Leveraging Advanced Analytics to Understand the Impact of the COVID-19 Pandemic on Trends in Substance Use Disorders

Abstract

Coronavirus disease (COVID-19) caused an overwhelming healthcare, economic, social, and psychological impact on the world during 2020 and first part of 2021. Certain populations, especially those with Substance Use Disorders (SUD), were particularly vulnerable to contract the virus and also likely to suffer from a greater psychosocial and psychological burden. COVID-19 and addiction are two conditions on the verge of a collision, potentially causing a major public health threat. There is surge of addictive behaviors (both new and relapse), including use of alcohol, nicotine, and recreational drugs. This book chapter analyzed the bi-directional relationship between COVID-19 and SUD by leveraging descriptive summaries, advanced analytics, and machine learning approaches. The data sources included healthcare claims dataset as well as state level alcohol consumption to help in investigating the bi-directional relationship between the two conditions. Results suggest that alcohol and nicotine use increased during the pandemic and that the profile of SUD patients included diagnoses and symptoms of COVID-19, depression and anxiety, as well as hypertensive conditions.

Adoption of machine learning systems within the health sector: a systematic review, synthesis and research agenda

“…In order to identify essential individuals for the purpose of pharmaceutical review and analysis, these computers are able to handle hundreds, if not millions, of simulations. Drug discovery models that are enabled by ML might save years or even decades off of the design process, and hence lowering the overall cost of manufacturing medication and shortening the amount of time it takes to treat a patient (Kleczyk et al. , 2021).…”

“…, 2021; Ahmed, Mohamed, Zeeshan, & Dong, 2020; Bravo, 2022; Carriere et al. , 2021; Char, Shah, & Magnus, 2018; Elfiky, Pany, Parikh, & Obermeyer, 2018; Kleczyk, Bana, & Arora, 2021; Panesar et al. , 2019; Sabry, Eltaras, Labda, Alzoubi, & Malluhi, 2022; Shinozaki, 2019).…”

“…The growing adoption in ML is evident in improving the health sector in areas such as diagnostic, clinical event prediction and the mortality trend prediction. Previous systematic literature reviews on adoption of ML in the health sector have highlighted the following (Abbasgholizadeh Rahimi et al, 2021;Ahmed, Mohamed, Zeeshan, & Dong, 2020;Bravo, 2022;Carriere et al, 2021;Char, Shah, & Magnus, 2018;Elfiky, Pany, Parikh, & Obermeyer, 2018;Kleczyk, Bana, & Arora, 2021;Panesar et al, 2019;Sabry, Eltaras, Labda, Alzoubi, & Malluhi, 2022;Shinozaki, 2019). Firstly, the accumulation of electrical health records has resulted in a meteoric rise in the volume of healthcare data, which has placed an enormous demand on healthcare technology and applications.…”