TY - GEN
T1 - Effects of COVID-19 on individuals in Opioid Addiction Recovery
AU - Saifuddin, Khaled Mohammed
AU - Akbas, Esra
AU - Khanov, Max
AU - Beaman, Jason
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Opioid Use Disorder (OUD) is one of the most severe health care problems in the USA. People addicted to opioids need various treatments, including Medication-Assisted Treatment (MAT), proper counseling, and behavioral therapies. However, during the peak time of the COVID-19 pandemic, the supply of emergency medications was disrupted seriously. Patients faced severe medical care scarcity since many pharmaceutical companies, drugstores, and local pharmacies were closed. Import-export was also canceled to consent to the government emergency law, i.e., lockdown, quarantine, and isolation. These circumstances and their negative effects on OUD patient's psychology could have led them to a drop out of MAT medications and persuaded to resume illicit opioid use. This project involves collecting and analyzing a large volume of Twitter data related to MAT medications for OUD patients. We discover the Active MAT Medicine Users (AMMUs) on twitter. For this, we build a seed dictionary of words related to OUD and MAT and apply association rules to expand it. Further, AMMUs' tweet posts are studied 'before the pandemic' (BP) and 'during the pandemic' (DP) to understand how the drug behaviors and habits have changed due to COVID-19. We also perform sentiment analysis on Tweets to determine the impact of the COVID-19 pandemic on the psychology of AMMUs. Our analysis shows that the use of MAT medications has decreased around 30.54%, where the use of illicit drugs and other prescription opioids increased 18.06% and 12.12%, respectively, based on AMMUs' tweets posted during the lockdown compared with before the lockdown statistics. The COVID-19 pandemic and lockdown may result in the resumption of illegal and prescription opioid abuse by OUD patients. Necessary steps and precautions should be taken by health care providers to ensure the emergency supply of medicines and also psychological support and thus prevent patients from illicit opioid use.
AB - Opioid Use Disorder (OUD) is one of the most severe health care problems in the USA. People addicted to opioids need various treatments, including Medication-Assisted Treatment (MAT), proper counseling, and behavioral therapies. However, during the peak time of the COVID-19 pandemic, the supply of emergency medications was disrupted seriously. Patients faced severe medical care scarcity since many pharmaceutical companies, drugstores, and local pharmacies were closed. Import-export was also canceled to consent to the government emergency law, i.e., lockdown, quarantine, and isolation. These circumstances and their negative effects on OUD patient's psychology could have led them to a drop out of MAT medications and persuaded to resume illicit opioid use. This project involves collecting and analyzing a large volume of Twitter data related to MAT medications for OUD patients. We discover the Active MAT Medicine Users (AMMUs) on twitter. For this, we build a seed dictionary of words related to OUD and MAT and apply association rules to expand it. Further, AMMUs' tweet posts are studied 'before the pandemic' (BP) and 'during the pandemic' (DP) to understand how the drug behaviors and habits have changed due to COVID-19. We also perform sentiment analysis on Tweets to determine the impact of the COVID-19 pandemic on the psychology of AMMUs. Our analysis shows that the use of MAT medications has decreased around 30.54%, where the use of illicit drugs and other prescription opioids increased 18.06% and 12.12%, respectively, based on AMMUs' tweets posted during the lockdown compared with before the lockdown statistics. The COVID-19 pandemic and lockdown may result in the resumption of illegal and prescription opioid abuse by OUD patients. Necessary steps and precautions should be taken by health care providers to ensure the emergency supply of medicines and also psychological support and thus prevent patients from illicit opioid use.
KW - COVID-19
KW - Drug Abuse
KW - Lockdown
KW - MAT
KW - OUD
UR - http://www.scopus.com/inward/record.url?scp=85125840144&partnerID=8YFLogxK
U2 - 10.1109/ICMLA52953.2021.00216
DO - 10.1109/ICMLA52953.2021.00216
M3 - Conference contribution
AN - SCOPUS:85125840144
T3 - Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
SP - 1333
EP - 1340
BT - Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
A2 - Wani, M. Arif
A2 - Sethi, Ishwar K.
A2 - Shi, Weisong
A2 - Qu, Guangzhi
A2 - Raicu, Daniela Stan
A2 - Jin, Ruoming
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Y2 - 13 December 2021 through 16 December 2021
ER -