School of Public Health, College of Health Sciences, Mekelle University, Mekelle, Ethiopia.
Laboratory Interdisciplinary Statistical Data Analysis, College of Health Sciences, Mekelle University, Mekelle, Ethiopia.
PLoS One. 2022 Aug 11;17(8):e0271124. doi: 10.1371/journal.pone.0271124. eCollection 2022.
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.
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.
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.
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.
COVID-19 是一种致命的大流行疾病，由一种属于 CORONA 病毒家族的 RNA 病毒引起。为了在资源有限的环境中应对 COVID-19 大流行，确定 COVID-19 死亡率的风险因素至关重要。本研究旨在确定在埃塞俄比亚北部提格雷的四个治疗中心住院的 COVID-19 患者的社会和临床决定因素与死亡率之间的关系。
我们回顾了 2020 年 5 月 7 日至 2020 年 10 月 28 日期间报告的 6637 例 COVID-19 阳性病例的数据。其中 925 例因病情严重而被送往治疗中心，并进行了回顾性分析。数据被输入 STATA 16 版本进行分析。使用中位数、四分位距、频率分布和百分比等描述性分析。使用二元逻辑回归模型来确定 COVID-19 患者死亡的潜在风险因素。使用调整后的优势比（AOR）和 95%置信区间来确定结局与预测变量之间的关联程度。
患者的中位年龄为 30 岁（IQR，25-44），约 70%为男性。非幸存者组的患者比幸存者组的患者年龄大得多（中位数 57.5 岁对 30 岁，p 值<0.001）。总的病死率为 6.1%（95%CI：4.5%-7.6%），在重症和危重症患者中上升至 40.3%（95%CI：32.2%-48.4%）。非幸存者组中严重和危重症的比例明显高于幸存者组（严重疾病为 19.6%对 5.1%，危重症为 80.4%对 4.5%，均 p 值<0.001）。12.5%的患者存在一种或多种预先存在的合并症：心血管疾病（42.2%）、糖尿病（25.0%）和呼吸系统疾病（16.4%）是最常见的合并症。非幸存者组（44.6%）的合并症发生率高于幸存者组（10.5%）。多变量二元回归的结果显示，心血管疾病（AOR=2.49，95%CI：1.03-6.03）、呼吸急促（AOR=9.71，95%CI：4.73-19.93）和身体虚弱（AOR=3.04，95%CI：1.50-6.18）的患者死亡的可能性更高。此外，患者年龄越大，死亡的估计几率就越高。
年龄、心血管疾病、呼吸急促和身体虚弱是 COVID-19 患者死亡的预测因素。了解这些因素可以帮助更好地识别高危 COVID-19 患者，并因此能够优先预防死亡。
