Developing an operational casualty estimate in a multinational headquarters to inform and drive medical resource allocation.
Davis, Soo Lee
|Publication:||Name: U.S. Army Medical Department Journal Publisher: U.S. Army Medical Department Center & School Audience: Professional Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2012 U.S. Army Medical Department Center & School ISSN: 1524-0436|
|Issue:||Date: Oct-Dec, 2012|
Medical planners serving on multinational operational staffs need
an easy-to-use, accurate, nonproprietary casualty estimate model to
properly plan for operations. The estimate tool cannot be software
specific because it can be difficult and costly to acquire the software.
The software may require special technical hardware and system
provisions to operate on NATO equipment, and such permission may not be
granted. The underlying data and statistical assumptions must be readily
accessible to any contributing nation. Oftentimes, US military-developed
casualty estimate models are classified or require US military accounts
to access them. This article describes a methodology that the Regional
Command South (RC(S)) developed to provide an operational casualty
estimate for a critical operation in Kandahar Province. The guidance
used to develop this model was NATO joint medical planning doctrine, an
excerpt of which follows:
RC(S) is one of 6 Afghanistan International Security Assistance Force (ISAF) regional commands. Four Afghan provinces fall under the responsibility of RC(S): Kandahar, Zabul, Uruzgan, and Daykundi. Kandahar Province is the largest, most populated, and was the most critical to ISAF strategic objectives at the time. The medical support in RC(S) was designed around a "hub and spoke" model, with Kandahar as the hub. Kandahar Airfield contained a NATO Role-3 Hospital. (2(pp1-9-1-12)) Camp Hero, adjacent to Kandahar Airfield, included an Afghan National Army hospital, the Kandahar Regional Military Hospital (KRMH). Kandahar City was home to Mir Weis, an Afghan civilian hospital operated by the International Committee of the Red Cross. Zabul and Uruzgan Provinces served as two of the primary spokes from the hub of the healthcare system, both containing Role 2 healthcare facilities.
During the summer of 2010, Afghan National Security Forces (ANSF), supported by ISAF, embarked on security operations in and around Kandahar City. The operational objectives were to protect the civilian population from insurgent intimidation and violence and to promote stability and improved governance by the Government of the Islamic Republic of Afghanistan. The operation, designated Hamkari (meaning "cooperation" in Dari), consisted of 3 stages, the first of which was the improvement of security and government presence in Kandahar City with the formation of a security ring protection force. This security ring consisted of a network of traffic checkpoints and police substations manned by partnered ANSF and ISAF personnel. This stage was nonkinetic, with the focus on increasing the number of police and security forces available in Kandahar City to directly support the civilian population. Concurrent to this effort, RC(S) began receiving US brigade combat teams to augment the one Canadian battle group whose operational area covered districts south and east of Kandahar City.
The second stage focused on clearing the Arghandab district northwest of Kandahar City. It commenced once 2nd Brigade, 101st Air Assault Division, Task Force Strike, was prepared to conduct deliberate operations in their area. Task Force Strike was successful in clearing key population areas and controlling major access routes into Kandahar City. This stage of the operation served as the anchor for the casualty estimate methodology outlined here.
Stage 3 required the clearance of 2 rural districts west and southwest of Kandahar City, Zhari and Panjwa'i. This stage was separated into phases 3A and 3B. Intelligence reports indicated the density of insurgent fighters was significantly greater in these areas than in Arghandab District. It also contained major insurgent logistical bases, which were heavily protected and fortified with defensive measures, such as improvised explosive device (IED) belts. The terrain, though rural, was much more difficult to operate in, and very conducive to concealing insurgent activities. One of the challenges of the terrain was the large number of grape fields, which consisted of undulating rows of dirt mounds, some as high as 6 feet. These grape fields were extremely difficult to maneuver through, and easily concealed tunnels, weapons caches, and antipersonnel mines, and channelized ISAF forces directly into IED belts emplaced by the insurgents.
The casualty rate in the Arghandab District operation was significant. The intelligence reports of an increased insurgent threat, and the advantage the terrain gave the enemy, suggested the third stage of Hamkari would be very challenging. We anticipated a potential for a higher casualty rate than in Arghandab. Therefore, it was imperative to conduct a thorough casualty estimate in order to assess whether or not RC(S) possessed sufficient medical capacity to support the potential casualty load. This analysis had to be completed early enough to give the healthcare system adequate time to shift assets if levels were determined to be insufficient. The analysis also had to be done quickly, because the operational plan unfolded rapidly and remained very dynamic. It required a casualty estimate methodology just as agile and efficient.
Applying Operational Understanding to Develop the Casualty Numbers and Flow Estimate
Casualty estimates are heavily data-driven and best informed through information about the current campaign. Therefore, it is absolutely imperative that as much data as possible is captured in a mineable format. Historically, the RC(S) Combined Joint Medical office (CJMED) maintained a very comprehensive medical evacuation database for every patient handled by the ISAF system. This database has been in place since 2006 and has evolved as medical planners learned which fields were most relevant to inform future questions and analysis.
Deliberate planning for Phase 3A began in mid July 2010 with operations planned to commence in early September. CJMED planners actively participated in the military decision-making process and were aware of the potential for a much higher rate of casualties. We analyzed the medical capacity in RC(S) to determine its adequacy for increased casualties. To further compound the risk, Phase 3A was planned to commence prior to the Afghanistan elections. This had been a historically high-risk period, with increased insurgent activities intended to disrupt the democratic process through violence and intimidation.
CJMED established the relevant parameters to factor into the estimate. In order to account for the risk associated with the elections period, we analyzed recent RC(S) significant activity data from CIDNE, concentrating on those significant activities that were most likely to cause casualties.
We applied these statistics against the average number of significant activities RC(S) experienced per day in the last month. Applying the intelligence estimate (Joint Staff J2) against this baseline and assuming a gradual increase in the weeks preceding election day, the peak on election day, followed by a gradual decrease in the weeks after election day, we produced a regional casualty estimate that was based on historical data. This accounted for the risk associated with baseline activities and the elections period.
The next step was to account for the risk associated with Phase 3A operations. We consulted with the Combined Joint Operations section (CJ35) and agreed that the Arghandab clear operation (Stage 2) would be most similar to Phase 3A operations. We determined which units comprised the task force assigned to the Arghandab clear operations, the size of each of those individual units, and the timeline for the operation. This gave us the total size of the force, or population at risk (PAR)--the denominator--as well as the duration of the operation. In order to determine the attrition rate for the Arghandab clear operations, we queried the casualty reporting databases to pull the actual numbers for all categories of casualties for the Arghandab clear operation--the numerator. This attrition rate was used as the "anchor" for determining
the estimated casualty rate for Phase 3A operations.
CJ35 then provided a sequence matrix of all events associated with Phase 3A operations, which listed the size of the force (PAR), as well as the planned start and end dates. Since the intelligence estimates indicated Phase 3A operations would be much more intense, given the higher density of insurgent fighters and difficult terrain, CJ35 also added an increased intensity factor (IIF) to the Arghandab attrition rate (AAR). CJ35 applied the following IIF to each operational event: low, 10% more casualties (amber); medium, 20% more casualties (red); high, 30% more casualties (black).
The operational sequence was then placed into another matrix to determine the estimated number of casualties per day. The following formula was used for each event:
(AAR+(AARx IIF)) x PAR = total number of casualties for the event
We then made assumptions about the distribution of those casualties over the duration of the event. We categorized each event into short (1 to 3 days), medium (4 to 10 days), and long (11 to 21 days) durations. For short events, we expected only 10% of the total number of casualties would be suffered and all of the casualties would occur on the first day of the event. For medium events, we assumed an even distribution of the total expected casualties across a 9-day period. For long events, we presumed casualties would be higher in the early days, and diminish towards the end. To get the total number of casualties expected on any given day, we calculated the number of casualties expected as a result of baseline and elections activities. We mapped the Phase 3A expected number of casualties by event to the actual day the event was to start. We then added across all the simultaneous activities for each day.
Matching The Casualty Numbers and Flow to the Capacity of the Medical System
The other component that must be understood is the capacity and efficiency of the Role 3 hospitals and the air evacuation assets in RC(S). The CJMED operations section recorded, tracked, and analyzed trends of the hospital capacity and air evacuation performance in RC(S). Because there was rarely an indication that there was insufficient capacity in the air evacuation systems, (3(p1-3)) we did not conduct in-depth analysis on these systems. However, we did know that hospital capacity could present an operational constraint and we sought to better understand the capacity and throughput of the hospital.
In April 2010, the RC(S) Medical Director requested the US Joint Combat Casualty Research Team to conduct a study to analyze the resource utilization in the Kandahar Air Field Role 3 hospital (KAFR3). They were asked to examine operating room utilization, and intensive care unit (ICU) and intermediate care ward (ICW) lengths of stay. Their findings provided a much better understanding of the flow of casualties through the hospital. Most significantly, we determined that casualty rates should be estimated per day, as opposed to per week or per month, because the lengths of stay of all categories of patients was almost always measured in days, and there was a wide variability in the number of casualties in any given day. There was no casualty estimate tool or model already developed that we could find which calculated number of casualties per day. This further necessitated the need for a locally developed methodology.
As expected, given the efficient strategic medical evacuation system and availability of higher levels of care outside of Afghanistan, ISAF casualties spent one day or less in the hospital. There was very little variability around this statistic. Afghan National Security Force casualties spent approximately 2 to 3 days in hospital before they were stable enough to be transferred to an Afghan facility for care. There was much more variability with this statistic. Afghan civilians and Afghan detainees spent the longest time in hospital, 3 to 6 days, and also had the most variability to their lengths of stay. This was primarily because it was not acceptable to transfer Afghan civilians away from their home province, and Afghan detainees that were still under the custody of ISAF forces could not be transferred out of ISAF facilities for security reasons. Of all the hospital capabilities, ICU bed capacity had to be managed the most carefully because it is a more resource intensive capability, the scarcest capability, and served our most critically injured casualties.
The Table depicts how the ISAF system of care describes the amount of capacity remaining in the hospital. It expresses the amount of risk in exceeding hospital capacity via the traditional military "stoplight" model. This table does not account for the surge capacity that all hospitals possessed to get through a casualty crisis. While BLACK does indicate the highest risk, it does not indicate that the hospital would not be able to manage casualties during that time. It was meant to communicate that the system was under a tremendous amount of pressure and perhaps should be considered an operational constraint.
Understanding the dynamics of patient flow at KAFR3 helped us understand the casualty profile that the ISAF system of care could absorb in a day and the subsequent impact on available capacity the next day. We determined the range of casualty numbers for GREEN, AMBER, RED, and BLACK status (those numbers are intentionally not presented in this article). This calculation became very important in expressing the risk. Though it would have been ideal to understand the capacity and throughput of Kandahar Regional Military Hospital (KRMH), there was not the same capability to track and store data for later analysis. Given the ISAF system of care underwrites the risk in the Afghan system of care, we did not pursue a more deliberate analysis of KRMH.
Presenting the Analysis and Developing Mitigation Strategies
Graphing the total number of expected casualties per day, with the hospital capacity superimposed, gives a visual of where the high-risk periods are and the magnitude of the anticipated risk. The results of the analysis indicated that there was a significant potential for medical risk. This risk could potentially become an operational constraint. We also anticipated days in which there was very little spare capacity in the system to deal with a mass casualty event. We briefed our analysis and risks to the combined medical team whose key stakeholders consisted of the KAFR3 hospital, the casualty aeromedevac staging facility (the aviation task forces providing rotary-wing air evacuation), the 62nd Medical Brigade, the Afghan National Army (ANA) 205 Corps Surgeon, and the ANA KRMH Commander, to ensure they all had the same operational information. We then worked together to develop mitigating strategies.
We determined the most important method to mitigate risk was to affect the number of casualties the Role 3 hospitals could take in a day and still have capacity left to handle the next day's casualty profile. The combined team agreed this would be the most powerful strategy. We reallocated resources from across the theater to increase the KAFR3 capacity to handle surgical cases, as well as staff additional ICU and ICW beds. Figure 1 presents the casualty estimate per day, relative to hospital capacity, as well as the potential impact of hospital augmentation. While the ANA system of care attempted to do similar capacity increases, they were not able to obtain the resources to establish the extra capacity. They did have a contingency plan to set up a 40-bed minimal care ward in case of a large casualty surge.
The rotary-wing and fixed-wing aviation task forces also increased their capacity to conduct air evacuations by adding additional crews and aircraft to support the increased tempo of operations. The combined medical team agreed to adopt aggressive bed clearing practices to ensure the medical system was best prepared to handle the next day's events. Aggressive bed clearing was defined as maximum use of tactical evacuation to clear RC(S) of casualties by transferring them to other in-theater hospitals. It also included more frequent use of strategic evacuation assets for those casualties requiring evacuation out of theater. The casualty data pattern established 1 to 3 day peaks followed by 1 to 2 day troughs. Because there was no historical evidence of a sustained period of peak casualty rates, we decided the mitigation strategies described would be appropriate and sufficient.
Informing the Operational Leadership and the Commander
It is important to keep the operational leadership and commander informed about the medical risk associated with operations. This is especially true if the risk cannot be sufficiently mitigated and remains an operational constraint. Current medical risk information allows commanders to decide if the operational plan should be adjusted to accommodate the constraint. Each headquarters will have a process for synchronizing the staff and gaining a forum with the commander to present information, seek guidance, or get decisions. RC(S) CJMED planners continued to stay integrated with the operational staff throughout the analysis and operational planning. We periodically briefed the results of the casualty estimate to operational leaders, the chief of staff, and the commander. This ensured the wider staff was informed of the analysis and gave the commander confidence that the medical risk was properly evaluated and addressed.
[FIGURE 1 OMITTED]
Refining the Model
As with any model, one must compare the assumptions and logic to determine its accuracy. When we plotted the estimate against the actual number of casualties that were handled by the augmented ISAF system of care, it was obvious that the assumptions built into the model overestimated the risk. We determined that because we used CIDNE data to drive the casualty estimate for baseline and the elections period, we built into the model the total number of casualties reported in the region, but not necessarily just those handled by the ISAF system. Kandahar City has 2 capable hospitals, one ANA and the other civilian. These hospitals handled a portion of the casualties that occurred in the region without ISAF involvement for evacuation or treatment. Figure 2 presents the actual data compared to the estimate.
[FIGURE 2 OMITTED]
We backed out the elections assumptions to assess the accuracy of the methodology associated with the Phase 3A operations. This is demonstrated on the left portion of Figure 3. Based on this review, we concluded that while the model does not necessarily predict the exact number of casualties per day (exceedingly difficult to do), it did indeed predict the magnitude of the risk we expected. Ultimately, this is what drives resource decisions. Given we had good confidence in the assumptions, we used the same methodology and applied it against Phase 3B operations. The results indicated the amount of medical risk was manageable without external augmentation. Therefore, we released those assets back to their parent organizations and continued to monitor actual casualty rates. The right portion of Figure 3 displays these results.
We note that the model overestimated the medical risk for Phase 3B, despite the improved accuracy the model demonstrated for Phase 3A. After studying the operational information, we concluded that ISAF used different tactics, techniques, and procedures than were used in Phase 2 operations in the Arghandab District to counter the most dangerous threat of IEDs. The intelligence estimates were correct; the terrain was extremely difficult and there were many IED belts. However, ISAF used special mine clearing and antipersonnel obstacle removal systems to clear maneuver pathways. A single removal effort often caused multiple sympathetic detonations of IEDs. These clearing systems were not used as frequently in the Arghandab District because the population density was higher and the risk of collateral damage was too great. The areas cleared in Phase 3B were much more rural and sparsely populated, allowing ISAF to use more aggressive tactics.
[FIGURE 3 OMITTED]
This methodology serves a very specific purpose not available in other casualty estimate models. It is an operational level casualty estimate that specifically addresses the daily medical capacity required within an area of responsibility. This is not the strategic process of assigning medical forces to the theater of operations to support the campaign. It is not the units' tactical decisions, such as moving medical capabilities around the battlefield to support daily operations. It is not intended to determine type and amount of logistical supplies to treat specific wounds. This model does not predict the level of granularity that some casualty estimate models attempt to do, such as evacuation category, (3(p1-10)) battle injury vs disease or nonbattle injury, or nationality of the casualty. We balanced the precision of the information and amount of complicated analysis necessary to factor in these elements against the simplicity and agility of the model. Making an assessment of the amount of casualties the Role 3 hospitals could absorb in a day, and expressing that as a degree of risk, accommodated for these detailed elements well enough. It enabled us to make an informed decision without investing in perfecting the information. The leadership of RC(S) changed from British-led to US-led division headquarters on November 2, 20l0, and both authors of this article concluded our analysis of this subject.
It is important for the medical planner to maintain excellent situational awareness of operational information and participate in the military decision-making process. This is achieved through membership in cross-functional planning teams and active listening at command-level summary briefs. Through this participation, a medical planner will develop an understanding of what operational information is relevant. This allows sufficient data to be captured with much less difficulty. Given the likelihood of future campaigns in a multinational environment, it is imperative to use simple and widely available software so that any contributing-nation medical planner can convert the discrete data into current medical planning information. The information can then be used to produce estimates and analysis to inform the medical risk and mitigation strategies. The steps outlined in this article are presented in Figure 4.
As stated in NATO medical support doctrine,
A mass casualty plan is appropriate for handling single or multiple catastrophic events (aircraft accident, suicide bomber, etc) that must be managed over a day or two. It is nearly impossible to predict when these events will occur, and every medical system must be prepared to surge to get through such a casualty crisis of relatively short duration. In contrast, a mass casualty plan is insufficient to contend with a high, sustained rate of casualties, and its inadequacies will result in an enormous amount of stress on the medical system and endanger probabilities of survival of casualties. Military medical planners can anticipate periods of sustained (measured in weeks) high levels of casualties with good casualty estimates. However, it is important to fully appreciate that augmentation of medical systems with personnel and/or equipment requires considerable time and coordination. This is especially true for projected personnel augmentation requirements. Healthcare is complex, and human-skill-set-centric, so personnel augmentations must be initiated as early as possible to allow the additional personnel time to travel and integrate into their new environments. It is, therefore, vital that projected casualty planning is accomplished with as much lead time as possible to allow the medical system adequate time to respond to identified augmentation requirements.
[FIGURE 4 OMITTED]
(1.) STANAG 2542 MEDSTD (Edition 1)--Allied Joint Medical Planning Doctrine-AJMedP-1. Brussels: North Atlantic Treaty Organization Standardization Agency; November 3,2009:3-15-3-16. Available at: http://nsa.nato.int/nsa/zpublic/stanags/cur rent/2542eed01.pdf. Accessed July 18, 2012.
(2.) AJP-4.10(A): Allied Joint Medical Support Doctrine. Brussels: North Atlantic Treaty Organization Standardization Agency; March 2006. Available at: http://nsa.nato.int/nsa/zPublic/ap/AJP-4.10(A).pdf. Accessed July 18, 2012.
(3.) STANAG 2546 MEDSTD (Edition 1-Allied Joint Doctrine for Medical Evacuation)-AJMedP-2. Brussels: North Atlantic Treaty Organization Standardization Agency; November 24, 2008. Available at: http://nsa.nato.int/nsa/zpublic/stanags/cur rent/2546eed01.pdf. Accessed July 18, 2012.
LTC Soo Lee Davis, MS, USA
Col Martin Bricknell, RMC, British Army
LTC Davis is Commander, 187th Medical Battalion, 32nd Medical Brigade, Fort Sam Houston, Texas. When this article was written, she was Deputy Medical Advisor, Regional Command (South), Kandahar, Afghanistan.
Col Bricknell is Assistant Director Medical Capability, Army Medical Directorate, Great Britain. When this article was written, he was Medical Director, Regional Command (South), Kandahar, Afghanistan.
A thorough threat assessment and a proper analysis of the environment together with a comparison with former campaigns provide the basis for the estimate of severity and patterns of casualties to be expected.... Medical planners will have to cooperate with J2 and J5 staff from the beginning in order to receive early ideas of the concept to be developed. The casualty rate estimation on the timeline produces a diagram showing the estimated casualty flow during the phases and subphases of the operation.... A tool suitable for NATO casualty rate estimation has to be developed based on recent valid data gathered from all types of operations. Hence, a prerequisite for a sufficient tool is a comprehensive database covering the whole spectrum of possible operations. A tool must be user-friendly, but take the complexity of the casualty rate estimation into account. In addition to this it has to be accessible for medical planners involved in NATO operations. (1)
Casualty estimates are one of the core tools of medical plans, they are major resource drivers and, although an inexact science, accuracy is important. (2(pl-32))
Hospital capacity expressed as risk. Status ICU Fill ICW Fill Overall Fill Risk Green [less than [less than or equal or equal to] 50% to] 50% Most Low Amber 51% to 75% 51% to 75% Constrained Medium Red 76% to 99% 76% to 99% of ICU High Black [greater [greater vs ICW Very High than or than or equal equal to] 100% to] 100%
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