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 Table of Contents  
Year : 2021  |  Volume : 65  |  Issue : 1  |  Page : 64-66  

Prediction of mortality by age and multi-morbidities among confirmed COVID-19 patients: Secondary analysis of surveillance data in Pune, Maharashtra, India

1 Junior Resident, Department of Community Medicine, Armed Forces Medical College, Pune, Maharashtra, India
2 Civil Surgeon, Public Health Department, Symbiosis International (Deemed University) (SIU) Lavale, Pune, Maharashtra, India
3 Program Officer, Symbiosis Community Outreach Program and Extension, (SCOPE), Faculty of Health Sciences, Symbiosis International (Deemed University) (SIU) Lavale, Pune, Maharashtra, India
4 Associate Professor, Department of Community Medicine, Armed Forces Medical College, Pune, Maharashtra, India

Date of Submission25-Aug-2020
Date of Decision13-Sep-2020
Date of Acceptance06-Dec-2020
Date of Web Publication20-Mar-2021

Correspondence Address:
Arun Kumar Yadav
Department of Community Medicine, Armed Forces Medical College, Wanowrie, Pune - 411 040, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijph.IJPH_1096_20

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Maharashtra has reported the maximum number of COVID-19 cases in India. This study was conducted to describe the predictors of death among the confirmed cases of COVID-19 by carrying out a secondary analysis of surveillance data of 11,278 lab-confirmed COVID-19 cases and admitted in dedicated COVID hospitals and dedicated COVID health-care centers between April 4, 2020, and July 17, 2020, in Pune district of Maharashtra. A total of 1270 (11.2%, 95% confidence interval [CI]: 10.7–11.9) deaths out of 11,278 patients were reported. Out of the 1270 deaths, 825 (65%) were male and 788 (62%) had one or more comorbidities. Logistic regression was done for predictors of death, and males (adjusted odds ratio: 1.6, 95% CI: 1.4–1.8), those with symptoms at the time of admission (adjusted odds ratio: 2.9, 95% CI: 2.5–3.4), and those with the presence of two or more comorbidities (adjusted odds ratio: 2.7, 95% CI: 2.2–3.4) were having a higher risk of death.

Keywords: Comorbidities, coronavirus disease, mortality, secondary analysis, surveillance

How to cite this article:
Kansara N, Nandapurkar AB, Maniyar R, Yadav AK. Prediction of mortality by age and multi-morbidities among confirmed COVID-19 patients: Secondary analysis of surveillance data in Pune, Maharashtra, India. Indian J Public Health 2021;65:64-6

How to cite this URL:
Kansara N, Nandapurkar AB, Maniyar R, Yadav AK. Prediction of mortality by age and multi-morbidities among confirmed COVID-19 patients: Secondary analysis of surveillance data in Pune, Maharashtra, India. Indian J Public Health [serial online] 2021 [cited 2022 Jan 18];65:64-6. Available from:

The World Health Organization declared coronavirus disease 2019 (COVID-19) a pandemic on March 11, 2020. COVID-19 is caused by a novel coronavirus, now named severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2).[1] As of August 6, 2020, COVID-19 has caused over 18.6 million confirmed cases and 702,642 reported deaths with a case fatality rate of 3.77% globally.[2] The case fatality rate varies from country to country.

Increasing age and the associated comorbidities such as diabetes, hypertension, cardiovascular disease, and chronic lung disease are known to be associated with unsatisfactory clinical outcome. Specific laboratory abnormalities such as elevated D-dimer, ferritin, and troponin have also been associated with poor outcomes.[3],[4]

Predictors of poor clinical outcomes have been studied in patients of COVID-19 across the world. However, there is limited published literature from India. The present study was conducted to identify the predictors of mortality among hospitalized COVID-19 cases in a state of India.

Maharashtra has reported the maximum number of cases in India. The epidemic is presently concentrated in three cities of Maharashtra, namely Pune, Thane, and Mumbai. Pune has 44 dedicated COVID hospitals (DCHs) with intensive care unit (ICU) facilities and 91 dedicated COVID health-care centers without ICU facilities. In addition, 91 COVID care centers function as sample collection centers and isolation facilities for asymptomatic and mild cases. The data of all confirmed COVID cases were entered into a centralized database. Secondary data analysis was done on 11,278 cases admitted from April 4, 2020, to July 17, 2020. The data on the demographic and clinical details and the outcome (discharge/death) of all confirmed cases were collected.

The outcome (Dead or recovered) of COVID-19 infection was the dependent variable, and the other variables were the predictor variable. Age was recorded in completed years. Patients were divided into two categories: symptomatic (cases with one or more symptoms) and asymptomatic at the time of admission (patients with no symptoms). Symptoms recorded were fever, sore throat, cough, common cold, breathlessness, diarrhea, vomiting, pain abdomen, myalgia, chest pain, etc. In others, symptoms recorded were chills, loss of appetite, dizziness, giddiness, pyuria, weakness, and hemoptysis. The information about the comorbidities (hypertension, diabetes, cancer, kidney disease, alcoholic liver disease, heart disease, asthma, thyroid disease, and obesity) was extracted. For analysis, they were grouped into three categories as follows: no comorbidities, one comorbidity, and two or more comorbidities. Preapproval for performing secondary analysis of the data received from the civil surgeon's office was taken from the Pune Municipal Corporation's competent health authority. The institute ethics committee gave a waiver for secondary analysis.

Out of 11,278 cases, data were finally analyzed for 11,263 (99.9%) cases. For 15 cases, requisite data were not available. There were 1270 deaths (adjusted odds ratio: 11.2%, 95% CI: 10.7–11.9). The characteristics of COVID cases as per outcome (death or alive) and logistic regression findings are summarized in [Table 1].
Table 1: Characteristics of COVID-19 cases as per the outcome and prediction of mortality among COVID-19 cases using logistic regression

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The results showed that male gender has a 1.6 times higher risk of mortality than females even after adjusting for age, symptoms, and comorbidities. The presence of comorbidities has a higher incremental risk of death. COVID case with any symptoms has higher (2.9 times) risk of mortality than COVID case with no symptoms even after adjustment for age, sex, and comorbidities.

The case fatality rate due to COVID-19 in India is 1.7% and in the Pune district is 1.8%. The mortality rate in hospitalized patients reported previously varies from 4% to 28%.[5] The Higher rates of mortality was found(11.2%) in this study as data was received from DCHs, that treat only moderate to severe cases. The differential reporting by the various hospitals could also contribute to this high mortality rate in our study.

Gender is an important, albeit neglected, risk factor and a health determinant of COVID-19. Certain studies have reported severe clinical outcomes and also higher rates of death among males. A meta-analysis found males to have significantly more adverse clinical consequences, including mortality due to COVID-19.[6] In this study too, male gender was associated with an increased risk of mortality from COVID-19 after adjusting for age and comorbidities. There are animal as well as human studies that have confirmed that angiotensin-converting enzyme-2 activity may be driven by estradiol. These findings have direct implications for the observed sex differences in COVID-19 outcomes.[7] Hence, early aggressive management may be warranted among males. Males with comorbidities and symptoms of COVID-19 should be encouraged for early reporting to the health-care system.

The China's Center for Disease Control and Prevention's report of 44,000 laboratory-confirmed COVID-19 cases found an association of older age, cardiovascular disease, diabetes, chronic respiratory disease, hypertension, and cancer with an increased risk of death. Older age has been consistently associated with COVID-19 mortality. A prospective study on 179 patients in Wuhan Pulmonary Hospital found an increased risk of 3.76 (95% confidence interval [CI]: 1.14–17.39) times in persons older than >65 years of age.[8] A systematic review and meta-analysis published by Tian et al. reported male sex and multi-morbidities as significant predictors of mortality.

Tian et al. did a systematic review and meta-analysis to find the predictors of mortality in hospitalized COVID-19 patients from 14 different studies with outcomes of 4659 patients. They concluded that comorbidities such as hypertension, coronary heart diseases, and diabetes were associated with a significantly higher risk of death among patients with COVID-19. No association was found between mortality and the presence of symptoms in the systematic review.[9] However, the association of symptoms at the time of disease onset with mortality was observed in the present study. The association of symptoms and mortality may be further explored as it may be important for the triage of the patient and early management to reduce the mortality.

The study has the limitation of secondary data analysis collected for surveillance of the disease. Hence, the few data variables such as duration of hospital stay were incomplete and could not be analyzed. However, secondary data analysis is cost-effective and may have less information bias. Another limitation of the study is that data about the laboratory or biochemistry parameters were not available, hence not analyzed. The classification of severity of disease at the time of admission was not available, resulting in a confounder. The data may not be generalizable due to differing testing and admission policies worldwide and within countries.

The present study done with a large data set has shown that older age, male sex, and comorbidities are important predictors of COVID-19 mortality in our population. The presence of symptoms at the time of admission is also a significant risk factor for mortality. The study may help identify and direct the resources to high-risk cases to reduce overall mortality in COVID-19 cases.

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Conflicts of interest

There are no conflicts of interest.

   References Top

Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and corona virus disease-2019 (COVID-19): The epidemic and the challenges. International Journal of Antimicrobial Agents 2020 Feb 17:105924.  Back to cited text no. 1
Coronavirus Disease (COVID-19) Situation Reports. Available from: [Last accessed on 2020 Aug 07].  Back to cited text no. 2
Jamil S, Mark N, Carlos G, Cruz CS, Gross JE, Pasnick S. Diagnosis and management of COVID-19 disease. Am J Respir Crit Care Med 2020;201:19-20.  Back to cited text no. 3
Struyf T, Deeks JJ, Dinnes J, Takwoingi Y, Davenport C, Leeflang MM, et al. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 disease. Cochrane Database Syst Rev 2020;7:CD013665. Available from: [Last accessed on 2020 Jul 27].  Back to cited text no. 4
Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. The Lancet 2020;395(10223):507-13.  Back to cited text no. 5
Galbadage T, Peterson BM, Awada J, Buck AS, Ramirez DA, Wilson J, et al. Systematic review and meta-analysis of sex-specific COVID-19 clinical outcomes. Front Med 2020;7:348. Available from: [Last accessed on 2020 Jul 27].  Back to cited text no. 6
Wenham C, Smith J, Morgan R. COVID-19: The gendered impacts of the outbreak. Lancet Publ Group 2020;395:846-8. Available from: [Last accessed on 2020 Sep 14].  Back to cited text no. 7
Du RH, Liang LR, Yang CQ, Wang W, Cao TZ, et al. Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: A prospective cohort study. European Respiratory Journal 2020;55(5).  Back to cited text no. 8
Tian W, Jiang W, Yao J, Nicholson CJ, Li RH, Sigurslid HH, et al. Predictors of mortality in hospitalized COVID-19 patients: A systematic review and meta-analysis. Journal of Medical Virology 2020.  Back to cited text no. 9


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