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Transcript
Health-Related Quality of Life in Primary Care Patients With Mental Disorders Results From the PRIME-MD 1000 Robert L. Spitzer, MD; Kurt Kroenke, MD; Mark Linzer, MD; Steven deGruy III, MD; David Brody, MD; Mark Davies, MS Study R. Hahn, MD; Janet B. W. Williams, DSW; Frank Verloin Objective.\p=m-\Todetermine if different mental disorders commonly seen in primary care are uniquely associated with distinctive patterns of impairment in the components of health-related quality of life (HRQL) and how this compares with the impairment seen in common medical disorders. Design.\p=m-\Survey. Setting.\p=m-\Fourprimary care clinics. Subjects.\p=m-\Atotal of 1000 adult patients (369 selected by convenience and 631 selected by site-specific methods to avoid sampling bias) assessed by 31 primary care physicians using PRIME-MD (Primary Care Evaluation of Mental Disorders) to make diagnoses of mood, anxiety, alcohol, somatoform, and eating disorders. Main Outcome Measures.\p=m-\Thesix scales of the Short-Form General Health Survey and self-reported disability days, adjusting for demographic variables as well as psychiatric and medical comorbidity. Results.\p=m-\Mood,anxiety, somatoform, and eating disorders were associated with substantial impairment in HRQL. Impairment was also present in patients who only had subthreshold mental disorder diagnoses, such as minor depression and anxiety disorder not otherwise specified. Mental disorders, particularly mood disorders, accounted for considerably more of the impairment on all domains of HRQL than did common medical disorders. Finally, we found marked differences in the pattern of impairment among different groups of mental disorders just as others have reported unique patterns associated with different medical disorders. Whereas mood disorders had a pervasive effect on all domains of HRQL, anxiety, somatoform, and eating disorders affected only selected domains. Conclusions.\p=m-\Mentaldisorders commonly seen in primary care are not only associated with more impairment in HRQL than common medical disorders, but also have distinct patterns of impairment. Primary care directed at improving HRQL needs to focus on the recognition and treatment of common mental disorders. Outcomes studies of mental disorders in both primary care and psychiatric settings should include multidimensional measures of HRQL. (JAMA. 1995;274:1511-1517) HEALTH-RELATED (HRQL) is increasingly quality of Ufe recognized as a From the Biometrics Research Department, New York State Psychiatric Institute and the Department of Psychiatry, Columbia University, New York, NY (Drs Spitzer and Williams and Mr Davies); Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Md (Dr Kroenke); Department of Medicine, University of Wisconsin Medical School, Madison (Dr Linzer); Department of Medicine, Albert Einstein College of Medicine, Bronx, NY (Dr Hahn); Department of Family Practice in Community Medicine, University of South Alabama College of Medicine, Mobile (Dr deGruy); and Department of Medicine, Mercy Catholic Medical Center, Darby, Pa (Dr Brody). Corresponding author: Robert L. Spitzer, MD, Biometrics Research Department, Unit 74, New York State Psychiatric Institute, 722 W 168 St, New York, NY 10032. major outcome in patient care and clini¬ cal research16 and as a term embraces the concepts of functional status, health status, quality of life, and well-being. A comprehensive assessment of HRQL typically measures the effects of health on a number of domains, including physi¬ cal, social and role functioning, freedom from bodily pain, the subject's percep¬ tion of general health, and mental health.5"8 In addition, the number of dis¬ ability days is frequently used as another measure offunctional impairment.9"11 Spe¬ cific medical disorders have been shown to have different effects on these com¬ ponents of HRQL. For example, arthri- tis is associated with increased bodily pain and impairment in physical func¬ tioning but little impairment in social or role functioning, whereas cardiac disease results in marked impairment across mul¬ tiple domains of HRQL.11·12 For editorial comment see 1557. An important question is whether dif¬ ferent mental disorders are also associ¬ ated with different patterns of impair¬ ment in the domains of HRQL. If so, this would not only identify the particular areas in which patients with different mental disorders are impaired, but also the domains that one would focus on when evaluating treatment. Since most pri¬ mary care patients with mental disor¬ ders have more than one type of mental disorder,13"15 an important additional ques¬ tion is the extent to which the overall impairment associated with a particular group of mental disorder (eg, mood, anxi¬ ety, eating, somatoform, and alcohol) is explained by the co-occurrence of other groups of mental disorders (psychiatric comorbidity) frequently present in pa¬ tients with that type of mental disorder. For example, to what extent is the im¬ pairment associated with anxiety disor¬ ders actually a function of the common occurrence of mood disorders among pa¬ tients with anxiety disorders? A third question is how the impair¬ ment associated with common mental dis¬ orders compares with that in patients with general medical disorders. The Medical Outcomes Study (MOS) demonstrated that patients with depressive symptoms had impairment in multiple domains of HRQL, equal to or exceeding that asso¬ ciated with patients with chronic general medical conditions.11·12 However, the MOS focused only on depression and did not assess the effects of other mental disor¬ ders common in primary care, such as anxiety, somatoform, and alcohol disor¬ ders. A recent World Health Organiza¬ tion (WHO) collaborative study14 in 15 Downloaded from jama.ama-assn.org at Florida State University on November 15, 2011 Specific Mental Table 1.—Association of Disorders With Health-Related Quality of Life Short-Form General Health No. of Mental Disorder No mental disorder, mean (SD) Average Difference Major depression Dysthymia Minor depression Partial depression! Anxiety, not otherwise specified Generalized anxiety Patients* 614 Physical Functioning 77.8(19.4) Bodily Pain 66.2 (23.7) Somatoform, not otherwise Hypochondriasis Alcohol! Binge eating specified General Functioning 84.4 (29.9) Health 80.8 in SF-20 Score Between Patients With No Mental Disorders and Patients With a Scales (20.8) Specific Mental Social Functioning 67.0 (20.0) Mental Health 65.3(12.9) Disorderft 115 -21.8 -22.7 -45.5 -30.4 -31.7 78 -25.1 -21.8 -52.6 -33.4 -34.3 -39.7 64 -17.7 63 -17.1 -16.2 -22.1 -27.6 -25.8 -23.0 -14.7 -32.3 -22.1 -43.6 -28.5 -28.1 -43.1 -29.6 -30.3 -36.3 -36.1 -29.0 -16.0 -12.4 -6.4 -17.7 -12.9 -13.5 -10.6 -27.7 -20.8 -24.5 -35.7 -27.5 70 -21.8 -24.1 82 -19.0 -28.6 -43.3 42 -16.0 -15.9 -11.2 -19.2 Panic Multisomatoform Survey (SF-20) Role -23.2 30 •Number in total sample with disorder; numbers are slightly less for each SF-20 scale due to missing data (see text). tAverage decrease in SF-20 scale score in patients with each disorder compared with the 614 patients with no mental disorder, adjusted for age, sex, education, site, and each of nine types of major medical disorders (see text). To determine the predicted score for a patient with a particular disorder, subtract the deviation score from the mean score for patients with no mental disorder. deviations are significant at P<.001, except P=.01 for bodily pain for hypochondriasis; and P=.11, bodily pain for alcohol. Indicates major depression in partial remission. ¿Partial depression HAIcohol indicates probable alcohol abuse or dependence. countries showed that impairment in func¬ tional status was observed with a variety of mental disorders, but there were only modest differences in the measures of functional status among the mental dis¬ orders when controlling for psychiatric comorbidity.14 However, important dif¬ ferences in the patterns of impairment might have emerged had this study in¬ cluded additional HRQL constructs, such as measures of well-being. In this article, we use data from the PRIME-MD 1000 study16 to address three major questions: What is the de¬ gree of impairment in HRQL associated with specific mental disorders commonly seen in primary care? Are certain groups of mental disorders associated with dis¬ tinctive patterns of impairment in the components of HRQL, after adjusting for psychiatric comorbidity? How does the impairment associated with differ¬ ent mental disorders compare with that seen in general medical disorders? METHODS One thousand patients at four primary care sites were evaluated with PRIMEMD (an acronym for Primary Care Evalu¬ ation of Mental Disorders), a two-stage screening and interview procedure that enables a primary care physician to rap¬ idly diagnose 18 specific mental disor¬ ders in five major groups: mood, anxiety, somatoform, alcohol, and eating disor¬ ders (see Table 1 for a list of the 12 most common specific PRIME-MD diagnoses). The 1000 patients were selected from 1360 patients who presented to the four sites from January 1992 to March 1993. The patients were selected by conve¬ nience (n=369) and by site-specific meth- ods (n=631) to avoid sampling bias. These two groups of patients did not differ sig¬ nificantly in terms of age, sex, ethnicity, education, functional status, or frequency of PRIME-MD diagnosis. The PRIMEMD evaluations were made by 31 pri¬ mary care physicians (seven to nine per site). Their mean age was 40 years. At three of the sites (76% of the 31 physi¬ cians), all were trained in internal medi¬ cine; the remainder were trained in fam¬ ily practice. The mean age of the patients was 55 years with a range of Í8 to 91 years; 60% were women, 58% were white, and 28% were college graduates. Of the total sample, 77% were established clinic patients; the remainder were seen for the first time. The physician noted the presence of eight types of general medi¬ cal disorders (plus "other"), the most com¬ mon being hypertension (48% of all pa¬ tients), arthritis (23%), diabetes (17%), and cardiac disease (15%). The number of types of current medical disorders was none in 18% of the patients, one in 34%, two in 29%, three in 14%, and four or more in 5%. Details of the PRIME-MD 1000 study, including data validating the diagnostic procedure, have been de¬ scribed previously.16 One or more PRIME-MD disorders were diagnosed in 39% of the patients, including a mood disorder in 26%, anxi¬ ety disorder in 18%, somatoform disor¬ der in 14%, alcohol disorder in 5%, and eating disorder in 3%. Whereas 26% of the patients had a mental disorder that met the full criteria for a specific disor¬ der according to the criteria for diagno¬ sis of the Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition (DSM-III-R), 13% of the patients had only a "subthreshold" diag¬ nosis, such as minor depression or anxi¬ ety disorder not otherwise specified (NOS). Subthreshold diagnoses encom¬ pass fewer symptoms than are required for any specific DSM-III-R diagnosis but were included because they are associ¬ ated with considerable impairment, and patients with these disorders may ben¬ efit from monitoring or treatment.9,11'17"19 Psychiatric comorbidity was common. Among patients with a psychiatric di¬ agnosis, 56% had a diagnosis from more than one major diagnostic group (eg, both a mood and an anxiety disorder), and 29% had diagnoses from three or groups. The co-occurrence rates (ie, percentage of patients who had a disorder from another major group) was high for all major diagnostic groups: eat¬ ing disorders, 84%; anxiety, 82%; somato¬ form, 73%; mood, 65%; and alcohol, 47%. The domains of HRQL were evalu¬ ated with the MOS Short-Form General Health Survey (SF-20).7·20 This 20-item self-report instrument includes six scales measuring the effect of health on physi¬ cal and role functioning, bodily pain, gen¬ eral health perception, social function¬ ing, and mental health. The six scales are scored from 0 (maximal impairment) to 100 (no impairment). The physical functioning scale consists of six items describing limitations due to health in more activities, such as sports, climbing stairs, walking, dressing, and bathing. The role functioning scale consists of two items describing the extent to which health interferes with work, housework, or schoolwork. The bodily pain scale is a single item measuring the degree to which the patient is free of pain. The Downloaded from jama.ama-assn.org at Florida State University on November 15, 2011 Table 2.—Unique Association of Mental and Medical Disorders with Health-Related Quality of Life Short-Form General Health No. of Disorder Mood Alcohoiy Eating Hypertension General Social Health Functioning -0.48 -0.49 Mental Health Physical Functioning Bodily 260 -0.50 -0.37 -0.25§ Functioning -0.58 -0.24§ -0.22 -0.42 -0.47 -0.67 -0.13 +0.07 -0.09 -0.13 -0.17 -0.21 -0.67+ -0.34 +0.03 -0.05 +0.07 +0.09 -0.12 -0.24§ 139 Pain -0.49§ +0.02 -0.61 -0.04 -0.39+ -0.59 -0.28 -0.22§ -0.17 170 -0.13 +0.07 -0.10 -0.35 -0.32+ -0.35 -0.12 -0.41 Cardiac 0.25§ -0.97 -0.82 +0.04 480 Arthritis Diabetes Deviation in Effect Sizef Role Patients* Anxiety Somatoform Survey (SF-20) Scales, Average -0.01 -0.06 -0.14 Pulmonary 78 -0.42 -0.07 -0.30§ -0.10 -0.14 -0.13 Renal 33 -0.33 -0.32 -0.23 -0.46§ -0.17 +0.09 Cancer 20 -0.52 +0.03 -0.40 -0.10 -0.03 Hepatic 19 -0.24 -0.05 -0.54 -0.35 *Number in total sample with disorder; numbers are slightly tess for each SF-20 scale due to missing data (see text). tPatients with each disorder are compared with patients without that disorder, adjusted for age, sex, education, site, and each of the other disorders. Effect size (see text) is average deviation divided by SD on that scale for entire sample. For example, patients with a mood disorder have an adjusted score on the SF-20 physical functioning scale that is half (0.50) of an SD lower than patients without a mood disorder. <.001. §P<.01. ¡Alcohol indicates probable alcohol abuse or dependence. functioning scale is a single item describing the extent to which health interferes with social activities, such as visiting friends or relatives. General health perceptions is a five-item scale describing the patient's overall rating oftheir current health. Finally, the men¬ social tal health scale consists of five items describing general mood or affect, in¬ cluding depression, anxiety, and posi¬ tive well-being. In the tables and fig¬ ures in this article, the scales are or¬ dered as presented herein to facilitate interpretation of the scales from left to right, as moving from the best indica¬ tors of physical health status (physical and role functioning and bodily pain) to measures that equally tap physical and mental health status (general health per¬ ceptions and social functioning) to the best measure of mental health status (mental health).6·21 As an additional mea¬ sure of functional status, disability days were estimated by asking patients how many days in the past 3 months they were unable to pursue their usual ac¬ tivities because of not feeling well. For each SF-20 scale, as recommended by the authors, total scores were esti¬ mated by prorating the available data if information was available for more than 60% of the scale items; this resulted in missing data on 7% to 12% of the sub¬ jects for the different scales. There was missing data on 10% of the subjects for disability days. RESULTS Impairment Associated With Specific Mental Disorders Table 1 shows the association of the 12 most common mental disorders included in PRIME-MD and the six SF-20 scales, without adjusting for psychiatric comorbidity, demonstrating the severity of im¬ pairment compared with patients with no mental disorder. The mean SF-20 score for patients with each of the disorders was calculated and compared with a single reference group: the 614 patients with¬ out a mental disorder. The first row pre¬ sents the mean SF-20 scores for the pa¬ tients without a mental disorder (most of whom had a medical disorder). The re¬ mainder of Table 1 presents the average difference of SF-20 scores for the 12 men¬ tal disorders, adjusted by regression analysis for age, sex, education, site, and each of nine types of physical disorders with the SF-20 scale. For example, the patients with major depression have an adjusted mean physical score 22 points lower than patients without a mental dis¬ order. (A decrement of >10 points on most SF-20 scales is generally similar to that associated with various chronic medi¬ cal disorders.7·12) As can be seen, all mental disorders are associated with substantial decre¬ ments in SF-20 scores across all six scales. Although in general the impair¬ ment is greatest for threshold diagnoses, even subthreshold diagnoses, such as minor depression, anxiety disorder NOS, somatoform disorder NOS, and binge eating disorder, have considerable im¬ pairment. Patterns of Impairment in HRQL Uniquely Associated With Specific Mental Disorders For the remainder of the analyses, our primary aim was to estimate the unique association of each type of mental and medical disorder with the components of The unique association of psy¬ chiatric and medical disorders with the components of HRQL was estimated by regressing each of the six SF-20 scores on the five major groups of mental dis¬ orders and the nine general medical dis¬ orders, again adjusting for age, sex, edu¬ cation, and site. Indicator (dummy) vari¬ ables were used for each of the mental and general medical disorders. This method of adjusting for comorbidity iden¬ tifies the independent association of each type of mental and medical disorder with each of the HRQL domains. Since the SF-20 scales have different variability, to make comparisons across the scales the results are presented as effect size (rather than as the raw scores used in Table 1). The effect size is the difference in scale score between patients with and without the disorder, divided by the SD of the scale in the total sample. Effect sizes 0.20, 0.50, and 0.80 or greater are considered small, moderate, and large, respectively.22 It should be noted that whereas in Table 1 all patients with a particular disorder were contrasted to the same reference group (patients with no mental disorder), in Table 2 the reference group is patients without the disorder being examined. The unique association of the five ma¬ jor groups of mental disorders and the nine types of general medical disorders with the six SF-20 scales is presented in Table 2. Patients with a mood disorder had adjusted scores on the physical func¬ tioning scale that were half (0.50) of an SD lower than patients without a mood disorder, a moderately large difference. After adjusting for demographic vari¬ ables and psychiatric and medical comorbidity, each of the five groups of mental disorders produces a distinctive HRQL. Downloaded from jama.ama-assn.org at Florida State University on November 15, 2011 Table 3.—Unique Association of Mental and General Medical Disorders With in the Past 3 Months Excess Disorder No. of Patients Hepatic 19 Cancer 20 Pulmonary 78 12.5t 9.0 Standardized Regression Coefficient 5.7 to 19.4 0.120 0.075 6.2§ 4.3§ 2.4 to 9.9 0.111 1.7 to 6.9 0.125 4.0t 3.5Í 0.8 to 7.1 0.090 150 2.8 -0.1 to 5.7 33 1.9 -3.7 to 7.5 -1.0 to 4.0 0.041 -0.1 -2.3 to 2.1 -0.004 260 Somatoform 139 Anxiety Renal Due to Disorder* 95% Confidence Interval 1.2 to 16.8 Mood Cardiac Disability Days Uniquely Self-reported Disability Days Arthritis 230 Hypertension 480 Diabetes 0.5 to 6.5 0.068 -0.6 -3.4 to 2.1 Eating 32 -1.1 -6.6 to 4.3 -0.014 Alcohol;' 51 -4.1 -8.4 to 0.3 -0.062 *Patients with each disorder are compared with patients without that disorder, adjusted for age, sex, education, site, and each of the other disorders. For example, patients with mood disorders report 4.3 excess disability days compared with patients without a mood disorder, while those with an alcohol disorder report 4.1 fewer disability days than those without an alcohol disorder. The entire patient sample had a tP<001. <.05. §P<01. HAIcohol indicates alcohol abuse or dependence. pattern of impairment. Whereas mood disorders are associated with all com¬ ponents of HRQL, anxiety disorders pri¬ marily are associated with impaired so¬ cial functioning and mental health. So¬ matoform disorders have a prominent association with role functioning, bodily pain, and general perception of health but have no association with the mental health scale. The unique impairment as¬ sociated with eating disorders is limited to social functioning and bodily pain. Sur¬ prisingly, alcohol disorder did not have a significant association with any of the measures. The patterns of impairment associated with medical disorders is gen¬ erally similar in this sample to that pre¬ viously reported in the MOS, even though the two study samples differed in the prevalence of medical disorders, and in the MOS, the data were not con¬ trolled for mental disorders.12 In a separate analysis, the 12 most common mental disorders were entered into the regression equation to deter¬ mine if, within each major diagnostic group, specific mood, anxiety, or somato¬ form disorders had different profiles from the larger diagnostic group (data not shown, available on request). Of in¬ terest, the unique impairment associ¬ ated with dysthymia was considerably less than the other three mood disor¬ ders (average effect size for SF-20 scales for dysthymia, 0.32; major depression, 0.61; partial remission of major depres¬ sion, 0.50; minor depression, 0.56). This suggests that much of the impairment seen in patients with dysthymia may be due to frequent comorbidity with other mental disorders (such as major depres¬ sion in patients with "double depres- mean of 5.0 disability days. sion"). In addition, the unique ment associated with specific impair¬ anxiety disorders, such as panic disorder and generalized anxiety disorder, was sub¬ stantially lower than any of the mood disorders (mean effect size for panic dis¬ order, 0.13; generalized anxiety disor¬ der, 0.19; anxiety disorder NOS, 0.32). The unique association of mental and general medical disorders with the num¬ ber of self-reported curability days in the past 3 months is shown in Table 3, using the same regressionanalysis that was used to predict the SF-20 scores presented in Table 2. The excess number of disability days is the difference between the num¬ ber of disability days of patients with and without each type of disorder, adjusted for demographic characteristics and comorbidity, and is presented in rank order. For example, patients with mood disor¬ ders, compared with patients without mood disorders, report 4.3 excess disabil¬ ity days. The fourth column is the stan¬ dardized regression coefficient and allows for comparisons among the disorders, ad¬ justing for prevalence. Thus, cancer, while associated with a larger number of excess disability days compared with mood dis¬ orders (9.0 vs 4.3 days), accounts for a smaller amount of the overall variation in disability days because of its much lower prevalence (ß=.075 vs ß=.125). Because mood, somatoform, and anxiety disorders are all prevalent and associated with a statistically significant number of excess disability days, they account for a sub¬ stantial amount of the overall impairment in our primary care patients. Considering both SF-20 scales and disability days, the common mental dis¬ orders are associated with greater im- pairment than several common medical disorders, such as cardiac disease, ar¬ thritis, hypertension, and diabetes, on most measures except physical function¬ ing. However, even for physical func¬ tioning, mood disorders are associated with impairment comparable with that seen in patients with cardiac disease and arthritis. Figure 1 illustrates the data from Table 2 for the three most common groups of mental disorders and forthree common medical disorders, cardiac dis¬ ease, arthritis, and diabetes. To determine the extent to which the impairment associated with different groups of mental disorders is explained by the co-occurrence of other groups of mental disorders (psychiatric comorbid¬ ity), the drop in the average effect size for the SF-20 scales was calculated for the five groups of mental disorders, ad¬ justing for psychiatric comorbidity. For example, the unadjusted average effect size of mood disorders on the six SF-20 scales was 0.83; after adjusting for comorbidity, it was 0.59, a drop of 29%. The drop in the other four groups of mental disorders was larger: anxiety, 53% (0.78 to 0.37), somatoform, 52% (0.69 to 0.33); alcohol, 53% (0.17 to 0.08); and eating, 52% (0.63 to 0.30). Comparison of Impairment in Mental Disorders With Impairment in General Medical Disorders To compare the impairment in HRQL associated with various mental disorders with the impairment associated with gen¬ eral medical disorders, the unique pro¬ portion of variance in impairment ac¬ counted for by mental disorders and medi¬ cal disorders was calculated. The unique proportion of variance explained by the mental disorders was determined by cal¬ culating the difference between the vari¬ ance explained by entering all factors (the five groups of mental disorders and the nine types of general medical disorders, as well as age, sex, education, and site) and the proportion explained by entering all but the mental disorders. An analagous procedure was done to calculate the unique proportion of variance explained by the medical disorders. For all scales, Figure 2 shows that more of the variance was uniquely accounted for by the men¬ tal disorders than by the medical disor¬ ders. This would be expected for the men¬ tal health scale, but the amount of vari¬ ance accounted for by mental disorders is also dramatically greater for the role func¬ tioning, social functioning, and general health scales. COMMENT Similar to previous studies,9"11'1417·18 we found that depressive disorders in pri¬ mary care are associated with substan- Downloaded from jama.ama-assn.org at Florida State University on November 15, 2011 tial impairment in HRQL. Second, we found that significant impairment is also seen in patients with anxiety, somato¬ form, and eating disorders. Third, the con¬ siderable impairment present in patients who only had subthreshold mental dis¬ order diagnoses adds to the growing body of evidence that primary care patients with subthreshold disorders also merit clinical attention.9'11'17"19 Fourth, mental disorders, particularly depressive disor¬ ders, accounted for considerably more of the impairment than did common medi¬ cal disorders on all of the domains of HQRL. Finally, we found that there are marked differences in the pattern of im¬ pairment among different groups of men¬ tal disorders just as others have reported unique patterns associated with different medical disorders.12 There are two reasons to believe that the results of our study are generalizable to other primary care settings. In comparing our sample with patients seen by internists in the United States over¬ all as determined by the National Am¬ bulatory Medical Care Survey,23 our pa¬ tients were nearly identical in terms of sex (60% vs 58% women) and age (55 vs 54.8 years). Although our sample had Figure 1.—Unique association of common mental and general Health Survey health-related quality-of-life scales. medical disorders with Short-Form General minority patients (42% vs 19%), minority status rarely had a significant more association with any of the outcome vari¬ ables. The most common chronic medi¬ cal conditions in our sample (hyperten¬ sion, arthritis, diabetes, and cardiac dis¬ ease) are also the leading diagnoses in internal medicine practice overall.24 In addition, the patterns and magnitude of impairment across the SF-20 scales that we observed in patients with common general medical disorders were gener¬ ally similar to that seen in the MOS, a landmark study of health outcomes in large samples of medical patients from multiple practice sites.12 We recognize that the cross-sectional design of our study does not prove that the mental disorders in our patients caused the associated impairment in HRQL. It is possible that for some pa¬ tients impaired functioning itself could result in psychopathology. However, multiple studies,25,26 primarily with de¬ pressive disorders, as well as a wealth of clinical experience, indicate that ef¬ fective treatment of mental disorders frequently improves functional status. Therefore it is reasonable to assume that mental disorders, as do general medical disorders, result in impaired HRQL. Our study has important implications for the primary care clinician. Mental dis¬ orders are not only common in primary care but are frequently overlooked because primary care patients with men¬ tal disorders typically present with physi¬ cal symptoms rather than psychological Figure 2.—Proportion of variance in Short-Form General Health Survey (SF-20) health-related quality-oflife scales uniquely accounted for by mental disorders and physical disorders (ie, general medical disorders). symptoms.27 Decrements in HRQL from mental disorders are usually greater than the decreases in HRQL resulting from common physical disorders. In short, men¬ tal disorders, such as depression, anxi¬ ety, and somatoform, may be the most important determinants of how our pa¬ tients feel about themselves, their lives, and the efficacy of their health care. If the goal of primary care practice is to im¬ prove HRQL, then our study suggests the value of more widespread screening for mental disorders (eg, in new patients, patients with poorly explained symptoms, and periodic health examinations) using a validated and efficient instrument. While a number of instruments are available to screen for depression in primary care,28 an advantage of PRIME-MD is that it also assesses other common mental dis¬ orders. In addition to screening for mental dis¬ orders, should primary care clinicians in¬ corporate measures of HRQL in their practice? Self-administered versions of the SF-3629 (and even briefer SF-12) as well as other simple HRQL measures30 are now available in versions that can be computer scored for easier use in the busy outpatient setting. As for impairment in Downloaded from jama.ama-assn.org at Florida State University on November 15, 2011 HRQL that is the result of psychopathology, our study suggests two uses of mea¬ sures of HRQL in clinical practice. First, in patients being treated for one or more mental disorders, the pattern of HRQL impairment may suggest parameters with which to follow the patient's progress to¬ ward recovery and perhaps even which aspects of management should be em¬ phasized. For example, restoration of nor¬ mal role function may be an important therapeutic goal for a patient diagnosed with a somatoform disorder, while efforts at normalization of social functioning may be a management focus for patients with mood, anxiety, and eating disorders. Sec¬ ond, in the patient without a diagnosed mental disorder, unexpectedly low scores of HRQL or a deterioration in scores over time not accounted for by a general medi¬ cal disorder, even on non-mental health subscales such as physical pain or role functioning, should immediately suggest undetected mental illness. The clinician may suspect a specific mental illness ac¬ cording to the patterns just described, but because the patterns of impairment do not approach the level of specificity necessary for making a mental disorder diagnosis, the use of a diagnostic proce¬ dure, such as PRIME-MD, would then be indicated. This use of HRQL measures as a screen for mental disorders would jus¬ tify its use in periodic health examina¬ tions and in evaluating new patients. Beyond its potential utility in screen¬ ing for or managing the care of patients with mental disorders, HRQL assessment is increasingly being recognized as an im¬ portant indicator ofthe quality of primary care.25 While impairments in HRQL are associated with increased utilization of health care services, such impairment is often not recognized by the clinician.31"34 When surveyed, patients actually report that they would like their physicians to assess their functional performance and emotional well-being as a part of medical care.33 Future studies may strengthen the argument that there are multiple clini¬ cal uses justifying broader use of HRQL measurements in primary care. Our findings have important implica¬ tions for outcomes research in psychia¬ try. Although the SF-20 has been used extensively in evaluating patients with medical disorders, our data clearly show that the multiple domains assessed by the instrument are relevant to studying the relationship between mental disor¬ ders and patient functioning and wellbeing. There may be value in routinely including multiple dimensions of HRQL6 in psychiatric outcomes studies rather than relying solely, as is often the case, on a single global measure of functioning, such as the Global Assessment Scale.35,36 Different groups of mental disorders each uniquely associated with dis¬ tinct patterns of impairment. Mood dis¬ orders were characterized by large as¬ sociations with all measures of HRQL and, compared with the other common mental disorders, had the smallest drop in overall impairment when adjusting for psychiatric comorbidity. This indicates that the impairment seen in patients with mood disorders is less attributable to psy¬ chiatric comorbidity than is the case for the other mental disorders. These find¬ ings justify the current emphasis on the recognition and treatment of mood dis¬ orders in primary care.37 The unique impairment associated with anxiety disorders was limited to three domains: social and role function¬ ing, mental health, and an increase in disability days. On the other hand, in a large study of mental disorders in the community,38 anxiety disorders had a substantial and independent association with physical functioning and general health perception, even though major depression had an even larger associa¬ tion. Whereas other studies have focused on the disability associated with panic disorder,39,40 in our primary care sample, patients who had generalized anxiety disorder or anxiety disorder NOS ex¬ were perienced comparable or even greater impairment. Clearly, further research is needed to clarify the independent as¬ sociation of specific anxiety diagnoses as well as anxiety disorders as a group with the multiple domains of HRQL. Somatoform disorders were associated with impairment in role functioning, bodily pain, and general health perceptions. The absence of any impairment on the mental health scale of the SF-20 in these patients is not surprising, considering that this scale consists of five items that primarily assess mood and anxiety symptoms. Since somatoform disorders are characterized by multiple unexplained physical symp¬ toms, the impairment shown on the role functioning scale is also expected, but it is curious that such patients did not re¬ port significant impairment on the physi¬ cal functioning scale. It is possible that the symptoms reported by such patients are not severe enough to interfere with the type of activities included in the physi¬ cal functioning scale, such as walking, climbing stairs, or bathing. Eating disorders, which in our sample consisted almost entirely of patients with binge eating disorder, were character¬ ized by significant impairment in social functioning, mental health, and bodily pain. The impairment in social function¬ ing is consistent with the fact that such patients, who are usually markedly over¬ weight, are extremely sensitive about their weight and appearance.41 The lack of impairment in any of the domains for patients with alcohol disor¬ ders, after controlling for psychiatric comorbidity, is puzzling since one would cer¬ tainly expect impairment in social and role functioning. This might in part be due to the less severe nature of alcohol disorders seen in primary care settings14,38,42 or, more likely, to poor sensitivity of our measure of alcohol problems caused by patient de¬ nial, commonly present in patients with alcohol abuse or dependence. In contrast to our findings, the recent WHO study found that differences in im¬ pairment among specific mental disorders diminished considerably after controlling for psychiatric comorbidity.14 One reason that we were able to identify unique pat¬ terns while the WHO study did not may in part be due to the fact that their study included only measures of functional sta¬ tus, whereas ours also included measures of well-being. In addition, we used multivariate analysis, and they used a dif¬ ferent method to adjust for psychiatric comorbidity. First they compared impair¬ ment among all patients with different groups of mental disorders. Then they restricted their analysis to examining im¬ pairment among patients with only "pure" forms of each disorder (a single mental disorder), thus excluding all patients with psychiatric comorbidity. Although our sample size does not allow us to perform this type of analysis, it is not clear which method of controlling for psychiatric comorbidity gives the most valid results. It is possible that patients with only a single disorder are not representative of all pa¬ tients with that disorder. Our study has several possible limita¬ tions. Although our physicians noted the presence or absence of specific groups of medical disorders, we had no measures of illness severity. Clearly, patients with more serious medical disorders would be expected to have greater impairment. However, we also had no measures of severity of mental disorders, and it would also be expected that patients with more severe mental disorders would also have greater impairment. Since our study group was a primary care sample, the majority of patients with mental and medical dis¬ orders had illnesses that were mild to mod¬ erate in severity. Therefore, we believe that our comparisons of the association of mental and medical disorders with HRQL were probably not biased by a lack of severity measures. However, in future studies it would be valuable to explore the effects that severity of both medical and mental disorders have on impairment. A second potential limitation of our study is that measures of impairment in HRQL relied solely on patient self-re¬ port. There is the possibility that many patients with mental disorders exagger¬ ate and patients with alcohol disorders Downloaded from jama.ama-assn.org at Florida State University on November 15, 2011 minimize the actual amount of their dis¬ ability. However, the different patterns of impairment across different groups of mental disorders argue against simple re¬ porting bias on the part of our patients with mental disorders. Furthermore, vir¬ tually all studies of impairment in HRQL rely solely on patient self-report, whether assessed by questionnaire or direct in¬ terview. This is because of the difficulty in obtaining independent observations of functional status by others (family mem¬ bers or friends) and the lack of objective ways to measure the relevant domains. Finally, the patient's perception of his or her functional status, even if discordant with independent measures, clearly can¬ not be ignored. Despite the limitations of self-report, the instrument that we used, the SF-20, has been the major measure of HRQL in leading studies, such as the MOS.11,12,20 Although the somewhat longer SF-3634 is increasingly being used as a criterion standard for assessing HRQL in clinical research, the SF-20 scales cor¬ relate highly with those of the SF-36,7-21·48 particularly when measuring differences between groups rather than changes within an individual patient. Mental disorders are not only preva¬ lent in primary care but are responsible for a large proportion of the impairment in HRQL experienced by patients, often exceeding the impairment associated with common medical disorders. Therefore, pri¬ mary care directed at improving HRQL needs to focus on the recognition and treatment of common mental disorders. While effective treatment has been de¬ veloped for major depression37 and panic disorder,44 our study provides additional evidence that there is also a need to de¬ velop treatment strategies for somato¬ form disorders, subthreshold mood and anxiety disorders, and chronic conditions, such as dysthymia and generalized anxi¬ ety disorder.45 Primary care patients fre¬ quently have multiple psychiatric and medical diagnoses. However, after adjust¬ ing for this comorbidity, the patterns of impairment differ substantially among the common groups of mental disorders. 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Mental Illness in General Health Care: An International Study. West Sussex, England: John Wiley & Sons Ltd; 1995. Downloaded from jama.ama-assn.org at Florida State University on November 15, 2011