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Survival Analysis of Patients With COVID-19 in India by Demographic Factors: Quantitative Study
BACKGROUND: Studies of the transmission dynamics of COVID-19 have depicted the rate, patterns, and predictions of cases of this pandemic disease. To combat transmission of the disease in India, the government declared a lockdown on March 25, 2020. Even after this strict lockdown was enacted nationwi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104005/ https://www.ncbi.nlm.nih.gov/pubmed/33882017 http://dx.doi.org/10.2196/23251 |
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author | Kundu, Sampurna Chauhan, Kirti Mandal, Debarghya |
author_facet | Kundu, Sampurna Chauhan, Kirti Mandal, Debarghya |
author_sort | Kundu, Sampurna |
collection | PubMed |
description | BACKGROUND: Studies of the transmission dynamics of COVID-19 have depicted the rate, patterns, and predictions of cases of this pandemic disease. To combat transmission of the disease in India, the government declared a lockdown on March 25, 2020. Even after this strict lockdown was enacted nationwide, the number of COVID-19 cases increased and surpassed 450,000. A positive point to note is that the number of recovered cases began to slowly exceed that of active cases. The survival of patients, taking death as the event that varies by age group and sex, is noteworthy. OBJECTIVE: The aim of this study was to conduct a survival analysis to establish the variability in survivorship of patients with COVID-19 in India by age group and sex at different levels, that is, the national, state, and district levels. METHODS: The study period was taken from the date of the first reported case of COVID-19 in India, which was January 30, 2020, up to June 30, 2020. Due to the amount of underreported data and removal of missing columns, a total sample of 26,815 patients was considered. Kaplan-Meier survival estimation, the Cox proportional hazard model, and the multilevel survival model were used to perform the survival analysis. RESULTS: The Kaplan-Meier survival function showed that the probability of survival of patients with COVID-19 declined during the study period of 5 months, which was supplemented by the log rank test (P<.001) and Wilcoxon test (P<.001) to compare the survival functions. Significant variability was observed in the age groups, as evident from all the survival estimates; with increasing age, the risk of dying of COVID-19 increased. The Cox proportional hazard model reiterated that male patients with COVID-19 had a 1.14 times higher risk of dying than female patients (hazard ratio 1.14; SE 0.11; 95% CI 0.93-1.38). Western and Central India showed decreasing survival rates in the framed time period, while Eastern, North Eastern, and Southern India showed slightly better results in terms of survival. CONCLUSIONS: This study depicts a grave scenario of decreasing survival rates in various regions of India and shows variability in these rates by age and sex. In essence, we can safely conclude that the critical appraisal of the survival rate and thorough analysis of patient data in this study equipped us to identify risk groups and perform comparative studies of various segments in India. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2020.08.01.20162115 |
format | Online Article Text |
id | pubmed-8104005 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-81040052021-05-12 Survival Analysis of Patients With COVID-19 in India by Demographic Factors: Quantitative Study Kundu, Sampurna Chauhan, Kirti Mandal, Debarghya JMIR Form Res Original Paper BACKGROUND: Studies of the transmission dynamics of COVID-19 have depicted the rate, patterns, and predictions of cases of this pandemic disease. To combat transmission of the disease in India, the government declared a lockdown on March 25, 2020. Even after this strict lockdown was enacted nationwide, the number of COVID-19 cases increased and surpassed 450,000. A positive point to note is that the number of recovered cases began to slowly exceed that of active cases. The survival of patients, taking death as the event that varies by age group and sex, is noteworthy. OBJECTIVE: The aim of this study was to conduct a survival analysis to establish the variability in survivorship of patients with COVID-19 in India by age group and sex at different levels, that is, the national, state, and district levels. METHODS: The study period was taken from the date of the first reported case of COVID-19 in India, which was January 30, 2020, up to June 30, 2020. Due to the amount of underreported data and removal of missing columns, a total sample of 26,815 patients was considered. Kaplan-Meier survival estimation, the Cox proportional hazard model, and the multilevel survival model were used to perform the survival analysis. RESULTS: The Kaplan-Meier survival function showed that the probability of survival of patients with COVID-19 declined during the study period of 5 months, which was supplemented by the log rank test (P<.001) and Wilcoxon test (P<.001) to compare the survival functions. Significant variability was observed in the age groups, as evident from all the survival estimates; with increasing age, the risk of dying of COVID-19 increased. The Cox proportional hazard model reiterated that male patients with COVID-19 had a 1.14 times higher risk of dying than female patients (hazard ratio 1.14; SE 0.11; 95% CI 0.93-1.38). Western and Central India showed decreasing survival rates in the framed time period, while Eastern, North Eastern, and Southern India showed slightly better results in terms of survival. CONCLUSIONS: This study depicts a grave scenario of decreasing survival rates in various regions of India and shows variability in these rates by age and sex. In essence, we can safely conclude that the critical appraisal of the survival rate and thorough analysis of patient data in this study equipped us to identify risk groups and perform comparative studies of various segments in India. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2020.08.01.20162115 JMIR Publications 2021-05-06 /pmc/articles/PMC8104005/ /pubmed/33882017 http://dx.doi.org/10.2196/23251 Text en ©Sampurna Kundu, Kirti Chauhan, Debarghya Mandal. Originally published in JMIR Formative Research (https://formative.jmir.org), 06.05.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Kundu, Sampurna Chauhan, Kirti Mandal, Debarghya Survival Analysis of Patients With COVID-19 in India by Demographic Factors: Quantitative Study |
title | Survival Analysis of Patients With COVID-19 in India by Demographic Factors: Quantitative Study |
title_full | Survival Analysis of Patients With COVID-19 in India by Demographic Factors: Quantitative Study |
title_fullStr | Survival Analysis of Patients With COVID-19 in India by Demographic Factors: Quantitative Study |
title_full_unstemmed | Survival Analysis of Patients With COVID-19 in India by Demographic Factors: Quantitative Study |
title_short | Survival Analysis of Patients With COVID-19 in India by Demographic Factors: Quantitative Study |
title_sort | survival analysis of patients with covid-19 in india by demographic factors: quantitative study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104005/ https://www.ncbi.nlm.nih.gov/pubmed/33882017 http://dx.doi.org/10.2196/23251 |
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