Public Health Emergency Preparedness
This resource was part of AHRQ's Public Health Emergency Preparedness (PHEP) program, which was discontinued on June 30, 2011, in a realignment of Federal efforts.
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With funding from the Agency for Healthcare Research and Quality (AHRQ), Abt Associates—together with researchers from Gryphon Scientific and Weill Medical College of Cornell University—developed the Hospital Surge Model, which estimates the amount of hospital resources needed to treat casualties of major disasters. The Hospital Surge Model is available at http://www.ahrq.gov/prep/hospsurgemodel/.
The Hospital Surge Model includes 13 different scenarios:
- Biological (anthrax, smallpox, pandemic influenza, or pneumonic plague).
- Chemical (chlorine, sulfur mustard, or sarin).
- Nuclear (1 KT or 10 KT explosion).
- Radiological (dispersion device or point source).
- Foodborne (botulism neurotoxin).
- Conventional explosions (improvised explosive device).
When the Hospital Surge Model is run, the user selects one of the above scenarios and specifies the number of casualties that their hospital(s) will need to treat. Casualties are treated, as necessary, in the emergency department (ED), in the intensive care unit (ICU), or on a general medical/surgical bed ward ("the floor"). Hospitals are assumed to have unlimited capacity and provide a standard level of care to all casualties-that is, the Hospital Surge Model assumes that care is not degraded by the surge in patients or by resource constraints. Eventually, casualties in the model are either discharged or die in the hospital(s). While patients are in the hospital(s), the Hospital Surge Model estimates the amount of resources (e.g., personnel, equipment, supplies) they require.
For the selected scenario, the Hospital Surge Model estimates:
- The number of casualties in the hospital(s) by hospital unit (ED, ICU, or floor) and day.
- The cumulative number of dead or discharged casualties by day.
- The required hospital resources (personnel, equipment, and supplies) to treat casualties by hospital unit and day.
This document is a description of the Version 1.31 of the Hospital Surge Model and its underlying assumptions. A companion report—the Hospital Surge Model User Manual—provides instructions on how to run the Hospital Surge Model. The remainder of this report is organized as follows:
- An overall description of the Hospital Surge Model and the Hospital Surge Model Web site.
- Chapters 1 through 12 discuss the assumptions for the individual scenarios.2
- References are cited in the individual sections.
The Hospital Surge Model is a modified version of the original Surge Model, developed by the project team in 2005. A project steering committee guided the original Surge Model project—go to the list of members in the Appendix.
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Model and Web Site Overview
This section provides an overview of the Hospital Surge Model and how it has been implemented on the Web site http://www.ahrq.gov/prep/hospsurgemodel/. Following this overview are 12 chapters (Chapters 1 through 12) that discuss the details of each of the ten scenarios.
Exhibit 1 shows the overall structure of the Hospital Surge Model. On the Hospital Surge Model Web site, the user specifies a number of different inputs, including a scenario (e.g., anthrax or smallpox) and the number and/or type of casualties assumed to present at their hospital(s).
Exhibit 1: Hospital Surge Model Structure
Users specify the number and/or type of casualties arriving at their hospital(s). The model then determines when these casualties present at the hospital(s) and estimates the hospital resources required to treat the casualties. The Hospital Module section provides an overview of the Hospital Module, which tracks the movement, treatment, and outcomes of patients in the hospital. The Hospital Module incorporates numerous assumptions, including:
- The length of stay by hospital unit (ED, ICU, or the floor).
- The probability that a given patient will be transferred between hospital units (i.e., "transition probabilities").
- The overall outcome probabilities (i.e., probability of discharge and probability of death).
- The assumed level of resource consumption per patient per day.
Go to Chapters 1 through 12 for specific assumptions for each scenario. . In general, there is a notable lack of published evidence to support establishing parameters for the medical treatment component of the scenarios (the improvised explosive device scenario is the exception). For example, since the botulinum scenario assumes a preformed toxin released in the food supply, the large body of literature on pediatric and infantile botulism is not directly applicable. While there is an extensive body of literature on bubonic plaque, relatively few studies have been published on the routine health care utilization of patients with pneumonic plague. In these and other instances the modelers have made what they believe to be plausible assumptions. Please contact project staff for more information on specific assumptions.
The Hospital Surge Model outputs, including details about the casualties, resources, and requirements, are summarized in the Hospital Surge Model Outputs section.
When the Hospital Surge Model is run, the user selects one of 13 scenarios; they include biological, chemical, nuclear, radiological, foodborne, and conventional explosive scenarios. After selecting one of the scenarios, the user must specify the number and (optionally) the type of casualties that will present at their hospital(s).
- The covert release of aerosol Bacillus anthracis through a city.
- The covert release of smallpox virus in a large indoor theater.
- A pandemic flu outbreak that sweeps across the country.
- The covert release of airborne Yersinia pestis, producing rapidly progressing cases of pneumonic plague.
These four scenarios include both communicable and non-communicable diseases, diseases that are treatable with antibiotics and those that are not, and indoor and outdoor attacks. The flu scenario incorporates hospital resource utilization and patient outcomes similar to those documented during the 2009 influenza A (H1N1) pandemic.
- The overt explosion of a liquid chlorine tank in a densely populated suburb.
- The overt release of the blister agent sulfur mustard at a large outdoor event.
- The semi-covert release of the nerve agent sarin in a crowded arena.
These three scenarios include both indoor and outdoor scenarios and include toxic industrial chemicals, blister agents, and nerve agents. Blood agents are not included because of the perceived difficulty in using blood agents to inflict mass casualties in a non-confined space; choking agents are not included because they are only lethal in extremely large quantities.
- The overt detonation of 1KT of nuclear material (improvised nuclear device) in a city center.
- The overt detonation of 10KT of nuclear material (improvised nuclear device) in a city center.
- The overt release of cesium-137 dust (commonly referred to as a "dirty bomb" or radiological dispersal device) in a city center.
- The covert placement of a cesium-137 source in a public place.
These four scenarios cover the threat space posed by radionuclides, which can be dispersed in an extremely violent reaction (nuclear), more gently (radiological dispersion device, or RDD), or not at all (a radioactive point source).
- The contamination of several ingredients of a popular food product with botulinum neurotoxin. This product is then processed at the factory and distributed nationwide.
- Terrorists place small-scale improvised explosive devices (IEDs) in several subway stations during rush hour.
This scenario has precedent both in the London and Madrid bombings, and represents a known threat.
Exhibit 2 shows how the user selects a scenario on the Hospital Surge Model Web site—http://www.ahrq.gov/prep/hospsurgemodel/.
Exhibit 2: Scenario Selection Screen
When the "Next" button is clicked, the user is asked to specify the number and (optionally) type of casualties that will present at their hospital(s).
For each of the scenarios, users have two options for specifying casualties:
- Specify the number and type of casualties. For example, users could specify that their hospital(s) receives 100 mild, 20 moderate, and 10 severe cases of sarin; or.
- Specify the number of casualties. For example, users could simply specify that their hospital(s) receives 130 cases of sarin. In this case, the casualties are randomly selected from among all casualties from the attack.
The table below lists the type of casualties users can specify for each scenario:
||Types of Casualties
- Mild: When victims become ill they are more than 3 days away from death, assuming no treatment.
- Severe: When victims become ill they are 3 or fewer days away from death, assuming no treatment.
- Onset: Mild, generalized symptoms.
- Moderate: Patients arriving at the hospital with moderate flu symptoms go to the floor for medical care.
- Severe: Patients arriving at the hospital with severe flu symptoms go to the ICU for medical care.
- Moderate: Patient requires hospitalization, but not in the ICU.
- Severe: Patient requires hospitalization in the ICU.
- Irritated: Hoarseness or burning in throat and lungs, irritation in eyes.
- Severe: Temporary blindness, permanent eye damage, bronchopneumonia, and skin damage.
- Mild: Miosis, ocular pain, tearing, rhinorrhea, bronchospasm, slight dyspnea, respiratory secretions, salivation, diaphoresis.
- Moderate: Moderate dyspnea, nausea, vomiting, diarrhea.
- Severe: Loss of consciousness, convulsions, paralysis, copious secretions, apnea.
- Burns/moderate: Second-degree burns on hands and face.
- Burns/severe: Third-degree burns on hands and face (and first-degree over the rest of the body).
- Trauma/people in collapsed skyscrapers .
- Trauma/people in collapsed houses and other light buildings.
- Trauma/people who receive multiple lacerations from flying glass .
- Trauma/people outside who receive blunt trauma .
- Radiation/mild: Nausea, vomiting, anorexia, fever, infections .
- Radiation/moderate: More severe mild symptoms, plus bleeding, fatigue, and weakness.
- Radiation/severe: More severe moderate symptoms, plus headache, prostration, dizziness, and disorientation.
- Fallout/mild: 1Gy equivalent dose for blood effects, no other equivalent dose (some bleeding and infection issues).
- Fallout/severe: 4Gy for blood effects (problems with bleeding and infection) and about 0.75 Gy for lethality and gastrointestinal (GI) effects (some small fraction of people will die, and others get nausea, vomiting, etc.).
|Radiological Dispersion Device (RDD)
- Mild: 1Gy equivalent dose for blood effects, no other equivalent dose (some bleeding and infection issues).
- Severe: 4Gy for blood effects (problems with bleeding and infection) and about 0.75 Gy for lethality and GI effects (some small fraction of people will die, and others get nausea, vomiting, etc.).
|Radiological Point Source
- Mild: Nausea, vomiting, anorexia, fever, infections.
- Moderate: Mild symptoms as above, plus bleeding, fatigue and weakness.
- Severe: Moderate symptoms as above, plus headache, prostration, dizziness and disorientation.
- Adult Moderate: Adults show symptoms but are able to be treated outside of the ICU.
- Child Moderate: Children show symptoms but are able to be treated outside of the ICU.
- Adult Severe: Adults present at the hospital with severe symptoms requiring a ventilator.
- Child Severe: Children present at the hospital with severe symptoms requiring a ventilator.
|Improvised Explosive Device (IED)
- Affected: Victims suffer from one type of blast injury including lacerations, fractures, burns, and pulmonary blast.
- Moderate: Victims suffer from two types of blast injuries including lacerations, fractures, burns, and pulmonary blast.
- Severe: Victims suffer from three or more types of blast injuries including lacerations, fractures, burns, and pulmonary blast.
The Hospital Module calculates resource requirements in different units (ED, floor, and ICU) of a hospital for each scenario, given the number and type (i.e., severity of injury) of patients arriving at a hospital.
Exhibit 3 shows the different ways in the Hospital Module that patients can move between different units in a hospital. Patients arrive at the ED of a hospital. The model groups them according to severity of their injury. The model assumes they spend a certain amount of time in the ED (dependent on severity). After the end of this period of stay, either they are discharged or they enter the floor or the ICU. On the floor, if their conditions worsen, they could be sent to the ICU. Otherwise, after a predetermined length of stay (depending on severity), they are discharged. Patients in the ICU recover after a predetermined length of stay (again, depending on the severity) and are transferred to the floor. These patients are assumed to not enter the ICU again. Patients can die in the ICU or on the floor. Lengths of stay, transition probabilities, and death rates are assumed to be fixed (i.e., the Hospital
Module is a deterministic, rather than random, model). However, different cases corresponding to different values of lengths of stay and death rates are considered. Assumptions regarding these data are in Chapters 1 through 12.
Exhibit 3: Hospital Module System Diagram
The hospital module sends all arriving patients through the system, transferring them from one hospital unit (i.e., the ED, the ICU, or the floor) to another according to the probabilities and assumed lengths of stay, and assuming certain mortality rates (Chapters 1 through 12). All patients will eventually exit a hospital either due to death or by being discharged. Probabilities of death per hospital unit are compounded daily. Once we have a count of how many patients are present in each hospital unit in each period, we can determine overall resource requirements by looking up a database of resource requirements per patient.
A database drives the hospital module. It contains the following information for each scenario:
- A list of resources.
- Daily requirements for each resource per patient in each hospital unit (i.e., the ED, the ICU, or the floor).
- Length of stay in each hospital unit.
- Mortality rates in each hospital unit—these are expressed as the probability that a patient in that hospital unit will die in the next period, given that he has survived until that period.
- Transition probabilities between hospital units (e.g., the probability that a patient of a certain severity will move from the ED to the ICU, as opposed to the ED to the floor).
While a patient is hospitalized, some resources are used at a constant rate; for other resources, a decreasing amount is used each day. For example, while a patient needs full use of a bed every day s/he is hospitalized, the amount of time physicians and other health care staff must devote to a single patient is likely to decrease each day that a patient is in the hospital. The daily "decay" rate for resource utilization is captured in a model variable called lambda. The following table defines "time to < 50% of use" (i.e., time to use of half of listed amount of resource) and "time to <10% of use" for different values of lambda.
We have included in the Hospital Surge Model those resources that are viewed as the most critical in a hospital setting for treating casualties of major disasters. We have not attempted to include all possible resources, although future versions of the Hospital Surge Model will include additional resources. The specific resources we have included for each scenario are listed in Chapters 1 through 12.
Scenario-specific references used to estimate parameters in the Hospital Module are noted in Chapters 1 through 12. In the absence of specific references, estimates for lengths of stay, transition probabilities, path routing through a hospital, and resource utilization were obtained from a combination of general references (see below), general references for each overall scenario type, and scenario-specific references.
1. Braun BI, Wineman NV, Finn NL, et al. Integrating hospitals into community emergency preparedness planning. Ann Intern Med 2006;144(11):799-811.
2. Brilli RJ, Spevetz A, Branson RD, et al. Critical care delivery in the intensive care unit: defining clinical roles and the best practice model. Crit Care Med 2001;29(10):2007-19.
3. Connelly LG, Bair AE. Discrete event simulation of emergency department activity: a platform for system-level operations research. Acad Emerg Med 2004;11(11):1177-85.
4. Fone D, Hollinghurst S, Temple M, et al. Systematic review of the use and value of computer simulation modeling in population health and health care delivery. J Public Health Med 2003;25(4):325-35.
5. Halpern NA, Pastores SM, Greenstein RJ. Critical care medicine in the United States 1985-2000: an analysis of bed numbers, use, and costs. Crit Care Med 2004;32(6):1254-9.
6. Halpern NA, Pastores SM, Thaler HT, et al. Changes in critical care beds and occupancy in the United States 1985-2000: differences attributable to hospital size. Crit Care Med 2006;34(8):2105-12.
7. Hick JL, O'Laughlin DT. Concept of operations for triage of mechanical ventilation in an epidemic. Acad Emerg Med 2006;13(2):223-9.
8. OSHA. OSHA Best Practices for Hospital-Based Receivers of Victims from Mass Casualty Incidents Involving the Release of Hazardous Substances. Occupational Safety and Health Administration, U.S.; 2004.
9. Rubinson L, Nuzzo JB, Talmor DS, et al. Augmentation of hospital critical care capacity after bioterrorist attacks or epidemics: recommendations of the Working Group on Emergency Mass Critical Care. Crit Care Med 2005;33(10):2393-403.
10. Shapiro DS. Surge capacity for response to bioterrorism in hospital clinical microbiology laboratories. J Clin Microbiol 2003;41(12):5372-6.
Hospital Surge Model Outputs
On the Hospital Surge Model Web site, the output from the Hospital Surge Model is organized into five sections: summary results, casualty arrival pattern at the hospital(s), number of patients in the hospital(s), number of dead and discharged patients, and resource requirements.
Two summary measures from the daily results are displayed:
- The day that the most patients arrive at the hospital(s), and the number of patients that arrive on that day.
- The day that the most patients are in the hospital(s), and the number of patients that are in the hospital(s) on that day.
Casualty arrival pattern at the hospital(s)
The casualty arrival pattern at the hospital(s) is shown in graphical and tabular form.
Casualties are assumed to arrive at your hospital(s) when symptoms present. For the nuclear and chemical attacks, casualties arrive at your hospital(s) immediately after the attack (i.e., on Day 1). For the biological and radiological attacks, there is a delay between exposure and when symptoms present. The Hospital Surge Model computes the delay for these attacks and generates an arrival pattern for the scenario. Exhibit 4 shows an illustrative arrival pattern for the anthrax scenario.
Exhibit 4: Illustrative Hospital Arrival Pattern
Number of patients in the hospital(s) by day
The number of patients in the hospital(s), by hospital unit and by day, is displayed in a table and in a graph (Exhibit 5). The assumptions regarding lengths of stay, transition probabilities between hospital units, and mortality rates for the different scenarios are listed in Chapters 1 through 12.
Exhibit 5: Illustrative Number of Patients in the Hospital(s)
Number of dead and discharged patients
The Web site shows, in both graphical and tabular form, the number of patients in the hospital(s), the cumulative number of deaths to date, and the cumulative number of patients discharged from the hospital(s) to date (Exhibit 6).
Exhibit 6: Number of Dead and Discharged Patients by Day
While patients are in the hospital(s), they require resources (e.g., medical personnel and equipment) and consume resources (e.g., medical supplies). (As noted earlier, the Hospital Surge Model assumes the hospital has unlimited resources.) The Web site displays the daily resource requirements for patients by resource, day, and hospital unit.
The following information is displayed for each resource:
- Resource name.
- Resource unit. The units of the resource requirements are: full-time employees (FTEs), machine time, and unit of use.
- Machine time refers to the amount of time needed per patient per day on a diagnostic machine, such as a CT scanner.
- Unit of use is a generic term for a daily dose or other definable amount of a consumable resource such as a medication or a set of laboratory reagents. For example, the unit of use for antibiotics for anthrax would be two 400 mg doses of intravenous ciprofloxacin or two 100 mg doses of intravenous doxycycline plus one or two additional antibiotics.
- Resource category and subcategory.
- Consumable (yes/no).
- Day of peak use. The day that the greatest amount of the resource is required.
- Amount of use on peak day. The amount of the resource that is required on the peak day.
- Daily requirements, as computed by the model.
Daily requirements are displayed in a table (Exhibit 7). Requirements for individual resources can be graphed (Exhibit 8).
Exhibit 7: Resource Requirements (Tabular Display)
Exhibit 8: Requirements for a Specific Resource (Graph Form)
1. Version 1.3, compared with Version 1.2, includes three additional scenarios—pneumonic plague, botulism neurotoxin, and the improvised explosive device.
The 1 KT and 10 KT nuclear scenarios have been combined into one section.
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