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Combat injury profiles among U.S. military personnel who survived serious wounds in Iraq and Afghanistan: A latent class analysis

  • Edwin W. D’Souza ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliations Medical Modeling, Simulation, and Mission Support Department, Naval Health Research Center, San Diego, California, United States of America, Leidos, Inc., San Diego, California, United States of America

  • Andrew J. MacGregor,

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing

    Affiliations Medical Modeling, Simulation, and Mission Support Department, Naval Health Research Center, San Diego, California, United States of America, Axiom Resource Management, Inc., San Diego, California, United States of America

  • Amber L. Dougherty,

    Roles Formal analysis, Visualization, Writing – original draft, Writing – review & editing

    Affiliations Medical Modeling, Simulation, and Mission Support Department, Naval Health Research Center, San Diego, California, United States of America, Leidos, Inc., San Diego, California, United States of America

  • Andrew S. Olson,

    Roles Conceptualization, Formal analysis, Resources, Supervision, Writing – review & editing

    Affiliation Medical Modeling, Simulation, and Mission Support Department, Naval Health Research Center, San Diego, California, United States of America

  • Howard R. Champion,

    Roles Formal analysis, Writing – review & editing

    Affiliations Uniformed Services University of the Health Sciences, Annapolis, Maryland, United States of America, Section of Surgery, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America

  • Michael R. Galarneau

    Roles Conceptualization, Formal analysis, Funding acquisition, Resources, Supervision, Writing – review & editing

    Affiliation Medical Modeling, Simulation, and Mission Support Department, Naval Health Research Center, San Diego, California, United States of America



The U.S. military conflicts in Iraq and Afghanistan had the most casualties since Vietnam with more than 53,000 wounded in action. Novel injury mechanisms, such as improvised explosive devices, and higher rates of survivability compared with previous wars led to a new pattern of combat injuries. The purpose of the present study was to use latent class analysis (LCA) to identify combat injury profiles among U.S. military personnel who survived serious wounds.


A total of 5,227 combat casualty events with an Injury Severity Score (ISS) of 9 or greater that occurred in Iraq and Afghanistan from December 2002 to July 2019 were identified from the Expeditionary Medical Encounter Database for analysis. The Barell Injury Diagnosis Matrix was used to classify injuries into binary variables by site and type of injury. LCA was employed to identify injury profiles that accounted for co-occurring injuries. Injury profiles were described and compared by demographic, operational, and injury-specific variables.


Seven injury profiles were identified and defined as: (1) open wounds (18.8%), (2) Type 1 traumatic brain injury (TBI)/facial injuries (14.2%), (3) disseminated injuries (6.8%), (4) Type 2 TBI (15.4%), (5) lower extremity injuries (19.8%), (6) burns (7.4%), and (7) chest and/or abdominal injuries (17.7%). Profiles differed by service branch, combat location, year of injury, injury mechanism, combat posture at the time of injury, and ISS.


LCA identified seven distinct and interpretable injury profiles among U.S. military personnel who survived serious combat injuries in Iraq or Afghanistan. These findings may be of interest to military medical planners as resource needs are evaluated and projected for future conflicts, and medical professionals involved in the rehabilitation of wounded service members.


The U.S. military conflicts in Iraq and Afghanistan, including Operations Iraqi and Enduring Freedom (OIF/OEF), had the most combat casualties since Vietnam with more than 53,000 wounded in action [1]. The epidemiology of combat injuries in OIF/OEF varied from previous wars [2], as asymmetric warfare became more common [36] and the survivability of combat wounds increased [3, 79]. Blasts, often caused by improvised explosive devices, predominated the battlefield. As a result, blast-related traumatic brain injury (TBI) emerged as a preeminent wound of these conflicts [5, 1012], and many casualties experienced polytrauma [4, 13, 14]. Case-fatality rates have sharply declined since World War II [3], as well as over the course of OIF/OEF [7, 9]. This change has been attributed to advances in personal protective equipment and field medicine [2, 3]. As more military personnel than ever are surviving combat wounds, clinical research efforts have prioritized long-term care and rehabilitation [15].

The novel epidemiology of combat injuries from OIF/OEF warrants further investigation. Previous studies have assessed injury patterns among specific samples of combat casualties, such as critically injured patients or those with certain types of injuries [13, 1622]. To date, no study has examined wounding patterns, or injury profiles, among a large population of seriously wounded combat survivors. Information on injury profiles, including common co-occurring injuries, may inform military medical planners and leadership for future armed conflicts, and provide guidance for the knowledge, skills, and abilities required of military medical personnel.

Knowledge of combat casualty injury profiles could also inform further research, such as the evaluation of rehabilitation and recovery outcome metrics. Although measures such as the Injury Severity Score (ISS) have proven useful in predicting mortality [2325], their utility in the assessment of long-term outcomes is unclear. One recent study found a pattern of postinjury multimorbidity (i.e., co-occurrence of two or more long-term health conditions) and poorer quality of life among military personnel with combat injury that was not associated with the highest levels of ISS [26]. This suggests that other factors, such as protracted impairments resulting from TBI or extremity trauma, may play a role beyond injury severity [11, 19].

The identification of combat casualty injury profiles may assist in refining patient management protocols to improve rehabilitation outcomes and overall well-being [27]. In addition, linking injury profiles to operational data may elucidate specific circumstances during wartime where certain combat injury profiles were more prevalent, and thus could influence future policies and prevention strategies. The objectives of the present study were to: (1) use latent class analysis (LCA) to identify injury profiles among U.S. military personnel who survived serious combat injuries; and (2) describe injury profiles by casualty and operational data.


Study population

Data for this study were obtained from the Expeditionary Medical Encounter Database (EMED), a deployment health repository at the Naval Health Research Center (NHRC), San Diego, California, that includes clinical records of U.S. service members injured during combat deployment. Clinical records were completed by providers in-theater and provided to NHRC where they were consolidated with patients’ medical records obtained from all levels of care. Patient records were retrospectively reviewed by certified nurse coders at NHRC and assigned International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes [28] and ISSs. Additional information on the EMED is available elsewhere [29]. The ISS is calculated as the sum of the squares of the highest Abbreviated Injury Scale [30] severity score in each of the three most severely injured body regions and quantifies overall injury severity for each casualty [2325]. Only those with serious or greater injury severity (i.e., ISS ≥ 9) who survived their wounds through all levels of care were included. The study population included 5,227 casualty events that occurred during combat operations in Iraq and Afghanistan from December 2002 to July 2019. This study complied with all federal regulations governing the protection of human subjects in research and was approved by the Institutional Review Board (IRB) at NHRC. The approved IRB protocol (NHRC.2003.0025) issued a waiver of informed consent for this study.


The Barell Injury Diagnosis Matrix, a two-dimensional table which categorizes injuries by body region (or site) and nature (or type) of injury, was used to classify injuries for each casualty [31]. In the Matrix, TBI is categorized as: Type 1 TBI (i.e., moderate-to-severe brain injury as indicated by an extended loss of consciousness and/or amnesia of the injury event); Type 2 TBI (i.e., mild brain injury as indicated by brief loss of consciousness or altered mental status); and Type 3 TBI (i.e., skull fracture without specification of intracranial injury). The Matrix has 36 rows that represent body regions and 12 columns that represent injury types. In this study, the “fractures” injury type was expanded to include “open” and “closed” fractures, which resulted in an additional column. Binary (1 or 0) injury variables were coded to indicate the presence or absence of the specific body region/injury type combination in each cell of the Matrix, and only populated cells in the Matrix were examined. Overall, 181 binary injury variables were derived for each combat casualty.

Other demographic, operational, and injury-specific variables were abstracted from the EMED for descriptive purposes. Demographic and operational variables included age at time of injury (18–24, 25–29, or 30+), sex, service branch (Army, Marine Corps, Navy, or Air Force), year of injury (2002–2008 or 2009–2019), and combat location (Iraq or Afghanistan). Injury-specific variables included injury mechanism (blast, gunshot wound, or other), combat posture at the time of injury (mounted [i.e., in a vehicle] or dismounted [i.e., on foot]), and ISS, which was categorized as serious (ISS 9–15), severe (ISS 16–24), and critical (ISS ≥ 25) [25].

Statistical analysis

Analyses were performed using SAS software, version 9.4 (SAS Institute, Cary, North Carolina) and R software, version 3.6.2 (The R Project for Statistical Computing). The R package poLCA [32] was used for LCA, a probability model-based clustering algorithm [33, 34]. LCA was used to map the 181 binary injury manifest variables [34] onto classes termed “injury profiles.” Injury profiles represented mutually exclusive groups of combat casualties with commonly co-occurring injuries. Several LCA models were built, with the number of latent classes ranging from 1 to 10. The best model for each group was chosen using a combination of qualitative and quantitative measures, preferring models with more coherent classes, fewer parameters, and better fit statistics [34]. Binary injury manifest variables with a conditional item probability (i.e., class-specific indicator probability) of at least 0.30 were used to identify and label the classes in the LCA models. Interpretation of injury profiles was based on LCA results and input from subject matter experts. Fit statistics, including the Bayesian information criterion (BIC), sample-size adjusted BIC (SABIC), Akaike information criterion (AIC), and consistent AIC (CAIC), were computed [35]. Casualties were assigned to each class in the LCA model using the maximum-probability assignment rule. To evaluate the likelihood of misclassification, mean classification posterior probabilities were estimated for each class. Values above 0.70 indicated well-separated classes in the model [36], and entropy values greater than 0.80 indicated “good” model classification of individual cases into classes [37]. The selected LCA model yielded latent classes (or injury profiles) that were described by injuries with conditional item probabilities above the 0.30 threshold in each group. Chi-square tests assessed the distribution of demographic, operational, and injury-specific variables across injury profiles. Multiple hypothesis tests were conducted to compare the proportions of the levels of each categorical variable over all possible pair combinations across injury profiles. P-values were adjusted using the Holm method to control the family-wise error rate for multiple comparisons. An alpha level of 0.05 was used for statistical significance.


Table 1 summarizes fit statistics of the LCA models with classes ranging from 1 to 10. The BIC, SABIC, AIC, and CAIC statistics indicated the ideal model had between 6 and 10 classes. The 7-class LCA model was selected as the best model based on a combination of good fit (smallest CAIC statistic), model parsimony (fewer model parameters), and coherent, interpretable classes. The 7-class LCA model had an entropy statistic of 0.857 and outperformed most of the models in delineating the classes. Assignment of cases to unique classes using the maximum-probability assignment rule yielded mean class membership posterior probabilities above 0.90 for all seven classes (Table 2). Posterior probabilities of membership among classes where cases were not assigned did not exceed 0.05, indicating a very low expected misclassification rate.

Table 2. Classification posterior probabilities of 7-class latent class analysis model.

Table 3 shows the injury types and sites with conditional item probabilities above the 0.30 threshold for each of the seven classes in the model. Injury profiles were defined as: open wounds (class 1); Type 1 TBI/facial injuries (class 2); disseminated injuries (class 3); Type 2 TBI (class 4); lower extremity injuries (class 5); burns (class 6); and chest and/or abdominal injuries (class 7). The distribution of the study population (N = 5,227) by injury profile was 18.8% (n = 981) in open wounds, 14.2% (n = 742) in Type 1 TBI/facial injuries, 6.8% (n = 353) in disseminated injuries, 15.4% (n = 804) in Type 2 TBI, 19.8% (n = 1,036) in lower extremity injuries, 7.4% (n = 387) in burns, and 17.7% (n = 924) in chest and/or abdominal injuries.

Table 3. Conditional item probabilities by injury type and site in the 7-class latent class analysis model.

Characteristics of the study population by injury profile are shown in Table 4. The study population was predominantly male (98.3%), aged 18–24 years (55.9%), and in the Army (70.6%). The majority were injured while deployed in Iraq (50.2%) between 2002–2008 (53.3%). Most casualties were injured by blasts (75.9%) and sustained serious injuries (ISS 9–15; 59.3%). A slightly higher proportion of the study population was mounted than dismounted at the time of injury (42.2% vs. 39.5%). All variables except for sex and age differed significantly across the injury profiles (ps < 0.001). The burns profile (class 6) had the highest percentage of Army service members (77.5%), whereas the open wounds profile (class 1) had the most Marines (33.5%). Compared with all other profiles, burns had the highest proportions of personnel injured in Iraq (73.6%) between 2002–2008 (79.3%), and Type 2 TBI (class 4) had the highest proportions injured in Afghanistan (69.2%) between 2009–2019 (66.3%). Blasts were the predominant injury mechanism for all injury profiles except for the chest and/or abdominal injuries group (class 7), which had a significantly higher proportion of gunshot wounds. The open wounds and disseminated injuries profiles (classes 1 and 3) had the highest proportions of service members dismounted and mounted at the time of injury, respectively. The disseminated injuries profile also had the highest percentages of severe (35.7%) and critical (47.0%) injuries. Conversely, the lower extremity injuries profile (class 5) was the least severe, with the lowest proportions of severe (14.7%) and critical (1.8%) injuries.

Table 4. Characteristics of combat casualties by injury profile in 7-class latent class analysis model.


The U.S. military conflicts in Iraq and Afghanistan resulted in a new pattern of injuries among combat casualties. To our knowledge, the present study is the first to describe injury profiles among combat casualties using LCA. Seven injury profiles were identified and described by demographic, operational, and injury-specific data, which reflected different periods of the OIF/OEF conflicts and highlighted ubiquitous injury types, such as TBI and lower extremity injuries [11, 19]. The findings may be of interest to military medical planners who project the logistics, resources, and skilled providers required to treat combat casualties with serious injuries in future conflicts, and to medical professionals involved in injury rehabilitation, as many military personnel with combat injuries may require life-long care [27].

One of the profiles identified in the present study indicated a wide range of open wounds marked by a high proportion of service members dismounted at the time of injury who were primarily injured by blasts. This profile appears to be similar to “dismounted complex blast injury,” which is characterized in the literature by extensive open wounds, including amputations and pelvic/urogenital injuries [21, 22]. The circumstances surrounding dismounted complex blast injury typically involve military personnel on foot patrol when an explosive device is activated nearby [22]. Survivors of these type of injuries face quality of life concerns due to resulting disabilities, and optimal rehabilitation strategies are necessary [38]. Pelvic protection has been developed for U.S. military personnel and future research is needed to determine its utility in a combat environment [39].

In contrast to the open wounds profile, the group with disseminated injuries, including injuries to internal organs and fractures (both open and closed), had the highest proportion of service members mounted in a vehicle at the time of injury. Most service members in this profile were also injured by blasts. A unique aspect of this profile was fractures to the vertebral column, which has been identified in previous research on mounted casualties [40, 41]. Though enclosure within a vehicle affords some protection compared with dismounted personnel, certain characteristics of the injury incident can increase risk for serious injury, such as vehicle rollover, and high velocity displacement [41]. A key variable missing from the present analysis was the type of vehicle mounted at the time of injury, which can impact injury patterns [40]. Over the course of OIF/OEF, vehicles have been improved to maximize operational effectiveness and increase the amount of protective armor. Humvees were widely used during the early phases of these conflicts, but were later phased out in favor of Mine-Resistant Ambush-Protected vehicles [42]. In addition, position in the vehicle can affect injury patterns, as a previous study found that gunners (i.e., operators of the weapon on top of the vehicle) had a higher percentage of extremity wounds compared with drivers and passengers [41]. As such, a more detailed analysis of mounted injuries is required to further define risk factors and develop potential preventive strategies.

The identification of TBI-related profiles was not surprising given that TBI emerged as one of the signature wounds of OIF/OEF, with an estimated 1 in 5 service members with mild TBI [10, 11]. One TBI profile consisted primarily of Type 2 TBI, whereas the other predominately involved Type 1 TBI, which generally results in worse long-term outcomes than mild Type 2 TBI [43]. Of note in the present study, both TBI profiles occurred in the presence of other injuries (e.g., wounds to the face). A prior descriptive account of combat-related TBI found that a significant proportion of service members sustain concomitant injuries [10], and these other injuries can slow the course of TBI recovery [44]. Future military TBI research should address co-occurring injuries, potentially by using injury severity specific to the non-head region, such as the extracranial ISS used by Stulemeijer et al. [44]. Furthermore, efforts should continue to identify innovative methods for monitoring and mitigating TBI on the battlefield, including sensor technology and improvements in helmet design [10, 45].

There were other notable findings of interest. The chest and/or abdominal injury profile was the only profile where the proportion of service members with gunshot wounds significantly outnumbered those injured by blasts. In addition, this profile was isolated to injuries to the internal organs in the mid-section, with no other injuries meeting the probability threshold. Current personal protective equipment may offer protection, but certain variables not accounted for in this study may impact its effectiveness, including overall fit and bullet/shrapnel trajectory. Another profile was predominated by burns. Most service members in this group were injured between 2002–2008 and mounted at the time of injury, which could reflect the vehicle types used earlier in the conflicts as described previously. Finally, the lower extremity injury profile was not surprising, as these injuries frequently occurred during OIF/OEF [19]. This profile also had the lowest overall injury severity, which may be indicative of low-energy blast injuries, such as when an individual is a significant distance from a blast event, or the improvised explosive device is of a lower explosive weight [22].

The present study had several strengths. The EMED allowed for abstraction of medical and tactical information (e.g., injury mechanism, combat posture) from the point of injury, which is generally difficult to obtain in austere combat environments. Further, the Barell Injury Diagnosis Matrix is a standard injury classification method endorsed by the Centers for Disease Control and Prevention [46], and all casualty records were reviewed and validated by professional nurse coders to ensure accuracy. There are also limitations that warrant mention. The injury profiles from LCA were probability-based in contrast to other potentially more precise methodologies such as three-dimensional surface wound mapping [47]. The conditional item probability threshold of 0.30 was a subjective criterion and injuries not meeting this threshold could have contributed to the injury profile within each class. It is also important to note that the combat posture variable (i.e., mounted/dismounted status) had a large proportion of missing data. Further research of incident-related factors is needed and may require collaboration with other U.S. government agencies to obtain sensitive data (e.g., amount of explosive, distance from blast). Additional studies are warranted to explore injury profiles among casualties with minor injuries and those who died of wounds or were killed in action, as the focus of the present study was service members who survived serious injuries and findings may not generalize to these other groups.


The present study used LCA to classify combat injury patterns among U.S. service members who survived serious wounds from OIF/OEF. Some of the injury profiles aligned with previous research that has identified dismounted complex blast injury, as well as preponderance of TBIs and lower extremity trauma during OIF/OEF. Combat posture at the time of injury was independently associated with various injury profiles, including the open wounds and disseminated injury groups, which requires further examination as these complex injury profiles impact long-term health outcomes. Additional research may be beneficial to identify injury-related sequelae and outlook for recovery. As modern warfare evolves and the U.S. military prepares for the next conflict, the identification and evaluation of combat injury patterns is paramount to medical planning and resource projections, and rehabilitation of wounded service members.


Disclaimer: The authors are military service members or employees of the U.S. Government. This work was prepared as part of our official duties. Title 17, U.S.C. §105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S.C. §101 defines a U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties. Report No. 21–48 was supported by the U.S. Navy Bureau of Medicine and Surgery under work unit no. 60808. The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Department of the Navy, Department of Defense, nor the U.S. Government. The study protocol was approved by the Naval Health Research Center Institutional Review Board in compliance with all applicable Federal regulations governing the protection of human subjects. Research data were derived from an approved Naval Health Research Center, Institutional Review Board protocol number NHRC.2003.0025.


  1. 1. DeBruyne NF, Leland A. American war and military operations casualties: lists and statistics. Congressional Research Service. 2020 Jul 29 [cited 2021 July 20]. Available from:
  2. 2. Belmont PJ, Schoenfeld AJ, Goodman G. Epidemiology of combat wounds in Operation Iraqi Freedom and Operation Enduring Freedom: orthopaedic burden of disease. J Surg Orthop Adv. 2010;19(1):2–7. pmid:20370999
  3. 3. Cannon JW, Holena DN, Geng Z, Stewart IJ, Huang Y, Yang W, et al. Comprehensive analysis of combat casualty outcomes in US service members from the beginning of World War II to the end of Operation Enduring Freedom. J Trauma Acute Care Surg. 2020;89(2S Suppl 2):S8–S15. pmid:32740296
  4. 4. Greer N, Sayer N, Kramer M, Koeller E, Velasquez T. Prevalence and epidemiology of combat blast injuries from the military cohort 2001–2014. Department of Veterans Affairs. 2016 [cited 2021 July 20]. Available from:
  5. 5. Phipps H, Mondello S, Wilson A, Dittmer T, Rohde NN, Schroeder PJ, et al. Characteristics and impact of U.S. military blast-related mild traumatic brain injury: a systematic review. Front Neurol. 2020;11:559318. pmid:33224086
  6. 6. Champion HR, Bellamy RF, Roberts CP, Leppaniemi A. A profile of combat injury. J Trauma. 2003;54(5 Suppl):S13–S19. pmid:12768096
  7. 7. Howard JT, Kotwal RS, Stern CA, Janak JC, Mazuchowski EL, Butler FK, et al. Use of combat casualty care data to assess the US military trauma system during the Afghanistan and Iraq conflicts, 2001–2017. JAMA Surg. 2019;154(7):600–608. pmid:30916730
  8. 8. Goldberg MS. Death and injury rates of U.S. military personnel in Iraq. Mil Med. 2010;175(4):220–226. pmid:20446496
  9. 9. Penn-Barwell JG, Roberts SA, Midwinter MJ, Bishop JR. Improved survival in UK combat casualties from Iraq and Afghanistan: 2003–2012. J Trauma Acute Care Surg. 2015;78(5):1014–1020. pmid:25909424
  10. 10. MacGregor AJ, Dougherty AL, Galarneau MR. Injury-specific correlates of combat-related traumatic brain injury in Operation Iraqi Freedom. J Head Trauma Rehabil. 2011;26:312–318. pmid:20808241
  11. 11. Institute of Medicine. Gulf War and health: Long-term consequences of traumatic brain injury. Vol 7. Washington, DC: The National Academies Press; 2009.
  12. 12. Tanielian T, Jaycox LH, (eds.). Invisible wounds of war: Psychological and cognitive injuries, their consequences, and services to assist recovery. Santa Monica, CA: RAND Corporation; 2008.
  13. 13. Champion HR, Holcomb JB, Young LA. Injuries from explosions: physics, biophysics, pathology, and required research focus. J Trauma. 2009;66(5):1468–1477. pmid:19430256
  14. 14. Eskridge SL, Macera CA, Galarneau MR, Holbrook TL, Woodruff SI, MacGregor AJ, et al. Injuries from combat explosions in Iraq: Injury type, location, and severity. Injury. 2012;43(10):1678–1682. pmid:22769977
  15. 15. Sayer NA, Cifu DX, McNamee S, Chiros CE, Sigford BJ, Scott S, et al. Rehabilitation needs of combat-injured service members admitted to the VA Polytrauma Rehabilitation Centers: the role of PM&R in the care of wounded warriors. PM R. 2009;1(1):23–28. pmid:19627869
  16. 16. Kotwal RS, Staudt AM, Trevino JD, Valdez-Delgado KK, Le TD, Gurney JM, et al. A review of casualties transported to Role 2 medical treatment facilities in Afghanistan. Mil Med. 2018;183(Suppl 1):134–145. pmid:29635602
  17. 17. Suresh MR, Valdez-Delgado KK, VanFosson CA, Trevino JD, Mann-Salinas EA, Shackelford SA, et al. Anatomic injury patterns in combat casualties treated by forward surgical teams. J Trauma Acute Care Surg. 2020;89(2S Suppl 2):S231–S236. pmid:32282757
  18. 18. Janak JC, Mazuchowski EL, Kotwal RS, Stockinger ZT, Howard JT, Butler FK, et al. Patterns of anatomic injury in critically injured combat casualties: a network analysis. Sci Rep. 2019;9(1):13767. pmid:31551454
  19. 19. Belmont PJ, Owens BD, Schoenfeld AJ. Musculoskeletal injuries in Iraq and Afghanistan: epidemiology and outcomes following a decade of war. J Am Acad Orthop Surg. 2016;24(6):341–348. pmid:27115793
  20. 20. Ivey KM, White CE, Wallum TE, Aden JK, Cannon JW, Chung KK, et al. Thoracic injuries in US combat casualties: a 10-year review of Operation Enduring Freedom and Iraqi Freedom. J Trauma Acute Care Surg. 2012;73(6 Suppl 5):S514–S519. pmid:23192079
  21. 21. Gordon W, Talbot M, Fleming M, Shero J, Potter B, Stockinger ZT. High bilateral amputations and dismounted complex blast injury (DCBI). Mil Med. 2018;183(Suppl 2):118–122. pmid:30189056
  22. 22. Cannon JW, Hofmann LJ, Glasgow SC, Potter BK, Rodriguez CJ, Cancio LC, et al. Dismounted complex blast injuries: a comprehensive review of the modern combat experience. J Am Coll Surg. 2016;223(4):652–664.e8. pmid:27481095
  23. 23. Baker SP, O’Neill B, Haddon W Jr, Long WB. The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma. 1974;14:187–196. pmid:4814394
  24. 24. Copes WS, Champion HR, Sacco WJ, Lawnick MM, Keast SL, Bain LW. The Injury Severity Score revisited. J Trauma. 1988;28:69–77. pmid:3123707
  25. 25. Stevenson M, Segui-Gomez M, Lescohier I, Di Scala C, McDonald-Smith G. An overview of the injury severity score and the new injury severity score. Inj Prev. 2001;7(1):10–13. pmid:11289527
  26. 26. MacGregor AJ, Zouris JM, Watrous JR, McCabe CT, Dougherty AL, Galarneau MR, et al. Multimorbidity and quality of life after blast-related injury among US military personnel: a cluster analysis of retrospective data. BMC Public Health. 2020;20(1):578. pmid:32345277
  27. 27. Woodruff SI, Galarneau MR, McCabe CT, Sack DI, Clouser MC. Health-related quality of life among US military personnel injured in combat: findings from the Wounded Warrior Recovery Project. Qual Life Res. 2018;27(5):1393–1402. pmid:29450855
  28. 28. Commission on Professional Hospital Activities. International classification of diseases, 9th revision, clinical modification. Ann Arbor, MI: Edwards Brothers; 1977.
  29. 29. Galarneau MR, Hancock WC, Konoske P, Melcer T, Vickers RR, Walker GJ, et al. The Navy-Marine Corps Combat Trauma Registry. Mil Med. 2006;171:691–697. pmid:16933807
  30. 30. Gennarelli TA, Wodzin E. AIS 2005: a contemporary injury scale. Injury. 2006;37:1083–1091. pmid:17092503
  31. 31. Barell V, Aharonson-Daniel L, Fingerhut LA, Mackenzie EJ, Ziv A, Boyko V, et al. An introduction to the Barell body region by nature of injury diagnosis matrix. Inj Prev. 2002;8(2):91–96. pmid:12120842
  32. 32. Linzer D, Lewis J. poLCA: polytomous variable latent class analysis [software]. 2014 [cited 2020 September 13]. Available from:
  33. 33. Masyn KE. Latent class analysis and finite mixture modeling. In: Little TD, editor. The Oxford handbook of quantitative methods: Statistical analysis. New York, NY: Oxford University Press; 2013. pp. 551–611.
  34. 34. MacGregor AJ, Dougherty AL, D’Souza EW, McCabe CT, Crouch DJ, Zouris JM, et al. Symptom profiles following combat injury and long-term quality of life: a latent class analysis. Qual Life Res. 2021;30(9):2531–2540. pmid:33884568
  35. 35. Nylund KL, Asparouhov T, Muthen B. Deciding on the number of classes in latent class analysis and growth mixture modeling. A Monte Carlo simulation study. Struct Equ Modeling. 2007;14:535–569.
  36. 36. Nagin DS. Group-based modeling of development. Cambridge, MA and London, England: Harvard University Press; 2005.
  37. 37. Clark SL, Muthén B. Relating latent class analysis results to variables not included in the analysis. University of California, Los Angeles. 2009 [cited 2021 July 20]. Available from:
  38. 38. Dismounted Complex Blast Injury Task Force. Dismounted complex blast injury: report of the Army dismounted complex blast injury task force. 2011 June 18 [cited 2021 July 20]. Available from:
  39. 39. Oh JS, Do NV, Clouser M, Galarneau M, Philips J, Katschke A, et al. Effectiveness of the combat pelvic protection system in the prevention of genital and urinary tract injuries: an observational study. J Trauma Acute Care Surg. 2015;79(4 Suppl 2):S193–S196. pmid:26406430
  40. 40. Possley DR, Blair JA, Freedman BA, Schoenfeld AJ, Lehman RA, Hsu JR, et al. The effect of vehicle protection on spine injuries in military conflict. Spine J. 2012;12(9):843–848. pmid:22177925
  41. 41. MacGregor AJ, Mayo JA, Dougherty AL, Girard PJ, Galarneau MR. Injuries sustained in noncombat motor vehicle accidents during Operation Iraqi Freedom. Injury. 2012; 43(9): 1551–1555. pmid:21612779
  42. 42. Pakulski K, Johnson J, Griffin R, Lo M, Wise D, St. Onge P, et al. Prevention of injury in mine resistant ambush protected (MRAP) vehicle accidents. Army Aeromedical Research Lab Fort Rucker AL. USAARL Report No. 2013–14. 2013 May [cited 2021 July 20]. Available from:
  43. 43. Schulz-Heik RJ, Poole JH, Dahdah MN, Sullivan C, Date ES, Salerno RM, et al. Long-term outcomes after moderate-to-severe traumatic brain injury among military veterans: successes and challenges. Brain Inj. 2016;30(3):271–279. pmid:26853377
  44. 44. Stulemeijer M, van der Werf SP, Jacobs B, Biert J, van Vugt AB, Brauer JM, et al. Impact of additional extracranial injuries on outcome after mild traumatic brain injury. J Neurotrauma. 2006;23(10):1561–1569. pmid:17020490
  45. 45. Rooks T, Logsdon K, McEntire BJ, Chancey VC. Evaluation of environmental sensors during laboratory direct and indirect head exposures. Mil Med. 2018;183(Suppl 1):294–302. pmid:29635599
  46. 46. Centers for Disease Control and Prevention. The Barell Injury Diagnosis Matrix, classification by body region and nature of the injury. National Center for Health Statistics. 2015 November 6 [cited 2021 July 20]. Available from:
  47. 47. Champion HR, Holcomb JB, Lawnick MM, Kelliher T, Spott MA, Galarneau MR, et al. Improved characterization of combat injury. J Trauma. 2010;68(5):1139–1150.8. pmid:20453770