Lifestyle and Comorbidity-Related Risk Factors of Severe and Critical COVID-19 Infection: A Comparative Study Among Survived COVID-19 Patients in Bangladesh

Sleep disturbance increases the risk of severity and acquisition of COVID-19: a systematic review and meta-analysis

BACKGROUND

Understanding the association between sleep quality and COVID-19 outcomes is crucial for effective preventive strategies and patient management. This systematic review aims to evaluate the impact of sleep quality as a risk factor for acquiring COVID-19 infection and the severity of the disease.

METHODS

A comprehensive search of electronic databases was conducted to identify relevant studies published from the inception of the COVID-19 pandemic which was 31st of December 2019 until 30 April 2023. Studies investigating the relationship between sleep quality and COVID-19 infection, or disease severity were included. Random effect meta-analysis was performed with odds ratios (OR) and their 95% confidence intervals (95% CI) as effect measures.

RESULTS

Out of the initial 1,132 articles identified, 12 studies met the inclusion criteria. All studies were observational studies (cohort, case-control, and cross-sectional). The association between sleep quality and COVID-19 infection risk was examined in 6 studies, The results of our meta-analysis showed that participants with poor sleep quality showed a 16% increase regarding the risk of COVID-19 acquisition (OR 1.16; 95% CI 1.03, 1.32; I2 = 65.2%, p = 0.02). Our results showed that participants with poor sleep quality showed a 51% increase in the incidence of primary composite outcome (OR 1.51; 95% CI 1.25, 1.81; I2 = 57.85%, p < 0.001). The result of our subgroup analysis also showed significantly increased risk of mortality (RR 0.67; 95% CI 0.50, 0.90; I2 = 31%, p = 0.008), and disease severity (OR 1.47; 95% CI 1.19, 1.80; I2 = 3.21%, p < 0.001) when comparing poor sleep group to those with good sleep quality.

CONCLUSION

This study highlights a significant association between poor sleep quality and an increased risk of COVID-19 infection as well as worse disease clinical outcomes.

Risk factors for COVID-19 mortality among telehealth patients in Bangladesh: A prospective cohort study

BACKGROUND AND OBJECTIVE

Estimating the contribution of risk factors of mortality due to COVID-19 is particularly important in settings with low vaccination coverage and limited public health and clinical resources. Very few studies of risk factors of COVID-19 mortality used high-quality data at an individual level from low- and middle-income countries (LMICs). We examined the contribution of demographic, socioeconomic and clinical risk factors of COVID-19 mortality in Bangladesh, a lower middle-income country in South Asia.

METHODS

We used data from 290,488 lab-confirmed COVID-19 patients who participated in a telehealth service in Bangladesh between May 2020 and June 2021, linked with COVID-19 death data from a national database to study the risk factors associated with mortality. Multivariable logistic regression models were used to estimate the association between risk factors and mortality. We used classification and regression trees to identify the risk factors that are the most important for clinical decision-making.

FINDINGS

This study is one of the largest prospective cohort studies of COVID-19 mortality in a LMIC, covering 36% of all lab-confirmed COVID-19 cases in the country during the study period. We found that being male, being very young or elderly, having low socioeconomic status, chronic kidney and liver disease, and being infected during the latter pandemic period were significantly associated with a higher risk of mortality from COVID-19. Males had 1.15 times higher odds (95% Confidence Interval, CI: 1.09, 1.22) of death compared to females. Compared to the reference age group (20-24 years olds), the odds ratio of mortality increased monotonically with age, ranging from an odds ratio of 1.35 (95% CI: 1.05, 1.73) for ages 30-34 to an odds ratio of 21.6 (95% CI: 17.08, 27.38) for ages 75-79 year group. For children 0-4 years old the odds of mortality were 3.93 (95% CI: 2.74, 5.64) times higher than 20-24 years olds. Other significant predictors were severe symptoms of COVID-19 such as breathing difficulty, fever, and diarrhea. Patients who were assessed by a physician as having a severe episode of COVID-19 based on the telehealth interview had 12.43 (95% CI: 11.04, 13.99) times higher odds of mortality compared to those assessed to have a mild episode. The finding that the telehealth doctors' assessment of disease severity was highly predictive of subsequent COVID-19 mortality, underscores the feasibility and value of the telehealth services.

CONCLUSIONS

Our findings confirm the universality of certain COVID-19 risk factors-such as gender and age-while highlighting other risk factors that appear to be more (or less) relevant in the context of Bangladesh. These findings on the demographic, socioeconomic, and clinical risk factors for COVID-19 mortality can help guide public health and clinical decision-making. Harnessing the benefits of the telehealth system and optimizing care for those most at risk of mortality, particularly in the context of a LMIC, are the key takeaways from this study.

Predictors Associated with In-hospital Mortality among COVID-19 Patients during the Second Wave in a Tertiary Care Hospital, Gujarat, India: A Retrospective Observational Study

BACKGROUND

Fatalities due to coronavirus disease 2019 (COVID-19) have already crossed to more than 5 million globally so far. Hence, it is crucial for us to identify the risk factors associated with hospital deaths starting from first contact which can help to give timely treatment to the targeted population.

OBJECTIVES

This retrospective cohort study was conducted to identify various factors related to in-hospital mortality related to COVID-19 in our region.

MATERIALS AND METHODS

The present study was a single-center, retrospective cohort study of 675 adult patients, admitted with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection between 1st April and 25th May 2021 in our tertiary care hospital. Baseline demographic profile, comorbidities, clinical characteristics, and investigatory findings were analyzed for increased odds of mortality.

RESULTS

A total of 181 (26.8%) patients died and 494 (73.2%) survived. There were 65.4% of males and no difference was found between genders in terms of mortality. Comorbidities associated with in-hospital death in our cohort were age group ≥50 years (p&lt;0.001), diabetes (p&lt;0.0007), and renal injury (p&lt;0.0001). More than half of the patients died during the first week of admission. Breathlessness (83%) was the most common symptom in non-survivors. Neutrophil-to-lymphocyte ratio (NLR), S. creatinine, D-dimer, ferritin, and C-reactive protein (CRP) were increased significantly among the patients who died. Multivariate logistic regression revealed age ≥50 years [adjusted odds ratio (AOR) 2.30, 95% confidence interval (CI) 1.45-3.64] and oxygen (O2) saturation &lt;94% at the time of admission (AOR 2.62, 95% CI 1.75-3.93) were associated with mortality.

CONCLUSION

Overall in-hospital mortality was 26.8%. Higher age and low O2 saturation were the major risk factors associated with in-hospital mortality.

A Social Media Infodemic-Based Prediction Model for the Number of Severe and Critical COVID-19 Patients in the Lockdown Area

Accurately predicting the number of severe and critical COVID-19 patients is critical for the treatment and control of the epidemic. Social media data have gained great popularity and widespread application in various research domains. The viral-related infodemic outbreaks have occurred alongside the COVID-19 outbreak. This paper aims to discover trustworthy sources of social media data to improve the prediction performance of severe and critical COVID-19 patients. The innovation of this paper lies in three aspects. First, it builds an improved prediction model based on machine learning. This model helps predict the number of severe and critical COVID-19 patients on a specific urban or regional scale. The effectiveness of the prediction model, shown as accuracy and satisfactory robustness, is verified by a case study of the lockdown in Hubei Province. Second, it finds the transition path of the impact of social media data for predicting the number of severe and critical COVID-19 patients. Third, this paper provides a promising and powerful model for COVID-19 prevention and control. The prediction model can help medical organizations to realize a prediction of COVID-19 severe and critical patients in multi-stage with lead time in specific areas. This model can guide the Centers for Disease Control and Prevention and other clinic institutions to expand the monitoring channels and research methods concerning COVID-19 by using web-based social media data. The model can also facilitate optimal scheduling of medical resources as well as prevention and control policy formulation.