Initial Assessment

In many clinical settings, an initial assessment is performed to obtain useful information in the development of a specific treatment plan. Information from an initial assessment is useful not only in understanding the patient but also in identifying the problems and issues that need to be addressed in treatment, and formulating recommendations about the most effective way to address these problems.

Several types of assessment methods can be used to conduct an initial assessment of a patient presenting with depression. A comprehensive review of available measures is beyond the scope of this chapter, and the reader is referred to Nezu, Ronan, Meadows, and McClure (2000) for a review of assessment measures of depression. We would like to draw attention to three areas that we believe are particularly important to assess in formulating a treatment plan. First, it is important to assess for co-occurring conditions that may impact treatment planning. For example, it is important to assess for co-occurring Axis I disorders; it has been reported that nearly three-fourths of people with lifetime depression and nearly two-thirds of people with 12-month major depression also meet criteria for at least one other Axis I disorder during their lifetime or in the past year, respectively (Kessler et al., 2003). Clinicians can comprehensively assess Axis I disorders using a diagnostic measure such as the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I), a semistructured interview that includes questions relating to specific symptoms that determine differential diagnoses. There are several versions of the SCID-I, including a shorter Clinician Version (SCID-CV; First, Spitzer, Gibbon, & Williams, 1996) and a longer Research Version (SCID-I-RV), which in turn has several versions. The SCID-CV covers the Axis I diagnoses most commonly seen in clinical practice, and excludes the subtypes and specifiers found in the longer SCID-I-RV. It is designed to be completed in a single sitting, and its administration takes 45-90 minutes, depending on the skill and experience of the clinician, the complexity of the psychiatric history, and the ability of patients to describe their psychopathology succinctly. In situations in which the clinician does not have time to administer the full SCID-I, specific modules may be administered to assess for the occurrence of specific disorders. The importance of structured interviews in the assessment of Axis I disorders come from research comparing comorbidity rates, as determined by unstructured clinical interviews and structured research diagnostic interviews, which suggests that comorbidity is underdetected in unstructured interviews routinely used in clinical practice (Zimmerman & Mattia, 1999).

Structured interviews such as the SCID-I provide important information for not only for identifying the presence of other Axis I disorders but also determining age of onset, and the history and number of prior episodes of depression and other conditions. Such information is necessary to determine whether the patient has a chronic and/or recurrent depression, and whether a comorbid condition emerged before, simultaneous with, or after the onset of depression. In addition to Axis I problems, the SCID-I includes important questions about the patient's physical health and social functioning, because depression has been found to covary with medical conditions (Stevens, Merikangas, & Merikangas, 1995), and impaired interpersonal and social relationships (Hirschfeld et al., 2000). If the patient reports health problems or impaired social functioning, then the clinician may want to conduct a more formal assessment that includes severity of impairment, as described below.

Whereas the SCID-I is useful in assessing Axis I disorders and alerting clinicians about the presence of comorbid medical conditions and social problems, it does not assess for the presence of Axis II disorders. The importance of assessment of Axis II conditions comes from findings that approximately 50-85% of depressed inpatients and 20-50% of depressed outpatients have personality disorders (Corruble, Ginestet, & Guelfi, 1996). There are many interview and self-report instruments for Axis II disorders, and the interested reader is referred to Widiger and Samuel (2005) for an overview of these instruments. The use of self-report measures to alert clinicians to the potential presence of Axis II disorders may be particularly useful in clinical settings, because completion of such measures requires minimal clinician time; semistructured interviews may then be used to verify the presence of Axis II disorders.

A second area of assessment is the patient's degree of functional impairment and subjective distress. Information about degree of severity may be important in generating hypotheses regarding prognosis, intensity, and length of treatment. Symptom measures, also useful for monitoring outcome, are measured from session to session, as well as from pre- to posttreatment. The many measures for assessing depression include, for example, the widely used Beck Depression Inventory—Second Edition (BDI-II; Beck, Steer, & Brown, 1996). In interpreting the BDI-II, a score < 13 indicates minimal depression; 14-19, mild; 20-28, moderate; and > 29 indicates severe depression. In addition to measuring depression, therapists may periodically want to assess symptom severity of co-occurring disorders for which the patient meets criteria, as well as to identify significant elevations in symptoms of disorders for which the patient does not meet diagnostic criteria (i.e., subthreshhold elevations). In addition, for the patients who report physical health problems or impaired social functioning, self-report data may be used to evaluate severity of impairment, as well as the impact of treatment. Although a review of applicable measures is beyond the scope of this chapter, the interested reader is referred to Maruish (1999) for a description of measures for treatment planning and outcome assessment.

A third area of assessment concerns the assessment of theory-specific mechanisms of change. As applied to CT for depression, this involves assessment ofcognitions. For example, a therapist may wish to assess the degree to which a patient endorses dysfunctional attitudes or automatic thoughts, or the extent to which a patient makes cognitive errors. Unlike the situation with symptom-based measures, relatively few guidelines for interpreting scores on cognitive measures of psychopathology may have resulted in clinicians being less likely to use them in clinical practice. However, Dozois, Covin, and Brinker (2003) have provided normative data on six self-report indices of depression-related cognitions, including measures of automatic thoughts, cognitive errors, and dysfunctional attitudes. The means and standard deviations for the commonly used cognitive measures obtained from this article are presented in Table 2.1.

TABLE 2.1. Normative Data on Cognitive and Personality Vulnerabilities for Depression

Measure

Mean

SD

Automatic Thoughts Questionnaire—

-Positive4

99

13

Automatic Thoughts Questionnaire—

-Negative4

53

18

Adolescents

64

21

Adults

53

18

Older persons

42

14

Women

54

19

Men

48

16

Beck Hopelessness Scale4

3

3

Cognitive Bias Questionnaire4

2

2

Cognitive Error Questionnaire4

17

12

Dysfunctional Attitude Scale4

119

27

Adolescents

135

32

Adults

115

27

Older persons

117

24

Revised Sociotropy—Autonomy Scale

Sociotropy scale

64

16

Solitude scale

26

6

Independence scale

45

8

Revised Personal Style Inventory

Sociotropy scale

95

16

Autonomy scale

84

14

4Data reported in Dozois, Covin, and Brinker (2003).

4Data reported in Dozois, Covin, and Brinker (2003).

As discussed by Dozois et al. (2003), normative data on depressive cognitions can be clinically useful in at least three ways. First, normative data can help to determine the extent to which a patient's score on a given cognitive measure falls outside the normal range of that measure in severity or frequency; that is, normative data on these cognitive measures can be used to create standardized scores, the most common of which are T-scores. The following equation can used in computing a T-score from a patient's raw score (X), the mean of the normative data (M), and the standard deviation of the normative data (SD):

For example, if an adult patient had a raw score of 168 on the Dysfunctional Attitude Scale (DAS), substituting this score and the normative data provided in Table 2.1 in the foregoing formula would produce a T-score of 70:

T = 50 + 10[(168 - 115)/27] = 50 + 10(2) = 50 + 20 = 70

T-scores have a mean of 50 and a standard deviation of 10. Thus, a T-score of 70 means that a score is 2.0 standard deviations above the mean. Therefore, whereas a raw score of 168 on the DAS means virtually nothing by itself, knowing that it corresponds to a T-score of 70 is informative insofar as it tells the clinician that the patient's score on this scale is 2.0 standard deviations above the mean of the normative data. In interpreting T-scores, values between 40 and 60 (i.e., ±1 standard deviation from the mean) are generally considered to fall within the normal range, whereas scores > 60 can be considered clinically elevated. In particular, T-scores above 65 or 70 are considerably elevated, because they correspond to 1.5 and 2.0 standard deviations above the mean, respectively.

As discussed by Dozois et al. (2003), a second use of normative data (as translated into T-scores) is in providing directions for intervention. For example, a clinician can compare a patient's T-scores on two measures to determine the patient's relative elevations across different cognitive variables. Such comparisons can usefully determine which specific types of depression-related cognitions are most elevated. For example, this information may be used to help the therapist determine whether it may be most useful to target reducing negative automatic thoughts or increasing positive automatic thoughts. Comparing T-scores across measures is based on the assumption that such scores have similar meaning from one measure to the next, which is valid, however, only if the scales involve similar distributions. Future research is needed to evaluate this assumption with respect to cognitive assessment measures.

As discussed by Dozois et al. (2003), a third use of normative data (as translated into T-scores) is to assist clinicians in deciding when to terminate treatment. For example, based on the perspective that cognitive change is important in preventing relapse and recurrence, a clinician might decide to continue treatment until a patient's scores on the DAS fall within the normative range (e.g., within 1 standard deviation from the normative mean).

Before leaving the topic of clinical applications of cognitive assessment, it should be noted that the impact of using such measures to guide treatment planning has not been tested empirically; that is, the recommendations to use these data in selecting cognitive domains to target for treatment, or to continue treatment until a certain level of end-state functioning is reached, are theoretical positions that need to be evaluated in future research.

Once the cognitive therapist has collected assessment information, he or she is ready to organize this information in developing a conceptualization of the patient. We turn now to an overview of case conceptualization in CT of depression.

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