A Machine Learning Analysis of Impact of the Covid-19 Pandemic on Alcohol Consumption Habit Changes Among Healthcare Workers in the U.S

TL;DR

This study uses machine learning techniques to analyze survey data and identify factors related to increased alcohol consumption among U.S. healthcare workers during the COVID-19 pandemic.

Contribution

It applies multiple machine learning models to survey data to uncover specific pandemic-related factors influencing alcohol habit changes.

Findings

School closures linked to increased alcohol use

Work schedule changes associated with drinking habits

Exposure to COVID-19 news correlates with alcohol consumption

Abstract

In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States. We utilize multiple supervised and unsupervised machine learning methods and models such as Decision Trees, Logistic Regression, Naive Bayes classifier, k-Nearest Neighbors, Support Vector Machines, Multilayer perceptron, XGBoost, CatBoost, LightGBM, Chi-Squared Test and mutual information method on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to find out relationships between COVID-19 related negative effects and alcohol consumption habit changes among healthcare workers. Our findings suggest that COVID-19-related school closures, COVID-19-related work schedule changes and COVID-related news exposure may lead to an increase in alcohol use among healthcare workers in the United States.