Verma Anjana, Patyal Ashish, Mathur Medha, Choudhary Suresh, Mathur Navgeet
Department of Community Medicine, Geetanjali Medical College and Hospital, Udaipur, Rajasthan, India.
Clinical Fellowship, Department of Neuroanaesthesia,Walton Centre, Liverpool, United Kingdom.
J Family Med Prim Care. 2021 Sep;10(9):3319-3324. doi: 10.4103/jfmpc.jfmpc_445_21. Epub 2021 Sep 30.
It has been over a year since the declaration of novel coronavirus disease (COVID-19) as pandemic by World Health Organization on March 11, 2020. Although mortality in India is low, as compared to western countries, the steady increase in the number of cases is still a worrying sign. The objectives of this study were to identify and quantify the association between sociodemographic and clinical characteristics with mortality among patients, suffering from COVID-19 at a tertiary care hospital in Udaipur, Rajasthan.
This retrospective observational study involved 824 patients hospitalized for COVID 19 at a tertiary hospital in Udaipur, who were discharged or had died. Electronic health records of the patients were accessed to retrieve the sociodemographic information (age, gender, residence, religion, socioeconomic status), history of exposure, clinical characteristics on admission, comorbidities, and outcomes (recovery or death). The Cox regression model was used to calculate associations between mortality and baseline characteristics in the form of hazard ratios (HRs).
Mortality in this study was found to be 5.82%. The mean age of the patients was 48.14 ± 16.2 years. The median time from time of admission to discharge was 8 days (interquartile range (IQR) 5-11), whereas the median time to death was 5 days (IQR 4-10). The variables found to be associated with higher mortality were age (HR 1.17; 95% confidence interval (CI) 1.15-1.24), residing in urban area (HR 1.29; 95% CI 1.17-2.15), diabetes mellitus (HR 1.3; CI 1.02-5.57), and patients having both diabetes and hypertension (HR 2.4; CI 1.69-3.14).
Sociodemographic variables and comorbidities impact the mortality among COVID 19 patients. The variables most clearly associated with a greater hazard of death were older age, urban area, diabetes, and having both diabetes and hypertension.
自2020年3月11日世界卫生组织宣布新型冠状病毒病（COVID-19）为大流行病以来，已经过去一年多了。尽管与西方国家相比，印度的死亡率较低，但病例数量的稳步增加仍是一个令人担忧的迹象。本研究的目的是确定并量化在拉贾斯坦邦乌代布尔的一家三级护理医院中，COVID-19患者的社会人口统计学和临床特征与死亡率之间的关联。
这项回顾性观察性研究纳入了在乌代布尔一家三级医院因COVID-19住院的824名患者，这些患者已出院或已死亡。通过查阅患者的电子健康记录来获取社会人口统计学信息（年龄、性别、居住地、宗教、社会经济地位）、接触史、入院时的临床特征、合并症以及结局（康复或死亡）。采用Cox回归模型以风险比（HRs）的形式计算死亡率与基线特征之间的关联。
本研究中的死亡率为5.82%。患者的平均年龄为48.14±16.2岁。从入院到出院的中位时间为8天（四分位间距（IQR）5 - 11），而死亡的中位时间为5天（IQR 4 - 10）。发现与较高死亡率相关的变量包括年龄（HR 1.17；95%置信区间（CI）1.15 - 1.24）、居住在城市地区（HR 1.29；95% CI 1.17 - 2.15）、糖尿病（HR 1.3；CI 1.02 - 5.57）以及同时患有糖尿病和高血压的患者（HR 2.4；CI 1.69 - 3.14）。
社会人口统计学变量和合并症会影响COVID - 19患者的死亡率。与死亡风险增加最明显相关的变量是年龄较大、城市地区、糖尿病以及同时患有糖尿病和高血压。
