 40–70 years age group [59.1%, (441/746)] and highest among diabetic patients with elevated urea levels [65.4% (68/104)]. The adjusted odds ratios of factors [OR (95% CI)] significant in the multivariable logistic regression were SaO2<95%; 2.96 (1.71–5.18), Urea ≥50 mg/dl: 4.51 (2.59–7.97), Neutrophil-lymphocytic ratio (NLR) >3; 3.01 (1.61–5.83), Age ≥50 years;2.52 (1.45–4.43), Pulse Rate ≥100/min: 2.02 (1.19–3.47) and coexisting Diabetes Mellitus; 1.73 (1.02–2.95) with hypertension and gender not retaining their significance. The individual risk scores for SaO2<95–11, Urea ≥50 mg/dl-15, NLR >3–11, Age ≥50 years-9, Pulse Rate ≥100/min-7 and coexisting diabetes mellitus-6, acronymed collectively as ‘OUR-ARDs score’ showed that the sum of scores ≥ 25 predicted mortality with a sensitivity-90%, specificity-64% and AUC of 0.85.


The ‘OUR ARDs’ risk score, derived from easily assessable factors predicting mortality, offered a tangible solution for prioritizing admission to COVID-19 tertiary care centre, that enhanced patient care but without unduly straining the health system.


Child, maternal, and adult mortality in Sierra Leone: nationally representative mortality survey 2018–20

Authors: Ronald Carshon-Marsh, MD,a,† Ashley Aimone, PhD,b Rashid Ansumana, Prof, PhD,c,† Ibrahim Bob Swaray, MD,b,c Anteneh Assalif, MSc,b,c Alimatu Musa, MSc,b Catherine Meh, MSc,b Francis Smart, MD,a Sze Hang Fu, MSc,b Leslie Newcombe, BSc,b Rajeev Kamadod, M Eng,b Nandita Saikia, Prof, PhD,d Hellen Gelband, MHS,b Amara Jambai, MD,a,†* and Prabhat Jha, Prof, DPhilb,**



Sierra Leone's child and maternal mortality rates are among the highest in the world. However, little is known about the causes of premature mortality in the country. To rectify this, the Ministry of Health and Sanitation of Sierra Leone launched the Sierra Leone Sample Registration System (SL-SRS) of births and deaths. Here, we report cause-specific mortality from the first SL-SRS round, representing deaths from 2018 to 2020.


The Countrywide Mortality Surveillance for Action platform established the SL-SRS, which involved conducting electronic verbal autopsies in 678 randomly selected villages and urban blocks throughout the country. 61 surveyors, in teams of four or five, enrolled people and ascertained deaths of individuals younger than 70 years in 2019–20, capturing verbal autopsies on deaths from 2018 to 2020. Centrally, two trained physicians independently assigned causes of death according to the International Classification of Diseases (tenth edition). SL-SRS death proportions were applied to 5-year mortality averages from the UN World Population Prospects (2019) to derive cause-specific death totals and risks of death nationally and in four Sierra Leone regions, with comparisons made with the Western region where Freetown, the capital, is located. We compared SL-SRS results with the cause-specific mortality estimates for Sierra Leone in the 2019 WHO Global Health Estimates.


Between Sept 1, 2019, and Dec 15, 2020, we enrolled 343 000 people and ascertained 8374 deaths of individuals younger than 70 years. Malaria was the leading cause of death in children and adults, nationally and in each region, representing 22% of deaths under age 70 years in 2020. Other infectious diseases accounted for an additional 16% of deaths. Overall maternal mortality ratio was 510 deaths per 100 000 livebirths (95% CI 483–538), and neonatal mortality rate was 31·1 deaths per 1000 livebirths (95% CI 30·4–31·8), both among the highest rates in the world. Haemorrhage was the major cause of maternal mortality and birth asphyxia or trauma was the major cause of neonatal mortality. Excess deaths were not detected in the months of 2020 corresponding to the peak of the COVID-19 pandemic. Half of the deaths occurred in rural areas and at home. If the Northern, Eastern, and Southern regions of Sierra Leone had the lower death rates observed in the Western region, about 20 000 deaths (just over a quarter of national total deaths in people younger than 70 years) would have been avoided. WHO model-based data vastly underestimated malaria deaths and some specific causes of injury deaths, and substantially overestimated maternal mortality.


Over 60% of individuals in Sierra Leone die prematurely, before age 70 years, most from preventable or treatable causes. Nationally representative mortality surveys such as the SL-SRS are of high value in providing reliable cause-of-death information to set public health priorities and target interventions in low-income countries.


Bill & Melinda Gates Foundation, Canadian Institutes of Health Research, Queen Elizabeth Scholarship Program.


An alternative estimation of the death toll of the Covid-19 pandemic in India

Authors: Christophe Z. GuilmotoID1,2*

1 Centre des Sciences Humaines, Delhi, India, 2 Ceped/IRD/Universite´ de Paris/INSERM, Paris, France

Abstract: The absence of reliable registration of Covid-19 deaths in India has prevented proper assessment and monitoring of the coronavirus pandemic. In addition, India’s relatively young age structure tends to conceal the severity of Covid-19 mortality, which is concentrated in older age groups. In this paper, we present four different demographic samples of Indian populations for which we have information on both their demographic structures and death outcomes. We show that we can model the age distribution of Covid-19 mortality in India and use this modeling to estimate Covid-19 mortality in the country. Our findings point to a death toll of approximately 3.2–3.7 million persons by early November 2021. Once India’s age structure is factored in, these figures correspond to one of the most severe cases of Covid-19 mortality in the world. India has recorded after February 2021 the second outbreak of coronavirus that has affected the entire country. The accuracy of official statistics of Covid-19 mortality has been questioned, and the real number of Covid-19 deaths is thought to be several times higher than reported. In this paper, we assembled four independent population samples to model and estimate the level of Covid-19 mortality in India. We first used a population sample with the age and sex of Covid-19 victims to develop a Gompertz model of Covid-19 mortality in India. We applied and adjusted this mortality model on two other national population samples after factoring in the demographic characteristics of these samples. We finally derive from these samples the most reasonable estimate of Covid-19 mortality level in India and confirm this result using a fourth population sample. Our findings point to a death toll of about 3.2–3.7 million persons by late May 2021. This is by far the largest number of Covid-19 deaths in the world. Once standardized for age and sex structure, India’s Covid-19 mortality rate is above Brazil and the USA. Our analysis shows that existing population samples allow an alternative estimation of deaths due to Covid-19 in India. The results imply that only one out of 7–8 deaths appear to have been recorded as a Covid-19 death in India. The estimates also point to a very high Covid-19 mortality rate, which is even higher after age and sex standardization. The magnitude of the pandemic in India requires immediate attention. In the absence of effective remedies, this calls for a strong response based on a combination of non-pharmaceutical interventions and the scale-up of vaccination to make them accessible to all, with an improved surveillance system to monitor the progression of the pandemic and its spread across India’s regions and social groups.


Impact of population density on Covid 19 infected and mortality rate in India

Authors: Arunava Bhadra1 · Arindam Mukherjee1 · Kabita Sarkar.

Abstract: The Covid-19 is a highly contagious disease which becomes a serious global health concern. The residents living in areas with high population density, such as big or metropolitan cities, have a higher probability to come into close contact with others and consequently any contagious disease is expected to spread rapidly in dense areas. However, recently, after analyzing Covid-19 cases in the USA researchers at the Johns Hopkins Bloomberg School of Public Health, London school of economics, and IZA—Institute of Labour Economics conclude that the spread of Covid-19 is not linked with population density. Here, we investigate the infuence of population density on Covid-19 spread and related mortality in the context of India. After a detailed correlation and regression analysis of infection and mortality rates due to Covid-19 at the district level, we fnd moderate association between Covid-19 spread and population density.


Analysing COVID-19 pandemic through cases, deaths, and recoveries.

Authors: Ilma Khan, Abid Haleem1 , Mohd Javaid.

Background and aims: The novel Coronavirus disease (COVID-19) in Wuhan, China, became a pandemic after its outbreak in January 2020. Countries one after the other are witnessing peak effects of the disease, and they need to learn from the experience of others already affected or peaked countries. Thus, this paper aims to analyse the effect of the COVID-19 pandemic on different countries through COVID-19 cases, resulting in deaths and recoveries. Methods: This study analyses quantitatively the lethal effects of the pandemic through the study of infections, deaths, and recoveries on the 13 most-affected COVID-19 countries as of 1 s t June. The daily change in cases, deaths, and recoveries for all the 13 countries were considered. Combined analysis for comparison and separate analysis for the detailed study were both taken for every country. All the graphs were made in RStudio using the R programming language, as it is best for statistical analysis. Results: The casual and ignorant behaviour of people is a major reason for such a large scale spread of the coronavirus. The government of every country should be strict as well as considerate to all sections of people while making policies. There is no room for mistakes, as one wrong decision or one delayed decision can worsen the situation. However, some countries which were once the epicentre of this pandemic are now corona-free, proving that this global threat can be tackled and we should all keep our morale high. Conclusions: The coronavirus disease is not any ordinary viral infection; it has become a pandemic as it has an impact on health, mortality, economy and social well being of the entire world. Qualitative and Quantitative analysis of the statistics related to COVID-19 in different countries is done based on their officials' data. The primary objective of this analysis is to learn about the relationships of various countries in containing the spread of COVID-19 and the various factors such as government policies, the cooperation of people, economy, and tourism.


Factors associated with disease severity and mortality among patients with COVID-19: A systematic review and meta-analysis .

Authors: Vignesh ChidambaramID1 , Nyan Lynn Tun1 , Waqas Z. Haque1 , Marie Gilbert MajellaID2 , Ranjith Kumar Sivakumar3 , Amudha Kumar4 , Angela Ting-Wei Hsu1 , Izza A. Ishak1 , Aqsha A. Nur1 , Samuel K. Ayeh5 , Emmanuella L. Salia6 , Ahsan Zil-E-Ali1 , Muhammad A. Saeed7 , Ayu P. B. Sarena8 , Bhavna Seth9 , Muzzammil Ahmadzada7 , Eman F. Haque10, Pranita Neupane5 , Kuang-Heng Wang1 , Tzu-Miao Pu1 , Syed M. H. Ali11, Muhammad A. Arshad12, Lin WangID1 , Sheriza BakshID1 , Petros C. Karakousis5 , Panagis Galiatsatos.

Abstract Background Understanding the factors associated with disease severity and mortality in Coronavirus disease (COVID-19) is imperative to effectively triage patients. We performed a systematic review to determine the demographic, clinical, laboratory and radiological factors associated with severity and mortality in COVID-19. Methods We searched PubMed, Embase and WHO database for English language articles from inception until May 8, 2020. We included Observational studies with direct comparison of clinical characteristics between a) patients who died and those who survived or b) patients with severe disease and those without severe disease. Data extraction and quality assessment were performed by two authors independently. Results Among 15680 articles from the literature search, 109 articles were included in the analysis. The risk of mortality was higher in patients with increasing age, male gender (RR 1.45, 95% PLOS ONE PLOS ONE | Chidambaram V, Tun NL, Haque WZ, Majella MG, Sivakumar RK, Kumar A, et al. (2020) Factors associated with disease severity and mortality among patients with COVID-19: A systematic review and meta-analysis. PLoS ONE 15(11): e0241541. pone.0241541 Editor: Girish Chandra Bhatt, All India Institute of Medical Sciences, Bhopal, INDIA Received: August 3, 2020 Accepted: October 17, 2020 Published: November 18, 2020 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: Copyright: © 2020 Chidambaram et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting information files. CI 1.23–1.71), dyspnea (RR 2.55, 95%CI 1.88–2.46), diabetes (RR 1.59, 95%CI 1.41– 1.78), hypertension (RR 1.90, 95%CI 1.69–2.15). Congestive heart failure (OR 4.76, 95%CI 1.34–16.97), hilar lymphadenopathy (OR 8.34, 95%CI 2.57–27.08), bilateral lung involvement (OR 4.86, 95%CI 3.19–7.39) and reticular pattern (OR 5.54, 95%CI 1.24–24.67) were associated with severe disease. Clinically relevant cut-offs for leukocytosis(>10.0 x109 /L), lymphopenia(< 1.1 x109 /L), elevated C-reactive protein(>100mg/L), LDH(>250U/L) and Ddimer(>1mg/L) had higher odds of severe disease and greater risk of mortality. Conclusion Knowledge of the factors associated of disease severity and mortality identified in our study may assist in clinical decision-making and critical-care resource allocation for patients with COVID-19.

Impact of complete lockdown on total infection and death rates: A hierarchical cluster analysis

Authors: Samit Ghosal a, * , Rahul Bhattacharyya b , Milan Majumder .

abstract Introduction and Aims: Retarding the spread of SARS-CoV-2 infection by preventive strategies is the first line of management. Several countries have declared a stringent lockdown in order to enforce social distancing and prevent the spread of infection. This analysis was conducted in an attempt to understand the impact of lockdown on infection and death rates over a period of time in countries with declared lock-down. Material and methods: A validated database was used to generate data related to countries with declared lockdown. Simple regression analysis was conducted to assess the rate of change in infection and death rates. Subsequently, a k-means and hierarchical cluster analysis was done to identify the countries that performed similarly. Sweden and South Korea were included as counties without lockdown in a secondphase cluster analysis. Results: There was a significant 61% and 43% reduction in infection rates 1-week post lockdown in the overall and India cohorts, respectively, supporting its effectiveness. Countries with higher baseline infections and deaths (Spain, Germany, Italy, UK, and France-cluster 1) fared poorly compared to those who declared lockdown early on (Belgium, Austria, New Zealand, India, Hungary, Poland and Malaysia-cluster 2). Sweden and South Korea, countries without lock-down, fared as good as the countries in cluster 2. Conclusion: Lockdown has proven to be an effective strategy is slowing down the SARS-CoV-2 disease progression (infection rate and death) exponentially. The success story of non-lock-down countries (Sweden and South Korea) need to be explored in detail, to identify the variables responsible for the positive result.


Predictors of morbidity and mortality in COVID-19.

Authors: R.N. GACCHE1, R.A. GACCHE2, J. CHEN3, H. LI4, G. LI5

Abstract. – The mortality of COVID-19 patients is increasing in logarithmic fashion and is mostly observed in older age people and patients having history of chronic ailments like chronic obstructive pulmonary disease (COPD), hypertension, diabetes, cardiovascular & cerebrovascular dysfunction, compromised immunity, renal comorbidities, hepatic, obesity problems etc., and recently investigated thrombotic complications. The molecular underpinnings linking the chronic human diseases with COVID-19 related morbidity and mortality are evolving and poorly understood. The aim of the present review is to discuss the mortality and morbidity in COVID-19 in relation to preexisting comorbidities across the globe, upcoming molecular mechanisms associated with expression profile of ACE2 and viral load, evolving pathophysiology of COVID-19 with special reference to thrombotic complication (‘Storm of Blood Clots’) and related predictive markers. The levels of plasminogen/plasmin in comorbid diseases of COVID-19 have been elaborated in the framework of risk and benefits of fibrinolysis in COVID-19. We have also attempted to discuss the puzzle of prescribing ARBs and ACEI drugs in COVID-19 management which are routinely prescribed for the management of hypertension in COVID-19 patients. A focused discourse on risk of cardiovascular complications and diabetes in concert with COVID-19 pathogenesis has been presented along with dynamics of SARS-CoV-2 induced immune dysfunctions in COVID-19 patients.


Differential mortality in COVID-19 patients from India and western countries .

Authors: Vijay Kumar Jain , * , Karthikeyan Iyengar , Abhishek Vaish , Raju Vaishya

world population. We try to elucidate various reasons for lower mortality rate in the Indian subcontinent due to COVID-19 pandemic. Method: We carried out a comprehensive review of the literature using suitable keywords such as ‘COVID-19’, ‘Pandemics’, ‘disease outbreaks’ and ‘India’ on the search engines of PubMed, SCOPUS, Google Scholar and Research Gate in the month of May 2020 during the current COVID-19 pandemic and assessed mortality data. Results: The mortality observed in Indian and south Asian subcontinent is lower than in the west. Multifactorial reasons indicated for this differential mortality due to COVID-19 have been described in the current literature. Conclusions: The effects of COVID-19 on the health of racial and ethnic minority groups are still emerging with disproportionate burden of illness and death amongst some black and ethnic minority groups. Overall the current COVID-19 related mortality appears to be lower in the health and resource challenged populous Indian subcontinent. Further scientific studies would be helpful to understand this disparity in mortality due to COVID-19 in the world population day.


COVID-19 mortality in cancer patients: a report from a tertiary cancer centre in India.

Authors: Anurag Mehta1 , Smreti Vasudevan2 , Anuj Parkash3 , Anurag Sharma2 , Tanu Vashist2 and Vidya Krishna.

Background: Cancer patients, especially those receiving cytotoxic therapy, are assumed to have a higher probability of death from COVID-19. We have conducted this study to identify the Case Fatality Rate (CFR) in cancer patients with COVID-19 and have explored the relationship of various clinical factors to mortality in our patient cohort. Methods: All confirmed cancer cases presented to the hospital from June 8 to August 20, 2020, and developed symptoms/radiological features suspicious of COVID-19 were tested by Real-time polymerase chain reaction assay and/or cartridge-based nucleic acid amplification test from a combination of naso-oropharyngeal swab for SARS-CoV-2. Clinical data, treatment details, and outcomes were assessed from the medical records. Results: Of the total 3,101 cancer patients admitted to the hospital, 1,088 patients were tested and 186 patients were positive for SARS-CoV-2. The CFR in the cohort was 27/186 (14.52%). Univariate analysis showed that the risk of death was significantly associated with the presence of any comorbidity (OR: 2.68; (95% CI [1.13–6.32]); P = 0.025), multiple comorbidities (OR: 3.01; (95% CI [1.02–9.07]); P = 0.047 for multiple vs. single), and the severity of COVID-19 presentation (OR: 27.48; (95% CI [5.34–141.49]); P < 0.001 for severe vs. not severe symptoms). Among all comorbidities, diabetes (OR: 3.31; (95% CI [1.35–8.09]); P = 0.009) and cardiovascular diseases (OR: 3.77; (95% CI [1.02–13.91]); P = 0.046) were significant risk factors for death. Anticancer treatments including chemotherapy, surgery, radiotherapy, targeted therapy, and immunotherapy administered within a month before the onset of COVID-19 symptoms had no significant effect on mortality. Conclusion: To the best of our knowledge, this is the first study from India reporting the CFR, clinical associations, and risk factors for mortality in SARS-CoV-2 infected cancer patients. Our study shows that the frequency of COVID-19 in cancer patients is high. Recent anticancer therapies are not associated with mortality. Pre-existing comorbidities, especially diabetes, multipcomorbidities, and severe symptoms at presentation are significantly linked with COVID-19 related death in the cohort.


Risk factors prediction, clinical outcomes, and mortality in COVID‐19 patients.

Authors: Roohallah Alizadehsani1 | Zahra Alizadeh Sani2,3 | Mohaddeseh Behjati2 | Zahra Roshanzamir4 | Sadiq Hussain5 | Niloofar Abedini6 | Fereshteh Hasanzadeh3 | Abbas Khosravi1 | Afshin Shoeibi7,8 | Mohamad Roshanzamir9 | Pardis Moradnejad2 | Saeid Nahavandi1 | Fahime Khozeimeh1 | Assef Zare10 | Maryam Panahiazar11 | U. Rajendra Acharya12,13,14 | Sheikh Mohammed Shariful Islam.

Abstract : Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu‐like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID‐19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank‐sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C‐reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of covid. COVID‐19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID‐19. To the best of our knowledge, a number of factors associated with mortality due to COVID‐19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID‐19.