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The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States. [ABSTRACT FROM AUTHOR]

Background and aims: Alcohol consumption increased in the early phases of the COVID‐19 pandemic in the United States. Alcohol use disorder (AUD) and risky drinking are linked to harmful health effects. This paper aimed to project future health and cost impacts of shifts in alcohol consumption during the COVID‐19 pandemic. Design: An individual‐level simulation model of the long‐term drinking patterns for people with life‐time AUD was used to simulate 10 000 individuals and project model outcomes to the estimated 25.9 million current drinkers with life‐time AUD in the United States. The model considered three scenarios: (1) no change (counterfactual for comparison); (2) increased drinking levels persist for 1 year ('increase‐1') and (3) increased drinking levels persist for 5 years ('increase‐5'). Setting: United States. Participants: Current drinkers with life‐time AUD. Measurements: Life expectancy [life‐years (LYs)], quality‐adjusted life‐years (QALYs), alcohol‐related hospitalizations and associated hospitalization costs and alcohol‐related deaths, during a 5‐year period. Findings Short‐term increases in alcohol consumption (increase‐1 scenario) resulted in a loss of 79 000 [95% uncertainty interval (UI]) 26 000–201 000] LYs, a loss of 332 000 (104 000–604 000) QALYs and 295 000 (82 000–501 000) more alcohol‐related hospitalizations, costing an additional $5.4 billion ($1.5–9.3 billion) over 5 years. Hospitalizations for cirrhosis of the liver accounted for approximately $3.0 billion ($0.9–4.8 billion) in hospitalization costs, more than half the increase across all alcohol‐related conditions. Health and cost impacts were more pronounced for older age groups (51+), women and non‐Hispanic black individuals. Increasing the duration of pandemic‐driven increases in alcohol consumption in the increase‐5 scenario resulted in larger impacts. Conclusions: Simulations show that if the increase in alcohol consumption observed in the United States in the first year of the pandemic continues, alcohol‐related mortality, morbidity and associated costs will increase substantially over the next 5 years. [ABSTRACT FROM AUTHOR]