bs_bs_banner
Perspectives in Psychiatric Care
ISSN 0031-5990
Correlates of Disability in Asian Patients With Major Depressive Disorder Kanokkwan Eurviriyanukul, MD, Manit Srisurapanont, MD, Pichet Udomratn, MD, Ahmad Hatim Sulaiman, MBBS, PhD, and Chia-Yih Liu, MD Kanokkwan Eurviriyanukul, MD, is Lecturer of Psychiatry, Department of Psychiatry, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; Manit Srisurapanont, MD, is Professor of Psychiatry, Department of Psychiatry, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; Pichet Udomratn, MD, is Professor of Psychiatry, Department of Psychiatry, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand; Ahmad Hatim Sulaiman, MBBS, PhD, is Associate Professor of Psychiatry, Department of Psychological Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; and Chia-Yih Liu, MD, is Associate Professor of Psychiatry and Chair, Department of Psychiatry, Chang Gung Medical Center and Chang Gung University, Tao-Yuan County, Taiwan.
Search terms: Correlate, depressive disorder, disability, function, predictor Author contact: [email protected], with a copy to the Editor: [email protected] Conflict of Interest Statement The authors report no actual or potential conflicts of interest. First Received September 30, 2014; Final Revision received March 17, 2015; Accepted for publication April 23, 2015. doi: 10.1111/ppc.12127
PURPOSE: To examine correlates of disability in Asian patients with major depressive disorder (MDD). DESIGN AND METHODS: Participants were outpatients with DSM-IV MDD. Global disability and three disability domains (i.e., work/school, social life/leisure, and family/home life) were key outcomes. Several socio-demographic and clinical characteristics were determined for their associations with disability. FINDINGS: The sample was 493 MDD patients. Apart from the number of hospitalizations, the global disability was significantly associated with depression severity, fatigue, physical health, and mental health. Several clinical but only few sociodemographic characteristics associated with the other three disability domains were similar. PRACTICE IMPLICATIONS: Disability among Asian patients with MDD correlates with the severity of psychiatric symptoms and the hospitalizations due to depression. Socio-demographic characteristics have little impact on the overall disability.
Disability or functional impairment is the limitation of certain functions in daily life due to an illness (Ustun & Kennedy, 2009). Based on the global burden of disease study in 2010, major depressive disorder (MDD) is the second leading cause of disability worldwide (Ferrari et al., 2013). Together with clinically significant distress, functional impairment is a criterion required for the diagnosis of MDD (American Psychiatric Association, 2013). In addition, some experts suggest that disability outcomes should be included in depression treatment research (Lam, Filteau, & Milev, 2011; McKnight & Kashdan, 2009). Although the causal pathways between depressive disorders and disability are not yet clear, it is widely accepted that depression is closely related to wide-ranging disability. More than half of depressed individuals have moderate or severe disability (Kessler et al., 2003). Disability associated with depression may be worse than that of other chronic medical illnesses (e.g., diabetes, respiratory diseases, and arthritis) (Alonso et al., 2011). It can adversely affect human functioning and leads to significant and pervasive work, family, and Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
social disability (Patten et al., 2009; Petty, Sachs-Ericsson, & Joiner, 2004). Determining correlates of disability in depressed patients may be a strategy to find treatment targets for functional improvement in these patients. While several lines of evidence support the association between depressive symptoms and disability, patients achieving full or asymptomatic remission from depressive symptoms can still have impaired functioning (Papakostas, 2009). Depression severity is moderately correlated but not paralleled with global functioning (McKnight & Kashdan, 2009). Factors that may correlate with disability in depressed patients include personality psychopathology (Markowitz et al., 2007; Ranjith, Farmer, McGuffin, & Cleare, 2005), anxiety symptoms (D’Avanzato et al., 2013), and social support (Strine et al., 2009). Asian and Western depressed patients may have culturally different views of disability. While a small study found that social life was the worst domain in Americans with depression (Kennedy, Lin, & Schwab, 2002), a recent study reported that work/school life was the most impacted domain in Asian 1
Correlates of Disability in Asian Patients With Major Depressive Disorder
patients with MDD (Srisurapanont et al., 2013). Although MDD ranks as the first cause of disability in Central and Southeast Asia (Ferrari et al., 2013), little is known about the correlates of disability in this population. The primary aim of this secondary data analysis was to examine factors associated with global disability in this population. The secondary aim was to investigate other factors specifically correlated with occupation, family role, and social life disabilities. Methods Overview This investigation is the secondary analysis of data obtained from the Study on Aspects of Asian Depression (SAAD), a multi-country, cross-sectional, observational clinical study of depression. The SAAD was conducted in six Asian countries, including China (three sites), Korea (four sites), Malaysia (one site), Singapore (one site), Taiwan (two sites), and Thailand (two sites). The SAAD did not involve any clinical care of participants and was approved by the Institutional Review Board or Ethics Committee of each site. Written informed consent was obtained from each participant after the study details had been fully explained. Given that the SAAD methodology and its original results have been presented in previous reports (Srisurapanont et al., 2013; Sulaiman et al., 2014), only the key methodology relevant to this secondary analysis is presented here. In addition, only the secondary analysis results are presented here. Participants Participants of the SAAD were outpatients who sought mental health services from the study sites. The inclusion criteria were male or female aged 18–65 years and meeting DSM-IV MDD diagnosed by using the Mini International Neuropsychiatric Interview (MINI) (Sheehan et al., 1998). The exclusion criteria were (a) unstable medical condition, (b) mood disorder due to medical conditions and/or substance abuse, (c) psychotic or bipolar disorder, (d) clinically significant cognitive impairment, (e) treatment with psychotropic medication within the previous month, (f) treatment with a benzodiazepine within the previous week, and (g) treatment with long-acting antipsychotic medication within the previous 3 months. All other psychiatric and comorbid conditions were permitted. Measures The SAAD measured several outcomes, but only those relevant to this secondary analysis are mentioned here. Other than disability, outcomes of interest were depression, fatigue, physical and mental health, and social support. Based on the 2
aforementioned review, except fatigue and physical health, all outcomes have shown their associations with disability in Western patients with MDD. All measures, except the Montgomery–Asberg Depression Rating Scale (MADRS) (Montgomery & Asberg, 1979), were questionnaires. The assessment started with the completion of self-report measures. Afterward, the site investigator conducted a face-to-face interview to elicit data needed for the MADRS. Data collection was accomplished in a single visit. Disability was assessed by using the Sheehan Disability Scale (SDS) (Sheehan, Harnett-Sheehan, & Raj, 1996). This is a self-rated measure assessing perceived disability in three areas, including work/school, social life/leisure, and family/ home life. Each area is rated from 0 (no disability) to 10 (extreme disability) and all items are summed to provide a total score ranging from 0 to 30. The SDS total score is therefore considered as a measure of global disability. We rated depressive symptoms by using the MADRS. A MADRS score of 30 or more was defined as severe depression (Bech, Andersen, & Wade, 2006). The participants self-rated their depression and fatigue by using the Symptoms Checklist 90-Revised (SCL-90-R) (Derogatis, 1977) and the Fatigue Severity Scale (FSS) (Krupp, LaRocca, Muir-Nash, & Steinberg, 1989), respectively. Each symptom of the SCL90-R was rated as 0 (no distress), 1 (a little bit distressed), 2 (moderately distressed), 3 (quite a bit distressed), and 4 (extremely distressed). Mean scores of the SCL-90-R depression subscale were averaged by the total number of symptoms. The subscale score of 2 therefore suggests a moderately distressed level of depression. An FSS total score of 4 or more was considered as clinically significant fatigue (Dittner, Wessely, & Brown, 2004). Physical and mental health levels were derived from the physical and mental health scores of the Medical Outcome Survey 36-Item Short Form (SF-36) (Ware & Sherbourne, 1992). The SF-36 physical or mental health score less than 50 indicates poor health in each respect. Social support was measured by using the Multidimensional Scale of Perceived Social Support (MSPSS) (Zimet, Powell, Farley, Werkman, & Berkoff, 1990). Given that an MSPSS score of 4 indicates neutral support, the total score larger than 4 suggests poor social support. While higher MADRS, SCL-90-R, and FSS scores indicated greater severity of symptoms, higher SF-36 and MSPSS scores suggest a better health status and social support, respectively. Statistical Analysis Because those who did not work or study at all for reasons unrelated to MDD could not rate their SDS work/school disability, we excluded these participants from the present analyses. We first calculated Pearson’s correlation coefficients to determine the significant associations between continuous Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
Correlates of Disability in Asian Patients With Major Depressive Disorder
socio-demographic/clinical variables and the SDS total/ subscale scores. For categorical variables, we examined the differences of SDS total and subscale scores between groups by using the Student’s t test. Only the variables with ps ≤ .05 (two-tailed test) were considered as eligible predictors of the SDS total and subscale scores. We applied the standard procedure of multiple linear regression analysis to determine the significant predictors (p to enter = .05; p to leave = .10). R2 and adjusted R2 were used to evaluate the ratio of the sum of squares explained by a regression model. The statistical significance for all tests was set at p < .05. Statistical analyses were performed by using the software package SPSS 17.0 (SPSS Inc, Chicago, IL, USA). Results Sample Characteristics Of 547 MDD patients participating in the SAAD study, 54 who were not working or studying due to reasons not related to depression were excluded from this secondary analysis. The investigation therefore included 493 participants from China (n = 110, 22.3%), Korea (n = 101, 20.5%), Malaysia (n = 88, 17.8%), Singapore (n = 34, 6.9%), Taiwan (n = 67, 13.6%), and Thailand (n = 93, 18.9%). Of these, 312 (63.3%) were female with a mean age of 39.1 (SD = 13.2) years (see Table 1). Two hundred forty patients (48.7%) completed secondary education. Approximately half of the participants were Chinese (50.1%), married/cohabiting (57.6%), and employed (50.9%). Mean scores of the MADRS (observerrated depression), SCL-90-R depression (self-rated depression), FSS (fatigue), and MSPSS total (social support) scores were 29.3 (SD = 8.0), 2.1 (SD = 0.8), 5.1 (SD = 1.4), and 4.4 (SD = 1.4), respectively. The SDS mean score of 18.1 (SD = 7.6) suggested that most participants had moderate disability or more. Correlations Between Variables and the SDS Total and Subscale Scores Apart from the number of past hospitalizations due to depression, variables associated with the SDS total and the SDS three subscale scores were the MADRS, SCL-90-R depression, MINI suicidal risk, FSS, SF-36 physical health, and SF-36 mental health scores (all p < .05) (see Table 2). Age and age at depression onset were significantly correlated with the SDS total, work/school subscale, and social life/leisure subscale scores (all p < .05). The MSPSS total scores were also significantly associated with the SDS total and family/home life subscale scores (all p < .05). For the dichotomous variables, the SDS total and/or subscale scores were significantly different between groups in a total of seven variables (see Table 3). Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
Table 1. Socio-Demographic and Clinical Features of 493 Asian Patients With Major Depressive Disorders Total (N = 493) Characteristics
Mean (SD)
n (%)
Age (years) Gender (% female) Education (% completed secondary education) Ethnicity Chinese Non-Chinese Korean Thai Other Asians Marital status Married/cohabiting Married Cohabiting Alone Never married Divorced/separated Widowed Work status Paid work Full-time employed Part-time employed Unpaid work Homemaker Student Retired Sick leave >3 months Unemployed Living situation With family Not with family Alone Institutionalized Others Religion No religion Having religion Buddhism Christian Hindu Muslim Others Age at depression onset Duration of index episode (weeks) Participants with history of hospitalization due to depression Number of past hospitalizations due to depression MADRS score SCL-90-R depression score Patients with suicidal risk (MINI suicide risk ≥1) MINI suicidal risk level (0–3, from no to high) FSS score SF-36 physical health score SF-36 mental health score MSPSS total score SDS total score SDS work/school score SDS social life/leisure SDS family life
39.1 (13.2) — —
— 312 (63.3) 240 (48.7)
— — — — —
247 246 101 92 53
(50.1) (49.9) (20.5) (18.7) (10.8)
— — — — — — —
284 261 23 209 153 38 18
(57.6) (52.9) (4.7) (42.4) (31.0) (7.7) (3.7)
— — — — — — — — —
251 228 23 242 93 68 36 26 19
(50.9) (46.3) (4.7) (49.1) (18.9) (13.8) (7.3) (5.3) (3.9)
— — — — —
387 106 64 25 17
(78.5) (21.5) (13.0) (5.1) (3.5)
— — — — — — — 36.0 (13.3) 83.8 (170.4) —
198 295 165 68 21 37 4
0.1 (0.4) 29.3 (8.0) 2.1 (0.8) — 0.8 (1.0) 5.1 (1.4) 55.0 (18.5) 34.5 (17.1) 4.4 (1.4) 18.1 (7.6) 6.5 (2.9) 6.0 (2.9) 5.7 (3.1)
— — — 260 (52.7) — — — — — — — — —
(40.6) (59.8) (33.5) (13.8) (4.3) (7.5) (0.8) — — 44 (8.9)
FSS, Fatigue Severity Scale; MADRS, Montgomery–Asberg Depression Rating Scale; MINI, Mini International Neuropsychiatric Interview; MSPSS, Multidimensional Scale of Perceived Social Support; SCL-90-R, Symptoms Checklist 90-Revised; SDS, Sheehan Disability Scale; SF-36, Medical Outcome Survey 36-Item Short Form.
3
Correlates of Disability in Asian Patients With Major Depressive Disorder
Table 2. Correlations Among SDS Total and SDS Subscale Scores and Variables Among 493 Asian Patients With Major Depressive Disorders Pearson’s correlations
SDS total
SDS work/ school
SDS social life/leisure
SDS family/ home life
Age Age at depression onset Duration of index episode (weeks) Number of past hospitalizations due to depression MADRS score SCL-90-R depression score MINI suicidal risk FSS score SF-36 physical health SF-36 mental health MSPSS total score
−0.116* −0.107* −0.002 0.146** 0.511** 0.531** 0.182** 0.460** −0.472** −0.690** −0.106*
−0.185** −0.176** −0.027 0.151** 0.416** 0.452** 0.153** 0.399** −0.413** −0.582** −0.031
−0.092* −0.093* 0.025 0.096* 0.455** 0.457** 0.133** 0.349** −0.369** −0.606** −0.115*
0.002 −0.010 −0.003 0.124** 0.428** 0.445** 0.175** 0.419** −0.416** −0.569** −0.122**
*p < .05; **p < .01. FSS, Fatigue Severity Scale; MADRS, Montgomery–Asberg Depression Rating Scale; MINI, Mini International Neuropsychiatric Interview; MSPSS, Multidimensional Scale of Perceived Social Support; SCL-90-R, Symptoms Checklist 90-Revised; SDS, Sheehan Disability Scale; SF-36, Medical Outcome Survey 36-Item Short Form.
Multiple Linear Regression Models
Global Disability
Table 4 shows independent variables included in the standard procedure of multiple linear regression analyses that were used for predicting the scores of SDS total, work/school, social life/leisure, and family/home life.
Apart from the number of past hospitalizations, the scores of MADRS, SCL-90-R depression, FSS, SF-36 physical health, and SF-36 mental health were statistically significantly correlated with the SDS total scores. The prediction model was
Table 3. Comparison of the SDS Total and Subscale Scores Among 493 Asian Patients With Major Depressive Disorders Using Analysis of Variance (ANOVA) Total SDS Mean (SD) Gender Female (n = 312) Male (n = 181) Education Secondary school education or less (n = 240) Higher than secondary school education (n = 253) Marital status Married/cohabiting (n = 284) Alone (n = 209) Work status Paid (n = 251) Unpaid (n = 237) Living situation With family (n = 387) Not with family (n = 106) Religion No religion (n = 198) Having religion (n = 295) Ethnicity Chinese (n = 247) Non-Chinese (n = 246)
Significant difference
Work/school
Social life/leisure
Family/home life
Mean (SD)
Mean (SD)
Mean (SD)
p = .874 18.1 (7.7) 18.2 (7.4)
Significant difference p = .174
6.3 (3.0) 6.7 (2.8) p = .094
Significant difference p = .937
6.0 (3.0) 6.0 (2.8) p = .010*
p = .415 5.8 (3.2) 5.5 (3.1)
p = .089
p = .926
17.6 (8.1)
6.1 (3.2)
5.8 (3.1)
5.7 (3.3)
18.7 (7.1)
6.8 (2.7)
6.2 (2.7)
5.7 (3.0)
p = .037* 17.5 (7.8) 19.0 (7.3)
p = .003** 6.1 (3.0) 6.9 (2.8)
p = .134 17.6 (7.5) 18.7 (7.8)
p = .271
p = .289 18.0 (7.7) 18.8 (7.4)
p = .070
p = .446
p = .004** 17.2 (7.1) 19.1 (8.0)
p = .384 5.7 (3.1) 5.4 (3.3)
p = .457 5.9 (2.8) 6.1 (3.0)
p = .050 6.2 (2.8) 6.7 (3.1)
5.5 (3.1) 5.8 (3.2)
5.9 (2.9) 6.5 (2.8)
6.4 (2.9) 6.6 (2.9)
p = .222
p = .136
p = .059
p = .695
p = .889 5.7 (3.2) 5.6 (3.0)
5.8 (2.9) 6.2 (2.9)
6.4 (2.9) 7.0 (2.9)
18.0 (7.4) 18.3 (7.7)
p = .010* 5.7 (3.0) 6.4 (2.8)
6.3 (2.9) 6.6 (3.0)
p = .650 5.7 (2.9) 5.6 (3.3)
p = .004** 5.6 (2.8) 6.4 (3.0)
Significant difference
p = .010* 5.3 (3.0) 6.0 (3.2)
*p < .05; **p < .01.
4
Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
Correlates of Disability in Asian Patients With Major Depressive Disorder
Table 4. Multiple Linear Regression Analysis of Factors Correlated With the SDS Total and Subscale Scores Among 493 Asian Patients With Major Depressive Disordersa SDS totalb
(Constant) Age Age at depression onset Number of past hospitalizations due to depression MADRS score SCL-90-R depression score MINI suicidal risk FSS score SF-36 physical health SF-36 mental health MSPSS total score Education Marital status Ethnicity
Work/schoolc
Social life/leisured
Family/home lifee β
β
SE (β)
t (p)
β
SE (β)
t (p)
β
SE (β)
t (p)
18.40 −0.09 0.07 1.39
2.60 0.05 0.05 0.57
7.09** −1.66 1.38 2.42*
7.60 −0.05 0.03 0.66
1.10 0.02 0.02 0.25
6.94** −2.17* 1.23 2.67**
6.40 −0.02 0.02 0.25
1.12 0.02 0.02 0.25
5.70** −0.87 0.94 1.02
5.23
1.128
4.634**
0.44
0.27
1.63
0.13 1.15 −0.46 0.47 −0.03 −0.20 0.09
0.04 0.39 0.28 0.22 0.02 0.02 0.18
3.30** 2.93** −1.64 2.15* −1.97* −9.47** 0.51
0.04 0.35 −0.15 0.19 −0.02 −0.06
0.02 0.17 0.12 0.09 0.01 0.01
2.108* 2.08* −1.27 2.00* −2.80** −6.44**
0.06 0.33 −0.27 0.02 −0.01 −0.08 0.01
0.02 0.17 0.12 0.09 0.01 0.01 0.08
3.26** 1.90 −2.26* 0.21 −0.67 −8.52** 0.08
0.04 0.43 −0.09 0.28 −0.01 −0.06 −0.09
0.02 0.19 0.13 0.10 0.01 0.01 0.08
2.33* 2.32* −0.70 2.69** −1.63 −5.86** −1.04
1.06 0.57
0.57 0.51
1.87 1.12
0.35 0.39
0.23 0.24
1.53 1.61
0.63 0.27
0.25 0.22
2.58* 1.22
0.14
0.24
SE (β)
t (p)
0.57
*p < .05; **p < .01. Gray cells: Based on the univariate analyses, these independent variables had no significant effect on the models. R = 0.540; adjusted R2 = 0.528; F = 46.279; p < .001. cR2 = 0.417; adjusted R2 = 0.404; F = 30.903; p < .001. dR2 = 0.414; adjusted R2 = 0.399; F = 27.804; p < .001. e 2 R = 0.376; adjusted R2 = 0.365; F = 31.995; p < .001. FSS, Fatigue Severity Scale; MADRS, Montgomery–Asberg Depression Rating Scale; MINI, Mini International Neuropsychiatric Interview; MSPSS, Multidimensional Scale of Perceived Social Support; SCL-90-R, Symptoms Checklist 90-Revised; SDS, Sheehan Disability Scale; SF-36, Medical Outcome Survey 36-Item Short Form. a
statistically significant, F(12, 473) = 46.279; p < .001, and accounted for approximately 53% of the variance of the SDS total scores (R2 = 0.540, adjusted R2 = 0.528). The SDS total scores were primarily predicted by the higher MADRS, the higher SCL-90-R depression, and the lower SF-36 mental health scores. Work/School Disability Apart from age and the number of past hospitalizations, the scores of MADRS, SCL-90-R depression, FSS, SF-36 physical health, and SF-36 mental health were statistically significantly correlated with the SDS work/school subscale scores. The prediction model was statistically significant, F(11, 475) = 30.903; p < .001, and 40% of the variance accounted for the SDS work/school scores (R2 = 0.417, adjusted R2 = 0.404). The SDS work/school subscale scores were primarily predicted by the larger numbers of past hospitalizations and the lower SF-36 physical and mental health scores. Social Life Disability Apart from the MINI suicide risk, the scores of MADRS and SF-36 mental health were statistically significant and correlated with the SDS social life/leisure subscale scores. The prediction model was statistically significant, F(12, 473) = 27.804; p < .001, and 40% of the variance accounted for the SDS social life/leisure subscale scores (R2 = 0.414, adjusted R2 Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
b 2
= 0.399). The SDS social life/leisure subscale scores were primarily predicted by the higher MADRS and the lower SF-36 mental health scores. Family Role Disability The scores of MADRS, SCL-90-R depression, FSS, and SF-36 mental health were statistically significant and correlated with the SDS family/home life subscale scores. The prediction model was statistically significant, F(9, 477) = 27.804; p < .001, and 40% of the variance accounted for the SDS family/home life subscale scores (R2 = 0.414, adjusted R2 = 0.399). The SDS family/home life subscale scores were primarily predicted by the higher FSS and the lower SF-36 mental health scores. Discussions The findings of this secondary data analysis suggest that the global disability, determined by the SDS total score, in Asian MDD patients is associated with the clinician-/self-rated severity of depressive symptoms, general mental health, fatigue levels, and hospitalizations due to depression. Most of these factors are also associated with occupational, family role, and social life disabilities. While marital status appears to be another correlate of social life disability, age and physical health may also be associated with occupational disability. These results support that the global and three domains of disability in this population mainly correlated with the 5
Correlates of Disability in Asian Patients With Major Depressive Disorder
symptoms and the illness course of depression. Sociodemographic characteristics had less impact on the global and three domains of disability. The moderate correlations between global disability and depression severity found in this study (r = 0.511 for the MADRS score and r = 0.531 for the SCL-90-R depression score) were in line with previous findings. In depressed patients, the correlation coefficients between functional outcomes and the Hamilton Depression Rating Scale scores are between −0.12 and −0.80 (McKnight & Kashdan, 2009). Specifically for the MADRS, scores are also moderately correlated with the Global Assessment of Function (GAF) with correlation coefficients of −0.32 and −0.79 (Benazzi, 1998; Koivumaa-Honkanen et al., 2008). To our knowledge, no study has ever assessed the association between the self-rated SCL-90-R depression subscale and the SDS in depressed patients. Given that clinicians and patients may rate the depression severity differently (Dorz, Borgherini, Conforti, Scarso, & Magni, 2004), the new finding of correlation between perceived disability and self-rated depression severity would support that the disability is associated with both clinician- and self-rated severity of depression. Two studies evaluating the association between the Beck Depression Inventory, another self-report measure of depression, and the GAF also found the moderate correlations between these two outcomes (correlation coefficients of −0.23 and −0.74) (Koivumaa-Honkanen et al., 2008). Not surprisingly, the present findings suggest the correlation between fatigue and disability. At least five of nine items of the FSS directly measure the functional impairment due to fatigue. In this secondary data analysis, only age and marital status were found to be correlated with disability: (a) younger age and work/school disability and (b) no spouse and social life disability. Because the SDS mainly measures perceived, but not actual, disability, the reversed association between age and occupational impairment found in this secondary data analysis should not be interpreted that younger patients with MDD have poorer work performance than the older ones. Due to their higher expectation on occupational function, compared with older patients, younger MDD patients may be more likely to overestimate their disability in this area. The more severe social disability perceived by those having no spouse may be also explained by the same reason. The lack of a spouse may cause more concern in this area. Those having no spouse, therefore, rate their social disability more severe than those having one. However, these postulates should be viewed with caution and need more studies to confirm. There were some limitations of this secondary data analysis. First, the present findings should be generalized with caution. All participants were from psychiatric service sites located in urban regions. So, it is unknown if the results are applicable to community samples, especially those living in a 6
rural area. The inclusion of only psychotropic drug-free patients allowed us to view a clear picture of the mental symptoms, but this requirement also caused the exclusion of many patients commonly seen in everyday clinical practice. Moreover, we enrolled only the patients in tertiary care settings. Second, although physical and psychiatric comorbidity is common in depressed patients and likely to have an impact on psychosocial functioning, the present study did not thoroughly assess these conditions. Third, this secondary data analysis used only the SDS, a self-reported measure, to assess the disability. Therefore, only the perceived but not actual disability was assessed in this secondary analysis. For example, those who perceive that they have severe occupational disability may actually work better than the ones who rate themselves as having less disability. Last, this secondary data analysis did not take into account many correlates reported previously (e.g., personality psychopathology, Markowitz et al., 2007; Ranjith et al., 2005, anxiety symptoms, D’Avanzato et al., 2013). The highest adjusted R2 of all regression models (52%) also suggested that many correlates were not examined in this secondary data analysis. The results of this secondary data analysis support that the disability found in Asian patients with MDD mainly correlates with the symptoms and the illness course of depression. Because these factors appear to be manageable, these correlates should be the treatment targets for functional improvement. The moderate correlates between global disability and depression severity also support that disability outcomes should be included in depression treatment research. In conclusions, measured by the use of SDS, disability among Asian patients with MDD mainly correlates with the severity of psychiatric symptoms and the hospitalizations due to depression. Socio-demographic characteristics have little impact on global disability but still correlate with work/ school and social life/leisure disabilities. Multidimension outcomes of disability should be included in depression treatment research. Acknowledgments This study was supported by unrestricted research grants from Lundbeck A/S and the Duke-National University of Singapore Office of Clinical Research. The funders had no role in study design, data collection and analysis, or decision to submit the manuscript for publication. Statistical analysis was provided by Singapore Clinical Research Institute. This study is the work of the MD-RAN (The Mood Disorders Research: Asian and Australian Network), which is comprised of the following members (in alphabetical order of family name [in capital letters]): Jae Nam BAE (Korea), Dianne BAUTISTA (Singapore), Edwin CHAN (Singapore), Sung-man CHANG (Korea), Chia-hui CHEN (Taiwan), CHUA Hong Choon (Singapore), Yiru FANG (China), Tom Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
Correlates of Disability in Asian Patients With Major Depressive Disorder
GEORGE (Australia), Ahmad HATIM (Malaysia), Yanling HE (China), Jin Pyo HONG (Korea), Hong Jin JEON (Korea), Augustus John RUSH (Singapore), Tianmei SI (China), Manit SRISURAPANONT (Thailand), Pichet UDOMRATN (Thailand), and Gang WANG (China). The authors would like to thank all study site personnel for contributing to the work achieved and Wen Yun Li of the Singapore Clinical Research Institute for her assistance on the data analysis.
References Alonso, J., Petukhova, M., Vilagut, G., Chatterji, S., Heeringa, S., Ustun, T. B., . . . Kessler, R. C. (2011). Days out of role due to common physical and mental conditions: Results from the WHO World Mental Health surveys. Molecular Psychiatry, 16(12), 1234–1246. doi:10.1038/mp.2010.101 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: Author. Bech, P., Andersen, H. F., & Wade, A. (2006). Effective dose of escitalopram in moderate versus severe DSM-IV major depression. Pharmacopsychiatry, 39(4), 128–134. Benazzi, F. (1998). A 10-item Hamilton Depression Rating Scale to measure major depressive episode severity in outpatients. International Journal of Geriatric Psychiatry, 13(8), 570–571. D’Avanzato, C., Martinez, J., Attiullah, N., Friedman, M., Toba, C., Boerescu, D. A., & Zimmerman, M. (2013). Anxiety symptoms among remitted depressed outpatients: Prevalence and association with quality of life and psychosocial functioning. Journal of Affective Disorders, 151(1), 401–404. doi:10.1016/j.jad.2013.06.040 Derogatis, L. (1977). SCL-90-R (revised) version manual I. Clinical psychometric research unit. Baltimore: John Hopkins University School of Medicine. Dittner, A. J., Wessely, S. C., & Brown, R. G. (2004). The assessment of fatigue: A practical guide for clinicians and researchers. Journal of Psychosomatic Research, 56(2), 157–170. Dorz, S., Borgherini, G., Conforti, D., Scarso, C., & Magni, G. (2004). Comparison of self-rated and clinician-rated measures of depressive symptoms: A naturalistic study. Psychology and Psychotherapy, 77(Pt. 3), 353–361. Ferrari, A. J., Charlson, F. J., Norman, R. E., Patten, S. B., Freedman, G., Murray, C. J., . . . Whiteford, H. A. (2013). Burden of depressive disorders by country, sex, age, and year: Findings from the global burden of disease study 2010. PLoS Medicine, 10(11), e1001547. doi:10.1371/journal.pmed.1001547 Kennedy, B. L., Lin, Y., & Schwab, J. J. (2002). Work, social, and family disabilities of subjects with anxiety and depression. Southern Medical Journal, 95(12), 1424–1427. Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., . . . Wang, P. S. (2003). The epidemiology of major depressive disorder: Results from the National
Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
Comorbidity Survey Replication (NCS-R). Journal of the American Medical Association, 289(23), 3095–3105. Koivumaa-Honkanen, H., Tuovinen, T. K., Honkalampi, K., Antikainen, R., Hintikka, J., Haatainen, K., & Viinamaki, H. (2008). Mental health and well-being in a 6-year follow-up of patients with depression: Assessments of patients and clinicians. Social Psychiatry and Psychiatric Epidemiology, 43(9), 688–696. doi:10.1007/s00127-008-0353-x Krupp, L. B., LaRocca, N. G., Muir-Nash, J., & Steinberg, A. D. (1989). The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Archives of Neurology, 46(10), 1121–1123. Lam, R. W., Filteau, M. J., & Milev, R. (2011). Clinical effectiveness: The importance of psychosocial functioning outcomes. Journal of Affective Disorders, 132(Suppl. 1), S9–S13. doi:10.1016/j.jad.2011.03.046 Markowitz, J. C., Skodol, A. E., Petkova, E., Cheng, J., Sanislow, C. A., Grilo, C. M., . . . McGlashan, T. H. (2007). Longitudinal effects of personality disorders on psychosocial functioning of patients with major depressive disorder. Journal of Clinical Psychiatry, 68, 186–193. McKnight, P. E., & Kashdan, T. B. (2009). The importance of functional impairment to mental health outcomes: A case for reassessing our goals in depression treatment research. Clinical Psychology Review, 29(3), 243–259. doi:10.1016/j.cpr.2009.01.005 Montgomery, S., & Asberg, M. (1979). A new depression scale designed to be sensitive to change. British Journal of Psychiatry, 134, 382–389. Papakostas, G. I. (2009). Major depressive disorder: Psychosocial impairment and key considerations in functional improvement. American Journal of Managed Care, 15(11 Suppl.), S316–S321. Patten, S. B., Wang, J. L., Williams, J. V., Lavorato, D. H., Bulloch, A., & Eliasziw, M. (2009). Prospective evaluation of the effect of major depression on working status in a population sample. Canadian Journal of Psychiatry, 54, 841–845. Petty, S. C., Sachs-Ericsson, N., & Joiner, T. E. J. (2004). Interpersonal functioning deficits: Temporary or stable characteristics of depressed individuals? Journal of Affective Disorders, 81, 115–122. Ranjith, G., Farmer, A., McGuffin, P., & Cleare, A. J. (2005). Personality as a determinant of social functioning in depression. Journal of Affective Disorders, 84, 73–76. Sheehan, D., Harnett-Sheehan, K., & Raj, B. (1996). The measurement of disability. International Clinical Psychopharmacology, 11, 89–95. Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., . . . Dunbar, G. C. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry, 59(Suppl. 20), 22–33. Srisurapanont, M., Hong, J. P., Tian-Mei, S., Hatim, A., Liu, C. Y., Udomratn, P., . . . Rush, A. J. (2013). Clinical features of
7
Correlates of Disability in Asian Patients With Major Depressive Disorder
depression in Asia: Results of a large prospective, cross-sectional study. Asia-Pacific Psychiatry, 5(4), 259–267. doi:10.1111/appy.12104 Strine, T. W., Kroenke, K., Dhingra, S., Balluz, L. S., Gonzalez, O., Berry, J. T., & Mokdad, A. H. (2009). The associations between depression, health-related quality of life, social support, life satisfaction, and disability in community-dwelling US adults. Journal of Nervous and Mental Disease, 197(1), 61–64. doi:10.1097/NMD.0b013e3181924ad8 Sulaiman, A. H., Bautista, D., Liu, C. Y., Udomratn, P., Bae, J. N., Fang, Y., . . . Rush, A. J. (2014). Differences in psychiatric symptoms among Asian patients with depression:
8
A multi-country cross-sectional study. Psychiatry and Clinical Neurosciences, 68(4), 245–254. Ustun, B., & Kennedy, C. (2009). What is “functional impairment”? Disentangling disability from clinical significance. World Psychiatry, 8(2), 82–85. Ware, J. E., Jr., & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical Care, 30(6), 473–483. Zimet, G. D., Powell, S. S., Farley, G. K., Werkman, S., & Berkoff, K. A. (1990). Psychometric characteristics of the Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment, 55(3–4), 610–617.
Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
Perspectives in Psychiatric Care
ISSN 0031-5990
Correlates of Disability in Asian Patients With Major Depressive Disorder Kanokkwan Eurviriyanukul, MD, Manit Srisurapanont, MD, Pichet Udomratn, MD, Ahmad Hatim Sulaiman, MBBS, PhD, and Chia-Yih Liu, MD Kanokkwan Eurviriyanukul, MD, is Lecturer of Psychiatry, Department of Psychiatry, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; Manit Srisurapanont, MD, is Professor of Psychiatry, Department of Psychiatry, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; Pichet Udomratn, MD, is Professor of Psychiatry, Department of Psychiatry, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand; Ahmad Hatim Sulaiman, MBBS, PhD, is Associate Professor of Psychiatry, Department of Psychological Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; and Chia-Yih Liu, MD, is Associate Professor of Psychiatry and Chair, Department of Psychiatry, Chang Gung Medical Center and Chang Gung University, Tao-Yuan County, Taiwan.
Search terms: Correlate, depressive disorder, disability, function, predictor Author contact: [email protected], with a copy to the Editor: [email protected] Conflict of Interest Statement The authors report no actual or potential conflicts of interest. First Received September 30, 2014; Final Revision received March 17, 2015; Accepted for publication April 23, 2015. doi: 10.1111/ppc.12127
PURPOSE: To examine correlates of disability in Asian patients with major depressive disorder (MDD). DESIGN AND METHODS: Participants were outpatients with DSM-IV MDD. Global disability and three disability domains (i.e., work/school, social life/leisure, and family/home life) were key outcomes. Several socio-demographic and clinical characteristics were determined for their associations with disability. FINDINGS: The sample was 493 MDD patients. Apart from the number of hospitalizations, the global disability was significantly associated with depression severity, fatigue, physical health, and mental health. Several clinical but only few sociodemographic characteristics associated with the other three disability domains were similar. PRACTICE IMPLICATIONS: Disability among Asian patients with MDD correlates with the severity of psychiatric symptoms and the hospitalizations due to depression. Socio-demographic characteristics have little impact on the overall disability.
Disability or functional impairment is the limitation of certain functions in daily life due to an illness (Ustun & Kennedy, 2009). Based on the global burden of disease study in 2010, major depressive disorder (MDD) is the second leading cause of disability worldwide (Ferrari et al., 2013). Together with clinically significant distress, functional impairment is a criterion required for the diagnosis of MDD (American Psychiatric Association, 2013). In addition, some experts suggest that disability outcomes should be included in depression treatment research (Lam, Filteau, & Milev, 2011; McKnight & Kashdan, 2009). Although the causal pathways between depressive disorders and disability are not yet clear, it is widely accepted that depression is closely related to wide-ranging disability. More than half of depressed individuals have moderate or severe disability (Kessler et al., 2003). Disability associated with depression may be worse than that of other chronic medical illnesses (e.g., diabetes, respiratory diseases, and arthritis) (Alonso et al., 2011). It can adversely affect human functioning and leads to significant and pervasive work, family, and Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
social disability (Patten et al., 2009; Petty, Sachs-Ericsson, & Joiner, 2004). Determining correlates of disability in depressed patients may be a strategy to find treatment targets for functional improvement in these patients. While several lines of evidence support the association between depressive symptoms and disability, patients achieving full or asymptomatic remission from depressive symptoms can still have impaired functioning (Papakostas, 2009). Depression severity is moderately correlated but not paralleled with global functioning (McKnight & Kashdan, 2009). Factors that may correlate with disability in depressed patients include personality psychopathology (Markowitz et al., 2007; Ranjith, Farmer, McGuffin, & Cleare, 2005), anxiety symptoms (D’Avanzato et al., 2013), and social support (Strine et al., 2009). Asian and Western depressed patients may have culturally different views of disability. While a small study found that social life was the worst domain in Americans with depression (Kennedy, Lin, & Schwab, 2002), a recent study reported that work/school life was the most impacted domain in Asian 1
Correlates of Disability in Asian Patients With Major Depressive Disorder
patients with MDD (Srisurapanont et al., 2013). Although MDD ranks as the first cause of disability in Central and Southeast Asia (Ferrari et al., 2013), little is known about the correlates of disability in this population. The primary aim of this secondary data analysis was to examine factors associated with global disability in this population. The secondary aim was to investigate other factors specifically correlated with occupation, family role, and social life disabilities. Methods Overview This investigation is the secondary analysis of data obtained from the Study on Aspects of Asian Depression (SAAD), a multi-country, cross-sectional, observational clinical study of depression. The SAAD was conducted in six Asian countries, including China (three sites), Korea (four sites), Malaysia (one site), Singapore (one site), Taiwan (two sites), and Thailand (two sites). The SAAD did not involve any clinical care of participants and was approved by the Institutional Review Board or Ethics Committee of each site. Written informed consent was obtained from each participant after the study details had been fully explained. Given that the SAAD methodology and its original results have been presented in previous reports (Srisurapanont et al., 2013; Sulaiman et al., 2014), only the key methodology relevant to this secondary analysis is presented here. In addition, only the secondary analysis results are presented here. Participants Participants of the SAAD were outpatients who sought mental health services from the study sites. The inclusion criteria were male or female aged 18–65 years and meeting DSM-IV MDD diagnosed by using the Mini International Neuropsychiatric Interview (MINI) (Sheehan et al., 1998). The exclusion criteria were (a) unstable medical condition, (b) mood disorder due to medical conditions and/or substance abuse, (c) psychotic or bipolar disorder, (d) clinically significant cognitive impairment, (e) treatment with psychotropic medication within the previous month, (f) treatment with a benzodiazepine within the previous week, and (g) treatment with long-acting antipsychotic medication within the previous 3 months. All other psychiatric and comorbid conditions were permitted. Measures The SAAD measured several outcomes, but only those relevant to this secondary analysis are mentioned here. Other than disability, outcomes of interest were depression, fatigue, physical and mental health, and social support. Based on the 2
aforementioned review, except fatigue and physical health, all outcomes have shown their associations with disability in Western patients with MDD. All measures, except the Montgomery–Asberg Depression Rating Scale (MADRS) (Montgomery & Asberg, 1979), were questionnaires. The assessment started with the completion of self-report measures. Afterward, the site investigator conducted a face-to-face interview to elicit data needed for the MADRS. Data collection was accomplished in a single visit. Disability was assessed by using the Sheehan Disability Scale (SDS) (Sheehan, Harnett-Sheehan, & Raj, 1996). This is a self-rated measure assessing perceived disability in three areas, including work/school, social life/leisure, and family/ home life. Each area is rated from 0 (no disability) to 10 (extreme disability) and all items are summed to provide a total score ranging from 0 to 30. The SDS total score is therefore considered as a measure of global disability. We rated depressive symptoms by using the MADRS. A MADRS score of 30 or more was defined as severe depression (Bech, Andersen, & Wade, 2006). The participants self-rated their depression and fatigue by using the Symptoms Checklist 90-Revised (SCL-90-R) (Derogatis, 1977) and the Fatigue Severity Scale (FSS) (Krupp, LaRocca, Muir-Nash, & Steinberg, 1989), respectively. Each symptom of the SCL90-R was rated as 0 (no distress), 1 (a little bit distressed), 2 (moderately distressed), 3 (quite a bit distressed), and 4 (extremely distressed). Mean scores of the SCL-90-R depression subscale were averaged by the total number of symptoms. The subscale score of 2 therefore suggests a moderately distressed level of depression. An FSS total score of 4 or more was considered as clinically significant fatigue (Dittner, Wessely, & Brown, 2004). Physical and mental health levels were derived from the physical and mental health scores of the Medical Outcome Survey 36-Item Short Form (SF-36) (Ware & Sherbourne, 1992). The SF-36 physical or mental health score less than 50 indicates poor health in each respect. Social support was measured by using the Multidimensional Scale of Perceived Social Support (MSPSS) (Zimet, Powell, Farley, Werkman, & Berkoff, 1990). Given that an MSPSS score of 4 indicates neutral support, the total score larger than 4 suggests poor social support. While higher MADRS, SCL-90-R, and FSS scores indicated greater severity of symptoms, higher SF-36 and MSPSS scores suggest a better health status and social support, respectively. Statistical Analysis Because those who did not work or study at all for reasons unrelated to MDD could not rate their SDS work/school disability, we excluded these participants from the present analyses. We first calculated Pearson’s correlation coefficients to determine the significant associations between continuous Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
Correlates of Disability in Asian Patients With Major Depressive Disorder
socio-demographic/clinical variables and the SDS total/ subscale scores. For categorical variables, we examined the differences of SDS total and subscale scores between groups by using the Student’s t test. Only the variables with ps ≤ .05 (two-tailed test) were considered as eligible predictors of the SDS total and subscale scores. We applied the standard procedure of multiple linear regression analysis to determine the significant predictors (p to enter = .05; p to leave = .10). R2 and adjusted R2 were used to evaluate the ratio of the sum of squares explained by a regression model. The statistical significance for all tests was set at p < .05. Statistical analyses were performed by using the software package SPSS 17.0 (SPSS Inc, Chicago, IL, USA). Results Sample Characteristics Of 547 MDD patients participating in the SAAD study, 54 who were not working or studying due to reasons not related to depression were excluded from this secondary analysis. The investigation therefore included 493 participants from China (n = 110, 22.3%), Korea (n = 101, 20.5%), Malaysia (n = 88, 17.8%), Singapore (n = 34, 6.9%), Taiwan (n = 67, 13.6%), and Thailand (n = 93, 18.9%). Of these, 312 (63.3%) were female with a mean age of 39.1 (SD = 13.2) years (see Table 1). Two hundred forty patients (48.7%) completed secondary education. Approximately half of the participants were Chinese (50.1%), married/cohabiting (57.6%), and employed (50.9%). Mean scores of the MADRS (observerrated depression), SCL-90-R depression (self-rated depression), FSS (fatigue), and MSPSS total (social support) scores were 29.3 (SD = 8.0), 2.1 (SD = 0.8), 5.1 (SD = 1.4), and 4.4 (SD = 1.4), respectively. The SDS mean score of 18.1 (SD = 7.6) suggested that most participants had moderate disability or more. Correlations Between Variables and the SDS Total and Subscale Scores Apart from the number of past hospitalizations due to depression, variables associated with the SDS total and the SDS three subscale scores were the MADRS, SCL-90-R depression, MINI suicidal risk, FSS, SF-36 physical health, and SF-36 mental health scores (all p < .05) (see Table 2). Age and age at depression onset were significantly correlated with the SDS total, work/school subscale, and social life/leisure subscale scores (all p < .05). The MSPSS total scores were also significantly associated with the SDS total and family/home life subscale scores (all p < .05). For the dichotomous variables, the SDS total and/or subscale scores were significantly different between groups in a total of seven variables (see Table 3). Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
Table 1. Socio-Demographic and Clinical Features of 493 Asian Patients With Major Depressive Disorders Total (N = 493) Characteristics
Mean (SD)
n (%)
Age (years) Gender (% female) Education (% completed secondary education) Ethnicity Chinese Non-Chinese Korean Thai Other Asians Marital status Married/cohabiting Married Cohabiting Alone Never married Divorced/separated Widowed Work status Paid work Full-time employed Part-time employed Unpaid work Homemaker Student Retired Sick leave >3 months Unemployed Living situation With family Not with family Alone Institutionalized Others Religion No religion Having religion Buddhism Christian Hindu Muslim Others Age at depression onset Duration of index episode (weeks) Participants with history of hospitalization due to depression Number of past hospitalizations due to depression MADRS score SCL-90-R depression score Patients with suicidal risk (MINI suicide risk ≥1) MINI suicidal risk level (0–3, from no to high) FSS score SF-36 physical health score SF-36 mental health score MSPSS total score SDS total score SDS work/school score SDS social life/leisure SDS family life
39.1 (13.2) — —
— 312 (63.3) 240 (48.7)
— — — — —
247 246 101 92 53
(50.1) (49.9) (20.5) (18.7) (10.8)
— — — — — — —
284 261 23 209 153 38 18
(57.6) (52.9) (4.7) (42.4) (31.0) (7.7) (3.7)
— — — — — — — — —
251 228 23 242 93 68 36 26 19
(50.9) (46.3) (4.7) (49.1) (18.9) (13.8) (7.3) (5.3) (3.9)
— — — — —
387 106 64 25 17
(78.5) (21.5) (13.0) (5.1) (3.5)
— — — — — — — 36.0 (13.3) 83.8 (170.4) —
198 295 165 68 21 37 4
0.1 (0.4) 29.3 (8.0) 2.1 (0.8) — 0.8 (1.0) 5.1 (1.4) 55.0 (18.5) 34.5 (17.1) 4.4 (1.4) 18.1 (7.6) 6.5 (2.9) 6.0 (2.9) 5.7 (3.1)
— — — 260 (52.7) — — — — — — — — —
(40.6) (59.8) (33.5) (13.8) (4.3) (7.5) (0.8) — — 44 (8.9)
FSS, Fatigue Severity Scale; MADRS, Montgomery–Asberg Depression Rating Scale; MINI, Mini International Neuropsychiatric Interview; MSPSS, Multidimensional Scale of Perceived Social Support; SCL-90-R, Symptoms Checklist 90-Revised; SDS, Sheehan Disability Scale; SF-36, Medical Outcome Survey 36-Item Short Form.
3
Correlates of Disability in Asian Patients With Major Depressive Disorder
Table 2. Correlations Among SDS Total and SDS Subscale Scores and Variables Among 493 Asian Patients With Major Depressive Disorders Pearson’s correlations
SDS total
SDS work/ school
SDS social life/leisure
SDS family/ home life
Age Age at depression onset Duration of index episode (weeks) Number of past hospitalizations due to depression MADRS score SCL-90-R depression score MINI suicidal risk FSS score SF-36 physical health SF-36 mental health MSPSS total score
−0.116* −0.107* −0.002 0.146** 0.511** 0.531** 0.182** 0.460** −0.472** −0.690** −0.106*
−0.185** −0.176** −0.027 0.151** 0.416** 0.452** 0.153** 0.399** −0.413** −0.582** −0.031
−0.092* −0.093* 0.025 0.096* 0.455** 0.457** 0.133** 0.349** −0.369** −0.606** −0.115*
0.002 −0.010 −0.003 0.124** 0.428** 0.445** 0.175** 0.419** −0.416** −0.569** −0.122**
*p < .05; **p < .01. FSS, Fatigue Severity Scale; MADRS, Montgomery–Asberg Depression Rating Scale; MINI, Mini International Neuropsychiatric Interview; MSPSS, Multidimensional Scale of Perceived Social Support; SCL-90-R, Symptoms Checklist 90-Revised; SDS, Sheehan Disability Scale; SF-36, Medical Outcome Survey 36-Item Short Form.
Multiple Linear Regression Models
Global Disability
Table 4 shows independent variables included in the standard procedure of multiple linear regression analyses that were used for predicting the scores of SDS total, work/school, social life/leisure, and family/home life.
Apart from the number of past hospitalizations, the scores of MADRS, SCL-90-R depression, FSS, SF-36 physical health, and SF-36 mental health were statistically significantly correlated with the SDS total scores. The prediction model was
Table 3. Comparison of the SDS Total and Subscale Scores Among 493 Asian Patients With Major Depressive Disorders Using Analysis of Variance (ANOVA) Total SDS Mean (SD) Gender Female (n = 312) Male (n = 181) Education Secondary school education or less (n = 240) Higher than secondary school education (n = 253) Marital status Married/cohabiting (n = 284) Alone (n = 209) Work status Paid (n = 251) Unpaid (n = 237) Living situation With family (n = 387) Not with family (n = 106) Religion No religion (n = 198) Having religion (n = 295) Ethnicity Chinese (n = 247) Non-Chinese (n = 246)
Significant difference
Work/school
Social life/leisure
Family/home life
Mean (SD)
Mean (SD)
Mean (SD)
p = .874 18.1 (7.7) 18.2 (7.4)
Significant difference p = .174
6.3 (3.0) 6.7 (2.8) p = .094
Significant difference p = .937
6.0 (3.0) 6.0 (2.8) p = .010*
p = .415 5.8 (3.2) 5.5 (3.1)
p = .089
p = .926
17.6 (8.1)
6.1 (3.2)
5.8 (3.1)
5.7 (3.3)
18.7 (7.1)
6.8 (2.7)
6.2 (2.7)
5.7 (3.0)
p = .037* 17.5 (7.8) 19.0 (7.3)
p = .003** 6.1 (3.0) 6.9 (2.8)
p = .134 17.6 (7.5) 18.7 (7.8)
p = .271
p = .289 18.0 (7.7) 18.8 (7.4)
p = .070
p = .446
p = .004** 17.2 (7.1) 19.1 (8.0)
p = .384 5.7 (3.1) 5.4 (3.3)
p = .457 5.9 (2.8) 6.1 (3.0)
p = .050 6.2 (2.8) 6.7 (3.1)
5.5 (3.1) 5.8 (3.2)
5.9 (2.9) 6.5 (2.8)
6.4 (2.9) 6.6 (2.9)
p = .222
p = .136
p = .059
p = .695
p = .889 5.7 (3.2) 5.6 (3.0)
5.8 (2.9) 6.2 (2.9)
6.4 (2.9) 7.0 (2.9)
18.0 (7.4) 18.3 (7.7)
p = .010* 5.7 (3.0) 6.4 (2.8)
6.3 (2.9) 6.6 (3.0)
p = .650 5.7 (2.9) 5.6 (3.3)
p = .004** 5.6 (2.8) 6.4 (3.0)
Significant difference
p = .010* 5.3 (3.0) 6.0 (3.2)
*p < .05; **p < .01.
4
Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
Correlates of Disability in Asian Patients With Major Depressive Disorder
Table 4. Multiple Linear Regression Analysis of Factors Correlated With the SDS Total and Subscale Scores Among 493 Asian Patients With Major Depressive Disordersa SDS totalb
(Constant) Age Age at depression onset Number of past hospitalizations due to depression MADRS score SCL-90-R depression score MINI suicidal risk FSS score SF-36 physical health SF-36 mental health MSPSS total score Education Marital status Ethnicity
Work/schoolc
Social life/leisured
Family/home lifee β
β
SE (β)
t (p)
β
SE (β)
t (p)
β
SE (β)
t (p)
18.40 −0.09 0.07 1.39
2.60 0.05 0.05 0.57
7.09** −1.66 1.38 2.42*
7.60 −0.05 0.03 0.66
1.10 0.02 0.02 0.25
6.94** −2.17* 1.23 2.67**
6.40 −0.02 0.02 0.25
1.12 0.02 0.02 0.25
5.70** −0.87 0.94 1.02
5.23
1.128
4.634**
0.44
0.27
1.63
0.13 1.15 −0.46 0.47 −0.03 −0.20 0.09
0.04 0.39 0.28 0.22 0.02 0.02 0.18
3.30** 2.93** −1.64 2.15* −1.97* −9.47** 0.51
0.04 0.35 −0.15 0.19 −0.02 −0.06
0.02 0.17 0.12 0.09 0.01 0.01
2.108* 2.08* −1.27 2.00* −2.80** −6.44**
0.06 0.33 −0.27 0.02 −0.01 −0.08 0.01
0.02 0.17 0.12 0.09 0.01 0.01 0.08
3.26** 1.90 −2.26* 0.21 −0.67 −8.52** 0.08
0.04 0.43 −0.09 0.28 −0.01 −0.06 −0.09
0.02 0.19 0.13 0.10 0.01 0.01 0.08
2.33* 2.32* −0.70 2.69** −1.63 −5.86** −1.04
1.06 0.57
0.57 0.51
1.87 1.12
0.35 0.39
0.23 0.24
1.53 1.61
0.63 0.27
0.25 0.22
2.58* 1.22
0.14
0.24
SE (β)
t (p)
0.57
*p < .05; **p < .01. Gray cells: Based on the univariate analyses, these independent variables had no significant effect on the models. R = 0.540; adjusted R2 = 0.528; F = 46.279; p < .001. cR2 = 0.417; adjusted R2 = 0.404; F = 30.903; p < .001. dR2 = 0.414; adjusted R2 = 0.399; F = 27.804; p < .001. e 2 R = 0.376; adjusted R2 = 0.365; F = 31.995; p < .001. FSS, Fatigue Severity Scale; MADRS, Montgomery–Asberg Depression Rating Scale; MINI, Mini International Neuropsychiatric Interview; MSPSS, Multidimensional Scale of Perceived Social Support; SCL-90-R, Symptoms Checklist 90-Revised; SDS, Sheehan Disability Scale; SF-36, Medical Outcome Survey 36-Item Short Form. a
statistically significant, F(12, 473) = 46.279; p < .001, and accounted for approximately 53% of the variance of the SDS total scores (R2 = 0.540, adjusted R2 = 0.528). The SDS total scores were primarily predicted by the higher MADRS, the higher SCL-90-R depression, and the lower SF-36 mental health scores. Work/School Disability Apart from age and the number of past hospitalizations, the scores of MADRS, SCL-90-R depression, FSS, SF-36 physical health, and SF-36 mental health were statistically significantly correlated with the SDS work/school subscale scores. The prediction model was statistically significant, F(11, 475) = 30.903; p < .001, and 40% of the variance accounted for the SDS work/school scores (R2 = 0.417, adjusted R2 = 0.404). The SDS work/school subscale scores were primarily predicted by the larger numbers of past hospitalizations and the lower SF-36 physical and mental health scores. Social Life Disability Apart from the MINI suicide risk, the scores of MADRS and SF-36 mental health were statistically significant and correlated with the SDS social life/leisure subscale scores. The prediction model was statistically significant, F(12, 473) = 27.804; p < .001, and 40% of the variance accounted for the SDS social life/leisure subscale scores (R2 = 0.414, adjusted R2 Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
b 2
= 0.399). The SDS social life/leisure subscale scores were primarily predicted by the higher MADRS and the lower SF-36 mental health scores. Family Role Disability The scores of MADRS, SCL-90-R depression, FSS, and SF-36 mental health were statistically significant and correlated with the SDS family/home life subscale scores. The prediction model was statistically significant, F(9, 477) = 27.804; p < .001, and 40% of the variance accounted for the SDS family/home life subscale scores (R2 = 0.414, adjusted R2 = 0.399). The SDS family/home life subscale scores were primarily predicted by the higher FSS and the lower SF-36 mental health scores. Discussions The findings of this secondary data analysis suggest that the global disability, determined by the SDS total score, in Asian MDD patients is associated with the clinician-/self-rated severity of depressive symptoms, general mental health, fatigue levels, and hospitalizations due to depression. Most of these factors are also associated with occupational, family role, and social life disabilities. While marital status appears to be another correlate of social life disability, age and physical health may also be associated with occupational disability. These results support that the global and three domains of disability in this population mainly correlated with the 5
Correlates of Disability in Asian Patients With Major Depressive Disorder
symptoms and the illness course of depression. Sociodemographic characteristics had less impact on the global and three domains of disability. The moderate correlations between global disability and depression severity found in this study (r = 0.511 for the MADRS score and r = 0.531 for the SCL-90-R depression score) were in line with previous findings. In depressed patients, the correlation coefficients between functional outcomes and the Hamilton Depression Rating Scale scores are between −0.12 and −0.80 (McKnight & Kashdan, 2009). Specifically for the MADRS, scores are also moderately correlated with the Global Assessment of Function (GAF) with correlation coefficients of −0.32 and −0.79 (Benazzi, 1998; Koivumaa-Honkanen et al., 2008). To our knowledge, no study has ever assessed the association between the self-rated SCL-90-R depression subscale and the SDS in depressed patients. Given that clinicians and patients may rate the depression severity differently (Dorz, Borgherini, Conforti, Scarso, & Magni, 2004), the new finding of correlation between perceived disability and self-rated depression severity would support that the disability is associated with both clinician- and self-rated severity of depression. Two studies evaluating the association between the Beck Depression Inventory, another self-report measure of depression, and the GAF also found the moderate correlations between these two outcomes (correlation coefficients of −0.23 and −0.74) (Koivumaa-Honkanen et al., 2008). Not surprisingly, the present findings suggest the correlation between fatigue and disability. At least five of nine items of the FSS directly measure the functional impairment due to fatigue. In this secondary data analysis, only age and marital status were found to be correlated with disability: (a) younger age and work/school disability and (b) no spouse and social life disability. Because the SDS mainly measures perceived, but not actual, disability, the reversed association between age and occupational impairment found in this secondary data analysis should not be interpreted that younger patients with MDD have poorer work performance than the older ones. Due to their higher expectation on occupational function, compared with older patients, younger MDD patients may be more likely to overestimate their disability in this area. The more severe social disability perceived by those having no spouse may be also explained by the same reason. The lack of a spouse may cause more concern in this area. Those having no spouse, therefore, rate their social disability more severe than those having one. However, these postulates should be viewed with caution and need more studies to confirm. There were some limitations of this secondary data analysis. First, the present findings should be generalized with caution. All participants were from psychiatric service sites located in urban regions. So, it is unknown if the results are applicable to community samples, especially those living in a 6
rural area. The inclusion of only psychotropic drug-free patients allowed us to view a clear picture of the mental symptoms, but this requirement also caused the exclusion of many patients commonly seen in everyday clinical practice. Moreover, we enrolled only the patients in tertiary care settings. Second, although physical and psychiatric comorbidity is common in depressed patients and likely to have an impact on psychosocial functioning, the present study did not thoroughly assess these conditions. Third, this secondary data analysis used only the SDS, a self-reported measure, to assess the disability. Therefore, only the perceived but not actual disability was assessed in this secondary analysis. For example, those who perceive that they have severe occupational disability may actually work better than the ones who rate themselves as having less disability. Last, this secondary data analysis did not take into account many correlates reported previously (e.g., personality psychopathology, Markowitz et al., 2007; Ranjith et al., 2005, anxiety symptoms, D’Avanzato et al., 2013). The highest adjusted R2 of all regression models (52%) also suggested that many correlates were not examined in this secondary data analysis. The results of this secondary data analysis support that the disability found in Asian patients with MDD mainly correlates with the symptoms and the illness course of depression. Because these factors appear to be manageable, these correlates should be the treatment targets for functional improvement. The moderate correlates between global disability and depression severity also support that disability outcomes should be included in depression treatment research. In conclusions, measured by the use of SDS, disability among Asian patients with MDD mainly correlates with the severity of psychiatric symptoms and the hospitalizations due to depression. Socio-demographic characteristics have little impact on global disability but still correlate with work/ school and social life/leisure disabilities. Multidimension outcomes of disability should be included in depression treatment research. Acknowledgments This study was supported by unrestricted research grants from Lundbeck A/S and the Duke-National University of Singapore Office of Clinical Research. The funders had no role in study design, data collection and analysis, or decision to submit the manuscript for publication. Statistical analysis was provided by Singapore Clinical Research Institute. This study is the work of the MD-RAN (The Mood Disorders Research: Asian and Australian Network), which is comprised of the following members (in alphabetical order of family name [in capital letters]): Jae Nam BAE (Korea), Dianne BAUTISTA (Singapore), Edwin CHAN (Singapore), Sung-man CHANG (Korea), Chia-hui CHEN (Taiwan), CHUA Hong Choon (Singapore), Yiru FANG (China), Tom Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
Correlates of Disability in Asian Patients With Major Depressive Disorder
GEORGE (Australia), Ahmad HATIM (Malaysia), Yanling HE (China), Jin Pyo HONG (Korea), Hong Jin JEON (Korea), Augustus John RUSH (Singapore), Tianmei SI (China), Manit SRISURAPANONT (Thailand), Pichet UDOMRATN (Thailand), and Gang WANG (China). The authors would like to thank all study site personnel for contributing to the work achieved and Wen Yun Li of the Singapore Clinical Research Institute for her assistance on the data analysis.
References Alonso, J., Petukhova, M., Vilagut, G., Chatterji, S., Heeringa, S., Ustun, T. B., . . . Kessler, R. C. (2011). Days out of role due to common physical and mental conditions: Results from the WHO World Mental Health surveys. Molecular Psychiatry, 16(12), 1234–1246. doi:10.1038/mp.2010.101 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: Author. Bech, P., Andersen, H. F., & Wade, A. (2006). Effective dose of escitalopram in moderate versus severe DSM-IV major depression. Pharmacopsychiatry, 39(4), 128–134. Benazzi, F. (1998). A 10-item Hamilton Depression Rating Scale to measure major depressive episode severity in outpatients. International Journal of Geriatric Psychiatry, 13(8), 570–571. D’Avanzato, C., Martinez, J., Attiullah, N., Friedman, M., Toba, C., Boerescu, D. A., & Zimmerman, M. (2013). Anxiety symptoms among remitted depressed outpatients: Prevalence and association with quality of life and psychosocial functioning. Journal of Affective Disorders, 151(1), 401–404. doi:10.1016/j.jad.2013.06.040 Derogatis, L. (1977). SCL-90-R (revised) version manual I. Clinical psychometric research unit. Baltimore: John Hopkins University School of Medicine. Dittner, A. J., Wessely, S. C., & Brown, R. G. (2004). The assessment of fatigue: A practical guide for clinicians and researchers. Journal of Psychosomatic Research, 56(2), 157–170. Dorz, S., Borgherini, G., Conforti, D., Scarso, C., & Magni, G. (2004). Comparison of self-rated and clinician-rated measures of depressive symptoms: A naturalistic study. Psychology and Psychotherapy, 77(Pt. 3), 353–361. Ferrari, A. J., Charlson, F. J., Norman, R. E., Patten, S. B., Freedman, G., Murray, C. J., . . . Whiteford, H. A. (2013). Burden of depressive disorders by country, sex, age, and year: Findings from the global burden of disease study 2010. PLoS Medicine, 10(11), e1001547. doi:10.1371/journal.pmed.1001547 Kennedy, B. L., Lin, Y., & Schwab, J. J. (2002). Work, social, and family disabilities of subjects with anxiety and depression. Southern Medical Journal, 95(12), 1424–1427. Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., . . . Wang, P. S. (2003). The epidemiology of major depressive disorder: Results from the National
Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
Comorbidity Survey Replication (NCS-R). Journal of the American Medical Association, 289(23), 3095–3105. Koivumaa-Honkanen, H., Tuovinen, T. K., Honkalampi, K., Antikainen, R., Hintikka, J., Haatainen, K., & Viinamaki, H. (2008). Mental health and well-being in a 6-year follow-up of patients with depression: Assessments of patients and clinicians. Social Psychiatry and Psychiatric Epidemiology, 43(9), 688–696. doi:10.1007/s00127-008-0353-x Krupp, L. B., LaRocca, N. G., Muir-Nash, J., & Steinberg, A. D. (1989). The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Archives of Neurology, 46(10), 1121–1123. Lam, R. W., Filteau, M. J., & Milev, R. (2011). Clinical effectiveness: The importance of psychosocial functioning outcomes. Journal of Affective Disorders, 132(Suppl. 1), S9–S13. doi:10.1016/j.jad.2011.03.046 Markowitz, J. C., Skodol, A. E., Petkova, E., Cheng, J., Sanislow, C. A., Grilo, C. M., . . . McGlashan, T. H. (2007). Longitudinal effects of personality disorders on psychosocial functioning of patients with major depressive disorder. Journal of Clinical Psychiatry, 68, 186–193. McKnight, P. E., & Kashdan, T. B. (2009). The importance of functional impairment to mental health outcomes: A case for reassessing our goals in depression treatment research. Clinical Psychology Review, 29(3), 243–259. doi:10.1016/j.cpr.2009.01.005 Montgomery, S., & Asberg, M. (1979). A new depression scale designed to be sensitive to change. British Journal of Psychiatry, 134, 382–389. Papakostas, G. I. (2009). Major depressive disorder: Psychosocial impairment and key considerations in functional improvement. American Journal of Managed Care, 15(11 Suppl.), S316–S321. Patten, S. B., Wang, J. L., Williams, J. V., Lavorato, D. H., Bulloch, A., & Eliasziw, M. (2009). Prospective evaluation of the effect of major depression on working status in a population sample. Canadian Journal of Psychiatry, 54, 841–845. Petty, S. C., Sachs-Ericsson, N., & Joiner, T. E. J. (2004). Interpersonal functioning deficits: Temporary or stable characteristics of depressed individuals? Journal of Affective Disorders, 81, 115–122. Ranjith, G., Farmer, A., McGuffin, P., & Cleare, A. J. (2005). Personality as a determinant of social functioning in depression. Journal of Affective Disorders, 84, 73–76. Sheehan, D., Harnett-Sheehan, K., & Raj, B. (1996). The measurement of disability. International Clinical Psychopharmacology, 11, 89–95. Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., . . . Dunbar, G. C. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry, 59(Suppl. 20), 22–33. Srisurapanont, M., Hong, J. P., Tian-Mei, S., Hatim, A., Liu, C. Y., Udomratn, P., . . . Rush, A. J. (2013). Clinical features of
7
Correlates of Disability in Asian Patients With Major Depressive Disorder
depression in Asia: Results of a large prospective, cross-sectional study. Asia-Pacific Psychiatry, 5(4), 259–267. doi:10.1111/appy.12104 Strine, T. W., Kroenke, K., Dhingra, S., Balluz, L. S., Gonzalez, O., Berry, J. T., & Mokdad, A. H. (2009). The associations between depression, health-related quality of life, social support, life satisfaction, and disability in community-dwelling US adults. Journal of Nervous and Mental Disease, 197(1), 61–64. doi:10.1097/NMD.0b013e3181924ad8 Sulaiman, A. H., Bautista, D., Liu, C. Y., Udomratn, P., Bae, J. N., Fang, Y., . . . Rush, A. J. (2014). Differences in psychiatric symptoms among Asian patients with depression:
8
A multi-country cross-sectional study. Psychiatry and Clinical Neurosciences, 68(4), 245–254. Ustun, B., & Kennedy, C. (2009). What is “functional impairment”? Disentangling disability from clinical significance. World Psychiatry, 8(2), 82–85. Ware, J. E., Jr., & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical Care, 30(6), 473–483. Zimet, G. D., Powell, S. S., Farley, G. K., Werkman, S., & Berkoff, K. A. (1990). Psychometric characteristics of the Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment, 55(3–4), 610–617.
Perspectives in Psychiatric Care •• (2015) ••–•• © 2015 Wiley Periodicals, Inc.
