Search

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.

Objectives In this study, we investigated the co-administration of ondansetron with morphine, and whether it could prevent the development of physical dependence in patients taking opioids for the treatment of chronic pain. Methods A total of 48 chronic back pain patients (N = 48) participated in this double-blinded, placebo-controlled, randomized study. Patients were titrated onto sustained-release oral morphine and randomized to take 8 mg ondansetron or placebo three times daily concurrently with morphine during the 30-day titration. Following titration, patients underwent Naloxone induced opioid withdrawal. Opioid withdrawal signs and symptoms were then assessed by a blinded research assistant (objective opioid withdrawal score: OOWS) and by the research participant (subjective opioid withdrawal score: SOWS). Results We observed clinically significant signs of naloxone-precipitated opioid withdrawal in all participants (ΔOOWS = 4.3 ± 2.4, p Conclusion We hypothesized that ondansetron would prevent the development of physical dependence in human subjects when co-administered with opioids, but found no difference in naloxone-precipitated opioid withdrawal scores between ondansetron and placebo treatment groups. These results suggest that further studies are needed to determine if 5HT 3 receptor antagonists are useful in preventing opioid physical dependence.

Background Opioid analgesics are frequently prescribed for chronic pain. One expected consequence of long-term opioid use is the development of physical dependence. Although previous resting state functional magnetic resonance imaging (fMRI) studies have demonstrated signal changes in reward-associated areas following morphine administration, the effects of acute withdrawal on the human brain have been less well-investigated. In an earlier study by our laboratory, ondansetron was shown to be effective in preventing symptoms associated with opioid withdrawal. The purpose of this current study was to characterize neural activity associated with acute opioid withdrawal and examine whether these changes are modified by ondansetron. Methods Ten participants were enrolled in this placebo-controlled, randomized, double-blind, crossover study and attended three acute opioid withdrawal sessions. Participants received either placebo or ondansetron (8 mg IV) before morphine administration (10 mg/70 kg IV). Participants then underwent acute naloxone-precipitated withdrawal during a resting state fMRI scan. Objective and subjective opioid withdrawal symptoms were assessed. Results Imaging results showed that naloxone-precipitated opioid withdrawal was associated with increased neural activity in several reward processing regions, including the right pregenual cingulate, putamen, and bilateral caudate, and decreased neural activity in networks involved in sensorimotor integration. Ondansetron pretreatment did not have a significant effect on the imaging correlates of opioid withdrawal. Conclusions This study presents a preliminary investigation of the regional changes in neural activity during acute opioid withdrawal. The fMRI acute opioid withdrawal model may serve as a tool for studying opioid dependence and withdrawal in human participants.