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Mortality Rate of Patients With COVID-19 Based on Underlying Health Conditions

Published online by Cambridge University Press:  03 May 2021

Won-Young Choi
Won-Young Choi*
Affiliation:
Division of Interdisciplinary Industrial Studies, Hanyang University, Seoul, Republic of Korea
*
Corresponding author: Won-Young Choi, Email: won5475@hanyang.ac.kr.
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Abstract

Objective:

The aim of this study was to evaluate the mortality rates of 566,602 patients with coronavirus disease (COVID-19) based on sex, age, and the presence or absence of underlying diseases and determine whether the underlying disease provides prognostic information specifically related to death.

Methods:

The mortality rate was evaluated using conditional probability to identify the significant factors, and adjusted odds ratios (ORs) using a multivariable logistic regression analysis were estimated.

Results:

The mortality rate of patients with underlying health conditions was 12%, which was 4 times higher than that of patients without underlying health conditions. Furthermore, the mortality rates of women and men with underlying health conditions were 5.5 and 3.4 times higher than the mortality rates of patients without underlying health conditions, respectively. In a multivariable logistic regression analysis including sex, age, and underlying health conditions, male sex (OR: 1.83), age ≥ 41 y (ORs > 2.70), and underlying health conditions (OR: 2.20) were confirmed as risk factors for death.

Conclusions:

More attention should be paid to older patients with underlying diseases and male patients with underlying diseases as the probability of death in this population was significantly higher.

Keywords

COVID-19mortality rateunderlying diseasehealth condition
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 16 , Issue 6 , December 2022 , pp. 2480 - 2485
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc

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References

World Health Organization. Coronavirus disease (COVID-19) pandemic. https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed May 7, 2021.Google Scholar
Clark, A, Jit, M, Warren-Gash, C, et al. Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study. Lancet Glob Health. 2020;8(8):e1003-e1017. doi: 10.1016/S2214-109X(20)30264-3 CrossRefGoogle ScholarPubMed
Bertsimas, D, Lukin, G, Mingardi, L, et al. COVID-19 mortality risk assessment: an international multi-center study. PLoS One. 2020;15(12). doi: 10.1371/journal.pone.0243262 CrossRefGoogle Scholar
Kang, Y-J. Lessons learned from cases of COVID-19 infection in South Korea. Disaster Med Public Health Prep. 2020;14(6):818-825. doi: 10.1017/dmp.2020.141 CrossRefGoogle ScholarPubMed
Giangreco, G. Case fatality rate analysis of Italian COVID-19 outbreak. J Med Virol. 2020;92(7):919-923. doi: 10.1002/jmv.25894 CrossRefGoogle ScholarPubMed
Sun, YJ, Feng, YJ, Chen, J, et al. Clinical features of fatalities in patients with COVID-19. Disaster Med Public Health Prep. 2020:1-3. doi: 10.1017/dmp.2020.235 CrossRefGoogle Scholar
Jamshidi, B, Bekrizadeh, H, Zargaran, SJ, et al. Modeling propagation of COVID-19 in the UK. Disaster Med Public Health Prep. 2020:1-2. doi: 10.1017/dmp.2020.383 CrossRefGoogle Scholar
Cortés-Álvarez, NY, Piñeiro-Lamas, R, Vuelvas-Olmos, CR. Psychological effects and associated factors of COVID-19 in a Mexican sample. Disaster Med Public Health Prep. 2020;14(3):413-424. doi: 10.1017/dmp.2020.215 CrossRefGoogle Scholar
Ramage-Morin, P, Polsky, JY. Health-related concerns and precautions during the COVID-19 pandemic: a comparison of Canadians with and without underlying health conditions. Health Rep. 2020;31(5):3-8. doi: 10.25318/82-003-x202000500001-eng Google ScholarPubMed
Chen, Y, Li, T, Ye, Y, et al. Impact of fundamental diseases on patients with COVID-19. Disaster Med Public Health Prep. 2020;14(6):776-781. doi: 10.1017/dmp.2020.139 CrossRefGoogle ScholarPubMed
Kang, Y-J. Mortality rate of infection with COVID-19 in Korea from the perspective of underlying disease. Disaster Med Public Health Prep. 2020;14(3):384-386. doi: 10.1017/dmp.2020.60 CrossRefGoogle ScholarPubMed
Bello-Chavolla, OY, Bahena-López, JP, Antonio-Villa, NE, et al. Predicting mortality due to SARS-CoV-2: a mechanistic score relating obesity and diabetes to COVID-19 outcomes in Mexico. J Clin Endocrinol Metab. 2020;105(8):dgaa346. doi: 10.1210/clinem/dgaa346 CrossRefGoogle ScholarPubMed
Martos-Benítez, FD, Soler-Morejón, CD, García-Del Barco, D. Chronic comorbidities and clinical outcomes in patients with and without COVID-19: a large population-based study using national administrative healthcare open data of Mexico. Intern Emerg Med. 2021:1-11. doi: 10.1007/s11739-020-02597-5 CrossRefGoogle Scholar
Mancilla-Galindo, J, Vera-Zertuche, JM, Navarro-Cruz, AR, et al. Development and validation of the patient history COVID-19 (PH-Covid19) scoring system: a multivariable prediction model of death in Mexican patients with COVID-19. Epidemiol Infect. 2020;148:e286. doi: 10.1017/S0950268820002903 CrossRefGoogle ScholarPubMed
McCullough, SA, Goyal, P, Krishnan, U, et al. Electrocardiographic findings in coronavirus disease-19: insights on mortality and underlying myocardial processes. J Card Fail. 2020;26(7):626-632. doi: 10.1016/j.cardfail.2020.06.005 CrossRefGoogle ScholarPubMed
Zhang, J, Lu, S, Wang, X, et al. Do underlying cardiovascular diseases have any impact on hospitalised patients with COVID-19? Heart. 2020;106(15):1148-1153. doi: 10.1136/heartjnl-2020-316909 CrossRefGoogle ScholarPubMed
Kaggle. COVID-19 patient pre-condition datset. https://www.kaggle.com/tanmoyx/covid19-patient-precondition-dataset. Accessed May 7, 2021.Google Scholar
Welch, BL. The generalization of student’s problem when several different population variances are involved. Biometrika. 1947;34(1-2):28-35. doi: 10.1093/biomet/34.1-2.28 Google ScholarPubMed
Zhang, Y, Wang, J, Tan, N, et al. Risk factors in patients with diabetes hospitalized for COVID-19: findings from a multicenter retrospective study. J Diabetes Res. 2021;2021:3170190. doi: 10.1155/2021/3170190 CrossRefGoogle ScholarPubMed
Ioannidis, JPA, Axfors, C, Contopoulos-Ioannidis, DG. Population-level COVID-19 mortality risk for non-elderly individuals overall and for non-elderly individuals without underlying diseases in pandemic epicenters. Environ Res. 2020;188:109890. doi: 10.1016/j.envres.2020.109890 CrossRefGoogle ScholarPubMed
Hu, Y, Deng, H, Huang, L, et al. Analysis of characteristics in death patients with COVID-19 pneumonia without underlying diseases. Acad Radiol. 2020;27(5):752. doi: 10.1016/j.acra.2020.03.023 CrossRefGoogle ScholarPubMed
Peters, SAE, MacMahon, S, Woodward, M. Obesity as a risk factor for COVID-19 mortality in women and men in the UK biobank: comparisons with influenza/pneumonia and coronary heart disease. Diabetes Obes Metab. 2021;23(1):258-262. doi: 10.1111/dom.14199 Google ScholarPubMed
Lunski, MJ, Burton, J, Tawagi, K, et al. Multivariate mortality analyses in COVID-19: comparing patients with cancer and patients without cancer in Louisiana. Cancer. 2021;127(2):266-274. doi: 10.1002/cncr.33243 Google ScholarPubMed