Coronavirus (COVID-19): A Systematic Review and Meta-analysis to Evaluate the Significance of Demographics and Comorbidities

Revisiting the complex time-varying effect of non-pharmaceutical interventions on COVID-19 transmission in the United States

Introduction

Although the global COVID-19 emergency ended, the real-world effects of multiple non-pharmaceutical interventions (NPIs) and the relative contribution of individual NPIs over time were poorly understood, limiting the mitigation of future potential epidemics.

Methods

Based on four large-scale datasets including epidemic parameters, virus variants, vaccines, and meteorological factors across 51 states in the United States from August 2020 to July 2022, we established a Bayesian hierarchical model with a spike-and-slab prior to assessing the time-varying effect of NPIs and vaccination on mitigating COVID-19 transmission and identifying important NPIs in the context of different variants pandemic.

Results

We found that (i) the empirical reduction in reproduction number attributable to integrated NPIs was 52.0% (95%CI: 44.4, 58.5%) by August and September 2020, whereas the reduction continuously decreased due to the relaxation of NPIs in following months; (ii) international travel restrictions, stay-at-home requirements, and restrictions on gathering size were important NPIs with the relative contribution higher than 12.5%; (iii) vaccination alone could not mitigate transmission when the fully vaccination coverage was less than 60%, but it could effectively synergize with NPIs; (iv) even with fully vaccination coverage >60%, combined use of NPIs and vaccination failed to reduce the reproduction number below 1 in many states by February 2022 because of elimination of above NPIs, following with a resurgence of COVID-19 after March 2022.

Conclusion

Our results suggest that NPIs and vaccination had a high synergy effect and eliminating NPIs should consider their relative effectiveness, vaccination coverage, and emerging variants.

Youth experiences with and perspectives on long covid

BACKGROUND

Research on the long-term effects of COVID-19 infection is ongoing, and the psychological and physical impacts of Long Covid on youth is poorly understood. To assess these impacts, we surveyed youth regarding their experiences with, and perspectives on, the long-term effects of COVID-19.

METHODS

We conducted a nationwide text message survey of youth ages 14-24 years in the United States. The survey asked four open ended questions regarding their experiences and perceptions regarding the long-term effects of COVID-19. Qualitative data was analyzed independently by three investigators using thematic analysis. Prevalence of codes were summarized using descriptive statistics.

RESULTS

Among 1150 participants, 991 responded to at least one survey question (response rate 86.1%). The vast majority of our sample had COVID-19 or knew someone who did (75%), and approximately one third (32%) of youth indicated that they knew someone who had experienced symptoms consistent with Long Covid. Many youth (50%) reported worry and concern about Long Covid even if they, or someone they knew, did not have Long Covid. Among youth who were not concerned about Long Covid, the most commonly reported reasons were having received the vaccine (29%) and not having a prior COVID-19 infection (24%).

CONCLUSIONS

Our findings suggest that among younger populations, there is significant concern regarding the long-term effects of COVID-19. Vaccination campaigns and youth-centered public health communication about Long Covid may not only reduce COVID-19 transmission, but also alleviate worries and concerns about Long Covid among youth.

Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis

Background

Multiple clinical phenotypes have been proposed for coronavirus disease (COVID-19), but few have used multimodal data. Using clinical and imaging data, we aimed to identify distinct clinical phenotypes in patients admitted with COVID-19 and to assess their clinical outcomes. Our secondary objective was to demonstrate the clinical applicability of this method by developing an interpretable model for phenotype assignment.

Methods

We analyzed data from 547 patients hospitalized with COVID-19 at a Canadian academic hospital. We processed the data by applying a factor analysis of mixed data (FAMD) and compared four clustering algorithms: k-means, partitioning around medoids (PAM), and divisive and agglomerative hierarchical clustering. We used imaging data and 34 clinical variables collected within the first 24 h of admission to train our algorithm. We conducted a survival analysis to compare the clinical outcomes across phenotypes. With the data split into training and validation sets (75/25 ratio), we developed a decision-tree-based model to facilitate the interpretation and assignment of the observed phenotypes.

Results

Agglomerative hierarchical clustering was the most robust algorithm. We identified three clinical phenotypes: 79 patients (14%) in Cluster 1, 275 patients (50%) in Cluster 2, and 203 (37%) in Cluster 3. Cluster 2 and Cluster 3 were both characterized by a low-risk respiratory and inflammatory profile but differed in terms of demographics. Compared with Cluster 3, Cluster 2 comprised older patients with more comorbidities. Cluster 1 represented the group with the most severe clinical presentation, as inferred by the highest rate of hypoxemia and the highest radiological burden. Intensive care unit (ICU) admission and mechanical ventilation risks were the highest in Cluster 1. Using only two to four decision rules, the classification and regression tree (CART) phenotype assignment model achieved an AUC of 84% (81.5-86.5%, 95 CI) on the validation set.

Conclusions

We conducted a multidimensional phenotypic analysis of adult inpatients with COVID-19 and identified three distinct phenotypes associated with different clinical outcomes. We also demonstrated the clinical usability of this approach, as phenotypes can be accurately assigned using a simple decision tree. Further research is still needed to properly incorporate these phenotypes in the management of patients with COVID-19.

Effectiveness of the Air-Filled Technique to Reduce the Dead Space in Syringes and Needles during ChAdox1-n CoV Vaccine Administration

In the current study, we calculated the vaccine volume and amount of dead space in a syringe and needle during ChAdox1-n CoV vaccine administration using the air-filled technique. The aim is to reduce the dead space in syringes and needles in order to administer up to 12 doses per vial. The hypothetical situation uses a vial with a similar size as the ChAdox1-n CoV vial. We used distilled water (6.5 mL) to fill the same volume as five vials of ChAdox1-n CoV. When 0.48 mL of distilled water is drawn according to the number on the side of the barrel, an additional 0.10 mL of air can be used in the dead space of the distilled water in the syringe and needle for 60 doses, which can be divided into an average of 0.5 mL per dose. ChAdox1-n CoV was administered using a 1-mL syringe and 25G needle into 12 doses using this air-filled technique. The volume of the recipient vaccine will increase by 20% and save on the budget for low dead space syringes (LDS).

Predicting In-Hospital Mortality in Severe COVID-19: A Systematic Review and External Validation of Clinical Prediction Rules

Multiple prediction models for risk of in-hospital mortality from COVID-19 have been developed, but not applied, to patient cohorts different to those from which they were derived. The MEDLINE, EMBASE, Scopus, and Web of Science (WOS) databases were searched. Risk of bias and applicability were assessed with PROBAST. Nomograms, whose variables were available in a well-defined cohort of 444 patients from our site, were externally validated. Overall, 71 studies, which derived a clinical prediction rule for mortality outcome from COVID-19, were identified. Predictive variables consisted of combinations of patients′ age, chronic conditions, dyspnea/taquipnea, radiographic chest alteration, and analytical values (LDH, CRP, lymphocytes, D-dimer); and markers of respiratory, renal, liver, and myocardial damage, which were mayor predictors in several nomograms. Twenty-five models could be externally validated. Areas under receiver operator curve (AUROC) in predicting mortality ranged from 0.71 to 1 in derivation cohorts; C-index values ranged from 0.823 to 0.970. Overall, 37/71 models provided very-good-to-outstanding test performance. Externally validated nomograms provided lower predictive performances for mortality in their respective derivation cohorts, with the AUROC being 0.654 to 0.806 (poor to acceptable performance). We can conclude that available nomograms were limited in predicting mortality when applied to different populations from which they were derived.

SARS-CoV-2 pre-exposure prophylaxis: A potential COVID-19 preventive strategy for high-risk populations, including healthcare workers, immunodeficient individuals, and poor vaccine responders

The unprecedented worldwide spread of SARS-CoV-2 has imposed severe challenges on global health care systems. The roll-out and widespread administration of COVID-19 vaccines has been deemed a major milestone in the race to restrict the severity of the infection. Vaccines have as yet not entirely suppressed the relentless progression of the pandemic, due mainly to the emergence of new virus variants, and also secondary to the waning of protective antibody titers over time. Encouragingly, an increasing number of antiviral drugs, such as remdesivir and the newly developed drug combination, Paxlovid® (nirmatrelvir/ritonavir), as well as molnupiravir, have shown significant benefits for COVID-19 patient outcomes. Pre-exposure prophylaxis (PrEP) has been proven to be an effective preventive strategy in high-risk uninfected people exposed to HIV. Building on knowledge from what is already known about the use of PrEP for HIV disease, and from recently gleaned knowledge of antivirals used against COVID-19, we propose that SARS-CoV-2 PrEP, using specific antiviral and adjuvant drugs against SARS-CoV-2, may represent a novel preventive strategy for high-risk populations, including healthcare workers, immunodeficient individuals, and poor vaccine responders. Herein, we critically review the risk factors for severe COVID-19 and discuss PrEP strategies against SARS-CoV-2. In addition, we outline details of candidate anti-SARS-CoV-2 PrEP drugs, thus creating a framework with respect to the development of alternative and/or complementary strategies to prevent COVID-19, and contributing to the global armamentarium that has been developed to limit SARS-CoV-2 infection, severity, and transmission.

Disparities in COVID-19 Outcomes by Race, Ethnicity, and Socioeconomic Status: A Systematic-Review and Meta-analysis

IMPORTANCE

COVID-19 has disproportionately affected racial and ethnic minority groups, and race and ethnicity have been associated with disease severity. However, the association of socioeconomic determinants with racial disparities in COVID-19 outcomes remains unclear.

OBJECTIVE

To evaluate the association of race and ethnicity with COVID-19 outcomes and to examine the association between race, ethnicity, COVID-19 outcomes, and socioeconomic determinants.

DATA SOURCES

A systematic search of PubMed, medRxiv, bioRxiv, Embase, and the World Health Organization COVID-19 databases was performed for studies published from January 1, 2020, to January 6, 2021.

STUDY SELECTION

Studies that reported data on associations between race and ethnicity and COVID-19 positivity, disease severity, and socioeconomic status were included and screened by 2 independent reviewers. Studies that did not have a satisfactory quality score were excluded. Overall, less than 1% (0.47%) of initially identified studies met selection criteria.

DATA EXTRACTION AND SYNTHESIS

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Associations were assessed using adjusted and unadjusted risk ratios (RRs) and odds ratios (ORs), combined prevalence, and metaregression. Data were pooled using a random-effects model.

MAIN OUTCOMES AND MEASURES

The main measures were RRs, ORs, and combined prevalence values.

RESULTS

A total of 4 318 929 patients from 68 studies were included in this meta-analysis. Overall, 370 933 patients (8.6%) were African American, 9082 (0.2%) were American Indian or Alaska Native, 101 793 (2.4%) were Asian American, 851 392 identified as Hispanic/Latino (19.7%), 7417 (0.2%) were Pacific Islander, 1 037 996 (24.0%) were White, and 269 040 (6.2%) identified as multiracial and another race or ethnicity. In age- and sex-adjusted analyses, African American individuals (RR, 3.54; 95% CI, 1.38-9.07; P = .008) and Hispanic individuals (RR, 4.68; 95% CI, 1.28-17.20; P = .02) were the most likely to test positive for COVID-19. Asian American individuals had the highest risk of intensive care unit admission (RR, 1.93; 95% CI, 1.60-2.34, P < .001). The area deprivation index was positively correlated with mortality rates in Asian American and Hispanic individuals (P < .001). Decreased access to clinical care was positively correlated with COVID-19 positivity in Hispanic individuals (P < .001) and African American individuals (P < .001).

CONCLUSIONS AND RELEVANCE

In this study, members of racial and ethnic minority groups had higher risks of COVID-19 positivity and disease severity. Furthermore, socioeconomic determinants were strongly associated with COVID-19 outcomes in racial and ethnic minority populations.

What Every Diabetologist Should Know about SARS-CoV-2: State of Knowledge at the Beginning of 2021

For almost a year, the major medical problem has been the pandemic caused by the SARS-CoV-2 virus. People with diabetes who contract COVID-19 are likely to experience more serious symptoms than patients without diabetes. This article presents new research about the epidemiology of COVID-19 in a group of patients with diabetes. It details the mortality and prognosis in such patients, as well as the relationship between COVID-19 and the diseases most often coexisting with diabetes: obesity, atherosclerosis, hypertension, and increased risk for infection. It also details how the virus infects and affects patients with hyperglycemia. The context of glycation and receptors for advanced glycation products (RAGE) seems to be of particular importance here. We also present a hypothesis related to the cause-and-effect axis-it turns out that diabetes can be both the cause of the more difficult course of COVID-19 and the result of SARS-CoV-2 infection. The last part of this article discusses the impact of antihyperglycemic drugs on the development of COVID-19 and other pharmacological implications, including which non-classical antihyperglycemic drugs seem to be effective in both the treatment of coronavirus infection and glucose homeostasis, and what strategies related to RAGE and glycation should be considered.