Scientists in California have found a way to use Google searches to predict heroin overdoses, Scientific American reports.
The University of California Institute for Prediction Technology's Sean D. Young led his colleagues in creating statistical models that can "forecast overdoses" by identifying opioid-related keywords, such as "China White," "Brown Sugar," "Avinza" and "Methadone," among others.
Their research was published in the September issue of the journal Drug and Alcohol Dependence. They wrote that "Internet search data may be a resource for predicting heroin-related admissions," and "A model explained 72 percent of the variance in the next year's heroin-related admissions," and "found regional differences in Internet searches for opioid-related information."
The researchers observed nine metropolitan statistical areas, or MSAs, in the United States, using data about Google searches related to opioids and data from the Substance Abuse and Mental Health Services Administration regarding emergency department visits related to opiates.
"The best-fitting model explained 72 percent of the variance in heroin-related ED visits," they found.
"Internet search-based modeling should be explored as a new source of insights for predicting heroin-related admissions," the researchers concluded. "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."
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