Can Big Data Predict the Rise of Novel Drug Abuse?

被引:27
|
作者
Perdue, Robert Todd [1 ]
Hawdon, James [2 ]
Thames, Kelly M. [3 ]
机构
[1] Elon Univ, 100 Campus Dr, Elon, NC 27244 USA
[2] Virginia Polytech Inst & State Univ, Blacksburg, VA 24061 USA
[3] Appalachian State Univ, Sociol, Boone, NC 28608 USA
关键词
novel drugs; opioids; big data; Google Trends; OxyContin; heroin; DISEASE; TRENDS;
D O I
10.1177/0022042618772294
中图分类号
R194 [卫生标准、卫生检查、医药管理];
学科分类号
摘要
Existing novel psychoactive drug (NPD) data are woefully inadequate. This gap is especially critical because NPDs are being developed and introduced at alarming rates and pose significant challenges to law enforcement and health care workers. Scholars in numerous fields have used Internet search query analysis to assess and predict health-related outcomes. Here, we explore the utility of these data for predicting NPD and established drug abuse. Google Trends searches for five novel and two established drugs were correlated with data pulled from the Monitoring the Future (MTF). Google Trends data proved highly correlated with data from MTF for all drugs analyzed. Despite limitations, Google Trends appears to be a promising compliment to existing data, providing real time data that may allow us to predict drug abuse trends and respond more quickly.
引用
收藏
页码:508 / 518
页数:11
相关论文
共 50 条