Anjana Verma1, Ashish Patyal2, Medha Mathur1, Suresh Choudhary1, Navgeet Mathur3. 1. Department of Community Medicine, Geetanjali Medical College and Hospital, Udaipur, Rajasthan, India. 2. Clinical Fellowship, Department of Neuroanaesthesia,Walton Centre, Liverpool, United Kingdom. 3. Department of General Medicine, Geetanjali Medical College and Hospital, Udaipur, Rajasthan, India.
Abstract
BACKGROUND: 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. MATERIAL AND METHODS: 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). RESULTS: 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). CONCLUSION: 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. Copyright:
BACKGROUND: 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. MATERIAL AND METHODS: 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). RESULTS: 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). CONCLUSION: 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. Copyright:
Authors: Mario Rivera-Izquierdo; María Del Carmen Valero-Ubierna; Juan Luis R-delAmo; Miguel Ángel Fernández-García; Silvia Martínez-Diz; Arezu Tahery-Mahmoud; Marta Rodríguez-Camacho; Ana Belén Gámiz-Molina; Nicolás Barba-Gyengo; Pablo Gámez-Baeza; Celia Cabrero-Rodríguez; Pedro Antonio Guirado-Ruiz; Divina Tatiana Martín-Romero; Antonio Jesús Láinez-Ramos-Bossini; María Rosa Sánchez-Pérez; José Mancera-Romero; Miguel García-Martín; Luis Miguel Martín-delosReyes; Virginia Martínez-Ruiz; Pablo Lardelli-Claret; Eladio Jiménez-Mejías Journal: PLoS One Date: 2020-06-25 Impact factor: 3.240
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Authors: Jennifer Summers; Hao-Yuan Cheng; Hsien-Ho Lin; Lucy Telfar Barnard; Amanda Kvalsvig; Nick Wilson; Michael G Baker Journal: Lancet Reg Health West Pac Date: 2020-10-21