Factors associated with mortality from COVID 19

Roy, Manas Pratim

doi:10.4103/lungindia.lungindia_403_21

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Patients with cardiovascular-related comorbidities also had an increased risk of mortality in this study compared to their counterparts. This finding has been substantiated by various studies where cardiovascular diseases such as hypertension, deep vein thrombosis and heart failure were reported to be positively associated with the risk of death among COVID-19 patients [10,28]. In a study conducted at Xinhua Hospital of Hubei Province, China, COVID-19 patients with hypertension had an increased risk of death compared to their counterparts [29].…”

Section: Discussionmentioning

confidence: 61%

Predictors of in-hospital mortality of patients with COVID-19 infection in a major treatment facility in Ghana.

OLIVER-COMMEY,

OWOO,

GHARTEY

et al. 2023

HSI Journal

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Background: An estimated 17 –18% of hospitalisedCOVID-19 patients die. This has been attributed to varying symptomatic, prophylactic, patient management and health care factors. However, these factors have been shown to vary with population and setting dynamics. There is limited literature on the impact of these factors on COVID-19 mortality in the Ghanaian setting. This study, therefore, assessed the factors associated with mortality among COVID-19 hospitalised patients. Objective: The study aimed to assess the clinical and health-related factors associated with the mortality of COVID-19 patients admitted to a major treatment facility in Ghana. Methods: We conducted a retrospective study at Ghana Infectious Disease Center, involving a review of data collected on patients admitted to the facility from January 2021 to December 2021. Using a data abstraction tool on Kobo Collect, data on patient sociodemographic characteristics, clinical presentation, underlying conditions and vaccination status were extracted from patient folders and other inpatient registers. We analysed the data and estimated Odds ratios and their respective 95%CI intervals. Results: Out of the 271 patients admitted to the hospital within the study period, 8.86% (95%CI: 5.76–12.89) died. Being diabetic (aOR = 2.62, 95%CI: 1.08-6.35), having a cardiovascular-related disease (aOR = 4.06, 95%CI: 1.03–15.91), having a kidney disease (aOR = 4.56, 95%CI: 1.40-14.92), a chronic lung disease (aOR = 4.42, 95%CI: 1.45-13.53), and longer duration of admission (aOR = 1.06, 95%CI: 1.01-1.11) were associated with increased odds of death among the patients. Being vaccinated (aOR = 0.40, 95%CI: 0.11-0.49) was associated with a reduction in the odds of death among the patients. Conclusion: This study revealed a less than 10% mortality rate among the patients. Being unvaccinated, having an underlying condition (diabetes, cardiovascular-related diseases, chronic lung diseases and kidney disease) and having a longer duration of admission in the hospital were associated with increased risk of death among the patients.

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Section: Discussionmentioning

confidence: 61%

Predictors of in-hospital mortality of patients with COVID-19 infection in a major treatment facility in Ghana.

OLIVER-COMMEY,

OWOO,

GHARTEY

et al. 2023

HSI Journal

View full text Add to dashboard Cite

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“…People engaged in informal work or unemployed were more prone to succumb to the infection. [ 34 ] Approximately half of India’s population of 1.3 billion is now in the middle class,[ 35 ] which justifies the higher death among these social groups proportionately. More middle-class people get themselves tested and treated.…”

Section: Discussionmentioning

confidence: 99%

The impact of Covid-19 in Chhattisgarh, India: A descriptive study

Agrawal,

Galhotra,

Gahwai

et al. 2024

Journal of Family Medicine and Primary Care

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Background: India is among the most severely affected countries in the ongoing Covid-19 pandemic, second to the USA. Until July 2022, there were 43 million confirmed Covid-19 cases, with 0.53 million Covid-19 deaths in India. Chhattisgarh, the twelfth-highest affected state by Covid-19 disease, has had an enormous mortality toll, with 14,046 deaths by 15th July 2022. This study is a descriptive analysis of the Covid-19 deaths across Chhattisgarh from September 2021 to February 2022 (the third pandemic wave). Materials and Methods: All Covid-19 deaths that occurred between September 2021 to February 2022 were line-listed, and family members of all who died in the hospital were interviewed. Results: 356 Covid-19 deaths were included in the present study. The top three districts which observed maximum deaths (63.48%) were Raipur (27.53%), Durg (15.45%), and Bilaspur (7.87%). Around 50% of the deceased were of age 65 years or above. The male deaths were twice as compared to the female deaths. The RT-PCR was the most used testing method, followed by RAT. Most of the admissions (58%) were in a government facility. Among the deceased, 60% had one or two associated comorbidities. Hypertension and Diabetes were the most common comorbidity identified. Almost 90% of patients were symptomatic. Breathlessness was the most common symptom. Half of the deceased were not vaccinated. The median expenditure on Covid-19 management was ₹ 50,000 (€ 565.13). Conclusion: The deaths in the study were urban-centric. Higher Covid-19 mortality was observed in unvaccinated older men (≥65 years) with one or more comorbidity.

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“…There was little variation in the rate of fatalities between the first and second waves of the pandemic, according to the Indian Council of Medical Research Director General [ 6 ]. Factors such as economic condition, diabetes, hypertension, overweight, obesity, and literacy were significantly associated with COVID-19 mortality [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Deaths Due to the COVID-19 Pandemic During the First and Second Waves in a Tertiary Care Center of a Rural Area in South India

Navab,

M A

et al. 2024

Cureus

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Background The coronavirus disease 2019 (COVID-19) pandemic affected life and livelihood worldwide, including India, with over five million deaths recorded over two years. In the present study, our objective was to analyze the COVID-19 deaths during the first and second waves in relation to demographic factors and comorbid conditions. Methods This was a hospital-based, retrospective comparative study of COVID-19 deaths that occurred in our hospital during the first and second waves of the COVID-19 pandemic. A total of 210 (6.69%) deaths recorded during both waves of the pandemic were analyzed. Microsoft Excel sheets (Microsoft Corporation, Redmond, WA, USA) were used to collect data from the medical records section, and the data were compiled. Descriptive statistics were used and analyzed using SPSS version 21 (IBM Corp., Armonk, NY, USA). Results Out of 3136 inpatients, mortality was 6.69% (n=210). Out of 210 deaths recorded in the study, 34 (2.25%) and 176 (10.7%) were during the first and second waves of the pandemic, respectively. The most common age group affected during the two waves was 50-75 years (67.6% & 47.7%; n=23 & n=84). People from urban (52.9%; n=18) and rural (67%; n=118) backgrounds were affected more during the first and second waves, respectively. Males were affected more (72.8%; n=153) in both waves. Age group (P=0.009) and locality (P=0.026) were statistically significant factors associated with mortality in the two COVID-19 waves. The time interval from admission in the hospital to death was less than seven days in both waves (70.5% & 69.8%; n=24 & n=123). A large number of subjects died after 48 hours of admission during both waves (70% (n=24 & n=124) in each wave). More than half of the subjects who died (52.9% (n=18) & 59% (n=104)) had comorbid conditions in both waves. Diabetes mellitus (17.6%; n=6) and hypertension (23.5%; n=8) were the most common comorbid conditions during the first wave of the pandemic while diabetes mellitus (30.6%; n=54) alone was the most common during the second wave. Conclusion The findings of this study stress the importance of considering demographic factors and geographic locations in understanding the impact of COVID-19, providing valuable inputs for public health interventions and resource allocation in response to similar pandemics.

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Factors associated with mortality from COVID 19

Cited by 3 publications

References 7 publications

Predictors of in-hospital mortality of patients with COVID-19 infection in a major treatment facility in Ghana.

Predictors of in-hospital mortality of patients with COVID-19 infection in a major treatment facility in Ghana.

The impact of Covid-19 in Chhattisgarh, India: A descriptive study

A Comparative Study of Deaths Due to the COVID-19 Pandemic During the First and Second Waves in a Tertiary Care Center of a Rural Area in South India

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