Drug and alcohol dependence · 2018
Internet searches for opioids predict future emergency department heroin admissions.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАПоддержка правительства СШАНазвание и аннотация: Перевод в очереди
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Young Sean D, Zheng Kai, Chu Larry F, Humphreys Keith
Аннотация
BACKGROUND: For a number of fiscal and practical reasons, data on heroin use have been of poor quality, which has hampered the ability to halt the growing epidemic. Internet search data, such as those made available by Google Trends, have been used as a low-cost, real-time data source for monitoring and predicting a variety of public health outcomes. We aimed to determine whether data on opioid-related internet searches might predict future heroin-related admissions to emergency departments (ED).
METHODS: Across nine metropolitan statistical areas (MSAs) in the United States, we obtained data on Google searches for prescription and non-prescription opioids, as well as Substance Abuse and Mental Health Services Administration (SAMHSA) data on heroin-related ED visits from 2004 to 2011. A linear mixed model assessed the relationship between opioid-related Internet searches and following year heroin-related visits, controlling for MSA GINI index and total number of ED visits.
RESULTS: The best-fitting model explained 72% of the variance in heroin-related ED visits. The final model included the search keywords "Avinza," "Brown Sugar," "China White," "Codeine," "Kadian," "Methadone," and "Oxymorphone." We found regional differences in where and how people searched for opioid-related information.
CONCLUSIONS: Internet search-based modeling should be explored as a new source of insights for predicting heroin-related admissions. In geographic regions where no current heroin-related data exist, Internet search modeling might be a particularly valuable and inexpensive tool for estimating changing heroin use trends. We discuss the immediate implications for using this approach to assist in managing opioid-related morbidity and mortality in the United States.
METHODS: Across nine metropolitan statistical areas (MSAs) in the United States, we obtained data on Google searches for prescription and non-prescription opioids, as well as Substance Abuse and Mental Health Services Administration (SAMHSA) data on heroin-related ED visits from 2004 to 2011. A linear mixed model assessed the relationship between opioid-related Internet searches and following year heroin-related visits, controlling for MSA GINI index and total number of ED visits.
RESULTS: The best-fitting model explained 72% of the variance in heroin-related ED visits. The final model included the search keywords "Avinza," "Brown Sugar," "China White," "Codeine," "Kadian," "Methadone," and "Oxymorphone." We found regional differences in where and how people searched for opioid-related information.
CONCLUSIONS: Internet search-based modeling should be explored as a new source of insights for predicting heroin-related admissions. In geographic regions where no current heroin-related data exist, Internet search modeling might be a particularly valuable and inexpensive tool for estimating changing heroin use trends. We discuss the immediate implications for using this approach to assist in managing opioid-related morbidity and mortality in the United States.