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

2019 ◽  
Vol 8 (7) ◽  
pp. 993 ◽  
Author(s):  
Lawrence Fulton ◽  
Zhijie Dong ◽  
F. Benjamin Zhan ◽  
Clemens Scott Kruse ◽  
Paula Stigler Granados
Keyword(s):  
Salt Lake ◽  
Salt Lake City ◽  
Temporal Analysis ◽  
Explanatory Models ◽  
Demand Analysis ◽  
Admission Rates ◽  
Financial Variables ◽  
Country Level ◽  
Demand Models ◽  
Lake City

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.

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

2019 ◽  
Author(s):  
Lawrence Fulton ◽  
Zhijie Dong ◽  
Benjamin Zhan ◽  
Clemens Scott Kruse ◽  
Paula Stigler Granados
Keyword(s):  
Cluster Analysis ◽  
Salt Lake ◽  
Salt Lake City ◽  
Temporal Analysis ◽  
Explanatory Models ◽  
Opioid Epidemic ◽  
Data Set ◽  
Financial Variables ◽  
Country Level ◽  
Demand Models

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, k-means cluster analysis, and extreme-gradient boosted random forests are used to evaluated ICD-10 F11 opioid-related admissions. The period of analysis was January 2016 through September 2018.  Results:  The analysis shows existing high-intensity areas in Chicago and New Jersey with emerging areas in Atlanta, Salt Lake City, Phoenix, and Las Vegas. Further, cluster analysis supports the current inflow from China through Mexico and Canada with another cluster in the Northeast likely associated with the Dominican flow.  Explanatory models suggest that hospital overall workload and financial variables may be used for allocating opioid-related funds effectively, as the gradient-boosted random forest models accounted for 88% of the variability on a blinded test data set.  Conclusions: Based on GIS analysis, the opioid epidemic is likely to spread or diffuse through the country, and 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.

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

2019 ◽  
Author(s):  
Lawrence Fulton ◽  
Zhijie Dong ◽  
Benjamin Zhan ◽  
C. Scott Kruse ◽  
Paula Stigler Granados
Keyword(s):  
Salt Lake ◽  
Temporal Analysis ◽  
Explanatory Models ◽  
Demand Analysis ◽  
Supply Side ◽  
Opioid Epidemic ◽  
Gis Analysis ◽  
Demand Side ◽  
Scarce Resources ◽  
Demand Models

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. Based on GIS analysis, the opioid epidemic is likely to spread or diffuse through the country, and 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.

Estimating Costs and Benefits of Transportation Corridor Alternatives

1997 ◽  
Vol 1606 (1) ◽  
pp. 115-123
Author(s):  
Patrick Decorla-Souza ◽  
Brian Gardner ◽  
Michael Culp ◽  
Jerry Everett ◽  
Chimai Ngo ◽  
...  
Keyword(s):  
Decision Making ◽  
Travel Demand ◽  
Salt Lake ◽  
Salt Lake City ◽  
Costs And Benefits ◽  
Benefit Cost Analysis ◽  
Demand Models ◽  
Data Output ◽  
Lake City ◽  
Benefit Cost

Although benefit-cost assessment is a useful tool in structuring the decision making process, it has not generally been used to assist in multi-modal decision making in metropolitan areas. Also, although detailed zone-to-zone trip information can be obtained from metropolitan travel-demand models, this information is not currently used by planners in developing detailed information on cross-modal comparisons of costs and benefits. A real-world application of benefit-cost analysis for multi-modal decision making using detailed zone-to-zone trip data output from travel-demand models for the I-15 corridor in Salt Lake City is presented. The analysis was conducted at two levels: corridor and region-wide. The research suggests that, when major investments are to be evaluated, the analyst should be very cautious in performing corridor-level analyses when such a trip-based approach is used, because of significant effects on the evaluation caused by traffic diverted into (or out of) the corridor.

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