Volume 17, Issue 2 (Vol 17,No.2, Summer 2021 2021)
irje 2021, 17(2): 184-193 |
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Kargarian-Marvasti S, Hasannezhad M, Abolghasemi J. Effective Factors in the Survival Time of Covid-19 Patients in Three Epidemic Waves: A Prospective Cohort Study. irje 2021; 17 (2) :184-193
URL: http://irje.tums.ac.ir/article-1-6996-en.html
URL: http://irje.tums.ac.ir/article-1-6996-en.html
1- MSc of Epidemiology, MPH in General Epidemiology, Faculty of Health, Iran University of Medical Sciences, Tehran, Iran;
2- Assistant Professor, MD, Department of Infectious Diseases, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
3- Associate Professor, Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
2- Assistant Professor, MD, Department of Infectious Diseases, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
3- Associate Professor, Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
Abstract: (2050 Views)
Background and Objectives: This study aimed to investigate the effective factors in the survival/hazard time of Covid-19 patients in three waves of epidemic.
Methods: All 880 Covid-19 patients were included in this prospective cohort study using the census method. Polymerase chain reaction was used to diagnose Covid-19. The survival status of these patients was followed up for 4 months. The analysis of this study was based on the time of infection in three epidemic waves in IRAN. To analyze the data, the Kaplan-Meier nonparametric approach and Cox proportional hazards regression model were used. Survival distributions were compared in three epidemic waves using the R software (version 3.6.2) (P<0.05).
Results: We diagnosed 880 positive case of Covid-19 using PCR test on 2269 susspected people who had respiratory symptomps. At the end of 1-year follow-up, cumulative incidence (risk) of Covid-19 was estimated 7%. Effective factors in the survival time of patients with Covid-19 based on Cox multivariate regression model were: 1- Age 2- Intensity of infection (Hospitalization) 3- Heart disease 4- Epidemic Wave and 5- Transmission mode of the disease (P <0.05). The Kaplan-Meier approach and log rank test (Mantel-Cox) showed a significant difference in the survival rate in three epidemic waves (P = 0.018).
Conclusion: Elderly patients, especially those with a history of heart disease, are at higher risk of death than other groups. In addition to regular screening, these patients will need active monitoring, especially at the time of hospitalization.
Methods: All 880 Covid-19 patients were included in this prospective cohort study using the census method. Polymerase chain reaction was used to diagnose Covid-19. The survival status of these patients was followed up for 4 months. The analysis of this study was based on the time of infection in three epidemic waves in IRAN. To analyze the data, the Kaplan-Meier nonparametric approach and Cox proportional hazards regression model were used. Survival distributions were compared in three epidemic waves using the R software (version 3.6.2) (P<0.05).
Results: We diagnosed 880 positive case of Covid-19 using PCR test on 2269 susspected people who had respiratory symptomps. At the end of 1-year follow-up, cumulative incidence (risk) of Covid-19 was estimated 7%. Effective factors in the survival time of patients with Covid-19 based on Cox multivariate regression model were: 1- Age 2- Intensity of infection (Hospitalization) 3- Heart disease 4- Epidemic Wave and 5- Transmission mode of the disease (P <0.05). The Kaplan-Meier approach and log rank test (Mantel-Cox) showed a significant difference in the survival rate in three epidemic waves (P = 0.018).
Conclusion: Elderly patients, especially those with a history of heart disease, are at higher risk of death than other groups. In addition to regular screening, these patients will need active monitoring, especially at the time of hospitalization.
Keywords: Covid-19, Survival analysis, Epidemic, Cox proportional hazards model, Kaplanmeier nonparametric approach
Type of Study: Research |
Subject:
Epidemiology
Received: 2021/11/24 | Accepted: 2021/09/21 | Published: 2021/09/21
Received: 2021/11/24 | Accepted: 2021/09/21 | Published: 2021/09/21
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