1

Integrating Psychological Assessment with Pharmacotherapy:A New Direction for Psychologists

Robert E. McGrath, Ph.D.

Fairleigh Dickinson University

2

Topics

• Enhancing Adherence

• Specificity in Problem Identification

• The Structure of Clinical States

3

Enhancing Adherence

• Medication adherence about 50% (Haynes et al., 2002)

• Research on improving adherence involves multidimensional interventions (McDonald et al., 2002)

• 25% of non-adherent patients (never got Rx) reported they were adherent (Kobak et al., 2002)

4

Lack of Effectiveness?

• Antidepressant effectiveness questionable (Kirsch et al., 2002, Prevention & Treatment)

• Overprescribing for mental disorders– PCPs: approx. 100% (National Depressive and Manic

Depressive Association, 2000)– Psychiatrists: approx. 90% (Pincus et al., 1999)– Psychologists: 15%? (John L. Sexton, personal communication,

August 4, 2000; Wiggins & Cummings, 1998)

• Still likely many people do not adhere for whom medication would be effective (anxiety, psychosis)

5

Predicting Non-Adherence: Personality Approach

• Do personality factors predict adherence?– NEO-PI (Costa & McCrae, 1992): Neuroticism,

Extraversion, Openness to Experience, Conscientiousness, Agreeableness

– Predicts adherence to psychotherapy (Miller, 1991; Muten, 1991), weight loss (Galluccio-Richardson et al., 2003), and kidney medication (Christensen & Smith, 1995):

– Small but significant effect for Conscientiousness and Rx regimen

6

Predicting Adherence: Social Approach

• Do social factors predict adherence? – Theory of Planned Behavior (Ajzen, 1988):

7

Predicting Adherence: Attitudinal Approach

• Predicts adherence to drug abuse treatment (Kleinman et al., 2002), weight loss (Mancini et al., 2002), and psychiatric medications (Conner et al., 1998):

• Three attitudinal factors accounted for 65% of variability in intention to adhere to meds; two factors accounted for 38% of variability in behavior

8

Factors Affecting Rx Adherence

• Ineffectiveness/preference for another medication• Personality factors: responsibility/conscientiousness,

resistance to authority• Attitudinal factors• Cost/reimbursement• Anxiety about side effects• Side effects• Inadequate understanding: latency, duration• Chaotic life circumstances

9

Therapeutic Assessment

• Developed as a model for collaborative assessment consultation (Finn, 1996).

• RCT found TA reduced general distress (d = .80), and improved self-esteem (1.04) and hopefulness (.84) when compared to attention placebo (Finn & Tonsager, 1992)

10

Initial Interview

• Build rapport– Introduce information-gathering and decision-making as a

collaboration– Listen attentively

• Frame questions collaboratively– Include whether medication is appropriate– Explicitly encourage questions about medications

• Collect background information– Begin with information relevant to questions– Ask permission for additional questioning and explain why you

need it– Explore issues likely to impeded adherence

• Ask about resistance/incomplete participation

11

Initial Interview• Ask about past medication experiences

– Show genuine interest– Empathize with previous experiences/hurts– State shortcomings of previous experiences– Offer contract that addresses previous hurts– Ask to be alerted if patient feels mistreated

• Offer tentative answers– Invite modification– Invite questions

• Encourage future questioning• Complete the prescription• Initiate treatment

– Monitoring– Contract about contact

12

Conclusions

• Prescriptions are a medical issue; prescribing is an interpersonal one

• Psychologists’ use of assessment can potentially improve adherence (and therefore, it is hoped, outcomes)

• Psychologists’ understanding of humanistic and interpersonal principles can potentially improve adherence and outcomes

13

Specificity in Problem Identification

• Actuarial versus clinical prediction and description– Meehl (1954, 1956)– Superiority of actuarial methods (Grove et al.,

2000)

• Cognitive errors (Arkes, 1981)– Covariance misestimation– Hindsight bias

14

Restructured Clinical Scales

• Affect research suggests that the discrimination of clinical states is muddied by the common Demoralization factor (Tellegen, 1985)

• RCSs consist of a measure of Demoralization, and scale-relevant items that are relatively independent of demoralization

15

16

17

Conclusions

• Assessment can improve the accuracy of diagnosis and therefore treatment

• Increasing specificity in assessment instruments can enhance decision-making

• Functional components of clinical state may be more useful than diagnosis

18

The Structure of Clinical States

• DSM assumes a categorical (biological) model– Comorbidity– NOS and mixed categories– Subclinical categories

• Assessors often assume dimensionality based on psychometric considerations

• Neither is universally correct

19

Taxometric Analysis

• Developed by Meehl and associates (Meehl & Yonce, 1994; Waller & Meehl, 1998)

• Identified several patterns that would emerge in relationships between measures only if their shared latent construct is categorical

20

MAXCOV (Maximum Covariance)

• Three measures of latent variable η

• Sample divided into sequential subsets on X

• Covariance of Y and Z computed within each subset

• A graph of covariances should make an inverted U only if η is categorical

21

22

Findings for Diagnosis

• Schizoid spectrum disorders seem categorical (Blanchard, Gangestad, Brown, & Horan, 2000; Erlenmeyer-Kimling, Golden, & Cornblatt, 1989)

• Melancholia appears categorical (Ambrosini, Bennett, Cleland, & Haslam, 2002; Haslam & Beck, 1994)

• Unipolar, non-melancholic depression consistently dimensional (Franklin, Strong, & Greene, 2002; Ruscio & Ruscio, 2000, 2002)

23

Implications

• Categorical status implies tight etiological net (biological?), dimensional a looser etiology (multidetermined?)

• Dimensional disorders unlikely to respond well to any one treatment

24

Discussion

• Prescribing is a complex interpersonal act• Case formulation and analysis of

treatment outcomes may be enhanced by specificity in characterization of clinical states

• A greater understanding of clinical states may overcome biological assumptions suggesting unimodal treatments

• Opportunities for scientist-practitioners