Internet search and medicaid prescription drug data as predictors of opioid emergency department visits

Abstract The primary contributors to the opioid crisis continue to rapidly evolve both geographically and temporally, hampering the ability to halt the growing epidemic. To address this issue, we evaluated whether integration of near real-time social/behavioral (i.e., Google Trends) and traditional...

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Autores principales:	Sean D. Young, Qingpeng Zhang, Jiandong Zhou, Rosalie Liccardo Pacula
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/dff4b85615994089b4844b3bf218c380
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spelling	oai:doaj.org-article:dff4b85615994089b4844b3bf218c3802021-12-02T14:39:24ZInternet search and medicaid prescription drug data as predictors of opioid emergency department visits10.1038/s41746-021-00392-w2398-6352https://doaj.org/article/dff4b85615994089b4844b3bf218c3802021-02-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00392-whttps://doaj.org/toc/2398-6352Abstract The primary contributors to the opioid crisis continue to rapidly evolve both geographically and temporally, hampering the ability to halt the growing epidemic. To address this issue, we evaluated whether integration of near real-time social/behavioral (i.e., Google Trends) and traditional health care (i.e., Medicaid prescription drug utilization) data might predict geographic and longitudinal trends in opioid-related Emergency Department (ED) visits. From January 2005 through December 2015, we collected quarterly State Drug Utilization Data; opioid-related internet search terms/phrases; and opioid-related ED visit data. Modeling was conducted using least absolute shrinkage and selection operator (LASSO) regression prediction. Models combining Google and Medicaid variables were a better fit and more accurate (R 2 values from 0.913 to 0.960, across states) than models using either data source alone. The combined model predicted sharp and state-specific changes in ED visits during the post 2013 transition from heroin to fentanyl. Models integrating internet search and drug utilization data might inform policy efforts about regional medical treatment preferences and needs.Sean D. YoungQingpeng ZhangJiandong ZhouRosalie Liccardo PaculaNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Sean D. Young Qingpeng Zhang Jiandong Zhou Rosalie Liccardo Pacula Internet search and medicaid prescription drug data as predictors of opioid emergency department visits
description	Abstract The primary contributors to the opioid crisis continue to rapidly evolve both geographically and temporally, hampering the ability to halt the growing epidemic. To address this issue, we evaluated whether integration of near real-time social/behavioral (i.e., Google Trends) and traditional health care (i.e., Medicaid prescription drug utilization) data might predict geographic and longitudinal trends in opioid-related Emergency Department (ED) visits. From January 2005 through December 2015, we collected quarterly State Drug Utilization Data; opioid-related internet search terms/phrases; and opioid-related ED visit data. Modeling was conducted using least absolute shrinkage and selection operator (LASSO) regression prediction. Models combining Google and Medicaid variables were a better fit and more accurate (R 2 values from 0.913 to 0.960, across states) than models using either data source alone. The combined model predicted sharp and state-specific changes in ED visits during the post 2013 transition from heroin to fentanyl. Models integrating internet search and drug utilization data might inform policy efforts about regional medical treatment preferences and needs.
format	article
author	Sean D. Young Qingpeng Zhang Jiandong Zhou Rosalie Liccardo Pacula
author_facet	Sean D. Young Qingpeng Zhang Jiandong Zhou Rosalie Liccardo Pacula
author_sort	Sean D. Young
title	Internet search and medicaid prescription drug data as predictors of opioid emergency department visits
title_short	Internet search and medicaid prescription drug data as predictors of opioid emergency department visits
title_full	Internet search and medicaid prescription drug data as predictors of opioid emergency department visits
title_fullStr	Internet search and medicaid prescription drug data as predictors of opioid emergency department visits
title_full_unstemmed	Internet search and medicaid prescription drug data as predictors of opioid emergency department visits
title_sort	internet search and medicaid prescription drug data as predictors of opioid emergency department visits
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dff4b85615994089b4844b3bf218c380
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Internet search and medicaid prescription drug data as predictors of opioid emergency department visits

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