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  Table of Contents  
Year : 2022  |  Volume : 15  |  Issue : 1  |  Page : 75-83  

Correlates of COVID-19 mortality: A descriptive study

1 Department of Community Medicine, Bankura Sammilani Medical College, Bankura, West Bengal, India
2 Department of Community Medicine, ID and BG Hospital, Kolkata, West Bengal, India
3 Department of Community Medicine, Murshidabad Medical College, Berhampore, West Bengal, India
4 Department of Community Medicine, Medical College, Kolkata, West Bengal, India
5 Department of Community Medicine, Calcutta National Medical College, Kolkata, India
6 General Practitioner, Kolkata, India

Date of Submission25-May-2020
Date of Decision10-Jun-2020
Date of Acceptance07-Jul-2020
Date of Web Publication14-Jul-2021

Correspondence Address:
Tanushree Mondal
Bidyadhari Housing Cooperative Society, CC-7, Flat No. 503, Newtown, Narkelbagan More, Near Biswa Bangla Gate, Kolkata - 700 156, West Bengal
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/mjdrdypu.mjdrdypu_282_20

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Background and Objectives: The enigma COVID-19 pandemic already involved major parts of the globe with a toll of 3,175,207 victims and 224,172 deaths from 215 countries/territories as on May 1, 2020. It cripples nations by the loss of human resources, economic decline, hunger, unemployment insecurities giving way to mental morbidities, and still many others to be discovered. A systematic search about correlates of its killing attribute is urgently warranted. Materials and Methods: A cross-sectional survey for 3 weeks (03/5/2020–23/5/2020) was conducted in a teaching institution at Kolkata aiming to describe the magnitude and correlates of COVID-19 mortality. Data pertaining to COVID-19 cases, deaths of affected countries, and their potential correlates were retrieved from various public domains, for example, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports, worldpopulationreview.com, data.worldbank.org. Results: Multiple linear regressions analysis through forward method revealed a maximum R2 of 57.7% (P = 0.03) and a significant model fit (P = 0.000) for COVID-19 mortality rate per million which was revealed to have a positive association with median age of the population of the country (β = 0.073), proportion of population sustaining obesity (β = 0.051) and %of population consumed alcohol over the past 12 months (β = 0.018). It meant for 1 year increase in median age COVID-19 mortality would be increased by 8.0%. Similarly, COVID-19 mortality would be increased by 2.0% and 5.0% by inclusion in the model of 1% alcoholic, and 1% obese individual, respectively, Conclusion: Notwithstanding variations in testing, reporting, and patients' management strategy the findings of this research have some implications to the scientific fraternity and policymakers.

Keywords: COVID-19 pandemic, median age, mortality, obesity

How to cite this article:
Haldar D, Maji B, Ray SK, Mondal T, Mandal PK, Haldar P. Correlates of COVID-19 mortality: A descriptive study. Med J DY Patil Vidyapeeth 2022;15:75-83

How to cite this URL:
Haldar D, Maji B, Ray SK, Mondal T, Mandal PK, Haldar P. Correlates of COVID-19 mortality: A descriptive study. Med J DY Patil Vidyapeeth [serial online] 2022 [cited 2022 Aug 11];15:75-83. Available from: https://www.mjdrdypv.org/text.asp?2022/15/1/75/321291

  Introduction Top

India, as well as the whole nation, is faced with a grim challenge that they have not encountered in the past few decades. It is the deadly attack of the novel Coronavirus (nCoV) producing fatal pneumonia (Covid-19).

After being emerged in Wuhan, China, in December 2019, the novel severe acute respiratory syndrome-CoV-2 coronavirus caused a large scale COVID-19 pandemic and engulfed countries in its process culminating in one of the most deadly pandemics of the century. As of May 1, 2020, the World Health Organization (WHO) reported 3175207 confirmed COVID-19 cases and 224,172 deaths from 215 countries/territories, including India's contribution of 35,043 cases and 1147 deaths.[1] Lacking herd immunity and in the absence of effective vaccines or antiviral therapies, countries worldwide are witnessing an unprecedented strain on health systems and disruption of economies as we start to understand the biology and mode of transmission of COVID-19.[2] As the entire world has been caught in the grip of COVID-19 pandemic, a race to understand the virus and to find an effective and safe vaccine or treatment has resulted in the emergence of a number of studies interested in the factors that could be contributing to mortality.[3] A pragmatic, systematic search is a prerequisite for any such query relating to death toll caused by an unknown pandemic. The present study was contemplated with the following objectives.


  • To estimate the mortality rate (MR) of different countries
  • To find out correlates, if any, of COVID-19 death

  Materials and Methods Top

Study design

A descriptive cross-sectional study was carried for 3 weeks (3rd to 23rd May, 2020) in a teaching Institute of Kolkata.


All the COVID-19 deaths from all the 215 affected countries/territories as on May 1, 2020.

Data sources

Data in relation to the COVID-19 pandemic were retrieved from WHO's portal: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports. Various other data were availed from other public domains such as www.bcg Atlas More Details.org, worldpopulationreview.com, data.worldbank.org, www.worldometer.info.


Information pertaining to the name of affected countries/territories, subcontinent which they belong to, their situation in relation to the tropical region of the world; socioeconomic status (SES), total population, population density per km2, adult literacy rate (%), urban population(%), slum population (%), population engaged in agriculture(%), population aged 65 years or more (%), total labor force (k), total tourist arrived in the last year (k), average annual temperature (°C) were retrieved from the above sources. Data relating to Malaria transmission (intense and rare/no), current Bacillus Calmette–Guérin (BCG) vaccination strategy (single dose/multiple dose and no use) of each of the affected countries; the prevalence of diabetes mellitus, malnutrition and obesity, HIV (adult), smoking (15 years and above), alcohol consumption in the last 12 months (15 years and above), raised blood pressure (BP) (systolic BP ≥140 mm of Hg and or diastolic BP ≥90 mm of Hg) and raised (≥190 mg/dl) age-standardized serum cholesterol were systematically gathered. Median age (year) of each affected country, proportional mortality from noncommunicable disease (NCD), out-of-pocket expenditure (OOPE) as %of total expenditure on health and mean annual exposure to PM2.5 air pollution (μg/m3) also included in the study as input variables. The total number of country-wise confirmed COVID-19 cases and deaths from 21st January, 2020–1st May 2020, nature of transmission in respective countries (sporadic, clustered, community, and pending) were obtained. Data were collected in a predesigned format.

Based on per capita Gross National Income in current US$, Atlas method, the World Bank classified countries into (1) Low income (<1026), (2) lower-middle income (1026–3995), (3) Upper-middle income (3996–12375) and (4) High income (>12375). The latest classification has been used in this study. Continents are considered as per the WHO's categorization used in (2019-nCoV) SITUATION REPORT from January 21, 2020.[1] The list of tropical countries was retrieved from public domain “worldpopulationreview.com.”

Statistical plan

Data were summarized by estimating crude rates, proportions, and displayed throug charts and tables. The normality of variables was tested using Kolmogorov–Smirnov test. Chi-square test, Independent t-test, Analysis of one-way variance (ANOVA) along with post-hoc test in least-square deviation method, Pearson correlation coefficient (r) and relative risk (RR) with its 95% confidence interval (CI) were used for bivariate analyses. Dummy variables were created for the SES of countries having four levels before entering them into multiple linear regression models through forward method. Soft-wares like International Business Machines Statistical Package for the Social Sciences (SPSS) version 22 and Epi info 3.4.3 version (CDC Atlanta, Georgia) were used for the data analysis. Value of P < 0.05 was considered significant at 5% precision.

Ethical issues

The necessary approval from concerned authority was sought for conducting this study.

  Results Top

All independent variables (IVs) were found to have skewed distribution except the %of the urban population. The dependent variable (DV), i.e., MR per million was found to become normally distributed (P = 0.200) after getting log-transformed (called as LogMR).

Epidemiological characteristics of COVID-19 Mortality

As of May 1, 2020, the COVID-19 pandemic gripped altogether 215 countries/territories, including 34 (15.96%) territories/areas. Out of 215 countries/territories one is international conveyance (Diamond Princess). Among the rest 214 affected countries/territories COVID-19 transmission was categorized by the WHO into sporadic (23.4%), cluster (41.6%), community (23.8%) and pending (11.2%).

A total of 3175207 confirmed COVID-19 cases along with 224172 deaths were reported from above-mentioned countries/territories. Crude incidence and MR per million people were estimated by dividing total cumulative COVID-19 cases and deaths by an estimated total population of the concerned countries.

Eight countries/territories, namely, Yemen, São Tomé and Príncipe, South Sudan, Saint Pierre and Miquelon; Bonaire, Sint Eustatius and Saba, and Falkland Islands (Malvinas), Tajikistan and Comoros were affected late and contributed only one COVID-19 death and were not considered in subsequent analyses as many information about them could not be traced.

Description of mortality in COVID-19 pandemic

The Euro-American region has lion's share of the burnt caused by Corona pandemic. Europe ranked on top by contributing 61.6% of total deaths, followed by the In contrast, the African part of the world shared only 0.43% of COVID19 deaths [Figure 1].
Figure 1: Distribution of subcontinents/regions according to proportion of deaths contributed by them

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Although in many studies, crude case fatality rate (CFR) was used, it cannot exactly be comparable as it depends, apart from the age-sex composition of the population of a country, on many other factors like testing and reporting policy, the effectiveness of management. Moreover, many Covid19 patients were yet to experience their outcome, i.e., death or recovery at the time of analysis. Rather, another indicator, i.e., crude MR per million, is a better indicator to compare. MRs for Africa, SEAR, Western Pacific, Eastern Mediterranean, American, and European regions were estimated to be 0.88, 1.09, 3.18, 10.05, 67.67, and 160.48 per million, respectively.

Correlates of COVID-19 mortality

Bivariable analyses

Analyses reflected that crude CFR calculated as the percentage of death among confirmed cases was lowest in the African region and highest in Europe. CFRs of Africa, SEAR, Western Pacific, and Eastern Mediterranean regions were comparable (vide RR and its 95% CI). However, it was found to be significantly higher in Euro-American regions compared to Africa [Table 1].
Table 1: Distribution of subcontinents/regions according to incident Covid19 cases and deaths as on May 01, 2020

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Countries situated in the tropical region of the world, implementing universal BCG vaccination, including booster dose, malaria transmission showed to sustain less mortality from COVID19 [Table 2].
Table 2: Relationship between LogMR and few categorical independent variables

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ANOVA explored that there were significant differences in MRs across the SES (F3, 155 = 38.187, P = 0.000). Post-hoc tests revealed that MRs were comparable between lower and lower-middle-income countries. However, the MRs of upper-middle and high-income countries were significantly higher than that of lower/lower-middle income countries.

Pearson's correlation coefficient (r) indicated that MR had linear relation with %of urban population (r = 0.450), %of aged population (r = 0.697), malaria transmission (r = −0.495), BCG vaccination policy (r = −0.472), SES (r = 0.647), smoking prevalence (r = 0.384), air pollution (r = −0.455), prevalence of raised BP (r = −0.432), proportional death from NCD (r = −0.703), %of people living with HIV (PLHIV) (r = −0.267), %of population consuming alcohol (r = 0.587), prevalence of obesity (r = 0.483), OOPE (r = −0.362), prevalence of hypercholesterolemia (r = 0.698), median age (r = 0.711), prevalence of malnutrition (r = −0.589), average annual temperature (r = −0.433), malaria incidents (r = −0.305), literacy rate (r = 0.530), % of people engaged in agriculture (r = −0.637), and % of urban people living in slum (r = −0.491).

Multiple linear regressions analyses

IVs found to have significant relation with DV as per bivariate analysis (%of urban population, %of slum population, %of aged population, %of population engaged in agriculture; the prevalence of obesity, smoking, alcohol consumption, raised BP and serum cholesterol, PLHIV; BCG immunization policy, magnitude of air pollution, proportional NCD death, malaria transmission, literacy rate, SES, annual malaria incidents, situation of country in respect of tropical region, median age) along with the prevalence of Diabetes mellitus were involved in multivariable analysis through the forward method. Model results revealed that the median age, the prevalence of obesity, and alcohol consumption had a positive association with COVID-19 mortality. Sincere attempts were made to obtain a better combination of IVs giving an optimum output. This was the highest significant R2 with the lowest residual at collinearity statistics (tolerance >0.1 and variance inflation factor <10%) favorable toward model predictions could be obtained in this study. However, literacy rate, annual malaria incidents, and situation of countries with respect to the tropical region were deleted without any change in R2 and residual. The IV %of population engaged in agriculture came out to be a negative predictor of COVID19 mortality but failed to secure the significance of its beta coefficient (0.091). Still, it was retained in the model as its addition gave 1% increase in R2 [Table 3].
Table 3: Multiple linear regressions analyses

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Interpretation of model's output[4]

In the case of multiple linear regressions involving log-transformed DV and IVs in original scales, the figure obtained by subtraction of 1.0 from the exponential value of beta coefficients multiplied by 100 gave the contribution of IVs in the variation of DV i.e., (exponential value of beta-1) *100. For example, beta 0.073 of the median age has its exponential value of 1.08. Subtraction of 1.0 from it * 100 gave a figure of 8.0%. It meant for 1 year increase in median age COVID-19 mortality would be increased by 8.0%. Similarly, COVID-19 mortality would be increased by 2.0% and 5.0% by inclusion in the model of 1% alcoholic and 1% obese individual, respectively.

  Discussion Top

Wide variation in COVID-19 mortality across the continents/countries is the query of the hour among the researchers. What is/are the influencing factor (s)-difference in genetic endowment of virus/inhabitants, local climatic and sociocultural determinants altering disease dynamics? An attempt was made in this study to unfold few potential climatic and sociocultural correlates of COVID-19 mortality. Multiple linear regressions revealed that the Covid19 mortality has a linear positive association with a median age of population, the prevalence of obesity, and alcohol consumption.

Despite poverty, illiteracy, inadequate health care; surprisingly, the African region is suffering less [Figure 1]. Factors such as less migration and less case detection in poor settings have come up in discussion. In the later scenario, the possibility of more cases of severe acute respiratory infection/influenza-like illness and deaths arising out of them might have been reported.

Having sustained explosive unknown epidemic with which the Western world is fighting now, China was able to get control it in a pragmatic way within a short span of time. Euro-American countries definitely got a bit more time to respond [Figure 2] and [Figure 3].
Figure 2: Weekly trend of deaths in different regions as from 21.01.2020–28/4/2020

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Figure 3: Time trend of Covid19 epidemic in China, Thailand, Italy and India (up to 21.4.2020)

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Why did the China model fail to halt the disastrous March of the pandemic in Italy and other Euro-American countries-is another matter of scientific search!

The pandemic visited India late on January 30, 2020, and awaited 2 months to take its hike generally started on 31.3.2020. Having some illustrative measures to follow, Government of India implemented evidence-based Nonpharmaceutical interventions (NPI) such as social distancing, cough etiquette, hand hygiene, quarantine, testing, isolation, and treatment of victims, etc., and now experiencing moderate death toll [Figure 4].
Figure 4: Trend of weekly death in India from 21st January up to 28th April, 2020

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In the initial part of this pandemic, Chinese studies found a positive association of COVID19 death with comorbidity, especially Diabetes but not with obesity, which is not a public health issue in China. Individuals of Asian descent have a higher propensity for ectopic and visceral fat storage, while those of the European store more of the excess fat in subcutaneous depots, with a lesser lipotoxic profile. Hence, though China has been experiencing an epidemic of type 2 diabetes, with prevalence rates similar to the US,[5] prevalence of obesity is more in America (42.4% in 2017–2018) along with a high burden of Class III obesity (body mass index [BMI] >40 kg/m2) among 9.2% of the population.[6] An obese, ill person requiring intensive care (5% of COVID-19 infections) becomes a concern to health system-more bariatric hospital beds, more challenging intubations, more difficult to obtain an imaging diagnosis (there are weight limits on imaging machines), more difficult to position and transport by nursing staff which may not be widely available elsewhere in hospitals. Very aptly said that at present the US health system is facing collision of the two public health epidemics, i.e., obesity and COVID-19.[2]

Unlike the findings of the present study, Squalli observed an association of certain factors with COVID-19 mortality, for example, countries with a greater share of spending on healthcare, a larger proportion of people >65 years of age. However, like the present study results they reported association of larger obesity rate with greater COVID-19 mortality and no evidence of link between COVID-19 mortality and BCG vaccination, smoking prevalence, and PM25 pollution.[3]

As per recent trends in the Euro-American region, a lot of young patients without comorbidities are getting very sick and going to deteriorate. Proposed “cytokine storm,” a hyper-immune reaction might impact one person severely and not occur at all in another.[7] This trend along with factors such as unrecognized comorbidity amongst people younger than 65 years, variation in testing and case definition might have role in diluting the association between older age ≥65 years and mortality among COVID-19 victims.

Based on the H1N1 mortality experience in 2009, Dietz and Santos-Burgoa apprehended the impact of obesity (BMI ≥30) and severe obesity (BMI ≥40) on Covid-19 patients due to its association with decreased expiratory reserve volume, functional capacity, and respiratory system compliance. Increased abdominal obesity further compromises pulmonary function in supine patients by decreasing diaphragmatic excursion, making ventilation more difficult. Furthermore, increased inflammatory cytokines associated with obesity may contribute to increased morbidity associated with obesity in COVID-19 infections.[8]

Among 4103 patients with Covid-19 at an academic health system in New York City, BMI >40 kg/m2 was the second strongest independent predictor of hospitalization, after old age.[9],[10] Furthermore, in a small study from a university hospital in Lille, France, reporting data from 124 patients with Covid-19, the need for invasive mechanical ventilation was associated with a BMI ≥35 kg/m2, independently of other comorbidities.[11] The parameters mediating this high risk are thought to include impaired respiratory mechanics, increased airway resistance and impaired gas exchange, as well as other pathophysiological features of obesity, such as low respiratory muscle strength and lung volumes.[12]

Other studies also proposed obesity as an extra risk to Covid-19 infection and mortality due to its dysregulation on metabolic activity, cardio-respiratory reserve, inflammatory response, innate immune mechanism, and the likelihood of extended duration of viral shedding and decreased responsiveness to future COVID-19 vaccine as evident from H1N1 pandemic.[13],[14],[15],[16],[17],[18]

Zhang et al. reported that median age of the deceased patients was 69.3 years (interquartile range [IQR] 61–78; range 34–90 years), which is significantly higher than that of the surviving patients (median age 56.1; IQR 43–68; range 16–95 years; P < 0.001). Older patients (>60 years), even without chronic comorbidity, were significantly more likely to die in the hospital than those 60 years old (p 0.004).[19]

Concurrent to the findings of the present study, Singh et al. reported that Covid-19 MRs (per million) were higher in countries with higher median age (>30 years) than in those with a lower median age (≤30 years). However, countries with a high (>40 years) median age had a greater chance of having higher mortality (odds ratio [OR] 8.18; 99% CI, 1.57–42.49) rates than those with a low (≤30 years) or medium (30–40 years) median age.[20] The median age of the population showed a strong positive correlation with Covid-19 mortality (r = 0.34, P = 0.0001) rates. However, the positive correlation between high median age and mortality (r = 0.35, P = 0.0001) rates in countries with a greater healthy life expectancy, indicated that the role of comorbidities might be modulated by some currently unknown confounders of COVID-19 and median age associations.[20]

Italian data reflected that the mean age of patients dying for Covid19 infection was 78.5 (median 80, range 31–103, IQR 73–85). The report shows that the median age of patients dying for COVID19 infection was >15 years higher as compared with the national sample diagnosed with Covid19 infection (median age 63 years).[21]

According to Dowd et al., the age structure of initial cases, along with early detection and treatment, likely explains the low numbers of fatalities in South Korea and Germany. The Korean outbreak concentrated among young Shincheonji religious group, with only 4.5% of cases thus far falling into the >80 years group led to a low overall CFR in South Korea compared to Italy (1.6% vs. 10.6%). Similarly, Germany had few deaths, with the median age of confirmed cases at 48 years compared to 62 years in Italy. As a counter product, COVID-19 transmission chains that begin in younger populations may go undetected longer, with countries slow to raise the alarm.[22]

Santesmasses et al. found that the death rate of COVID-19 (adjusted to population size) increases exponentially with the median age of the country, presumably because countries with older populations have a higher fraction of people who succumb to this disease. The incidence of COVID-19 also grows exponentially with the median age of the country.[23]

In his editorial, Banerjee A proposed obesity and median age as determinants of higher mortality among COVID-19 victims. Obesity modulates COVID-19 mortality by virtue of its own impact on the management of severe COVID-19 patients as well as a surrogate marker for noncommunicable comorbidities such as hypertension, diabetes, cardiovascular, and cerebrovascular morbidities.[24]

As per the WHO, alcohol use, especially heavy use, weakens the immune system and thus reduces the ability to cope with infectious diseases. Heavy use of alcohol increases the risk of acute respiratory distress syndrome (ARDS), one of the most severe complications of COVID-19. Alcohol alters one's thought process, judgment, decision-making, and behavior which are important in implementing social distancing, hand, and cough hygiene. People tend to smoke or smoke more if they drink alcohol, and smoking is associated with more complicated and dangerous progression of COVID-19. Individuals sustaining an alcohol use disorder are at greater risk of Covid-19 not only because of the health impact of alcohol but also because they are more likely to experience homelessness or incarceration than other people.[25]

Testino reviewed literature and concluded that inflammatory chaos that is created by chronic alcohol consumption significantly increases the risk of contracting bacterial and viral lung infections (including COVID-19). This is supported by the well-known fact that there is a correlation between alcohol consumption (also social-moderate) and the amount of angiotensin-converting enzyme 2 present in the body and in particular in the respiratory site.[26]

From a meta-analysis of 13 studies, Simou et al. showed that any measure of high relative to low alcohol consumption was associated with a significantly increased risk of ARDS (OR, 1.89; 95% CI, 1.45–2.48; I2 = 48%; 13 studies) which is one of the severe indexes of COVID19 leading to death.[27]

The present study failed to establish an association between COVID-19 mortality, and potential correlates, for example, the proportion of the population aged 65 years or beyond, suffering from diabetes, hypertension in contrast to recent studies.[28],[29] It has concurrence with the observations reported by Simonnet et al. who, from their study, inferred that the associations of age, diabetes, and hypertension were not significant when adjusted for obesity.[11] It is an important observation likely to draw the attention of the medical research fraternity.


This observational study is based on a single time-point data set with several confounding issues such as varied testing and reporting policy. There are evidently other factors not considered in this study that could contribute to COVID-19 mortality. For instance, a country's timely response to the pandemic, irrespective of its spending on healthcare, could have measurable effects on COVID-19 mortality. Data pertaining to age, gender, religion, and SES of individual patients could not be included for calculating specific and adjusted attack rate. The estimated population in 2020 was used. Missing data are another shortfall of this study-all data could not be availed for all countries.

  Conclusion Top

The COVID-19 enigma requires collaborative efforts for its halt. Small researches are coming up with hints for further study and guide for policymakers in adopting evidence-based approach. Notwithstanding its constraints, the present research has important policy implications. This observation emphasizes the need for increased vigilance, priority on detection and testing, and aggressive therapy for patients with obesity and COVID-19 infections. Policymakers should allocate resources toward the protection of the most vulnerable members of society from the pandemic, namely the elderly and obese, and the alcoholic as well for whom NPI seems to be futile.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Coronavirus Disease 2019 (COVID-19) Situation Report –85. WHO 2020 May 01; Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200414-sitrep-85-covid-19.pdf?sfvrsn=7b8629bb_4. [Last accessed on 2020 May 02].  Back to cited text no. 1
Ryan DH, Ravussin E, Heymsfield S. COVID 19 and the patient with obesity – The editors speak out. Obesity 2020;28:847.  Back to cited text no. 2
Squalli J. Evaluating the determinants of COVID19 mortality: A crosscountry study. Med Rxiv Preprint 2020; p.1-14. doi: https://doi.org/10.1101/2020.05.12.20099093.  Back to cited text no. 3
Ford C. Interpreting Log Transformations in a Linear Model; August, 2018. Available from: https://data.library.virginia.edu/interpreting-log-transformations-in-a-linear-model/. [Last accessed on 2020 Apr 14].  Back to cited text no. 4
Hu C, Jia W. Diabetes in China: Epidemiology and genetic risk factors and their clinical utility in personalized medication. Diabetes 2018;67:3-11.  Back to cited text no. 5
Centers for Disease Control and Prevention. Overweight & obesity. 27 Updated February, 2020. Available from: https://www.cdc.gov/obesity/data/adult.html. [Last accessed on 2020 Mar 18].  Back to cited text no. 6
Resnick B. Scientists are Trying to Figure out Why Covid19 Hits Some Young, Healthy People Hard (Internet). New York: Vox Today; 08 Apr, 2020. Availed from: https://www.vox.com/scienceandhealth/2020/4/8/21207269/covid19coronavirusriskfactors.  Back to cited text no. 7
Dietz W, Santos-Burgoa C. Obesity and its Implications for COVID-19 Mortality. Obesity (Silver Spring) 2020;28:1005.  Back to cited text no. 8
Stefan N, Birkenfeld AL, Schulze MB, Ludwig DS. Obesity and impaired metabolic health in patients with COVID-19. Nat Rev Endocrinol 2020;16:341-2.  Back to cited text no. 9
Petrilli CM, Jones SA, Yang J, Rajagopalan H, O'Donnell L, Chernyak Y, et al. Factors associated with hospitalization and critical illness among 4,103 patients with COVID-19 disease in New York City. Preprint at medRxiv 2020; doi: 10.1136/bmj.m1966. Available at: https://www.bmj.com/content/bmj/369/bmj.m1966.full.pdf.  Back to cited text no. 10
Simonnet A, Chetboun M, Poissy J, Raverdy V, Noulette J, Duhamel A, et al. High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation. Obesity (Silver Spring, Md) 2020;doi: 10.1002/oby.22831. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7262326/pdf/OBY-9999-na.pdf  Back to cited text no. 11
Murugan AT, Sharma G. Obesity and respiratory diseases. Chron Respir Dis 2008;5:233-42.  Back to cited text no. 12
Bhargava A, Bhargava M. Obesity and COVID19 outcomes:A risk factor that needs attention (internet). Curr Sci 2020 ; 118:1-2. Available at: https://www.researchgate.net/publication/340846393  Back to cited text no. 13
Sattar N, McInnes IB, McMurray John JV. Obesity a Risk Factor for Severe COVID19 Infection: Multiple Potential Mechanisms. Circulation 2020;142:4-6.https://doi.org/10.1161/CIRCULATIONAHA.120.047659.  Back to cited text no. 14
Cai Q, Chen F, Luo F, Liu X, Wang T, Wu Q, et al. Obesity and COVID19 Severity in a Designated Hospital in Shenzhen, China. Diabetes Care 2020;43:1392-1398. https://doi.org/10.2337/dc20-0576  Back to cited text no. 15
Williamson E, Walker AJ, Bhaskaran K, BaconS, Bates C,Morton CE, et al. Open SAFELY: factors associated with COVID19related hospital death in the linked electronic healthre cords of 17 million adult NHS patients (Internet). MedRxiv 2020; doi: https://doi.org/10.1101/2020.05.06.20092999.  Back to cited text no. 16
Docherty A B, Harrison EM, Green CA, Hardwick H, Pius R, Norman L, et al. Features of 16,749 hospitalised UK patients with COVID19 using the ISARIC WHO clinical characterization protocol (internet). medRxiv 2020;doi: https://doi.org/10.1101/2020.04.23.20076042.  Back to cited text no. 17
Lighter J, Phillips M, Hochman S, Sterling S, Johnson D,Francois F, et al. Obesity in patients younger than 60 years is a risk factor for Covid19 hospital admission (internet). Clin Infect Dis. 2020; doi: 10.1093/cid/ciaa415.  Back to cited text no. 18
Zhang J, Wang X, Jia X, Li J, Hu K, Chen G, et al. Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China. Clin Microbiol Infect 2020;26:767-72.  Back to cited text no. 19
ingh BR, Gandharva R, Karthikeyan R, Singh SV, Yadav A,Kumar Vinod OR, et al. Epidemiological determinants of acute respiratory syndrome coronavirus2 disease pandemic and the role of the bacillecalmetteguerin vaccine in reducing morbidity and mortality (internet). J Pure Appl Microbiol 2020 ;14:1-10.  Back to cited text no. 20
Istitvto Svperiore Di Sanita. Characteristics of COVID-19 patients dying in Italy Report based on available data on; 20 March, 2020. Available from: https://www.epicentro.iss.it/coronavirus/bollettino/Report-COVID-2019_20_marzo_eng.pdf. [Last accessed on 2020 May 20].  Back to cited text no. 21
Dowd JB, Andriano L, Brazel DM, Rotondi V, Block P, Ding X, et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. PNAS 2020;117:9696-8.  Back to cited text no. 22
Santesmasses D, Castro JP, Zenin AA, Shindyapina AV,Gerashchenko MV, Zhang B, et al. COVID19 is an emergent disease of aging (internet). medRxiv Preprint 2020; doi: https://doi.org/10.1101/2020.04.15.20060095  Back to cited text no. 23
Banerjee A. COVID-19: The curious case of the dog that did not bark. Med J DY Patil Vidyapeeth 2020;13:189-91.  Back to cited text no. 24
  [Full text]  
Alcohol and COVID-19: What you Need to know. World Health Organization; 2020. Availed from: http://www.euro.who.int/__data/assets/pdf_file/0010/437608/Alcohol-and-COVID-19-what-you-need-to-know.pdf. [Last accessed on 2020 May 19].  Back to cited text no. 25
Testino G. Are patients with alcohol use disorders at increased risk for Covid-19 infection? Alcohol Alcohol 2020;55:344-6.  Back to cited text no. 26
Simou E, Leonardi-Bee J, Britton J. The effect of alcohol consumption on the risk of ARDS a systematic review and meta-analysis. CHEST 2018;154:58-68.  Back to cited text no. 27
Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19).16-24th February, 2020. Available from: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf. [Last accessed on 2020 Apr 10].  Back to cited text no. 28
Niu S, Tian S, Lou J, Kang X, Zhang L, Lian H, et al. Clinical characteristics of older patients infected with COVID19: A descriptive study (internet). Arch Gerontol Geriatr 2020 ; 89:104058. https://doi.org/10.1016/j.archger.2020.104058.  Back to cited text no. 29


  [Figure 1], [Figure 2], [Figure 3], [Figure 4]

  [Table 1], [Table 2], [Table 3]


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