Articles related to Mortality due to Covid-19
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
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
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.
Impact of complete lockdown on total infection and death rates: A hierarchical cluster analysis
Authors: Samit Ghosal a, * , Rahul Bhattacharyya b , Milan Majumder .
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.