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dc.contributor.authorFulton, Lawrence V. ( Orcid Icon 0000-0001-8603-1913 )
dc.contributor.authorDong, Zhijie ( Orcid Icon 0000-0003-0979-812X )
dc.contributor.authorZhan, F. Benjamin ( )
dc.contributor.authorKruse, Clemens Scott ( Orcid Icon 0000-0002-7636-1086 )
dc.contributor.authorGranados, Paula Stigler ( Orcid Icon 0000-0002-8993-9879 )
dc.identifier.citationFulton, L. V., Dong, Z., Zhan, F. B., Kruse, C. S., & Stigler Granados, P. (2019). Geospatial-Temporal and Demand Models for Opioid Admissions, Implications for Policy. Journal of Clinical Medicine, 8(7).en_US

Background: As the opioid epidemic continues, understanding the geospatial, temporal, and demand patterns is important for policymakers to assign resources and interdict individual, organization, and country-level bad actors. Methods: GIS geospatial-temporal analysis and extreme-gradient boosted random forests evaluate ICD-10 F11 opioid-related admissions and admission rates using geospatial analysis, demand analysis, and explanatory models, respectively. The period of analysis was January 2016 through September 2018. Results: The analysis shows existing high opioid admissions in Chicago and New Jersey with emerging areas in Atlanta, Salt Lake City, Phoenix, and Las Vegas. High rates of admission (claims per 10,000 population) exist in the Appalachian area and on the Northeastern seaboard. Explanatory models suggest that hospital overall workload and financial variables might be used for allocating opioid-related treatment funds effectively. Gradient-boosted random forest models accounted for 87.8% of the variability of claims on blinded 20% test data. Conclusions: Based on the GIS analysis, opioid admissions appear to have spread geographically, while higher frequency rates are still found in some regions. Interdiction efforts require demand-analysis such as that provided in this study to allocate scarce resources for supply-side and demand-side interdiction: Prevention, treatment, and enforcement.

dc.format.extent18 pages
dc.format.medium1 file (.pdf)
dc.sourceJournal of Clinical Medicine, 2019, Vol. 8, No. 7
dc.subjectRandom forests
dc.titleGeospatial-Temporal and Demand Models for Opioid Admissions, Implications for Policyen_US
txstate.departmentHealth Administration



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