Alcohol consumption and obesity: The hidden scare with COVID-19 severity

The hidden cost of sophistication: economic complexity and obesity

Obesity has become a global health crisis, affecting people of all ages, regions, and socio-economic backgrounds. While individual behaviour and genetic factors contribute to obesity, the role of economic complexity in the evolution of obesity rates has not yet been empirically studied. Using a large panel of 110 countries over the period 1976-2015, this article estimates the linear and non-linear links between obesity and economic complexity. According to baseline results, an improvement in economic complexity will lead to an increase in obesity up to a certain threshold. Beyond this turning point, any further increase in economic complexity will significantly contribute to obesity reduction. The issue of simultaneity is tackled using the two-stage instrumental variable method. Our findings support the Obesity Kuznets Curve (OKC) pattern, which suggests that economic progress and obesity have an inverted U-shaped relationship. Our results suggest that greater embeddedness of knowledge in the products produced and exported by a country increases the likelihood of obesity in society, at least up to a threshold. From these results, some important policy implications are discussed.

Substances of abuse and their effect on SAR-CoV-2 pathogenesis

Following the emergence of SARS-CoV-2, various reports suggest that there has been a significant increase in substance abuse due to social distancing and related issues. Several reports have suggested the impact of chronic substance use on individuals' physiological and psychological health. Therefore, there is a need to know the impact of SARS-CoV-2 on persons with substance use disorders. Individuals with substance use disorders are the most vulnerable groups and are at a high risk of SARS-CoV-2 infection due to their already existing health issues associated with substance use. This review discusses some of the molecular and systemic/organic effects chronic substance use such as alcohol, nicotine, marijuana (cannabis), opioids, methamphetamine, and cocaine have on SARS-CoV-2 infectivity and its potential cause for worsened disease outcomes in persons with substance use disorder. This will provide healthcare providers, public health policies, and researchers with the needed knowledge to address some of the many challenges faced during the Covid-19 pandemic to facilitate treatment strategies for persons with substance use disorders.

MELD-Na score, Acute Physiologic and Chronic Health Evaluation II score, and SOFA score and their association with mortality in critically ill COVID-19 patients with liver injury: A retrospective single-center study

Background

The Acute Physiologic and Chronic Health Evaluation II (APACHE-II), Sequential Organ Failure Assessment (SOFA), and Model for End-Stage Liver Disease modified for Sodium concentration (MELD-Na) scores are validated to predict disease mortality. We studied the prognostic utility of these scoring systems in critically ill coronavirus disease 2019 (COVID-19) patients with liver injury.

Methods

This was a retrospective study of 291 confirmed COVID-19 and liver injury patients requiring intensive care unit level of care. These patients required supplemental oxygen requirement with fraction of inspired oxygen >55% and/or the use of vasopressor. MELD-Na, SOFA, and APACHE-II scores were adjusted. Outcomes were mortality and length of stay (LOS).

Results

SOFA (odds ratio: 0.78, 95% confidence interval: 0.63-0.98, P < 0.05) was associated with decreased odds for mortality. APACHE-II and MELD-Na were not associated with mortality or LOS.

Conclusions

We suggest that the novel nature of COVID-19 necessitates new scoring systems to predict outcomes in critically ill COVID-19 patients with liver injury.

Identifying Predictors of COVID-19 Mortality Using Machine Learning

(1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to investigate COVID-19 mortality in patients with pre-existing health conditions and to examine the association between COVID-19 mortality and other morbidities. (2) Methods: De-identified data from 113,882, including 14,877 COVID-19 patients, were collected from the UK Biobank. Different types of data, such as disease history and lifestyle factors, from the COVID-19 patients, were input into the following three machine learning models: Deep Neural Networks (DNN), Random Forest Classifier (RF), eXtreme Gradient Boosting classifier (XGB) and Support Vector Machine (SVM). The Area under the Curve (AUC) was used to measure the experiment result as a performance metric. (3) Results: Data from 14,876 COVID-19 patients were input into the machine learning model for risk-level mortality prediction, with the predicted risk level ranging from 0 to 1. Of the three models used in the experiment, the RF model achieved the best result, with an AUC value of 0.86 (95% CI 0.84-0.88). (4) Conclusions: A risk-level prediction model for COVID-19 mortality was developed. Age, lifestyle, illness, income, and family disease history were identified as important predictors of COVID-19 mortality. The identified factors were related to COVID-19 mortality.

Investigating the effect of macro-scale estimators on worldwide COVID-19 occurrence and mortality through regression analysis using online country-based data sources

OBJECTIVE

To investigate macro-scale estimators of the variations in COVID-19 cases and deaths among countries.

DESIGN

Epidemiological study.

SETTING

Country-based data from publicly available online databases of international organisations.

PARTICIPANTS

The study involved 170 countries/territories, each of which had complete COVID-19 and tuberculosis data, as well as specific health-related estimators (obesity, hypertension, diabetes and hypercholesterolaemia).

PRIMARY AND SECONDARY OUTCOME MEASURES

The worldwide heterogeneity of the total number of COVID-19 cases and deaths per million on 31 December 2020 was analysed by 17 macro-scale estimators around the health-related, socioeconomic, climatic and political factors. In 139 of 170 nations, the best subsets regression was used to investigate all potential models of COVID-19 variations among countries. A multiple linear regression analysis was conducted to explore the predictive capacity of these variables. The same analysis was applied to the number of deaths per hundred thousand due to tuberculosis, a quite different infectious disease, to validate and control the differences with the proposed models for COVID-19.

RESULTS

In the model for the COVID-19 cases (R²=0.45), obesity (β=0.460), hypertension (β=0.214), sunshine (β=-0.157) and transparency (β=0.147); whereas in the model for COVID-19 deaths (R²=0.41), obesity (β=0.279), hypertension (β=0.285), alcohol consumption (β=0.173) and urbanisation (β=0.204) were significant factors (p<0.05). Unlike COVID-19, the tuberculosis model contained significant indicators like obesity, undernourishment, air pollution, age, schooling, democracy and Gini Inequality Index.

CONCLUSIONS

This study recommends the new predictors explaining the global variability of COVID-19. Thus, it might assist policymakers in developing health policies and social strategies to deal with COVID-19.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT04486508).

Alcohol Use and the Risk of Communicable Diseases

The body of knowledge on alcohol use and communicable diseases has been growing in recent years. Using a narrative review approach, this paper discusses alcohol's role in the acquisition of and treatment outcomes from four different communicable diseases: these include three conditions included in comparative risk assessments to date-Human Immunodeficiency Virus (HIV)/AIDS, tuberculosis (TB), and lower respiratory infections/pneumonia-as well as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) because of its recent and rapid ascension as a global health concern. Alcohol-attributable TB, HIV, and pneumonia combined were responsible for approximately 360,000 deaths and 13 million disability-adjusted life years lost (DALYs) in 2016, with alcohol-attributable TB deaths and DALYs predominating. There is strong evidence that alcohol is associated with increased incidence of and poorer treatment outcomes from HIV, TB, and pneumonia, via both behavioral and biological mechanisms. Preliminary studies suggest that heavy drinkers and those with alcohol use disorders are at increased risk of COVID-19 infection and severe illness. Aside from HIV research, limited research exists that can guide interventions for addressing alcohol-attributable TB and pneumonia or COVID-19. Implementation of effective individual-level interventions and alcohol control policies as a means of reducing the burden of communicable diseases is recommended.