A Preliminary Classification System for Homeless Veterans With Mental Illness

Transcription

1 Psychological Services Copyright 2008 by the American Psychological Association 2008, Vol. 5, No. 1, /08/$12.00 DOI: / A Preliminary Classification System for Homeless Veterans With Mental Illness Gerald Goldstein and James F. Luther Veterans Affairs Pittsburgh Healthcare System and University of Pittsburgh Aaron M. Jacoby Veterans Affairs Pittsburgh Healthcare System Gretchen L. Haas and Adam J. Gordon Veterans Affairs Pittsburgh Healthcare System and University of Pittsburgh The purpose of this study was that of defining psychiatric profiles among veterans based on a structured interview of 3,595 individuals administered by outreach mental health clinicians to individuals who were presently or recently homeless. The interview included ratings of presence or absence of current psychiatric disorders; alcoholism, drug abuse, psychosis, mood disorders, personality disorders, PTSD, and adjustment disorders. We identified three subgroups using cluster analysis each showing different diagnostic profiles that were characterized as addiction (n 3,061), psychosis (n 218), and personality disorders (n 54). Cluster membership was related to demographic characteristics, living situation, length of homelessness, and symptoms and complaints including cognitive difficulties, suicidality, violence, and depression. Group comparison statistics were used to compare intercluster differences in demographics, homeless situation, symptoms, and subjective complaints. There were no major intercluster differences in socioethnic, demographic, and homeless situation variables. Differences occurred in complaints of depression, positive symptoms of psychosis, and suicidality. It was concluded that despite the disproportionate sizes of the clusters homeless veterans with mental illness are nevertheless heterogeneous with regard to their psychiatric profiles. Keywords: homelessness, mental illness, cluster analysis Although much is known about the epidemiology of homeless individuals with mental illness, much less is known about the clinical Gerald Goldstein and James F. Luther, Mental Illness Research, Education and Clinical Center, VA Pittsburgh Healthcare System and University of Pittsburgh, Pittsburgh, Pennsylvania; Aaron M. Jacoby, Behavioral Health Service, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; Gretchen L. Haas and Adam J. Gordon, Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, and University of Pittsburgh, Pittsburgh, Pennsylvania. This research was supported by the Mental Illness Research, Education and Clinical Center, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, and the Medical Research Service, Department of Veterans Affairs. We thank the Department of Veterans Affairs Medical Research Service, VISN 4 Mental Illness Research, Education, and Clinical Center, and Department of Veterans Affairs Northeast Program Evaluation Center for support of this research. Correspondence concerning this article should be addressed to Gerald Goldstein, Research Service (151R), VA Pittsburgh Healthcare System, 7180 Highland Drive, Pittsburgh, PA ggold@nb.net characteristics and the impact of those characteristics on homeless individuals with mental disorders. There is consensus that the prevalence of mental disorder is far greater among the homeless than is the case for the general population (Martens, 2001). The problem of mental illness in homeless veterans specifically has been extensively investigated by Rosenheck and various collaborators beginning in 1991 with a series of studies of homelessness among Vietnam era veterans. These studies continued to the present time providing the opportunity to study mental illness in homeless veterans essentially from the time the phenomenon first emerged until the present. These studies examined the areas of vulnerability to and antecedents of homelessness (Mares & Rosenheck, 2004; McGuire & Rosenheck, 2004; Pickett-Schenk et al., 2002; Rosenheck & Fontana, 1994), representation of various ethnic groups among homeless veterans (Kasprow, & Rosenheck, 1998; Lim, Kasprow, & Rosenheck, 36

2 A PRELIMINARY CLASSIFICATION SYSTEM ), differences between homeless veterans and nonveterans (Rosenheck, Gallup, & Leda, 1991), problems associated with substance abuse (Kasprow, & Rosenheck, 1998; Lim et al., 2006), suicidality (Desai, Dausey, & Rosenheck, 2005; Desai, Liu-Mares, Dausey, & Rosenheck, 2003), and outcome of treatment (Gonzalez & Rosenheck, 2002). With regard to determination of who becomes homeless, Rosenheck and Fontana (1994) proposed a multidimensional model, with postmilitary social isolation, substance abuse, and psychiatric illness being the most important factors. However, age and history of childhood abuse or trauma were also significant factors. There is an increased risk of homelessness in men aged 30 to 44, and non-white homeless combat veterans are more likely to have psychiatric, alcohol, and medical problems than White homeless veterans. Homeless veterans with a criminal history were found to have more severe illness and less improvement than veterans without such histories (McGuire & Rosenheck, 2004). Serving during the Vietnam era did not increase risk of homelessness and military service itself was not found to increase the risk of homelessness (Mares & Rosenheck, 2004). The Rosenheck group conducted specific studies of Asian American (Lim et al., 2006) and Native American veterans. Asian American veterans were found to have a low risk of homelessness compared with African Americans and Hispanics, and a lower prevalence of alcohol and drug use disorders. Native American veterans were found to be overrepresented in the homeless population by 19%. They were found to have more alcohol use disorders than reference groups, but less drug abuse and psychiatric problems. This material on background variables indicates that risk of homelessness or severity of mental illness is associated with substance abuse and psychiatric difficulties after military service, being in the 30 to 44 years age range, and criminal history. Other factors, such as military history or serving during the Vietnam era were ruled out. Substance abuse is essentially pandemic among homeless individuals in general. The presence of comorbid substance abuse and serious mental illness is very common. The Rosenheck group studies showed that the prevalence in different types of substance abuse varies among different ethnic groups. Gonzalez and Rosenheck (2002) reported that homeless veterans with this comorbidity had poorer clinical and social adjustment, and poorer outcome than individuals without comorbidity. The presence of an association between substance abuse and homelessness in other than veteran populations has been reported since the beginnings of the homeless literature (e.g., Lipton, Sabatini, & Katz, 1983; Susser, Struening, & Conover, 1989). Alcohol and other substance abuse and dependence may contribute to both homelessness and psychiatric morbidity. An universal finding is that homelessness is associated with alcohol and other substance use, and that as many as half of the homeless people with schizophrenia have a comorbid drug or alcohol disorder (Reardon, Burns, Preist, Sachs- Ericcson, & Lang, 2003). Comorbidity is typically associated with poor outcomes. Alcohol and other substance use are often the root cause of homelessness and this use independently influences medical and psychiatric morbidity. Suicidal behavior has been identified as a significant problem among homeless individuals. Desai et al. (2003) found that homeless individuals are at particularly high risk for suicidal behaviors. In their study of 7,224 cases they found a 66.2% lifetime prevalence of suicidal ideation and 26.9% of the cases reported an attempt resulting in psychiatric hospitalization. Prigerson, Desai, Liu-Mares, and Rosenheck (2003) reported that homeless mentally ill individuals have suicide risk factors that are different from what is reported for the nonhomeless population. There is an increased risk between the ages of 30 to 39 and risk increases with presence of drug and alcohol use disorders in individuals who are homeless and in the elderly age range. A study confirming the prevalence findings based on homeless individuals from the general population found a 61% prevalence of ideation and a 34% prevalence of actual attempts (Eynan et al., 2002). A systematic review of the published research on the prevalence of psychosis among the homeless indicated that the prevalence among patients who are homeless is 11% with a range of 4 to 16% (Folsom & Jeste, 2002). In a more recent report Folsom et al. (2005) studied 10,340 cases in a general population of homeless individuals identifying 860 (54.8% of sample) individuals with schizophrenia, 311 (19.8%) with bipolar disorder and 396 (25.2%)

3 38 GOLDSTEIN, LUTHER, JACOBY, HAAS, AND GORDON with major depression. There were 949 (60.5%) of these patients with substance use disorders. These percentages were substantially higher than they reported for their sample of 8,771 nonhomeless patients. The presence of substance use disorder was found in 20.9% of the nonhomeless patients. It would therefore appear that homelessness may be a substantial risk factor for mental illness, and that the prevalence of substance abuse is higher among homeless than nonhomeless mentally ill regardless of whether one is considering a veteran or general population. Many patients with serious mental disorders are homeless. For example, among all first-admission psychotic disorder patients admitted to acute inpatient units in the Suffolk County Mental Health Project, 27% were homeless (Herman, Susser, Jandorf, Lavelle, & Bromet, 1998). Other studies reported that 8% of patients with schizophrenia experienced an episode of homelessness within 3 months following admission (Herman et al., 1998; Olfson, Mechanic, Hansell, Boyer, & Walkup, 1999). Although these epidemiological aspects of mental disorders among the homeless have been well studied, they have not yet been well characterized clinically among those homeless individuals, particularly with regard to prevalence of various forms of psychopathology. As part of a nation-wide effort to provide services to the large population of homeless veterans, the Department of Veterans Affairs (DVA) designed and administered an interview-based questionnaire to veterans who were presently or recently homeless. Based on data made available from a consecutive series of 4,150 interviews conducted over a 2-year period (October 1, 2001 to September 30, 2003) in one VA regional network, the present study aimed to identify the patterns of psychopathology found in this sample of homeless veterans. Ultimately, different clinical profiles may be associated with differing treatment approaches, course, and outcomes, particularly when the presence of comorbidity complicates case management and planning. In this study, a cluster analysis was performed utilizing major diagnostic categories coded by the interviewer as the variable set. Cluster analysis is a collection of algorithms that classifies cases into homogeneous groups based on shared similarity in measurements made on the cases. The advantage of cluster analysis over a simple frequency distribution in this case is that (a) it can more easily identify structure in the data in which there are numerous diagnostic combinations and (b) it can empirically embed infrequently occurring profiles into larger, more general patterns of comorbidity. On establishing an adequate clustering solution, other portions of the interview were evaluated to determine possibly differing statuses among the clusters of areas of concern covered by the interview. This determination could ultimately provide a preliminary basis for taxonomy of psychiatric disorders in homelessness, with subgroups potentially having differing antecedents and outcomes. Such a taxonomy could support further hypothesis testing research utilizing multivariate models that validate the clusters against relevant criteria not contained in the cluster analysis itself, and that may ultimate lead to establishment of clinically meaningful subtypes of psychopathology. Participants Method Data were collected by DVA outreach workers using a demographic and clinical history interview conducted with 3,595 homeless veterans from the Veterans Integrated Services Network 4 (VISN 4) that includes all of Pennsylvania, Delaware, and parts of West Virginia, New Jersey, and Ohio. The sample included all presently or recently homeless veterans identified and contacted in various settings located in both urban and rural areas within VISN 4, including community facilities for the homeless, VA hospitals, outpatient clinics, prisons, and veteran s centers in the community for the purpose of providing information about DVA sponsored health services. Forty-six percent of the participants lived in shelters (n 1,665) or with acquaintances on a temporary basis (18%; n 657). Data describing characteristics of the analysis sample are contained in Table 1. The data were collected as a national survey of veterans under conditions that assured privacy and confidentiality. Participants were contacted by the DVA as part of an outreach program and were asked to volunteer to take the interview without penalty for refusal. The present investigators did not actually collect the data but were given access to it by the DVA Northeast Program Evaluation Center

4 A PRELIMINARY CLASSIFICATION SYSTEM 39 Table 1 Sample Characteristics Characteristic N 3,333 a Age M N (%) Ethnicity White 1,534 (46.2%) African American 1,755 (52.8%) Other 118 (3.6%) Marital status Never married 1,173 (35.2%) Married/remarried 131 (3.9%) Separated/divorced 1,925 (57.8%) Widowed 100 (3.0%) Employment, past 3 years Employed 1,696 (51.2%) Unemployed 729 (22.0%) Retired/disability 888 (26.8%) Pensions and other support Service-connected/psychiatric 211 (6.4%) Service-connected/other 326 (9.8%) Nonservice connected 340 (10.2%) Non-VA disability 515 (15.5%) Other public support 524 (15.9%) Money received, past 30 days None 895 (27.2%) $1to (2.8%) $50 to (4.1%) $100 to (24.2%) $500 to (30.1%) $1, (11.6%) Service period Pre-Vietnam 278 (8.4%) Vietnam 1,584 (47.6%) Post-Vietnam 1,279 (38.4%) Persian Gulf 188 (5.6%) Where slept last night Own place 112 (3.4%) Relative/friend 597 (18.3%) Shelter 1,542 (47.3%) Outdoors 516 (15.8%) Institution 423 (13.0%) Prison/jail 69 (2.1%) Time homeless Housed at present 225 (6.8%) 1 night to 6 months 1,871 (56.8%) 6 months to 1 year 443 (13.4%) 1 year or more 755 (22.9%) a There were small amounts of missing data for some of the variables. (NEPEC). The present investigators therefore had no direct contact with the study participants and did not conduct the consent procedure. Several publications of the original investigators indicate that written informed consent was obtained (e.g., McGuire & Rosenheck, 2004; Rosenheck, Dausey, Frisman, & Kasprow, 2000). The data were transmitted to the investigators in de-identified form meeting HIPAA standards, and so it was not possible to review individual consent forms for verification. However, approval to conduct the analysis based on the obtained data and to submit the results for publication was granted by the IRB of the VA Pittsburgh Healthcare System. Interview procedures: Interviews were conducted by experienced mental health workers, mainly social workers and psychiatric nurses, associated with the DVA sponsored healthcare for homeless veterans program that routinely performs assessments for identifying and tracking veterans in need of services. A personal interview with the homeless veteran was conducted by these mental health workers who were trained in the administration of the semistructured interview The interviews included sections on demographic information, characteristics of the contact with the veteran, military history, living situation, medical history, substance abuse, psychiatric status, employment status, and observations made by the interviewer covering the areas of clinical psychiatric disorder and needs for referral and treatment. Several studies have appeared in the literature utilizing data obtained from this survey process that were reported on beginning in 1991 (e.g., Kasprow & Rosenheck, 1998). Presence or absence ratings of a set of psychiatric diagnostic categories were made by the interviewers based on self-report information provided by the veteran and supplemented by medical chart information, when available. As indicated in the Kasprow and Rosenheck study, these ratings were made on the basis of unstructured assessments and therefore relied on clinical judgment sometimes supported by record review. Although they should not be construed as being formal Diagnostic and Statistical Manual of Mental Disorders (4th ed.; [DSM IV] American Psychiatric Association, 1994) diagnoses based on a standard structured interview, these clinical ratings have been associated in previous large sample studies to meaningful clinical and sociocultural variables in homeless individuals such as ethnicity (Lim et al., 2006) and suicide risk (Desai et al., 2005). In any event the diagnostic ratings used in the present study were for broad groups of disorders including alcohol and drug use disorders, psychosis, mood disorder, posttraumatic stress disorder (PTSD), personality disorder, and

5 40 GOLDSTEIN, LUTHER, JACOBY, HAAS, AND GORDON adjustment disorder. Specific DSM IV diagnoses were not used as bases for classification, and only current diagnoses were noted, although questions were asked about past alcohol and drug use. Although computer-based medical records were available to the interviewers for determining diagnoses and for additional demographic information, we do not have information concerning how frequently they were used. However, interviewers were trained clinicians and knowledgeable concerning the disorders rated, and therefore capable of making accurate ratings of the broad diagnostic categories used here based on self-report during a clinical interview. As indicated, the Rosenheck group used these clinical diagnostic assessments in numerous published studies. Kasprow and Rosenheck (1998) indicated that large samples obtained by numerous clinicians, as was the case in the present study, should attenuate diagnostic biases, because they would have to be consistently applied by dozens of clinicians to thousands of participants. The psychiatric status section also included direct questions about depression, anxiety, hallucinations, concentration and memory, violent behavior, and past suicidal ideation or past suicide attempts. Contacts with the outreach interviewing program were made through DVA-initiated outreach, referrals from shelter staff, and referrals from DVA patient care facilities, through the veteran s community centers or through special programs for the homeless. The data analysis was based on 3,333 individuals (93% of the entire sample) having at least one diagnosis. Cluster Analyses The data were collated and entered into computer databases by staff from the NEPEC. Cluster analyses were conducted using diagnostic classifications made by interviewers as to the presence or absence of seven psychiatric diagnostic groups: alcohol abuse/dependency, drug abuse/dependency, psychosis (combining schizophrenia and other psychotic disorders), mood disorder, personality disorder, PTSD from combat, and adjustment disorder. Disorders not on this list were rated as other psychiatric disorder (8.4%; n 281) but this variable was not included in the analyses because of its lack of specificity. The interview form used the term Alcohol or Drug Abuse/Dependency but interviewers were not asked to record distinctions between abuse and dependency in their ratings. Participants could have more than one diagnosis and often had multiple diagnoses. Cases were classified into clusters using the between groups average linkage method. Average linkage is one of several hierarchical agglomerative methods. It computes an average of the similarity of a case under consideration with all cases in a cluster and joins the case to that cluster if a given similarity level is achieved using this average value (Aldenderfer & Blashfield, 1984). The Dice similarity measure was used as the proximity measure because it excludes negative matches (joint absences) and weights positive matches double (Everitt, Landau, & Leese, 2001). Doubling the value of positive matches takes into consideration the large number of combinations of diagnoses and the relative infrequency with which many occur. It thereby attenuates the problem of outliers distorting the analysis. This combination of method and distance measure is appropriate for binary data. The number of clusters was determined by preliminary evaluations with varying numbers of cluster solutions aimed at avoiding trivial clusters in a manner similar to scree testing, and more definitively plotting clusters in discriminant function space, finding adequate separation among group centroids, as recommended by Aldenderfer and Blashfield (1984) in the absence of objective methods for definitive determination of number of clusters. The use of these heuristic methods does not rule out alternative cluster solutions, but does suggest the location of the point at which further clustering is not productive because of reduced distance among the cluster spaces. Following the completion of the cluster analysis, cluster membership was used as an independent variable seeking intercluster differences among demographic, situational, and clinical variables not included in the cluster analyses. Kruskal Wallis tests were used for nonparametric continuous variables and chi-square tests were used for frequency data. The Cluster Analysis Results Following extensive evaluations with other solutions it was determined through the use of discriminant analyses of alternative solutions that a three cluster solution was both statisti-

6 A PRELIMINARY CLASSIFICATION SYSTEM 41 cally internally valid and clinically meaningful. Solutions involving larger numbers of clusters produced small, probably trivial clusters, likely to be associated with outliers, or did not achieve adequate separation in discriminant function space. The clusters varied greatly in size. Cluster 1 accounted for the vast majority (92%; n 3,061) of the participants, while Cluster 2 had 218 (6.5%) members, and Cluster 3 had 54 (1.6%) members. Percentages of the individual psychiatric disorder ratings within each cluster are presented in Table 2. In most cases there were individuals with each of the seven diagnostic ratings in each cluster. The cluster profile is presented in Figure 1. The large first cluster describes a complex diagnostic profile mainly involving substance use, mood, and adjustment disorders. It characterizes the great majority of cases who can be characterized as having comorbid diagnoses involving a form of substance abuse and/or depression and significant adjustment difficulties. It contains a large proportion of the cases with substance use disorders but with other comorbid disorders as well. The second cluster contains the bulk of the participants with psychosis, sometimes accompanied by mood disorder and alcohol but not drug abuse or dependency. It is probably best thought of as a psychosis subgroup with the understanding that there were also individuals with psychosis in the other clusters. The small third cluster is largely restricted to individuals with personality and adjustment disorders. In summary, the three Table 2 Percentages of Psychiatric Diagnostic Ratings Within Each Cluster Diagnostic rating Addiction (n 3,061) Cluster Psychosis (n 218) Personality (n 54) Alcohol use disorder Drug use disorder Psychosis Mood disorder Personality disorder Posttraumatic stress Adjustment disorder clusters are not coextensive with individual psychiatric diagnoses but reflect various patterns of comorbidity, particularly involving substance use disorders. In the following we will describe Cluster 1 as the Addiction cluster, Cluster 2 as the Psychosis cluster, and Cluster 3 as the Personality cluster. As indicated in Figure 1, the second and third clusters are relatively homogeneous, but there were substantial numbers of cases with psychosis and addiction in Cluster 1. Thus, the extraction of Clusters 2 and 3 should not be taken to mean that the cases in these clusters are representative of all psychosis and personality disorder in the sample but rather that they represent a portion of individuals with those disorders who do not have substantial psychiatric comorbidity. A preliminary cross-validation was performed by splitting the sample in half and repeating the analysis for each half separately. In this split-half design the 3,333 participants were randomly assigned to one of two subsamples (n 1,649 and n 1,684, respectively). A cluster analysis was performed on each of the subsamples using the identical methods employed for the full sample. Calculations of the original partition to each of the new partitions revealed misclassification rates of 18% for the first subsample and 5% for the second subsample. The Addiction and Psychosis clusters were highly concordant with minimal changes in the diagnostic profiles of the Personality cluster. It should be noted, however, that variation is to be expected in this last cluster due to small numbers of members. Overall, it appears that the cluster solution for the total sample is stable and not distorted by individual outliers or choice of variables. Comorbidity The cluster analysis results suggested the presence of numerous patterns of psychiatric disorder among homeless veterans. In general, the presence of comorbidity was extensive with a median of two diagnoses for the total sample. The patterns of comorbidity are presented in Table 3. This table contains percentages of co-occurrence of other diagnostic ratings with each individual rating used as a target. Thus, for example, using PTSD as a target 65.5% (n 165) of the participants with PTSD had alcoholism as a comorbid disorder. Using alcoholism as the target, 7.5% (n 164) of

7 42 GOLDSTEIN, LUTHER, JACOBY, HAAS, AND GORDON Mean Frequency of Presence (0 = Absent; 1 = Present) Alcohol abuse/dependency Drug abuse/dependency Mood disorder Personality disorder PTSD from combat Adjustment disorder Psychosis (n=3061) 2 (n=218) Average Linkage (Between Groups) 3 (n=54) 3(n=54) Figure 1. Cluster profile. participants with this diagnosis also had PTSD. Using the substance use disorders as targets, there were particularly high levels of comorbidity with mood and adjustment disorders. Alcoholism was the most frequently occurring comorbid disorder. Factors Associated With Cluster Membership Cluster membership was used as the independent variable in analyses of other items con- Table 3 Comorbidity by Diagnostic Rating Comorbidity Diagnosis N ALC DRG PSY MD PER PTS ADJ Not comorbid Alcohol abuse (ALC) 2, Drug abuse (DRG) 1, Psychosis (PSY) Mood disorder (MD) 1, Personality disorder (PER) Posttraumatic stress (PTS) Adjustment disorder (ADJ) 1, Note. Data presented as percentages indicating the proportion of participants with diagnosis who are comorbid.

8 A PRELIMINARY CLASSIFICATION SYSTEM 43 tained in the interview. Most of the items produced nominal data and were analyzed with chi-square tests. These items mainly involved yes/no or present/absent ratings. Some items were continuous, such as age, and were analyzed using a nonparametric analysis of variance (ANOVA), the Kruskal Wallis test, if the variable was nonnormally distributed. The purpose of these analyses was to determine the relationship between cluster membership, based on psychiatric disorder, and the various domains covered by the interview including demographics, homelessness situation, historical information provided by participants, and subjective declarations of various aspects of mental disorder, such as depression. The historical and subjective complaint information can be viewed as evaluating the external validity of the cluster solution, described by Aldenderfer and Blashfield (1984) as testing the cluster solution against relevant criteria. That is, validity would be supported by historical and subjective material, as, for example, when significantly more individuals in the Addiction cluster report more days of drinking over the past 30 days than in the other clusters, or more individuals in the Psychosis cluster report having hallucinations. Table 4 describes relationships between cluster membership and interview items concerning demographic information. Mean age ranges from 48 to 53 across the clusters, with the Addiction cluster having a lower mean age than the Psychosis and Personality clusters. There were very few female participants, but there was a higher percentage (7.3%; n 16) of them in the Psychosis cluster. The lowest percentage (27.8%; n 15) of non-whites (nearly all African American) was in the Personality cluster. Very small percentages of participants were married, with little difference in percentage among the clusters. With regard to employment, defined as part- or full-time work over the past 3 years, there were marked intercluster differences: 18.8% (n 41) of the Psychosis cluster was employed in contrast to 53.6% (n 1,151) of the Addiction cluster and 38.9% (n 21) of the Personality cluster. In 19.7% (n 43) of the cases in the Psychosis cluster participants were receiving service connected disability payments for psychiatric illness, in contrast to 0% in the Personality cluster. Homelessness situation results are reported by cluster in Table 5. There was not a significant difference in length of homelessness among the clusters. In the total sample, length of homelessness ranged from less than 1 month to 2 years or more. However, most of the participants were homeless for 6 months or less. Table 4 Sociodemographics by Cluster Membership Addiction (n 3,061) Cluster Psychosis (n 218) Personality (n 54) p Analyses Measure Pairwise tests Mean age a A Y&P Male A Y White A Y&P Married/remarried Employed, past 3 years A P Y Mean work days, past 30 b Y A&P $500 income, past 30 days A Y&P Financial support VA psychiatric Y A&P VA other NSC pension A Y Disability Y A&P Other Received combat zone fire Note. Data presented as percentages, tests chi-square, and effect sizes calculated as square root of ( 2 /N) unless otherwise indicated. A Addiction cluster; Y Psychosis cluster; P Personality cluster. a Test: analysis of variance (ANOVA); effect size: square root of R 2. b Test: Kruskal Wallis; effect size: square root of ( 2 /N). Effect size

9 44 GOLDSTEIN, LUTHER, JACOBY, HAAS, AND GORDON The greatest discrepancies among clusters regarding housing were for domiciliaries and hotels/boarding houses. None of the participants in the Psychosis cluster made use of the domiciliaries, while there was some use by members of the other clusters. Conversely, there was relatively greater use of hotels or boarding houses by members of the Psychosis cluster (3%; n 7) with little use by members of the Addiction cluster (.6%; n 19). Psychiatric history and complaint results appear in Table 6. The Personality cluster had no members with substance use diagnoses, and correspondingly low percentages of subjective reports of alcohol or drug difficulties. Percentages of subjective report of depression did not differ significantly among clusters and was high in all three of them, ranging from 53% (n 29) in the Personality cluster to 62% (n 1,900) in the Addiction cluster with similar results for subjective reports of anxiety and tension. Reports of hallucinations and concentration and memory problems were particularly frequent in the Psychosis cluster. Report of violence was not high in any of the clusters, but was particularly low in the Personality cluster (7.4%; n 4). There was little intercluster difference in suicidal thinking. Suicidal attempts were particularly low in the Personality cluster, with only one case. The other two clusters produced about 5% of cases attempting suicide. The actual numbers were 136 in the Addiction Cluster and 11 in the Psychosis cluster. These figures appear to be high relative to the general population base rate that has been estimated at 2.7% (Nock & Kessler, 2006). Discussion A cluster analysis of clinical mental disorder diagnostic ratings obtained from interviews of 3,333 veterans with current or recent history of homelessness suggested a three cluster solution based on ratings of seven psychiatric disorders. The participants were individuals who responded to an outreach program sponsored by the DVA aimed at extending health care to homeless veterans. The sample consisted largely (93%) of individuals who had at least one psychiatric disorder and should not be considered as a random sample of the homeless veteran population. Table 5 Homeless Situation by Cluster Membership Addiction (n 3,061) Cluster Psychosis (n 218) Personality (n 54) p Analyses Variable Pairwise tests Days slept, past 30 a Own place A Y Someone else s place A Y Hospital/nursing home Domiciliary Y A&P VA halfway house Other halfway house A Y Hotel/boarding room A Y Homeless shelter P A&Y Outdoors Vehicle Prison/jail Housed A Y Time homeless months year years Note. Data presented as percentages, tests chi-square, and effect sizes calculated as square root of ( 2 /N) unless otherwise indicated. A Addiction cluster; Y Psychosis cluster; P Personality cluster. a Test: Kruskal Wallis; effect size: square root of ( 2 /N). Effect size

10 A PRELIMINARY CLASSIFICATION SYSTEM 45 Table 6 Psychiatric History by Cluster Membership Addiction (n 3,061) Cluster Psychosis (n 218) Personality (n 54) p Analyses Variable Pairwise tests Psychiatric hospitalization Y A&P Used VA, past 6 months Y A P Current alcohol use disorder A Y P Past alcohol use disorder A Y P Days drank, past A Y P Current drug use disorder A Y&P Past drug use disorder A Y P Days used, past 30 a A Y&P Psychological problem Depression Anxiety Hallucinations Y A&P Concentration Y A P Violent behavior Y A&P Suicidal thoughts Suicide attempt Note. Data presented as percentages, tests chi-square, and effect sizes calculated as square root of ( 2 /N) unless otherwise indicated. A Addiction cluster; Y Psychosis cluster; P Personality cluster. a Test: Kruskal Wallis; effect size: square root of ( 2 /N). Effect size The internal validity of a three cluster solution was established with a clear separation among the centroids for a three cluster solution. The clusters were characterized with the terms Addiction, Psychosis, and Personality. These profiles appear to represent clinical presentations that involve extensive comorbidity as well as profiles that largely reflect individual diagnoses with more limited comorbidity, such as between psychosis and alcoholism. The three clusters were not found to be coextensive with the clinical diagnoses, something most apparent in the first cluster that was very large and that contained many cases with comorbid depression and substance use disorders. A second cluster consisted mainly of individuals with psychosis combining schizophrenia with other psychotic disorders. Nineteen percent (n 42) of them had alcoholism, but only 2 cases had drug abuse. The third cluster consisted largely of individuals with personality disorder. Thus, psychiatric diagnoses found among the homeless apparently reflect a broad range of psychopathology varying in severity from adjustment and personality disorder to psychosis. The large first cluster reflected extensive comorbidity involving substance use and mood disorders, but the second and third clusters were more homogeneous, consisting of individuals with psychosis or personality disorder. There were, however, substantial numbers of individuals with psychosis or personality disorders classed in the Addiction cluster. Thus, although many participants with psychosis or personality disorder had no comorbid conditions, a substantial number had substance use disorders. One could conclude that heterogeneity was present but it was disproportionate among disorders. Comorbid substance use disorders were exceedingly common, but as indicated in Table 3, there were small percentages of cases in which a substance use disorder was present without other diagnoses, and there are also many cases in which a substance use disorder was not present. However, the substance use disorder cases did not form a unique cluster but became part of a large, complex cluster reflecting comorbid substance use, mood, and adjustment disorders. Furthermore, the extraction of a Personality cluster without substance abuse should not be taken to mean that individuals with personality disorder typically do not have substance abuse. Rather it means that there is an empirically identifiable subgroup of individuals

11 46 GOLDSTEIN, LUTHER, JACOBY, HAAS, AND GORDON with personality disorder who do not have comorbid substance abuse. Because of the large number of Vietnam veterans in the sample, questions may be raised as to why there was not a PTSD cluster and more PTSD participants in general in view of the extensive literature concerning the prevalence of PTSD among homeless individuals (e.g., North & Smith, 1992; Rosenheck & Fontana, 1994; Savoca & Rosenheck, 2000). In support of the PTSD diagnoses, 84.4% of all the PTSD cases reported receiving fire in a combat zone, as contrasted with percentages ranging from 16% to 20% in the other diagnostic groups. However, only 252 (7.5%) cases of the total sample respondents received a PTSD diagnostic rating, and most of them were embedded in the Addiction cluster. The basis for this classification appears to be the great deal of comorbidity in the participants with PTSD. Looking at the number of cases with PTSD that were assigned additional diagnoses we found 95% comorbidity. The cluster analytic results indicate that PTSD, at least among homeless individuals, exists in the context of complex psychiatric illness of a rather severe nature. They share extensive comorbidity with participants in several of the other diagnostic groups. The most clinically relevant point is that these homeless individuals with PTSD appear to be a markedly distressed group with a high prevalence of comorbidity, most often including substance abuse, significant problems with anxiety, depression, and suicidal behaviors. Although comorbidity was common in all diagnostic groups, almost all of the participants with PTSD had at least one comorbid disorder. Seventeen of the 252 cases (6.7%) with PTSD reported making at least one suicidal attempt. This percentage slightly exceeds the 5.6% (95/1,688) found in participants with mood disorders suggesting that the attempt rate approximates that found in individuals with mood disorders, in which suicide is generally thought to be a risk. Future studies may help to clarify the matter of whether this comorbidity pattern is unique to combat related PTSD found in veterans or is seen in other types of PTSD. The stereotype that homeless veterans consist largely of individuals in the military during the Vietnam conflict and have PTSD was not confirmed. That is the case in some instances, but it does not characterize the psychopathology found in the great majority of homeless veterans. Substance use disorders are pandemic in this sample, being involved in all but one of the clusters. Polysubstance abuse is common, constituting 41.4% (n 1,379) of the 3,333 cases. There is, however, a group of individuals with personality disorders, represented as Cluster 3, that do not have either form of substance use disorder. With regard to this Personality cluster, these individuals had low levels of symptomatology not only in the substance use area but also with regard to subjective complaints of anxiety, depression, violence, suicidal behavior, and disturbances of thinking. One may wonder if the rating of personality disorder was made to a lesser or greater extent on the basis of homelessness, and if these individuals do not have significant psychopathology. None of the individuals in the Personality cluster was receiving a DVA disability pension for a psychiatric disorder, very few of them were homeless at least 2 years, and very few of them reported cognitive problems. Unfortunately, we do not have information concerning the type of personality disorder, particularly because an association has been made between homelessness and the presence of antisocial personality disorder (North, Smith, & Spitznagel, 1993) preceding the onset of homelessness and sometimes traceable to childhood conduct disorders. Whether the Personality cluster consists largely of individuals with antisocial personality disorders without accompanying substance abuse would appear to merit further study. With regard to the external validity of the cluster solutions, it appears that there is evidence of anticipated relationships between cluster membership and aspects of behavior, functioning, and life situation. Some of these associations are minor including those involving cluster membership and demographic and homelessness related variables. Major intercluster differences were not found for age, ethnicity, marital status, or length of homelessness. With regard to residential situation, almost half of the sample lived in shelters. There is, however, evidence that the Psychosis cluster is more dysfunctional, experiencing more difficulty thinking and positive psychotic symptoms than the other clusters. Employment, percentage having DVA service connected disabilities, pensions for a psychiatric disorder, history of

12 A PRELIMINARY CLASSIFICATION SYSTEM 47 psychiatric hospitalization, use of DVA care facilities, and percentages having difficulties in understanding and comprehension, all reflect greater disability than is the case for the other clusters. With regard to the Addiction cluster, there are higher percentages of individuals reporting relatively high numbers of days of drinking and intoxication and histories of treatment for alcoholism or drug addiction. Regarding the Personality cluster evidence for validity is shown in the absence of substantial substance abuse or dependence, fewer subjective reports of cognitive dysfunction, and fewer indications of suicidal behavior, although the latter difference is not significant. Thus, cluster membership was found to be associated with a number of life situation and clinical variables that support the external validity of the solution proposed. However, the effect sizes for most of these differences are small, and one would have to assume that the various external validity criteria are determined by many more considerations than psychopathology. A limitation of these conclusions, aside from absence of detailed diagnostic evaluations, is the possible lack of clarity between comorbidity and the existence of individual subgroups with differing diagnoses. The matter is a complex one because, for example, a PTSD subgroup was not isolated as a separate cluster. Most clearly, the co-occurrence of PTSD and mood disorders is common, as is comorbidity involving adjustment disorders both with substance use and other disorders. The interactions between these disorders and the impact of homelessness would merit further evaluation. Of particular interest would be the further clarification of the clusters with regard to whether they may reflect valid subtypes. A limitation of cluster analysis is that it does not provide the more definitive evidence of the validity of a syndrome that can often be provided by working with clearly defined groups within the framework of such procedures as logistic regression or discriminant analysis. There is no objective, definitive method of determining the number of clusters and one is left with various heuristic procedures as described in Aldenderfer and Blashfield (1984) and utilized here. A more systematic multivariate methodology for determining the validity of the clusters would be desirable for determination of conditions associated with the distinctions among the cluster analysis derived groups. It is generally understood that cluster analysis is a classification and not a hypothesis-testing technique, and that any classification system needs to be validated with hypothesis testing procedures. However, even the preliminary classification system presented here points out the extensive problem of comorbidity among psychiatric and substance use disorders, and may help to correct stereotypic beliefs about the mental health status of homeless individuals, particularly those regarding the prevalence of PTSD. Cross-validation of cluster solutions on new samples and using differing algorithms are also desirable, particularly because different algorithms can generate substantially different solutions. References Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis. Beverly Hills, CA: Sage. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. Desai, R. A., Dausey, D. J., & Rosenheck, R. A. (2005). Mental health service delivery and suicide risk: The role of individual patient and facility factors. American Journal of Psychiatry, 162, Desai, R. A., Liu-Mares, W., Dausey, D. J., & Rosenheck, R. A. (2003). Suicidal ideation and suicide attempts in a sample of homeless people with mental illness. Journal of Nervous and Mental Disease, 191, Everitt, B. S., Landau, S., & Leese, M. (2001). Cluster analysis (4th ed.). New York: Oxford University Press. Eynan, R., Langley, J., Tolomiczenko, G., Rhodes, A. E., Links, P., Wasylenki, D., et al. (2002). The association between homelessness and suicidal ideation and behaviors: Results of a cross-sectional survey. Suicide and Life Threatening Behaviors, 32, Folsom, D. P., Hawthorne, W., Lindamer, L., Gilmer, T., Bailey, A., Golshan, S., et al. (2005). Prevalence and risk factors for homelessness and utilization of mental health services among 10,340 patients with serious mental illness in a large public mental health system. American Journal of Psychiatry, 162, Folsom, D. P., & Jeste, D. V. (2002). Schizophrenia in homeless persons: A systematic review of the literature. Acta Psychiatrica Scandinavica, 105, Gonzalez, G., & Rosenheck, R. A. (2002). Outcomes and service use among homeless persons with se-

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