Factors Predicting Progression to Severe COVID-19: A Competing Risk Survival Analysis of 1753 Patients in Community Isolation in Wuhan, China

Mortality rate and predictors of COVID-19 inpatients in Ethiopia: a systematic review and meta-analysis

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an extremely rare virus that devastates the economy and claims human lives. Despite countries' urgent and tenacious public health responses to the COVID-19 pandemic, the disease is killing a large number of people. The results of prior studies have not been used by policymakers and programmers due to the presence of conflicting results. As a result, this study was conducted to fill the knowledge gap and develop a research agenda.

Objective

This study aimed to assess the mortality rate and predictors of COVID-19 hospitalized patients in Ethiopia.

Methods

Electronic databases were searched to find articles that were conducted using a retrospective cohort study design and published in English up to 2022. The data were extracted using a Microsoft Excel spreadsheet and exported to StataTM version 17.0 for further analysis. The presence of heterogeneity was assessed and presented using a forest plot. The subgroup analysis, meta-regression, and publication bias were computed to identify the source of heterogeneity. The pool COVID-19 mortality rate and its predictors were calculated and identified using the random effects meta-analysis model, respectively. The significant predictors identified were reported using a relative risk ratio and 95% confidence interval (CI).

Results

Seven studies with 31,498 participants were included. The pooled mortality rate of COVID-19 was 9.13 (95% CI: 5.38, 12.88) per 1,000 person-days of mortality-free observation. Those study participants who had chronic kidney disease had 2.29 (95% CI: 1.14, 4.60) times higher chance of experiencing mortality than their corresponding counterparts, diabetics had 2.14 (95% CI: 1.22, 3.76), HIV patients had 2.98 (95% CI: 1.26, 7.03), hypertensive patients had 1.63 (95% CI: 1.43, 1.85), and smoker had 2.35 (95% CI: 1.48, 3.73).

Conclusion

COVID-19 mortality rate was high to tackle the epidemic of the disease in Ethiopia. COVID-19 patients with chronic renal disease, diabetes, hypertension, smoking, and HIV were the significant predictors of mortality among COVID-19 patients in Ethiopia. COVID-19 patients with chronic diseases and comorbidities need special attention, close follow-up, and care from all stakeholders.

Deliberation, context, emotion and trust - understanding the dynamics of adults' COVID-19 vaccination decisions in Germany

BACKGROUND

Willingness to vaccinate against coronavirus disease 2019 (COVID-19), which is vital to successful vaccination campaigns, is wavering and suboptimal. In Germany, quantitative research highlighted concerns regarding the safety and efficacy of COVID-19 vaccines as barriers to uptake, but qualitative insights regarding individuals' decisions about COVID-19 vaccines and how personal perceptions reflect or refute existing behavioral theories are lacking.

METHODS

To identify how individuals make COVID-19 vaccination decisions within real-life contexts, we conducted 33 semi-structured, in-depth qualitative interviews with individuals in Germany between March and April 2021 using maximum variation sampling, focusing on perceptions of COVID-19 vaccines. Analysis, informed by a framework approach, began in the field via debriefings and was amplified upon the conclusion of data collection.

RESULTS

Four interconnected themes (deliberation, context, emotion, trust) shaped respondents' decisions about vaccination. Personal deliberation regarding benefits and risks of vaccines and perceptions of the broader social and political context sparked a spectrum of emotions that underpinned vaccination decisions. Trust in science and researchers emerged as a powerful protective factor facilitating the decision to get vaccinated even amidst a rapidly changing context and disconcerting information.

CONCLUSIONS

Our findings add to ongoing debates about the breadth of vaccination decisions by highlighting how respondents are influenced by their perceptions of the political context and the emotional heft of their decisions. The role of cognitive evaluation, context, and emotions mirrors other decision-making frameworks, particularly the Risk as Feelings Theory. We extend on the elements of this theory by highlighting trust as a protective factor when making decisions particularly in highly uncertain contexts. Success of vaccination campaigns, more important than ever as new variants of COVID-19 emerge, is interwoven with an ability to bolster trust in science. Communicating public-health decisions and information about vaccines transparently without instilling fear offers promising chances to strengthen public trust in COVID-19 vaccines.

TRIAL REGISTRATION

German Clinical Trials Register ( DRKS00024505 ).

Comorbidities and complications of COVID-19 associated with disease severity, progression, and mortality in China with centralized isolation and hospitalization: A systematic review and meta-analysis

Background

Coronavirus disease 2019 (COVID-19) causes life-threatening with the high-fatality rates and spreads with high-infectious disease worldwide. We aimed to systematically review the comorbidities and complications of COVID-19 that are associated with various disease severity, progression, and mortality in China, to provide contemporary and reliable estimates in settings with centralized isolation and hospitalization.

Methods

In this systematic review and meta-analysis, we searched four main English language databases, and four main Chinese language databases for observational studies published from inception to January 2022, to identify all the related comorbidities and complications of COVID-19, in the China region with centralized isolation and hospitalization, with disease severity, progression, and mortality. Literature search, data extraction, and quality assessment were independently conducted by two reviewers. We used the generalized linear mixed model to estimate pooled effect sizes for any comorbidities and complications, and subgroup in gender ratio was done to further address the potential heterogeneity.

Results

Overall, 187 studies describing 77,013 patients, namely, 54 different comorbidities and 46 various complications of COVID-19, were identified who met our inclusion criteria. The most prevalent comorbidities were hypertension [20.37% 95% CI (15.28-26.63), 19.29% (16.17-22.85), 34.72% (31.48-38.10), and 43.94% (38.94-49.06)] and diabetes [7.84% (5.78-10.54), 8.59% (7.25-10.16), 17.99% (16.29-19.84), and 22.68% (19.93-25.69)] in mild, moderate, severe, and critical cases. The most prevalent complications were liver injury [10.00% (1.39-46.72), 23.04% (14.20-35.13), and 43.48% (39.88-47.15)] in mild, moderate, and severe cases, and acute respiratory distress syndrome [ARDS; 94.17% (20.78-99.90)] and respiratory failure [90.69% (28.08-99.59)] in critical cases. Renal insufficiency [odds ratio (OR) 17.43 (6.69-45.43)] in comorbidities and respiratory failure [OR 105.12 (49.48-223.33)] in complications were strongly associated in severe/critical than in mild/moderate cases. The highest estimated risk in intensive care unit (ICU) admission, progression, and mortality was an autoimmune disease, nervous system disease, and stroke in comorbidities, shock, and ARDS in complications.

Conclusion

Comorbidities and complications in inpatients with COVID-19 were positively associated with increased risk in severe and critical cases, ICU admission, exacerbation, and death during centralized isolation and hospitalization. Prompt identification of comorbidities and complications in inpatients with COVID-19 can enhance the prevention of disease progression and death and improve the precision of risk predictions.

Analysis of Survival of Patients Hospitalized with COVID-19 in Espírito Santo, Brazil

Objective: To analyze the survival of patients hospitalized with COVID-19 and its associated factors. Methods: Retrospective study of survival analysis in individuals notified and hospitalized with COVID-19 in the state of Espírito Santo, Brazil. As data source, the reports of hospitalized patients in the period from 1 March 2020, to 31 July 2021 were used. The Cox regression analysis plus the proportional risk assessment (assumption) were used to compare hospitalization time until the occurrence of the event (death from COVID-19) associated with possible risk factors. Results: The sample comprised 9806 notifications of cases, with the occurrence of 1885 deaths from the disease (19.22%). The mean age of the group was 58 years (SD ± 18.3) and the mean hospital length of stay was 10.5 days (SD ± 11.8). The factors that presented a higher risk of death from COVID-19, associated with a lower survival rate, were non-work-related infection (HR = 4.33; p < 0.001), age group 60−79 years (HR: 1.62; p < 0.001) and 80 years or older (HR = 2.56; p < 0.001), presence of chronic cardiovascular disease (HR = 1.18; p = 0.028), chronic kidney disease (HR = 1.5; p = 0.004), smoking (HR = 1.41; p < 0.001), obesity (HR = 2.28; p < 0.001), neoplasms (HR = 1.81; p < 0.001) and chronic neurological disease (HR = 1.68; p < 0.001). Conclusion: It was concluded that non-work-related infection, age group above or equal to 60 years, presence of chronic cardiovascular disease, chronic kidney disease, chronic neurological disease, smoking, obesity and neoplasms were associated with a higher risk of death, and, therefore, a lower survival in Brazilian patients hospitalized with COVID-19. The identification of priority groups is crucial for Health Surveillance and can guide prevention, control, monitoring, and intervention strategies against the new coronavirus.

Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China

Purpose

To identify more objectively predictive factors of severe outcome among patients hospitalized for coronavirus disease 2019 (COVID-19).

Patients and Methods

A retrospective cohort of 479 hospitalized patients diagnosed with COVID-19 in Hunan Province was selected. The prognostic effects of factors such as age and laboratory indicators were analyzed using the Kaplan–Meier method and Cox proportional hazards model. A prognostic nomogram model was established to predict the progression of patients with COVID-19.

Results

A total of 524 patients in Hunan province with COVID-19 from December 2019 to October 2020 were retrospectively recruited. Among them, 479 eligible patients were randomly assigned into the training cohort (n = 383) and validation cohort (n = 96), at a ratio of 8:2. Sixty-eight (17.8%) and 15 (15.6%) patients developed severe COVID-19 after admission in the training cohort and validation cohort, respectively. The differences in baseline characteristics were not statistically significant between the two cohorts with regard to age, sex, and comorbidities (P > 0.05). Multivariable analyses included age, C-reactive protein, fibrinogen, lactic dehydrogenase, neutrophil-to-lymphocyte ratio, urea, albumin-to-globulin ratio, and eosinophil count as predictive factors for patients with progression to severe COVID-19. A nomogram was constructed with sufficient discriminatory power (C index = 0.81), and proper consistency between the prediction and observation, with an area under the ROC curve of 0.81 and 0.86 in the training and validation cohort, respectively.

Conclusion

We proposed a simple nomogram for early detection of patients with non-severe COVID-19 but at high risk of progression to severe COVID-19, which could help optimize clinical care and personalized decision-making therapies.

In Patients with Obesity, Are Affective Temperaments Associated with Attrition? An Evaluation during and before the SARS-CoV-2 Pandemic

Timely data on attrition from weight loss programs for patients with obesity during the SARS-CoV-2 pandemic are lacking, so we aimed to contribute to filling this gap in the literature by comparing attrition during or outside of the SARS-CoV-2 pandemic and its possible association with patients' affective temperaments, psychopathology, and clinical variables. Two-hundred and eleven outpatients with obesity were recruited and completed the Temperament Evaluation of Memphis, Pisa, and San Diego Auto-questionnaire, Binge Eating Scale, Beck Depression Inventory, and State-Trait Anxiety Inventory. Those who dropped out during the pandemic period were mostly men, with younger age of weight gain, and with a larger waist circumference than completers. Patients with obesity who dropped out outside of the SARS-CoV-2 pandemic showed marked levels of depression, anxiety, binge eating episodes, and higher affective temperaments (but the hyperthymic one) when compared to their counterparts. The cyclothymic temperament slightly increased attrition (OR = 1.13, 95% CI 1.00-1.27 p = 0.05) outside the pandemic, while during the pandemic, male gender (OR = 3.50, 1.04-11.7, p = 0.04) was associated with attrition. These findings suggested that male patients with obesity are at particular risk of drop-out from weight-loss treatment during the SARS-CoV-2 pandemic; contrariwise, outside the pandemic, affective temperaments could be a useful baseline assessment for defining the attrition risk in these patients.