ORIGINAL ARTICLE
Complex Comorbidity Clusters in OEF/OIF Veterans The Polytrauma Clinical Triad and Beyond Mary Jo V. Pugh, PhD, RN,*wz Erin P. Finley, PhD, MPH,*y Laurel A. Copeland, PhD,z8 Chen-Pin Wang, PhD,*w Polly H. Noel, PhD,*y Megan E. Amuan, MPH,z Helen M. Parsons, PhD, MPH,w Margaret Wells, BS,*w Barbara Elizondo, BA,*w and Jacqueline A. Pugh, MD*#
Background: A growing body of research on US Veterans from Afghanistan and Iraq [Operations Enduring and Iraqi Freedom, and Operation New Dawn (OEF/OIF)] has described the polytrauma clinical triad (PCT): traumatic brain injury (TBI), posttraumatic stress disorder (PTSD), and pain. Extant research has not explored comorbidity clusters in this population more broadly, particularly co-occurring chronic diseases. Objectives: The aim of the study was to identify comorbidity clusters among diagnoses of deployment-specific (TBI, PTSD, pain) and chronic (eg, hypertension, diabetes) conditions, and to examine the association of these clusters with health care utilization and adverse outcomes. Research Design: This was a retrospective cohort study. Subjects: The cohort comprised OEF/OIF Veterans who received care in the Veterans Health Administration in fiscal years (FY) 2008–2010.
From the *South Texas Veterans Health Care System; wDepartment of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio; zTexas A&M Health Science Center, Bryan; yDepartment of Medicine, Division of Clinical Epidemiology, University of Texas Health Science Center San Antonio, San Antonio; 8Center for Applied Health Research, jointly sponsored by Central Texas Veterans Health Care System, and Scott and White Healthcare System, Temple, TX; zCenter for Health Quality, Outcomes and Economic Research, Edith Nourse Rogers Memorial VA Hospital, Bedford, MA; and #Department of Medicine, Division of Hospital Medicine, University of Texas Health Science Center San Antonio, San Antonio, TX. The content of this article is solely the responsibility of the authors and does not necessarily reflect the official views of the Veterans’ Health Administration. Funded by the Department of Veterans Affairs, Office of Research and Development, VA Health Services Research and Development Service (DHI 09-237). The authors declare no conflict of interest. Reprints: Mary Jo V. Pugh, PhD, RN, South Texas Veterans Health Care System, 7400 Merton Minter Blvd, San Antonio, TX 78229. E-mail: [email protected]. Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Website, www.lww-medical care.com. Copyright r 2013 by Lippincott Williams & Wilkins ISSN: 0025-7079/14/5202-0172
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Measures: We identified comorbidity using validated ICD-9-CM code–based algorithms and FY08–09 data, followed by which we applied latent class analysis to identify the most statistically distinct and clinically meaningful patterns of comorbidity. We examined the association of these clusters with process measures/outcomes using logistic regression to correlate medication use, acute health care utilization, and adverse outcomes in FY10. Results: In this cohort (N = 191,797), we found 6 comorbidity clusters. Cluster 1: PCT + Chronic Disease (5%); Cluster 2: PCT (9%); Cluster 3: Mental Health + Substance Abuse (24%); Cluster 4: Sleep, Amputation, Chronic Disease (4%); Cluster 5: Pain, Moderate PTSD (6%); and Cluster 6: Relatively Healthy (53%). Subsequent health care utilization patterns and adverse events were consistent with disease patterns. Conclusions: These comorbidity clusters extend beyond the PCT and may be used as a foundation to examine coordination/quality of care and outcomes for OEF/OIF Veterans with different patterns of comorbidity. Key Words: Afghan Campaign 2001 (Operation Enduring Freedom), comorbidity, Iraq War 2003 (Operation Iraqi Freedom), veterans (Med Care 2014;52: 172–181)
T
he years since 2001 have presented extraordinary challenges to the US Armed Forces and the Veterans Health Administration (VA) in their efforts to provide high-quality care to Veterans returning from deployment to Afghanistan and Iraq (Operations Enduring and Iraqi Freedom, and Operation New Dawn, hereafter OEF/OIF). Studies in this population have identified high rates of combat-related injuries and mental health conditions that commonly co-occur and that have implications for health care utilization, treatment, and long-term outcomes.1–4 The majority of prior studies have focused on the signature injuries of traumatic brain injury (TBI), posttraumatic stress disorder (PTSD), and pain, also known as the polytrauma clinical triad (PCT), individually or in combination (eg, TBI and PTSD, PTSD and pain, etc.).5–10 Although trauma-based injuries and mental health problems dominate research in this population,10–13 Veterans Medical Care
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of OEF/OIF also experience a significant chronic disease burden14,15 and non–combat-related injuries (eg, motor vehicle accidents), musculoskeletal issues, and medical conditions (eg, digestive, genitourinary, and circulatory).16 As a result, studies have begun to describe comorbidity of mental health and physical health conditions in this population. Cohen et al17 found that OEF/OIF Veterans with PTSD are more likely to have cardiovascular disease–related conditions, and Frayne et al found that those with PTSD had more medical comorbidities diagnosed than those without PTSD.18 Nonetheless, studies of patterns of mental and physical comorbidity and associated outcomes beyond the PCT are not available. Thus, despite growing emphasis across the medical literature on providing integrated treatment approaches for patients with multiple chronic conditions,19 we lack information regarding whether patterns of multiple chronic conditions in this relatively young population exist that can inform recommendations for improving treatment and services and support more comprehensive evaluation of processes and outcomes of care.18 Our goal was to identify patterns of co-occurring physical and mental health conditions among OEF/OIF Veterans using latent class analysis (LCA), and to examine the association of those patterns with health care utilization and adverse events. We hypothesized that (1) previously unidentified clusters of comorbidity among OEF/OIF Veterans exist and include a variety of combinations of TBI, PTSD, pain, and chronic disease, and (2) variation in comorbidity clusters are associated with differences in processes of care and adverse outcomes. We specifically expected the following: 2a/Mental Health—comorbidity clusters with higher likelihood of depression and/or other mental health disorders have higher utilization of psychotropic medications, emergency care, inpatient mental health/ substance abuse treatment, and adverse outcomes such as suicide-related behavior (SRB); 2b/Pain—comorbidity clusters with higher likelihood of pain have higher utilization of medications commonly used for pain (eg, anticonvulsants, opioid pain relievers), and inpatient neurosurgery/orthopedic care; and 2c/Chronic Disease—comorbidity clusters with higher likelihood of chronic disease states have higher utilization of related medications (eg, antihypertensive agents), inpatient medical hospitalizations, and mortality rate.
METHODS Data Sources After securing IRB approval, we obtained data for Fiscal Years (FY) 2008 to 2010 (FY08–10; October 1, 2007 to September 30, 2010) from the VA Decision Support System National Data Extracts (DSS inpatient, outpatient, and pharmacy data), the OEF/OIF roster, and the VA Vital Status file. Inpatient and outpatient files included ICD-9-CM codes, clinic or hospital bedsection in which care was received, and demographic characteristics. Outpatient pharmacy data included the generic drug name. We identified latent classes in diagnostic data from FY08–09; health care utilization and adverse outcomes were identified in FY10, after comorbidity latent classes (clusters) were identified. r
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Beyond the Polytrauma Clinical Triad
Participants Participants flagged as OEF/OIF Veterans were selected for inclusion if they received VA inpatient or outpatient care at least 1 time yearly in each of FY08 and FY09.
Measures Comorbid Conditions Inpatient and outpatient diagnoses from face-to-face clinic/hospital care during FY08–09 were used to identify comorbid conditions for subsequent LCA; diagnoses from ancillary care such as laboratory, radiology, etc. were excluded. We used ICD-9-CM codes previously validated for use in administrative data (including Charlson, Elixhauser)20,21 to create dichotomous indicators for 32 physical/ mental health and postdeployment conditions including TBI, PTSD, pain, and other conditions commonly identified in this population.22–24 ICD-9-CM codes for each condition are presented in Appendix 1, Supplemental Digital Content 1, http://links.lww.com/MLR/A637.
Demographic Characteristics Demographics included age, sex, race/ethnicity, and marital status, obtained from inpatient and outpatient data. Individuals with missing race were included as a separate category. We also identified individuals who deployed with the National Guard/Reserve (per OEF/OIF roster).
Health Care Utilization We identified process measures describing pharmacotherapy, emergency care, and inpatient care during FY10. We created dichotomous variables for 10 theoretically relevant drug groups related to pain, mental health, and chronic disease management (Table 2). Individuals with at least 1 prescription for each drug group during FY10 were classified as receiving a prescription in that drug group (eg, opioids). We created indicators of health care utilization using outpatient identifiers for emergency/urgent care (130, 131) and bedsection (treating clinician’s specialty; see Table 3).
Adverse Outcomes Adverse outcomes included proxies for homelessness, diagnoses of drug overdose, suicidal ideation or attempt (hereafter SRB), and death. ICD-9-CM and clinic stop codes for SRB and drug overdose are provided in Appendix 1, (Supplemental Digital Content 1, http://links.lww.com/MLR/ A637). Mortality through FY10 was defined using the VA Vital Status File.
Analysis We used LCA, a method of structural equation modeling that identifies unobservable subgroups (latent classes) within a population on the basis of the distribution of binary diagnosis outcomes to identify patterns of comorbidity in this population. Thus, each latent class represents a “cluster” of comorbidities that occur most commonly in a subgroup of the population. Like other latent variable modeling techniques, LCA is an iterative process that assesses the fit of the model to the data. Our analysis started with a 2-class www.lww-medicalcare.com |
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solution, modeling the joint probability of the conditions under the assumption that they are correlated through class membership. We then conducted LCA with 3–7 classes, allowing 20 different start values for each LCA to ensure that the global maximum was reached and results were consistent regardless of the starting point. We identified the model with
Probability of Having Diagnosis
Probability of Having Diagnosis
Cluster 2 Polytrauma Clinical Triad 100 90 80 70 60 50 40 30 20 10 0
Comorbid Conditions
Comorbid Conditions Cluster 4 Sleep, Amputation, Chronic Disease Probability of Having Diagnosis
Probability of Having Diagnosis
Cluster 3 Mental Health, Substance Abuse 100 90 80 70 60 50 40 30 20 10 0
100 90 80 70 60 50 40 30 20 10 0
Cluster 5 Pain, Moderate PTSD
Comorbid Conditions
Comorbid Conditions Probability of Having Diagnosis
Probability of Having Diagnosis
Comorbid Conditions
100 90 80 70 60 50 40 30 20 10 0
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the best fit on the basis of reliability of the estimates, Akaike information criterion, and Bayesian information criterion. Once the best fitting model was identified, we used the pseudoclass technique to estimate each individual’s class (hereafter cluster) membership25 by drawing a random sample from the multinomial distribution based on posterior
Cluster 1 Polytrauma Clinical Triad, Depression, Chronic Disease 100 90 80 70 60 50 40 30 20 10 0
Cluster 6 Relatively Healthy 100 90 80 70 60 50 40 30 20 10 0
Comorbid Conditions
FIGURE 1. Distribution of comorbidity types by cluster.
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9.6 50.3 3.3 21.5 48.5 46.5 11.0 54.5 3.2 14.6 56.5 51.4 10.6 47.6 3.5 15.2 65.1 51.7 10.2 59.3 3.2 15.9 48.8 44.6 12.5 55.1 3.1 10.1 58.3 59.0
9.4 57.2 3.1 15.8 40.1 41.8
16.6 23.0 11.4 19.2
14.6
38.9 (10.0) 86.8 41.4 (9.8) 92.9 33.9 (8.6) 83.9 33.6 (8.4) 91.4 40.2 (10.2) 88.3
All comparisons had statistically significant differences at the P < 0.001 level. PTSD indicates posttraumatic stress disorder.
Table 2 displays the proportion of individuals from each cluster who received specific medication types, and the odds ratios from individual logistic regression models compared with Cluster 6. The final column of Table 2 shows the clusters with similar results, identified by examining the overlap of confidence intervals from the logistic regression analysis.When confidence intervals overlap, those clusters are not significantly different from each other, allowing comparisons between clusters beyond the reference group.
Cluster 1: Polytrauma Clinical Triad+Depression, Chronic Disease N = 8801 (4.6%)
Health Care Utilization and Adverse Outcomes
TABLE 1. Demographic Characteristics by Comorbidity Cluster
RESULTS Our analysis of care-seeking Veterans (N = 191,797) found that the LCA with 6 classes obtained global maximum likelihood estimates and had the smallest Akaike information criterion and Bayesian information criterion compared with other LCAs whose results converged. Figure 1 summarizes the probability of an individual in each cluster having specific conditions and the general distribution of comorbidity types across the 6-class model, with descriptions of the 21 conditions that were most notable in each cluster. Clusters 1 and 2 exhibited characteristics of the PCT (TBI, PTSD, Pain). Cluster 1 also had significantly higher probabilities of low back pain, chronic pain, insomnia, depression, hypertension, and osteoarthritis, and lower probabilities of TBI than Cluster 2 (Cluster 1: PCT, Depression, Chronic Disease; Cluster 2: PCT). Cluster 3 was characterized by depression, PTSD, anxiety, and substance abuse (Mental Health, Substance Abuse). Cluster 4 was characterized by insomnia, amputation, and chronic disease (Sleep, Amputation, Chronic Disease). Cluster 5 was characterized by back pain and other pain (Pain, Moderate PTSD). Cluster 6 was characterized by low probabilities of all conditions (Relatively Healthy). Although all bivariate analyses of demographic characteristics demonstrated statistically significant differences (Table 1), the most clinically relevant differences were related to age. In Clusters 1 and 4 the mean age was over 40, whereas Clusters 2 and 3 had mean ages in the early 30s [F = 1731 (df = 5, 191,723), P < 0.01]. Clusters 1 and 4 had significantly higher proportions of African Americans and lower proportions of whites than expected [w2 = 2367 (df = 20), P < 0.001]. Individuals in Clusters 1, 4, and 5 were significantly more likely to be married and to be deployed as National Guard/Reservists than expected [w2 = 2686 (df = 5), P < 0.001; w2 = 1013 (df = 5), P 5> 3, 6 > 2 1, 4 > 5, 6 > 3> 2 1, 4 > 5> 2, 3, 6
1> 3> 2> 4,5 > 6 1> 2,3,5 > 4> 6 1> 2,3 > 4> 5> 6
1> 2,5 > 3,4 6 1> 5> 2,4 > 3> 6 1> 5> 4> 2> 3> 6 1> 5> 2,4 > 3> 6
Groups With Similar Results
40.2 4.4 (4.2–4.6)
52.5 5.8 (5.6–6.0)
4.0 3.7 (3.3–4.1)
12.8 6.8 (6.4–7.3)
12.9 1.6 (1.6–1.7)
28.8 1.5 (1.5–1.6)
19.3 1.8 (1.8–1.9)
14.2 3.9 (3.7–4.0)
Cluster 3: Mental Health, Substance Abuse N = 45,242 (23.6%)
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Chronic disease Antihypertensive agents
7.5 7.1 (6.4–7.8)
Antipsychotic: typical
26.7 16.9 (15.9–18.0)
37.8 6.6 (6.3–6.9)
Skeletal muscle relaxants
Psychiatric Antipsychotic: atypical
36.7 2.2 (2.1–2.3)
56.0 4.8 (4.6–5.0)
Nonsteroidal antiinflammatory drug
18.7 2.5 (2.4–2.6)
25.1 2.5 (2.4–2.6)
20.2 5.9 (5.6–6.2)
48.5 7.1 (6.8–7.5)
41.5 16.6 (15.7–17.5)
Opioid pain reliever
Pain related Anticonvulsants
Cluster 2: Cluster 1: Polytrauma Clinical Polytrauma Clinical Triad, Depression, Chronic Disease Triad N = 8801 (4.6%) N = 16,585 (8.6%)
% OR (95% CI)
TABLE 2. Types of Prescription Medications Received in FY10: Comparisons With the Relatively Healthy Cluster (Cluster 6)
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Medications commonly used for pain were far more likely to be received by individuals in Cluster 1 than by patients in other clusters, with the lowest utilization of pain medications being in Cluster 6. Utilization of pain medications was also higher for Cluster 5 compared with most other groups. Similar patterns were found for psychotropic medications, with those in Cluster 1 having the highest odds of receiving each type of psychotropic medication, whereas individuals in Cluster 6 had the lowest odds. Those in Clusters 2 and 3 also were consistently more likely to receive atypical antipsychotics and antidepressants than Clusters 4, 5, and 6. With regard to medications for chronic disease, individuals in Clusters 1 and 4 had the highest likelihood of receiving antihypertensives, hypoglycemics, and lipid medications, whereas individuals in Cluster 2 consistently had the lowest likelihood of receiving these medications. Individuals in Cluster 6 were more likely than Clusters 2 and 3 to receive hypoglycemic medications. Table 3 shows the prevalence of acute care utilization in FY10 and odds ratios comparing Cluster 6 with other clusters. The final column of Table 3 lists groups with similar results, identified by examining the overlap of confidence intervals from the logistic regression analysis. Individuals in Cluster 1 had the highest levels of acute care utilization across the board, whereas those in Cluster 6 had the lowest levels. For inpatient psychiatric/substance abuse care, those in Clusters 3 and 2 were significantly higher than all remaining clusters. Finally, individuals in Cluster 5 were equally likely to have orthopedic/neurosurgery inpatient care as those in Cluster 1, and individuals in Clusters 2 and 4 were less likely to have orthopedic/neurosurgery inpatient care than Clusters 1 and 5, but more likely than those in Clusters 3 and 6. Adverse outcomes were consistently highest in Clusters 1 and 3 and lowest for individuals in Cluster 6, with the exception of overdose, in which Cluster 4 was not different from Cluster 6. Of interest, individuals in Cluster 4, who had a higher likelihood of chronic disease, were at lower likelihood of mortality than those in Cluster 3, which included individuals with primarily mental health/substance use disorder (Table 4).
DISCUSSION This study is the first to describe broad comorbidity clusters in Veterans from the conflicts in Afghanistan and Iraq, accounting for conditions related to TBI, mental health, chronic pain, and chronic disease. As predicted (hypothesis 1), we found 6 clusters, 2 of which were characterized by all 3 signature PCT injuries—TBI, PTSD, and pain. However, individuals in Cluster 1 had lower probability of TBI and higher probability of headache, back pain, other pain than those in Cluster 2. Individuals in Cluster 1 had higher probabilities of conditions associated with the PCT such as sleep disturbance, depression, and anxiety. Finally, individuals in Cluster 1 were older and more likely to have chronic disease diagnoses including hypertension, diabetes, osteoarthritis, and obesity. r
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Beyond the Polytrauma Clinical Triad
Although the PCT was not evident in Clusters 3, 4, and 5, other conditions, including individual PCT conditions, emerged. Cluster 3 was characterized by multiple mental health conditions including PTSD, depression, anxiety, and substance use disorder, whereas Cluster 5 was characterized primarily by back pain and other pain, with a likelihood of PTSD similar to our overall cohort (32%). Although the likelihood for PTSD was similar between Clusters 4 and 5, all individuals in Cluster 4 had diagnoses of sleep disorder, and higher probabilities of hypertension, obesity, osteoarthritis, and amputation diagnoses. These findings are consistent with PCT literature, and recent work that found Veterans with PTSD were more likely to have chronic disease than those without PTSD.9,19 However, they suggest that there are distinct clusters of individuals with PTSD, rather than 1 prototypical PTSD patient. Additional research using these clusters may help us identify early on those individuals with PTSD who may progress more quickly to chronic disease versus those with less chronic disease burden. Such research may also allow us to develop interventions that ultimately reduce disease burden in the PTSD chronic disease clusters. Finally, despite the plethora of literature identifying profound disease burden in this population,5,23,26–29 we found a cluster of relatively healthy individuals who accounted for about half of the cohort (53%). Although these individuals had a likelihood of hypertension slightly higher than the population prevalence, the likelihood of other conditions was significantly lower than the respective population prevalences. Across clusters, examination of sociodemographic characteristics suggests that clusters with a higher mean age (Clusters 1, 4, 5, and 6) were more likely to have associated chronic disease. Age was not associated with mental health conditions, however, as Clusters 1 and 3 had the highest likelihood of a variety of psychiatric diagnoses. Analyses of health care utilization and adverse outcomes also generally supported our second hypothesis. With regard to psychiatric outcomes (Hypothesis 2a/Mental Health), those clusters with high probability of multiple psychiatric diagnoses (Clusters 1 and 3) were consistently among those with the highest likelihood of psychotropic medications, emergency care, inpatient psychiatric/substance abuse care, and adverse outcomes of homelessness, overdose, SRB, and mortality. Cluster 1 in particular had a higher likelihood of psychotropic medications and inpatient psychiatric care than Cluster 3. Other findings suggest that there may be a “dose response” effect for psychiatric comorbidity. For example, Clusters 2, 4, and 5, each characterized by a lower likelihood of PTSD and other psychiatric conditions than Clusters 1 and 3, had psychiatrically related outcomes higher than Cluster 6 but lower than Clusters 1 and 3. This finding echoes similar findings in studies of suicidal ideation30 and suggests that the impact of PTSD-depression comorbidity in this population extends beyond suicidal ideation. With regard to Hypothesis 2b/Pain, we found that clusters characterized by pain (Clusters 1, 2, and 5) had the highest likelihood of anticonvulsants and opioids; Clusters 1 www.lww-medicalcare.com |
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6.5 13.4 (11.9–15.1)
1.0 7.3 (5.6–9.6)
1.2 3.3 (2.7–4.1)
Inpatient psychiatric/ substance abuse
1.2 1.9 (1.6–2.2)
0.5 1.4 (1.2–1.7)
0.2 1.3 (1.0–1.7)
4.5 9.2 (8.4–10.2)
1.5 2.3 (2.1–2.6)
0.6 6.3 (5.0–7.9)
25.2 2.0 (2.0–2.1)
Cluster 3: Mental Health, Substance Abuse N = 45,242 (23.6%)
0.9 2.4 (1.8–3.1)
0.4 2.7 (1.8–4.0)
1.7 3.3 (2.8–4.0)
1.6 2.6 (2.1–3.1)
0.3 3.1 (2.0–4.7)
22.8 1.8 (1.7–1.9)
Cluster 4: Sleep, Amputation, Chronic Disease N = 8158 (4.3%)
0.8 2.1 (1.7–2.7)
0.9 6.8 (5.2–8.8)
1.9 3.7 (3.1–4.3)
1.3 2.0 (1.7–2.4)
0.4 4.1 (2.9–5.8)
25.1 2.0 (1.9–2.1)
Cluster 5: Pain, Moderate PTSD N = 10,769 (5.6%)
0.4 Reference
0.1 Reference
0.5 Reference
0.6 Reference
0.1 Reference
14.4 Reference
Cluster 6: Relatively Healthy N = 102,242 (53.3%)
1> 2,3,5 > 4> 6 1> 2,3 > 4,5 > 6 1> 2,3,4,5 > 6 1> 3> 2> 4,5 > 6 1,5 > 2,4 > 3,6 1> 2,4,5 > 3> 6
Groups With Similar Results
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CI indicates confidence interval; OR, odds ratio; PTSD, posttraumatic stress disorder.
Inpatient orthopedic/ neurosurgery Other inpatient surgery
3.1 6.2 (5.5–7.0)
3.8 6.2 (5.4–7.1)
Inpatient medicine
0.6 6.0 (4.6–7.9)
1.3 12.6 (9.6–16.4)
Inpatient Neurology
25.5 2.0 (2.0–2.1)
39.4 3.9 (3.7–4.1)
Emergency/urgent care
Cluster 2: Cluster 1: Polytrauma Clinical Polytrauma Triad, Depression, Chronic Disease Clinical Triad N = 8801 (4.6%) N = 16,585 (8.6%)
% OR (95% CI)
TABLE 3. Acute Care Received in FY10: Comparisons With the Relatively Healthy Cluster (Cluster 6)
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0.2 Reference 0.3 1.4 (1.0–2.0) 0.7 2.9 (2.1–3.8) Mortality
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CI indicates confidence interval; N, cluster similar to >1 group; OR, odds ratio; PTSD, posttraumatic stress disorder.
0.4 1.7 (1.2–2.5) 0.6 2.5 (2.1–2.9)
0.4 3.6 (2.4–5.3) 1.0 9.3 (7.6–11.5) 0.8 7.2 (5.6–9.3) 1.4 13.3 (10.3–17.2) Suiciderelated behaviors
0.3 1.4 (1.1–1.9)
0.1 Reference
0.1 Reference
0.2 2.7 (1.5–4.7) 0.4 3.3 (2.3–4.8) 0.1 1.2 (0.52–2.9) 0.2 2.5 (1.5–4.1) 0.6 8.4 (5.6–12.6) Overdose
0.3 4.5 (3.2–6.4)
0.6 Reference 1.3 2.1 (1.8–2.6) 1.1 1.8 (1.5–2.3) 3.6 6.1 (5.6–6.7) 2.1 3.5 (3.1–4.0) 3.9 6.6 (5.8–7.5)
Cluster 6: Relatively Healthy N = 102,242 (53.3%) Cluster 5: Pain, Moderate PTSD N = 10,769 (5.6%) Cluster 4: Sleep, Amputation, Chronic Disease N = 8158 (4.3%) Cluster 3: Mental Health, Substance Abuse N = 45,242 (23.6%) Cluster 2: Cluster 1: Polytrauma Clinical Triad, Polytrauma Clinical Depression, Chronic Disease Triad N = 8801 (4.6%) N = 16,585 (8.6%)
% OR (95% CI)
TABLE 4. Adverse Outcomes in FY10: Comparisons With the Relatively Healthy Cluster (Cluster 6) r
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1, 3 > 2> 4, 5 > 6 1, 3 > (3), 2, 5 > 4, 6 1, 3 > 2, 3 > 4, 5 > 6 1, 3 > 2, 4, 5 > 6
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Homeless specific care
Groups With Similar Results
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and 5 were also more likely to receive nonsteroidal antiinflammatory drugs and skeletal muscle relaxants than others, especially Cluster 1. Of interest, opioid use was 10% even in Cluster 6, suggesting that opioid exposure extends beyond risk factors identified by Seal et al31 and that additional research may be needed to examine opioid exposures and outcomes in this “healthy” sector of the OEF/OIF population. Clusters 1 and 5, which had the highest likelihood of back pain, predictably also had the highest likelihood of inpatient care for orthopedics/neurosurgery. With regard to adverse outcomes, we saw no consistent pattern with pain, unless pain was combined with significant psychiatric comorbidity. Cluster 5 was characterized primarily by high probability of back pain and other pain, but was lower in rankings of homeless care, overdose, and SRB than Cluster 1, in which pain was accompanied by high rates of PTSD and depression. Finally, Hypothesis 2c/Chronic Disease was also largely supported. Clusters 1 and 4, in which chronic disease was a distinguishing feature, were most likely to receive antihypertensive, hypoglycemic, and lipid medications. Cluster 1 had the highest likelihood of medical hospitalizations, but findings were not as consistent for Cluster 4. Unexpectedly, Cluster 6 was more likely to receive antihypertensive and hypoglycemic medications than Cluster 2; however, Cluster 6 had the lowest odds of medical hospitalization and mortality. The finding for antihypertensive medications is consistent with the probability of hypertension in Cluster 6, suggesting that, although these individuals do have some chronic disease, they are treated and do not yet demonstrate adverse outcomes as defined by mortality. In addition to these hypotheses, several findings were notable. First, despite the emphasis in the literature on the comorbidity of TBI, PTSD, and pain, only about 13% of the cohort was defined by this pattern. We found that those in Cluster 1, who also had a higher probability of depression and chronic disease, had significantly more health care utilization and adverse outcomes than any of the other clusters including Cluster 2 with only PCT. Moreover, whereas PTSD was prominent in 4 distinct clusters (Clusters 1, 2, 3, and 5), TBI was prominent only in conjunction with PTSD and pain. Although not identified as a signature injury of war, this analysis found that depression was a significant comorbidity, which characterized 2 clusters (Clusters 1 and 3) both of which were also characterized by PTSD. Findings indicate that these 2 clusters were most likely to have adverse outcomes and were also more likely to have a variety of different forms of acute care including emergency care, and neurology, medicine, and psychiatric inpatient care. Thus, depression is also an important comorbidity that contributes substantially to health care utilization and adverse outcomes. Second, although clusters characterized by high likelihood of pain (Clusters 1 and 5) had the highest prevalence of opioid exposure (49% and 36%, respectively), opioid exposure in Cluster 6 was nearly 12%. Because we did not have access to the number of days of medication received, we do not know whether these were short-term prescriptions www.lww-medicalcare.com |
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after a minor procedure or injury. We do, however, know that the majority of the prescriptions were for hydrocodone—the most commonly prescribed drug in the United States overall.32 In addition, the extremely high prevalence of opioids, skeletal muscle relaxants, and antidepressants, etc. in Clusters 1 and 5 suggests that a significant number of individuals are receiving multiple central nervous system drugs simultaneously, which may lead to adverse drug side effects and adverse outcomes. Finally, it is notable that veterans of the National Guard/Reserve were overrepresented in several clusters. These clusters were also characterized by older age and higher likelihood of being married, and 2 were characterized by age-related chronic disease. This finding is consistent with reports of service members, many of whom (66%) were National Guard/Reserve, seeking treatment for cardiovascular disease while deployed in Iraq.14
Limitations These data provide a unique perspective on patterns of comorbidity among Veterans of the Afghanistan and Iraq wars but have limitations that must also be considered. First, our data are derived from VA health care databases. As such, only those who have received care in the VA are included. There is no way to identify individuals who may also have been deployed to Desert Storm. Thus, Gulf War Illness from prior deployments may also be represented in these clusters. This concern can be addressed, however, in future components of the study using survey data. Moreover, the participants received VA care for several years. The extent to which those receiving care within the VA differ from those who opt to receive care elsewhere is not well understood, although Veterans in general are better off in terms of health and wealth compared with Veterans seeking VA care.33 Our data do not reflect non-VA care or diagnoses received by individuals in non-VA settings, and our measure of mortality does not reflect cause of death, which was not available for this study. Thus, mortality may have been due to SRB, accidents, or chronic disease. Future research is needed to explicate cause of death. Moreover, use of dichotomous variables that only account for whether a condition, service, or prescription was present/absent during the period examined does not provide information about disease severity or intensity of care received across clusters. These data were also cross-sectional in nature. Some individuals had been in VA care for 8 years, whereas for others these were the first years of care. Longitudinal analysis beginning in the first year of VA care and continuing over an extended period of time is needed to identify characteristics associated with development of new comorbidities and/or increasing severity of initial comorbidities. Finally, these clusters were statistically derived, and it is very likely that these clusters may differ in a number of ways that could not be measured given the data available.34 Future research adding survey and medical chart abstraction data will allow closer comparison of some currently unknown characteristics, and longitudinal analyses will allow us to determine whether these clusters represent stable
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trajectories of comorbidity or whether there is change in patterns of comorbidity over time. This study provides an initial perspective to assist clinicians both in and outside the VA system in understanding patterns of comorbidity clusters among OEF/OIF Veterans and subsequent care patterns and adverse outcomes associated with these clusters. Information from these data may inform basic science and stimulate research that investigates common mechanisms for specific conditions (eg, epilepsy and depression), defines integrated treatment approaches for Veterans with complex mental and physical health comorbidities, and examines quality of care for Veterans with different constellations of comorbidity. REFERENCES 1. Gailey R, McFarland LV, Cooper RA, et al. Unilateral lower-limb loss: prosthetic device use and functional outcomes in service members from Vietnam War and OIF/OEF conflicts. J Rehabil Res Dev. 2010;47:317–331. 2. Sayer N, Chiros C, Sigford B, et al. Characteristics and rehabilitation outcomes among patients with blast and other injuries sustained during the Global War on Terror. Arch Phys Med Rehabil. 2008;89:163–170. 3. Hoge CW, Toboni HE, Messer SC, et al. The occupational burden of mental disorders in the U.S. Military: psychiatric hospitalizations, involuntary separations, and disability. Am J Psychiatry. 2005;162: 585–591. 4. Hoge CW, Castro CA, Messer SC, et al. Combat duty in Iraq and Afghanistan, mental health problems, and barriers to care. N Engl J Med. 2004;351:13–22. 5. Lew HL, Otis JD, Tun C, et al. Prevalence of chronic pain, posttraumatic stress disorder, and persistent postconcussive symptoms in OIF/OEF veterans: polytrauma clinical triad. J Rehabil Res Dev. 2009;46:697–702. 6. Gironda R, Clark ME, Ruff RL, et al. Traumatic brain injury, polytrauma, and pain: challenges and treatment strategies for the polytrauma rehabilitation. Rehabil Psychol. 2009;54:247–258. 7. Carlson KF, Kehle SM, Meis LA, et al. Prevalence, assessment, and treatment of mild traumatic brain injury and posttraumatic stress disorder: a systematic review of the evidence. J Head Trauma Rehabil. 2011;26:103–115. 8. Taylor BC, Hagel EM, Carlson KF, et al. Prevalence and costs of cooccurring traumatic brain injury with and without psychiatric disturbance and pain among Afghanistan and Iraq War Veteran VA users. Med Care. 2012;50:342–346. 9. Nazarian D, Kimerling R, Frayne SM. Posttraumatic stress disorder, substance use disorders, and medical comorbidity among returning U.S. Veterans. J Trauma Stress. 2012;25:220–225. 10. Milliken CS, Auchterlonie JL, Hoge CW. Longitudinal assessment of mental health problems among active and reserve component soldiers returning from the Iraq war. JAMA. 2007;298:2141–2148. 11. Clark ME, Bair MJ, Buckenmaier CC 3rd, et al. Pain and combat injuries in soldiers returning from Operations Enduring Freedom and Iraqi Freedom: implications for research and practice. J Rehabil Res Dev. 2007;44:179–194. 12. White JM, Stannard A, Burkhardt GE, et al. The epidemiology of vascular injury in the wars in Iraq and Afghanistan. Ann Surg. 2011;253:1184–1189. 13. Pietrzak RH, Whealin JM, Stotzer RL, et al. An examination of the relation between combat experiences and combat-related posttraumatic stress disorder in a sample of Connecticut OEF-OIF Veterans. J Psychiatr Res. 2011;45:1579–1584. 14. Sullenberger L, Gentlesk PJ. Cardiovascular disease in a forward military hospital during Operation Iraqi Freedom: a report from deployed cardiologists. Mil Med. 2008;173:193–197. 15. Defense Science Board Task Force. Deployment of Members of the National Guard and Reserve in the Global War on Terrorism. Washington, DC: Office of the Under Secretary of Defense for Acquisition, Technology, and Logistics; 2007. r
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16. Zouris JM, Wade AL, Magno CP. Injury and illness casualty distributions among U.S. Army and Marine Corps personnel during Operation Iraqi Freedom. Mil Med. 2008;173:247–252. 17. Cohen BE, Marmar C, Ren L, et al. Association of cardiovascular risk factors with mental health diagnoses in Iraq and Afghanistan War Veterans using VA health care. JAMA. 2009;302:489–492. 18. Frayne SM, Chiu VY, Iqbal S, et al. Medical care needs of returning veterans with PTSD: their other burden. J Gen Intern Med. 2011;26:33–39. 19. Agency for Healthcare Research and Quality. Multiple chronic conditions. Available at: http://www.ahrq.gov/professionals/preventionchronic-care/decision/mcc. Accessed July 21, 2013. 20. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613–619. 21. Elixhauser A, Steiner C, Harris DR, et al. Comorbidity measures for use with administrative data. Med Care. 1998;36:8–27. 22. Ruff RL, Ruff SS, Wang XF. Headaches among Operation Iraqi Freedom/Operation Enduring Freedom Veterans with mild traumatic brain injury associated with exposures to explosions. J Rehabil Res Dev. 2008;45:941–952. 23. Wallace DM, Shafazand S, Ramos AR, et al. Insomnia characteristics and clinical correlates in Operation Enduring Freedom/Operation Iraqi Freedom Veterans with post-traumatic stress disorder and mild traumatic brain injury: an exploratory study. Sleep Med. 2011;12:850–859. 24. Jakupcak M, Luterek J, Hunt S, et al. PTSD symptom clusters in relationship to alcohol misuse among Iraq and Afghanistan war veterans seeking post-deployment VA health care. Addict Behav. 2010;35:840–843.
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25. Bandeen-roche K, Miglioretti DL, Zeger SL, et al. Latent variable regression for multiple discrete outcomes. J Am Stat Assoc. 1997;92:1375–1386. 26. Carter AC, Capone C, Short EE. Co-occurring posttraumatic stress disorder and alcohol use disorders in Veteran populations. J Dual Diagn. 2011;7:285–299. 27. Helmer DA, Chandler HK, Quigley KS, et al. Chronic widespread pain, mental health, and physical role function in OEF/OIF veterans. Pain Med. 2009;10:1174–1182. 28. Petrakis IL, Rosenheck R, Desai R. Substance use comorbidity among veterans with posttraumatic stress disorder and other psychiatric illness. Am J Addict. 2011;20:185–189. 29. Seal KH, Bertenthal D, Miner CR, et al. Bringing the war back home: mental health disorders among 103,788 US Veterans returning from Iraq and Afghanistan seen at Department of Veterans Affairs facilities. Arch Intern Med. 2007;167:476–482. 30. Lemaire CM, Graham DP. Factors associated with suicidal ideation in OEF/OIF Veterans. J Affect Disord. 2010;130:231–238. 31. Seal KH, Shi Y, Cohen G, et al. Association of mental health disorders with prescription opioids and high-risk opioid use in US Veterans of Iraq and Afghanistan. JAMA. 2012;307:940–947. 32. Manchikanti L, Helm S 2nd, Fellows B, et al. Opioid epidemic in the United States. Pain Physician. 2012;15:ES9–38. 33. Morgan RO, Teal CR, Reddy SG, et al. Measurement in Veterans Affairs Health Services research: Veterans as a special population. Health Serv Res. 2005;40:1573–1583. 34. Shepperd S, Lewin S, Straus S, et al. Can we systematically review studies that evaluate complex interventions? PLoS Med. 2009;6:e1000086.
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Complex Comorbidity Clusters in OEF/OIF Veterans The Polytrauma Clinical Triad and Beyond Mary Jo V. Pugh, PhD, RN,*wz Erin P. Finley, PhD, MPH,*y Laurel A. Copeland, PhD,z8 Chen-Pin Wang, PhD,*w Polly H. Noel, PhD,*y Megan E. Amuan, MPH,z Helen M. Parsons, PhD, MPH,w Margaret Wells, BS,*w Barbara Elizondo, BA,*w and Jacqueline A. Pugh, MD*#
Background: A growing body of research on US Veterans from Afghanistan and Iraq [Operations Enduring and Iraqi Freedom, and Operation New Dawn (OEF/OIF)] has described the polytrauma clinical triad (PCT): traumatic brain injury (TBI), posttraumatic stress disorder (PTSD), and pain. Extant research has not explored comorbidity clusters in this population more broadly, particularly co-occurring chronic diseases. Objectives: The aim of the study was to identify comorbidity clusters among diagnoses of deployment-specific (TBI, PTSD, pain) and chronic (eg, hypertension, diabetes) conditions, and to examine the association of these clusters with health care utilization and adverse outcomes. Research Design: This was a retrospective cohort study. Subjects: The cohort comprised OEF/OIF Veterans who received care in the Veterans Health Administration in fiscal years (FY) 2008–2010.
From the *South Texas Veterans Health Care System; wDepartment of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio; zTexas A&M Health Science Center, Bryan; yDepartment of Medicine, Division of Clinical Epidemiology, University of Texas Health Science Center San Antonio, San Antonio; 8Center for Applied Health Research, jointly sponsored by Central Texas Veterans Health Care System, and Scott and White Healthcare System, Temple, TX; zCenter for Health Quality, Outcomes and Economic Research, Edith Nourse Rogers Memorial VA Hospital, Bedford, MA; and #Department of Medicine, Division of Hospital Medicine, University of Texas Health Science Center San Antonio, San Antonio, TX. The content of this article is solely the responsibility of the authors and does not necessarily reflect the official views of the Veterans’ Health Administration. Funded by the Department of Veterans Affairs, Office of Research and Development, VA Health Services Research and Development Service (DHI 09-237). The authors declare no conflict of interest. Reprints: Mary Jo V. Pugh, PhD, RN, South Texas Veterans Health Care System, 7400 Merton Minter Blvd, San Antonio, TX 78229. E-mail: [email protected]. Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Website, www.lww-medical care.com. Copyright r 2013 by Lippincott Williams & Wilkins ISSN: 0025-7079/14/5202-0172
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Measures: We identified comorbidity using validated ICD-9-CM code–based algorithms and FY08–09 data, followed by which we applied latent class analysis to identify the most statistically distinct and clinically meaningful patterns of comorbidity. We examined the association of these clusters with process measures/outcomes using logistic regression to correlate medication use, acute health care utilization, and adverse outcomes in FY10. Results: In this cohort (N = 191,797), we found 6 comorbidity clusters. Cluster 1: PCT + Chronic Disease (5%); Cluster 2: PCT (9%); Cluster 3: Mental Health + Substance Abuse (24%); Cluster 4: Sleep, Amputation, Chronic Disease (4%); Cluster 5: Pain, Moderate PTSD (6%); and Cluster 6: Relatively Healthy (53%). Subsequent health care utilization patterns and adverse events were consistent with disease patterns. Conclusions: These comorbidity clusters extend beyond the PCT and may be used as a foundation to examine coordination/quality of care and outcomes for OEF/OIF Veterans with different patterns of comorbidity. Key Words: Afghan Campaign 2001 (Operation Enduring Freedom), comorbidity, Iraq War 2003 (Operation Iraqi Freedom), veterans (Med Care 2014;52: 172–181)
T
he years since 2001 have presented extraordinary challenges to the US Armed Forces and the Veterans Health Administration (VA) in their efforts to provide high-quality care to Veterans returning from deployment to Afghanistan and Iraq (Operations Enduring and Iraqi Freedom, and Operation New Dawn, hereafter OEF/OIF). Studies in this population have identified high rates of combat-related injuries and mental health conditions that commonly co-occur and that have implications for health care utilization, treatment, and long-term outcomes.1–4 The majority of prior studies have focused on the signature injuries of traumatic brain injury (TBI), posttraumatic stress disorder (PTSD), and pain, also known as the polytrauma clinical triad (PCT), individually or in combination (eg, TBI and PTSD, PTSD and pain, etc.).5–10 Although trauma-based injuries and mental health problems dominate research in this population,10–13 Veterans Medical Care
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of OEF/OIF also experience a significant chronic disease burden14,15 and non–combat-related injuries (eg, motor vehicle accidents), musculoskeletal issues, and medical conditions (eg, digestive, genitourinary, and circulatory).16 As a result, studies have begun to describe comorbidity of mental health and physical health conditions in this population. Cohen et al17 found that OEF/OIF Veterans with PTSD are more likely to have cardiovascular disease–related conditions, and Frayne et al found that those with PTSD had more medical comorbidities diagnosed than those without PTSD.18 Nonetheless, studies of patterns of mental and physical comorbidity and associated outcomes beyond the PCT are not available. Thus, despite growing emphasis across the medical literature on providing integrated treatment approaches for patients with multiple chronic conditions,19 we lack information regarding whether patterns of multiple chronic conditions in this relatively young population exist that can inform recommendations for improving treatment and services and support more comprehensive evaluation of processes and outcomes of care.18 Our goal was to identify patterns of co-occurring physical and mental health conditions among OEF/OIF Veterans using latent class analysis (LCA), and to examine the association of those patterns with health care utilization and adverse events. We hypothesized that (1) previously unidentified clusters of comorbidity among OEF/OIF Veterans exist and include a variety of combinations of TBI, PTSD, pain, and chronic disease, and (2) variation in comorbidity clusters are associated with differences in processes of care and adverse outcomes. We specifically expected the following: 2a/Mental Health—comorbidity clusters with higher likelihood of depression and/or other mental health disorders have higher utilization of psychotropic medications, emergency care, inpatient mental health/ substance abuse treatment, and adverse outcomes such as suicide-related behavior (SRB); 2b/Pain—comorbidity clusters with higher likelihood of pain have higher utilization of medications commonly used for pain (eg, anticonvulsants, opioid pain relievers), and inpatient neurosurgery/orthopedic care; and 2c/Chronic Disease—comorbidity clusters with higher likelihood of chronic disease states have higher utilization of related medications (eg, antihypertensive agents), inpatient medical hospitalizations, and mortality rate.
METHODS Data Sources After securing IRB approval, we obtained data for Fiscal Years (FY) 2008 to 2010 (FY08–10; October 1, 2007 to September 30, 2010) from the VA Decision Support System National Data Extracts (DSS inpatient, outpatient, and pharmacy data), the OEF/OIF roster, and the VA Vital Status file. Inpatient and outpatient files included ICD-9-CM codes, clinic or hospital bedsection in which care was received, and demographic characteristics. Outpatient pharmacy data included the generic drug name. We identified latent classes in diagnostic data from FY08–09; health care utilization and adverse outcomes were identified in FY10, after comorbidity latent classes (clusters) were identified. r
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Participants Participants flagged as OEF/OIF Veterans were selected for inclusion if they received VA inpatient or outpatient care at least 1 time yearly in each of FY08 and FY09.
Measures Comorbid Conditions Inpatient and outpatient diagnoses from face-to-face clinic/hospital care during FY08–09 were used to identify comorbid conditions for subsequent LCA; diagnoses from ancillary care such as laboratory, radiology, etc. were excluded. We used ICD-9-CM codes previously validated for use in administrative data (including Charlson, Elixhauser)20,21 to create dichotomous indicators for 32 physical/ mental health and postdeployment conditions including TBI, PTSD, pain, and other conditions commonly identified in this population.22–24 ICD-9-CM codes for each condition are presented in Appendix 1, Supplemental Digital Content 1, http://links.lww.com/MLR/A637.
Demographic Characteristics Demographics included age, sex, race/ethnicity, and marital status, obtained from inpatient and outpatient data. Individuals with missing race were included as a separate category. We also identified individuals who deployed with the National Guard/Reserve (per OEF/OIF roster).
Health Care Utilization We identified process measures describing pharmacotherapy, emergency care, and inpatient care during FY10. We created dichotomous variables for 10 theoretically relevant drug groups related to pain, mental health, and chronic disease management (Table 2). Individuals with at least 1 prescription for each drug group during FY10 were classified as receiving a prescription in that drug group (eg, opioids). We created indicators of health care utilization using outpatient identifiers for emergency/urgent care (130, 131) and bedsection (treating clinician’s specialty; see Table 3).
Adverse Outcomes Adverse outcomes included proxies for homelessness, diagnoses of drug overdose, suicidal ideation or attempt (hereafter SRB), and death. ICD-9-CM and clinic stop codes for SRB and drug overdose are provided in Appendix 1, (Supplemental Digital Content 1, http://links.lww.com/MLR/ A637). Mortality through FY10 was defined using the VA Vital Status File.
Analysis We used LCA, a method of structural equation modeling that identifies unobservable subgroups (latent classes) within a population on the basis of the distribution of binary diagnosis outcomes to identify patterns of comorbidity in this population. Thus, each latent class represents a “cluster” of comorbidities that occur most commonly in a subgroup of the population. Like other latent variable modeling techniques, LCA is an iterative process that assesses the fit of the model to the data. Our analysis started with a 2-class www.lww-medicalcare.com |
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solution, modeling the joint probability of the conditions under the assumption that they are correlated through class membership. We then conducted LCA with 3–7 classes, allowing 20 different start values for each LCA to ensure that the global maximum was reached and results were consistent regardless of the starting point. We identified the model with
Probability of Having Diagnosis
Probability of Having Diagnosis
Cluster 2 Polytrauma Clinical Triad 100 90 80 70 60 50 40 30 20 10 0
Comorbid Conditions
Comorbid Conditions Cluster 4 Sleep, Amputation, Chronic Disease Probability of Having Diagnosis
Probability of Having Diagnosis
Cluster 3 Mental Health, Substance Abuse 100 90 80 70 60 50 40 30 20 10 0
100 90 80 70 60 50 40 30 20 10 0
Cluster 5 Pain, Moderate PTSD
Comorbid Conditions
Comorbid Conditions Probability of Having Diagnosis
Probability of Having Diagnosis
Comorbid Conditions
100 90 80 70 60 50 40 30 20 10 0
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the best fit on the basis of reliability of the estimates, Akaike information criterion, and Bayesian information criterion. Once the best fitting model was identified, we used the pseudoclass technique to estimate each individual’s class (hereafter cluster) membership25 by drawing a random sample from the multinomial distribution based on posterior
Cluster 1 Polytrauma Clinical Triad, Depression, Chronic Disease 100 90 80 70 60 50 40 30 20 10 0
Cluster 6 Relatively Healthy 100 90 80 70 60 50 40 30 20 10 0
Comorbid Conditions
FIGURE 1. Distribution of comorbidity types by cluster.
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9.6 50.3 3.3 21.5 48.5 46.5 11.0 54.5 3.2 14.6 56.5 51.4 10.6 47.6 3.5 15.2 65.1 51.7 10.2 59.3 3.2 15.9 48.8 44.6 12.5 55.1 3.1 10.1 58.3 59.0
9.4 57.2 3.1 15.8 40.1 41.8
16.6 23.0 11.4 19.2
14.6
38.9 (10.0) 86.8 41.4 (9.8) 92.9 33.9 (8.6) 83.9 33.6 (8.4) 91.4 40.2 (10.2) 88.3
All comparisons had statistically significant differences at the P < 0.001 level. PTSD indicates posttraumatic stress disorder.
Table 2 displays the proportion of individuals from each cluster who received specific medication types, and the odds ratios from individual logistic regression models compared with Cluster 6. The final column of Table 2 shows the clusters with similar results, identified by examining the overlap of confidence intervals from the logistic regression analysis.When confidence intervals overlap, those clusters are not significantly different from each other, allowing comparisons between clusters beyond the reference group.
Cluster 1: Polytrauma Clinical Triad+Depression, Chronic Disease N = 8801 (4.6%)
Health Care Utilization and Adverse Outcomes
TABLE 1. Demographic Characteristics by Comorbidity Cluster
RESULTS Our analysis of care-seeking Veterans (N = 191,797) found that the LCA with 6 classes obtained global maximum likelihood estimates and had the smallest Akaike information criterion and Bayesian information criterion compared with other LCAs whose results converged. Figure 1 summarizes the probability of an individual in each cluster having specific conditions and the general distribution of comorbidity types across the 6-class model, with descriptions of the 21 conditions that were most notable in each cluster. Clusters 1 and 2 exhibited characteristics of the PCT (TBI, PTSD, Pain). Cluster 1 also had significantly higher probabilities of low back pain, chronic pain, insomnia, depression, hypertension, and osteoarthritis, and lower probabilities of TBI than Cluster 2 (Cluster 1: PCT, Depression, Chronic Disease; Cluster 2: PCT). Cluster 3 was characterized by depression, PTSD, anxiety, and substance abuse (Mental Health, Substance Abuse). Cluster 4 was characterized by insomnia, amputation, and chronic disease (Sleep, Amputation, Chronic Disease). Cluster 5 was characterized by back pain and other pain (Pain, Moderate PTSD). Cluster 6 was characterized by low probabilities of all conditions (Relatively Healthy). Although all bivariate analyses of demographic characteristics demonstrated statistically significant differences (Table 1), the most clinically relevant differences were related to age. In Clusters 1 and 4 the mean age was over 40, whereas Clusters 2 and 3 had mean ages in the early 30s [F = 1731 (df = 5, 191,723), P < 0.01]. Clusters 1 and 4 had significantly higher proportions of African Americans and lower proportions of whites than expected [w2 = 2367 (df = 20), P < 0.001]. Individuals in Clusters 1, 4, and 5 were significantly more likely to be married and to be deployed as National Guard/Reservists than expected [w2 = 2686 (df = 5), P < 0.001; w2 = 1013 (df = 5), P 5> 3, 6 > 2 1, 4 > 5, 6 > 3> 2 1, 4 > 5> 2, 3, 6
1> 3> 2> 4,5 > 6 1> 2,3,5 > 4> 6 1> 2,3 > 4> 5> 6
1> 2,5 > 3,4 6 1> 5> 2,4 > 3> 6 1> 5> 4> 2> 3> 6 1> 5> 2,4 > 3> 6
Groups With Similar Results
40.2 4.4 (4.2–4.6)
52.5 5.8 (5.6–6.0)
4.0 3.7 (3.3–4.1)
12.8 6.8 (6.4–7.3)
12.9 1.6 (1.6–1.7)
28.8 1.5 (1.5–1.6)
19.3 1.8 (1.8–1.9)
14.2 3.9 (3.7–4.0)
Cluster 3: Mental Health, Substance Abuse N = 45,242 (23.6%)
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Chronic disease Antihypertensive agents
7.5 7.1 (6.4–7.8)
Antipsychotic: typical
26.7 16.9 (15.9–18.0)
37.8 6.6 (6.3–6.9)
Skeletal muscle relaxants
Psychiatric Antipsychotic: atypical
36.7 2.2 (2.1–2.3)
56.0 4.8 (4.6–5.0)
Nonsteroidal antiinflammatory drug
18.7 2.5 (2.4–2.6)
25.1 2.5 (2.4–2.6)
20.2 5.9 (5.6–6.2)
48.5 7.1 (6.8–7.5)
41.5 16.6 (15.7–17.5)
Opioid pain reliever
Pain related Anticonvulsants
Cluster 2: Cluster 1: Polytrauma Clinical Polytrauma Clinical Triad, Depression, Chronic Disease Triad N = 8801 (4.6%) N = 16,585 (8.6%)
% OR (95% CI)
TABLE 2. Types of Prescription Medications Received in FY10: Comparisons With the Relatively Healthy Cluster (Cluster 6)
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Medications commonly used for pain were far more likely to be received by individuals in Cluster 1 than by patients in other clusters, with the lowest utilization of pain medications being in Cluster 6. Utilization of pain medications was also higher for Cluster 5 compared with most other groups. Similar patterns were found for psychotropic medications, with those in Cluster 1 having the highest odds of receiving each type of psychotropic medication, whereas individuals in Cluster 6 had the lowest odds. Those in Clusters 2 and 3 also were consistently more likely to receive atypical antipsychotics and antidepressants than Clusters 4, 5, and 6. With regard to medications for chronic disease, individuals in Clusters 1 and 4 had the highest likelihood of receiving antihypertensives, hypoglycemics, and lipid medications, whereas individuals in Cluster 2 consistently had the lowest likelihood of receiving these medications. Individuals in Cluster 6 were more likely than Clusters 2 and 3 to receive hypoglycemic medications. Table 3 shows the prevalence of acute care utilization in FY10 and odds ratios comparing Cluster 6 with other clusters. The final column of Table 3 lists groups with similar results, identified by examining the overlap of confidence intervals from the logistic regression analysis. Individuals in Cluster 1 had the highest levels of acute care utilization across the board, whereas those in Cluster 6 had the lowest levels. For inpatient psychiatric/substance abuse care, those in Clusters 3 and 2 were significantly higher than all remaining clusters. Finally, individuals in Cluster 5 were equally likely to have orthopedic/neurosurgery inpatient care as those in Cluster 1, and individuals in Clusters 2 and 4 were less likely to have orthopedic/neurosurgery inpatient care than Clusters 1 and 5, but more likely than those in Clusters 3 and 6. Adverse outcomes were consistently highest in Clusters 1 and 3 and lowest for individuals in Cluster 6, with the exception of overdose, in which Cluster 4 was not different from Cluster 6. Of interest, individuals in Cluster 4, who had a higher likelihood of chronic disease, were at lower likelihood of mortality than those in Cluster 3, which included individuals with primarily mental health/substance use disorder (Table 4).
DISCUSSION This study is the first to describe broad comorbidity clusters in Veterans from the conflicts in Afghanistan and Iraq, accounting for conditions related to TBI, mental health, chronic pain, and chronic disease. As predicted (hypothesis 1), we found 6 clusters, 2 of which were characterized by all 3 signature PCT injuries—TBI, PTSD, and pain. However, individuals in Cluster 1 had lower probability of TBI and higher probability of headache, back pain, other pain than those in Cluster 2. Individuals in Cluster 1 had higher probabilities of conditions associated with the PCT such as sleep disturbance, depression, and anxiety. Finally, individuals in Cluster 1 were older and more likely to have chronic disease diagnoses including hypertension, diabetes, osteoarthritis, and obesity. r
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Although the PCT was not evident in Clusters 3, 4, and 5, other conditions, including individual PCT conditions, emerged. Cluster 3 was characterized by multiple mental health conditions including PTSD, depression, anxiety, and substance use disorder, whereas Cluster 5 was characterized primarily by back pain and other pain, with a likelihood of PTSD similar to our overall cohort (32%). Although the likelihood for PTSD was similar between Clusters 4 and 5, all individuals in Cluster 4 had diagnoses of sleep disorder, and higher probabilities of hypertension, obesity, osteoarthritis, and amputation diagnoses. These findings are consistent with PCT literature, and recent work that found Veterans with PTSD were more likely to have chronic disease than those without PTSD.9,19 However, they suggest that there are distinct clusters of individuals with PTSD, rather than 1 prototypical PTSD patient. Additional research using these clusters may help us identify early on those individuals with PTSD who may progress more quickly to chronic disease versus those with less chronic disease burden. Such research may also allow us to develop interventions that ultimately reduce disease burden in the PTSD chronic disease clusters. Finally, despite the plethora of literature identifying profound disease burden in this population,5,23,26–29 we found a cluster of relatively healthy individuals who accounted for about half of the cohort (53%). Although these individuals had a likelihood of hypertension slightly higher than the population prevalence, the likelihood of other conditions was significantly lower than the respective population prevalences. Across clusters, examination of sociodemographic characteristics suggests that clusters with a higher mean age (Clusters 1, 4, 5, and 6) were more likely to have associated chronic disease. Age was not associated with mental health conditions, however, as Clusters 1 and 3 had the highest likelihood of a variety of psychiatric diagnoses. Analyses of health care utilization and adverse outcomes also generally supported our second hypothesis. With regard to psychiatric outcomes (Hypothesis 2a/Mental Health), those clusters with high probability of multiple psychiatric diagnoses (Clusters 1 and 3) were consistently among those with the highest likelihood of psychotropic medications, emergency care, inpatient psychiatric/substance abuse care, and adverse outcomes of homelessness, overdose, SRB, and mortality. Cluster 1 in particular had a higher likelihood of psychotropic medications and inpatient psychiatric care than Cluster 3. Other findings suggest that there may be a “dose response” effect for psychiatric comorbidity. For example, Clusters 2, 4, and 5, each characterized by a lower likelihood of PTSD and other psychiatric conditions than Clusters 1 and 3, had psychiatrically related outcomes higher than Cluster 6 but lower than Clusters 1 and 3. This finding echoes similar findings in studies of suicidal ideation30 and suggests that the impact of PTSD-depression comorbidity in this population extends beyond suicidal ideation. With regard to Hypothesis 2b/Pain, we found that clusters characterized by pain (Clusters 1, 2, and 5) had the highest likelihood of anticonvulsants and opioids; Clusters 1 www.lww-medicalcare.com |
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6.5 13.4 (11.9–15.1)
1.0 7.3 (5.6–9.6)
1.2 3.3 (2.7–4.1)
Inpatient psychiatric/ substance abuse
1.2 1.9 (1.6–2.2)
0.5 1.4 (1.2–1.7)
0.2 1.3 (1.0–1.7)
4.5 9.2 (8.4–10.2)
1.5 2.3 (2.1–2.6)
0.6 6.3 (5.0–7.9)
25.2 2.0 (2.0–2.1)
Cluster 3: Mental Health, Substance Abuse N = 45,242 (23.6%)
0.9 2.4 (1.8–3.1)
0.4 2.7 (1.8–4.0)
1.7 3.3 (2.8–4.0)
1.6 2.6 (2.1–3.1)
0.3 3.1 (2.0–4.7)
22.8 1.8 (1.7–1.9)
Cluster 4: Sleep, Amputation, Chronic Disease N = 8158 (4.3%)
0.8 2.1 (1.7–2.7)
0.9 6.8 (5.2–8.8)
1.9 3.7 (3.1–4.3)
1.3 2.0 (1.7–2.4)
0.4 4.1 (2.9–5.8)
25.1 2.0 (1.9–2.1)
Cluster 5: Pain, Moderate PTSD N = 10,769 (5.6%)
0.4 Reference
0.1 Reference
0.5 Reference
0.6 Reference
0.1 Reference
14.4 Reference
Cluster 6: Relatively Healthy N = 102,242 (53.3%)
1> 2,3,5 > 4> 6 1> 2,3 > 4,5 > 6 1> 2,3,4,5 > 6 1> 3> 2> 4,5 > 6 1,5 > 2,4 > 3,6 1> 2,4,5 > 3> 6
Groups With Similar Results
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CI indicates confidence interval; OR, odds ratio; PTSD, posttraumatic stress disorder.
Inpatient orthopedic/ neurosurgery Other inpatient surgery
3.1 6.2 (5.5–7.0)
3.8 6.2 (5.4–7.1)
Inpatient medicine
0.6 6.0 (4.6–7.9)
1.3 12.6 (9.6–16.4)
Inpatient Neurology
25.5 2.0 (2.0–2.1)
39.4 3.9 (3.7–4.1)
Emergency/urgent care
Cluster 2: Cluster 1: Polytrauma Clinical Polytrauma Triad, Depression, Chronic Disease Clinical Triad N = 8801 (4.6%) N = 16,585 (8.6%)
% OR (95% CI)
TABLE 3. Acute Care Received in FY10: Comparisons With the Relatively Healthy Cluster (Cluster 6)
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0.2 Reference 0.3 1.4 (1.0–2.0) 0.7 2.9 (2.1–3.8) Mortality
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CI indicates confidence interval; N, cluster similar to >1 group; OR, odds ratio; PTSD, posttraumatic stress disorder.
0.4 1.7 (1.2–2.5) 0.6 2.5 (2.1–2.9)
0.4 3.6 (2.4–5.3) 1.0 9.3 (7.6–11.5) 0.8 7.2 (5.6–9.3) 1.4 13.3 (10.3–17.2) Suiciderelated behaviors
0.3 1.4 (1.1–1.9)
0.1 Reference
0.1 Reference
0.2 2.7 (1.5–4.7) 0.4 3.3 (2.3–4.8) 0.1 1.2 (0.52–2.9) 0.2 2.5 (1.5–4.1) 0.6 8.4 (5.6–12.6) Overdose
0.3 4.5 (3.2–6.4)
0.6 Reference 1.3 2.1 (1.8–2.6) 1.1 1.8 (1.5–2.3) 3.6 6.1 (5.6–6.7) 2.1 3.5 (3.1–4.0) 3.9 6.6 (5.8–7.5)
Cluster 6: Relatively Healthy N = 102,242 (53.3%) Cluster 5: Pain, Moderate PTSD N = 10,769 (5.6%) Cluster 4: Sleep, Amputation, Chronic Disease N = 8158 (4.3%) Cluster 3: Mental Health, Substance Abuse N = 45,242 (23.6%) Cluster 2: Cluster 1: Polytrauma Clinical Triad, Polytrauma Clinical Depression, Chronic Disease Triad N = 8801 (4.6%) N = 16,585 (8.6%)
% OR (95% CI)
TABLE 4. Adverse Outcomes in FY10: Comparisons With the Relatively Healthy Cluster (Cluster 6) r
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1, 3 > 2> 4, 5 > 6 1, 3 > (3), 2, 5 > 4, 6 1, 3 > 2, 3 > 4, 5 > 6 1, 3 > 2, 4, 5 > 6
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and 5 were also more likely to receive nonsteroidal antiinflammatory drugs and skeletal muscle relaxants than others, especially Cluster 1. Of interest, opioid use was 10% even in Cluster 6, suggesting that opioid exposure extends beyond risk factors identified by Seal et al31 and that additional research may be needed to examine opioid exposures and outcomes in this “healthy” sector of the OEF/OIF population. Clusters 1 and 5, which had the highest likelihood of back pain, predictably also had the highest likelihood of inpatient care for orthopedics/neurosurgery. With regard to adverse outcomes, we saw no consistent pattern with pain, unless pain was combined with significant psychiatric comorbidity. Cluster 5 was characterized primarily by high probability of back pain and other pain, but was lower in rankings of homeless care, overdose, and SRB than Cluster 1, in which pain was accompanied by high rates of PTSD and depression. Finally, Hypothesis 2c/Chronic Disease was also largely supported. Clusters 1 and 4, in which chronic disease was a distinguishing feature, were most likely to receive antihypertensive, hypoglycemic, and lipid medications. Cluster 1 had the highest likelihood of medical hospitalizations, but findings were not as consistent for Cluster 4. Unexpectedly, Cluster 6 was more likely to receive antihypertensive and hypoglycemic medications than Cluster 2; however, Cluster 6 had the lowest odds of medical hospitalization and mortality. The finding for antihypertensive medications is consistent with the probability of hypertension in Cluster 6, suggesting that, although these individuals do have some chronic disease, they are treated and do not yet demonstrate adverse outcomes as defined by mortality. In addition to these hypotheses, several findings were notable. First, despite the emphasis in the literature on the comorbidity of TBI, PTSD, and pain, only about 13% of the cohort was defined by this pattern. We found that those in Cluster 1, who also had a higher probability of depression and chronic disease, had significantly more health care utilization and adverse outcomes than any of the other clusters including Cluster 2 with only PCT. Moreover, whereas PTSD was prominent in 4 distinct clusters (Clusters 1, 2, 3, and 5), TBI was prominent only in conjunction with PTSD and pain. Although not identified as a signature injury of war, this analysis found that depression was a significant comorbidity, which characterized 2 clusters (Clusters 1 and 3) both of which were also characterized by PTSD. Findings indicate that these 2 clusters were most likely to have adverse outcomes and were also more likely to have a variety of different forms of acute care including emergency care, and neurology, medicine, and psychiatric inpatient care. Thus, depression is also an important comorbidity that contributes substantially to health care utilization and adverse outcomes. Second, although clusters characterized by high likelihood of pain (Clusters 1 and 5) had the highest prevalence of opioid exposure (49% and 36%, respectively), opioid exposure in Cluster 6 was nearly 12%. Because we did not have access to the number of days of medication received, we do not know whether these were short-term prescriptions www.lww-medicalcare.com |
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after a minor procedure or injury. We do, however, know that the majority of the prescriptions were for hydrocodone—the most commonly prescribed drug in the United States overall.32 In addition, the extremely high prevalence of opioids, skeletal muscle relaxants, and antidepressants, etc. in Clusters 1 and 5 suggests that a significant number of individuals are receiving multiple central nervous system drugs simultaneously, which may lead to adverse drug side effects and adverse outcomes. Finally, it is notable that veterans of the National Guard/Reserve were overrepresented in several clusters. These clusters were also characterized by older age and higher likelihood of being married, and 2 were characterized by age-related chronic disease. This finding is consistent with reports of service members, many of whom (66%) were National Guard/Reserve, seeking treatment for cardiovascular disease while deployed in Iraq.14
Limitations These data provide a unique perspective on patterns of comorbidity among Veterans of the Afghanistan and Iraq wars but have limitations that must also be considered. First, our data are derived from VA health care databases. As such, only those who have received care in the VA are included. There is no way to identify individuals who may also have been deployed to Desert Storm. Thus, Gulf War Illness from prior deployments may also be represented in these clusters. This concern can be addressed, however, in future components of the study using survey data. Moreover, the participants received VA care for several years. The extent to which those receiving care within the VA differ from those who opt to receive care elsewhere is not well understood, although Veterans in general are better off in terms of health and wealth compared with Veterans seeking VA care.33 Our data do not reflect non-VA care or diagnoses received by individuals in non-VA settings, and our measure of mortality does not reflect cause of death, which was not available for this study. Thus, mortality may have been due to SRB, accidents, or chronic disease. Future research is needed to explicate cause of death. Moreover, use of dichotomous variables that only account for whether a condition, service, or prescription was present/absent during the period examined does not provide information about disease severity or intensity of care received across clusters. These data were also cross-sectional in nature. Some individuals had been in VA care for 8 years, whereas for others these were the first years of care. Longitudinal analysis beginning in the first year of VA care and continuing over an extended period of time is needed to identify characteristics associated with development of new comorbidities and/or increasing severity of initial comorbidities. Finally, these clusters were statistically derived, and it is very likely that these clusters may differ in a number of ways that could not be measured given the data available.34 Future research adding survey and medical chart abstraction data will allow closer comparison of some currently unknown characteristics, and longitudinal analyses will allow us to determine whether these clusters represent stable
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trajectories of comorbidity or whether there is change in patterns of comorbidity over time. This study provides an initial perspective to assist clinicians both in and outside the VA system in understanding patterns of comorbidity clusters among OEF/OIF Veterans and subsequent care patterns and adverse outcomes associated with these clusters. Information from these data may inform basic science and stimulate research that investigates common mechanisms for specific conditions (eg, epilepsy and depression), defines integrated treatment approaches for Veterans with complex mental and physical health comorbidities, and examines quality of care for Veterans with different constellations of comorbidity. REFERENCES 1. Gailey R, McFarland LV, Cooper RA, et al. Unilateral lower-limb loss: prosthetic device use and functional outcomes in service members from Vietnam War and OIF/OEF conflicts. J Rehabil Res Dev. 2010;47:317–331. 2. Sayer N, Chiros C, Sigford B, et al. Characteristics and rehabilitation outcomes among patients with blast and other injuries sustained during the Global War on Terror. Arch Phys Med Rehabil. 2008;89:163–170. 3. Hoge CW, Toboni HE, Messer SC, et al. The occupational burden of mental disorders in the U.S. Military: psychiatric hospitalizations, involuntary separations, and disability. Am J Psychiatry. 2005;162: 585–591. 4. Hoge CW, Castro CA, Messer SC, et al. Combat duty in Iraq and Afghanistan, mental health problems, and barriers to care. N Engl J Med. 2004;351:13–22. 5. Lew HL, Otis JD, Tun C, et al. Prevalence of chronic pain, posttraumatic stress disorder, and persistent postconcussive symptoms in OIF/OEF veterans: polytrauma clinical triad. J Rehabil Res Dev. 2009;46:697–702. 6. Gironda R, Clark ME, Ruff RL, et al. Traumatic brain injury, polytrauma, and pain: challenges and treatment strategies for the polytrauma rehabilitation. Rehabil Psychol. 2009;54:247–258. 7. Carlson KF, Kehle SM, Meis LA, et al. Prevalence, assessment, and treatment of mild traumatic brain injury and posttraumatic stress disorder: a systematic review of the evidence. J Head Trauma Rehabil. 2011;26:103–115. 8. Taylor BC, Hagel EM, Carlson KF, et al. Prevalence and costs of cooccurring traumatic brain injury with and without psychiatric disturbance and pain among Afghanistan and Iraq War Veteran VA users. Med Care. 2012;50:342–346. 9. Nazarian D, Kimerling R, Frayne SM. Posttraumatic stress disorder, substance use disorders, and medical comorbidity among returning U.S. Veterans. J Trauma Stress. 2012;25:220–225. 10. Milliken CS, Auchterlonie JL, Hoge CW. Longitudinal assessment of mental health problems among active and reserve component soldiers returning from the Iraq war. JAMA. 2007;298:2141–2148. 11. Clark ME, Bair MJ, Buckenmaier CC 3rd, et al. Pain and combat injuries in soldiers returning from Operations Enduring Freedom and Iraqi Freedom: implications for research and practice. J Rehabil Res Dev. 2007;44:179–194. 12. White JM, Stannard A, Burkhardt GE, et al. The epidemiology of vascular injury in the wars in Iraq and Afghanistan. Ann Surg. 2011;253:1184–1189. 13. Pietrzak RH, Whealin JM, Stotzer RL, et al. An examination of the relation between combat experiences and combat-related posttraumatic stress disorder in a sample of Connecticut OEF-OIF Veterans. J Psychiatr Res. 2011;45:1579–1584. 14. Sullenberger L, Gentlesk PJ. Cardiovascular disease in a forward military hospital during Operation Iraqi Freedom: a report from deployed cardiologists. Mil Med. 2008;173:193–197. 15. Defense Science Board Task Force. Deployment of Members of the National Guard and Reserve in the Global War on Terrorism. Washington, DC: Office of the Under Secretary of Defense for Acquisition, Technology, and Logistics; 2007. r
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