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Risk factors for mortality among hospitalized COVID-19 patients in Northern Ethiopia: A retrospective analysis

BACKGROUND: COVID-19 is a deadly pandemic caused by an RNA virus that belongs to the family of CORONA virus. To counter the COVID-19 pandemic in resource limited settings, it is essential to identify the risk factors of COVID-19 mortality. This study was conducted to identify the social and clinical...

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Detalles Bibliográficos
Autores principales: Abebe, Haftom Temesgen, Mulugeta, Afework, Berhe, Yibrah, Berhane, Kiros, Siraj, Amir, Siraj, Dawd, Aregawi, Maru, Fseha, Berhane, Mohamedniguss Ebrahim, Mohamedawel, Hintsa, Solomon, Gebre, Hagazi, Mohammed, Abrahim Hassen, Godefay, Hagos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371316/
https://www.ncbi.nlm.nih.gov/pubmed/35951497
http://dx.doi.org/10.1371/journal.pone.0271124
Descripción
Sumario:BACKGROUND: COVID-19 is a deadly pandemic caused by an RNA virus that belongs to the family of CORONA virus. To counter the COVID-19 pandemic in resource limited settings, it is essential to identify the risk factors of COVID-19 mortality. This study was conducted to identify the social and clinical determinants of mortality in COVID-19 patients hospitalized in four treatment centers of Tigray, Northern Ethiopia. METHODS: We reviewed data from 6,637 COVID-19 positive cases that were reported from May 7, 2020 to October 28, 2020. Among these, 925 were admitted to the treatment centers because of their severity and retrospectively analyzed. The data were entered into STATA 16 version for analysis. The descriptive analysis such as median, interquartile range, frequency distribution and percentage were used. Binary logistic regression model was fitted to identify the potential risk factors of mortality of COVID-19 patients. The adjusted odds ratio (AOR) with 95% confidence interval was used to determine the magnitude of the association between the outcome and predictor variables. RESULTS: The median age of the patients was 30 years (IQR, 25–44) and about 70% were male patients. The patients in the non-survivor group were much older than those in the survivor group (median 57.5 years versus 30 years, p-value < 0.001). The overall case fatality rate was 6.1% (95% CI: 4.5% - 7.6%) and was increased to 40.3% (95% CI: 32.2% - 48.4%) among patients with critical and severe illness. The proportions of severe and critical illness in the non-survivor group were significantly higher than those in the survivor group (19.6% versus 5.1% for severe illness and 80.4% versus 4.5% for critical illness, all p-value < 0.001). One or more pre-existing comorbidities were present in 12.5% of the patients: cardiovascular diseases (42.2%), diabetes mellitus (25.0%) and respiratory diseases (16.4%) being the most common comorbidities. The comorbidity rate in the non-survivor group (44.6%) was higher than in the survivor group (10.5%). The results from the multivariable binary regression showed that the odds of mortality was higher for patients who had cardiovascular diseases (AOR = 2.49, 95% CI: 1.03–6.03), shortness of breath (AOR = 9.71, 95% CI: 4.73–19.93) and body weakness (AOR = 3.04, 95% CI: 1.50–6.18). Moreover, the estimated odds of mortality significantly increased with patient’s age. CONCLUSIONS: Age, cardiovascular diseases, shortness of breath and body weakness were the predictors for mortality of COVID-19 patients. Knowledge of these could lead to better identification of high risk COVID-19 patients and thus allow prioritization to prevent mortality.