In recent years, mass-casualty incidents (MCIs) have become more frequent and deadly, while emergency department (ED) crowding has grown steadily worse and widespread. The ability of hospitals to implement an effective mass-casualty surge plan, immediately and expertly, has therefore never been more important. Yet, mass-casualty exercises tend to be highly choreographed, pre-scheduled events that provide limited insight into hospitals’ true capacity to respond to a no-notice event under real-world conditions. To address this gap, the US Department of Health and Human Services (Washington, DC USA), Office of the Assistant Secretary for Preparedness and Response (ASPR), sponsored development of a set of tools meant to allow any hospital to run a real-time, no-notice exercise, focusing on the first hour and 15 minutes of a hospital’s response to a sudden MCI, with the goals of minimizing burden, maximizing realism, and providing meaningful, outcome-oriented metrics to facilitate self-assessment. The resulting exercise, which was iteratively developed, piloted at nine hospitals nationwide, and completed in 2015, is now freely available for anyone to use or adapt. This report demonstrates the feasibility of implementing a no-notice exercise in the hospital setting and describes insights gained during the development process that might be helpful to future exercise developers. It also introduces the use of ED “immediate bed availability (IBA)” as an objective, dynamic measure of an ED’s physical capacity for new arrivals.

WaxmanDA, ChanEW, PillemerF, SmithTWJ, AbirM, NelsonC. Assessing and Improving Hospital Mass-Casualty Preparedness: A No-Notice Exercise. Prehosp Disaster Med. 2017;32(6):662–666.

Objectives: Hospital surge capacity in multiple casualty events (MCE) is the core of hospital medical response, and an integral part of the total medical capacity of the community affected. To date, however, there has been no consensus regarding the definition or quantification of hospital surge capacity. The first objective of this study was to quantitatively benchmark the various components of hospital surge capacity pertaining to the care of critically and moderately injured patients in trauma-related MCE. The second objective was to illustrate the applications of those quantitative parameters in local, regional, national, and international disaster planning; in the distribution of patients to various hospitals by prehospital medical services; and in the decision-making process for ambulance diversion.

Methods: A 2-step approach was adopted in the methodology of this study. First, an extensive literature search was performed, followed by mathematical modeling. Quantitative studies on hospital surge capacity for trauma injuries were used as the framework for our model. The North Atlantic Treaty Organization triage categories (T1-T4) were used in the modeling process for simplicity purposes.

Results: Hospital Acute Care Surge Capacity (HACSC) was defined as the maximum number of critical (T1) and moderate (T2) casualties a hospital can adequately care for per hour, after recruiting all possible additional medical assets. HACSC was modeled to be equal to the number of emergency department beds (#EDB), divided by the emergency department time (EDT); HACSC = #EDB/EDT. In trauma-related MCE, the EDT was quantitatively benchmarked to be 2.5 (hours). Because most of the critical and moderate casualties arrive at hospitals within a 6-hour period requiring admission (by definition), the hospital bed surge capacity must match the HACSC at 6 hours to ensure coordinated care, and it was mathematically benchmarked to be 18% of the staffed hospital bed capacity.

Conclusions: Defining and quantitatively benchmarking the different components of hospital surge capacity is vital to hospital preparedness in MCE. Prospective studies of our mathematical model are needed to verify its applicability, generalizability, and validity.

(Disaster Med Public Health Preparedness. 2011;5:117–124)

The hallmark of a successful response to a nuclear detonation will be the resilience of the community, region, and nation. An incident of this magnitude will rapidly become a national incident; however, the initial critical steps to reduce lives lost, save the lives that can be saved with the resources available, and understand and apply resources available to a complex and dynamic situation will be the responsibility of the local and regional responders and planners. Expectations of the public health and health care systems will be met to the extent possible by coordination, cooperation, and an effort to produce as consistent a response as possible for the victims. Responders will face extraordinarily stressful situations, and their own physical and psychological health is of great importance to optimizing the response. This article illustrates through vignettes and supporting text how the incident may unfold for the various components of the health and medical systems and provides additional context for the discipline-related actions outlined in the state and local planners’ playbook.

(Disaster Med Public Health Preparedness. 2011;5:S73-S88)

Objectives: To identify health care workers most at risk for H1N1 infection before vaccination and compare health outcomes after vaccination.

Methods: The indices used to gauge employee health were laboratory-confirmed H1N1 data, laboratory-confirmed influenza A data, and employee sick hours records. In phase 1 of this 2-phase study, absenteeism records for 6,093 hospital employees before vaccine administration were analyzed according to department and employee position during the spring 2009 H1N1 pandemic.

Results: Records of 123 confirmed reports of laboratory-confirmed influenza A or novel H1N1 infections in hospital employees were also analyzed. Two thirds of the H1N1 cases occurred during June (infection rates in parentheses): 34 in physicians and medical personnel (6.7%), 36 in nurses and clinical technicians (2.2%), 39 in Administrative & Support Personnel (infection rate = 1.2%), 3 in Social Workers & Counselors (infection rate = 1.0%), 8 in Housekeeping & Food Services (infection rate = 2.7%), and 3 in Security & Transportation (infection rate=3.9%). When analyzed according to department, the adult emergency department (infection rate = 28.8%) and the pediatric emergency department (infection rate = 25.0%) had the highest infection rates per department.

Conclusions: Of the reported cases of H1N1 in health care workers, 49% occurred in a population that constitutes less than 20% of the total population studied. Physicians and medical personnel had a higher infection rate than other employee positions, whereas ED personnel had the highest infection rate.

(Disaster Med Public Health Preparedness. 2010;4:47-54)

Objectives: The November 26-29, 2008, terrorist attacks on Mumbai were unique in its international media attention, multiple strategies of attack, and the disproportionate national fear they triggered. Everyone was a target: random members of the general population, iconic targets, and foreigners alike were under attack by the terrorists.

Methods: A retrospective, descriptive study of the distribution of terror victims to various city hospitals, critical radius, surge capacity, and the nature of specialized medical interventions was gathered through police, legal reports, and interviews with key informants.

Results: Among the 172 killed and 304 injured people, about four-fifths were men (average age, 33 years) and 12% were foreign nationals. The case-fatality ratio for this event was 2.75:1, and the mortality rate among those who were critically injured was 12%. A total of 38.5% of patients arriving at the hospitals required major surgical intervention. Emergency surgical operations were mainly orthopedic (external fixation for compound fractures) and general surgical interventions (abdominal explorations for penetrating bullet/shrapnel injuries).

Conclusions: The use of heavy-duty automatic weapons, explosives, hostages, and arson in these terrorist attacks alerts us to new challenges to medical counterterrorism response. The need for building central medical control for a coordinated response and for strengthening public hospital capacity are lessons learned for future attacks. These particular terrorist attacks had global consequences, in terms of increased security checks and alerts for and fears of further similar “Mumbai-style” attacks. The resilience of the citizens of Mumbai is a critical measure of the long-term effects of terror attacks.

(Disaster Med Public Health Preparedness. 2011;5:273–279)

Objective: To assess the adequacy of the pediatric triage system in an acute care general hospital.

Methods: All children younger than 14 years of age who presented with a primary medical condition to the accident and emergency department (ED) during January to March 2009 were recruited. Suitability of the triage system was assessed according to the vital parameters taken and the priority code assigned. Triage workload was assessed from the number of children presenting to ED and the timing of presentation.

Results: Of 2269 children presenting to ED, 1617 (71.3%) were younger than 5 years, and 883 (38.9%) were younger than 2 years. Only 0.26% (6/2269) had four vital parameters crucial for priority assignment measured, and 19.3% (437/2269) had at least one parameter measured. A priority code was assigned to 10% (225/2269).

Conclusions: Our study revealed inadequacies in the pediatric triage system. A simple and objective triage system that is based on the measurement of crucial vital parameters and on prompt recognition of warning signs and symptoms to correctly identify high-risk groups has been introduced to ensure appropriate and effective triage of sick children.

(Disaster Med Public Health Preparedness. 2012;6:151–154)

Objective: This project evaluated New York (NY) hospitals outside of New York City (upstate) for their awareness and utilization of the NY State Department of Health Pediatric and Obstetric Emergency Preparedness Toolkit (toolkit) and presence of pediatric emergency preparedness planning elements.

Methods: A survey assessing toolkit awareness and utilization was distributed to all 145 upstate NY hospitals. Quantitative survey data were analyzed using summary statistics, χ2 analysis, and odds ratios (OR) in aggregate, by hospital size, and by presence of pediatric medicine/surgery, pediatric intensive care unit (PICU), and/or neonatal ICU (NICU) beds (pediatric beds).

Results: Of the 145 hospitals, 116 (80%) completed the survey; 86% of these had reviewed the toolkit. Most had staff clinicians with pediatric expertise, but fewer had appointed pediatric clinical (physician or nurse) coordinators. Hospitals with at least one pediatric bed were more than 2.5 times more likely to have an emergency management plan (EMP) for pediatric patients (P =. 0223) and nearly 8 times more likely to have appointed a pediatric physician coordinator (P <. 0001) than were hospitals without pediatric beds. Appointment of a pediatric clinical coordinator was significantly associated (P <. 001) with presence of various pediatric emergency plan elements (OR range: 3.06-15.13), while staff pediatric clinical expertise or toolkit review were not.

Conclusions: Appointment of at least one pediatric clinical coordinator and the presence of one or more pediatric beds were significantly associated with having developed key EMP pediatric elements. Further research should examine barriers to pediatric clinical coordinator appointment and explore the awareness that pediatric patients may arrive at nonpediatric hospitals during a disaster with no option for transfer.

(Disaster Med Public Health Preparedness. 2012;6:138–145)

Objective: We evaluated emergency department (ED) data, emergency medical services (EMS) data, and public utilities data for describing an outbreak of carbon monoxide (CO) poisoning following a windstorm.

Methods: Syndromic ED data were matched against previously collected chart abstraction data. We ran detection algorithms on selected time series derived from all 3 data sources to identify health events associated with the CO poisoning outbreak. We used spatial and spatiotemporal scan statistics to identify geographic areas that were most heavily affected by the CO poisoning event.

Results: Of the 241 CO cases confirmed by chart review, 190 (78.8%) were identified in the syndromic surveillance data as exact matches. Records from the ED and EMS data detected an increase in CO-consistent syndromes after the storm. The ED data identified significant clusters of CO-consistent syndromes, including zip codes that had widespread power outages. Weak temporal gastrointestinal (GI) signals, possibly resulting from ingestion of food spoiled by lack of refrigeration, were detected in the ED data but not in the EMS data. Spatial clustering of GI-based groupings in the ED data was not detected.

Conclusions: Data from this evaluation support the value of ED data for surveillance after natural disasters. Enhanced EMS data may be useful for monitoring a CO poisoning event, if these data are available to the health department promptly.

(Disaster Med Public Health Preparedness. 2011;5:37-45)