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Predictors of nonresponse to treatment in primary care patients with dysthymia
General Hospital Psychiatry 24 (2002) 20 –27
Predictors of nonresponse to treatment in primary care patients with dysthymia Wayne Katon, M.D.a,*, Joan Russo, Ph.D.a Ellen Frank, Ph.D.b, James Barrett, M.D.c, John W. Williams, Jr., M.D., MHSd, Thomas Oxman, M.D.e, Mark Sullivan, M.D., Ph.D.a, John Cornell, Ph.D.d,f a
Department of Psychiatry and Behavioral Sciences, University of Washington Medical School, Seattle, WA, USA Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh, Pittsburgh, PA, USA c Department of Community and Family Medicine, Dartmouth Medical School, Hanover, NH, USA d The South Texas Veterans Health Care System, Audie Murphy Division and the Division of General Internal Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA e Department of Psychiatry, Dartmouth Medical School, Hanover, NH, USA f Division of Geriatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA b
Abstract Dysthymia is one of the most prevalent problems in primary care, especially in the elderly. In this study, we evaluated the demographic and clinical predictors of nonresponse to treatment in primary care patients with dysthymia. The study sample consisted of 338 primary care patients meeting DSMIII-R criteria for dysthymia from 4 diverse geographic sites in a randomized controlled 11-week trial of paroxetine, problem-solving therapy or placebo. Patients who attended at least 4 treatment sessions were used in the analysis. A score of less than 7 on the Hamilton was defined as a positive response to treatment. By Week 11, 52.2% of patients had a positive response to treatment. Patients with lower levels of education (odds ratio 0.44, 95% CI 0.23, 0.86), higher scores on the personality dimension of neuroticism (odds ratio 0.58, 95% CI 0.36, 0.92) and those with more severe medical illness (odds ratio 0.97, 95% CI 0.95, 0.99) were less likely to recover with either active or placebo treatments. Elderly women (⬎60 years of age; odds ratio 0.19, 95% CI 0.05, 0.66) were also less likely to respond to all treatments; however, females had a significantly higher response to placebo treatment compared to males. The factors associated with lack of response to treatment included lower-levels of education, high neuroticism, more severe medical illness and being an older female. This analysis is based on patients agreeing to participate in a randomized controlled trial, limiting representativeness of the sample, however, the demographic and clinical characteristics are common in elderly depressed primary care patients, and may signal the need for increased mental health specialty consultation. © 2002 Elsevier Science Inc. All rights reserved. Keywords: Dysthymia; Depression; Paroxetine
1. Introduction Dysthymia occurs in approximately 3 to 5% of primary care patients [1] and is associated with increased health care utilization and health care costs [2–3] impaired functioning [4] and multiple unexplained physical symptoms such as headaches and fatigue [5]. Most nonelderly patients with dysthymia have experienced major depressive episodes [6] and when they co-occur, there is additive disability [7] and a high rate of relapse after recovery from the major depres-
* Corresponding author. Tel.: ⫹1-206-543-7177; fax: ⫹1-206-221-5414. E-mail address: [email protected] (W.J. Katon).
sive episode [6,7]. Data from the Medical Outcome Study (MOS) showed that dysthymia was associated with poor symptomatic and functional outcomes, even in the absence of major depression [6]. Although many intervention studies have examined predictors of nonresponse among patients with major depression, few studies have examined variables associated with nonresponse to treatment in patients with chronic depression. Moreover, the limited research on predictors of treatment response in chronic depressives has been largely completed in tertiary care specialty populations. In the largest tertiary-based study, Hirschfeld and colleagues examined predictors of response in 623 patients enrolled in the acute phase of the Chronic Major Depression
0163-8343/02/$ – see front matter © 2002 Elsevier Science Inc. All rights reserved. PII: P I S 0 1 6 3 - 8 3 4 3 ( 0 1 ) 0 0 1 7 1 - 2
W.J. Katon et al. / General Hospital Psychiatry 24 (2002) 20 –27
and Double-Depression Study, who were randomized to 12 weeks of imipramine or sertraline [8]. A higher baseline quality of life, living with spouse or partner, having more education and two personality variables (passive aggressive personality and lower levels of tenseness and introversion) predicted positive treatment response. The authors of this study acknowledged that a significant limitation of this research was the lack of placebo-control group that “would have permitted more global correlates of favorable outcome to be disaggregated from more specific predictors of response to active pharmacotherapy.” A second double-blind placebo-controlled study of imipramine in chronically depressed psychiatric patients found that “neurotic” characteristics in the Cattell 16-Personality Factor Scale were associated with poor outcome [9]. We have recently reported the results of a large primary care based randomized controlled trial of paroxetine, problem solving therapy (PST-PC) adapted for primary care and placebo in both a younger age (18 to 59) and older age (60 and over) sample of patients with minor depression and dysthymia [10,11]. This trial allowed us to study predictors of nonresponse to active treatments and placebo in a sample of patients with dysthymia more generalizable to primary care populations. An accompanying paper in this edition of General Hospital Psychiatry studied predictors of response in patients with minor depression.
2. Methods 2.1. Subjects The details of the study design have been described elsewhere [11]. In summary, primary care patients aged 18 to 90 were recruited for a comparative treatment trial from patients currently enrolled in primary care practices (family medicine or general internal medicine) in four communities (Lebanon, NH, Pittsburgh, PA, San Antonio, TX, and Seattle, WA, USA). The Seattle and Lebanon sites enrolled both patients in a younger age cohort (18 –59) and an older age cohort (60 –90), while San Antonio and Pittsburgh only enrolled the older age cohort. In order to increase elderly recruitment, three sites included Veterans Administration primary care clinics. To be included a subject needed to have three or four of the DSM-IV symptoms of depression on the Prime-MD major depression module [12], one of which was depressed mood or anhedonia as assessed by clinical interview, and to have a 17-item Hamilton Rating Scale for Depression score of ten or greater [13]. Only the patients with dysthymia were included in this paper and dysthymics were required to have experienced symptoms for at least two years. Patients were excluded if, within the past six months, they had major depression, active substance abuse, uncomplicated bereavement, parasuicidal behavior, or antisocial personality. Patients currently taking psychotropic drugs, seeing a psychotherapist or with cog-
21
nitive impairment (Mini Mental State Exam ⱕ 23) [14 ]or a terminal illness (less than six months to live) were also excluded. 2.2. Procedures Each site used a variety of methods to educate participating primary care providers about referral. Brief depression screening instruments were also used at some sites to further identify potential subjects. Subjects thus were identified in a primary care practice setting as potentially having either dysthymia or minor depression and were referred for a research evaluation. A two-phase evaluation took place within one week of identification. The initial phase was a semistructured clinical interview to determine eligibility [12]. For patients meeting criteria, a complete description of the study was provided, and written informed consent approved by the local institutional review board was obtained. Those who agreed to participate were then administered additional baseline measures and randomized to one of the three treatment arms. Subjects in all three arms were offered six subsequent treatment visits at 1, 2, 4, 6, 8 and 10 (for PST-PC) [15–16] or 11 (for paroxetine and placebo) weeks. 2.3. Measures The semistructured interview included mood, anxiety and alcohol modules from the Prime-MD [12], the 17-item Hamilton Rating Scale for Depression [13] and the substance abuse, and psychosis modules of the Structured Clinical Interview for Diagnosis (SCID) [17]. To assess severity of depression, we used the interview-based 17-item Hamilton Rating Scale for Depression (HAM-D) [13]. The HAM-D was administered at baseline, 6 and 11 weeks by an independent rater who was blind to intervention status. Scores ranged from 10 to 25 (mean ⫽ 13.73 ⫾ 2.92) for the 17-item version used in this study. The HAM-D has been shown to have high reliability and validity in measuring depression.) [13]. A 57-item Hopkins Symptom Checklist self-report measure, consisting of the items that make up five scales (depression, anxiety, interpersonal sensitivity, hostility and somatization) was utilized [18]. The 20-item depression scale has been found to be a sensitive indicator of change in primary care samples [19]. Scores on this 20-item scale ranged from 0.15 to 3.45 (mean ⫽ 1.5 ⫾ 0.68). The Duke Severity of Illness Index (DUSOI) provided a quantification of the amount of medical comorbidity present in each patient [20]. Scores were based on medical information abstracted from medical records by a physician. The NEO Neuroticism Scale consisted of 12 self-report items to assess neuroticism as a personality trait [21]. In previous primary care studies, it was found to predict persistence of depressive symptoms [22].
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2.4. Statistical analyses
Table 1 Sample description (n ⫽ 282)
Recovery was defined as having a Hamilton Depression Rating Scale score of 6 or less at 11 weeks. An adequacy of treatment analysis was used. Subjects who attended at least 4 treatment sessions and had received a Hamilton depression score at 6 or 11 weeks by an independent rater were used in analyses. If the 11-week Hamilton score was missing, the 6-week score was carried forward. Hamilton Depression Scales were completed at 6 and 11 weeks by an independent rater blind to group status. 2 analyses with corrections for continuity and t-tests were used to compare patients who were and were not included in the statistical analyses. Univariate tests were performed examining the relationship between the potential predictor variables and recovery status. 2 analyses with corrections for continuity were used for the discrete predictors of gender, age cohort, site, treatment group, ethnicity (minority vs. majority), marital status (married vs. not married), employment status (full time or part-time, retired-homemaker, unemployed or disabled), income (⬍ 20 K, ⬎ 20 K), veteran (patient of the Veteran’s Administration Hospital) status and percent with generalized anxiety disorder or a history of major depression. T-tests were used for the Duke Medical Illness Severity Scale, NEO neuroticism scale, and the baseline SCL 20item depression scale. Potential predictor variables with univariate relationships to recovery status of P⫽.10 or less were included in the logistic regression model as potential predictors of recovery. A logistic regression modeling strategy was used to determine the best predictors of recovery. Due to the design of the study, 14 design terms were entered into the model regardless of statistical significance: site (dummy coded into 3 variables with Seattle as the reference group), age cohort (dummy coded), treatment group (2 dummy coded variables with placebo as the reference group), site by treatment interaction (6 terms), and the treatment by age interaction (2 terms). Although gender was not a design variable, it was entered into the model due to its relationship to the age cohort variable. Preliminary examination of that data revealed a strong relationship between gender and age cohort (2⫽13.43, P⬍.001). In the older cohort (60 years and over), 62% were men and 38% were women, compared to the younger sample (18 to 59 years) that was 60% women and 40% men. Therefore the age cohort by gender interaction was also entered into the model to control for these imbalances. Next the potential predictor variables were entered into the model. Interactions of the potential predictor variables and treatment were tested individually. Significant interaction terms were retained and the model was refit. The final model contained the 14 design variables, significant interaction terms, significant main effect predictors and predictors necessary for the interactions.
Characteristic
N
%
Treatment: Paroxetine PST Placebo Site: Lebanon Pittsburgh San Antonio Seattle Age Cohort: 18–59–Younger 60⫹–Older Gender: Female Ethnicity: Caucasian Marital Status: Married Employment: full or part-time retired or homemaker Education: At least 1 year of college Income: ⬎ $20,000 Veteran: Yes Comorbid Panic Disorder Comorbid GAD History of Major Depression
87 99 96 80 37 69 96 105 177 132 229 156 107 135 162 132 91 20 70 106 Mean 61.4 2.12 20.9 1.5
30.9 35.1 34.0 28.4 13.1 24.5 34.0 37.2 62.8 46.8 81.2 55.3 38.2 48.2 57.4 47.3 33.0 7.2 28.0 37.6 S.D. 15.1 0.65 13.5 0.68
Age Neuroticism Scale–NEO Duke Severity of Medical Illness Baseline SCL-D20 Depression Scale
3. Results The study sample consisted of 338 dysthymic patients. Fifty-six patients (16.6%) were not used in the statistical analyses: 25 patients had no treatment sessions, 23 patients had 1 to 3 treatment sessions and 8 patients had 4 or more treatment sessions, but were missing both Week 6 and Week 11 Hamilton Depression Ratings. Patients who were included in the analyses (n⫽282) were compared to those not included (n⫽56) on all study design variables (site, treatment status, age cohort), demographic variables (age, gender, employment, education level, ethnicity, marital status, veteran status), and clinical variables (neuroticism and baseline depression level). There were no statistically significant differences between patients who were used for analyses and those who were not used on any study variable. Table 1 contains the sample description for the 282 patients used in the analyses. The sample was evenly distributed into treatment groups. About half of the sample was female, averaging about 61 years of age, and primarily Caucasian. About half of the sample were retired or homemakers, married and had incomes greater than $20,000. The majority of patients had at least 1 year of college, and one third were veterans. 148 (52.2%) of the 282 dysthymia patients were recovered by Week 11 (Hamilton depression rating ⬍ 7). Table 2 presents the design and predictor variables along with statistical tests for the recovery groups. Recovery was significantly related to treatment, site, and age cohort. The uni-
W.J. Katon et al. / General Hospital Psychiatry 24 (2002) 20 –27 Table 2 Potential predictor and design variables stratified by the recovery groups Measures
Not Recovered (n ⫽ 134)
Recovered (n ⫽ 148)
N
%
N
33 46 55
38 46 57
54 53 41
62 54 43
9.63 (df ⫽ 2) p ⫽ .03
26 20 44 44
33 54 64 46
54 17 25 52
67 46 36 54
15.28 (df ⫽ 3) p ⫽ .002
40 94
38 53
65 83
62 47
61 73
46 49
71 77
54 51
99 35
43 66
130 18
57 34
70 64
56 41
56 92
44 59
38 72 23
36 53 60
69 63 15
64 47 40
71 63
59 39
49 99
41 61
79 54
54 41
68 78
46 59
79 50
43 55
106 41
57 45
84 50
48 47
92 56
52 53
84 36 Mean 23.8
46 50 SD 13.4
99 36 Mean 18.3
54 50 SD 13.1
NEO Neuroticism
2.2
0.6
2.1
0.7
Baseline HSCL Depression
1.6
0.6
1.4
0.7
Treatment: Paroxetine PST Placebo Site: Lebanon Pittsburgh San Antonio Seattle Age Cohort: 18 to 59–Younger 60⫹–Older Gender: Female Male Ethnicity: Caucasian Non-Caucasian Marital Status: Not Married Married Employment: Full or part-time Retired–Homemaker Other Education: Up to 12 years 13⫹ years Income: ⬍ ⫽ $20,000 ⬎ 20,000 Veteran Status: No Yes History of Major Depression No Yes GAD No Yes Duke Severity Score
Chi-Square
%
5.37 (df ⫽ 1) p ⫽ .02 0.09 (df ⫽ 1) p ⫽ .77 8.08 (df ⫽ 1) p ⫽ .004 5.33 (df ⫽ 1) p ⫽ .02 10.71 (df ⫽ 2) p ⫽ .005 10.57 (df ⫽ 1) p ⫽ .001 4.09 (df ⫽ 1) p ⫽ .04 3.20 (df ⫽ 1) p ⫽ .07 0.01 (df ⫽ 1) p ⫽ .99 0.02 (df ⫽ 1) p ⫽ .65 t 3.43 (df ⫽ 270) p ⫽ .001 1.65 (df ⫽ 277) p ⫽ .10 1.91 df ⫽ 276 p ⫽ .06
variate tests showed that all the predictor variables with the exception of gender, history of major depression and comorbid generalized anxiety disorder (i.e., ethnicity, marital status, employment status, education, income, veteran sta-
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tus, Duke severity score, Neuroticism score, and baseline SCL) were significantly related to recovery (P⬍.10). Due to missing data, the final model was based on 269 patients, 128 not recovered and 141 recovered. A logistic regression model containing site, treatment status, treatment status by site interaction, age cohort, treatment status by age cohort interaction, gender, and gender by age cohort interaction was first fit to the data. In the second step, the potential predictor variables were entered and the interactions of the predictors and treatment were tested individually. The only interaction that was statistically significant was gender and treatment status. Lastly, only statistically significant potential predictors were retained in the model. These were education, Duke Severity Score and NEO neuroticism. Patients with lower educational levels, high medical comorbidity (DUSOI), and high neuroticism (NEO) were found to be less likely to recover. Because the three two way interactions interactions involving gender, age cohort and treatment status were all statistically significant, we tested the 3-way interaction of gender by age cohort by treatment status. This interaction was not statistically significant (P⬎.30) and did not contribute significantly to the fit of the model, and was therefore not included in the final model. The results of this model are presented in Table 3. The main effects (e.g., gender, site, treatment status, age cohort) involved in interactions should not be interpreted due to the dummy coding used in these models. The interaction of age cohort and gender was the strongest in the model (see Fig. 1). Collapsed over treatments, the relationship between recovery and age was very significant for female subjects: 71% of the younger female sample recovered in comparison to 37% of the older females [2⫽13.54, df⫽1, P⬍.001]. For the male subjects, there was no significant relationship between age and recovery [2⫽0.15, df⫽1, P⫽.70], with 53% and 48% of the older and younger sample of men recovering. Gender by treatment status was significant because there was an interaction between recover and treatment status for male subjects, but not for female subjects [2⫽1.03, df⫽1, P⫽.60] (see Fig. 2). In females, 60% of the Paroxetine group recovered in comparison to 50% of the PST group and 52% of the placebo group. For the male subjects, the relationship was significant [2⫽9.61, df⫽1, P⫽.008], with recovery rates of 63%, 57% and 33% in the Paroxetine, PST and placebo groups, respectively. It appears that the salient factor in this observation is that the male patients had a lower placebo response rate than the female patients. Post hoc analyses were performed to further examine the relationship between age and recovery for the female patients. Quartiles were formed based on the age distribution in the female cohort. The percentage of patients who recovered within the age quartiles generally decreased with age (2⫽17.52, df⫽3, P⫽.001): For the women 45 years or younger, the recovery rate was 64%, while in women 45.5 to 60 years of age, the recovery rate was 77%. This is in
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W.J. Katon et al. / General Hospital Psychiatry 24 (2002) 20 –27
Table 3 Logistic regression model for recovery based on the hamilton depression rating scale score ⬍ ⫽ 6, recovery is coded as 1 Variable
B
Wald’s ta
Adj Odds Ratio
95% CI
Site S1 ⫽ Lebanon–Seattle S2 ⫽ Pittsburgh–Seattle S3 ⫽ San Antonio–Seattle Treatment Status T1 ⫽ Paroxetine–Placebo T2 ⫽ PST–Placebo Site by Treatment Status S1 ⴱ T1 S1 ⴱ T2 S2 ⴱ T1 S2 ⴱ T2 S3 ⴱ T1 S3 ⴱ T2 Age Cohort (Older ⫽ 1) Age Cohort by Treatment Status Age * Parox–Placebo Age * PST–Placebo Gender (Female ⫽ 1) Gender by Age Cohort† Gender by Treatment Status Sex * Parox–Placebo1 Sex * PST–Placebo1 Educational Level (up to 12 yrs ⫽ 1) Duke Illness Severity Scale NEO Neuroticism
— 0.52 0.89 0.30 — 2.58 1.91 — 2.57 ⫺0.41 0.87 ⫺1.57 0.68 ⫺0.95 1.39 — ⫺2.46 ⫺0.35 1.96 ⫺1.67 — ⫺1.82 ⫺1.52 ⫺0.80 ⫺0.03 ⫺0.55
1.36b 0.70 0.96 0.20 8.89***c 8.19*** 5.24** 9.03d 4.77** 0.22 0.44 1.76 0.37 0.96 3.41* 5.46** 5.11** 0.17 8.81*** 6.83*** 6.85**c 5.20** 4.58** 5.90** 5.63** 5.24**
— 1.68 2.45 1.35 — 13.16 6.75 — 13.02 0.66 2.39 0.21 1.96 0.39 4.01 — 0.09 0.70 7.10 0.19 — 0.16 0.22 0.44 0.97 0.58
— 0.50–5.66 0.41–14.62 0.36–5.03 — 2.25–76.85 1.32–34.63 — 1.30–130.27 0.12–3.63 0.13–31.39 0.14–2.11 0.15–17.47 0.01–0.72 0.92–17.51 — 0.01–0.72 0.13–3.82 1.95–25.87 0.05–0.66 — 0.03–0.77 0.05–0.88 0.23–0.86 0.95–0.99 0.36–0.92
1
Active treatments are less effective than placebo in women compared to men (see Figure 2) †† Older females are less likely to recover * p ⬍.10; ** p ⬍ .05; ***p ⬍.01 a df ⫽ 1; b df ⫽ 3; c df ⫽ 2; d df ⫽ 6
contrast to the women who were 60.5 to 71 years who had a recovery rate of 44%, and the women over 71 who had a recovery rate of only 30%. This trend was not evident for the male patients.
4. Discussion In this study of primary care patients with dysthymia, patients who were less educated, more medically ill and with higher neuroticism scores were significantly less likely
Fig. 1. The interaction of age cohort and gender on remission.
to recover with all three treatment conditions. In addition, female patients were found to be more likely to have a positive response to placebo treatment than males; however, females over age 60 were less likely to recover. This is the first primary care-based study to report predictors of nonresponse to active and placebo treatments in patients with dysthymia. Unlike many tertiary care-based efficacy trials, patients were diverse with wide ranges of medical comorbidity, age, race and socioeconomic status. This diversity is likely to make our results more generalizeable to the larger United States primary care population than those of most treatment trials. Research has shown that primary care is the main setting where patients with depressive disorders access care [3– 4]. We report that subjects with less education were less likely to recover across all conditions studied; however, education is likely a proxy measure for socioeconomic class and many studies have found lower socioeconomic class to be a predictor of lower recovery rates from both affective illness and medical disorders [23–26]. This may reflect the presence of more chronic social stressors such as housing and financial problems, living in more dangerous environments, having less adaptive coping mechanisms, and less social support. In surveys of the general population, lower
W.J. Katon et al. / General Hospital Psychiatry 24 (2002) 20 –27
Fig. 2. The interaction of gender and treatment status on remission.
levels of education have been found to be associated with poorer psychological function (less mastery, happiness and self-efficacy), less optimal health behaviors (increased cigarette smoking and decreased physical activity), poorer medical health (decreased pulmonary function and increased obesity) and larger social networks (increased number of social contacts and decreased negative support) [26]. There is a large amount of literature on neuroticism (N) that may explain why higher levels of N are associated with lack of recovery in patients with dysthymia. Patients with high neuroticism tend to describe themselves as having low tolerance for stress, feeling alienated, having low-self-esteem, being anxious worriers and feeling easily victimized and resentful [21,27–32]. Several studies have shown that high neuroticism levels predict increased exposure to adverse life events [27–29] as well as a perceived increase in severity of everyday stressors [30 –31]. Researchers have also found that individuals with high neuroticism are more likely to develop major depression when faced with stressful life events [27,32]. In prior treatment studies of patients with major depression, including one primary care-based study, higher levels of neuroticism have been found to be associated with lower recovery rates with active treatments such as antidepressants or specific psychotherapies [22,33– 34]. High levels of neuroticism have also been found to be associated with increased psychiatric comorbidity (i.e., major depression and panic disorder) [35] as well as Axis II disorders [36]. When major depression is associated with a comorbid anxiety or an Axis II disorder, studies have shown less response to pharmacologic treatment or specific psychotherapies in both psychiatry and primary care patients [1,37–38]. This raises the question of what neuroticism actually measures and whether it really represents a mixed anxiety/depression diathesis with higher N simply reflecting greater severity and chronicity of these conditions. Medical comorbidity with affective disorder has also been found in some but not all studies to be correlated with lower recovery rates with treatment [39]. Few previous studies have enrolled patients as medically ill as many patients were in the current trial. A recent meta-analysis from elderly community and primary care populations found that physical illness was associated with poor outcomes in four studies, but not in two others [40]. The rating
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of physical comorbidity in the current study may have been more sensitive than ratings of previous studies since a physician used the Duke Severity of Illness Scale to rate by chart review each individual’s medical illness severity. Recent research has suggested that chronic medical disorders may precipitate major depression especially when they begin to decrease physical and role functioning [41]. Depression interventions in patients with significant comorbid physical illness and functional impairment may need to pay attention to both active depression treatments and treatment aimed at enhancing function (such as physical therapy) and decreasing pain and other aversive symptoms. The fact that elderly dysthymic women were less responsive to all treatments is intriguing. A secondary analysis of a large treatment trial database found that elderly women with major depression who had not been treated with estrogen replacement were less likely to respond to Selective Serotonin Reuptake Inhibitors (SSRIs) [42]. Unfortunately, we did not measure estrogen replacement status in this study, but we did find less response to all treatment conditions in women with dysthymia age 60 and over. This raises the question of whether there is more global difficulties in responding to depression treatments among all postmenopausal women that is not specific to the SSRI-estrogen link. Although this study found a high placebo response in females, prior studies of patients with major depression have not consistently found female gender to be associated with higher placebo response [42– 43]. One study found higher placebo response rates in males and a second found higher placebo response rates in females with a single episode but not multiple episodes of major depression [44]. The results that we report here should be interpreted with some caution since they are based on patients completing at least four sessions as well as having an independent evaluation rating of depression on the Hamilton at 6 or 11 weeks (e.g., not based on an intent to treat analysis). The inclusion of measures of chronic stress, coping, social support and life events may have provided additional understanding about mechanisms involved in lack of recovery. The addition of structured interviews to determine more specific Axis II personality diagnoses might have also shed further light on our results. Finally, the study needs to be replicated to validate the clinical and demographic predictors found in this population. In summary, this treatment outcome study of primary care patients with dysthymia found that patients with lower levels of education, higher neuroticism, and more severe medical comorbidity were less likely to recover with either active pharmacologic, psychotherapeutic or placebo treatment. Thus, demographic, psychological and medical factors appear relevant to the prognosis of dysthymia in primary care patients. Currently, few patients with dysthymia in primary care are being recognized and treated. We need to develop enhanced methods to provide basic treatments such as antidepressant medications and problem-solving
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W.J. Katon et al. / General Hospital Psychiatry 24 (2002) 20 –27
therapy to more of these patients. For the more complex patients with additional demographic and clinical risk factors for poor outcome, a treatment model involving longer term and more intensive specialty mental health treatment may be necessary to improve outcomes [45]. It is essential to develop and test active treatments for dysthymia that will be more effective in less educated, medically ill populations since these patients represent a sizeable minority of elderly primary care populations. Future research also needs to examine whether estrogen replacement therapy in postmenopausal women with depression would bolster the effect of all active treatments or whether this effect is specific to SSRIs. Acknowledgment This research was supported by grants from the John A. Hartford and MacArthur Foundations. This material is the result of work supported with resources from, and the use of, the South Texas Veterans Health Care System, the Audie L. Murphy Division, the Seattle Veterans Hospital and the VA Pittsburgh Health Care System. The views expressed are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. References [1] Brown C, Schulberg H, Madonia M, Shear M, Houck P. Treatment outcomes for primary care patients with major depression, and lifetime anxiety disorders. Am J Psychiatry 1996;153:1293–1300. [2] Howland, R. General health, health care utilization, and medical comorbidity in dysthymia. Internat J Psychiatry Med 1993;23:211– 38. [3] Katon W, Von Korff M, Lin E, Bush T, Ormel J. Distressed high utilizers of medical care. Gen Hosp Psychiatry 1990;12:355– 62. [4] Wells K, Stewart A, Hays R, et al. The functioning, and well-being of depressed patients. JAMA 1989;262:914 –9. [5] Kroenke K, Spitzer R, Williams J, et al. Physical symptoms in primary care: predictors of psychiatric disorders, and functional impairment. Arch Fam Med 1994;3:774 –9. [6] Wells K, Burnham M, Rogers W, Camp P. The course of depression in adult outpatients. Results from the Medical Outcomes Study. Arch Gen Psychiatry 1992;49:788 –94. [7] Keller M, Hirschfeld R, Hanks D. Double depression: a distinctive subtype of unipolar depression. J Affect Dis 1997;45:65–73. [8] Hirschfeld R, Russel J, Delgado P. Predictors of response to acute treatment of chronic, and double-depression with sertraline or impramine. J Clin Psychiatry 1998;12:669 –75. [9] Kocsis J, Mason B, Francis A. Prediction of response of chronic depression to imipramine. J Affect Dis 1989;17:255– 60. [10] Barrett JE, Williams JW Jr, Oxman TE, et al. Treatment of dysthymia, and minor depression in primary care-a randomized trial in patients aged 18 to 59 years comparing placebo, paroxetine and problem-solving treatment. J Fam Pract 2001;50(5):405–12. [11] Williams JW Jr, Barret J, Oxman T, et al. Treatment of dysthymic, and minor depression in primary care: a randomized trial in older adults. JAMA 2000;284:1519 –26. [12] Spitzer R, Williams J Jr, Kroenke K, et al. Utility of a new procedure for diagnosing mental disorders in primary care. The Prime-MD 1000 Study. JAMA 1994;272:1749 –56.
[13] Hamilton M. A rating scale for depression. J Neurol Neuro-Surg Psych 1960;23:56 – 62. [14] Folstein M, Folstein S, McHugh P. “Mini-mental state:” a practical method for grading the cognitive state of patients for the clinician. J Psychiatric Res 1975;12:189 –98. [15] Mynors-Wallis L. Problem-solving treatment: evidence for effectiveness, and feasibility in primary care. Internat J Psychiatry Med 1996; 3:249 – 62. [16] Hegel M, Barrett J, Oxman T. Training therapists in problem-solving treatment of depressive disorders. Lessons learned from the Treatment Effectiveness Project. Families, Systems, and Health 2000;18: 133– 45. [17] Spitzer R, Williams J, Gibbon M, First M. The structured clinical interview for DSM-IIIR (SCID) I: history, reliability, and description. Arch Gen Psychiatry 1992;49:624 –9. [18] Lipman R, Covi L, Shapiro A. The Hopkins Symptoms Checklist (HSCL): factors derived from the HSCL-90. J Affect Disord 1979; 1:9 –24. [19] Katon W, Von Korff M, Lin E, et al. Collaborative management to achieve treatment guidelines. Impact on depression in primary care. JAMA 1995;273:1026 –31. [20] Parkerson G, Broadhead W, Tse C. The Duke Severity of Illness Checklist (DUSOI) for measurement of severity, and comorbidity. J Clin Epidemiol 1993;46:379 –93. [21] Costa P, McCrae R. NEO PI/FFI Manual Supplement. Psychological Assessment Resources. Odessa, FL 1989. [22] Katon W, Lin E, Von Korff M, et al. The predictors of persistence of depression in primary care. J Affect Disord 1994; 31, 81–90. [23] Link B, Phelan J. Understanding sociodemographic differences in health—the role of fundamental social causes. Am J Public Health 1996;86:471–2. [24] Kaplan G. You can’t get there from here: understanding the link between socioeconomic status, and health requires going upstream. Adv J Mind Body Health 1995;11:15– 6. [25] Swindle R, Cronkite R, Moos R. Risk factors for sustained nonremission of depressive symptoms: a 4-year follow-up. J Nerv Mental Dis 1998;186:462–9. [26] Kubzanky L, Berkman L, Glass T, Seeman T. Is educational attainment associated with shared determinants of health in the elderly? Finding from the MacArthur Studies of successful aging. Psychosom Med 1998;60:578 – 85. [27] Van Os J, Jones P. Early risk factors, and adult person-environment relationships in affective disorder. Psychol Med 1999;29:1055– 67. [28] Magnus K, Diener E, Fujita F, Favot W. Extraversion, and neuroticism as predictors of objective life events: a longitudinal study. J Pers Soc Psychol 1993;65:1046 –53. [29] Heady B, Wearing A. Personality, life events, and subjective wellbeing: toward a dynamic equilibrium model. J Pers Soc Psychol 1989;57:731–9. [30] Bolger N, Schilling E. Personality, and the problems of everyday life: the role of neuroticism in exposure, and reactivity to daily stressors. J Personality 1991;59:355– 86. [31] Watson D, Pennebaker J. Health complaints, stress, and distress: exploring the role of negative affectivity. Psychol Rev 1989;96:234 – 54. [32] Ormel J, Wohlforth T. How neuroticism, long-term difficulties, and life situation changes influence psychological distress: a longitudinal model. J Pers Soc Psychol 1991;60:744 –55. [33] Joyce P, Paykel E. Predictors of drug response in depression. Gen Psychiatry 1989;46:89 –99. [34] Frank E, Kupfer D, Jacob M, Jarret D. Personality measures, and response to acute treatment in major depression. J Pers Dis 1987;1: 14 –26. [35] Andrews G, Stewart G, Morris-Yates A, Hot P, Henderson S. Evidence for a general neurotic syndrome. Br J Psychiatry 1990;157:6 – 12.
W.J. Katon et al. / General Hospital Psychiatry 24 (2002) 20 –27 [36] Trull T. DSM-III-R personality disorders, and the five-factor model of personality. J Abnorm Psychol 1992;101:553– 60. [37] Charney D, Nelson J, Quinlan D. Personality traits, and disorder in depression. Am J Psychiatry 1981;138:1601– 4. [38] Shea M, Widiger T, Klein M. Comorbidity of personality disorders, and depression. Complications from treatment. J Consult Clin Psychol 1992;60:857– 68. [39] Katon W, Schulberg H. Epidemiology of depression in primary care. Gen Hosp Psychiatry 1992;14:237– 47. [40] Cole M, Bellavance F, Mansour A. Prognosis of depression in elderly community, and primary care populations: A systematic review and meta-analysis. Am J Psychiatry 1999;156:1182– 89. [41] Ormel J, Kemper J, Penninx B, Brilman E, Beeman A, van Sonderen E. Chronic medical conditions, and mental health in older people:
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disability, and psychosocial resources mediate specific mental health effects. Psychol Med 1997;27:1065–77. Schneider L, Small G, Hamilton S, Bystritsky A, Nemeroff C, Meyers B. Estrogen replacement, and response to fluoxetine in a multi-center geriatric depression trial. Am J Geriatr Psychiatry 1997;5:97–106. Wilcox C, Cohn J, Linden R, et al. Predictors of placebo response: a retrospective analysis. Psychopharm Bull 1992;28:157– 62. Bialik R, Ravindran A, Bukish D, Lapierre Y. A comparison of placebo responders, and nonresponders in subgroups of depressive disorder. J Psychiatry Neurosci 1995;20:265–70. Katon W, Von Korff M, Lin E, et al. Stepped collaborative care for primary care patients with persistent symptoms of depression: A randomized trial. Arch Gen Psychiatry 1999;56:1109 –15.
Predictors of nonresponse to treatment in primary care patients with dysthymia Wayne Katon, M.D.a,*, Joan Russo, Ph.D.a Ellen Frank, Ph.D.b, James Barrett, M.D.c, John W. Williams, Jr., M.D., MHSd, Thomas Oxman, M.D.e, Mark Sullivan, M.D., Ph.D.a, John Cornell, Ph.D.d,f a
Department of Psychiatry and Behavioral Sciences, University of Washington Medical School, Seattle, WA, USA Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh, Pittsburgh, PA, USA c Department of Community and Family Medicine, Dartmouth Medical School, Hanover, NH, USA d The South Texas Veterans Health Care System, Audie Murphy Division and the Division of General Internal Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA e Department of Psychiatry, Dartmouth Medical School, Hanover, NH, USA f Division of Geriatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA b
Abstract Dysthymia is one of the most prevalent problems in primary care, especially in the elderly. In this study, we evaluated the demographic and clinical predictors of nonresponse to treatment in primary care patients with dysthymia. The study sample consisted of 338 primary care patients meeting DSMIII-R criteria for dysthymia from 4 diverse geographic sites in a randomized controlled 11-week trial of paroxetine, problem-solving therapy or placebo. Patients who attended at least 4 treatment sessions were used in the analysis. A score of less than 7 on the Hamilton was defined as a positive response to treatment. By Week 11, 52.2% of patients had a positive response to treatment. Patients with lower levels of education (odds ratio 0.44, 95% CI 0.23, 0.86), higher scores on the personality dimension of neuroticism (odds ratio 0.58, 95% CI 0.36, 0.92) and those with more severe medical illness (odds ratio 0.97, 95% CI 0.95, 0.99) were less likely to recover with either active or placebo treatments. Elderly women (⬎60 years of age; odds ratio 0.19, 95% CI 0.05, 0.66) were also less likely to respond to all treatments; however, females had a significantly higher response to placebo treatment compared to males. The factors associated with lack of response to treatment included lower-levels of education, high neuroticism, more severe medical illness and being an older female. This analysis is based on patients agreeing to participate in a randomized controlled trial, limiting representativeness of the sample, however, the demographic and clinical characteristics are common in elderly depressed primary care patients, and may signal the need for increased mental health specialty consultation. © 2002 Elsevier Science Inc. All rights reserved. Keywords: Dysthymia; Depression; Paroxetine
1. Introduction Dysthymia occurs in approximately 3 to 5% of primary care patients [1] and is associated with increased health care utilization and health care costs [2–3] impaired functioning [4] and multiple unexplained physical symptoms such as headaches and fatigue [5]. Most nonelderly patients with dysthymia have experienced major depressive episodes [6] and when they co-occur, there is additive disability [7] and a high rate of relapse after recovery from the major depres-
* Corresponding author. Tel.: ⫹1-206-543-7177; fax: ⫹1-206-221-5414. E-mail address: [email protected] (W.J. Katon).
sive episode [6,7]. Data from the Medical Outcome Study (MOS) showed that dysthymia was associated with poor symptomatic and functional outcomes, even in the absence of major depression [6]. Although many intervention studies have examined predictors of nonresponse among patients with major depression, few studies have examined variables associated with nonresponse to treatment in patients with chronic depression. Moreover, the limited research on predictors of treatment response in chronic depressives has been largely completed in tertiary care specialty populations. In the largest tertiary-based study, Hirschfeld and colleagues examined predictors of response in 623 patients enrolled in the acute phase of the Chronic Major Depression
0163-8343/02/$ – see front matter © 2002 Elsevier Science Inc. All rights reserved. PII: P I S 0 1 6 3 - 8 3 4 3 ( 0 1 ) 0 0 1 7 1 - 2
W.J. Katon et al. / General Hospital Psychiatry 24 (2002) 20 –27
and Double-Depression Study, who were randomized to 12 weeks of imipramine or sertraline [8]. A higher baseline quality of life, living with spouse or partner, having more education and two personality variables (passive aggressive personality and lower levels of tenseness and introversion) predicted positive treatment response. The authors of this study acknowledged that a significant limitation of this research was the lack of placebo-control group that “would have permitted more global correlates of favorable outcome to be disaggregated from more specific predictors of response to active pharmacotherapy.” A second double-blind placebo-controlled study of imipramine in chronically depressed psychiatric patients found that “neurotic” characteristics in the Cattell 16-Personality Factor Scale were associated with poor outcome [9]. We have recently reported the results of a large primary care based randomized controlled trial of paroxetine, problem solving therapy (PST-PC) adapted for primary care and placebo in both a younger age (18 to 59) and older age (60 and over) sample of patients with minor depression and dysthymia [10,11]. This trial allowed us to study predictors of nonresponse to active treatments and placebo in a sample of patients with dysthymia more generalizable to primary care populations. An accompanying paper in this edition of General Hospital Psychiatry studied predictors of response in patients with minor depression.
2. Methods 2.1. Subjects The details of the study design have been described elsewhere [11]. In summary, primary care patients aged 18 to 90 were recruited for a comparative treatment trial from patients currently enrolled in primary care practices (family medicine or general internal medicine) in four communities (Lebanon, NH, Pittsburgh, PA, San Antonio, TX, and Seattle, WA, USA). The Seattle and Lebanon sites enrolled both patients in a younger age cohort (18 –59) and an older age cohort (60 –90), while San Antonio and Pittsburgh only enrolled the older age cohort. In order to increase elderly recruitment, three sites included Veterans Administration primary care clinics. To be included a subject needed to have three or four of the DSM-IV symptoms of depression on the Prime-MD major depression module [12], one of which was depressed mood or anhedonia as assessed by clinical interview, and to have a 17-item Hamilton Rating Scale for Depression score of ten or greater [13]. Only the patients with dysthymia were included in this paper and dysthymics were required to have experienced symptoms for at least two years. Patients were excluded if, within the past six months, they had major depression, active substance abuse, uncomplicated bereavement, parasuicidal behavior, or antisocial personality. Patients currently taking psychotropic drugs, seeing a psychotherapist or with cog-
21
nitive impairment (Mini Mental State Exam ⱕ 23) [14 ]or a terminal illness (less than six months to live) were also excluded. 2.2. Procedures Each site used a variety of methods to educate participating primary care providers about referral. Brief depression screening instruments were also used at some sites to further identify potential subjects. Subjects thus were identified in a primary care practice setting as potentially having either dysthymia or minor depression and were referred for a research evaluation. A two-phase evaluation took place within one week of identification. The initial phase was a semistructured clinical interview to determine eligibility [12]. For patients meeting criteria, a complete description of the study was provided, and written informed consent approved by the local institutional review board was obtained. Those who agreed to participate were then administered additional baseline measures and randomized to one of the three treatment arms. Subjects in all three arms were offered six subsequent treatment visits at 1, 2, 4, 6, 8 and 10 (for PST-PC) [15–16] or 11 (for paroxetine and placebo) weeks. 2.3. Measures The semistructured interview included mood, anxiety and alcohol modules from the Prime-MD [12], the 17-item Hamilton Rating Scale for Depression [13] and the substance abuse, and psychosis modules of the Structured Clinical Interview for Diagnosis (SCID) [17]. To assess severity of depression, we used the interview-based 17-item Hamilton Rating Scale for Depression (HAM-D) [13]. The HAM-D was administered at baseline, 6 and 11 weeks by an independent rater who was blind to intervention status. Scores ranged from 10 to 25 (mean ⫽ 13.73 ⫾ 2.92) for the 17-item version used in this study. The HAM-D has been shown to have high reliability and validity in measuring depression.) [13]. A 57-item Hopkins Symptom Checklist self-report measure, consisting of the items that make up five scales (depression, anxiety, interpersonal sensitivity, hostility and somatization) was utilized [18]. The 20-item depression scale has been found to be a sensitive indicator of change in primary care samples [19]. Scores on this 20-item scale ranged from 0.15 to 3.45 (mean ⫽ 1.5 ⫾ 0.68). The Duke Severity of Illness Index (DUSOI) provided a quantification of the amount of medical comorbidity present in each patient [20]. Scores were based on medical information abstracted from medical records by a physician. The NEO Neuroticism Scale consisted of 12 self-report items to assess neuroticism as a personality trait [21]. In previous primary care studies, it was found to predict persistence of depressive symptoms [22].
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W.J. Katon et al. / General Hospital Psychiatry 24 (2002) 20 –27
2.4. Statistical analyses
Table 1 Sample description (n ⫽ 282)
Recovery was defined as having a Hamilton Depression Rating Scale score of 6 or less at 11 weeks. An adequacy of treatment analysis was used. Subjects who attended at least 4 treatment sessions and had received a Hamilton depression score at 6 or 11 weeks by an independent rater were used in analyses. If the 11-week Hamilton score was missing, the 6-week score was carried forward. Hamilton Depression Scales were completed at 6 and 11 weeks by an independent rater blind to group status. 2 analyses with corrections for continuity and t-tests were used to compare patients who were and were not included in the statistical analyses. Univariate tests were performed examining the relationship between the potential predictor variables and recovery status. 2 analyses with corrections for continuity were used for the discrete predictors of gender, age cohort, site, treatment group, ethnicity (minority vs. majority), marital status (married vs. not married), employment status (full time or part-time, retired-homemaker, unemployed or disabled), income (⬍ 20 K, ⬎ 20 K), veteran (patient of the Veteran’s Administration Hospital) status and percent with generalized anxiety disorder or a history of major depression. T-tests were used for the Duke Medical Illness Severity Scale, NEO neuroticism scale, and the baseline SCL 20item depression scale. Potential predictor variables with univariate relationships to recovery status of P⫽.10 or less were included in the logistic regression model as potential predictors of recovery. A logistic regression modeling strategy was used to determine the best predictors of recovery. Due to the design of the study, 14 design terms were entered into the model regardless of statistical significance: site (dummy coded into 3 variables with Seattle as the reference group), age cohort (dummy coded), treatment group (2 dummy coded variables with placebo as the reference group), site by treatment interaction (6 terms), and the treatment by age interaction (2 terms). Although gender was not a design variable, it was entered into the model due to its relationship to the age cohort variable. Preliminary examination of that data revealed a strong relationship between gender and age cohort (2⫽13.43, P⬍.001). In the older cohort (60 years and over), 62% were men and 38% were women, compared to the younger sample (18 to 59 years) that was 60% women and 40% men. Therefore the age cohort by gender interaction was also entered into the model to control for these imbalances. Next the potential predictor variables were entered into the model. Interactions of the potential predictor variables and treatment were tested individually. Significant interaction terms were retained and the model was refit. The final model contained the 14 design variables, significant interaction terms, significant main effect predictors and predictors necessary for the interactions.
Characteristic
N
%
Treatment: Paroxetine PST Placebo Site: Lebanon Pittsburgh San Antonio Seattle Age Cohort: 18–59–Younger 60⫹–Older Gender: Female Ethnicity: Caucasian Marital Status: Married Employment: full or part-time retired or homemaker Education: At least 1 year of college Income: ⬎ $20,000 Veteran: Yes Comorbid Panic Disorder Comorbid GAD History of Major Depression
87 99 96 80 37 69 96 105 177 132 229 156 107 135 162 132 91 20 70 106 Mean 61.4 2.12 20.9 1.5
30.9 35.1 34.0 28.4 13.1 24.5 34.0 37.2 62.8 46.8 81.2 55.3 38.2 48.2 57.4 47.3 33.0 7.2 28.0 37.6 S.D. 15.1 0.65 13.5 0.68
Age Neuroticism Scale–NEO Duke Severity of Medical Illness Baseline SCL-D20 Depression Scale
3. Results The study sample consisted of 338 dysthymic patients. Fifty-six patients (16.6%) were not used in the statistical analyses: 25 patients had no treatment sessions, 23 patients had 1 to 3 treatment sessions and 8 patients had 4 or more treatment sessions, but were missing both Week 6 and Week 11 Hamilton Depression Ratings. Patients who were included in the analyses (n⫽282) were compared to those not included (n⫽56) on all study design variables (site, treatment status, age cohort), demographic variables (age, gender, employment, education level, ethnicity, marital status, veteran status), and clinical variables (neuroticism and baseline depression level). There were no statistically significant differences between patients who were used for analyses and those who were not used on any study variable. Table 1 contains the sample description for the 282 patients used in the analyses. The sample was evenly distributed into treatment groups. About half of the sample was female, averaging about 61 years of age, and primarily Caucasian. About half of the sample were retired or homemakers, married and had incomes greater than $20,000. The majority of patients had at least 1 year of college, and one third were veterans. 148 (52.2%) of the 282 dysthymia patients were recovered by Week 11 (Hamilton depression rating ⬍ 7). Table 2 presents the design and predictor variables along with statistical tests for the recovery groups. Recovery was significantly related to treatment, site, and age cohort. The uni-
W.J. Katon et al. / General Hospital Psychiatry 24 (2002) 20 –27 Table 2 Potential predictor and design variables stratified by the recovery groups Measures
Not Recovered (n ⫽ 134)
Recovered (n ⫽ 148)
N
%
N
33 46 55
38 46 57
54 53 41
62 54 43
9.63 (df ⫽ 2) p ⫽ .03
26 20 44 44
33 54 64 46
54 17 25 52
67 46 36 54
15.28 (df ⫽ 3) p ⫽ .002
40 94
38 53
65 83
62 47
61 73
46 49
71 77
54 51
99 35
43 66
130 18
57 34
70 64
56 41
56 92
44 59
38 72 23
36 53 60
69 63 15
64 47 40
71 63
59 39
49 99
41 61
79 54
54 41
68 78
46 59
79 50
43 55
106 41
57 45
84 50
48 47
92 56
52 53
84 36 Mean 23.8
46 50 SD 13.4
99 36 Mean 18.3
54 50 SD 13.1
NEO Neuroticism
2.2
0.6
2.1
0.7
Baseline HSCL Depression
1.6
0.6
1.4
0.7
Treatment: Paroxetine PST Placebo Site: Lebanon Pittsburgh San Antonio Seattle Age Cohort: 18 to 59–Younger 60⫹–Older Gender: Female Male Ethnicity: Caucasian Non-Caucasian Marital Status: Not Married Married Employment: Full or part-time Retired–Homemaker Other Education: Up to 12 years 13⫹ years Income: ⬍ ⫽ $20,000 ⬎ 20,000 Veteran Status: No Yes History of Major Depression No Yes GAD No Yes Duke Severity Score
Chi-Square
%
5.37 (df ⫽ 1) p ⫽ .02 0.09 (df ⫽ 1) p ⫽ .77 8.08 (df ⫽ 1) p ⫽ .004 5.33 (df ⫽ 1) p ⫽ .02 10.71 (df ⫽ 2) p ⫽ .005 10.57 (df ⫽ 1) p ⫽ .001 4.09 (df ⫽ 1) p ⫽ .04 3.20 (df ⫽ 1) p ⫽ .07 0.01 (df ⫽ 1) p ⫽ .99 0.02 (df ⫽ 1) p ⫽ .65 t 3.43 (df ⫽ 270) p ⫽ .001 1.65 (df ⫽ 277) p ⫽ .10 1.91 df ⫽ 276 p ⫽ .06
variate tests showed that all the predictor variables with the exception of gender, history of major depression and comorbid generalized anxiety disorder (i.e., ethnicity, marital status, employment status, education, income, veteran sta-
23
tus, Duke severity score, Neuroticism score, and baseline SCL) were significantly related to recovery (P⬍.10). Due to missing data, the final model was based on 269 patients, 128 not recovered and 141 recovered. A logistic regression model containing site, treatment status, treatment status by site interaction, age cohort, treatment status by age cohort interaction, gender, and gender by age cohort interaction was first fit to the data. In the second step, the potential predictor variables were entered and the interactions of the predictors and treatment were tested individually. The only interaction that was statistically significant was gender and treatment status. Lastly, only statistically significant potential predictors were retained in the model. These were education, Duke Severity Score and NEO neuroticism. Patients with lower educational levels, high medical comorbidity (DUSOI), and high neuroticism (NEO) were found to be less likely to recover. Because the three two way interactions interactions involving gender, age cohort and treatment status were all statistically significant, we tested the 3-way interaction of gender by age cohort by treatment status. This interaction was not statistically significant (P⬎.30) and did not contribute significantly to the fit of the model, and was therefore not included in the final model. The results of this model are presented in Table 3. The main effects (e.g., gender, site, treatment status, age cohort) involved in interactions should not be interpreted due to the dummy coding used in these models. The interaction of age cohort and gender was the strongest in the model (see Fig. 1). Collapsed over treatments, the relationship between recovery and age was very significant for female subjects: 71% of the younger female sample recovered in comparison to 37% of the older females [2⫽13.54, df⫽1, P⬍.001]. For the male subjects, there was no significant relationship between age and recovery [2⫽0.15, df⫽1, P⫽.70], with 53% and 48% of the older and younger sample of men recovering. Gender by treatment status was significant because there was an interaction between recover and treatment status for male subjects, but not for female subjects [2⫽1.03, df⫽1, P⫽.60] (see Fig. 2). In females, 60% of the Paroxetine group recovered in comparison to 50% of the PST group and 52% of the placebo group. For the male subjects, the relationship was significant [2⫽9.61, df⫽1, P⫽.008], with recovery rates of 63%, 57% and 33% in the Paroxetine, PST and placebo groups, respectively. It appears that the salient factor in this observation is that the male patients had a lower placebo response rate than the female patients. Post hoc analyses were performed to further examine the relationship between age and recovery for the female patients. Quartiles were formed based on the age distribution in the female cohort. The percentage of patients who recovered within the age quartiles generally decreased with age (2⫽17.52, df⫽3, P⫽.001): For the women 45 years or younger, the recovery rate was 64%, while in women 45.5 to 60 years of age, the recovery rate was 77%. This is in
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W.J. Katon et al. / General Hospital Psychiatry 24 (2002) 20 –27
Table 3 Logistic regression model for recovery based on the hamilton depression rating scale score ⬍ ⫽ 6, recovery is coded as 1 Variable
B
Wald’s ta
Adj Odds Ratio
95% CI
Site S1 ⫽ Lebanon–Seattle S2 ⫽ Pittsburgh–Seattle S3 ⫽ San Antonio–Seattle Treatment Status T1 ⫽ Paroxetine–Placebo T2 ⫽ PST–Placebo Site by Treatment Status S1 ⴱ T1 S1 ⴱ T2 S2 ⴱ T1 S2 ⴱ T2 S3 ⴱ T1 S3 ⴱ T2 Age Cohort (Older ⫽ 1) Age Cohort by Treatment Status Age * Parox–Placebo Age * PST–Placebo Gender (Female ⫽ 1) Gender by Age Cohort† Gender by Treatment Status Sex * Parox–Placebo1 Sex * PST–Placebo1 Educational Level (up to 12 yrs ⫽ 1) Duke Illness Severity Scale NEO Neuroticism
— 0.52 0.89 0.30 — 2.58 1.91 — 2.57 ⫺0.41 0.87 ⫺1.57 0.68 ⫺0.95 1.39 — ⫺2.46 ⫺0.35 1.96 ⫺1.67 — ⫺1.82 ⫺1.52 ⫺0.80 ⫺0.03 ⫺0.55
1.36b 0.70 0.96 0.20 8.89***c 8.19*** 5.24** 9.03d 4.77** 0.22 0.44 1.76 0.37 0.96 3.41* 5.46** 5.11** 0.17 8.81*** 6.83*** 6.85**c 5.20** 4.58** 5.90** 5.63** 5.24**
— 1.68 2.45 1.35 — 13.16 6.75 — 13.02 0.66 2.39 0.21 1.96 0.39 4.01 — 0.09 0.70 7.10 0.19 — 0.16 0.22 0.44 0.97 0.58
— 0.50–5.66 0.41–14.62 0.36–5.03 — 2.25–76.85 1.32–34.63 — 1.30–130.27 0.12–3.63 0.13–31.39 0.14–2.11 0.15–17.47 0.01–0.72 0.92–17.51 — 0.01–0.72 0.13–3.82 1.95–25.87 0.05–0.66 — 0.03–0.77 0.05–0.88 0.23–0.86 0.95–0.99 0.36–0.92
1
Active treatments are less effective than placebo in women compared to men (see Figure 2) †† Older females are less likely to recover * p ⬍.10; ** p ⬍ .05; ***p ⬍.01 a df ⫽ 1; b df ⫽ 3; c df ⫽ 2; d df ⫽ 6
contrast to the women who were 60.5 to 71 years who had a recovery rate of 44%, and the women over 71 who had a recovery rate of only 30%. This trend was not evident for the male patients.
4. Discussion In this study of primary care patients with dysthymia, patients who were less educated, more medically ill and with higher neuroticism scores were significantly less likely
Fig. 1. The interaction of age cohort and gender on remission.
to recover with all three treatment conditions. In addition, female patients were found to be more likely to have a positive response to placebo treatment than males; however, females over age 60 were less likely to recover. This is the first primary care-based study to report predictors of nonresponse to active and placebo treatments in patients with dysthymia. Unlike many tertiary care-based efficacy trials, patients were diverse with wide ranges of medical comorbidity, age, race and socioeconomic status. This diversity is likely to make our results more generalizeable to the larger United States primary care population than those of most treatment trials. Research has shown that primary care is the main setting where patients with depressive disorders access care [3– 4]. We report that subjects with less education were less likely to recover across all conditions studied; however, education is likely a proxy measure for socioeconomic class and many studies have found lower socioeconomic class to be a predictor of lower recovery rates from both affective illness and medical disorders [23–26]. This may reflect the presence of more chronic social stressors such as housing and financial problems, living in more dangerous environments, having less adaptive coping mechanisms, and less social support. In surveys of the general population, lower
W.J. Katon et al. / General Hospital Psychiatry 24 (2002) 20 –27
Fig. 2. The interaction of gender and treatment status on remission.
levels of education have been found to be associated with poorer psychological function (less mastery, happiness and self-efficacy), less optimal health behaviors (increased cigarette smoking and decreased physical activity), poorer medical health (decreased pulmonary function and increased obesity) and larger social networks (increased number of social contacts and decreased negative support) [26]. There is a large amount of literature on neuroticism (N) that may explain why higher levels of N are associated with lack of recovery in patients with dysthymia. Patients with high neuroticism tend to describe themselves as having low tolerance for stress, feeling alienated, having low-self-esteem, being anxious worriers and feeling easily victimized and resentful [21,27–32]. Several studies have shown that high neuroticism levels predict increased exposure to adverse life events [27–29] as well as a perceived increase in severity of everyday stressors [30 –31]. Researchers have also found that individuals with high neuroticism are more likely to develop major depression when faced with stressful life events [27,32]. In prior treatment studies of patients with major depression, including one primary care-based study, higher levels of neuroticism have been found to be associated with lower recovery rates with active treatments such as antidepressants or specific psychotherapies [22,33– 34]. High levels of neuroticism have also been found to be associated with increased psychiatric comorbidity (i.e., major depression and panic disorder) [35] as well as Axis II disorders [36]. When major depression is associated with a comorbid anxiety or an Axis II disorder, studies have shown less response to pharmacologic treatment or specific psychotherapies in both psychiatry and primary care patients [1,37–38]. This raises the question of what neuroticism actually measures and whether it really represents a mixed anxiety/depression diathesis with higher N simply reflecting greater severity and chronicity of these conditions. Medical comorbidity with affective disorder has also been found in some but not all studies to be correlated with lower recovery rates with treatment [39]. Few previous studies have enrolled patients as medically ill as many patients were in the current trial. A recent meta-analysis from elderly community and primary care populations found that physical illness was associated with poor outcomes in four studies, but not in two others [40]. The rating
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of physical comorbidity in the current study may have been more sensitive than ratings of previous studies since a physician used the Duke Severity of Illness Scale to rate by chart review each individual’s medical illness severity. Recent research has suggested that chronic medical disorders may precipitate major depression especially when they begin to decrease physical and role functioning [41]. Depression interventions in patients with significant comorbid physical illness and functional impairment may need to pay attention to both active depression treatments and treatment aimed at enhancing function (such as physical therapy) and decreasing pain and other aversive symptoms. The fact that elderly dysthymic women were less responsive to all treatments is intriguing. A secondary analysis of a large treatment trial database found that elderly women with major depression who had not been treated with estrogen replacement were less likely to respond to Selective Serotonin Reuptake Inhibitors (SSRIs) [42]. Unfortunately, we did not measure estrogen replacement status in this study, but we did find less response to all treatment conditions in women with dysthymia age 60 and over. This raises the question of whether there is more global difficulties in responding to depression treatments among all postmenopausal women that is not specific to the SSRI-estrogen link. Although this study found a high placebo response in females, prior studies of patients with major depression have not consistently found female gender to be associated with higher placebo response [42– 43]. One study found higher placebo response rates in males and a second found higher placebo response rates in females with a single episode but not multiple episodes of major depression [44]. The results that we report here should be interpreted with some caution since they are based on patients completing at least four sessions as well as having an independent evaluation rating of depression on the Hamilton at 6 or 11 weeks (e.g., not based on an intent to treat analysis). The inclusion of measures of chronic stress, coping, social support and life events may have provided additional understanding about mechanisms involved in lack of recovery. The addition of structured interviews to determine more specific Axis II personality diagnoses might have also shed further light on our results. Finally, the study needs to be replicated to validate the clinical and demographic predictors found in this population. In summary, this treatment outcome study of primary care patients with dysthymia found that patients with lower levels of education, higher neuroticism, and more severe medical comorbidity were less likely to recover with either active pharmacologic, psychotherapeutic or placebo treatment. Thus, demographic, psychological and medical factors appear relevant to the prognosis of dysthymia in primary care patients. Currently, few patients with dysthymia in primary care are being recognized and treated. We need to develop enhanced methods to provide basic treatments such as antidepressant medications and problem-solving
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