Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 
Print this page Email this page Users Online: 181

 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 11  |  Issue : 3  |  Page : 208-214

The effect of age on COVID-19 patient's outcome


1 Department of Physiology, Gayatri Vidya Parishad Institute of Health Care and Medical Technology, Marikavalasa, Visakhapatnam, Andhra Pradesh, India
2 Department of Obstetrics & Gynaecology, Krishna Hospital, Maharani Peta, Visakhapatnam, Andhra Pradesh, India
3 Department of Community Medicine, Gayatri Vidya Parishad Institute of Health Care and Medical Technology, Marikavalasa, Visakhapatnam, Andhra Pradesh, India

Date of Submission01-Dec-2021
Date of Acceptance29-Dec-2021
Date of Web Publication26-Dec-2022

Correspondence Address:
Dr. Venkata Suresh Babu Adapa
Professor in Physiology, Gayatri Vidya Parishad Institute of Health Care and Medical Technology, Marikavalasa, Visakhapatnam - 530 048, Andhra Pradesh
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jdrntruhs.jdrntruhs_156_21

Rights and Permissions
  Abstract 


Background: The first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified in Wuhan, China, in late 2019. In India, the first case was reported on 30.01.2020, and first COVID-19 death occurred on 10.03.2020. The case fatality rate (CFR) was 3.4% estimate by the World Health Organization (WHO) as of 03.03.2020. All age groups had significantly higher mortality compared with the immediately younger age group. The largest increase in mortality risk was observed in patients aged 60 to 69 years compared with those aged 50 to 59 years.
Objective: The aim of this study is to determine the effect of age on COVID-19 patient's outcome.
Methods: All the secondary data were collected either from the Indian institute statistics, Bangalore website, or COVID-19 india website, or GitHub website, or Indian government websites. The effect of age on COVID-19 patient's outcome was determined.
Results: Age at first quartile was 50 years in the deceased group, whereas in recovered, it was 25 years. The median age in the deceased group and recovered group were 59 and 34 years, respectively. Significant difference was observed in age between the deceased and recovered group. Age was showing a medium effect size (0.574) in the outcome of the COVID-19. The cut-off value of age for estimating risk of death was established by using Receiver Operating Characteristic (ROC) analysis. The cut-off value was 48 years. The sensitivity was 77.5% and the specificity was 78.8%. More than 48 years age group had a 13 times higher risk than the less than 48 years age group. Area under the curve was 0.855 (95% CI: 0.846–0.864).
Conclusions: This study suggests that the strong association between the age and outcome of COVID-19 patients. We can predict the outcome of COVID-19 patient based on their age. The outcome of COVID-19 patient prediction may give better results with associated comorbid conditions. The cut-off value of age for outcome was 48 years.

Keywords: Age, COVID-19, outcome


How to cite this article:
Adapa VS, Adapa SS, Narni H. The effect of age on COVID-19 patient's outcome. J NTR Univ Health Sci 2022;11:208-14

How to cite this URL:
Adapa VS, Adapa SS, Narni H. The effect of age on COVID-19 patient's outcome. J NTR Univ Health Sci [serial online] 2022 [cited 2023 Jan 29];11:208-14. Available from: https://www.jdrntruhs.org/text.asp?2022/11/3/208/365014




  Introduction Top


Coronaviruses (CoVs) are a large group of enveloped, positive-sense, single-stranded, highly diverse RNA viruses infecting mammals and birds and producing a wide variety of diseases.[1],[2],[3]

The first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified in Wuhan, China, in late 2019, the epicenter of the COVID-19 outbreak. COVID-19 (coronavirus disease 2019) is a respiratory tract infection due to a novel coronavirus, SARS-CoV-2 [initially called 2019-nCOV].[4] International Committee on Taxonomy of Viruses (ICTV) announced “severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)” as the name of the new virus on 11 February 2020.[5] WHO announced “COVID-19” as the name of this new disease on 11 February 2020.[6] The virus quickly spread across China and the globe, until the World Health Organization (WHO) declared COVID-19 a pandemic on 11 March 2020.[6]

The WHO announced that Wuhan's CFR, China, considered the epicenter of the outbreak, is between 2% and 4%.[3] In China, as of 20 February 2020, the CFR was 3.8% nationwide, 5.8% in Wuhan, and 0.7% other areas.[7] The CFR was 3.4% estimate by the WHO as of 03.03.2020.[8] The mortality rate among diagnosed cases (CFR) is generally about 2% to 3% but varies by country; the correct overall mortality rate is uncertain. The total number of cases, including undiagnosed persons with milder illness, is unknown.[9] Knowledge of this disease is incomplete and evolving; moreover, coronaviruses are known to mutate and often recombine, presenting an ongoing challenge to our understanding and clinical management.

The virus is transmitted mainly through respiratory droplets, or through contaminated surfaces and objects. In general, case fatality rate and recovery rate of infectious disease will be influenced by Host factors like Age, Gender, Nutritional Status, and Immunity Status of the individual, associated comorbid conditions like Hypertension, Diabetes Mellitus, Smoking, and Malignancy. Pathogen factors like virulence of infectious agent, infectious agent load, and the management including early diagnosis and early treatment also influence the case fatality.[10]

When it comes to COVID-19, studies have shown that advancing age and the presence of underlying conditions like diabetes, hypertension, and cardiovascular disease can lead to more severe illness. Other factors include age, gender, Bacille Calmette-Guérin (BCG) vaccination, Measles, Mumps, and Rubella (MMR) vaccination, smoking, and malaria prevalence.[10],[11],[12],[13],[14]

In the first week of April 2020, young Indians are at higher risk of contracting the disease as about 60% of the patients are under the age of 50. Data show that 391 confirmed cases, or 22%, are between the ages of 30 and 39, followed by 376 patients (21%) who are in their 20s, while 17% are in their 40s. Early reports from other countries, such as China and Italy, have shown that the most vulnerable category is that of older people. However, in India, patients over the age of 60 are only 19% of the total confirmed cases, while people aged 80 and older account for less than 2% of the total confirmed cases of COVID-19. During early days of the pandemic <10 years and >60 years were considered as high-risk age group for mortality as the immunity is less in these group. Looking at global trends, the elderly population that is over the age of 50 and people with comorbidities are at severe risk of contracting the disease. However, everyone is vulnerable to the virus.[15]

The first COVID-19 case was reported in India on 30.01.2020 in Kerala State, an imported one. The first local COVID-19 case was reported on 04.03.2020 in Uttar Pradesh State; the source of infection was in contact with the confirmed imported case, belong to their family.[16],[17],[18]

The first COVID-19 case of death in India occurred on 10.03.2020. However, it was reported on late 12.03.2020 and updated on 13.03.2020 in Karnataka State, an imported one. The first local COVID-19 case death was reported on 12.03.2020 in Delhi, source of infection was contact with son, who was an imported confirmed case.[16],[17],[18],[19]

This study aims to determine the effect of age on COVID-19 patient's outcome.

The objectives of this study were,

  1. To determine the association between age and outcome of COVID-19.
  2. To determine the association between gender and outcome of COVID-19.



  Material and Methods Top


A retrospective study was conducted from March to August 2020. All the secondary data were collected either from the Indian institute statistics, Bangalore website, COVID-19 india website, GitHub website, Indian government websites (MOHFW and Karnataka state), or media quoting the government announcements.

Inclusion criteria

All the patients whose outcome was known were included.

Exclusion criteria

All the patients whose outcome were not known were excluded like still hospitalized and those outcome data missing.

The study period was from 09.03.2020 to 21.07.2020. In this study with the Inclusion criteria and Exclusion criteria out of the 75,462 patients from the Karnataka state, only 26,664 patients were included. A total of 26,664 patients' data were analyzed. All the patients were divided into 9 groups from 0–9 years to 80 years and above based on their age with an interval of 10 years in the each group except 9th group, which includes 80 years and above. All these groups were divided further basing their gender and outcome.

Statistical analysis

Data were entered and analyzed using SPSS V24. Descriptive statistics were represented with percentiles, percentages, mean with SD, or Median with IQR. Kolmogorov–Smirnov test was applied to find normality in age distribution. Age has not followed the normal distribution; hence, non parametric tests were applied. Mann–Whitney U test was applied to find the significance of age in deceased and recovered group. Cohen's d Effect size was calculated to find out the strength of association between age and outcome of COVID-19. Chi-square test and Fisher exact test were applied to determine the association between gender and outcome of COVID-19 in each age group interval. ROC analysis was done. The cut-off value of age was taken at the highest sensitivity and specificity. Area under the curve (AUC) with 95% confidence interval were calculated. The Box plot and ROC curve were drawn. The P < 0.05 was considered as statistical significance.

Ethical Clearance

Institutional ethics committee approved the research proposal on 01-08-2020.


  Results Top


In this study, age wise distribution of the patients was 0–9 years -1464 (5.5%), 10–19 years -2312 (8.7%), 20–29 years -6019 (22.6%), 30–39 years -6191 (23.2%), 40–49 years -4544 (17.0%), 50–59 years -3238 (12.1%), 60–69 years -1969 (7.4%), 70–79 years -705 (2.6%), and 80 years and above -222 (0.8%). The most commonly affected age group was 30–39 years followed by 20–29 years, and 40–49 years. The least affected age group was 80 years and above. In this study's gender wise distribution, 16,841 (63.16%) were male, 9823 (36.84%) were female, and 1 transgender. As transgender was only one patient, it was excluded from the study. The deceased were 1418 (5.3%) and recovered were 25,246 (94.7%). The mean age was 58.43 with SD 14.09 in deceased group. The mean age was 35.30 with SD 16.32 in recovered group [Table 1] and [Graph 3] and [Graph 4].
Table 1: Age, gender, and outcome wise distribution of the patients

Click here to view



The gender wise distribution of the patients in different age groups males were 0–9 years -750 (4.45%), 10–19 years -1292 (7.67%), 20–29 years -3644 (21.64%), 30–39 years -4027 (23.91%), 40–49 years -3146 (18.68%), 50–59 years -2147 (12.75%), 60–69 years -1261 (7.49%), 70–79 years -457 (2.71%), and 80 years and above -117 (0.69%). The females were 0–9 years -714 (7.27%), 10–19 years -1020 (10.38%), 20–29 years -2375 (24.18%), 30–39 years -2164 (22.03%), 40–49 years -1398 (14.23%), 50–59 years -1091 (11.11%), 60–69 years -708 (7.21%), 70–79 years -248 (2.52%), and 80 years and above -105 (1.07%).

Age at first quartile was 50 years in the deceased group whereas in recovered, it was 25 years. The median age in the deceased group and recovered group were 59 and 34 years, respectively. Age at third quartile was 68 years in the deceased group where as in recovered, it was 46 years. At all the percentiles, age in the deceased group was higher than the recovered group. Significant difference was observed in age between the deceased and recovered group. Age was showing a medium effect size (0.574) in the outcome of the COVID-19 [Table 2].
Table 2: Percentiles of age by the outcome of COVID-19

Click here to view


The cut-off value of age for estimating risk of death was established by using ROC analysis. The cut-off value was 48 years. The sensitivity was 77.5% and the specificity was 78.8%. More than 48 years age group had a 13 times higher risk than the less than 48 years age group. Area under the curve was 0.855 (95% CI: 0.846–0.864) [Table 3] and [Graph 1].
TABLE 3: Association of age and outcome of COVID-19

Click here to view



The gender was not significant in the outcome of COVID-19 patients. In the age group 60 to 69 years, gender showed statistically significant in the outcome of the COVID-19 patients and female recovered better than males. The proportion of deaths in males was 21.6% whereas in females, it was 15.8% in this age group. In all other age groups, it was not statistically significant. As whole, the proportion of deaths between males and females was showing no statistical significance and the gender does not influence the outcome of the COVID-19 patients [Table 4].
Table 4: Association of gender and outcome of COVID-19 in different age groups

Click here to view


In females, median age in deceased group and recovered group were 59 and 32 years, respectively, where as in males, median age in deceased group and recovered group were 60 and 35 years, respectively. Both in males and females, higher age people were observed in deceased group when compared with recovered group [Table 5] and [Graph 2] and [Graph 5]. There is no statistical significance (P = 0.65) age difference between male and female population in deceased group.
Table 5: Gender wise percentiles of age by the outcome of COVID-19

Click here to view




  Discussion Top


In this study, the most commonly affected age group in males was 30 to 39 years followed by 20 to 29 years and 40 to 49 years similar to overall age group distribution. The most commonly affected age group in females was 20 to 29 years, followed by 30 to 39 years, and 40 to 49 years on the contrary to males and overall age group distribution. The least affected age group in both the gender was 80 years and above.

In China, the most commonly affected age group was 50 to 59 years and least affected age group was 80 years and above. In Italy, Spain, and United Kingdom, the most commonly affected age group was 80 years and above and least affected age group was 29 years and below. In New York State, the most commonly affected age group was 60 to 69 years and least affected age group was 29 years and below.[20]

In this study, the highest mortality age group was 80 years and above and the lowest mortality age group was 0 to 9 years. In China, Italy, Spain, New York State, and United Kingdom, the highest mortality age group was 80 years and above. In China, the lowest mortality age group was both 29 years and below and 30 to 39 years. In Italy, Spain, New York State, and United Kingdom, the lowest mortality age group was 29 years and below. Our study results were similar to those other countries age-specific mortality. The age-specific mortality pattern was following closely to that of the United Kingdom.[20]

In this study, the males were affected 1.72 times more than females. In China and New York State, males were affected more than females, but not the same proportion as in this study. In Italy, Spain, and United Kingdom, females were affected compared with males.[20]

COVID-19 was associated with a “linear” shaped mortality curve, with high numbers of deaths in the 80 years and above age range. The age has significance in the outcome of COVID-19 patients. The recovery was inversely proportional to the age of the individual and the mortality directly proportional to the age of the individual. We can predict the outcome of COVID-19 patients based on their age. The cut-off value of age for outcome was 48 years. Patients aged >48 years had a higher risk of mortality compared with patients with age ≤48 years. It was 13-fold higher with an odds ratio of 12.82 (95% CI: 11.28–14.58) when they were compared with all patients aged ≤48 years.


  Conclusion Top


This study suggests that the strong association between the age and outcome of COVID-19 patients. The cut-off value of age for estimating death was established by using ROC analysis. The cut-off value was 48 years. The sensitivity was 77.5% and the specificity was 78.8%. More than 48 years age group had a 13 times higher risk than the less than 48 years age group. Area under the curve was 0.855 (95% CI: 0.846–0.864). We can predict the outcome of COVID-19 patient based on their age. The cut-off value of age for outcome was 48 years. There is no association between gender and outcome of COVID-19 patients. Further studies and molecular level research required for better understanding pathophysiology of COVID-19 in different age groups. Development of newer strategies in the prevention and management of COVID-19 will decrease the case fatality rate and morbidity. The results have important implications, such as identifying the high-risk age group and planning specific preventive measures in that age group to decrease the incidence and mortality. This study reemphasizes age as a vital determinant in COVID-19 patients' prognosis.

Limitations

The data regarding the associated comorbidities of the patients were not available. The outcome of COVID-19 patient prediction may give better results with associated comorbid conditions. This study was limited to the patients whose outcome was available.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Weiss SR, Navas-Martin S. Coronavirus pathogenesis and the emerging pathogen severe acute respiratory syndrome Coronavirus. Microbiol Mol Biol Rev 2005;69:635-64.  Back to cited text no. 1
    
2.
Perlman S, McIntosh K. Coronaviruses, including severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In: Bennett JE, Dolin R, Blaser MJ, editors. Mandell Douglas and Bennett's Principles and Practice of Infectious Diseases. 9th ed. Philadelphia, PA: Elsevier; 2019. p. 2072-9.  Back to cited text no. 2
    
3.
Infection Prevention and Control Canada. Information About Coronavirus. https://ipac-canada.org/coronavirus-resources.php. [Last accessed on 2020 Mar 25].  Back to cited text no. 3
    
4.
World Health Organization. Coronavirus Disease 2019 (COVID-19): Situation Report-51. https://www.who.int/docs/default-source/coronaviruse//situation-reports/20200311-sitrep-51-covid-19.pdf. [Last accessed on 2020 Mar 26].  Back to cited text no. 4
    
5.
Gorbalenya AE, Baker SC, Baric RS, de Groot RJ, Drosten C, Gulyaeva AA, et al. The species Severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020;5:536-44.  Back to cited text no. 5
    
6.
World Health Organization. Director-General's remarks at the media briefing on 2019-nCoV on 11 February 2020.https://www.who.int/dg/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020. [Last accessed on 2020 Mar 26].  Back to cited text no. 6
    
7.
World Health Organization. WHO Report of the WHO-China Joint Mission on Coronavirus Disease 28 February, 2020.https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf. [Last accessed on 2020 Mar 30].  Back to cited text no. 7
    
8.
Dutta SS. 11 types of novel coronavirus now but only one driving pandemic, find Indian scientists, The New Indian Express. https://www.newindianexpress.com/lifestyle/health/2020/apr/27/11-types-of-novel-coronavirus-now-but-only-one-driving-pandemic-find-indian-scientists-2136070.html. [Last accessed on 2020 Apr 27].  Back to cited text no. 8
    
9.
Oke J, Heneghan C.Global Covid-19 Case Fatality Rates. https://www.cebm.net/covid-19/global-covid-19-case-fatality-rates/. [Last accessed on 2020 May 20].  Back to cited text no. 9
    
10.
Goh HP, Mahari WI, Ahad NI, Chaw LL, Kifli N, Goh BH, et al.Risk factors affecting COVID-19 case fatality rate: A quantitative analysis of top 50 affected countries. medRxiv. doi: https://doi.org/10.1101/2020.05.20.20108449. [Last accessed on 2020 May 28].  Back to cited text no. 10
    
11.
Spencer R.Some COVID-19 vs. Malaria Numbers: Countries with Malaria have Virtually no Coronavirus Cases Reported. https://www.drroyspencer.com/2020/03/some-covid-19-vs-malaria-numbers-countries-with-malaria-have-virtually-no-coronavirus-cases-reported/. [Last accessed on 2020 Mar 15].  Back to cited text no. 11
    
12.
Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: Retrospective study. BMJ 2020;368:m1295.  Back to cited text no. 12
    
13.
Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical characteristics of Coronavirus disease 2019 in China. N Engl J Med 2020;382:1708-20.  Back to cited text no. 13
    
14.
Miller A, Reandelar MJ, Fasciglione K, Roumenova V, Li Y, Otazu GH. Correlation between universal BCG vaccination policy and reduced morbidity and mortality for COVID-19:An epidemiological study. medRxiv2020. https://www.medrxiv.org/content/10.1101/20200.03.24.20042937v1. [Last accessed on 2020 Mar 10].  Back to cited text no. 14
    
15.
Rai D. Young Indians comprise more than half of confirmed Covid-19 cases', India Today. (https://www.indiatoday.in/diu/story/coronavirus-india-young-patients-age-groups-covid19-1662698-2020-04-03).  Back to cited text no. 15
    
16.
Ministry of Health and Family Welfare Government of India. COVID-19 INDIA Status. https://www.mohfw.gov.in/. [Last accessed on 2020 Aug 2].  Back to cited text no. 16
    
17.
COVID19 INDIA. Coronavirus Outbreak in India. https://www.covid19india.org/. [Last accessed on 2020 Aug 2].  Back to cited text no. 17
    
18.
GitHub. GitHub - covid19india/api: Our Database. https://github.com/covid19india/api. [Last accessed on 2020 Aug 2].  Back to cited text no. 18
    
19.
Athreya S, Gadhiwala N, Mishra A.COVID-19 India-Timeline an understanding across States and Union Territories. https://www.isibang.ac.in/~athreya/incovid19/. [Last accessed on 2020 Aug 29].  Back to cited text no. 19
    
20.
Bonanad C, García-Blas S, Tarazona-Santabalbina F, Sanchis J, Bertomeu-González V, Fácila L. The effect of age on mortality in patients with COVID-19: A meta-analysis with 611,583 subjects. J Am Med Dir Assoc 2020;21:915-8.  Back to cited text no. 20
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Material and Methods
Results
Discussion
Conclusion
References
Article Tables

 Article Access Statistics
    Viewed230    
    Printed22    
    Emailed0    
    PDF Downloaded33    
    Comments [Add]    

Recommend this journal