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Psychosomatic Medicine 65:764-770 (2003)
© 2003 American Psychosomatic Society

ORIGINAL ARTICLES

Predictors of Psychiatric Comorbidity in Medical Outpatients

Bernd Löwe, MD, Dipl-Psych, Kerstin Gräfe, Dipl-Psych, Kurt Kroenke, MD, Stephan Zipfel, MD, Andrea Quenter, Dipl-Psych, Beate Wild, Dipl-Math, Dipl-Psych, Christoph Fiehn, MD and Wolfgang Herzog, MD

From the Department of General Internal and Psychosomatic Medicine (B.L., K.G., S.Z., A.Q., B.W., W.H.) and the Department of Hematology, Oncology, and Rheumatology (C.F.), University of Heidelberg, Medical Hospital, Heidelberg, Germany; and Regenstrief Institute for Health Care, Indiana University School of Medicine, Indianapolis, Indiana (K.K.).

Address reprint requests to: Bernd Löwe, MD, Dipl-Psych, Department of General Internal and Psychosomatic Medicine, University of Heidelberg, Medical Hospital, Bergheimer Straße 58, D-69115 Heidelberg, Germany. Email: bloewe{at}regenstrief.org


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 ACKNOWLEDGMENTS
 REFERENCES
 
OBJECTIVE: Psychiatric comorbidity in medical outpatients is associated with personal suffering and reduced psychosocial functioning. Simple clinical indicators are needed to improve recognition and treatment of psychiatric comorbidity. This study aimed to identify predictors of psychiatric comorbidity for diagnostic use in busy medical settings and to describe their criterion validity.

METHODS: The SCID was adopted as the independent criterion standard for the presence of a psychiatric comorbidity in 357 patients (68% female; mean age, 43 years) of six internal medicine outpatient clinics and 12 general practices. Potential indicators of psychiatric comorbidity were investigated by means of patient and physician questionnaires. Logistic regression analyses were used to identify independent predictors of psychiatric comorbidity, and their operating characteristics were determined.

RESULTS: Of 18 indicators, the four most important predictors of psychiatric comorbidity were identified: a screening question for nervousness, anxiety, or worries (odds ratio, 11.9; p < .001), a screening question for depressed mood (odds ratio, 8.8; p < .001), the self-report of three or more bothersome physical symptoms (odds ratio, 3.2; p = .001), and feeling distressed by partner difficulties (odds ratio, 2.7; p = .006). The combined assessment of the four predictors resulted in positive predictive values as high as 100%, negative predictive values as high as 91%, sensitivities as high as 86%, and specificities as high as 100%.

CONCLUSIONS: The identification of mental disorders in medical outpatients could be substantially improved by the knowledge and use of four easily accessible predictors. When the presence of one or more of these predictors can be confirmed, it is suggested that the patient undergo further evaluation to determine more precisely the presence and specific type of psychiatric disorder being identified.

Key Words: mental disorders, • diagnosis, • questionnaires, • predictive value of tests, • sensitivity and specificity, • primary health care.

Abbreviations: CI = confidence interval;; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders;; FKB-20 = Body Image Questionnaire;; PHQ = Patient Health Questionnaire;; SCID = Structured Clinical Interview for DSM-IV;; SF-12 = 12-Item Short Form Health Survey;; WBI-5 = World Health Organization (five item) Well-Being Index.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 ACKNOWLEDGMENTS
 REFERENCES
 
Psychiatric comorbidity, of which depressive and anxiety disorders are the most common, is present in approximately one third of medical outpatients (1–4). In addition to the personal suffering of the patient, psychiatric comorbidity is strongly associated with disability, increased health care costs, and elevated risk of mortality (5–10). Efficient psychosocial and pharmacological treatments are available for psychiatric comorbidity in medical outpatients, especially for depressive disorders (11–14). Despite the important clinical significance, only 30% to 50% of the cases are identified by medical doctors (15–19). Obviously, diagnosing mental disorders alone is not sufficient to achieve a better outcome in primary care patients with psychiatric disorders. However, if the recognition of a mental disorder in primary care is closely linked to the initiation of an adequate treatment, patient outcome improves significantly with regard to clinical outcome, satisfaction with care, and health service costs (20–22). To improve recognition, physicians in general hospitals and primary care need to know which diagnostic questions and which indicators can help them detect mental disorders. Unfortunately, only a few studies have tried to identify simple clinical indicators for the diagnosis of psychiatric comorbidity. These studies did demonstrate that independent predictors of mental disorders in medical outpatients include multiple physical symptoms, higher patient ratings of symptom severity, recent stress, lower patient ratings of overall health, and patient age younger than 50 years (23–27). However, none of these studies provide complete operating characteristics (predictive values, sensitivity and specificity, agreement with criterion standard) necessary to compare the validity and utility of these predictors with other diagnostic methods, such as screening instruments. The use of simple screening questions is another promising approach to identify patients with psychiatric comorbidity better among medical outpatients. To date, the diagnostic value of specific questions has been evaluated only for depressive disorders. It has been demonstrated that questioning for the two core symptoms of major depression (depressed mood and loss of interest or pleasure) are efficient for detection of depression in primary care (28–30). The fact that screening can be cost-effective (31), that primary care patients desire mental health screening by their doctors (32), and that these patients wish to have psychiatric symptoms treated (33) underline the necessity for a sound evaluation of psychiatric comorbidity.

The purpose of this study was to identify simple clinical predictors that would lead to heightened clinical suspicion of mental disorders (ie, red flags) in busy medical settings, and to characterize their validity and utility. Primarily, we aimed to identify potential psychiatric caseness rather than specific disorders, with the presumption that more precise diagnostic questions would be reserved for those patients screening positive. Also, clinical predictors may be less specific for a particular disorder because of the substantial comorbidity between the most common mental disorders: patients with depressive, anxiety, or somatoform disorders have a 50% or greater likelihood of having one or more of the other disorders (4, 34). To this end, we examined and compared the diagnostic value of indicators of any psychiatric comorbidity. These indicators included demographic information, patient ratings, specific screening questions, treatment variables, and physician ratings. Of all variables, we aimed to identify the most important set of independent predictors that could efficiently help identify psychiatric disorders in busy medical settings. To assess the validity of the identified predictors, and to allow a comparison with other case-finding methods, the respective operating characteristics were analyzed. This analysis was followed by an investigation of their test characteristics for diagnosing separate disorders. Finally, we aimed to describe well-being, health-related quality of life, body image, and health care use of the patients with psychiatric comorbidity compared with patients without psychiatric comorbidity. To guarantee a valid psychiatric diagnosis, a well established clinical interview was used as a criterion standard.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 ACKNOWLEDGMENTS
 REFERENCES
 
Subjects
On predetermined days from August 2000 to July 2001, and according to fixed selection rates, patients visiting six specialized internal medicine outpatient clinics from Heidelberg University Hospital and from 12 general practitioners were approached and invited to complete a questionnaire during their waiting time and undergo the SCID, Axis 1 Disorders (35, 36), directly after having seen the doctor or within 1 week. Twenty-six percent of the approached patients declined participation or were unable either to fill out the questionnaire or to participate in the SCID. A total of 357 patients gave informed consent, completed the questionnaires, and underwent the SCID. Of the participating subjects, 247 (69.2%) were patients of the Medical University outpatient clinics and 110 (30.8%) were patients of general practitioners. The mean age was 43.3 (±13.0) years, 67.8% of the patients were female, and 77.0% were living with a spouse or partner. Patients who declined participation did not differ from the participating patients regarding sex (61.0% female, p = .16) but were, however, significantly older (mean age, 55.1 ± 13.0 years; p < .001). The primary physical diagnoses of the participating patients according to the Tenth Revision of the International Classification of Diseases were disorders of the muscular-skeletal system (28.8%), endocrinological and metabolic disorders (20.7%), cardiovascular/circulatory diseases (14.3%), gastrointestinal diseases (10.1%), pulmonary/upper respiratory infection (8.4%), and other disorders (17.7%). The study protocol was approved by the ethics committee of the Medical Faculty of Heidelberg University.

Design and Measures
The potential predictors of psychiatric comorbidity were incorporated as part of patient and physician questionnaires. They were drawn from studies mentioned (23–30), from theoretical considerations, and from clinical experience. In addition, screening questions, physical symptoms, and psychosocial stressors were taken from the PHQ (15, 37–39), a self-report questionnaire designed for use in primary care that actually diagnoses specific disorders using diagnostic criteria from DSM-IV (40). Well-being of the patients was measured with the WBI-5 (41), and the SF-12 (42) was used for assessing health-related quality of life. Body image was appraised with the FKB-20 (43, 44), originally a German-language instrument with good reliability and validity and a stable factor structure consisting of two factors: perception of body dynamics and negative evaluation of the body.

The physicians, who were unaware of the results of either the patient questionnaire or the clinical interview, were asked on a separate questionnaire for medical diagnoses, psychiatric comorbidity, the cause of the complaints, and for the severity of illness. The SCID, Axis 1 Disorders (35, 36), was used as criterion standard for the presence of a psychiatric comorbidity. The four interviewers were blinded to the results of the questionnaire and the physicians’ ratings. All four raters attended a special SCID training course, which consisted of two full-day courses at an interval of 7 weeks and the supervised training of interviews between the courses. During the study, the interviews were videotaped and supervised weekly by an experienced clinician. Interrater reliability for the interviewers was assessed by reviewing videotapes of 60 original interviews. Each of the four raters reviewed randomly assigned interviews originally performed by another rater. Interrater reliability, measured as coefficient {kappa} (95% CI) (45), for the SCID diagnoses of depressive disorders was 1.0 (.68–1.0); for anxiety disorders, .96 (.67–1.0); for somatization disorders, .70 (.13–1.0); and for eating disorders, 1.0 (.33–1.0).

Statistical Analysis
Subgroup differences in the frequency of psychiatric disorders were analyzed using {chi}2 tests (two-tailed), or Fisher exact tests (two-tailed) if the cells had counts less than 10. t Tests (two-tailed) were used to compare well-being, quality of life, body image, and number of visits to the doctor by patients with and without psychiatric comorbidity.

To identify predictors of psychiatric comorbidity, logistic regression analyses (46) were conducted. The dependent variable for logistic regression analyses was the presence or absence of any psychiatric disorder in the SCID. All predictor variables were dichotomized. First, separate logistic regression analyses were performed for each indicator while controlling for age, sex, and educational level. Differences between odds ratios were considered significant if the 95% confidence intervals did not overlap. Second, to identify which indicators were independent predictors, a multivariate logistic regression analysis with stepwise forward variable selection was performed with inclusion of the patient variables that were significant in the separate regression analyses. Because the physician ratings and the treatment characteristics are the only variables not assessed by the patients themselves, they were not included in the multivariate regression analyses.

The final subset of variables represents the most valid predictors that can be investigated in the clinical interview to identify psychiatric comorbidity in medical outpatients. To estimate the likelihood that these predictors correspond to either the presence or the absence of a psychiatric comorbidity, the predictive values of a positive test and a negative test were evaluated. Additionally, the criterion validity of the predictors was characterized by sensitivity and specificity. The agreement of the predictors with the SCID was measured as coefficient {kappa} (47). The ability of the identified predictors to diagnose certain disorders was investigated for the three most common types of mental disorders in primary care: depressive, anxiety, and somatization disorders. Specifically, sensitivity and specificity were analyzed because they are the only test measures that do not depend on prevalence rate and can therefore be compared between separate disorders. Statistical analyses were performed using the software package of the SAS System for Windows (Release 8.01; SAS Institute Inc., Cary, NC).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 ACKNOWLEDGMENTS
 REFERENCES
 
One or more SCID Axis 1 psychiatric disorders were diagnosed in 102 patients (28.6%), with 29 patients (8.1%) having two or more psychiatric disorders. Most common were major depressive disorder (N = 22, 6.2%), other depressive disorder (N = 33, 9.2%), anxiety disorders (N = 48, 13.5%), somatization disorders (N = 14, 3.9%), and eating disorders (N = 12, 3.4%). There was no significant difference in disorder type or frequency between the outpatients from the Medical Hospital and the patients from general practitioners. Compared with patients without psychiatric comorbidity, the patients with psychiatric comorbidity reported significantly more visits to the doctor, poorer well-being (WBI-5), poorer mental functioning (SF-12), and a poorer body image (FKB-20; Table 1).


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  		TABLE 1. Differences of patients with and without psychiatric comorbiditya
 
The separate logistic regression analyses showed that the only demographic variable associated with an increased risk for psychiatric comorbidity was an age younger than 50 years (Table 2). Among the patient ratings, the perceptions that one’s illness had a psychological cause or had been inadequately explained by the physician were both significant predictors of psychiatric comorbidity. Other predictors included feeling misunderstood by physicians, being significantly troubled by three or more physical symptoms, social isolation, difficulty with one’s partner, and financial problems. The self-report of an impaired psychological condition and the experience of physical violence or abuse during the previous year had the highest odds ratios for psychiatric comorbidity. However, considering the overlap of the confidence intervals, these odds ratios were not significantly higher than the odds ratios of the other patient ratings. Finally, single screening questions on depressed mood and on nervousness, anxiety, or worries had a strong positive association with psychiatric comorbidity (odds ratio, 18.9, 95% CI, 8.9–39.8; and odds ratio, 28.9, 95% CI, 10.8–77.4, respectively). These two questions were significantly stronger predictors of psychiatric comorbidity than a doctor’s diagnosis (odds ratio, 3.6; 95% CI, 2.0–6.2). Significant treatment predictors included an existing psychopharmacological or psychotherapeutic treatment and visits to three or more different physicians during the last 3 months.


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  		TABLE 2. Indicators of psychiatric comorbidity: separate analysesa
 
Of all the patient variables, the multivariate stepwise logistic regression analysis identified four independent predictors of psychiatric comorbidity: the screening question for nervousness, anxiety, or worries; the screening question for depressed mood; the self-report of three or more bothersome physical symptoms; and difficulties in one’s partnership (Table 3). The operating characteristics for each predictor are shown in Table 4. The two screening questions, when assessed alone, showed respectable positive and negative predictive values and specificities, indicating that the response of the patient correctly predicts the presence of a psychiatric comorbidity in more than 80% of cases. However, sensitivity of the single predictors was between 41% and 51%, suggesting that their use as the sole case-finding instrument for psychiatric disorders may be problematic. When the predictors were analyzed in combination, which reflects the situation of most clinical interviews, another picture emerges: if the patient consented to any of the four predictors, sensitivity was approximately twice as high compared with the use of a single predictor. Moreover, every patient giving an affirmative response to both screening questions had an Axis 1 disorder diagnosed in the clinical interview. To summarize, the combined assessment of the four predictors resulted in positive predictive values as high as 100%, negative predictive values as high as 91%, sensitivities as high as 86%, and specificities as high as 100%. Agreement of the predictors with the SCID can be described according to the benchmarks furnished by Landis and Koch (47) as "fair" ({kappa} = .27) to almost "substantial" ({kappa} = .56).


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  		TABLE 3. Predictors of psychiatric comorbidity: multivariate analysisa
 

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  		TABLE 4. Operating characteristics of independent predictors of psychiatric comorbiditya
 
The operating characteristics of the four independent predictors for identifying certain mental disorders are displayed in Table 5. Among the four predictors, the screening question for depressed mood had the highest sensitivity for depressive disorders, and the presence of three or more physical symptoms was most predictive for somatization disorders. The other two predictors had a similar strength of association with depressive, anxiety, and somatization disorders. The operating characteristics of any single predictor, however, were inferior to those of the four predictors taken together.


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  		TABLE 5. Operating characteristics of individual items for certain mental disordersa
 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
ACKNOWLEDGMENTS
 REFERENCES
 
As described in earlier studies (15–19), the diagnosis of psychiatric comorbidity by medical doctors is often suboptimal. Besides simply recognizing mental disorders when they are present, doctors must also contend with other disincentives such as limited time, stigmatization, reimbursement barriers, inadequate access to mental health specialty care, and other competing demands (48–50). Efforts to increase diagnosis of comorbid psychiatric disorders in medical patients may be hampered unless there are system changes that optimize treatment and follow-up. Thus, many of the earlier studies that focused simply on enhanced recognition without supporting the primary physician in management and follow-up failed to improve patient outcomes substantially (51) More recent studies incorporating recognition of mental disorders as one component that also involves enhanced care management and mental health specialist access have produced more favorable outcomes (52).

Our study identified factors that increase the likelihood of a patient having an Axis 1 psychiatric disorder. Multivariate analysis revealed that four factors in particular were independent predictors of psychiatric comorbidity. These factors were all variables measured by patient self-report and included 1) nervousness, anxiety, edginess, or worrying; 2) depressed mood; 3) three or more troublesome physical symptoms; and 4) being bothered by difficulties with the partner. The especially high odds ratio for the screening question for nervousness, anxiety, edginess, or worrying can probably be explained by the broad range of depressive and anxiety symptoms covered by that question. Information about these four predictors can identify as many as 86% of cases with psychiatric comorbidity. If the patient affirms one of these predictors, psychiatric comorbidity is probable, and affirmation of two or more predictors makes psychiatric comorbidity almost certain.

The predictors we identified are best viewed as complementary to current case-finding instruments rather than substitutes for them. The operating characteristics of a combined use of these four predictors are comparable with those of established case-finding instruments for psychiatric disorders (15, 37, 53, 54) . However, some of these screening instruments, eg, the PHQ (37) or the Hospital Anxiety and Depression Scale (53), also allow screening for specific mental disorders. Nevertheless, there are several ways in which knowledge of these four predictors could be clinically useful. First, they are factors that may readily become apparent or be elicited in routine patient inquiry, such as anxious or depressed mood, multiple somatic symptoms, or interpersonal distress. As such, they can be efficiently and naturalistically incorporated into the provider’s standard clinical interview, in contrast with the separate administration of longer psychiatric questionnaires. Along this line, our findings confirm several previous predictors and substantiate several others (23–27). Second, having a few easy-to-remember clinical variables with high diagnostic yield has proven a popular and pragmatic means of screening for common mental disorders in a variety of busy practice settings. Examples include the CAGE questionnaire for alcohol disorders (55) and one to two screening questions for depressive disorders (28, 30). Patients for whom one or more of these clinical predictors are identified would be candidates for further inquiry, by using either validated diagnostic questionnaires or structured criteria-based interviews, to determine precisely the presence and specific type of psychiatric disorder.

Although our sample included clinic outpatients and primary care patients, there was no significant difference in the frequency and type of psychiatric disorders between the two groups. Nonetheless, external cross validation of the predictors in different samples is advisable. Of course, the predictors described here do not reflect the only questions that may increase the suspicion of psychiatric comorbidity in medical outpatients, and neither should they replace a trusting patient-doctor relationship. They do, however, represent simple clinical information that may heighten the recognition of common mental disorders.


   ACKNOWLEDGMENTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 ACKNOWLEDGMENTS
REFERENCES
 
This study was supported by the medical faculty of the University of Heidelberg, Germany (project 121/2000), and an additional unrestricted research grant from Pfizer, Germany. Our sincere thanks to the doctors and patients who collaborated in this study. We are very grateful to Christine Buchholz, Dipl-Psych, who organized data collection and performed a substantial part of the SCIDs, and to Dr. Werner de Cruppé for his helpful comments regarding the manuscript. Finally, we would like to thank our students Levke Willand and Ingeborg Warnke, who supported us during important phases of the study and did a great job during data collection.

Received for publication September 15, 2002.


   REFERENCES
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 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 ACKNOWLEDGMENTS
 REFERENCES
 

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