Geospatial-Temporal and Demand Models for Opioid Admissions, Implications for Policy.

Area of Science:

• Public Health
• Epidemiology
• Geospatial Analysis

Background:

• The ongoing opioid epidemic necessitates understanding its patterns for effective resource allocation and interdiction.
• Policymakers require insights into geospatial, temporal, and demand dynamics to combat opioid misuse.

Purpose of the Study:

• To analyze geospatial and temporal patterns of opioid-related admissions.
• To identify emerging hotspots and high-admission rate regions.
• To evaluate factors influencing opioid admission rates for resource allocation.

Main Methods:

• Geographic Information System (GIS) geospatial-temporal analysis.
• Evaluation of ICD-10 F11 opioid-related admissions and rates.
• Extreme-gradient boosted random forests for explanatory modeling and demand analysis.

Main Results:

• Opioid admissions show geographic spread, with established high rates in Chicago and New Jersey, and emerging areas in Atlanta, Salt Lake City, Phoenix, and Las Vegas.
• High admission rates (claims per 10,000 population) are concentrated in the Appalachian region and along the Northeastern seaboard.
• Explanatory models indicate hospital workload and financial variables are key for effective treatment fund allocation, with random forest models explaining 87.8% of claim variability.

Conclusions:

• Opioid admissions exhibit geographic expansion, though high-frequency areas persist.
• Demand analysis is essential for optimizing resource allocation in supply-side and demand-side interdiction strategies.
• Effective interdiction requires a multi-faceted approach encompassing prevention, treatment, and enforcement.