University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School 2-27-2009 Predicting the Medical Management Requirements of Large Scale Mass Casualty Events Using Computer Simulation Scott A. Zuerlein University of South Florida Follow this and additional works at: https://scholarcommons.usf.edu/etd Part of the American Studies Commons Scholar Commons Citation Zuerlein, Scott A., "Predicting the Medical Management Requirements of Large Scale Mass Casualty Events Using Computer Simulation" (2009). Graduate Theses and Dissertations. https://scholarcommons.usf.edu/etd/105 This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Predicting the Medical Management Requirements of Large Scale Mass Casualty Events Using Computer Simulation by Scott A. Zuerlein A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Health Policy and Management College of Public Health University of South Florida Major Professor: Alan M. Sear, Ph.D. Barbara L. Orban, Ph.D. James Studnicki, Sc.D. Yiliang Zhu, Ph.D. Date of Approval: February 27, 2009 Keywords: computer modeling, simulation, blasts, planning, emergency care, care processes, health system preparedness, health system resources ©Copyright 2009, Scott A. Zuerlein Table of Contents List of Tables iv List of Figures vi Abstract vii Chapter 1: Introduction/Statement of the Research Problem 1 Chapter 2: Review of the Literature 8 Systems Theory, Computer Modeling and Simulation 9 Research Methods 16 Arena specific computer modeling structures/methods 20 Probability Distributions 25 Verification and Validation 28 Blast Events and Terrorist Actions 30 Capacity (Structure) of the Health Care System and Mass Casualty 37 Events Public Policy 43 Studies Supporting the Development of a Computer Simulation Model 46 Chapter 3: Objectives of the Research 64 Chapter 4: Research Methods 70 Methods for Objective 1 71 Methods for Objective 2 75 Methods for Objective 3 81 Methods for Objectives 4 and 5 86 Methods for Objective 6 94 Chapter 5: Results 96 Results for Objective 1 97 Facility Characteristics 97 Frequency of Use 99 Structural Characteristics 100 Explosive Delivery Mechanisms 106 Location of Blast 110 Strength of Blast 111 Results for Objective 2 113 i Results for Objective 3 121 Results for Objectives 4 ad 5 147 Results for Objective 6 167 Chapter 6: Summary and Conclusions 183 Chapter 7: Limitations of the Study 197 References 202 Appendices 208 Appendix 1: Discussion of Additional Probability Distributions Available In Arena Software 209 Appendix 2: Bombings of Fixed Structures Using Conventional Explosives: 1988 to 2005 214 Appendix 3: Distribution of Outdoor Stadiums within the United States (includes domed stadiums) 217 Appendix 4: Distribution of Indoor Arenas within the United States 218 Appendix 5: Example of a Resource Dense Geographic Location 219 Appendix 6: Example of a Resource Sparse Geographic Location 220 Appendix 7: The Barell Injury Diagnosis Matrix 221 Appendix 8: The Barell Injury Diagnosis Matrix, Classification by Body Region and Nature of the Injury 222 Appendix 9: Matrix of Studies Identifying Injury by Body Region 228 Appendix 10: Matrix of Studies Identifying Injury by Injury Type 229 Appendix 11: In Hospital Emergency Response Models 230 Appendix 12: Process and Decision Parameters as Defined in Hirshberg Study 233 Appendix 13: Distribution of Arena Stadiums by State 234 Appendix 14: Arenas and Stadiums by State 236 Appendix 15: Distribution of Indoor Arenas by Location Population 247 Appendix 16: Distribution of Outdoor Stadiums by Location Population 247 Appendix 17: List of Explosive Materials 248 Appendix 18: Injury Prediction Model Based on Frykberg Study 254 Appendix 19: Injury Prediction Model Based on Barell Injury Matrix 257 Appendix 20: ICD-9-CM Codes and Injury Description to Match Injury Prediction Model Based on Frykberg Study (Appendix 18) 261 Appendix 21: Resource Prediction Model 274 Appendix 22: Simulation Decision Module, Process Module, and Termination Module Parameters 280 Appendix 23: Simulation Results - 7,000 Injured Survivors (no constraints) 288 ii Appendix 24: Simulation Results – 45,000 Injured Survivors (no constraints) 289 Appendix 25: Simulation Results - 7,000 Injured Survivors (12 hours, resource sparse) 290 Appendix 26: Simulation Results - 7,000 Injured Survivors (12 hours, resource dense) 291 Appendix 27: Simulation Results - 45,000 Injured Survivors (12 hours, resource sparse) 292 Appendix 28: Simulation Results - 45,000 Injured Survivors (12 hours, resource dense) 293 Appendix 29: Simulation Results – 7,000 Injured Survivors (12 hour resource constraints) 294 Appendix 30: Simulation Results- 45,000 Injured Survivors (12 hour resource constraints) 295 About the Author End Page iii List of Tables Table 1. Probability Distributions Available in Arena 26 Table 2. Published studies providing data/information for this research 47 Table 3. CDC Overview of Explosive-related Injuries 50 Table 4. Terrorist Bombing Injury Analysis (220 events, 2,934 immediate survivors) 52 Table 5. Oklahoma City Injury Analysis (592 Injured Survivors) 53 Table 6. Oklahoma City - Location of Soft Tissue Injuries (506 injuries) 54 Table 7. Oklahoma City – Location of Musculoskeletal Injuries (Fractures And Dislocations (60 injuries) and Sprains (152 injuries)) 55 Table 8. Peleg Study – Injury Categorization (623 patients) 57 Table 9. World Trade Center Attack Survivors (790 injured survivors) 58 Table 10. Khobar Towers Injury Analysis (401 injured survivors) 60 Table 11. Abbreviated Injury Scale 62 Table 12. Injury Severity Scores among Published Studies 63 Table 13. Terrorist related blast events causing more than 500 injured survivors 76 Table 14. Simulation Run Matrix 87 Table 15. Resource Constraints 90 Table 13. Injury Prediction Model (Frykberg Data, 1988) 116 Table 14. Injury Prediction Model (Nature of Injury categories) 119 Table 15. Injury Predictions (7,000 injured survivors, no constraints, 10 replications) 148 Table 16. Injury Predictions (45,000 injured survivors, no constraints, 10 replications) 150 Table 17. Injury Predictions (7,000 injured survivors, 12 hours/resource dense, 10 replications) 152 Table 18. Injury Predictions (45,000 injured survivors, 12 hours/resource dense, 10 replications) 153 Table 19. Comparison (7,000 injured survivors, 10 replication means) 154 Table 20. Comparison (45,000 injured survivors, 10 replication means) 156 Table 21. Resource Requirement Prediction (7,000 injured survivors, no constraints, 10 replications) 157 Table 22. Resource Requirement Prediction (45,000 injured survivors, no constraints, 10 replications) 159 Table 23. Resource Requirement Prediction (7,000 injured survivors, 12 hours/resource dense, 10 replications) 160 iv Table 24. Resource Requirement Prediction (7,000 injured survivors, 12 hours/resource sparse, 10 replications) 161 Table 25. Scenario Comparison of Predicted Resource Requirements (7,000 injured survivors, 10 replication means) 163 Table 26. Scenario Comparison of Predicted Resource Requirements (45,000 injured survivors, 10 replication means) 164 Table 27. 7,000 Injured Survivors/Resource Dense Geographic Area (8 hour window) 168 Table 28. 7,000 Injured Survivors/Resource Sparse Geographic Area (8 hour window) 172 Table 29. 45,000 Injured Survivors/Resource Dense Geographic Area (8 hour window) 173 Table 30. 45,000 Injured Survivors/Resource Sparse Geographic Area (8 hour window 174 Table 31. 7,000 Injured Survivors/Resource Dense Geographic Area (12 hour window) 175 Table 35. 7,000 Injured Survivors/Resource Sparse Geographic Area (12 hour window) 176 Table 36. 45,000 Injured Survivors/Resource Dense Geographic Area (12 hour window) 177 Table 37. 45,000 Injured Survivors/Resource Sparse Geographic Area (12 hour window) 178 Table 38. 7,000 Injured Survivors/Resource Dense Geographic Area 179 Table 36. 45,000 Injured Survivors/Resource Sparse Geographic Area 181 v List of Figures Figure 1. Simulation Model Example 21 Figure 2. Casualty Flow in a Disaster Situation 83 Figure 3. Basic Structure of the Simulation Model Used for the Present Research 84 Figure 4. Indoor Facility above Ground Structure (Conseco Fieldhouse, Indianapolis, IN) 103 Figure 5. Indoor Facility below Ground Structure (University Arena, Albuquerque, NM) 104 Figure 6. Outdoor Facility above Ground Structure (Raymond James Stadium Tampa, Florida) 107 Figure 7. Outdoor Facility below Ground Structure (Michigan Stadium Ann Arbor, MI) 108 Figure 8. Injury Prediction Algorithm 115 Figure 9. Resource Prediction Algorithm (Rescue/Transportation Component) 123 Figure 10. Simulation Module Descriptions 125 Figure 11. Resource Prediction Algorithm (CCP Component) 130 Figure 12. Resource Prediction Algorithm (Hospital Component) 135 Figure 13. Resource Prediction Algorithm (Surgery Component) 139 Figure 14. Resource Prediction Algorithm (Surgery Component) 141 Figure 15. Resource Prediction Algorithm (Hospital (2) Component) 144 vi Predicting the Medical Management Requirements of Large Scale Mass Casualty Events Using Computer Simulation Scott A. Zuerlein ABSTRACT Recent events throughout the world and in the US lend support to the belief that another terrorist attack on the US is likely, perhaps probable.