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ABSTRACT
Background: Early reports on coronavirus disease 2019 (COVID-19) case fatalities in India
suggests that males are at a greater disadvantage of than females, but it is unclear whether
males experience a higher risk of mortality throughout the age-spectrum or there are sex-
dierentials in survival risk. We adopt a gender lens and present a disaggregated view of age-
sex specic COVID-19 infection and mortality risk in India.
Methods: We use crowdsourced data (https://www.covid19india.org/) to provide preliminary
estimates for age-sex specic COVID-19 case fatality rate (CFR) for India. We analyse the
burden of the cases and deaths for age-sex categories. CFR is estimated as the ratio of
conrmed deaths in total conrmed cases. We report binomial condence interval for the
CFR estimates. Also, an adjusted-CFR is developed to capture the potential mortality among
the currently active infections.
Results: As of May 20, 2020, males share a higher burden (66%) of COVID-19 infections than
females (34%) but the infection is more or less evenly distributed in under-ve as well as
elderly age groups. The CFR among males and females is 2.9% and 3.3%, respectively. The
age-specic COVID-19 CFR assumes ‘Nike-swoosh’ pattern with elevated risks among the
elderly. The World Health Organization world standard population structure standardized
CFR for India is 3.34%. The adjusted-CFR is estimated to be 4.8%.
Conclusion: Early evidence indicates that males have higher overall burden, but females have
a higher relative-risk of COVID-19 mortality in India. Elderly males and females both display
high mortality risk and require special care when infected. Greater focus on data collection
and sharing of age-sex specic COVID-19 cases and mortality data is necessary to develop
robust estimates of COVID-19 case fatality to support policy decisions.
Keywords: COVID-19; Case fatality rate; Gender dierence; Elderly; India
J Glob Health Sci. 2020 Jun;2(1):e17
https://doi.org/10.35500/jghs.2020.2.e17
pISSN 2671-6925·eISSN 2671-6933
Original Article
Received: May 24, 2020
Accepted: May 30, 2020
Correspondence to
S V Subramanian
Department of Social and Behavioral Sciences,
Harvard T.H. Chan School of Public Health, 677
Huntington Avenue, Boston MA 02115, USA.
E-mail: svsubram@hsph.harvard.edu
© 2020 Korean Society of Global Health.
This is an Open Access article distributed
under the terms of the Creative Commons
Attribution Non-Commercial License (https://
creativecommons.org/licenses/by-nc/4.0/)
which permits unrestricted non-commercial
use, distribution, and reproduction in any
medium, provided the original work is properly
cited.
ORCID iDs
William Joe
https://orcid.org/0000-0003-3282-658X
Abhishek Kumar
https://orcid.org/0000-0001-5959-3195
Sunil Rajpal
https://orcid.org/0000-0001-8607-1878
U.S. Mishra
https://orcid.org/0000-0003-3900-0342
S V Subramanian
https://orcid.org/0000-0003-2365-4165
Conflict of Interest
No potential conflict of interest relevant to this
article was reported.
William Joe ,1 Abhishek Kumar ,2 Sunil Rajpal ,3 U.S. Mishra ,4
S V Subramanian 5,6
1Population Research Centre, Institute of Economic Growth, Delhi, India
2Institute of Economic Growth, Delhi, India
3Institute of Health Management Research, IIHMR University, Jaipur, India
4Centre for Development Studies, Thiruvananthapuram, India
5Harvard Center for Population and Development Studies, Cambridge, MA, USA
6Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Equal risk, unequal burden? Gender
differentials in COVID-19 mortality in
India
https://e-jghs.org
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Author Contributions
Conceptualization: Joe W, Subramanian SV;
Data curation: Joe W, Kumar A, Rajpal S;
Formal analysis: Joe W, Kumar A, Rajpal S;
Methodology: Joe W, Kumar A, Mishra US,
Subramanian SV; Supervision: Joe W, Mishra
US, Subramanian SV; Writing - original draft:
Joe W; Writing - review & editing: Joe W,
Mishra US, Subramanian SV.
INTRODUCTION
In India, the rst case of coronavirus disease 2019 (COVID-19) was detected on Jan 30, 2020.
Until Mar 1, 2020, India had only three conrmed cases but ever since the contagious infection
has grown exponentially. As of May 21, 2020, with over 112,000 cases1 India accounts for 11th
highest share of 2.24% in global burden of COVID-19. India also reports over 3400 COVID-19
deaths and accounts for 1.05% of the COVID-19 deaths worldwide. Meanwhile, the policy
response to COVID-19 has revolved around an all-encompassing nationwide lockdown that
helped curb2 the COVID-19 outbreak in the initial phases.3,4 But, sooner rather than later,
lockdown would be relaxed leading to greater exposure to the virus. In fact, with no vaccine or
(validated) cure in sight, the fate of all individuals remains as good as their immune systems.5
Clearly, further evidence and insights on infection risks and survival chances of this novel
infection assumes salience for all individual and policy decisions.
In this regard, the concept of case fatality rate (CFR) has considerable relevance and can provide
much needed inferences regarding survival patterns. With an ongoing epidemic, the CFR is
best described as a dynamic rate and is dened as the percentage of conrmed deaths in total
conrmed cases. For instance, during mid-April the Ministry of Health and Family Welfare6
reports a CFR of 3.3% for India. Nevertheless, there is increasing evidence that the COVID-19
survival chances are sensitive to age-pattern and other co-morbidity conditions of the infected
population.7 In particular, early evidence from China and Italy reveal a steep age gradient in
risk of death and can vary across contexts.8,9 For instance, based on the cases observed till
April 20, 2020, a recent study estimates an overall COVID-19 CFR of 3.2% for India.10 The
study nds CFR of 14.3% for those aged 60 and above whereas the mortality risk is found to
be much lower (below 1%) among the younger population (aged below 25 years). Although,
the age-group classications are too broad, but these provide insights regarding variations in
survival experience of children (or the older elderly), the young adult and the elderly (60+ years).
Besides, with evolving nature of the pandemic and increasing number of cases it is critical
to track the CFR with all available information and add to our understanding on COVID-19
mortality risk among infected children and elderly population in India.
An important concern here is to examine the overall as well as age-specic COVID-19
infection and mortality risk from a gender lens. Preliminary evidence from various countries
suggests that men are at greater risk of both infections and deaths, but these inferences
should be carefully interpreted.11 The early reports of COVID-19 cases and deaths in India
suggests that males are at a greater disadvantage than females with CFR of 3.3% and 2.9%,
respectively.10 However, it is unclear whether males experience a higher risk of mortality
throughout the age-spectrum or there is sex-dierentials in survival risk. It is argued that pre-
existing conditions, behavioural risk factors (smoking) and biological factors all elevate the
risk of mortality among males12 but these patterns need to be veried with new and emerging
information on COVID-19 outbreak in India.
A related concern is regarding the distinction between sex-specic burden and risk of
COVID-19 deaths in India. Overall, it is noted that more men than women are infected with
COVID-19 and are also more likely to die from the infection. Some of the early estimates from
the Ministry of Health and Family Welfare13 indicated that three-fourth of all conrmed cases
are males. But it is important to disaggregate the burden to understand whether the inference
is valid for children as well as the elderly age group. In fact, bulk of the evidence on age-sex
patterns in COVID-19 mortality has focused on adults and the elderly14 but very little is known
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about the age-sex specic relative risks and patterns of COVID-19 mortality, particularly for
populous and resource-poor settings such as India.15 Understanding the risks for children
and women is also critical because of widespread poverty and undernourishment which is
likely to weaken immunity against infectious diseases.16,17 The issue assumes relevance for
India because of large young population and also because of health system deciencies and
ongoing economic slowdown.18 Against this backdrop, the main objective of the paper is to
present an age-sex disaggregated assessment of COVID-19 cases and COVID-19 deaths in
India. The analysis specically aims to draw attention toward analysis and interpretation of
the two distinct concepts of burden and risk COVID-19 cases and deaths in India. The analysis
is expected to provide a rst set of estimates for age-sex specic CFR and emphasises on
presenting an accurate interpretation of the dierentials in sex-specic risk and burden of
COVID-19 infections and fatalities in India.
METHODS
The analysis is based on the crowdsourced data on COVID-19 cases in India publicly available
for download at https://www.covid19india.org/. The website provides information on
time-series of cumulative and daily numbers of COVID-19 cases, recoveries and deaths. The
information on these indicators is consistent with the ocial estimates provided by the
Ministry of Health and Family Welfare as well as other international online databases on
COVID-19 such as https://coronavirus.jhu.edu/map.html from the Johns Hopkins University
and Medicine or the https://ourworldindata.org/coronavirus.19 Unit level information of cases
by age and sex and is also provided. The main analysis is based on the COVID-19 infections
and deaths data available till May 20, 2020. However, given the exponential growth in cases
and the need to conrm robustness of estimates, we also conduct sensitivity analysis and
track COVID-19 infections and deaths at 4 dierent time points: April 10, May 1, May 9,
and May 20, 2020. These time points reect 4 dierent phases of nationwide lockdown as
follows: Phase 1 (Mar 24 to Apr 14, 2020), Phase 2 (April 15 to May 3, 2020), Phase 3 (May
4 to May 17, 2020), and ongoing Phase 4 (May 18 to May 31, 2020). This helps verify the
consistency in the distribution of reported cases of COVID-19 infections and deaths by age-
sex specics. Also, it helps understand the dynamics of CFR estimates up to the most recent
data point and base the inferences on cumulative observations. Specically, for April 10, May
1, May 9, and May 20, 2020 we have reports on 1,160, 3,384, 6,914, and 15,341 number of
COVID-19 cases and 127, 316, 507, and 569 number of COVID-19 deaths, respectively. During
these dates the total (including those with missing age-sex information) cumulative number
of conrmed cases were 7,618, 37,257, 67,161, and 112,027 whereas the cumulative number of
conrmed deaths were 249, 1,223, 2,212, and 3,433, respectively. The distribution of missing
and reported cases is presented as Supplementary Table 1. Since the individual-level age-sex
information is not available for all the conrmed cases, therefore, for analytical purposes
we apply the standard assumption that the age-sex distribution of the cases with missing
information is similar to the age-sex distribution of the reported cases.10 We use the data to
describe the growth in COVID-19 infections using a curve-tting exercise using the curvet
module of Stata (StataCorp LLC, College Station, TX, USA).20
The CFR is estimated as the ratio of conrmed deaths in total conrmed cases and denotes
the risk of mortality from COVID-19 infection. The CFR is estimated for males and females
for overall and for 9 age-groups as follows: 0–4 years, 5–19 years, 20–29 years, 30–39 years,
40–49 years, 50–59 years, 60–69 years, 70–79 years, and 80+ years. We report the Binomial 95%
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condence interval (CI) for the CFR estimates. Further, we analyse the burden of the cases
and deaths. The burden of cases or death is dened as the number of deaths of a particular sex
(male or female) in total deaths (both sexes). This can also be dened as share of a particular
age-group in total number of cases or deaths. The burden of cases or death for males and
females is dened as total share of a particular age-group of given sex in total number of cases
or deaths for that particular sex. Moreover, a further standardization is necessary to improve
the interpretation of the CFR if these were applicable to the entire country. For this purpose, we
develop the population standardized CFR estimate for India. The standardization is based on
the World Health Organization (WHO) world standard population distribution.21
The CFR, nevertheless, encounters two specic concerns: rst, if the epidemic is still
ongoing, then the estimate of CFR may vary because the probability of death among currently
active cases is likely to be non-zero, and; second, it is critical that the CFR accounts for the
risk of mortality among the currently active cases. Although, there are methods22 to adjust
CFR for this censored period but there are considerable data gaps in India to understand
the duration since onset of infection and nal status (deceased or recovered). Accordingly,
we propose an alternative denition of adjusted CFR as follows: CFR* = θ(1 + α); where, θ
denotes conventional CFR i.e., the ratio of total deaths to total cases (Dt/Ct) and α denotes
the ratio of active cases (At) to total cases (At/Ct). The CFR ination factor assumes that the
active cases will also experience similar relative probabilities of death and recoveries.22 As the
epidemic ends, the active cases would gradually reduce to zero (α=0) and accordingly CFR*
would approach the true CFR. Taken together, CFR and CFR* provides a reasonable estimate
and the trends in CFR and CFR* converge at the end of the epidemic to reveal the nal CFR.
RESULTS
COVID-19 cases and growth
As of May 20, 2020, India reports more than 112,000 conrmed COVID-19 infections. Since
March 24, 2020, India is under lockdown (in four dierent phases) with varying intensity and
restrictions across regions depending on the COVID-19 outbreak situation. A cur ve-tting
exercise reveals that since March 1, 2020 COVID-19 outbreak has witnessed an exponential
growth of 6.4% per day. The exponential growth was the highest at 12.9% during the rst
phase of lockdown (March 24 to April 14, 2020) but has declined to 6.5% and 5.4% during
the second (April 15 to May 3, 2020), and third (May 4 to May 17, 2020) phase, respectively.
The reduced growth rate, nevertheless, gets translated into large and ever-increasing number
of infections per day. The geo-spatial dispersion of COVID-19 is also an emerging concern.
Two important aspects of the spread are: i) heavy concentration in western India (major
urban centres) and ii) increasing transmission to Eastern India. It is noted that more than
60% of the COVID-19 cases are concentrated in ve cities namely Ahmedabad, Chennai,
Delhi, Mumbai, and Thane. In India the progress of the epidemic is also monitored through
concepts such as doubling time. It is worth noting that the COVID-19 cases in India doubled
from 1,019 to 2,059 in 4 days (March 29 to April 1, 2020) whereas it took 11 days (April 23 to
May 3, 2020) for a 2-fold increase in cases from 21,373 to 42,546 and 13 days (May 5 to May 18,
2020) to increase from 49,405 to 100,327.
Burden of COVID-19 infections and deaths
Table 1 presents the absolute burden of male and female in total COVID-19 infections and
deaths for overall cases as well as for specic age-groups. Overall, females have 34.3% share
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in total burden of COVID-19 infections. The burden is more or less similar among under-ve
boys (51.5%) and girls (48.5%). The burden increases among males in middle age group and
reaches a maximum of 70.4% for the age group 30–39 years (Fig. 1). Thereaer, the share of
females in total burden increases with age. The burden among females is over 40% for the
age groups 70–79 years and 80+ years.
The burden of females in total COVID-19 deaths is 36.9%. It may be noted that the absolute
burden of deaths among females is higher than males than the absolute burden among females
in total number of infections. The dierence in female burden in infections and deaths is 2.6%
and is statistically signicant (
P
-value = 0.001). Females in the age group 30–39 years have
lowest burden of death (21.3%) whereas elderly females in the age group 80+ years have more
or similar burden (48.5%) in total death for this particular age group. Although, females share a
lower mortality burden than males in general but there is no reported case of male mortality in
the age group 5–19 years hence the entire burden is borne by females.
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Table 1. Sex-specific absolute burden of COVID-19 infections and deaths by age groups, India, May 20, 2020
Age group Cases (% Share) Deaths (% Share)
Male Female Total Male Female Total
0 to 4 years 51.5 48.5 1 00.0 64.9 35 .1 100.0
5 to 19 years 60.3 39.7 10 0.0 0.0 100.0 100.0
20 to 29 years 65.1 34.9 100.0 64.9 35.1 100.0
30 to 39 years 70.4 29.6 100.0 78.7 21.3 100.0
40 to 49 years 68.3 31.7 100.0 58.4 41.6 100.0
50 to 59 years 66.5 33.5 100.0 68.4 31.6 100.0
60 to 69 years 63.3 36.7 100.0 64.7 35.3 100.0
70 to 79 years 54.5 45.5 100.0 57.6 42.4 100.0
80+ years 56.6 43.4 100.0 51.4 48.6 100.0
All 65.7 34.3 100.0 6 3.1 36.9 100.0
Totala73,654 38,373 112,027 2,165 1,268 3,433
Source: Authors based on https://www.covid19india.org/ (accessed on May 21, 2020).
COVID-19 = coronavirus disease 2019.
aNote: Denotes cumulative number of confirmed cases and deaths as on May 20, 2020. The age-sex distribution is based on the assumption that the cases with
missing information have similar age-sex distribution as of the reported cases.
0
100
80
40
60
20
Share of COVID cases (%)
Age-group (yr) Age-group (yr)
0–4 5–19 20–29 30–39 40–49 50–59 60–69 70–79 80+ All
0
100
80
40
60
20
Share of COVID deaths (%)
0–4 5–19 20–29 30–39 40–49 50–59 60–69 70–79 80+ All

   



   


FemaleMale FemaleMale




  






  

Fig. 1. Male and female share in total burden of COVID-19 cases and deaths in India, May 20, 2020.
Estimated burden is based on the assumption that the age-sex distribution of the cases with missing information is similar to the age-sex distribution of the
reported cases. Source: Authors based on https://www.covid19india.org/ (accessed on May 20, 2020).
COVID-19 = coronavirus disease 2019.
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Table 2 presents the age-specic relative burden in infections and deaths among males and
females as well as for the combined cases. As per the data till May 20, 2020, Children and
adolescents (below age 20 years) account for 13.8% share in total COVID-19 infections but
have relatively lower burden of 2.1% in total COVID-19 deaths (Fig. 2). Elderly aged (60 and
above) although account for 9.7% share in total infection but they account for 51.6% share
in total deaths. Table 2 further shows that the age-specic relative burden in deaths among
males aged 60 years is 50.7% whereas the same is 54.5% among females. The population
in the age group 20–59 years have a higher relative burden of 76.4% in total cases but
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Table 2. Age-specific relative burden of COVID-19 infections and deaths by sex in India, May 20, 2020
Age group Male (% Share) Female (% Share) Total (% Share)
Cases Deaths Cases Deaths Cases Deaths
0 to 4 years 1.7 1.1 3.0 1.0 2.1 1. 2
5 to 19 years 10.9 0.0 13.8 2.5 11.7 0.9
20 to 29 years 23.6 2.2 24.2 2.0 23.8 2 .1
30 to 39 years 24.5 5.4 19.8 2.5 2 3.0 4.6
40 to 49 years 1 8.1 1 2.7 1 6.1 15.5 1 7. 5 13.7
50 to 59 years 12.2 27.9 11.7 22.0 1 2.1 2 6.0
60 to 69 years 6.6 32.8 7.4 3 0.5 6.8 31.8
70 to 79 years 1.9 13.6 3.0 17.0 2.2 14.7
80+ years 0.6 4.3 0.9 7. 0 0.7 5.1
All 100.0 100.0 100.0 10 0.0 100.0 10 0.0
Totala73,654 2,165 38,373 1,268 112,027 3,433
Source: Authors based on https://www.covid19india.org/ (accessed on May 21, 2020).
COVID-19 = coronavirus disease 2019.
aNote: Denotes cumulative number of confirmed cases and deaths as on May 20, 2020. The age-sex distribution is based on the assumption that the cases with
missing information have similar age-sex distribution as of the reported cases.
Age-group share in total burden of COVID
cases in India, May , 
Age-group share in total burden of COVID
deaths in India, May , 
 y r,
.%
 yr,
.%
 yr, .%
 yr,
.%
 yr,
. %
 yr,
 .%
 yr,
.%
 yr, .%
 yr, .%
 yr, .%
 yr, .%
 yr, .%
 yr,
.%
 yr,
.%
 yr,
.%
 yr,
.%
 yr,
.%
 yr,
.%
Fig. 2. Age-group share in total burden of COVID-19 cases and deaths in India, May 20, 2020.
Estimated burden based on the assumption that the age-distribution of the cases with missing information is similar to the age-distribution of reported cases.
Source: Authors based on https://www.covid19india.org/ (accessed on May 20, 2020).
COVID-19 = coronavirus disease 2019.
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account for 46.4% share in total deaths. The relative burden of total cases is higher among
the middle-aged population but the relative burden in deaths is mostly borne by the elderly
population. Sensitivity analysis based on the sex-specic burden in total infections and
deaths in India at four dierent time points shows that the share of females in total infections
and deaths has been increasing since April onwards (Supplementary Table 2).
CFR and relative risk of COVID-19 deaths
As of May 20, 2020, the CFR for India is estimated to be 3.1% (95% CI, 3.0%–3.2%) (Table 3).
With an ongoing epidemic, the CFR becomes a dynamic statistic and has to be closely monitored
to understand its evolution along with the increasing number of COVID-19 infections and deaths.
Therefore, as a sensitivity analysis, we also estimated CFR at four dierent points in time: April
10, May 1, May 9, and May 20, 2020. During these four time points, the CFR has declined from
3.3% to 3.1% (Supplementary Table 3).
The age-specic CFR for India presumes a ‘Nike-Swoosh’ pattern with high risk of mortality
for under-ve children, lower risks among adolescents and young adults and then increasing
risks for older adults and elderly (Fig. 3). The CFR is much higher for all infected elderly
but with particularly elevated mortality risk of over 20% for those aged 70 and above. An
important observation from age-specic temporal comparison is that COVID-19 among
elderly is more fatal than what was revealed through some of the early estimates.10 For
instance, April 10, 2020 data reveals an adjusted CFR of 8.9%, 12.1%, and 22.4% among
those aged 60–69 years, 70–79 years and 80+ years. Whereas, the May 20, 2020 data shows
CFR of 14.3%, 20.1%, and 22.2% for these age groups, respectively (Supplementary Table 3).
As of May 20, 2020, the CFR for males and females is estimated to 2.9% (95% CI, 2.8%–
3.1%) and 3.3% (95% CI, 3.1%–3.5%), respectively, indicating a relatively higher risk of death
among females. The dierence in overall male and female CFR is also found to be signicant
(
P
-value = 0.001). Fig. 3 displays age-specic CFR pattern for males and females. The CFR
among males is usually higher than females for most of the age groups. Male and female
CFR also have distinct patterns with greater disadvantage for male survival in under-ve as
well as in older age groups (Supplementary Figs. 1-4). Females have higher risk of mortality
in the age group 40–49 years (
P
-value = 0.000) and this leads to a marginally higher overall
risk of COVID-19 mortality for females. The dierence is found to be statistically signicant
(at 5% level) only for three age groups (5–19 years, 30–39 years, and 40–49 years). Further
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Table 3. Age-sex specific COVID-19 CFR (in %) in India, May 20, 2020
Age group Males Females Both sexes
0 to 4 years 1.9 (1. 2–2.8) 1.1 (1.6–3.4) 1.7 (1.2–2.3)
5 to 19 years - 0.6 (0.4–0.9) 0.2 (0.1–0.3)
20 to 29 years 0.3 (0.2–0.4) 0.3 (0.2–0.4) 0.3 (0.2–0.3)
30 to 39 years 0.6 (0.5–0.8) 0.4 (0.3–0.6) 0.6 (0.5–0.7)
40 to 49 years 2.1 (1.8–2.3) 3.2 (2.8–3.7) 2.4 (2.2–2.6)
50 to 59 years 6.7 (6.2–7.3) 6.2 (5.5–6.9) 6.6 (6.2–7.0)
60 to 69 years 14.5 (13.5–15.5) 13.6 (12.4–14.9) 14.3 (13.5–15.1)
70 to 79 years 21.1 (18.9–23.3) 18.6 (16.4–20.1) 20.1 (18.6–21.7)
80+ years 20.5 (16.9–24.5) 25.3 (20.8–30.2) 22.2 (19.3–25.3)
All 2.9 (2.8–3.1) 3.3 (3.1–3.5) 3.1 (3.0–3.2)
The WHO world standard population age-sex structure based standardized CFR estimated for May 20, 2020 is
3.34%. The estimated burden is based on the assumption that the age-sex distribution of the cases with missing
information is similar to the age-sex distribution of the reported cases. Values are presented as CFR with 95%
confidence interval. Source: Authors based on https://www.covid19india.org/ (accessed on May 21, 2020).
COVID-19 = coronavirus disease 2019; CFR = case fatality rate.
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standardization as per the population age-structure is necessary to interpret the CFR. For this
purpose, we use the WHO world standard population structure and nd a standardized CFR
of 3.34% for all-India.
Adjusting the CFR for active infections (CFR*)
When an epidemic is underway, the CFR presents an incomplete assessment of the currently
active infections as it is only aer a certain number of days that these censored cases will be
ultimately categorized into recoveries or deaths. Figure 4 presents the trends in CFR as well
as the adjusted-CFR estimate (CFR*) that accounts for the potential risk of death among the
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0
30
10
20
25
5
15
COVID-19 death rate (unstandardized) based on
adjusted distribution of cases (%)
Male
Female
Overall
Age-group (yr)
0–4 5–19 20–29 30–39 40–49 50–59 60–69 70–79 80+
1.7 0.2 0.3 0.6
2.4
6.6
14.3
20.1
22.2
Fig. 3. Sex-differentials in COVID-19 CFR, India, May 20, 2020.
The estimated burden is based on the assumption that the age-sex distribution of the cases with missing
information is similar to the age-sex distribution of the reported cases. The male-female CFR difference is
statistically significant (at 5%) for the overall CFR and for the CFRs of the following age groups: 5–19 years, 30–39
years, and 40–49 years. Source: Authors based on https://www.covid19india.org/ (accessed on May 21, 2020).
COVID-19 = coronavirus disease 2019; CFR = case fatality rate.
0
7
6
5
4
2
3
1
COVID-19 CFR and CFR* estimates (in %)
Mar 24 Mar 31 Apr 7 Apr 14 Apr 21 Apr 28 May 5 May 12 May 19
CFR CFR*
Fig. 4. Estimates of COVID-19 CFR and CFR*, India, Mar 24 to May 20, 2020.
The 3 vertical lines denote the lockdown: Phase 1 (March 24 to April 14, 2020), Phase 2 (April 15 to May 3, 2020),
and Phase 3 (May 4 to May 17, 2020). Source: Authors based on https://www.covid19india.org/ (accessed on May
21, 2020).
COVID-19 = coronavirus disease 2019; CFR = case fatality rate; CFR* = adjusted-case fatality rate estimate.
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currently active cases. The adjustment is done by inating the CFR by a factor that assumes
that the active cases of COVID-19 will also experience the same risk of death as the current
known level of CFR. As of May 20, 2020, the CFR for India is 3.1% but in comparison, the
CFR* for India is estimated to be 4.8%. Unless there is a dramatic uctuation in the number
of infections and deaths, the CFR and CFR* reect a reasonable range for estimate COVID-19
mortality in India.
DISCUSSION
The analysis puts forth four salient inferences as follows. First, while males have a higher
overall burden (66%) of COVID-19 infections than females but the infection is evenly
distributed in the under-ve age group and, to some extent, even among the elderly age
groups (particularly 70+ years). Second, the COVID-19 CFR assumes a ‘Nike-swoosh’ type
age-specic mortality pattern in India with particularly elevated risk of mortality among the
elderly population. The pattern is evident so far but may further evolve as more and new data
is accumulated. Third, although males share a higher burden of death but at the same time it
is important to note that females have relatively high risk of COVID-19 death. The data reveals
that the overall CFR among males is 2.9% but the same is signicantly (
P
-value = 0.001)
higher among females (3.3%). In particular, the dierence in male and female CFR in the
middle age groups is statistically signicant (
P
-value = 0.000) indicating greater caution for
females in the age group 40–49 years. Finally, the CFR for India is likely to increase because
of a large number of active infections. In fact, the adjusted CFR* as of May 20, 2020 that
accounts for potential mortality among the currently active cases is estimated to be 4.8%.
The WHO world standard population based adjusted estimate of CFR for India is estimated
to be 3.3%. It is important that for international comparisons the WHO world standard
population is used because countries vary substantially in their population age-structure.
Countries with aging population, in particular, are likely to have high CFR whereas those
with higher share of young population may have low CFR but both set of estimates should be
standardized to facilitate comparisons.15,23,24
The study has 2 key limitations. First, the analysis is based on crowdsourced data with
considerable gaps in reporting of age-sex specic information of all the COVID-19 infections
and deaths. To overcome this, we assume that the distribution of missing cases follows the
same distribution as of the reported cases. Our estimate of age-sex specic CFR is sensitive
to this assumption. However, as sensitivity check we have conducted this analysis at various
time points have noted consistent ndings regarding the age-sex CFR patterns indicating
that the assumption is reasonable. Second, the number of conrmed cases in India depends
upon the testing facility and capture of the data. Although, testing frequencies, data capture
and sharing on age-sex specicities of the COVID-19 cases has been inadequate but data
cumulation has allowed much needed robustness for CFR estimations. However, it is likely
that the estimates may further evolve with greater availability of data.
The gendered impacts of COVID-19 outbreak need to be eectively analysed for potential
public health and policy inferences.11 The analysis reveals that while the overall burden of
COVID-19 is higher among men but it is important to note that females are at an overall
higher relative risk of mortality. This nding calls for equal, if not greater, attention
toward females for COVID-19 care. In the under-ve age group as well as elderly age group
particularly, those aged 70+ years the risk of COVID-19 death is more or less similar for
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both males and females. Evidence from China also indicates that there is no signicant sex
dierence in risk of COVID-19 infection among children.25 Also, further data and insights
are necessary to understand whether the excess risk for males and females are associated
with socioeconomic circumstances, gender bias in care-seeking or specic biological
characteristics. For instance, it is argued that female hormones, specically estrogen, has
a benecial eect on upper and lower airways and is associated with stimulation of the
immune response to upper airway infections.26 The reduction in estrogen in menopausal
and elderly age group may also be responsible for increase in the relative risk of death among
females in the age group 40+ years onwards.26 However, evidence from developed countries
such as Italy and United States conrms a higher CFR among males.24,26 A higher risk among
females can also be associated with reporting gaps in India. For instance, there are more than
1,100 cases of COVID-19 infection among male aged 5–19 years is reported but there is no
report of any single COVID-19 deaths in this age-sex groups.
In the Indian context, it is evident that bulk of infections and deaths are among males and
part of the explanation may be found in the gendered nature of work and society in India.27-29
Men are relatively more likely to undertake visits for household chores or for socializing but
at the same time the gendered nature of occupations and employment also pose greater risk
of infection among men. In fact, bulk of the infection in India is concentrated across 4 to 5
major urban centres in Western India that has considerable share of young male migrants
who are also more likely to acquire and transmit infection.30,31 Given increasing reports of
reverse urban-to-rural migration, if such COVID-19 transmissions are le unchecked than it
would lead to increase in number of COVID-19 clusters across rural areas. This can also have
a direct impact on rural population where every household has higher proportion of children,
women and elderly. Clearly, the ever-increasing numbers of daily new cases of COVID-19
infections is a major concern for India.
With greater details on age-sex specics, the aggregate statistic of CFR can be more useful
for policy action. Nevertheless, the CFR strongly relies on the conrmation of cases which;
however, can be underestimated during epidemics. Particularly, the underestimation can be
higher in resource-poor settings because of limited testing and health care facilities. The CFR
is also sensitive to age-sex specics, co-morbidity conditions, and health care condition.14 A
high burden of deaths among the older adults and elderly can be associated with certain pre-
existing conditions including diabetes and cardiovascular diseases.32,33 Lifestyle risk factors
are also an important aspect and also leading to early onset of non-communicable diseases
in India.34 Men are also more likely to have multimorbidity conditions with early onset of
such diseases.9 Also, resource-poor settings with inadequate facilities for clinical care and
intervention can further elevate the risks of infection and mortality.35
Since CFR does not capture the risk of mortality among currently active infections therefore,
we also suggest the indicator CFR* that aims to mirror the actual CFR that is known only
when the epidemic ends. In fact, it would be useful to briey compare the CFR* estimate with
countries where the COVID-19 epidemic has more or less subsided or the curve has attened.
We use CFR* of India and compare this with CFR of China, South Korea and Thailand
mainly because these countries now witness very small number of daily cases.36 Under such
situation, CFR* converges with CFR because the share of active infection to total cases is very
low. China, South Korea and Thailand display CFR of 5.5%, 2.4%, and 1.9%, respectively.
Whereas, it is worth noting that India's CFR* is higher than South Korea and Thailand and it
is, more or less, likely to settle near the Chinese experience.
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Alternative approaches for CFR estimation are also available.22 For instance, 2 alternative
CFRs can be dened as follows: CFR1 = Dt/Ct-15 and CFR2 = Dt/(Dt + Rt); where t, C, D and
R refer to time point (days), cases, deaths and recoveries, respectively37,38. Based on these
methods, an all-India CFR of about 13%–15% is estimated during rst half of April 2020.37,38
But both methods may not provide reasonable estimates because the numerator Dt does not
capture all potential future deaths (leading to underestimation) whereas the denominators
are restricted to cases with 2–3 weeks lag (leading to overestimation). Besides, the estimated
CFR1 and CFR2 appear to be much higher than the CFR noted for countries that have attened
the curve or are in penultimate stages of the epidemic. In contrast, the CFR* suggested here
is eective in capturing the mortality risk based on twin parameters that combines both the
proportion of active cases as well as prevailing CFR levels.
Nevertheless, since the CFR does not include all possible infections this limitation is
overcome by estimating the infection fatality rate (IFR) which is dened as the total number
of infection-related deaths divided by the total number of infected cases. But IFR estimation
is also fraught with diculties because true count of cases is seldom known due to
inadequate testing and presence of asymptomatic infections. The infectivity potential of such
asymptomatic cases is also an unknown parameter. Besides, within event period, the IFR will
also encounter similar problems of accounting for deaths and recoveries among currently
active cases. This would also need a robust sur veillance system to keep track of infection
spread for estimating such parameters.
To a large extent, the response to COVID-19 infections depends on the immune system of
the individuals. It is widely acknowledged that individuals with poor nutritional status are
likely to have weak immune system. In fact, among children under-ve with widespread
undernutrition there will be further compromising of the immune system and this can elevate
the risk of mortality if such children are exposed to COVID-19.17,39 A signicant proportion of
women in the age group 15–49 years are undernourished and this also leaves them vulnerable
to an elevated risk of COVID-19 infection and severe outcomes. The COVID-19 pandemic
has also severely disrupted food and health systems worldwide. Recently released Global
Nutrition Report (2020) takes cognizance of these adversities and is particularly concerned
about its disproportionate impact on the poor and the vulnerable populations.40 India is no
exception whereby the COVID-19 outbreak and the ensuing policy response has a devastating
eect on millions. Amidst elevated risks to lives and livelihood, there is also a surge in reports
of hunger and food deprivation in both rural and urban areas of India. Restrictions and
declines in economic activity also imply that children from subsisting households may have
to compromise with both the quantity as well as quality of the dietary intake.41 The disruption
of the health care services is also inimical to nutritional health and well-being of the children
and women.14,42,43
The analysis reveals that the risk of COVID-19 case fatality in India is relatively higher among
females than males. The burden of infections and death is shared equally among under-ve
boys and girls and to some extent among the oldest old men and women. Besides, women
in age group 40–49 years have relatively higher risk of mortality. With increasing trend of
active cases, and increasing urgency to roll back lockdown measures, this analysis oers
two important policy insights. First, the healthcare system should strictly reduce exposure
of elderly and children. In fact, with such high CFR it is critical that all elderly COVID-19
patients should be treated at tertiary care facilities with adequate life-saving support system.
Also, special care should be ensured at institutional facility for elderly as well as nursing
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homes. Second, it is important to improve data capture and reporting as there are huge gaps
in age-sex specic reporting of information. Also, testing information by age-sex groups
should be available to understand the specic trends and patterns in COVID-19 infections.
So far, testing for COVID-19 is mainly being done among at-risk individuals (e.g., those
with inuenza-like symptoms, people who have had contact with an individual testing
positive for COVID-19, healthcare professionals, or those with a travel history to an aected
region). As a consequence, an accurate value for how many individuals are truly infected
is not known. Since at-risk individuals are not representative of the general population, it
is impossible to ascertain the true prevalence of COVID-19 in the population. Establishing
this value is vital to understand the COVID-19 related morbidity and mortality age-sex
specic risk in the population, particularly in India, which cannot absorb the economic
and public health fallout of a national lockdown of uncertain duration. India has increased
its testing intensity, and, to a large extent, this has led to increased detection of COVID-19
infections. At the same time, while COVID-19 deaths have gone up, the CFR appears to be
considerably low given the size and density of India. In fact, given the low CFR among the
younger population, it is also proposed that they may be gradually allowed to return to work
or education subject to certain medical conditions and rules.44 But even when we assume
the death data to be more or less accurate, this implies that the infection fatality risk (i.e.,
the risk of dying) is also lower but, due to the sheer population size, even lower prevalence
translates to large absolute numbers.
Clearly, a full comprehension of the risk prole is very critical to decide upon lockdown
relaxation approach and exibilities in resumption of economic activities for dierent
population age-sex groups. India is yet to witness the peak of the COVID-19 curve. Given
a lengthy recovery period, social distancing or lockdown essentially delays the intensity of
the epidemic outbreak. But an extended lockdown phase can disrupt the food system and
other economic activities to signicantly elevate the risks of mortality among the vulnerable
population groups of children, women and elderly. Evidence to understand the equity aspect
of COVID-19 infection burden and mortality risk is an important area for further research.
SUPPLEMENTARY MATERIALS
Supplementary Table 1
Number of reported COVID-19 cases and deaths with age-sex information, India
Click here to view
Supplementary Table 2
Sex-specic absolute burden of COVID-19 infections and deaths in India
Click here to view
Supplementary Table 3
COVID-19 CFR (in %) for India by age-group, April 10, May 1, May 9, and May 20, 2020
Click here to view
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Supplementary Fig. 1
Sex-dierentials in COVID-19 CFR, India, April 10, 2020.
Click here to view
Supplementary Fig. 2
Sex-dierentials in COVID-19 CFR, India, May 1, 2020.
Click here to view
Supplementary Fig. 3
Sex-dierentials in COVID-19 CFR, India, May 9, 2020.
Click here to view
Supplementary Fig. 4
Sex-dierentials in COVID-19 CFR, India, May 20, 2020.
Click here to view
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... According to several reports, lethality rates are reported as generally high among men compared to women [10,29,30]. However, the current study revealed a higher lethality rate among females, which aligns with Joe et al. [31], who found a similar statistic (3.3% among women vs. 2.9% for men) in his India-based study. This has been chalked up to the lack of access to health services; studies have observed a difference in willingness to be admitted to the hospital across the two genders [32]. ...
... This explanation is in line with previous studies, which state that comorbidities and low immune function in older patients may have been an important factor that contributes to higher mortality rates among COVID-19-infected patients [42]. Similarly, the extant literature states that mortality and lethal rates were generally found to increase with age, which aligns with the findings in this study [10,22,31,43]. Diabetes, obesity, and various cardiovascular illnesses have been linked to the variance in mortality rates [26,34]. ...
... In our study, advanced age was found to be a potential risk factor (p < 0.05) for COVID-19 mortality. Similar results were also reported by numerous authors who showed that older age is commonly associated with higher mortality risk among COVID-19 patients [10,22,31,34,43,63]. Our results indicated that among the deceased patients, 80.2% were of age 60 or above, with an odd ratio of death risk that was sixtimes the ratio for patients below this age range. ...
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Since its initial appearance in December 2019, COVID-19 has posed a serious challenge to healthcare authorities worldwide. The purpose of the current study was to identify the epidemiological context associated with the respiratory illness propagated by the spread of COVID-19 and outline various risk factors related to its evolution in the province of Debila (Southeastern Algeria). A retrospective analysis was carried out for a cohort of 612 COVID-19 patients admitted to hospitals between March 2020 and February 2022. The results were analyzed using descriptive statistics. Further, logistic regression analysis was employed to perform the odds ratio. In gendered comparison, males were found to have a higher rate of incidence and mortality compared to females. In terms of age, individuals with advanced ages of 60 years or over were typically correlated with higher rates of incidence and mortality in comparison toindividuals below this age. Furthermore, the current research indicated that peri-urban areas were less affected that the urban regions, which had relatively significant incidence and mortality rates. The summer season was marked with the highest incidence and mortality rate in comparison with other seasons. Patients who were hospitalized, were the age of 60 or over, or characterized by comorbidity, were mainly associated with death evolution (odds ratio [OR] = 8.695; p = 0.000), (OR = 6.192; p = 0.000), and (OR = 2.538; p = 0.000), respectively. The study identifies an important relationship between the sanitary status of patients, hospitalization, over-age categories, and the case severity of the COVID-19 patient.
... Based on the study, it is evident that the interaction between age and sex has a signi cant effect on whether ICU patients with COVID-19 die over the course of hospital stay. The reported observation corroborates the ndings reported by a study that focused on COVID-19 mortality cases from India, which indicated a non-uniform mortality rate across different age groups (27). The study noted that women with COVID-19 aged between 40-49 years were at a greater risk of death compared to men (27). ...
... The reported observation corroborates the ndings reported by a study that focused on COVID-19 mortality cases from India, which indicated a non-uniform mortality rate across different age groups (27). The study noted that women with COVID-19 aged between 40-49 years were at a greater risk of death compared to men (27). ...
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Introduction Understanding the factors that predict the likelihood of death among intensive care unit (ICU) patients with COVID-19 is important in reducing mortality. This study sought to determine the indicators of mortality risk among ICU patients with COVID-19. Methods A retrospective chart review was conducted among 289 files of patients managed at the ICU of two COVID-19 hospitals in Trinidad and Tobago from March 2021 to December 2021. Included patients belonged to either the ICU survivors cohort (n = 82) or the ICU non-survivors cohort (n = 207). Variables collected included CBC and biochemistry reports, and vital signs upon ICU admission and discharge, comorbidities, age, sex, vaccination status, and mortality status up until discharge from the COVID-19 facility. Data was analysed using logistic regressions and Cox proportional hazards models. Results The median age of ICU non-survivors was higher (57.0, IQR = 19 years) compared to those who survived (51.50, IQR = 21 years). The univariate analysis indicated that vaccination status has a significant effect on whether ICU patients with COVID-19 survive in hospital (p = 0.041). The interaction between age and sex has a significant effect on whether ICU patients with COVID-19 survive in hospital (p = 0.047). However, the vitals and the CBC parameters are not reliable predictors of survival among ICU patients with COVID-19, but chronic kidney disease and sickle cell disease are significant predictors of survival. Conclusion To enhance survival, there is a need a need to pay attention to vaccination status, age and sex of the ICU patients with COVID-19.
... − A human-centered perspective on nature and health by most citizens, including the majority of scientists, contributing to, for example, the extinction and climate crises (Yeung et al., 2021); − Gender discrimination at all levels of society, including decision-making institutions, contributing to, for example, uneven burden of diseases throughout society (Washington et al., 2021). − Cultural discrimination, resulting in the violation of human rights of Indigenous Peoples, ethnic minorities and refugees (Joe et al., 2020). ...
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One Health recognises the interdependence between the health of humans, animals, plants and the environment. With the increasing inclusion of One Health in multiple global health strategies, the One Health workforce must be prepared to protect and sustain the health and well-being of life on the planet. In this paper, a review of past and currently accepted One Health core competencies was conducted, with competence gaps identified. Here, the Network for Ecohealth and One Health (NEOH) propose updated core competencies designed to simplify what can be a complex area, grouping competencies into three main areas of: Skills; Values and Attitudes; and Knowledge and Awareness; with several layers underlying each. These are intentionally applicable to stakeholders from various sectors and across all levels to support capacity-building efforts within the One Health workforce. The updated competencies from NEOH can be used to evaluate and enhance current curricula, create new ones, or inform professional training programs at all levels, including students, university teaching staff, or government officials as well as continual professional development for frontline health practitioners and policy makers. The competencies are aligned with the new definition of One Health developed by the One Health High-Level Expert Panel (OHHLEP), and when supported by subjectspecific expertise, will deliver the transformation needed to prevent and respond to complex global challenges. One Health Impact Statement Within a rapidly changing global environment, the need for practitioners competent in integrated approaches to health has increased substantially. Narrow approaches may not only limit opportunities for global and local solutions but, initiatives that do not consider other disciplines or social, economic and cultural contexts, may result in unforeseen and detrimental consequences. In keeping with principles of One Health, the Network for Ecohealth and One Health (NEOH) competencies entail a collaborative effort between multiple disciplines and sectors. They focus on enabling practitioners, from any background, at any level or scale of involvement, to promote and support a transformation to integrated health approaches. The updated competencies can be layered with existing disciplinary competencies and used to evaluate and enhance current education curricula, create new ones, or inform professional training programs at all levels-including for students, teachers and government officials as well as continual professional development for frontline health practitioners and policymakers. The competencies outlined here are applicable to all professionals and disciplines who may contribute to One Health, and are complimentary to, not a replacement for, any discipline-specific competencies. We believe the NEOH competencies meet the need outlined by the Quadripartite’s (Food and Agriculture Organisation, United Nations Environment Programme, World Health Organisation, World Organisation for Animal Health) Joint Plan of Action on One Health which calls for cross-sectoral competencies.
... Several common factors associated with COVID-19 severity have been established across many populations, while some were shown to be population-specific [3,4]. As an example, the male to female fatality ratio in many populations was estimated at 3.5 [5][6][7], except in India where COVID-19 fatality rate was shown to be higher in women than men (3.3% versus 2.9%) [8,9]. ...
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Background: The COVID-19 pandemic claimed millions of lives worldwide without clear signs of abating despite several mitigation efforts and vaccination campaigns. There have been tremendous interests in understanding the etiology of the disease particularly in what makes it severe and fatal in certain patients. Studies have shown that COVID-19 patients with kidney injury on admission were more likely to develop severe disease, and acute kidney disease was associated with high mortality in COVID-19 hospitalized patients. Methods: This study investigated 819 COVID-19 patients admitted between January 2020-April 2021 to the COVID-19 ward at a tertiary care center in Lebanon and evaluated their vital signs and biomarkers while probing for two main outcomes: intubation and fatality. Logistic and Cox regressions were performed to investigate the association between clinical and metabolic variables and disease outcomes, mainly intubation and mortality. Times were defined in terms of admission and discharge/fatality for COVID-19, with no other exclusions. Results: Regression analysis revealed that the following are independent risk factors for both intubation and fatality respectively: diabetes (p = 0.021 and p = 0.04), being overweight (p = 0.021 and p = 0.072), chronic kidney disease (p = 0.045 and p = 0.001), and gender (p = 0.016 and p = 0.114). Further, shortness of breath (p<0.001), age (p<0.001) and being overweight (p = 0.014) associated with intubation, while fatality with shortness of breath (p<0.001) in our group of patients. Elevated level of serum creatinine was the highest factor associated with fatality (p = 0.002), while both white blood count (p<0.001) and serum glutamic-oxaloacetic transaminase levels (p<0.001) emerged as independent risk factors for intubation. Conclusions: Collectively our data show that high creatinine levels were significantly associated with fatality in our COVID-19 study patients, underscoring the importance of kidney function as a main modulator of SARS-CoV-2 morbidity and favor a careful and proactive management of patients with elevated creatinine levels on admission.
... Those with comorbidity and with female gender also had a higher seroprevalence of the three antibodies as compared to their counterparts; same factors as stated in the elderly age can explain the scenario. Regarding females in India, studies have suggested higher mortality though fewer infections among them as compared to males (34), but across the globe, females have been found to experience less mortality (35), and in Kashmir, the resemblance was to the latter case. Fewer infections among them can be because of more asymptomatic or milder infections, thus not undergoing investigation and diagnosis. ...
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Background Within Kashmir, which is one of the topographically distinct areas in the Himalayan belt of India, a total of 2,236 cumulative deaths occurred by the end of the second wave. We aimed to conduct this population-based study in the age group of 7 years and above to estimate the seropositivity and its attributes in Kashmir valley. Methods We conducted a community-based household-level cross-sectional study, with a multistage, population-stratified, probability-proportionate-to-size, cluster sampling method to select 400 participants from each of the 10 districts of Kashmir. We also selected a quota of healthcare workers, police personnel, and antenatal women from each of the districts. Households were selected from each cluster and all family members with age 7 years or more were invited to participate. Information was collected through a standardized questionnaire and entered into Epicollect 5 software. Trained healthcare personnel were assigned for collecting venous blood samples from each of the participants which were transferred and processed for immunological testing. Testing was done for the presence of SARS-CoV-2-specific anti-spike IgM, IgG antibodies, and anti-nucleocapsid IgG antibodies. Weighted seropositivity was estimated along with the adjustment done for the sensitivity and specificity of the test used. Findings The data were collected from a total of 4,229 participants from the general population within the 10 districts of Kashmir. Our results showed that 84.84% (95% CI 84.51–85.18%) of the participants were seropositive in the weighted imputed data among the general population. In multiple logistic regression, the variables significantly affecting the seroprevalence were the age group 45–59 years (odds ratio of 0.73; 95% CI 0.67–0.78), self-reported history of comorbidity (odds ratio of 1.47; 95% CI 1.33–1.61), and positive vaccination history (odds ratio of 0.85; 95% CI 0.79–0.90) for anti-nucleocapsid IgG antibodies. The entire assessed variables showed a significant role during multiple logistic regression analysis for affecting IgM anti-spike antibodies with an odds ratio of 1.45 (95% CI 1.32–1.57) for age more than 60 years, 1.21 (95% CI 1.15–1.27) for the female gender, 0.87 (95% CI 0.82–0.92) for urban residents, 0.86 (95% CI 0.76–0.92) for self-reported comorbidity, and an odds ratio of 1.16 (95% CI 1.08–1.24) for a positive history of vaccination. The estimated infection fatality ratio was 0.033% (95% CI: 0.034–0.032%) between 22 May and 31 July 2021 against the seropositivity for IgM antibodies. Interpretation During the second wave of the SARS-CoV-2 pandemic, 84.84% (95% CI 84.51–85.18%) of participants from this population-based cross-sectional sample were seropositive against SARS-CoV-2. Despite a comparatively lower number of cases reported and lower vaccination coverage in the region, our study found such high seropositivity across all age groups, which indicates the higher number of subclinical and less severe unnoticed caseload in the community.
... In addition, globally, evidence suggests that COVID-19 results in higher mortality among men in most countries, except for India, Nepal, Vietnam, and Slovenia, where more women die than men. These countries might have recorded higher mortality among women due to the biases in sex identification or more significant risks already threatening women in these countries due to the presence of demographic factors or even events related to local health profiles [39][40][41]. ...
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The male sex, due to the presence of genetic, immunological, hormonal, social, and environmental factors, is associated with higher severity and death in Coronavirus Disease (COVID)-19. We conducted an epidemiological study to characterize the COVID-19 clinical profile, severity, and outcome according to sex in patients with the severe acute respiratory syndrome (SARS) due to the fact of this disease. We carried out an epidemiological analysis using epidemiological data made available by the OpenDataSUS, which stores information about SARS in Brazil. We recorded the features of the patients admitted to the hospital for SARS treatment due to the presence of COVID-19 (in the absence of comorbidities) and associated these characteristics with sex and risk of death. The study comprised 336,463 patients, 213,151 of whom were men. Male patients presented a higher number of clinical signs, for example, fever (OR = 1.424; 95%CI = 1.399–1.448), peripheral arterial oxygen saturation (SpO2) < 95% (OR = 1.253; 95%CI = 1.232–1.274), and dyspnea (OR = 1.146; 95%CI = 1.125–1.166) as well as greater need for admission in intensive care unit (ICU, OR = 1.189; 95%CI = 1.168–1.210), and the use of invasive ventilatory support (OR = 1.306; 95%CI = 1.273–1.339) and noninvasive ventilatory support (OR = 1.238; 95%CI = 1.216–1.260) when compared with female patients. Curiously, the male sex was associated only with a small increase in the risk of death when compared with the female sex (OR = 1.041; 95%CI = 1.023–1.060). We did a secondary analysis to identify the main predictors of death. In that sense, the multivariate analysis enabled the prediction of the risk of death, and the male sex was one of the predictors (OR = 1.101; 95%CI = 1.011–1.199); however, with a small effect size. In addition, other factors also contributed to this prediction and presented a great effect size, they are listed below: older age (61–72 years old (OR = 15.778; 95%CI = 1.865–133.492), 73–85 years old (OR = 31.978; 95%CI = 3.779–270.600), and +85 years old (OR = 68.385; 95%CI = 8.164–589.705)); race (Black (OR = 1.247; 95%CI = 1.016–1.531), Pardos (multiracial background; OR = 1.585; 95%CI = 1.450–1.732), and Indigenous (OR = 3.186; 95%CI = 1.927–5.266)); clinical signs (for instance, dyspnea (OR = 1.231; 95%CI = 1.110–1.365) and SpO2 < 95% (OR = 1.367; 95%CI = 1.238–1.508)); need for admission in the ICU (OR = 3.069; 95%CI = 2.789–3.377); and for ventilatory support (invasive (OR = 10.174; 95%CI = 8.803–11.759) and noninvasive (OR = 1.609; 95%CI = 1.438–1.800)). In conclusion, in Brazil, male patients tend to present the phenotype of higher severity in COVID-19, however, with a small effect on the risk of death.
... Large-scale data from two meta-analysis studies demonstrated that although there are no significant differences in the proportion of individuals infected with the virus, men are much more likely to develop serious illnesses and die compared to women [82,83], except for some countries such as India [84]. According to Bhopal and Bhopal [85], hypotheses based on risk factors that are known to change with both sex and age seem to be the most probable explanations for the observed differences. ...
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The COVID-19 pandemic has had a major impact on a global scale. Understanding the innate and lifestyle-related factors influencing the rate and severity of COVID-19 is important for making evidence-based recommendations. This cross-sectional study aims at establishing a potential relationship between human characteristics and vulnerability/resistance to SARS-CoV-2. We hypothesize that the impact of the virus is not the same due to cultural and ethnic differences. A cross-sectional study was performed using an online questionnaire. The methodology included the development of a multi-language survey, expert evaluation, and data analysis. Data were collected using a 13-item pre-tested questionnaire based on a literature review between 9 December 2020 and 21 July 2021. Data were statistically analyzed using logistic regression. For a total of 1125 respondents, 332 (29.5%) were COVID-19 positive; among them, 130 (11.5%) required home-based treatment, and 14 (1.2%) intensive care. The significant and most influential factors on infection included age, physical activity, and health status (p < 0.05), i.e., better physical activity and better health status significantly reduced the possibility of infection, while older age significantly increased it. The severity of infection was negatively associated with the acceptance (adherence and respect) of preventive measures and positively associated with tobacco (p < 0.05), i.e., smoking regularly significantly increases the severity of COVID-19 infection. This suggests the importance of behavioral factors compared to innate ones. Apparently, individual behavior is mainly responsible for the spread of the virus. Therefore, adopting a healthy lifestyle and scrupulously observing preventive measures, including vaccination, would greatly limit the probability of infection and prevent the development of severe COVID-19.
... Nevertheless, the mortality due to COVID-19 was more probable in females than males in a few countries, such as India, one of the worst-affected countries. As of Sept 30, 2020, India had more than 6.4 million proven COVID-19 pa-tients [5], and the COVID-19 mortality rate for males was 2.9%, while it was 3.3% for females [6]. Both Group 1 and Group 2 patients had comorbid diseases, but comorbid diseases were not signifi-cant between the two groups. ...
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Aim: Herein, we aimed to examine the laboratory parameters of COVID-19 patients and differentiate the parameters that can be used in mortality prediction. Materials and Methods: The study retrospectively examined the patients who needed intensive care unit due to COVID-19 between March 2020 and December 2020. Three hundred and seventy-four patients who met the study criteria were included in the study. Two main groups were formed: patients discharged from the intensive care unit with no mortality and patients with a mortal course. Patients discharged constituted Group- 1, and patients who died constituted Group- 2. Patients were examined regarding demographic features, clinical, and laboratory characteristics. Results: Group- 1 consisted of survived patients (n=148, 39.5%), while Group- 2 patients were the patients with mortal course (n=226, 60.4%). In Group-1, 84 (56.8%) of the patients were male, while in Group-2, 127 (56.2%) were male. In the mortality group, procalcitonin, CRP, BUN, D-dimer, troponin, LDH, lactate, and INR values were significantly higher, albumin value was lower (p< 0.001). PLT and D-dimer were found as independent variables of mortality according to the logistic regression analysis. Conclusion: High procalcitonin and D-dimer values obtained with routinely examined rapid and easily accessible blood tests of COVID-19 patients may contribute to mortality prediction
Chapter
The COVID-19 pandemic has affected the global healthcare system in many countries. India has faced complex multidimensional problems concerning the healthcare system during the COVID-19 outbreak. This article explores some of the implications of COVID-19 on the health system. Also, we attempt to study health economics and other related issues. We have developed the susceptible-exposed-infection-recovered model, logistic growth model, time interrupted regression model, and a stochastic approach for these problems. These models focus on the effect of prevention measures and other interventions for a pandemic on the healthcare system. Our study suggests that the above models are appropriate for COVID-19 at break and effective models for the implications of the pandemic on the healthcare system.
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Background & objectives: Due to shortcomings in death registration and medical certification, the excess death approach is recommended for COVID-19 mortality burden estimation. In this study the data from the civil registration system (CRS) from one district in India was explored for its suitability in the estimation of excess deaths, both directly and indirectly attributable to COVID-19. Methods: All deaths registered on the CRS portal at the selected registrar's office of Faridabad district in Haryana between January 2016 and September 2021 were included. The deaths registered in 2020 and 2021 were compared to previous years (2016-2019), and excess mortality in both years was estimated by gender and age groups as the difference between the registered deaths and historical average month wise during 2016-2019 using three approaches - mean and 95 per cent confidence interval, FORECAST.ETS function in Microsoft Excel and linear regression. To assess the completeness of registration in the district, 150 deaths were sampled from crematoria and graveyards during 2020 and checked for registration in the CRS portal. Agreement in the cause of death (CoD) in CRS with the International Classification of Diseases-10 codes assigned for a subset of 585 deaths after verbal autopsy was calculated. Results: A total of 7017 deaths were registered in 2020, whereas 6792 deaths were registered till 30 September 2021 which represent a 9 and 44 per cent increase, respectively, from the historical average for that period. The highest increase was seen in the age group >60 yr (19% in 2020 and 56% in 2021). All deaths identified in crematoria and graveyards in 2020 had been registered. Observed peaks of all-cause excess deaths corresponded temporally and in magnitude to infection surges in the district. All three approaches gave overlapping estimates of the ratio of excess mortality to reported COVID-19 deaths of 1.8-4 in 2020 and 10.9-13.9 in 2021. There was poor agreement (κ<0.4) between CoD in CRS and that assigned after physician review for most causes, except tuberculosis and injuries. Interpretation & conclusions: CRS data, despite the limitations, appeared to be appropriate for all-cause excess mortality estimation by age and sex but not by cause. There was an increase in death registration in 2020 and 2021 in the district.
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Background The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. Methods In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK–CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation. Findings We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41–4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0. Interpretation We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality. Funding National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK.
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In this letter we discussed how Estrogen can explain the difference in COVID-19 outcome between male and female. Hormone acts on upper and lower respiratory tract protecting from virus.
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Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the coronavirus disease 2019 (COVID-19) pandemic. Because deaths have been concentrated at older ages, we highlight the important role of demography, particularly, how the age structure of a population may help explain differences in fatality rates across countries and how transmission unfolds. We examine the role of age structure in deaths thus far in Italy and South Korea and illustrate how the pandemic could unfold in populations with similar population sizes but different age structures, showing a dramatically higher burden of mortality in countries with older versus younger populations. This powerful interaction of demography and current age-specific mortality for COVID-19 suggests that social distancing and other policies to slow transmission should consider the age composition of local and national contexts as well as intergenerational interactions. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted forecasting of hospitalization and critical care needs.
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: Little is known about surgical practice in the initial phase of coronavirus disease 2019 (COVID-19) global crisis. This is a retrospective case series of 4 surgical patients (cholecystectomy, hernia repair, gastric bypass, and hysterectomy) who developed perioperative complications in the first few weeks of COVID-19 outbreak in Tehran, Iran in the month of February 2020. COVID-19 can complicate the perioperative course with diagnostic challenge and a high potential fatality rate. In locations with widespread infections and limited resources, the risk of elective surgical procedures for index patient and community may outweigh the benefit.
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Background The coronavirus disease 2019 (Covid-19) outbreak is evolving rapidly worldwide. Objective To evaluate the risk of serious adverse outcomes in patients with coronavirus disease 2019 (Covid-19) by stratifying the comorbidity status. Methods We analysed the data from 1590 laboratory-confirmed hospitalised patients 575 hospitals in 31 province/autonomous regions/provincial municipalities across mainland China between December 11 th , 2019 and January 31 st , 2020. We analyse the composite endpoints, which consisted of admission to intensive care unit, or invasive ventilation, or death. The risk of reaching to the composite endpoints was compared according to the presence and number of comorbidities. Results The mean age was 48.9 years. 686 patients (42.7%) were females. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached to the composite endpoints. 399 (25.1%) reported having at least one comorbidity. The most prevalent comorbidity was hypertension (16.9%), followed by diabetes (8.2%). 130 (8.2%) patients reported having two or more comorbidities. After adjusting for age and smoking status, COPD [hazards ratio (HR) 2.681, 95% confidence interval (95%CI) 1.424–5.048], diabetes (HR 1.59, 95%CI 1.03–2.45), hypertension (HR 1.58, 95%CI 1.07–2.32) and malignancy (HR 3.50, 95%CI 1.60–7.64) were risk factors of reaching to the composite endpoints. The HR was 1.79 (95%CI 1.16–2.77) among patients with at least one comorbidity and 2.59 (95%CI 1.61–4.17) among patients with two or more comorbidities. Conclusion Among laboratory-confirmed cases of Covid-19, patients with any comorbidity yielded poorer clinical outcomes than those without. A greater number of comorbidities also correlated with poorer clinical outcomes.
Article
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Alongside investigations into the virology of SARS-CoV-2, understanding the fundamental physiological and immunological processes underlying the clinical manifestations of COVID-19 is vital for the identification and rational design of effective therapies. Here, we provide an overview of the pathophysiology of SARS-CoV-2 infection. We describe the interaction of SARS-CoV-2 with the immune system and the subsequent contribution of dysfunctional immune responses to disease progression. From nascent reports describing SARS-CoV-2, we make inferences on the basis of the parallel pathophysiological and immunological features of the other human coronaviruses targeting the lower respiratory tract — severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). Finally, we highlight the implications of these approaches for potential therapeutic interventions that target viral infection and/or immunoregulation. In the short time since SARS-CoV-2 infections emerged in humans, much has been learned about the immunological processes that underlie the clinical manifestation of COVID-19. Here, the authors provide an overview of the pathophysiology of SARS-CoV-2 infection and discuss potential therapeutic approaches.
Article
A novel coronavirus, SARS-CoV-2, emerged in December 2019, leading within a few months to a global pandemic. COVID-19, the disease caused by this highly contagious virus, can have serious health consequences, though risks of complications are highly age-dependent. Rates of hospitalization and death are less than 0.1% in children, but increase to 10% or more in older people. Moreover, at all ages, men are more likely than women to suffer serious consequences from COVID-19. These patterns are familiar to the geroscience community. The effects of age and sex on mortality rates from COVID-19 mirror the effects of aging on almost all major causes of mortality. These similarities are explored here, and underscore the need to consider the role of basic biological mechanisms of aging on potential treatment and outcomes of COVID-19.
Article
Importance Coronavirus disease 2019 (COVID-19) has resulted in considerable morbidity and mortality worldwide since December 2019. However, information on cardiac injury in patients affected by COVID-19 is limited. Objective To explore the association between cardiac injury and mortality in patients with COVID-19. Design, Setting, and Participants This cohort study was conducted from January 20, 2020, to February 10, 2020, in a single center at Renmin Hospital of Wuhan University, Wuhan, China; the final date of follow-up was February 15, 2020. All consecutive inpatients with laboratory-confirmed COVID-19 were included in this study. Main Outcomes and Measures Clinical laboratory, radiological, and treatment data were collected and analyzed. Outcomes of patients with and without cardiac injury were compared. The association between cardiac injury and mortality was analyzed. Results A total of 416 hospitalized patients with COVID-19 were included in the final analysis; the median age was 64 years (range, 21-95 years), and 211 (50.7%) were female. Common symptoms included fever (334 patients [80.3%]), cough (144 [34.6%]), and shortness of breath (117 [28.1%]). A total of 82 patients (19.7%) had cardiac injury, and compared with patients without cardiac injury, these patients were older (median [range] age, 74 [34-95] vs 60 [21-90] years; P < .001); had more comorbidities (eg, hypertension in 49 of 82 [59.8%] vs 78 of 334 [23.4%]; P < .001); had higher leukocyte counts (median [interquartile range (IQR)], 9400 [6900-13 800] vs 5500 [4200-7400] cells/μL) and levels of C-reactive protein (median [IQR], 10.2 [6.4-17.0] vs 3.7 [1.0-7.3] mg/dL), procalcitonin (median [IQR], 0.27 [0.10-1.22] vs 0.06 [0.03-0.10] ng/mL), creatinine kinase–myocardial band (median [IQR], 3.2 [1.8-6.2] vs 0.9 [0.6-1.3] ng/mL), myohemoglobin (median [IQR], 128 [68-305] vs 39 [27-65] μg/L), high-sensitivity troponin I (median [IQR], 0.19 [0.08-1.12] vs <0.006 [<0.006-0.009] μg/L), N-terminal pro-B-type natriuretic peptide (median [IQR], 1689 [698-3327] vs 139 [51-335] pg/mL), aspartate aminotransferase (median [IQR], 40 [27-60] vs 29 [21-40] U/L), and creatinine (median [IQR], 1.15 [0.72-1.92] vs 0.64 [0.54-0.78] mg/dL); and had a higher proportion of multiple mottling and ground-glass opacity in radiographic findings (53 of 82 patients [64.6%] vs 15 of 334 patients [4.5%]). Greater proportions of patients with cardiac injury required noninvasive mechanical ventilation (38 of 82 [46.3%] vs 13 of 334 [3.9%]; P < .001) or invasive mechanical ventilation (18 of 82 [22.0%] vs 14 of 334 [4.2%]; P < .001) than those without cardiac injury. Complications were more common in patients with cardiac injury than those without cardiac injury and included acute respiratory distress syndrome (48 of 82 [58.5%] vs 49 of 334 [14.7%]; P < .001), acute kidney injury (7 of 82 [8.5%] vs 1 of 334 [0.3%]; P < .001), electrolyte disturbances (13 of 82 [15.9%] vs 17 of 334 [5.1%]; P = .003), hypoproteinemia (11 of 82 [13.4%] vs 16 of 334 [4.8%]; P = .01), and coagulation disorders (6 of 82 [7.3%] vs 6 of 334 [1.8%]; P = .02). Patients with cardiac injury had higher mortality than those without cardiac injury (42 of 82 [51.2%] vs 15 of 334 [4.5%]; P < .001). In a Cox regression model, patients with vs those without cardiac injury were at a higher risk of death, both during the time from symptom onset (hazard ratio, 4.26 [95% CI, 1.92-9.49]) and from admission to end point (hazard ratio, 3.41 [95% CI, 1.62-7.16]). Conclusions and Relevance Cardiac injury is a common condition among hospitalized patients with COVID-19 in Wuhan, China, and it is associated with higher risk of in-hospital mortality.