Problems assessment for substance using psychiatric patients: development and initial psychometric evaluation

Drug and Alcohol Dependence 75 (2004) 67–77

Problems assessment for substance using psychiatric patients: development and initial psychometric evaluation Kate B. Carey a,∗ , Lisa J. Roberts b , Daniel R. Kivlahan b , Michael P. Carey a , Dan J. Neal a a

Syracuse University, Center for Health and Behavior, 430 Huntington Hall, Syracuse, NY 13244-2340, USA b VA Puget Sound Health Care System, 1660 S. Columbian Way (116 MHC), Seattle, WA 98108, USA Received 11 June 2003; received in revised form 9 January 2004; accepted 23 January 2004

Abstract Background: Persons with co-occurring Axis I mental disorders and substance use disorders often experience multiple negative consequences as a result of their substance use. Because no existing measure adequately assesses these population-specific problems, we developed the Problems Assessment for Substance Using Psychiatric Patients (PASUPP). This paper describes the scale development and factor structure, and provides initial reliability and validity evidence for the PASUPP. Methods: An initial pool of 54 items was assembled by reviewing existing measures for relevant items and generating new items. Then, 239 patients (90% male, 61% White) with documented Axis I psychiatric and current substance use disorders rated the lifetime and last 3-month occurrence of each problem, and completed additional measures of substance use and related functioning. Results: Lifetime endorsements ranged from 31 to 95%, whereas 3-month endorsements ranged from 24 to 78%. Item analyses reduced the set to 50 items. The PASUPP is internally consistent (alpha = 0.97) and unidimensional. Scale validity was suggested by moderate correlation with other measures of substance problem severity. Conclusions: Promising psychometric properties are reported for a population-specific measure of substance use problems. Such a measure could be useful for initial assessments and outcome evaluations with substance using psychiatric patients. © 2004 Elsevier Ireland Ltd. All rights reserved. Keywords: Substance use; Problems; Psychiatric disorders; Psychometrics

1. Introduction Co-occurring substance abuse or dependence occurs among nearly half of all persons living with severe and persistent mental illness (Regier et al., 1990). Persons diagnosed with both an Axis I mental disorder and a substance use disorder often experience multiple negative consequences, including many of the typical social, occupational, medical, interpersonal, and psychological sequel of substance use. In addition, they often experience exacerbation of their psychiatric symptoms. Substance use also compromises the ability to benefit from psychiatric rehabilitation services, such as maintaining supportive housing and case management assistance, adherence with psychiatric medi-

∗ Corresponding author. Tel.: +1-315-443-2706; fax: +1-315-443-4123. E-mail address: [email protected] (K.B. Carey).

cations, learning and practicing self management skills, and participating in vocational rehabilitation. A comprehensive evaluation of persons with co-occurring substance use and psychiatric disorders should include an assessment of the negative psycho-social consequences of substance use (Carey and Correia, 1998; Managed Care Initiative Panel on Co-occurring Disorders, 1998). Assessment of negative consequences is necessary for three reasons. First, because substance use disorders are defined in terms of their consequences for adaptive functioning (American Psychiatric Association, 1994), a comprehensive diagnostic assessment needs to measure adaptive function and life problems. This information is helpful for treatment planning. Second, changes in substance-related problems can be used to measure treatment outcome. Use and resulting problems are distinct constructs, and the ability to monitor both can increase the sensitivity of outcome evaluations. Third, from a motivational perspective, identification of problems resulting from substance use may help to clarify

0376-8716/$ – see front matter © 2004 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.drugalcdep.2004.01.010

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the reasons for quitting and the costs of continuing to use substances. An assessment of negative consequences may help to increase a person’s problem recognition, and can be used during treatment as personalized feedback (Carey, 2002). Psychometrically sound problems measures such as the Drinker Inventory of Consequences (Miller et al., 1995), the Drinking Problems Index (Finney et al., 1991), and the Rutgers Alcohol Problems Index (White and Labouvie, 1989) are available for use with other populations. However, none of these measures were specifically developed for persons with co-occurring disorders and are inadequate for two reasons. First, existing measures do not adequately assess the unique substance-related problems experienced by the mentally ill (Drake et al., 1990). Many psychiatric patients take psychotropic medications and engage in ongoing psychiatric rehabilitation. Substance use often leads to medication noncompliance and undesirable side effects resulting from mixing prescribed medications and alcohol or illicit drugs (Owen et al., 1996; Weiss et al., 1998). Substance use limits the eligibility of mentally ill persons for supportive housing or employment opportunities (Saunders and Robinson, 2002; Lipton et al., 2000), and the presence of a comorbid substance use disorder increases the likelihood of homelessness (Caton et al., 1994; Drake et al., 1991). Ample evidence indicates that substance use exacerbates psychiatric symptoms, social isolation, and adaptive coping abilities (Barry et al., 1996; Carey et al., 1991; Shaner et al., 1995). Rates of re-hospitalization and use of emergency services are higher among dually diagnosed patients than their non-abusing counterparts (Carey, 1995). Many of these adverse consequences are not assessed by existing problems measures intended for more general populations. Thus, there is a need to enhance the content validity of negative consequences assessments for individuals with comorbid psychiatric and substance use disorders. Second, these measures contain items that may not pertain to persons living with mental illness. For example, the effect of substance use on missing work, or driving a motor vehicle may not apply to persons who often are unemployed and medically prohibited from driving. Furthermore, items that refer to conflicts within established relationships, or to the effects of substance use on one’s popularity or reputation, may lack sensitivity for persons who may be socially ostracized because of their psychiatric disabilities (Corse et al., 1995). This paper describes the process of development and the initial psychometric evaluation of the Problems Assessment for Substance Using Psychiatric Patients (PASUPP), a self-report questionnaire designed to be sensitive to the range of substance-related problems experienced by dually diagnosed persons. A key consideration in the development of the PASUPP was a focus on undesirable events or effects that could result from substance use. We elected not to include items that might be indicators of dependence or addiction (e.g. “Can you stop drinking without a struggle after one or two drinks?” or “Do you drink before noon

fairly often?”) to create a more pure assessment of negative consequences. In this way, we designed the PASUPP to reflect the psycho-social impact of substance use separate from dependence symptoms. We refined the scale using item and factor analyses, then evaluated evidence for the reliability and validity of the remaining items. With regard to discriminant and convergent validation, we defined three sets of variables and predicted different relationships with the PASUPP. First, demographic variables (age and education) were not expected to be related to substance-related negative consequences. Second, general measures of adaptive function and psychiatric status were expected to be only modestly related to PASUPP scores. Although these dimensions are not directly related to substance use per se, the rationale for this prediction is based on observations that substance use can have a broad impact on the functional status of psychiatric patients (Drake and Brunette, 1998). Third, other measures of substance use problems were expected to be significantly related to PASUPP scores; because these substance-specific validation measures differ from the PASUPP in both time frames and comprehensiveness, we expected moderately strong relationships.

2. Method 2.1. Item generation and refinement The goal of the item generation process was to create a broad, overinclusive set to maximize content validity (Clark and Watson, 1995).We followed a three-step process to determine items for the PASUPP. First, we identified five existing problems measures with good psychometric properties. The Drinkers Inventory of Consequences (DrInC) (Miller et al., 1995), the Michigan Alcoholism Screening Tests (MAST) (Selzer, 1971), and the Drug Abuse Screening Test (DAST) (Skinner, 1982) have been used previously with adult, treatment-seeking samples. The Rutgers Alcoholism Problems Index (RAPI) (White and Labouvie, 1989) was designed for adolescents, and the Drinking Problems Index (DPI) (Finney et al., 1991) was designed for older adults. In total, these measures supplied 135 potential items. We eliminated 22 items referring to dependence symptoms or otherwise not clear descriptions of negative consequences (e.g. “Are you always able to stop drinking when you want to?” from the MAST; “Gone to anyone for help about your drug use/drinking” from both the DAST and the DPI; “Drinking has helped me to relax” from the DrInC; “Tried to cut down on drinking” from the RAPI). From the remaining items, we combined items with overlapping content and generated a composite list of relevant items. Second, we supplemented this set of items with new items generated from focus groups of dually diagnosed outpatients. Previous qualitative research yielded focus group transcripts in which psychiatric outpatients discussed the costs and benefits of their personal substance use (Carey

K.B. Carey et al. / Drug and Alcohol Dependence 75 (2004) 67–77

et al., 1999). We reviewed these transcripts and generated a list of negative consequences reported by the participants, which included specific examples of problems in the following domains: money management, physical health, emotional health and psychiatric symptoms, family and friendly relationships, legal consequences, and violence. This list was then compared to the list of items derived from the existing problems measures, many overlapped with item content already present, but a total of nine new items were not already represented in the existing item pool and these were added to the list. Third, we reviewed the literature to identify additional problems documented in descriptive research studies. Based on this review, a list of 23 general categories of negative consequences (e.g. medication non-compliance, legal problems, sexual abuse, problems with money management, affect lability, and increased paranoia) was developed and compared to the existing items, resulting in the generation of four supplemental items. This three-step process resulted in a set of 54 items: 34 came from two or more sources, seven derived from a single questionnaire, nine from focus groups, and four from the literature. All items were reworded to follow the stem: Because of your drinking or drug use, did you . . . (experience this problem). Two separate response options were provided: lifetime and last 3 months. The experience of negative consequences during the person’s lifetime was obtained by asking: “Did this ever happen to you? Yes or No.” More recent negative consequences were assessed by the following response options reflecting frequency or intensity, respectively: (a) “In the last 3 months, how often did this happen?” (never/once or twice/a few times/many times), or (b) “In the last 3 months, has this happened to you?” (not at all/a little/somewhat/a lot). Three of the authors with extensive experience working with dually disordered patients reviewed and clarified the wording of the items. This pool of 54 items was then administered to 23 psychiatric outpatients (17 men and 6 women; ages 27–46 years). They were told that their responses would be used to improve the PASUPP. Thus, they were treated as expert informants and invited to ask questions or provide feedback at any time. The patients’ feedback was used to improve the clarity of instructions, the items themselves, and response options. The final version of the PASUPP was determined to be at the sixth grade reading level, according to the Flesch–Kincaid formula (produced by Microsoft Word 2001). 2.2. Sites Participants were recruited from the Veterans Affairs (VA) Puget Sound Health Care System and Harborview Medical Center, in Seattle. Both hospitals are large, tertiary care facilities affiliated with the University of Washington School of Medicine. They both provide comprehensive medical and psychiatric services, including specialized treatment for individuals with co-occurring disorders. Institutional Review

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Board approval was obtained from the University of Washington, King County, VA Puget Sound Health Care System, and Syracuse University. 2.3. Participants Participants were 239 patients receiving psychiatric treatment services (92% outpatients); 156 (66%) were recruited from the VA and 82 (34%) were recruited from Harborview. All were receiving clinical services including medication management, case management, and psycho-educational groups. Eligibility criteria were: (a) an Axis I psychiatric disorder documented in the medical record, using criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fourth edition (DSM-IV) (First et al., 1995); (b) a current DSM-IV substance use disorder, also documented in the medical record; (c) capacity to provide informed consent and to complete a questionnaire battery meaningfully, as determined by research staff in consultation with treatment staff; and (d) no signs of substance intoxication at the time of assessment. Most of the participants were male (90%); 61% identified themselves as Caucasian or White, 31% as African-American or Black, 3% as Hispanic, 2% as Native American, and 2% as Asian or Pacific Islander. The mean age of the sample was 46 years (S.D. = 8.1), with a range of 22–71 years. Only 6% of the sample was currently married, 40% were never married, 51% were separated or divorced, and 3% were widowed. Patients described themselves as living alone (29%), in temporary/unstable arrangements (30%), or in a controlled environment (23%), a minority resided with friends or family (18%). Of the 156 veterans in the sample, 35% had service-connected disabilities and the remaining 65% were being treated for non-service-connected illness. Typical pattern of employment over the past 3 years was reported as full-time work (18%), part-time work (29%), retired or disabled (18%), or unemployed (34%). Equivalent numbers had a primary diagnoses in the schizophrenia spectrum (38%) and mood disorders (40%, of that 23% had major depressive disorder and 17% had bipolar disorder), and 18% had a primary diagnosis of PTSD. Most of the sample (91%) was taking psychiatric medications. Primary substance use disorders included alcohol abuse/dependence (56%) and drug abuse/dependence (44%). Mean AUDIT score was 16.2 (S.D. = 12.6; range 0–40) and mean DAST score was 5.5 (S.D. = 3.6; range 0–10). Both are well above the recommended cut-points for identifying alcohol (AUDIT ≥ 8) and drug use (DAST ≥ 3) disorders in psychiatric samples (Maisto et al., 2000). 2.4. Measures 2.4.1. Problems Assessment For Substance Using Psychiatric Patients (PASUPP) The PASUPP assesses whether respondents experienced any of 54 substance use-related consequences across two

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time intervals (lifetime and past 3 months). Participants were instructed (a) to rate lifetime occurrence as “yes or no,” and (b) to indicate the frequency or intensity of consequences in the last 3 months. The first 32 items represented events (e.g. accidentally injure yourself and lose your housing) and they were rated using the frequency scale: not at all = 0, once or twice = 1, a few times = 2, and many times = 3. The last 22 items represented states (e.g. have trouble remembering things, become more anxious and fearful than usual), and these were rated using the intensity scale: not at all = 0, a little = 1, somewhat = 2, or a lot = 3. 2.4.2. Addiction Severity Index (ASI) Subjective severity of substance use disorders was measured using the self-report version of the ASI (Rosen et al., 2000). Similar to the to the ASI interview (McLellan et al., 1992), the self-report survey covers seven domains: background and employment, health (medical and psychiatric), family relationships, legal situation and alcohol and drug use (Rosen et al., 2000). Evidence for the validity and reliability of the ASI has been reported for mentally-ill substance abusers (Appleby et al., 1997; Carey et al., 1997). Items used in the present study include three items available in parallel form in the alcohol, drug, and psychiatric sections, in the past 30 days, how troubled or bothered have you been by these alcohol/drug/psychiatric problems in the past 30 days? (response options range from “0 = not at all” to “4 = extremely”). In addition, the number of substance use days (including alcohol) in the past 30 days was used as a measure of recent substance involvement. 2.4.3. Behavior and Symptom Identification Scale (BASIS-32) The BASIS-32 (Eisen et al., 1986) is a 32-item patient report instrument designed to assess psychiatric symptoms and life functioning. Respondents are asked to describe the level of difficulty experienced in the last week, ranging from “0 = no difficulty” to “4 = extreme difficulty.” The BASIS-32 has sound psychometric properties (Eisen et al., 1999), and yields five subscales representing (a) relation to self and others, (b) depression and anxiety, (c) daily living and role functioning, (d) impulsive and addictive behaviors, and (e) psychosis. Alphas for the five subscales based on the data in the current study were 0.86, 0.83, 0.85, 0.80, and 0.75, respectively. 2.4.4. Medical outcomes short form 12-item health survey (SF-12) The SF-12 is a widely used measure of functional health status that has been demonstrated to be an appropriate outcome measure for changes in physical and role functioning in outpatient mental health settings. The SF-12 has good internal consistency and test–retest reliability (Ware et al., 1996), including samples of individuals with

psychiatric disorders (Salyers et al., 2000). We used a single item global rating of general health status from the SF-12: “in general, would you say your health is: 0 = excellent, 1 = very good, 2 = good, 3 = fair, and 4 = poor.” 2.4.5. Alcohol Use Disorders Identification Test (AUDIT) The AUDIT is a 10-item self-administered or interview screening questionnaire developed by the World Health Organization to identify persons whose alcohol consumption has become hazardous or harmful to their health (Saunders et al., 1993; Babor et al., 2002). Extensive research in many settings has documented the psychometric qualities of the AUDIT (Reinert and Allen, 2002). Coefficient alpha in this sample was 0.94. The AUDIT has been shown to be a sensitive and specific screen for alcohol use disorders in psychiatric samples (Dawe et al., 2000; Maisto et al., 2000). We used the AUDIT total score as an index of drinking problems in the last year. 2.4.6. Drug abuse screening test-10 (DAST-10) The DAST-10 is a 10-item screening measure that assesses problems associated with drug use during the last year (Skinner, 1982). In samples of psychiatric outpatients, the DAST-10 has been found to be both reliable and valid (Cocco and Carey, 1998; Maisto et al., 2000), coefficient alpha in this sample was 0.89. The total DAST-10 score was used as an index of drug problems in the last year. 2.5. Procedures Eligible patients were approached by one of their clinical providers (i.e. case manager or psychiatrist) who inquired about their interest in participating in a research project to understand consequences of substance use. Interested individuals were referred to research personnel for further information. Research staff reviewed procedures and obtained written informed consent. Participants then completed a collated packet of measures organized in the following order: AUDIT, DAST-10, PASUPP, ASI, BASIS-32, and SF-12. The questionnaires were completed in one or two sessions, and participants were allowed to take breaks if needed. At all times, a research assistant was in the room and accessible for assistance if needed. All participants were able to complete the questionnaires. The majority of participants did not request assistance, but when requested the types of assistance provided included: (a) clarification of directions (e.g. if question did not apply to them, such as not having kids) N = 30, (b) clarification of wording (definition of words such as “autonomy” in BASIS-32) N = 10, and (c) read questionnaire items due to participant’s impaired vision N = 5. If participants had questions about whether to consider prescribed medications in their responses, the research assistant clarified that the questions were about non-prescribed drugs or medications.

K.B. Carey et al. / Drug and Alcohol Dependence 75 (2004) 67–77

Participants were paid US$ 15 after completion of the questionnaires. 2.6. Data analytic plan First, item analyses were used to eliminate redundant or poorly performing items. Second, items were submitted to a principal (common) factor analysis, using the squared multiple correlation as estimates of the communalities. The factor structure was then rotated using a Promax rotation, allowing for an oblique (correlated) factor structure. Third, Cronbach alphas and item-total correlations were calculated to determine the internal consistency of the identified factor(s). Fourth, two total scores (lifetime and 3 months) were derived, and relationships (t-tests, chi-squares, and correlations) between PASUPP scores and other variables (i.e. demographic variables, general measures of adjustment and psychiatric status, and other measures of substance use problems) were evaluated to obtain discriminant and convergent evidence for validity. All analyses were conducted using Stata 7.0 (Stata Corporation, 2001).

3. Results 3.1. Item analyses 3.1.1. Lifetime endorsement patterns As summarized in Table 1, lifetime endorsements of PASUPP items ranged from 31% (traded sex for money or drugs) to 95% (felt bad or guilty about alcohol or drug use); the median of the item endorsements was 77%. A desirable range of item endorsements was achieved, and no items were eliminated based upon extremely infrequent or frequent endorsement (Clark and Watson, 1995). 3.1.2. Three-month endorsement patterns Item endorsements for the last 3 months (i.e. any positive, non-zero rating for the item) ranged from 24 to 78%, demonstrating adequate variability across items. As displayed in Table 1, the mean frequency ratings range from 0.4 to 1.9. No items were so infrequently or frequently endorsed as to justify deletion. Seven of the 11 items endorsed by 75% or more of the sample were derived from sources other than existing measures (i.e. focus groups, reviews of the literature). These include “get irritated and angry at people,” have difficulty concentrating or paying attention,” “spend too much time alone,” and “become more bothered by thoughts of past events.” 3.1.3. Inter-item relationships Examination of item intercorrelation within the 3-month responses was used to identify redundant items and candidates for deletion. Pearson product moment correlation ranged from 0.01 to 0.81, with a mean of 0.41 (S.D. =

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0.17). The mean falls within the recommended range of r = 0.15–0.50 (Clark and Watson, 1995). Nearly all of the smaller inter-item correlation could be accounted for by the two items with relatively low base rates (arrested for drug possession, and sex trading). Inspection of the items with the highest correlation revealed redundancy in four pairs of items, elimination of one of each pair of items resulted in the set of 50 items contained in Table 1. 3.2. Factor analysis Factor analysis using the principal factor method was performed on the 50 remaining items, using the 3-month responses. Factor loadings were computed using the squared multiple correlation as estimates of the communalities on the diagonals of the correlation matrix (Floyd and Widaman, 1995; Russell, 2002). The screen test suggested one- or two-factor solutions (eigenvalues for the first five factors extracted were 20.96, 4.31, 1.57, 1.36, and 1.02). The first factor accounted for 57% of the variance, and the second accounted for an additional 12%. The two factors were submitted to Promax rotation, allowing them to correlate as might be expected in a set of negative consequences. A clear pattern emerged in which the first factor corresponded to the first 30 items, which used frequency response options “not at all/once or twice/a few/many times,” and the second factor corresponded to the second 20 items, which shared intensity response options “not at all/a little/somewhat/a lot.” Because this two-factor solution appeared to reflect methodological rather than conceptual considerations, a single factor solution was examined. All items loaded highly on a single factor (range 0.46–0.81) with the exception of the two low base rate items (arrested for drug possession loaded 0.29 and sex trading loaded 0.32; see Table 1). We elected to retain these two items because they have great public health importance, and were endorsed frequently when the lifetime time frame was used. Thus, the single-factor solution was deemed as the most interpretable solution because (a) the first factor accounts for a majority of the variance, (b) the single-factor loadings were strong, and (c) the two-factor solution appeared to reflect a shift in response options rather than a conceptual distinction among problems. A unidimensional scale also maximizes ease of scoring and interpretation. 3.3. Internal consistency For the 3-month PASUPP, item-total (less the item) correlation ranged from 0.29 to 0.75 (see Table 1), suggesting good item discrimination (Nunnally and Bernstein, 1994). Coefficient alpha was 0.97, and alpha could not be improved by dropping any of the retained items. Combined with the moderate average inter-item correlation, this evidence provides strong evidence of internal consistency. Internal consistency of the dichotomous lifetime PASUPP responses was determined using the Kuder–Richardson formula (KR-20);

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Table 1 Factor loadings, item-rest correlations for 3-month responses, and lifetime and 3-month item endorsement patterns Item

Have arguments with a family member, spouse, or friends Get into trouble at work or school because of drinking or drug use Get into physical fights when under the influence Do something illegal to get drugs or alcohol Have withdrawal symptoms (felt sick) when you stopped drinking or using drugs Get arrested because of your behavior when drunk or high Get arrested for possession of drugs Miss appointments or fail to get to places on time Lose your job Lose your housing Spend money on alcohol or drugs that you needed for other things Get sick or vomit Have headaches Skip meals or not eat properly Become confused or disoriented Neglect responsibilities to family members, pets, or others that you take care of Pass out or have a blackout Accidentally injure yourself Cause injury to someone else Damage property or break things Feel suicidal or feel like hurting yourself Cause injury to yourself on purpose Traded sex for money or drugs Have sex with someone you wish you had not Sell your possessions to get alcohol or drugs Get robbed or attacked when drunk or high Get arrested for driving while intoxicated Get hospitalized after drinking or using drugs Stop taking your prescribed medication Have a bad reaction from mixing medication with alcohol or drugs Feel bad or guilty about your alcohol/drug use Notice a change in your personality that you did not like Have trouble remembering things Neglect your appearance Spend too much time alone Having trouble sleeping, staying asleep, or nightmares Have medical or physical problems (such as ulcers or liver problems) Have problems managing your money Have restrictions placed on your income Feel out of control Lose friends Lose contact with your children Become more depressed than usual Become more paranoid than usual Have hallucinations, such as seeing or hearing things that were not really there Feel manic, like your thoughts were racing or you were “on top of the world” Become more anxious or fearful than usual Become more bothered by thoughts of past events Get irritated and angry at people Have difficulty concentrating or paying attention

Factor loading

Item-rest r

% endorsing lifetime

% endorsing 3 months

3-month frequency rating Mean

S.D.

0.63 0.66

0.58 0.60

84 73

75 50

1.6 1.0

1.1 1.1

0.62 0.51 0.71

0.58 0.50 0.69

69 62 77

44 47 59

0.8 1.0 1.3

1.0 1.2 1.2

0.68 0.29 0.68 0.58 0.61 0.72

0.64 0.29 0.67 0.59 0.57 0.67

72 41 85 70 69 92

49 24 68 46 49 75

0.9 0.4 1.3 0.8 0.8 1.7

1.1 0.7 1.1 1.0 1.0 1.2

0.72 0.77 0.81 0.81 0.78

0.67 0.72 0.75 0.73 0.70

87 87 92 86 79

63 70 78 70 61

1.3 1.4 1.9 1.6 1.3

1.2 1.2 1.2 1.2 1.2

0.72 0.66 0.58 0.64 0.57 0.46 0.32 0.56 0.73 0.56 0.49 0.63 0.62 0.64

0.70 0.69 0.55 0.63 0.54 0.46 0.38 0.56 0.67 0.57 0.47 0.62 0.59 0.62

75 71 48 74 75 49 31 66 76 62 57 77 76 62

55 48 28 50 59 30 24 47 55 40 30 53 58 46

1.2 0.9 0.5 0.8 1.1 0.6 0.4 0.7 1.0 0.6 0.5 0.8 1.1 0.8

1.2 1.1 0.9 1.0 1.1 1.0 0.8 0.9 1.1 0.9 0.8 1.0 1.1 1.0

0.67 0.76 0.68 0.68 0.62 0.71 0.51

0.56 0.62 0.63 0.60 0.56 0.68 0.51

95 92 87 88 88 89 56

78 74 75 72 76 78 46

1.8 1.6 1.6 1.4 1.7 1.8 1.0

1.2 1.2 1.2 1.1 1.2 1.2 1.2

0.64 0.44 0.71 0.68 0.52 0.77 0.72 0.56

0.58 0.44 0.64 0.68 0.49 0.72 0.65 0.53

90 53 88 83 57 91 85 77

73 40 73 63 45 76 73 58

1.6 0.9 1.5 1.2 1.1 1.8 1.5 1.1

1.2 1.2 1.1 1.2 1.3 1.2 1.2 1.2

0.66

0.64

79

62

1.3

1.2

0.71 0.71 0.67 0.68

0.66 0.65 0.64 0.63

88 91 91 91

75 76 78 78

1.6 1.7 1.7 1.6

1.1 1.2 1.1 1.2

K.B. Carey et al. / Drug and Alcohol Dependence 75 (2004) 67–77

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20 18 16

Count

14 12 10 8 6 4 2 0 0

5

10

15

20

25

30

35

40

45

50

Lifetime PASUPP Score Fig. 1. Distribution and frequency counts of PASUPP lifetime scores (range 5–50).

the KR-20 was 0.92, also indicative of strong internal consistency.

were correlated with three sets of variables: demographic, psychiatric status, and substance use problems. Table 2 summarizes the validity evidence. Note that none of the correlation between validation measures exceeded 0.48.

3.4. Validity analyses

3.4.2. Discriminant evidence Summary scores did not vary significantly by age, gender, educational level, or race/ethnicity, all P > 0.10.

3.4.1. Summary scores Figs. 1 and 2 illustrate the distributions of the two-summary scores. On the lifetime score, the sample endorsed a mean of 38 (S.D. = 9.2) out of the 50 problems (median = 40), no one in the sample endorsed fewer than five lifetime problems. The lifetime score was slightly negatively skewed (skew = −0.91). For the 3-month time-frame, the sample received a mean score of 57.6 (S.D. = 34.4), and a median of 60. Scores ranged from 0 to 139 (out of a possible 150), with relatively little skew (skew = 0.01). The two summary scores were correlated r = 0.53 (P < 0.001), indicating a significant but non-redundant relationship (i.e. approximately 25% of variance in recent consequences is associated with lifetime consequences). Evidence for the validity of the PASUPP scores can be derived from their relationships with other measures of psychological functioning. Both the lifetime and 3-month scores

3.4.3. Convergent evidence Two sets of variables were expected to correlate positively with PASUPP scores. First, modest associations were expected with measures of current functioning and psychiatric status, because they are negatively affected by substance use, and because the response windows on questions varied from the past 3 months (PASUPP) to the past month (ASI) to the past week (BASIS-32) and unspecified (SF-12). As expected, four of the subscale scores derived from the BASIS-32 and the rating of general health status from the SF-12 were moderately correlated with the PASUPP 3-month scores (0.15 ≤ r ≤ 0.32). Most correlated significantly with PASUPP lifetime scores as well (0.08 ≤

16 14

Count

12 10 8 6 4 2 0 0

10

20

30

40

50

60

70

80

90

100 110 120 130

Three-Month PASUPP Score Fig. 2. Distribution and frequency counts of PASUPP 3-month scores (range 0–139).

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Table 2 Associations between PASUPP lifetime and 3-month scores and relevant constructs Construct and predicted relationship

Demographics: no relationship Gender, t (206) Race/ethnicity, F (4, 207) Age (years) Education (years)

PASUPP scores Lifetime

3-month

1.09 0.37 0.02 −0.08

0.68 0.59 0.01 −0.04

Adjustment/psychiatric status: modest relationship SF-12 general health status (range 0–4)a BASIS-32 relation to self and othersa BASIS-32 depression and anxietya BASIS-32 daily living and role functioninga BASIS-32 psychosisa ASI distress due to psychiatric problems (range 0–4)a

0.20∗∗ 0.35∗∗∗ 0.28∗∗∗ 0.36∗∗∗ 0.08 0.14∗

0.15∗ 0.32∗∗∗ 0.30∗∗∗ 0.32∗∗∗ 0.25∗∗∗ 0.14∗

Substance use: moderate relationship ASI distress due to alcohol problems (range 0–4)a ASI distress due to drug problems (range 0–4)a ASI substance use days, in last 30 BASIS-32 impulsive and addictive behaviorsa DAST total score (range 0–10) AUDIT total score (range 0–40)

0.14∗ 0.27∗∗∗ 0.13 0.25∗∗∗ 0.51∗∗∗ 0.32∗∗∗

0.31∗∗∗ 0.39∗∗∗ 0.28∗∗∗ 0.46∗∗∗ 0.49∗∗∗ 0.36∗∗∗

Note: Values represent Pearson product-moment correlations, except when indicated otherwise. PASUPP is—Problems Assessment for Substance Using Psychiatric Patients, SF-12—Medical Outcomes 12-item Health Survey, BASIS-32—Behavior and Symptom Identification Scale, ASI—Addiction Severity Index, DAST-10—Drug Abuse Screening Test, and AUDIT—Alcohol Use Disorder Identification Test. a Higher ratings reflect greater problems. ∗ P < 0.05. ∗∗ P < 0.01. ∗∗∗ P < 0.001.

r ≤ 0.36). Subjective distress due to psychiatric problems in the last month (from the ASI) was weakly correlated (r = 0.14) with both 3-month and lifetime scores. The set of other measures of substance use problems did, as expected, show a stronger pattern of correlation with 3-month PASUPP scores (0.28 ≤ r ≤ 0.49). The 3-month score was significantly correlated with ASI-derived ratings for (a) distress due to alcohol problems in the last month (r = 0.31), (b) distress due to drug problems (r = 0.39), and (c) number of substance use days in the last month (r = 0.28). Also significantly correlated with PASUPP 3-month score, as was (d) the BASIS-32 impulsive and addictive behaviors subscale (r = 0.46). Finally, both (e) the AUDIT (r = 0.36) and (f) the DAST-10 scores (r = 0.49) exhibited strong relationships with 3-month PASUPP scores. As expected, a similar but attenuated pattern of associations was found for lifetime PASUPP scores.

4. Discussion The primary purpose of this study was to develop a population-sensitive and psychometrically sound measure of the range of negative consequences due to substance use experienced by persons with co-occurring psychiatric and substance use disorders. Following a formative process of scale development, we generated an initial pool of

items, which was then reduced based on item analyses. The resulting 50-item PASUPP was a unidimensional scale with strong internal consistency. Interestingly, seven of the 11 most frequently endorsed items were derived from population-specific sources (literature searches and focus groups) rather from existing measures. This finding supports the value of a measure that assesses the unique ways in which substance use affects the lives of patients with concurrent mental disorders. Correlational analyses provide both convergent and preliminary discriminant evidence for the validity of the PASUPP. The 3-month scores were consistently and significantly associated with all other measures of alcohol and drug problems. The obtained correlations (ranging from 0.28 to 0.49) correspond to medium-to-large effect sizes (Cohen, 1988). Moderately strong associations among these measures were anticipated because the PASUPP comprises a more comprehensive set of substance-related problems when compared to the comparison measures. The strengths of these correlation suggest that the problems reported in this sample tended to be chronic, because the time frame for the comparison measures differed from that of the PASUPP (for example, the AUDIT and DAST-10 assess alcohol and drug problems over the past year, and the ASI items cover only the past 30 days). The lifetime PASUPP score was strongly related to the AUDIT and DAST-10 totals, and to a lesser extent to the

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ASI items. These relationships were expected in light of the fact that lifetime accumulations of problems may not reflect recent behaviors. Epidemiological data confirm that lifetime prevalence of substance use disorders regularly exceeds 30-day point prevalence estimates (Kessler et al., 1994). Overall, the pattern of associations among multiple measures of substance use problems provides convergent support for the validity of the PASUPP. The relationships among the PASUPP scores and measures of adjustment and psychiatric status are not surprising given the tendency of substance use to affect many areas of functioning of psychiatric patients (Drake and Brunette, 1998). Concurrent substance use can exacerbate psychiatric symptoms (Carey et al., 1991; Cuffel and Chase, 1994), and lead to relapse and re-hospitalization (Swofford et al., 1996). Similarly, comorbid substance use is associated with lower quality of life ratings among psychiatric patients (Russo et al., 1997), whereas reductions in alcohol and drug abuse over time predict improvements in quality of life (Russo et al., 1997; Schaar and Ojehagen, 2003). Consistent with this literature, low-to-moderate correlation were found between PASUPP scores and recent psychiatric distress, mood and psychotic symptoms, adaptive role functioning, and ratings of general health status. These relationships confirm a modest covariation among these domains, corresponding to small to medium effect sizes according to Cohen (1988). Although, consistent with a generalized impact of substance use problems on psychiatric status and quality of life, directional inferences cannot be supported with these data. Overall, demographic variables were not related to PASUPP lifetime or 3-month scores (i.e. gender, race/ethnicity, age, and education). This finding allays concern about a systematic bias due to demographic characteristics, but generalizability to other samples remains to be evaluated. Furthermore, stronger demonstrations of discriminant validity are needed. In this study, participants provided responses for two separate timeframes: lifetime and last 3 months. The PASUPP can be used with either (or both) response window(s). As expected, the range of problems endorsed for lifetime exceeded that of problems experienced in the last 3 months. Both timeframes may be useful in initial evaluations of patients in order to supplement diagnostic assessments. Knowledge of recent psycho-social problems can help treatment planning, establish rehabilitation needs, and identify targets for intervention. In addition, either time-frame may be useful in the context of personalized feedback (Carey, 2002). Motivational interventions often include feedback on substance use patterns and problems as a way of raising awareness of the impact of substance use on a patient’s life (Carey et al., 2002; Miller et al., 1992). Feedback on self-reported problems can help to initiate a discussion of the potential costs of substance use. The 3-month time-frame may be particularly useful for repeated assessments, where it is necessary to evaluate changes in substance-related

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problems over time. Because the PASUPP includes consequences that are not contained in other problems measures, it may be a more sensitive measure of change for patients with co-occurring disorders. Furthermore, inclusion of problems measures in outcome evaluations is consistent with either abstinence-focused or harm reduction intervention approaches (Carey, 1996; Marlatt and Roberts, 1998). The limitations of the current research deserve mention. First, diagnoses were determined by chart review and not by structured research interviews. However, the generally low functional status of the sample corroborates the presence of Axis I disorders. Second, response patterns may vary among subgroups of patients with different comorbid disorders. That is, problems endorsed by persons with cocaine dependence and schizophrenia could differ systematically from those endorsed by persons with alcohol dependence and bipolar disorder. Unfortunately, the recruitment process used in this study does not allow for such subgroup comparisons. Third, women were underrepresented in our sample. It is possible that the frequency of endorsement of certain items might differ in samples with greater proportions of women, a possibility that warrants investigation. Fourth, the majority of participants were veterans, which might limit the generalizability of the findings. Taking the last three points together, it is important to conduct further psychometric studies of the PASUPP in more heterogeneous samples and across culturally diverse settings. A fifth limitation pertains to the use of the DAST-10 as both one of the sources of PASUPP items and also as a validation measure. Although only three out of the 50 items on the PASUPP came uniquely from the DAST-10, this overlap may have inflated the association between the two. Finally, although internal consistency reliability was high for both PASUPP scores, retest stability was not assessed. The PASUPP is the first instrument designed specifically to assess the negative consequences of substance use experienced by persons with comorbid mental illness. This instrument may facilitate needed outcome evaluations for treatment programs designed for this population (Drake et al., 1998). The encouraging reliability and validity evidence produced by this study justifies further research on the psychometric properties and the utility of the PASUPP. In particular, three directions for future research can be identified. First, cross-validation of the factor analytic solution in an independent sample would enhance confidence in the factor structure. Second, it would be informative to compare the results of the PASUPP to other established problems measures in order to evaluate our expectation that the PASUPP may be a more sensitive tool for substance users with comorbid psychiatric disorders. Third, it may be useful to explore the effect of alternate administration modalities (e.g. computer-assisted interview, face-to-face interview) in comparison to the self-administered format on patterns of response. In sum, the availability of the PASUPP provides treatment providers and researchers working with these patients with a population-specific measure

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of negative psycho-social consequences due to substance use.

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