The epidemic of drug abuse is a serious public health issue in the U.S. The number of overdose deaths involving prescription opioids and illicit drugs has continuously increased over the last few years. The objective of this study is to develop a geospatial model that identifies geospatial clusters in terms of socioeconomic, and demographic characteristics with an unsupervised machine learning algorithm. Then, we suggest the most important features affecting heroin overdose both negatively and positively. The findings of this study may inform policymakers about strategies to mitigate the drug overdose crisis.

This study investigates safety and efficacy of a large-dose, needle-based epidural technique where the anesthetic dose is administered through an epidural needle prior to insertion of the epidural catheter. Using a data-driven machine learning (ML) approach, the findings show that the needle-based approach is faster and more dose-effective in achieving sensory level than the catheter-based approach. The authors also find that injecting large doses in the epidural space through the needle is safe. And a needle dose of at most 18 ml offers lower hypotension complication. ML predicts hypotension with 85% accuracy and shows that total dose, injection duration, weight, and physician experience are top features impacting sensory level. The findings facilitate pain relief improvement and establish new clinical practice guideline for training and dissemination of safe administration. The successful prediction of hypotension allows for early intervention. Although almost 50% of drug combinations used involve fentanyl, the findings show that fentanyl has little effect on outcome and should be avoided.

Background Drug overdose is one of the top leading causes of accidental death in the U.S., largely due to the opioid epidemic. Although the opioid epidemic is a nationwide issue, it has not affected the nation uniformly. Methods We combined multiple data sources, including emergency medical service response, American Community Survey data, and health facilities datasets to analyze distributions of heroin-related overdose incidents in Cincinnati, Ohio at the census block group level. The Ripley’s K function and the local Moran’s I statistics were performed to examine geographic variation patterns in heroin-related overdose incidents within the study area. Then, conditional cluster maps were plotted to examine a relationship between heroin-related incident rates and sociodemographic characteristics of areas as well as the resources for opioid use disorder treatment. Results The global spatial analysis indicated that there was a clustered pattern of heroin-related overdose incident rates at every distance across the study area. The univariate local spatial analysis identified 7 hot spot clusters, 27 cold spot clusters, and 1 outlier cluster. Conditional cluster maps showed characteristics of neighborhoods with high heroin overdose rates, such as a higher crime rate, a high percentage of the male, a high poverty level, a lower education level, and a lower income level. The hot spots in the Southwest areas of Cincinnati had longer distances to opioid treatment programs and buprenorphine prescribing physicians than the median, while the hot spots in the South-Central areas of the city had shorter distances to those health resources. Conclusions Our study showed that the opioid epidemic disproportionately affected Cincinnati. Multi-phased spatial clustering models based on various data sources can be useful to identify areas that require more policy attention and targeted interventions to alleviate high heroin-related overdose rates.