research articles on depression symptoms

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Advanced Search
• Journal List
• Dialogues Clin Neurosci
• v.10(3); 2008 Sep

Language: English | Spanish | French

Core symptoms of major depressive disorder: relevance to diagnosis and treatment

Sintomas centrales del trastorno depresivo mayor: relevancia para el diagnóstico y el tratamiento, symptômes essentiels des troubles dépressifs majeurs: importance pour le diagnostic et le traitement, sidney h. kennedy.

Department of Psychiatry, University Health Network, University of Toronto, Ontario, Canada

The construct of major depressive disorder makes no etiological assumptions about populations with diverse symptom clusters. “Depressed mood” and “loss of interest or pleasure in nearly all activities” are core features of a major depressive episode, though a strong case can be made to pay increasing attention to symptoms of fatigue, sleep disturbance, anxiety, and neurocognitive and sexual dysfunction in the diagnosis and evaluation of treatment outcome. Mood, guilt, work, and interest, as well as psychic anxiety, are consistently identified across validated subscales of the Hamilton Depression Rating Scale as prevalent and sensitive to change with existing treatments. A major limitation of these antidepressant therapies is their narrow spectrum of action. While the core “mood and interest” symptoms have been the main focus of attention, the associated symptoms listed above are often unaffected or exacerbated by current treatments. Careful clinical evaluation should address all of these dimensions, recognizing that improvement may occur sooner in some symptoms (eg, mood) compared with others (eg, sleep disturbance).

El constructo trastorno depresivo mayor no asume etioiogías para las poblaciones con diversas agrupaciones sintomáticas. El “ánimo depresivo” y la “pérdida de interés o placer en casi todas las actividades” son características centrales de un episodio depresivo mayor, aunque ban existido buenos argumentos para prestar atención creciente a síntomas como fatiga, alteraciones del sueño, ansiedad, y disfunciones neurocognitivas y sexuales para el diagnóstico y la evaluación de los resultados del tratamiento. El ánimo, la culpa, el trabajo y el interés como también la ansiedad psíquica son identificados consistentemente a través de subescalas validadas de la escala de depresión de Hamilton como prevalentes y sensibles de cambiar con Ios tratamientos disponibles. Una limitación importante de estas terapias antidepresivas es su limitado espectro de acción. Mientras que los síntomas centrales “ánimo e interés” han sido el principal foco de atención, Ios síntomas asociados antes señalados a menudo no son afectados o exacerbados por los tratamientos actuales. La evaluación clínica debe ser cuidadosa y orientarse a todas estas dimensiones, reconociendo que la mejoría puede ocurrir más rápido en algunos síntomas (como el ánimo) en comparación con otros (como las alteraciones del sueño).

La constitution des troubles dépressifs majeurs ne présuppose pas d'origine étiologique chez des patients aux symptômes variés. « L'humeur dépressive » et « la perte d'intérêt ou de plaisir dans presque toutes les activités » sont des critères essentiels d'un épisode dépressif majeur, bien qu'il y ait beaucoup à dire sur l'intérêt croissant que suscitent les symptômes de fatigue, les perturbations du sommeil, l'anxiété et les dysfonctions sexuelles et cognitives dans le diagnostic et l'évaluation du traitement Des sous-échelles validées de l'HAMD (Hamilton Depression Rating Scale) montrent régulièrement que l'humeur, la culpabilité, le travail et l'intérêt comme l'anxiété psychique sont des symptômes prévalents et susceptibles de variations avec les traitements existants. Ces traitements antidépresseurs sont très limités par leur spectre d'action étroit Alors que l'attention s'est majoritairement focalisée sur les symptômes majeurs « humeur et intérêt », les symptômes associés cités ci-dessus sont souvent inchangés ou exacerbés par les traitements actuels. Toutes ces questions devraient faire l'objet d'une évaluation clinique soigneuse, certains symptômes (par ex, l'humeur) pouvant être améliorés avant d'autres (par ex, troubles du sommeil).

Core and associated symptoms within the diagnosis of major depressive disorder

The current polythetic approach to diagnostic classification of “Major Depressive Disorder (MDD)” in the Diagnostic and Statistical Manual of Mental Disorders. 4th ed. (DSM-IV 1 or “Recurrent Depressive Episodes” in The ICD-10 Classification of Mental and Behavioral Disorders: Clinical descriptions and diagnostic guidelines. (ICD-10) 2 is devoid of implications about etiopathology or treatment response. Only “depressed mood” (mood) or “loss of interest or pleasure in nearly all activities” (anhedonia) are considered to be essential requirements for the diagnosis of a .Major Depressive Episode (MDE) in DSM-IV. When these two “core symptoms” were used to screen for MDD using a 2-item version of the Patient Health Questionnaire (PHQ-2), they displayed a sensitivity of 83% and a specificity of 92% for “caseness” based on a Structured Clinical Interview for DSM-IV (SCTD) 3 and comparable results were obtained in a subsequent. .European replication. 4

Confirmatory diagnosis of an MDE, according to DSM-IV, requires a minimum of five symptoms (at least one being mood or anhedonia) for a minimum of 2 weeks (see Table I for DSM-IV). It is easy to see how the multiple permutations and combinations of these symptoms contribute to substantial intraclass heterogeneity.

Major depressive episode subtypes

Specifiers may be added to imply greater homogeneity within a subpopulation. For example, “with melancholic features” requires at least three of the following symptoms: complete loss of pleasure, lack of reactivity, psychomotor retardation, significant weight loss, excessive guilt, or distinct quality of depressed mood. Some authors have emphasized the presence of psychomotor retardation as a core feature of melancholic depression. 5 The presence of “atypical features” requires two or more of the following symptoms: overeating/weight gain, hypersomnia, leaden paralysis, preservation of mood reactivity, or interpersonal rejection sensitivity. These latter two symptoms (preservation of mood reactivity and interpersonal rejection sensitivity) have been criticized on the basis of poor reliability, and some authors have recommended that only the reverse vegetative symptoms, hypersomnia, and overeating as well as leaden paralysis form the core of atypical depression. 6

There have been attempts to dichotomize these two depression subtypes on both treatment, responsiveness and psychobiology. Historically, tricyclic antidepressants and electroconvulsive therapy were recommended for the melancholic patient, 7 while patients with atypical features appeared to respond better to classical monoamine oxidase inhibitors 8 , 9 than to tricyclic antidepressants. These distinctions have been less apparent with the current, generation of selective serotonin reuptake inhibitor (SSRI) and serotonin-norepinephrine reuptake inhibitors (SNRI) antidepressants, and no currently available antidepressant carries a specific indication for either melancholic or atypical symptoms. In fact, Parker's group recently acknowledged that, symptom profiles within the “melancholia” population may vary with age. Hypersomnia was noted to be more common in the younger age group, while late insomnia became the dominant sleep disturbance of older patients. 10

Evidence of core symptoms from rating scales

It is common to evaluate the severity of a depressive episode using classic rating scales, particularly the Hamilton Rating Scale for Depression (HAMD-17) 11 or the Montgomery Asberg Depression Rating Scale (MADRS). 12 Differences in medication type and in the symptom profiles of the population being evaluated may influence outcomes on a rating scale. Among individual items, the core “depressed mood” item on either the HAMD-17 or the MADRS was more sensitive to drug-placebo separation and to establishing optimal dosing, compared with the full scales in several controlled trials. 13 , 14

The sensitivity of some items to differentiate between active drug and placebo can be compromised when a drug has an unfavorable effect on certain items. For example, increased anxiety may occur during the early weeks of SSRI therapy, and activating antidepressants may disrupt some aspects of sleep. 15 The net result is that, prevalent items may not. emerge on rating scales that are designed to detect improvements during antidepressant, therapy. When symptom prevalence and sensitivity to change have been evaluated in large data sets using item analysis or factor analysis, several core symptoms emerge with greater sensitivity to change and less distortion by treatment emergent side effects than with the full versions of the scale.

Three such scales derived from the HAMD-17 are the “Been 6,” 16 “Maier subscale,” 17 and “HAMD-7” 18 (Table II). Four items are common to each of these scales: mood, guilt, anhedonia, and psychic anxiety. In HAMD-7 and Bech 6, loss of energy (fatigue) was also present, as was psychomotor retardation in Bech 6 and Maier 6, while the HAMD-7 included somatic anxiety and suicidal ideation. All three scales include anxiety symptoms, in contrast to current diagnostic systems.

The prominence of anxiety symptoms and syndromes

Surprisingly, anxiety is not considered as a core or associated symptom of depression according to either DSM-IV or ICD-10 criteria. Neither is “with anxious features” a specifier within DSM-IV, yet. up to 90% of patients have co-occurring anxiety symptoms, and approximately 50% of depressed patients meet, criteria for a comorbid anxiety disorder. 19 , 20 This lack of syndrome independence on Axis 1 is a major limitation to the current concept, of comorbidity. Comorbid disorders should only exist, at a level expected by chance, yet. in the case of M'DD, comorbidity is the rule and not the exception. 21 .

A recent proposal for mood and anxiety spectrum disorders, to be considered in DSM-V, has been advanced by Watson 22 who proposes three subclasses of emotional disorders: “bipolar disorders,” “distress disorders,” (MDD, dysthymic disorder, generalized anxiety disorder, and post-traumatic stress disorder) and “fear disorders” (panic disorder, agoraphobia, social phobia and specific phobia). This reflects a pendulum swing to the unitary position of Mapother 23 and Lewis 24 who viewed states of anxiety along a continuum with depressive disorders, in contrast. to the progressive separation of mood and anxiety disorders initiated more than three decades ago. 25 , 26 It is likely that the inconsistent impact of some antidepressants on anxiety has distorted measurement of anxiety symptoms during treatment.

What is less in dispute is the impact of anxiety comorbidity on response to the treatment of depression. Patients without anxiety symptoms at. the time of remission are significantly more likely to remain well than those patients with residual anxiety. 27 There is also consistent, evidence of lower response rates and higher relapse in comorbidly anxious depressed patients. Although there is a strong justification to consider “anxious depression” as a depressive subtype, 28 a case can be made to maintain the separation of Generalized Anxiety Disorder (GAD) from MDD. 29

Sleep disturbance, apathy, and fatigue

Sleep disturbance.

The relationship between sleep and depression is complex. Insomnia is a frequent symptom of depression, and there is evidence to suggest that, sleep disturbances are often a prodrome to a MDE,. 30 Paradoxically, sleep deprivation has been advocated as an antidepressant, therapy 31 while several antidepressant agents actually worsen sleep. 15 Sleep disturbance also lends itself to objective evaluation through polysomnography. Disturbances in the ratio of rapid eye movement (REM) sleep to non-RFM sleep, decreased slow-wave sleep, and impaired sleep continuity are among the most robust, markers for MDD. Whether reductions in slow-wave sleep and REM latency are trait, or state abnormalities is a controversial issue, 32 and attempts to establish robust diagnostic electroencephalographic markers for MDD have been confounded by the effects of age and gender. 33

Among the symptoms of depression, sleep disturbance is a prominent, symptom that is frequently unresponsive to current, antidepressants, or is overtreated with consequent daytime somnolence. In a family practice evaluation of physician diagnosis and patient self-report of depressive symptoms, “insomnia or hypersomnia” along with “depressed mood” were the symptoms most frequently elicited by physicians, although only “suicidal ideation” and “insomnia or hypersomnia” were associated with a statistically significant likelihood of depression diagnosis. 34 Middle (71%), early (62%), and late (55%) insomnia were frequently reported items from the HAMD-17 in a sample of almost 300 depressed clinic patients. 18 However, underscoring the limited effectiveness of current antidepressants to improve sleep, none of these three sleep items were among the seven with greatest sensitivity to change during treatment (Table III). In fact, middle insomnia emerged as the eighth most sensitive item to reflect antidepressant change. 18

The importance of sleep disturbance as a residual symptom in MDD has also been highlighted by Nierenberg and colleagues, 35 who examined threshold and subthreshold symptoms among patients who achieved remission (HAMD-17“ 7) after 8 weeks of antidepressant treatment with fluoxetine. The three most prevalent, residual symptoms were disturbances in sleep (44%), fatigue (38%), and anhedonia (27%). Since the majority of these patients reported sleep disturbance prior to treatment with fluox-etine it. is less likely to have been a treatment-emergent adverse event. The persistence of insomnia is a particular concern, given the propensity for residual sleep disturbance to predict relapse. 36

Persistent sleep disturbances in SSRT “responders” include prolonged sleep latency (beyond 1 hour), reduced total sleep time, and multiple awakenings. Although coprescription of a hypnotic may have a beneficial effect, 37 concerns about long-term hypnotic use limit this recommendation. Elsewhere, advantages beyond sleep restoration were demonstrated when cszopiclone and fluoxetine were combined in the acute treatment of MDD. 38 Given the role of sleep disruption in predicting relapse, there is a strong argument, to consider sleep disturbance as a core symptom in depression, and to emphasize the importance of sleep restoration early in the treatment of an MDE. The daytime effects of persistent sleep disruption should not be underestimated in depressed patients.

Fatigue and apathy

Particularly in primary care settings, depressed patients are likely to present, with complaints of exhaustion or inability to carry out physical or mental work. In fact, fatigue was the commonest, depressive symptom in a survey of family practice settings. 39 In the large European collaborative study of almost 2000 depressed patients across 6 countries (DEPRES II), 73% of patients “felt, tired”; this symptom was associated with severity of the episode and was more prevalent in women. 40 Although “fatigue or loss of energy nearly every day” is not. considered an essential depressive symptom according to DSM-IV, it. is emphasized within the atypical symptom cluster, with “leaden paralysis” as the extreme variant. However, reduced energy is considered a “core feature” in the definition of depressive episode according to ICD-10, emphasizing that marked tiredness may occur after only slight, effort. 41 It is a reasonable assumption that sleep disturbance and daytime fatigue are related (as previously reviewed - over 40% of remitters to fluoxetine had sleep disturbance and just, under 40% had fatigue), although there are no data to confirm this relationship.

Similarly, apathy may overlap with diminished interest, loss of energy, and even indecisiveness, but this construct is too nonspecific to be considered a core symptom. In fact, apathy has been reported more frequently as a side effect, in up to 20% of patients who receive SSRI antidepressants. 42

Cognitive dysfunction

Subjective neurocognitive disturbance in depression is represented by “diminished ability to think or concentrate” in DSM-IV, although broader neurocognitive disturbances can be measured using standardized neuropsychological test batteries. Neuropsychological deficits have most often been detected in older individuals and include disturbances in psychomotor speed, 43 memory, 44 verbal fluency, 45 attention, 45 executive function, 45 and processing speed. 48 Whether restoration of cognitive function occurs with symptom remission in MDD has been a topic of considerable interest in recent, years. Mostly in elderly patients, the data suggest enduring deficits in both memory and executive function. 49 Links between recurrent depressive episodes, reduced hippocampal volume and memory deficits have also been reported. 50 Although it is premature to endorse any specific neurocognitive deficit as a core symptom of depression, residual memory disturbance has major implications for functional recovery and deserves ongoing attention in clinical management.

Sexual dysfunction

Sexual dysfunction is also a complex issue among patients with depression. Common complaints include reduction in desire or libido, diminished arousal, a decline in the frequency of intercourse, or an undesirable delay in achieving orgasm. The prevalence of sexual dysfunction in the community is high; 51 it is even higher in untreated depressed patients 52 and may be further exacerbated by antidepressants. 53 In a large European study designed to evaluate sexual function in both treated and untreated depressed patients, more than two thirds of men and women reported decreased libido and the prevalence increased with severity and duration of the depressive episode. 54

The reluctance among many patients to spontaneously report sexual dysfunction as a disturbing symptom of depression has resulted in a relatively low and misleading prevalence rate. The true importance of sexual dysfunction as a depressive symptom has not. been recognized either in diagnosis or during antidepressant therapy.

Nevertheless, low libido may contribute to deteriorating interpersonal/marital relations and further exacerbate depression. In the case of SSRI antidepressants, up to 60% of patients report treatment-emergent sexual function. 55 , 56 Antidepressants that do not stimulate serotonin release are less likely to induce or exacerbate sexual dysfunction. 53 , 57 , 58 This has implications for treatment adherence, as sexual dysfunction remains one of the commonest, reasons for treatment, discontinuation. 53

Future directions

Both DSM-IV and ICD-10 represent descriptive systems of classification. With DSM-V in mind, several authors have advocated a role for phenotypic characteristics, genetic data, as well as cognitive or other biological markers. 59 , 60 Endophenotypes reflect the gap between the gene and the expression of the disease process. In depression, putative biological candidates include disruptions in circadian rhythm, immune function, neurotransmitter-receptor signaling pathways, and neuroendocrine axes, as well as brain structure and function. Studies exploring the influence of gene-environment interactions (involving polymorphisms of the serotonin transporter) on symptom presentation and treatment response in depression have attracted considerable attention. 59 , 60 Reduction in hippocampal volume has been consistently reported in MDD 63 and linked to duration of untreated depression, 64 as well as deficits in neurocognition. 50 There are also preliminary reports on potential markers for treatment resistance. Lower serotonin transporter binding in the midbrain, medulla, and anterior cingulate cortex was associated with nonremission, 65 while hypermetabolism in the ventral anterior cingulate area brain region was a predictor of nonresponse to both cognitive therapy and venlafaxine. 66 Though provocative, these interesting findings are unlikely to influence diagnostic or treatment, selection practices in the near future. In the meantime, a re-examination of core symptoms in depressed patients and careful clinical attention to their response to disparate antidepressant, strategies will remain the cornerstone of good clinical practice.

Selected abbreviations and acronyms

• Reference Manager
• Simple TEXT file

People also looked at

Original research article, symptoms of major depressive disorder and their impact on psychosocial functioning in the different phases of the disease: do the perspectives of patients and healthcare providers differ.

• 1 Medical Affairs, H. Lundbeck A/S, Valby, Denmark
• 2 Biostatistics, Lundbeck Singapore Pte. Ltd., Singapore, Singapore
• 3 Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
• 4 Department of Psychiatry, Melbourne Medical School, University of Melbourne, Melbourne, VIC, Australia
• 5 The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia

This analysis was undertaken to examine the relationship between different symptoms of major depressive disorder (MDD) and psychosocial functioning from the perspectives of patients and healthcare providers (HCPs) across the different phases of the disease (acute, post-acute, and remission). Data regarding symptoms of MDD and psychosocial functioning, assessed by an adapted version of the Functioning Assessment Short Test (FAST) scale, were elicited via an online survey from 2,008 patients diagnosed with MDD (based on their personal experience of the disease) and 3,138 patients observed by 1,046 HCPs (based on individual patient records). Correlations between patient-reported and HCP-reported MDD symptoms and impairment of psychosocial functioning were assessed by multivariate regression analysis. The population comprised 1,946 patient respondents and 3,042 HCP-reported patients. Patients reported experiencing a wider range of symptoms and greater impairment of functioning than reported by HCPs across all phases of the disease. At the domain level, only cognitive symptoms were found to be significantly associated with functioning during the acute phase from the perspective of patients, while from the HCPs’ perspective both mood and cognitive symptoms significantly impacted functioning in this phase. Significant associations were seen between mood, physical, and cognitive symptom domains and functioning in both cohorts during the post-acute and remission phases. Differences in associations between individual MDD symptoms and functioning were also observed between the two cohorts across all disease phases; in particular, HCPs found that more physical symptoms impacted functioning during remission than did patients. In summary, the results suggest that perceptions of MDD symptoms and the associations between these symptoms and functioning differ significantly between patients and HCPs across all phases of the disease. These findings further highlight the need for improved communication between patients and HCPs in order to set appropriate treatment goals and promote symptomatic and functional recovery in MDD.

Introduction

Major depressive disorder (MDD) is a complex and multidimensional condition ( 1 ), which is associated with significant impairment of psychosocial functioning and health-related quality of life ( 2 – 4 ). In addition to depressed mood and/or anhedonia, patients with MDD may experience a wide range of physical and cognitive symptoms ( 5 ). Clinical presentation is highly heterogeneous, and large variations in symptom profiles exist between individual patients ( 6 , 7 ).

Both symptomatic and functional recovery are required if patients with MDD are to return to productive and fulfilling daily lives ( 8 – 10 ); however, achieving these treatment goals remains challenging in clinical practice. Approximately 50% of patients with MDD do not respond adequately to initial antidepressant treatment ( 11 ), with patients who achieve only partial response experiencing significant impairments in overall functioning compared with those who achieve remission ( 12 ). Residual symptoms during periods of remission have been shown to be strong predictors of subsequent relapse in patients with MDD ( 13 – 18 ).

Successful management of MDD necessitates shared decision-making between patients and healthcare providers (HCPs) to set appropriate treatment goals ( 19 ). However, available data suggest that patients and HCPs differ in their views as to what they consider important for recovery from MDD ( 20 – 23 ). We have previously reported results of a large, international, online survey undertaken to assess potential differences in perceptions of MDD symptoms and treatment priorities between patients and HCPs across the different phases of the disease ( 23 ). We found that patients more frequently reported mood, physical, and cognitive symptoms than HCPs reported with regard to their patients, particularly during the post-acute and remission phases of MDD. Patients also reported greater impact of symptoms on psychosocial functioning than did HCPs. In addition, patients more frequently reported inadequately treated symptoms across all domains and phases of MDD compared with HCPs. While alleviation of mood symptoms was found to be a priority for both patients and HCPs in the acute phase of MDD, patients also reported the need for improvements in physical and cognitive symptoms in order to address the impact of MDD on psychosocial functioning. In contrast, HCPs underestimated across all disease phases the number of patients who wanted physical and cognitive symptoms to be addressed.

This observed discordance between patient and HCP perceptions has important implications for the diagnosis and management of MDD. Greater alignment of patient and HCP perceptions of depression impact and treatment goals may be expected to facilitate functional recovery and improve long-term outcomes. This analysis of the survey data was undertaken to more comprehensively explore the relationship between individual symptoms of MDD and psychosocial functioning from the perspectives of both patients and HCPs across the different phases of the disease using multivariate regression analysis.

Study Design

This is a detailed analysis of data from an online survey undertaken between February 14 and March 28, 2017, in patients with MDD and HCPs (primary care physicians, psychiatrists, and neurologists) treating patients with MDD in eight countries (Brazil, Canada, Mexico, South Korea, USA, France, Italy, and Spain). Study design, inclusion/exclusion criteria, and survey development have been reported in detail previously ( 23 ). In brief, respondents were recruited through existing online panels of consumers and HCPs. Participating patients were required to be ≥18 years old (≥25 years in the USA) and have a profile indicative of a history of depression during the past 12 months. Patients confirmed that they had been diagnosed with depression by a physician and were either currently using prescribed antidepressant medication for their depression or had used medication to treat depression in the past 3 months. Patients were asked to carefully review three statements describing different phases of depression and indicate which of these best described their current disease state.

Participating HCPs were required to undergo a rigorous screening and authentication process that included an introductory telephone call and validation of identity. Participating HCPs were also required to have treated and managed a minimum number of MDD patients per month: primary care physicians, ≥15 patients (≥10 in Brazil); psychiatrists, ≥40 patients; and neurologists, ≥25 patients. Other HCP eligibility criteria included: ≥75% of working hours to be spent in direct patient care; prescribing antidepressants to ≥75% of their patients with MDD; and, in the USA, <10% of patients living in long-term care facilities. For inclusion, HCPs were also required to confirm that they were able to refer to case records for individual patients matching each of the three different disease phases during completion of the survey.

Patients and HCPs were excluded from survey participation if they were employed by or affiliated with a pharmaceutical company, marketing agency, or any other unsuitable agency ( e.g. , a government agency, health insurance company, or pharmacy/drug store).

All respondents had previously consented to participate in research. Consent was also obtained from all respondents specifically for this survey before participation. Respondents were offered reward points for participating in the survey, which could be redeemed for PayPal credit, gift vouchers, or air miles, or donated to charity.

Survey Assessments

The full questionnaires have been published previously ( 23 ). The 25-minute online patient survey elicited responses based mainly on current experiences with MDD, including disease phase ( i.e. , acute, post-acute, and remission), type of physician consulted, type and relative importance of symptoms experienced, functional impairment, and antidepressant treatment received. The 30-minute online HCP survey required completion of three patient record forms corresponding to the last patient treated for each phase of depression; HCPs were requested to refer to patient records when completing the patient record forms.

Respondents were asked to indicate all the symptoms experienced in the current disease phase, from a list of 23 symptoms. The total symptom score comprised the number of symptoms experienced ( i.e. , 0–23). The score for each domain was also calculated, based on the number of symptoms experienced within the domain (mood 0–7, physical 0–10, and cognitive 0–6).

Both the patient and HCP surveys incorporated the Functioning Assessment Short Test (FAST) questionnaire, the wording of which had been slightly amended from the original version to facilitate data collection. The FAST questionnaire is a brief instrument designed to assess the main problems in daily functioning experienced by psychiatric patients ( 24 ), including those with MDD ( 25 , 26 ). It comprises 24 items that assess impairment or disability across six domains of psychosocial functioning: autonomy, occupational functioning, cognitive functioning, financial issues, interpersonal relationships, and leisure time. Respondents were asked to select the degree of difficulty (‘no difficulty,’ ‘mild difficulty,’ ‘moderate difficulty,’ ‘severe difficulty,’ or ‘don’t know’) associated with each item. Total FAST score ranges from 0 to 72; higher scores indicate greater disability.

Statistical Analysis

For patients and HCPs, the population for analysis comprised all respondents who met all inclusion criteria and no exclusion criteria and completed their respective online survey. Respondents were excluded from the final sample for analysis if they discontinued the survey for any reason, were deemed to have provided identical answers to a series of questions, completed the survey in less than 40% of the mean time taken by other respondents, or provided nonsensical and/or open-ended responses. Respondents who selected ‘don’t know’ for five or more items on the FAST questionnaire were removed from the analysis of the FAST total scores, but their other questionnaire responses were included in other descriptive analyses ( e.g. , analysis of total symptom scores).

Data were analyzed separately for patients and HCPs. Summary statistics (mean and standard deviation for continuous variables, and counts and percentages for categorical variables) were estimated for demographic and clinical characteristics, symptoms experienced, and the FAST scores. Multivariate regression analysis was applied to examine the significance of correlations between MDD symptom scores (at the domain and individual level) and the FAST total score. Data on age, sex, country of origin, and level of education were included as covariates. All statistical tests were two-sided; the significance level was 5%. Analyses were performed using the statistical software R (version 3.5.0) ( 27 ).

Survey Population

Of the 14,048 patients identified as having depression through initial screening and invited to participate, 2,379 took part in the survey (16.9%). Of the 2,428 HCPs invited to participate, 1,223 took part in the survey (50.4%). A total of 2,008 patients and 1,046 HCPs (366 primary care physicians, 650 psychiatrists, and 30 neurologists) accurately completed the surveys, with HCPs providing data for a total of 3,138 patients. The population for the multivariate analysis comprised 1,946 patient respondents and 3,042 HCP-reported patients; 62 and 96 patients having been excluded from the two cohorts, respectively, as they had missing FAST total score. In terms of disease phase, 406, 767, and 773 responses were included for the patient-reported cohort and 1,005, 1,017 and 1,020 for the HCP-reported cohort for the acute, post-acute, and remission phases, respectively.

Baseline Characteristics

Sociodemographic characteristics at baseline were similar in the two patient cohorts ( Table 1 ). In both cohorts, the majority of patients were female and ≥31 years old. No differences were seen between the two cohorts in terms of education and working status. Most patients were currently receiving antidepressants for MDD (80% in the patient-reported cohort and 96% in HCP-reported cohort).

Table 1 Baseline sociodemographic characteristics and symptom profile.

Mean total symptom score and all symptom domain scores were higher in the patient-reported cohort than in the HCP-reported cohort ( Table 1 ). This trend was apparent across all disease phases ( Figure 1 ). Overall mean total FAST score was also higher in the patient-reported cohort than in the HCP-reported cohort ( Table 1 ). Mean total FAST scores were generally similar in both cohorts for the acute phase but higher in the patient-reported cohort than in the HCP-reported cohort for the post-acute and remission phases. Mean scores for individual FAST domains were also similar in the two cohorts for the acute phase, but were generally higher across all domains in the patient-reported cohort compared with the HCP-reported cohort for the post-acute and remission phases ( Figure 2 ).

Figure 1 Mean overall and symptom domain scores in the two patient cohorts according to disease phase: (A) acute; (B) post-acute; and (C) remission. HCP, healthcare provider.

Figure 2 Mean Functioning Assessment Short Test score for each domain in the two patient cohorts according to disease phase: (A) acute; (B) post-acute; and (C) remission. HCP, healthcare provider.

Multivariate Analysis

Multivariate analysis of associations between MDD symptom domains (physical, mood, and cognitive) and total FAST score showed only cognitive symptoms to be significantly associated with functioning in the patient-reported cohort in the acute phase of MDD. However, both mood and cognitive symptoms were found to be significantly associated with patient functioning in this disease phase in the HCP-reported cohort ( Table 2 ). Significant associations were seen between all three symptom domains and functioning in both cohorts during the post-acute and remission phases.

Table 2 Associations between FAST total score and symptoms reported by phase of depression (acute, post-acute, and remission) in the patient- and HCP-reported cohorts; regression coefficients at the symptom domain level.

Differences in associations between individual symptoms of MDD and functioning were also observed between the two cohorts across all disease phases ( Table 3 ). In terms of mood symptoms, significant associations were seen in the patient-reported cohort for ‘lack of interest in general’ in the acute phase and ‘low self-esteem’ in the remission phase. In the HCP-reported cohort, significant associations were seen for ‘feelings of guilt or worthlessness’ and ‘suicidal thoughts’ in the acute phase, for ‘low self-esteem’ in the post-acute phase, and for ‘lack of confidence’ in the remission phase. No associations were seen between any individual physical symptoms and functioning in either cohort in the acute phase; however, significant associations between physical symptoms and functioning were seen in both cohorts during the post-acute and remission phases. Of note, significant associations between physical symptoms and functioning were more frequent in the HCP-reported cohort than in the patient-reported cohort during the remission phase. Significant associations were seen between at least one cognitive symptom and functioning in both cohorts during all disease phases. For the patient-reported cohort, significant associations were seen for the symptoms of ‘difficulty in making plans’ during the acute phase, ‘slowness of thinking’ and ‘difficulty concentrating’ during the post-acute phase, and ‘difficulty concentrating’ and ‘difficulty in making plans’ during the remission phase. For the HCP-reported cohort, significant associations were seen for ‘forgetfulness/difficulty remembering’ during the acute and post-acute phases, and for ‘slowness of thinking’ and ‘difficulty in making plans’ during the remission phase. The magnitude of effect of the individual symptoms on functioning did not differ significantly in either cohort.

Table 3 Associations between FAST total score and symptoms reported by phase of depression (acute, post-acute, and remission) in the patient- and HCP-reported cohorts; regression coefficients at the individual symptom level. a

This multivariate analysis was undertaken to explore statistically how perceived individual symptoms of MDD and psychosocial functioning differ between patients and HCPs across the different phases of the disease. We found that patients reported more symptoms of MDD and greater impairment of functioning than HCPs across all disease phases. Our findings also highlight the importance of cognitive symptoms in patients with MDD across all disease phases. Indeed, in patients with MDD, at the domain level only cognitive symptoms were found to be significantly associated with functioning during the acute phase of MDD; however, significant associations between both mood and cognitive symptoms and functioning were observed in the HCP-reported cohort during this disease phase. Differences in associations between individual MDD symptoms and functioning were also observed between the two cohorts across all disease phases, with more cognitive symptoms found to be associated with functioning in the patient-reported cohort and more physical symptoms of MDD associated with functioning in the HCP-reported cohort.

Our results support earlier findings reporting important differences in perspectives between patients and HCPs regarding what “success looks like” for the treatment of depression. Demyttenaere et al. ( 22 ) found patients’ top three priorities to be to what extent life is meaningful, level of enjoyment in life, and satisfaction in life, while HCPs ranked addressing negative feelings (blue mood, despair, anxiety, depression), feeling down/depressed/hopeless, and little interest or pleasure in doing things as their top three priorities. These top-ranking treatment priorities in patients were similar to those reported by Zimmerman et al. ( 28 ), who found the presence of positive mental health, feeling your usual self, and restoring functioning to be the most important priorities from the patients’ perspective. Alleviation of depressive symptoms was ranked only sixth ( 28 ). Our finding of cognitive symptoms being the only significant symptom domain significantly associated with functioning in the acute phase of MDD from a patient perspective, unlike both cognitive and mood symptoms from an HCP perspective, is intriguing in this regard.

Cognitive symptoms are prevalent during the course of MDD, and important for patient functioning. In one prospective study, cognitive symptoms were found to be present for 85–94% of the duration of depressive episodes and 39–44% of the duration of periods of remission ( 29 ). The presence of cognitive symptoms affects patient functioning broadly, including difficulty in maintaining performance at work, experiencing household and financial strains, and difficulty in participating in social life ( 30 – 32 ). Improvements in these symptoms have been shown to precede improvements in functional outcome, even after adjustment for depressive symptom severity ( 33 – 35 ). Our findings may indicate that patients experience the functional consequences of cognitive symptoms in the short term to a much greater extent than realized or observed by HCPs and therefore report them as significant over and above depressive symptoms.

Patient-centered consulting approaches allow treatment to be based on individual patient symptoms and preferences, which may result in improved outcomes ( 36 , 37 ). Other studies have also reported low recognition by HCPs of cognitive symptoms in patients with MDD ( 38 – 40 ). A survey of Italian psychiatrists showed that, although psychiatrists considered cognitive symptoms among the most relevant residual symptoms in MDD, these symptoms were not always taken into consideration when selecting antidepressant therapy ( 38 ). This is of clinical significance as currently available antidepressants have been shown to have differential effects in terms of improving cognitive symptoms in patients with MDD ( 41 ).

The presented research has strengths and limitations, which have been described in detail previously ( 23 ). In brief, this is the first study to our knowledge to investigate real-world differences in perceptions of MDD symptoms between patients and HCPs across the different phases of the disease (acute, post-acute, and remission phases) and their associations with perceived functioning. Furthermore, data were provided by primary care physicians, psychiatrists and neurologists, representing the full spectrum of HCPs likely to be consulted by patients with MDD in real-world practice. Study limitations include the potential for selection and/or response bias, lack of information about comorbid psychiatric disorders and the presence of subthreshold symptoms, and the potential for cultural differences within the survey population. In terms of this analysis, it should again be noted that comparisons between the two patient cohorts should be interpreted with caution as patient- and HCP-provided responses were unmatched ( i.e. , patients who participated in this survey were not the same patients as those described by the HCPs). In addition, as the survey by definition is cross-sectional, the data presented here cannot be used to draw conclusions about any causal relationship between symptoms of MDD and psychosocial functioning.

Conclusions

In summary, the results suggest that patients and HCPs differ in their perceptions of MDD symptoms and their impact on functioning across all phases of the disease. In particular, patients emphasized cognitive rather than mood and physical symptoms in the acute phase of the disease, while HCPs were more likely than patients to associate more specific physical symptoms with functioning during the remission phase. These findings further highlight the need for improved communication between patients and HCPs in order to set appropriate treatment goals based on the individual’s specific symptom profile and promote symptomatic and functional recovery in patients with MDD.

Data Availability Statement

The datasets presented in this article are not readily available given the informed consent provided by survey participants. Requests to access the datasets should be directed to MC.

Ethics Statement

All respondents (patients and HCPs) accepted the online panel partners’ privacy policies and terms and conditions when they signed up to become a member of the panel. They thus provided consent to receive invitations to participate in market research, and their consent was sought again to participate in this particular study. The market research protocol was not formally approved by a medical ethics committee.

Author Contributions

MC and BB were both instrumental in the development of the study, study design, analysis plan, and interpretation of data. CW undertook the statistical analysis and contributed to data interpretation. All authors were involved at all stages of manuscript development and approved the final version.

This study was funded by H. Lundbeck A/S, who contributed to the data analysis, review of the data, and review of the manuscript.

Conflict of Interest

MC is an employee of H. Lundbeck A/S. CW is an employee of Lundbeck Singapore Pte. Ltd. BB has received speaker/consultation fees from AstraZeneca, Lundbeck, Pfizer, Takeda, Servier, Bristol-Myers Squibb, Otsuka, and Janssen-Cilag.

Acknowledgments

The internet survey and corresponding analyses were provided by Matt Brooks and Bridget Pumfrey of BPR Pharma Ltd, funded by H. Lundbeck A/S. Medical writing assistance was provided by Jennifer Coward of Anthemis Consulting Ltd, funded by H. Lundbeck A/S.

1. Culpepper L, Muskin PR, Stahl SM. Major depressive disorder: Understanding the significance of residual symptoms and balancing efficacy with tolerability. Am J Med (2015) 128(9 Suppl):S1–15. doi: 10.1016/j.amjmed.2015.07.001

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Greer TL, Kurian BT, Trivedi MH. Defining and measuring functional recovery from depression. CNS Drugs (2010) 24(4):267–84. doi: 10.2165/11530230-000000000-00000

3. Hammer-Helmich L, Haro JM, Jönsson B, Tanguy Melac A, Di Nicola S, Chollet J, et al. Functional impairment in patients with major depressive disorder: the 2-year PERFORM study. Neuropsychiatr Dis Treat (2018) 14:239–49. doi: 10.2147/NDT.S146098

4. Saragoussi D, Christensen MC, Hammer-Helmich L, Rive B, Touya M, Haro JM. Long-term follow-up on health-related quality of life in major depressive disorder: a 2-year European cohort study. Neuropsychiatr Dis Treat (2018) 14:1339–50. doi: 10.2147/NDT.S159276

5. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders . 5th ed. (DSM-5) Washington, DC: American Psychiatric Association (2013).

Google Scholar

6. Ostergaard SD, Jensen SO, Bech P. The heterogeneity of the depressive syndrome: when numbers get serious. Acta Psychiatr Scand (2011) 124(6):495–6. doi: 10.1111/j.1600-0447.2011.01744.x

7. Fried EI, Nesse RM. Depression is not a consistent syndrome: An investigation of unique symptom patterns in the STAR*D study. J Affect Disord (2015) 172:96–102. doi: 10.1016/j.jad.2014.10.010

8. American Psychiatry Association. Practice Guideline for the Treatment of Patients With Major Depressive Disorder . 3rd ed. Washington, DC: American Psychiatric Association (2010).

9. McIntyre RS, Lee Y, Mansur RB. Treating to target in major depressive disorder: response to remission to functional recovery. CNS Spectr (2015) 20:20–30. doi: 10.1017/S1092852915000826

10. Lam RW, McIntosh D, Wang J, Enns MW, Kolivakis T, Michalak EE, et al. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical guidelines for the management of adults with major depressive disorder: Section 1. Disease burden and principles of care. Can J Psychiatry (2016) 61(9):510–23. doi: 10.1177/0706743716659416

11. Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry (2006) 163(11):1905–17. doi: 10.1176/ajp.2006.163.11.1905

12. Trivedi MH, Corey-Lisle PK, Guo Z, Lennox RD, Pikalov A, Kim E. Remission, response without remission, and nonresponse in major depressive disorder: impact on functioning. Int Clin Psychopharmacol (2009) 24(3):133–8. doi: 10.1097/YIC.0b013e3283277614

13. Paykel ES, Ramana R, Cooper Z, Hayhurst H, Kerr J, Barocka A. Residual symptoms after partial remission: an important outcome in depression. Psychol Med (1995) 25(6):1171–80. doi: 10.1017/s0033291700033146

14. Judd LL, Akiskal HS, Maser JD, Zeller PJ, Endicott J, Coryell W, et al. Major depressive disorder: a prospective study of residual subthreshold depressive symptoms as predictor of rapid relapse. J Affect Disord (1998) 50(2–3):97–108. doi: 10.1016/S0165-0327(98)00138-4

15. Nierenberg AA, Husain MM, Trivedi MH, Fava M, Warden D, Wisniewski SR, et al. Residual symptoms after remission of major depressive disorder with citalopram and risk of relapse: a STAR*D report. Psychol Med (2010) 40(1):41–50. doi: 10.1017/S0033291709006011

16. Judd LL, Schettler PJ, Rush AJ. A brief clinical tool to estimate individual patients’ risk of depressive relapse following remission: Proof of concept. Am J Psychiatry (2016) 173(11):1140–6. doi: 10.1176/appi.ajp.2016.15111462

17. Saragoussi D, Touya M, Haro JM, Jönsson B, Knapp M, Botrel B, et al. Factors associated with failure to achieve remission and with relapse after remission in patients with major depressive disorder in the PERFORM study. Neuropsychiatr Dis Treat (2017) 13:2151–65. doi: 10.2147/NDT.S136343

18. Verhoeven FEA, Wardenaar KJ, Ruhe HGE, Conradi HJ, de Jonge P. Seeing the signs: Using the course of residual depressive symptomatology to predict patterns of relapse and recurrence of major depressive disorder. Depress Anxiety (2018) 35(2):148–59. doi: 10.1002/da.22695

19. Kennedy SH, Lam RW, McIntyre RS, Tourjman SV, Bhat V, Blier P, et al. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical guidelines for the management of adults with major depressive disorder: Section 3. Pharmacological treatments. Can J Psychiatry (2016) 61(9):540–60. doi: 10.1177/0706743716659417

20. Battle CL, Uebelacker L, Friedman MA, Cardemil EV, Beevers CG, Miller IW. Treatment goals of depressed outpatients: a qualitative investigation of goals identified by participants in a depression treatment trial. J Psychiatr Pract (2010) 16(6):425–30. doi: 10.1097/01.pra.0000390763.57946.93

21. Zimmerman M, Martinez JA, Attiullah N, Friedman M, Toba C, Boerescu DA, et al. Why do some depressed outpatients who are in remission according to the Hamilton Depression Rating Scale not consider themselves to be in remission? J Clin Psychiatry (2012) 73(6):790–5. doi: 10.4088/JCP.11m07203

22. Demyttenaere K, Donneau AF, Albert A, Ansseau M, Constant E, van Heeringen K. What is important in being cured from depression? Discordance between physicians and patients. J Affect Disord (2015) 174:390–6. doi: 10.1016/j.jad.2014.12.004

23. Baune BT, Christensen MC. Differences in perceptions of major depressive disorder symptoms and treatment priorities between patients and health care providers across the acute, post-acute, and remission phases of depression. Front Psychiatry (2019) 10:335. doi: 10.3389/fpsyt.2019.00335

24. Rosa AR, Sanchez-Moreno J, Martinez-Aran A, Salamero M, Torrent C, Reinares M, et al. Validity and reliability of the Functioning Assessment Short Test (FAST) in bipolar disorder. Clin Pract Epidemiol Ment Health (2007) 3:5. doi: 10.1186/1745-0179-3-5

25. Baune BT, Air T. Clinical, functional, and biological correlates of cognitive dimensions in major depressive disorder – Rationale, design, and characteristics of the Cognitive Function and Mood Study (CoFaM-Study). Front Psychiatry (2016) 7:150. doi: 10.3389/fpsyt.2016.00150

26. Knight MJ, Air T, Baune BT. The role of cognitive impairment in psychosocial functioning in remitted depression. J Affect Disord (2018) 235:129–34. doi: 10.1016/j.jad.2018.04.051

27. R Core Team. R: A language and environment for statistical computing . 3-900051-07-0 http://www.R-project.org/ . Vienna, Austria: R Foundation for Statistical Computing. (2018).

28. Zimmerman M, McGlinchey JB, Posternak MA, Friedman M, Attiullah N, Boerescu D. How should remission from depression be defined? The depressed patient’s perspective. Am J Psychiatry (2006) 163:148–50. doi: 10.1176/appi.ajp.163.1.148

29. Conradi HJ, Ormel J, de Jonge P. Presence of individual (residual) symptoms during depressive episodes and periods of remission: a 3-year prospective study. Psychol Med (2011) 41:1165–74. doi: 10.1017/S0033291710001911

30. McIntyre RS, Cha DS, Soczynska JK, Woldeyohannes HO, Gallaugher LA, Kudlow P, et al. Cognitive deficits and functional outcomes in major depressive disorder: determinants, substrates, and treatment interventions. Depress Anxiety (2013) 30:515–27. doi: 10.1002/da.22063

31. Hammar A, Ardal G. Cognitive functioning in major depression–a summary. Front Hum Neurosci (2009) 3:26. doi: 10.3389/neuro.09.026.2009

32. Baune BT, Miller R, McAfoose J, Johnson M, Quirk F, Mitchell D. The role of cognitive impairment in general functioning in major depression. Psychiatry Res (2010) 176:183–9. doi: 10.1016/j.psychres.2008.12.001

33. Chokka P, Bougie J, Proulx J, Holmegaard Tvistholm A, Ettrup A. Long-term functioning outcomes are predicted by cognitive symptoms in working patients with major depressive disorder treated with vortioxetine: results from the AtWoRC study. CNS Spectr (2019) 24:616–27. doi: 10.1017/S1092852919000786

34. Wang G, Si TM, Li L, Fang Y, Wang CX, Wang LN, et al. Cognitive symptoms in major depressive disorder: associations with clinical and functional outcomes in a 6-month, non-interventional, prospective study in China. Neuropsychiatr Dis Treat (2019) 15:1723–36. doi: 10.2147/NDT.S195505

35. Haro JM, Hammer-Helmich L, Saragoussi D, Ettrup E, Larsen KG. Patient-reported depression severity and cognitive symptoms as determinants of functioning in patients with major depressive disorder: a secondary analysis of the 2-year prospective PERFORM study. Neuropsychiatr Dis Treat (2019) 15:2313–23. doi: 10.2147/NDT.S206825

36. Wittink MN, Cary M, Tenhave T, Baron J, Gallo JJ. Towards patient-centered care for depression: conjoint methods to tailor treatment based on preferences. Patient (2010) 3(3):145–57. doi: 10.2165/11530660

37. Jani B, Bikker AP, Higgins M, Fitzpatrick B, Little P, Watt GC, et al. Patient centredness and the outcome of primary care consultations with patients with depression in areas of high and low socioeconomic deprivation. Br J Gen Pract (2012) 62(601):e576–81. doi: 10.3399/bjgp12X653633

38. Albert U, Brugnoli R, Caraci F, Dell’Osso B, Di Sciascio G, Tortorella A, et al. Italian psychiatrists’ perception on cognitive symptoms in major depressive disorder. Int J Psychiatry Clin Pract (2016) 20(1):2–9. doi: 10.3109/13651501.2015.1093147

39. Neri G, Serrati C, Zolo P, Cataldo N, Ripellino C. Italian neurologists’ perception on cognitive symptoms in major depressive disorder. Neurol Sci (2016) 37(9):1437–42. doi: 10.1007/s10072-016-2601-2

40. McAllister-Williams RH, Bones K, Goodwin GM, Harrison J, Katona C, Rasmussen J, et al. Analysing UK clinicians’ understanding of cognitive symptoms in major depression: A survey of primary care physicians and psychiatrists. J Affect Disord (2017) 207:346–52. doi: 10.1016/j.jad.2016.09.036

41. Baune BT, Brignone M, Larsen KG. A network meta-analysis comparing effects of various antidepressant classes on the digit symbol substitution test (DSST) as a measure of cognitive dysfunction in patients with major depressive disorder. Int J Neuropsychopharmacol (2018) 21(2):97–107. doi: 10.1093/ijnp/pyx070

Keywords: major depressive disorder, psychosocial functioning, cognitive symptoms, treatment phase, acute, post-acute, remission, recovery

Citation: Christensen MC, Wong CMJ and Baune BT (2020) Symptoms of Major Depressive Disorder and Their Impact on Psychosocial Functioning in the Different Phases of the Disease: Do the Perspectives of Patients and Healthcare Providers Differ? Front. Psychiatry 11:280. doi: 10.3389/fpsyt.2020.00280

Received: 07 January 2020; Accepted: 23 March 2020; Published: 24 April 2020.

Reviewed by:

Copyright © 2020 Christensen, Wong and Baune. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Michael Cronquist Christensen, [email protected]

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• For authors
• Browse by collection
• BMJ Journals More You are viewing from: Google Indexer

You are here

• Volume 7, Issue 8
• Prevalence of depression and depressive symptoms among outpatients: a systematic review and meta-analysis
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

• Jinghui Wang 1 ,
• Xiaohang Wu 1 ,
• Weiyi Lai 1 ,
• Erping Long 1 ,
• Xiayin Zhang 1 ,
• Wangting Li 1 ,
• Yi Zhu 1 , 2 ,
• Chuan Chen 1 , 2 ,
• Xiaojian Zhong 1 ,
• Zhenzhen Liu 1 ,
• Dongni Wang 1 ,
• Haotian Lin 1
• 1 State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center , Sun Yat-sen University , Guangzhou , People’s Republic of China
• 2 Department of Molecular and Cellular Pharmacology , University of Miami Miller School of Medicine , Miami , Florida , USA
• Correspondence to Dr Haotian Lin; haot.lin{at}hotmail.com

Objectives Depression and depressive symptoms are common mental disorders that have a considerable effect on patients’ health-related quality of life and satisfaction with medical care, but the prevalence of these conditions varies substantially between published studies. The aim of this study is to conduct a systematic review and meta-analysis to provide a precise estimate of the prevalence of depression or depressive symptoms among outpatients in different clinical specialties.

Design Systematic review and meta-analysis.

Data sources and eligibility criteria The PubMed and PsycINFO, EMBASE and Cochrane Library databases were searched to identify observational studies that contained information on the prevalence of depression and depressive symptoms in outpatients. All studies included were published before January 2016. Data characteristics were extracted independently by two investigators. The point prevalence of depression or depressive symptoms was measured using validated self-report questionnaires or structured interviews. Assessments were pooled using a random-effects model. Differences in study-level characteristics were estimated by meta-regression analysis. Heterogeneity was assessed using standard χ 2 tests and the I 2 statistic. The study protocol has been registered with PROSPERO under number CRD42017054738.

Results Eighty-three cross-sectional studies involving 41 344 individuals were included in this study. The overall pooled prevalence of depression or depressive symptoms was 27.0% (10 943/41 344 individuals; 95% CI 24.0% to 29.0%), with significant heterogeneity between studies (p<0.0001, τ 2 =0.3742, I 2 =96.7%). Notably, a significantly higher prevalence of depression and depressive symptoms was observed in outpatients than in the healthy controls (OR 3.16, 95% CI 2.66 to 3.76, I 2 =72.0%, χ 2 =25.33). The highest depression/depressive symptom prevalence estimates occurred in studies of outpatients from otolaryngology clinics (53.0%), followed by dermatology clinics (39.0%) and neurology clinics (35.0%). Subgroup analyses showed that the prevalence of depression and depressive symptoms in different specialties varied from 17.0% to 53.0%. The prevalence of depression and depressive symptoms was higher among outpatients in developing countries than in outpatients from developed countries. Moreover, the prevalence of depression and depressive symptoms in outpatients slightly decreased from 1996 to 2010. Regarding screening instruments, the Beck Depression Inventory led to a higher estimate of the prevalence of depression and depressive symptoms (1316/4702, 36.0%, 95% CI 29.0% to 44.0%, I 2 =94.8%) than the Hospital Anxiety and Depression Scale (1003/2025, 22.0%, 95% CI 12.0% to 35.0%, I 2 =96.6%).

Conclusion Our study provides evidence that a significant proportion of outpatients experience depression or depressive symptoms, highlighting the importance of developing effective management strategies for the early identification and treatment of these conditions among outpatients in clinical practice. The substantial heterogeneity between studies was not fully explained by the variables examined.

• mental health
• meta-analysis

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

http://dx.doi.org/10.1136/bmjopen-2017-017173

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Strengths and limitations of this study

This is the first systematic review and meta-analysis to provide a comprehensive estimate of the prevalence of depression or depressive symptoms among outpatients considering different clinical specialties.

The information provided by this study highlights the importance of developing effective management strategies for the early identification and treatment of depression among outpatients in clinical practice.

The substantial heterogeneity between included studies was not fully explained by the variables examined.

The paucity of longitudinal data may decrease the generalisability of the study outcomes.

Introduction

Depression is the leading cause of disability and is a major contributor to the disease burden worldwide. The global prevalence of depression and depressive symptoms has been increasing in recent decades. 1 The lifetime prevalence of depression ranges from 20% to 25% in women and 7% to 12% in men. 2 Depression is a significant determinant of quality of life and survival, accounting for approximately 50% of psychiatric consultations and 12% of all hospital admissions. 3 Notably, the prevalence of depression or depressive symptoms is higher in patients than in the general public. 3–6 The underlying reasons include the illness itself and the heavy medical cost, unsatisfactory medical care service and poor doctor–patient relationship. 7 8 Several informative systematic reviews on specific groups of outpatients have been published. For example, Mitchell  et al estimated that the prevalence of depression in oncology and haematology patients was 9.6%–16.5%. 5 Depression is a significant comorbidity of chronic medical disorders. The prevalence of depression in chronic medical conditions is as follows: asthma (27%), 9 atopic dermatitis (5%), 10 chronic obstructive pulmonary disease (24.6%), 11 gouty arthritis (20%), 12 rheumatoid arthritis (15%), 13 systemic lupus erythematosus (22%) 12 and stroke (30%). 14 Ismail et al conducted a meta-analysis of 57 studies and showed that the overall pooled prevalence of depression in patients with mild cognitive impairment was 32%. 4 Estimates of the prevalence of depression and depressive symptoms vary substantially between published studies, particularly with respect to specialty, patient age and residence. The inconsistency across different studies may originate from the lack of a clear definition or gold standard for the diagnosis of depression and depressive symptoms. Many previous studies have focused on depression and depressive symptoms in inpatient settings; however, mental disorders in outpatients are largely underestimated. 6 15 Depression in outpatients is associated with high indirect costs due to loss of productivity and unemployment. 8 The combination of chronic medical illnesses and depression will lead to significant economic burden. 8 Additionally, it is important for healthcare workers to identify mental status changes in outpatients, as mental states may affect the doctor–patient relationship and can influence patient satisfaction with medical care. 16 To the best of our knowledge, no previous studies have quantitatively analysed a robust dataset with information on depression and depressive symptoms among outpatients in different clinical departments. Therefore, conducting a systematic review and meta-analysis of the depression prevalence detected during doctor visits is essential to informing efforts to prevent and treat depression and depressive symptoms among outpatients. In this study, we aimed to quantitatively summarise the prevalence of depression and depressive symptoms in different clinical departments.

Study selection

Relevant studies published before January 2016 that described the prevalence of depression or depressive symptoms in patients from different specialties were identified using the PubMed and PsycINFO, EMBASE and Cochrane Library databases (by WJH and WXH); the selected articles were then screened by title, abstract and reference lists in collaboration with study investigators using the approach recommended by the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. 17 Potentially relevant papers were first identified through title and abstract searches. The full text of the retrieved articles was then assessed. The search strategy involved applying the ‘explode’ command to search the MeSH terms ‘depression’ and ‘depressive symptoms’ and ‘outpatient*’; the combined terms were related to ‘prevalence’, ‘cross-sectional studies’ or ‘controlled studies’ and ‘different specialties’, such as ‘internal medicine’, ‘surgical specialties’ and ‘paediatrics’, with language restrictions (articles published in English only). More search strategy details can be found in online supplementary method 1 . The study inclusion criteria were the following: (1) articles that included patients diagnosed with a specific disease other than psychiatric disorders; (2) articles in peer-reviewed journals that involved only patients with a current degree of clinically relevant depression sufficient to warrant clinical intervention, regardless of the depression severity (mild, moderate or severe) 18 ; (3) studies in which depression was confirmed by validated self-report instruments or diagnostic structured interviews 19 ; (4) articles with study populations who were recruited from outpatient clinics only. The exclusion criteria were as follows: (1) studies that failed to report the specific prevalence of depression, (2) studies on patients whose depression predated any other physical disorder and (3) studies on patients diagnosed with more than one psychiatric disorder (in addition to depression).

Supplementary file 1

Data extraction and quality assessment.

Data extraction was a multistep process based on the eligibility criteria. The following information was extracted from each study independently by two investigators (JHW and XHW) independently using a standardised form: study design, research year, country, specialty category, disease, sample size, diagnostic or screening method used and reported prevalence of depression and depressive disorders. A modified version of the Newcastle-Ottawa Scale was used to evaluate the quality of non-randomised studies. 20 Studies were identified as having a low risk of bias (≥3 points) or a high risk of bias (<3 points). The effect of individual studies on the overall prevalence estimate was explored by serially excluding each study in a sensitivity analysis. Additionally, two reviewers (JHW and XHW) cross-checked the reference lists of all selected articles to identify other relevant studies. All discrepancies were resolved by discussion and consensus.

Statistical analysis

As considerable heterogeneity was expected because of the multiple sources of variance, a random-effects model was used to estimate the pooled prevalence of depressive symptoms. 21 Random-effect model attempted to generalise findings beyond the included studies by assuming that the selected studies are random samples from a larger population. 22 The observed heterogeneity in the depression prevalence among outpatients may be attributed to differences in the assessment methods used to detect depression, the variation in thresholds in the different validated depression measurements, the specialties examined, the study countries, study year, patient ages and other factors. Thus, subgroup analyses were performed. Binomial proportion CIs for individual studies were calculated using the Clopper-Pearson method, which allows for asymmetry. 23 Between-study heterogeneity was evaluated using standard χ 2 tests and the I 2 statistic. 21 I 2 statistics were calculated to describe the percentages of total variation across studies caused by heterogeneity. A 0% value indicated no heterogeneity, and higher values represented an increase in heterogeneity. Generally, heterogeneity is categorised as 25% (low), 50% (moderate) and 75% (high). 24 The results of the analysis were compared in terms of descriptive characteristics (age, specialty, study year, diagnostic method and country) using subgroup analysis and meta-regression. For models with considerable heterogeneity, a meta-regression was performed to identify the moderators that might contribute to the heterogeneity of the effect sizes. 25 Publication bias of the studies was examined using funnel plots and Egger’s test. 26 All analyses were performed using R version 3.3.1 (R Foundation for Statistical Computing, Vienna, Austria) and Review Manager version 5.3 (The Cochrane Collaboration, 2015, the Nordic Cochrane Centre, Copenhagen, Denmark). Statistical tests were two-sided with a significance threshold of p<0.05. This study is registered with PROSPERO, number CRD42017054738.

Patient involvement

No patients eligible for screening were involved in the design and conduct of the study or involved in defining the research question or outcome measures. We have no intentions to disseminate our results to patients eligible for screening.

Screening the titles and abstracts resulted in 3165 articles, 110 of which were duplicates, and only 207 articles were retrieved for a detailed, full-text assessment. Of these, 83 studies fulfilled the inclusion criteria; 101 studies did not meet the eligible population criteria, 8 failed to present point prevalence data and 15 used improper outcome measures and were excluded.

Eighty-three cross-sectional studies involving a total of 41 344 individuals were included in the study ( figure 1 ). Study participants were recruited from 11 departments: 22 studies recruited patients from internal medicine clinics, 12 from primary care, 10 from neurology, 8 from dermatology, 7 from obstetrics/gynaecology, 6 from ophthalmology, 6 from oncology, 5 from infectious diseases, 4 from surgery, 3 from paediatrics and 3 from otolaryngology departments. Most (29) of the studies were conducted in Europe; 21 were performed in Asia, 19 in North America, 4 in South America, 4 in Oceania, 3 in the Middle East and 1 in Africa. Seventeen studies used the Beck Depression Inventory (BDI) to assess depression; 10, the Hospital Anxiety and Depression Scale (HADS); 7, the Patient Health Questionnaire; 6, the Hamilton Depression Scale, also called the Hamilton Depression Rating Scale; and 43, other methods. The full study characteristics are summarised in table 1 . The overall prevalence estimates of depression or depressive symptoms reported by the 83 studies yielded a summary prevalence of 27.0% (10 943/41 344 individuals, 95% CI 24.0% to 29.0%), with significant between-study heterogeneity (p<0.0001, τ 2 =0.3742, I 2 =96.7%). Subgroup analyses by age, clinical department, study year, country and diagnosis method were conducted to explore the potential heterogeneity between studies. Of the 83 studies, the highest depression/depressive symptom prevalence estimates occurred in studies of outpatients from otolaryngology clinics (357/796, 53.0%, 95% CI 39.0% to 66.0%, I 2 =79.8%), followed by dermatology clinics (520/1558, 39.0%, 95% CI 24.0% to 56.0%, I 2 =96.9%) and neurology clinics (3328/9280, 35.0%, 95% CI 30.0% to 40.0%, I 2 =94.4%). The prevalences of depression among outpatients from other specialties are summarised in figure 2 . Subgroup analysis was conducted to compare studies in developed countries versus in developing countries (7788/29 208, 24.0%, 95% CI 21.0% to 27.0%, I 2 =97.0%, p<0.0001 vs 3188/12 050, 33.0%, 95% CI 28.0% to 38.0%, I 2 =96.8%, p<0.0001). The prevalence of depression/depressive symptoms in outpatients decreased from 36.0% to 24.0% from 1990 to 2010, followed by a slight increase from 2011 to 2016. Outpatients who were younger than 30 years old showed the lowest depression prevalence, at 20.0% (170/797, 95% CI 14.0% to 28.0%, I 2 =81.6%, p=0.0010), whereas the highest depression prevalence was reported in outpatients older than 80 years at 34.0% (397/2128, 95% CI 15.0% to 69.0%, I 2 =96.8%, p<0.0001). The prevalences reported in studies stratified by year are presented in figure 3 . Eight studies with healthy controls were included in a subgroup. There was a significantly higher prevalence of depression and depressive symptoms in outpatients than in healthy controls (OR 3.16, 95% CI 2.66 to 3.76, I 2 =72.0%, χ 2 =25.33) ( figure 4 ).

• Download figure
• Open in new tab
• Download powerpoint

Flow diagram of study inclusion.

Forest plot of the prevalence of depression or depressive symptoms among outpatients. E.N.T., ear, nose, throat.

Bar graph of meta-analysis of the prevalence of depression or depressive symptoms among outpatients stratified by age and study year. (A) Prevalence of depression or depressive symptoms among outpatients stratified by year of study publication. (B) Prevalence of depression or depressive symptoms among outpatients stratified by age.

Forest plot of the eight studies that included control groups.

• View inline

Selected characteristics of the 83 studies included in this systematic review and meta-analysis

Regarding the screening instruments used, higher prevalence estimates were found among studies that used BDI (1316/4702, 36.0%, 95% CI 29.0% to 44.0%, I 2 =94.8%) compared with those that used HADS (1003/2025, 22.0%, 95% CI 12.0% to 35.0%, I 2 =96.6%). A meta-regression analysis was conducted to evaluate the potential heterogeneity in combined subtypes to assess study year, country and specialty as sources of heterogeneity. In this analysis, country, study year and specialties represented a small amount of heterogeneity (p=0.0327, <0.05). Funnel plots and tests indicated evidence of publication bias (Egger’s test, p<0.001, figure 5 ). Sensitivity analyses, in which the meta-analysis was serially repeated after excluding each study, suggested that no individual study affected the overall prevalence estimate by more than 1% (online  supplementary table 1 ).

Supplementary file 2

Funnel plots to test the publication bias of the 8 studies that included control groups. Each point represents a separate study on the indicated association. The points were distributed asymmetrically, indicating the existence of publication bias.

We performed a systematic review and meta-analysis to best estimate the prevalence of depression and depressive symptoms in different clinical departments. Overall, the prevalence of depression or depressive symptoms among outpatients was 27.0%, ranging from 17.0% to 53.0% in different clinical departments. This study found that outpatients from otolaryngology clinics had the highest prevalence of depression (53.0%). Depression was found to be an important mediator for otolaryngologic conditions such as chronic tinnitus. 27 It was not surprising that dermatology ranked the second highest and 39.0% of outpatients from dermatology clinics suffered from depression. Atopic dermatitis was found to be associated with depression because the skin stigmata often causes embarrassment, low confidence and sadness. 10 28 Atopic dermatitis is one of the most common dermatological disorders and was found to be associated with negative impact on the quality of life of patients, families and caregivers. 29 30 There is psychoneuroimmunology connection between depression and medical illness. 31 The production of pro-inflammatory cytokine (eg, IL-6) was found to be higher in patients with atopic dermatitis 32 and IL-6 was found to be raised in patients with depression. 33 Raised IL-6 may cause depression in patient with atopic dermatitis. This study found that 35% of outpatients from neurology clinic suffered from depression. Genetic factors and autoantibodies play an important role in causing neuropsychiatric complications including depression. 34 35 Stroke is a common neurological disorder and causes significant health burden. 36 The burden of stroke causes depression in both stroke patients and their caregivers. 37 Novel rehabilitation intervention targeting at motor deficit was designed to improve functional status and quality of life of patients with stroke. 36 This intervention might offer hope and reduce prevalence of depression in patients with stroke. Our study confirmed previous findings of the higher prevalence of depression or depressive symptoms in outpatients than in the general public. 3–6 The prevalence of depression/depressive symptoms in outpatients slightly decreased from 1990 to 2010. This decrease may be due to the potentially improved recognition of the illness and increased awareness for seeking help among the general public. However, this explanation has yet to be confirmed with population-based research. Depression or depressive symptoms are often overlooked during daily medical care by busy professionals without specific training in mental health, and our findings suggests that specialists should focus on patients’ physical problems and their mental problems. We should enhance the awareness of mental disorders during medical works and strengthen the communication between doctors and patients. Depression is expected to vary throughout the life course, as ageing is a risk factor for the development of depression and depressive symptoms. In this study, the distribution of age-related depression had two peaks and varied in different groups. Outpatients aged 30–40 years old had a similar depression prevalence as outpatients aged 80–90 years old, with rates ranging between 30.0% and 40.0%. However, previous research on the association between age and depression has shown contradictory patterns. 38–40 Klerman noted a particular emergence of childhood depression and an increase in suicide attempts and death among adolescents and young adults. 39 Outpatients aged 30–40 years suffering from chronic medical illnesses are at higher risk for developing depression. Depressed outpatients might develop maladaptive rumination and illness perception towards their chronic medical illness. 41 Chronic medical illness may increase the risk of suicide in adult outpatients because psychosomatic complaint such as headache was found to be an important risk factor for suicide in adults. 42  Yang showed that depression declined with age. 40 By contrast, Jorm revealed that there was no consistent pattern across studies regarding age differences in the occurrence of anxiety, depression or distress. 38 Our results showed that the prevalence of depression and depressive symptoms peaked among individuals aged 30–40 years and 80–90 years, consistent with the U-shaped ageing trajectory of depression reported by a previous study. 43 It has been suggested that depression reaches its highest level in elderly aged 80 years or older because physical dysfunction and low personal control add to personal and status losses. 43 Risk factors of geriatric depression include poor health, brain injury, low folate and vitamin B12 and raised plasma homocysteine levels. 44 The association between depression and chronic medical illnesses in elderly is due to accompanying poor self-reported health and functional status. 45 Further, history of depression and antidepressant treatment are important risk factors for elderly suicide. 46 The prevalence of depression and depressive symptoms was found to be higher (p<0.0001) in developing countries (24.0%) than in developed countries (33.0%), a greater difference and much higher than the 12-month prevalence estimates in developed (5.5%) and developing countries (5.9%) found in Kessler's study. 47 However, a possible limitation of this finding is that Kessler's study was restricted to a small number of countries, a narrower range of severe patients and a shorter research time. 47 More specifically, 13 studies from China were included in the present meta-analysis. The prevalence of depression or depressive symptoms among Chinese outpatients was 27.0% (1941/7194, 95% CI 22.0% to 33.0%, I 2 =95.4%), which fell between the prevalence observed in developing and developed countries (24.0%–33.0%) and consistent with China’s national development.

Various factors may account for the heterogeneity in this meta-analysis. First, differences in the assessments instruments and cut-offs may have affected the diagnostic sensitivity and specificity. Modified diagnostic criteria for depression and depressive symptoms have been proposed for use in different health settings, but there is no consensus regarding the optimal diagnostic approach. Whether the existing diagnostic criteria are ideal in different health settings remains to be determined. Additionally, little attention has been devoted to the ICD (International Classification of Diseases) criteria, in which a depressive episode is defined based on the number and severity of the symptoms only. 48 Second, heterogeneity between individual studies existed due to the different diagnostic methods applied in different countries. Third, the study qualities varied. For example, some studies used screening instruments with non-standard methods (eg, with cut-off scores that have not been validated) or having different thresholds in depression measurements that may increase the errors of prevalence estimates. These variations were captured in part by the modified Newcastle-Ottawa score, which assessed the risk of bias in each study.

Publication bias was assessed in this review. First, the exclusion of non-English publications likely contributed to the bias in our analysis. However, given the large number of included studies, we would not expect missed studies to significantly affect the findings. Second, because of the nature of the specialty, some studies examined the rates of depression in females only. For example, the prevalence of depression in obstetrics and gynaecology departments was 25.0%, which may have caused selection bias. Also, the ageing of the population phenomenon may have a more profound impact of depression estimates in developed counties comparing with developing countries. Third, the estimates of prevalence in some specialties were based on an inadequate number of studies, which may have affected the accuracy of the overall depression prevalence. For instance, the prevalence of depression in otorhinolaryngology departments was 53.0%, which was calculated using data from only three studies. Third, studies with fewer participants generally yielded more extreme prevalence estimates, further suggesting the presence of publication bias. 49 The study quality is also an important factor for evaluating the presence of publication bias. However, the sensitivity analysis showed that no individual study affected the overall prevalence estimate.

Limitations of this meta-analysis

Limitations should be considered when interpreting the results of this study. First, the substantial heterogeneity between studies was not fully explained by the variables examined. For example, various disease categories, the onset of depressive episode, medical expenses, medical workers’ attitudes and patients’ race and gender may contribute to the risk of depressive symptoms among outpatients. Furthermore, compared with self-report scales, interview methods commonly underestimate the prevalence of psychiatric disorders. 5 Second, the major update from the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) to the fifth edition in 2013 may have affected the accuracy of the prevalence estimates. 50 51 The severity indicator for depression and depressive symptoms was updated to be more precise, and this change may explain the increase in prevalence between 2010 and 2016. Third, the data were collected from studies that used different cross-sectional study designs, including different diseases and sample sizes. For example, in otorhinolaryngology departments, only three diseases were included in the meta-analysis, leading to low representativeness; however, given the available publications in the database, we would not expect this limitation to significantly affect the findings. Another limitation is the paucity of longitudinal data, which decreased the generalisability of the study outcomes. Therefore, high-quality studies that use cohort study designs to conduct follow-ups of depression might provide more precise outcomes. 52 Fourth, using a single measure to assess depression and depressive symptom might improve the accuracy and sensitivity of the outcomes. Finally, focusing on depression alone is insufficient. Depression and depressive symptoms with other mental disorders remain an important and overlooked complication among outpatients, and this oversight calls for a more systematic approach to clinical assessment and follow-up. In conclusion, this systematic review and meta-analysis highlighted the overall high prevalence of depression and depressive symptoms, which may have long been overlooked in outpatients worldwide. Our study also provided substantial quantitative subgroup analyses that laid the foundation for researchers, clinicians and policy makers to develop effective strategies for depression management.

In summary, our study has several implications for clinical practice. First, we performed a systematic review and meta-analysis to estimate the prevalence of depression and depressive symptoms in different clinical departments. Second, our results suggest that more attention should be devoted to outpatient mental health, particularly in clinical departments with a high depression prevalence (eg, outpatients at otolaryngology clinics had the highest prevalence of depression (53.0%)). The inconsistency of the findings across different specialties regarding the prevalence of outpatients with depression could help modify and improve clinical guidelines for the evaluation and diagnosis of depression or depressive symptoms in different medical settings. Third, we identified that different screening instruments produce different estimates, and these findings may provide a reliable reference for developing an effective and unified measurement for diagnosing depression. Fourth, the substantial heterogeneity between studies was not fully explained by the variables examined.

• 1. ↵ GBD 2015 Disease and Injury Incidence and Prevalence Collaborators . Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the global burden of disease study 2015 . Lancet 2016 ; 388 : 1545 – 602 . doi:10.1016/S0140-6736(16)31678-6 OpenUrl CrossRef PubMed
• 2. ↵ World Health Organization . The World Health Report 2002: Reducing Risks, Promoting Healthy Life . 2002 .
• Shah AA , et al
• Elbayoumi H ,
• Fischer CE , et al
• Mitchell AJ ,
• Bhatti H , et al
• Raugust S ,
• Marschner N , et al
• Fritzsche K , et al
• Chua AN , et al
• van Bever HP , et al
• Tee SI , et al
• Cheung MW , et al
• Chan MF , et al
• Mak A , et al
• Massie MJ ,
• Danesh DM VC ,
• Knobloch K ,
• Anderson RJ ,
• Freedland KE ,
• Clouse RE , et al
• Kerr LDK LK
• Borenstein M ,
• Hedges LV ,
• Higgins JP , et al
• Cheung MW ,
• Lim Y , et al
• Takkouche PK B
• Higgins JP ,
• Thompson SG ,
• Deeks JJ , et al
• Fu E , et al
• Sterne JA ,
• Trevis KJ ,
• McLachlan NM ,
• Chernyshov PV ,
• Monti F , et al
• Ng TP , et al
• Jirakova A ,
• Ho RC , et al
• Gharagozlou M ,
• Farhadi E ,
• Khaledi M , et al
• Thiaghu C , et al
• Thiaghu C ,
• Ong H , et al
• Zhang MW , et al
• Liow CS , et al
• Diederich J ,
• Song I , et al
• Mirowsky J ,
• Kua EH , et al
• Tai BC , et al
• Kessler RC ,
• Birnbaum HG ,
• Shahly V , et al
• 48. ↵ The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines . Geneva : World Health Organization , 1992 .
• Bansal N , et al
• 50. ↵ American Psychiatric Association . Diagnostic and statistical manual of mental disorders (DSM-IV) . 4th edn . Washington : American Psychiatric Press , 1994 .
• 51. ↵ American Psychiatric Association . Diagnostic and statistical manual of mental disorders (DSM-V) . 5th edn . Washington : American Psychiatric Press , 2013 .
• Carson AJ ,
• Stone J , et al
• Bokemeyer B ,
• Hüppe D , et al
• Tedeschini E ,
• Pingani L ,
• Simoni E , et al
• Yohannes AM ,
• Baldwin RC ,
• Connolly MJ
• Baldwin RC , et al
• Lee HJ , et al
• Gregor M , et al
• Fritzsche K ,
• Leonhart R , et al
• Zhang L , et al
• Pontone S ,
• Marianetti M , et al
• Urrutia I ,
• Aguirre U ,
• Pascual S , et al
• Lam M , et al
• Birket-Smith M ,
• Hansen BH ,
• Hanash JA , et al
• Nowicka-Sauer K ,
• Smoleńska Ż , et al
• Jin Q , et al
• Inagaki M ,
• Ohtsuki T ,
• Yonemoto N , et al
• Schaefert R ,
• Salm F , et al
• Addolorato G ,
• Mirijello A ,
• D'Angelo C , et al
• Geelhoed-Duijvestijn PH ,
• Tack CJ , et al
• Huang TL , et al
• Wei J , et al
• Tsunoda A ,
• Hiratsuka K , et al
• Polidoro Lima M ,
• Alcalar N ,
• Kucucuk S , et al
• Strux A , et al
• Chen W , et al
• Choi SR , et al
• Weisbord SD ,
• Sevick MA , et al
• Sundström-Poromaa I ,
• Björn I , et al
• Gaillard A ,
• Le Strat Y ,
• Mandelbrot L , et al
• Lorençatto C ,
• Navarro MJ , et al
• Poleshuck EL ,
• Luan R , et al
• Araszkiewicz A , et al
• Stewart SM ,
• Emslie GJ , et al
• Zduńczyk B ,
• Sendela J ,
• Szypowska A
• Epifanova-Bertschi N ,
• Sankowski R , et al
• Ringbauer B ,
• MacKenzie L , et al
• de Oliveira GN ,
• Marchetti RL , et al
• Dickstein LP ,
• Viguera AC ,
• Nowacki AS , et al
• Hu WD , et al
• Mitsikostas DD ,
• Bhattacharya S ,
• Larsen JL , et al
• Williams LS ,
• Shen J , et al
• Postels DG , et al
• Attah Johnson FY ,
• Mostaghimi H
• Balieva F ,
• Kupfer J , et al
• Poon TC , et al
• Mattoo SK ,
• Kaur I , et al
• Rasoulian M ,
• Ebrahimi AA ,
• Zare M , et al
• Wigley FM ,
• Dogra S , et al
• Tsintsadze N ,
• Beridze L ,
• Tsintsadze N , et al
• Daaleman TP ,
• Drayer RA ,
• Piraino B ,
• Reynolds CF , et al
• Hankin CS ,
• Miller DR , et al
• Hollifield M ,
• Ishikawa Y ,
• Takeshima T ,
• Mise J , et al
• Michalski D ,
• Thomae E , et al
• Okumura T ,
• Ohhira M , et al
• Kaoutar B , et al
• Cavalieri TA ,
• Leonard DM , et al
• Tamburrino MB ,
• Nagel RW , et al
• Mancuso CA ,
• Peterson MG ,
• Charlson ME
• Beaver HA ,
• Jogerst G , et al
• Rovner BW ,
• Casten RJ ,
• Hegel MT , et al
• Fletcher AE ,
• Williams RA , et al
• Mathew RS ,
• Delbaere K ,
• Lord SR , et al
• Popescu ML ,
• Boisjoly H ,
• Schmaltz H , et al
• Tsutsumi T ,
• Oishi N , et al
• Asghari A ,
• Mohammadi F ,
• Kamrava SK , et al
• Wu CL , et al
• Seedat S , et al
• Kolarić B ,
• Ivanković D , et al
• Arayawichanont A , et al
• Chua P , et al

Contributors HL contributed to the study design, data analysis and interpretation and manuscript preparation. JW and XW wrote the first draft of the report. JW and XW performed the literature search. WLa, EL, XZha, WLi, ZL, XZho and DW independently reassessed the integrity and accuracy of the data results. HL, YZ and CC critically revised the manuscript. HL contributed to the research funding, coordinated the research and oversaw the project. All authors reviewed the manuscript for important intellectual content and approved the final manuscript.

Funding The principal investigator of this study (HL) is currently supported by the Pearl River Scholar Program of Guangdong Province, the Outstanding Young Teacher Cultivation Projects in Guangdong Province (YQ2015006) and the Guangdong Provincial Natural Science Foundation for Distinguished Young Scholars of China (2014A030306030). The sponsors or funding organizations had no role in the design or performance of this study.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement No additional data are available.

Correction notice This article has been corrected since it was published online. The affiliation of Haotian Lin has been corrected.

Read the full text or download the PDF:

Welcome to Gale North America

Depression is a common mental health disorder characterized by sadness, lethargy, and loss of interest in daily life activities. Read the overview below to gain an understanding of this illness and explore the previews of other articles examining different aspects of depression.

Access Through Your library >>  

Topic Home      |      Social Issues      |      Literature      |      Lifelong Learning & DIY      |      World History

Depression topic overview.

"Watson, Stephanie. "Depression." Gale Health and Wellness Online Collection , Gale, 2021. 

Depression is a mood disorder that causes persistent feelings of sadness, hopelessness, loss of interest, and decreased energy. A diagnosis of major depression means symptoms have been consistent nearly every day for at least two weeks. However, depression is more than just unhappiness. It can be severe enough to interfere with relationships, work, school, and other daily activities. Doctors also refer to this condition as major depressive disorder or clinical depression.

An estimated 1 in 6 people, or nearly 17 percent of Americans, will experience depression at some point in their lifetime, according to the American Psychiatric Association. About 7 percent of Americans have at least one major depressive episode per year. Although the symptoms can start at any age, depression is most likely to begin during a person's teens or 20s.

Types of depression

• Along with major depression, there are several other types of depression, which are characterized by their symptoms or causes: Postpartum depression involves feelings of extreme sadness, fatigue, and anxiety that start after a woman gives birth.
• Premenstrual dysphoric disorder (PMDD) is severe depression, irritability, and anxiety that occurs in the week or two before a woman's period.
• Seasonal affective disorder (SAD) is depression that occurs during the winter months and is relieved by the change of season.
• Bipolar disorder, formerly called manic depression, is characterized by alternating episodes of extremely low mood (depression) and exuberant highs (mania).
• Persistent depressive disorder, which is depressions lasting two years or more. It combines dysthymia, which is a low-grade but persistent depression with chronic major depression. People with persistent depressive disorder generally lack energy, have low self-esteem, and often feel helpless.
• Psychotic depression includes features of both depression and psychosis, such as having false beliefs (delusions) or seeing and hearing things that are not there (hallucinations).

Depression stems from a combination of biological, environmental, and psychological factors. People with depression often have family members with the condition, which suggests that genetics are involved. If one biological twin has depression, the other twin has a 70 percent chance of also having the condition.

Researchers have discovered differences in the brains of people with depression, as well as in the function of chemical messengers called neurotransmitters. Hormonal changes also can set off depressive symptoms; for example, during a woman's menstrual cycle or after she gives birth.

The following factors increase the risk for depression:

• a personal or family history of depression or other mental health disorder
• trauma or stress, such as physical or sexual abuse, relationship issues, or financial worries drug or alcohol abuse
• medical conditions such as diabetes, cancer, heart disease, stroke, and Parkinson's disease certain medications, including those used to treat high blood pressure and insomnia

People who have depression will experience some or all of the following symptoms (almost every day for at least two weeks for major depression):

• Persistent sad or empty mood
• Feelings of hopelessness, helplessness, emptiness, worthlessness, or guilt
• Low energy, fatigue
• Irritability, restlessness, anxiety
• Slowed thinking, speaking, or movement
• Loss of interest in activities they once enjoyed
• Trouble concentrating, remembering, or making decisions
• Loss of appetite, or eating too much
• Weight gain or loss
• Trouble sleeping, or sleeping too much
• Headache, stomachache, and other aches and pains that do not have a clear physical cause
• Thoughts of death or suicide

Some people with depression will experience many of these symptoms. Others will have just a few. The severity of depression symptoms can range from mild to severe enough to affect a person's day-to-day life.

Doctors start the diagnostic process with a physical exam and lab work to rule out possible physical causes of depression, such as a thyroid disorder or vitamin deficiency. A psychologist or physician can do a psychological evaluation, asking questions and assessing symptoms according to established criteria for identifying depression and arriving at a diagnosis.

Since chemistry in the brain is linked to depression, a person might take an antidepressant, which can modify the brain's chemistry. Typical treatment includes antidepressants or other medications, psychotherapy (talk therapy), or a combination of the two interventions. Personalizing treatment to the individual can increase the chances that it will be successful.

Antidepressants are a class of drugs used to treat depression. They include the following types:

• Selective serotonin reuptake inhibitors (SSRIs) are often the first drugs doctors prescribe for depression. These drugs affect the chemical messenger, serotonin, which helps to regulate mood. Low serotonin levels have been linked to depression. Serotonin-norepinephrine reuptake inhibitors (SNRIs) work on two brain chemicals—serotonin and norepinephrine.
• Atypical antidepressants act on the brain in a different way from other antidepressants. These drugs may be an option for people who have not found relief from SSRIs or SNRIs.
• Tricyclic antidepressants are an older class of antidepressant. They work on three brain chemicals: serotonin, norepinephrine, and dopamine. Tricyclics are not used as often as they once were because they have a higher risk for side effects than newer antidepressants.
• Monoamine oxidase inhibitors (MAOIs), the first class of antidepressants developed, are used infrequently today due to their numerous interactions with other drugs and foods.
• In 2019, the FDA approved a nasal spray called esketamine (Spravato) to treat people with major depression and for whom other antidepressants are not working. The spray works more quickly than do SSRIs taken by mouth.

Sometimes doctors will prescribe another type of medication—such as an anti-anxiety drug, antipsychotic medicine, or stimulant—along with the antidepressant. Antidepressants can take up to four weeks to achieve their full benefits. Other drugs might work specifically for one type of depression. For example, brexanolone became the first therapy approved (in 2019) for treating postpartum depression specifically. It can take a few tries to find the best drug and dosage combination that will relieve your depression.

Talk therapy programs like cognitive-behavioral therapy (CBT) help people with depression identify the negative thoughts and behaviors that result from depression, and replace them with more positive strategies for building coping skills and psychological resilience. Therapy can be done one-on-one with a therapist, as part of a group, or together with a partner or other family members.

If these treatments are not effective, brain stimulation therapies including electroconvulsive therapy (ECT) might be an option. ECT is done while a person is under general anesthesia. Small electrical currents are passed through the brain to induce a seizure. Research finds ECT is often effective in cases where antidepressants and talk therapy fail.

A few alternative remedies and supplements are used to treat depression, including acupuncture, meditation, guided imagery, and tai chi. Evidence that herbal supplements like St. John's wort and SAMe help with depression symptoms is inconclusive, and they are

not FDA-approved treatments. Because these supplements can often cause side effects or interact with other medications you take, alert your doctor first if you would like to try them.

Depression is highly treatable. Up to 90 percent of people will eventually improve with medication, therapy, a combination of the two, or another treatment. However, it can take some trial and error to find the therapy that works best. Help is available for people who are struggling with depression through the U.S. Substance Abuse and Mental Health Services Administration (SAMHSA) helpline at 1-800-662-HELP (4357).

Suicidal thoughts and actions can occur in people who are severely depressed. Anyone who might be considering harming themselves should call a trusted health care provider right away, reach out to supportive friends or family members, or call the National Suicide Prevention Lifeline at 1-800-273-TALK (1-800-273-8255). If a friend or loved one is at immediate risk for self-harm, it is advised to dial 911 or local emergency services.

"Depression." MedlinePlus. October 28, 2020.   https://medlineplus.gov/depression.html (accessed November 16, 2020). "Depression." National Institute of Mental Health. February  2018. https://www.nimh.nih.gov/health/topics/depression/index.shtml  (accessed November 16, 2020).

"Depression (major depressive disorder)." Mayo Clinic. February 3, 2018.   https://www.mayoclinic.org/diseases - conditions/depression/symptoms-causes/syc-20356007 (accessed November 16, 2020).

"Major Depression in Teens." Health Encyclopedia, University of Rochester Medical Center.  https://www.urmc.rochester.edu/encyclopedia/content.aspxcontenttypeid=90&contentid=P01614 (accessed November 16, 2020).

"What Is Depression?" American Psychiatric Association. October 2020.  https://www.psychiatry.org/patients- families/depression/what-is depression (accessed November 16, 2020).

Organizations

American Psychiatric Association,   800 Maine Avenue, S.W., Suite 900,   Washington,   D.C.,   20024 (888) 357-7924,   (202) 559 https://www.psychiatry.org .

National Alliance on Mental Illness (NAMI),   3803 N. Fairfax Drive, Suite 100,   Arlington,   VA,   22203,  (800) 950-6264,   (703) 524 https://www.nami.org .

National Institute of Mental Health,   6001 Executive Boulevard, Room 6200,  Bethesda,   MD,   20892-9663,   (866) 615-6464,   Fax 443-4279,    [email protected] , https://www.nimh.nih.gov .

More Articles

Clearing the hurdles: how to overcome obstacles in depression treatment.

This article discusses the obstacles people face in treating depression, and how to overcome them.

How Exercising More May Help Prevent the Onset of Depression

This article reports the results of study showing that exercise can help prevent depression, even among people genetically predisposed to the condition.

Are Your Medications Affecting Your Mood?

This article reports a study showing that many people take medications that are known to have depression as a side effect. People who take these drugs have increased reports of depression, especially if they take more than one of these medications.

New Theory May Explain Cause of Depression and Improve Treatments

This article reports a study that suggests a new explanation for the causes of some cases of depression: dysfunction in mitochondria. This could lead to new approaches to the treatment of depression.

Depressive Disorders

Source: "Academy of American Family Physicians, http://www.aafp.org. Disclaimer: This information is not a tool for self-diagnosis or a substitute for professional care. This image may be used for personal, non-commercial purposes only.

"Depressive Disorders." The Gale Encyclopedia of Children's Health: Infancy through Adolescence , edited by Deirdre S. Blanchfield, 3rd ed., vol. 2, Gale Health and Wellness , Gale, 2016, www.gale.com.

Looking for information on other topics?

Access Through Your Library >>

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

The Impact of Individual Depressive Symptoms on Impairment of Psychosocial Functioning

* E-mail: [email protected]

Affiliations Cluster of Excellence “Languages of Emotion”, Freie Universität Berlin, Berlin, Germany, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany

Affiliation School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America

• Eiko I. Fried, 
• Randolph M. Nesse

• Published: February 28, 2014
• https://doi.org/10.1371/journal.pone.0090311
• Reader Comments

Previous studies have established that scores on Major Depressive Disorder scales are correlated with measures of impairment of psychosocial functioning. It remains unclear, however, whether individual depressive symptoms vary in their effect on impairment, and if so, what the magnitude of these differences might be. We analyzed data from 3,703 depressed outpatients in the first treatment stage of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. Participants reported on the severity of 14 depressive symptoms, and stated to what degree their depression impaired psychosocial functioning (in general, and in the five domains work, home management, social activities, private activities, and close relationships). We tested whether symptoms differed in their associations with impairment, estimated unique shared variances of each symptom with impairment to assess the degree of difference, and examined whether symptoms had variable impacts across impairment domains. Our results show that symptoms varied substantially in their associations with impairment, and contributed to the total explained variance in a range from 0.7% (hypersomnia) to 20.9% (sad mood). Furthermore, symptoms had significantly different impacts on the five impairment domains. Overall, sad mood and concentration problems had the highest unique associations with impairment and were among the most debilitating symptoms in all five domains. Our findings are in line with a growing chorus of voices suggesting that symptom sum-scores obfuscate relevant differences between depressed patients and that substantial rewards will come from close attention to individual depression symptoms.

Citation: Fried EI, Nesse RM (2014) The Impact of Individual Depressive Symptoms on Impairment of Psychosocial Functioning. PLoS ONE 9(2): e90311. https://doi.org/10.1371/journal.pone.0090311

Editor: Qiyong Gong, West China Hospital of Sichuan University, China

Received: October 14, 2013; Accepted: January 30, 2014; Published: February 28, 2014

Copyright: © 2014 Fried, Nesse. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The STAR*D study was supported by NIMH Contract # N01MH90003 to the University of Texas Southwestern Medical Center ( http://www.nimh.nih.gov ). The ClinicalTrials.gov identifier is NCT00021528. This manuscript reflects the views of the authors and may not reflect the opinions or views of the STAR*D study investigators or the NIMH. Mr. Fried is supported by fellowships from the Cluster of Excellence “Languages of Emotion” (grant no. EXC302, http://www.loe.fu-berlin.de ) and the German Research Foundation ( www.dfg.de ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

About 60% of individuals who meet criteria for Major Depressive Disorder (MDD) as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [1] report severe or very severe impairment of functioning [2] . Impairment associated with depression is long-lasting [3] and equal or greater than impairment caused by other common, chronic medical conditions such as diabetes, hypertension, heart attack, and congestive heart failure [4] , [5] . Moreover, depression impairs functioning in various domains such as home life, workplace, friends, and family [6] , [7] – severely compromising the capacity for self-care and independent living in many cases.

A recent review found moderate correlations between scores on various screening instruments for depression and measures of impairment [8] . It has been unclear, however, whether certain symptoms are more impairing than others, and if so, what the magnitude of these differences might be. This question is highly relevant because of large differences in the symptoms experienced by patients diagnosed with MDD.

Qualifying for a diagnosis of MDD requires experiencing at least five of the nine DSM symptomatic criteria, among which at least one has to be either sad mood or loss of interest, for at least 2 weeks. Four symptoms are compound symptoms comprised by different subsymptoms (feelings of worthlessness or inappropriate guilt) or opposite subsymptoms (insomnia or hypersomnia, psychomotor agitation or retardation, weight loss or weight gain), leading to 1,497 unique symptom profiles that all qualify for the same diagnosis [9] , including profiles that do not have a single symptom in common. Considerable symptom variability has been reported across individuals [10] – [12] and within individuals across time [13] , [14] .

Specific depressive symptoms have received comparably little attention because they are assumed to be diagnostically interchangeable indicators of a common diagnosis. This assumption of symptom equivalence [15] goes hand in hand with the conceptualization of depression within the framework of reflective latent variable modeling [16] , [17] : variation in the latent disorder depression causes variation of the observable symptoms. Depression is viewed as the common cause for diverse symptoms such as insomnia, psychomotor agitation, or loss of interest – which is the reason why symptoms are measured in order to assess depression. Since all symptoms indicate the same latent disease, only the number of symptoms is relevant, not their natures . The notion that different symptoms are diagnostically equivalent justifies the common practice of summing the number of symptoms to reflect depression severity.

However, several authors have suggested that there are substantial benefits to analyzing depressive symptoms individually [15] , [18] – [20] . This is supported by evidence showing that symptoms differ from each other in their associations with demographic variables, personality traits, lifetime comorbidities, and risk factors [15] , [21] , and it has been established that specific stressful life events are predictive of distinct MDD symptom profiles [22] – [25] . Furthermore, particular gene polymorphisms are associated with specific depressive symptoms [26] , [27] , and a recent study of 7,500 twins concluded that the DSM symptomatic criteria for depression do not reflect a single underlying genetic factor [28] .

We are aware of only a single previous study that explored concurrent effects of individual depressive symptoms on impairment of psychosocial functioning [29] . In this analysis of a general population sample, six DSM-III [30] symptoms were significantly associated with impairment (depressed mood, dysthymia, cognitive difficulties, suicidal ideation, fatigue, and sexual disinterest).

The present study extends the previous report [29] in four important aspects: (1) we examine the differential impact of symptoms on impairment in a large and highly representative sample of 3,703 depressed patients; (2) we use the updated DSM-5 criterion symptoms; (3) we investigate subsymptoms (e.g., psychomotor agitation and psychomotor retardation) instead of compound symptoms (e.g., psychomotor problems); (4) lastly, we test whether symptoms vary in their impacts across five impairment domains.

Materials and Methods

Study description.

Data from the first treatment stage (level 1) of the NIH-supported “Sequenced Treatment Alternatives to Relieve Depression” (STAR*D) study [31] , [32] were analyzed for this report. Data can be obtained from the NIMH and were provided to the authors under terms of an NIHM Data Use Certificate that protects confidentiality; dataset version 3 was used. STAR*D was a multisite randomized clinical trial conducted in the USA to investigate which of several treatment options would be most effective for nonpsychotic MDD outpatients; 4,041 patients were enrolled into the first treatment stage, in which all participants received citalopram, a selective serotonin reuptake inhibitor (SSRI) antidepressant. Outcome data were obtained via telephone interviews that were conducted either by interviewers, or by an interactive voice response system (IVR). STAR*D was approved and monitored by the institutional review boards at each of the 14 participating institutions, a national coordinating center, a data coordinating center, and the data safety and monitoring board at the NIMH. All participants provided written informed consent at study entry. Detailed information about design, methods, exclusion criteria, and the rationale of STAR*D are described elsewhere [31] , [32] .

Participants

STAR*D used relatively inclusive selection criteria in order to obtain a highly representative sample of patients seeking treatment for MDD. Participants had to be between 18 and 75 years, fulfill DSM-IV criteria for single or recurrent nonpsychotic MDD, and have at least moderately severe depression corresponding to a score of at least 14 on the 17-item Hamilton Rating Scale for Depression (HAM-D) [33] . Participants with a history of bipolar disorder, schizophrenia, schizoaffective disorder, or psychosis were excluded, as were patients with current anorexia, bulimia, or primary obsessive compulsive disorder. Further exclusion criteria were a history of intolerability to antidepressant medication, lack of response to an adequate trial of SSRI in the current episode of MDD, or failure to respond to 16 or more sessions of cognitive therapy in the current episode of MDD. Our analyses are limited to the 3,703 individuals that were assessed within the first week of level 1 via IVR.

Outcomes measures

STAR*D used the Quick Inventory of Depressive Symptoms (QIDS-16 [34] ) to assess depressive symptoms. The QIDS-16 has good psychometric properties [34] , and the results of the IVR version are comparable to the results produced by the self-rated and the clinician-rated QIDS-16 [35] . The QIDS-16 assesses the nine DSM symptom domains with 16 questions ( Table 1 ). Each domain yields a score between 0 and 3, 0 indicating no problems, 3 indicating severe problems. While six symptoms are measured with single questions, the three compound symptoms ( sleep problems , psychomotor problems , appetite / weight problems ) are assessed with multiple questions. The QIDS-16 constructs these compound symptoms by using the highest symptom score in each symptom group, resulting in one score on each of the nine DSM criterion symptoms. Since we were interested in individual symptoms, we used all available items instead of symptom domains. Detailed information for the domain appetite and weight problems was not available, since either appetite decrease or appetite increase , and either weight decrease or weight increase was scored. Overall, this resulted in twelve individual symptoms plus the two compound symptoms appetite problems and weight problems ( Table 1 ).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0090311.t001

The Work and Social Adjustment Scale (WSAS [36] ) was used to measure impairment of functioning. The WSAS is a simple, reliable, and valid self-report instrument that uses Likert-scale ratings of 5 items to assess impairment in the domains of work, home management, social activities, private activities, and close relationships. Each question is rated on a 0–8 Likert scale, with 0 indicating no impairment and 8 indicating very severe impairment. WSAS scores below 10 are associated with subclinical populations; scores of 10–20 are associated with significant functional impairment, while scores above 20 suggest at least moderately severe functional impairment (total range 0–40). The WSAS has been used mainly in samples with mood and anxiety disorders, and has been shown to have good internal consistency (0.70 to 0.94) and retest-reliability (0.73), and high concurrent validity of IVR administrations with clinician interviews (0.81 and 0.86) [37] . In STAR*D, the WSAS specifically queried participants how much their depression impaired work and social activities. For instance, work impairment was measured via the following item: “Because of my depression, my ability to work is impaired. 0 means not at all impaired and 8 means very severely impaired to the point I can't work.”

Statistical analysis

Three analyses were performed. First, we used the 14 QIDS-16 depression symptoms to predict overall impairment as measured by the WSAS sum-score, controlling for age and sex. We then compared two linear regression models: in model I (heterogeneity model), regression weights for symptoms were free to vary, whereas model II (homogeneity model) constrained regression weights to be equal. While model I allows for differential impairment-symptoms associations, model II represents the hypothesis that symptoms have equal associations with impairment. A χ 2 -test was used to compare the two models. Because depressive symptoms are generally correlated with each other, we performed multicollinearity diagnostics for both regression analyses. The variance inflation factor (VIF) did not exceed the value of five for any symptom, indicating no multicollinearity problems [38] .

Second, we aimed to allocate unique R 2 shares (proportion of explained variance) to each regressor to examine how much unique variance each individual symptom shared with impairment. We used the LMG metric via the R-package RELAIMPO [39] to estimate the relative importance (RI [40] – [42] ) of each symptom. LMG estimates the importance of each regressor by splitting the total R 2 into one non-negative R 2 share per regressor, all of which sum to the total explained R 2 . This is done by calculating the contribution of each predictor at all possible points of entry into the model, and taking the average of those contributions. In other words, an estimate of RI for each variable is obtained by calculating as many regressions as there are possible orders of regressors (in the present case, 8.7×10 10 regressions), and then averaging individual R 2 values over all models. RI estimates are then adjusted to sum to 100% for easier interpretation. Confidence interval (CI) estimates of the RI coefficients, as well as p -values indicating whether regressors differed significantly from each other in their RI contributions (in an exploratory sense), were obtained using the bootstrapping capabilities of the RELAIMPO package. It is important to note that predictors with a non-significant regression coefficient can nonetheless contribute to the total explained variance, that is, have a non-zero LMG contribution. This is the case when regressors are correlated with each other and thus can indirectly influence the outcome via other regressors [42] . Therefore, all symptoms, even those without significant regression coefficients, were included in subsequent RI calculations.

Third, we tested whether individual symptoms differed in their associations across the five WSAS impairment domains work, home management, social activities, private activities and close relationships. We estimated two structural equation models (SEM), using the Maximum-Likelihood Estimator. Both models contained five linear regressions, one for each domain of impairment. In each of these five regressions, we used the 14 depressive symptoms as predictors of one impairment domain, controlling for age and sex. While the first SEM allowed free estimation of all regression coefficients (model I), the second constrained each symptom to have equal effects (i.e. regression coefficients) across the five impairment domains (model II). This second model represents the hypothesis that a given symptom has similar impacts on all five domains. We compared the models using a χ 2 -test.

Analyses one and three were performed in MPLUS v7.0 [43] , and analysis two was estimated in R v2.13.0 [44] .

Of the 3,703 outpatients in the study, 2,234 (60.3%) were female, and the mean age was 41.2 years ( sd  = 13.2). See Table 2 for detailed demographic information.

https://doi.org/10.1371/journal.pone.0090311.t002

The average impairment score was 23.52 ( sd  = 9.29), corresponding to moderately severe levels of impairment; 307 (8.3%) individuals did not show impaired functioning, 875 (23.6%) exhibited significant functional impairment, while 2,521 (68.1%) reported severe functional impairment.

Homogeneity versus heterogeneity of associations

The heterogeneity model (allowing variable contributions of symptoms to impairment) fit the data significantly better than the homogeneity model (in which symptoms were constrained to have the same contributions to impairment) ( χ 2  = 394.5, df  = 13, p <0.001). In the heterogeneity model, 11 of the 14 depression symptoms as well as male sex and older age significantly predicted impairment, explaining 40.8% of the variance ( F (16, 3686) = 159.1, p <0.001) ( Table 3 ). The heterogeneity model was thus used for subsequent RI estimations.

https://doi.org/10.1371/journal.pone.0090311.t003

Relative importance analysis

The RI estimates of all regressors, representing the allocated individual R 2 contributions of symptoms on impairment, are displayed in Figure 1 . Different symptoms had drastically different effects on impairment, ranging from RI values of 0.7% ( hypersomnia ) to 20.9% ( sad mood ). Out of 91 symptom pairs, 76 (83.5%) significantly differed in their RI contributions to impairment (all p <0.05). RI coefficients within the two compound symptoms ( sleep problems and psychomotor problems ) showed differential RI: early insomnia (3.6%) was associated with significantly more impairment than middle insomnia (0.8%) and hypersomnia (0.7%), while slowed (8.7%) had a significantly larger RI estimate than agitated (2.1%) (all p <0.05).

Relative importance coefficients of depressive symptoms on overall impairment, including bootstrapped confidence intervals. Each value represents the unique shared variance between a symptom and impairment, controlling for age and sex. Estimates are adjusted to sum to 100%.

https://doi.org/10.1371/journal.pone.0090311.g001

Are the large differences in the impact of different symptom on disability due to the nature of symptoms, or due to their severity ? If severity, then severity differences between symptoms should explain a large proportion of the differences of the RI estimates (i.e. symptoms with high mean values are highly debilitating, whereas symptoms with a low mean are associated with much less impairment). To test this hypothesis we used a linear regression to predict the RI of each of the 14 symptoms by its mean severity. Symptom severity did not reach statistical significance as predictor for symptom RI estimates ( F (1,12) = 4.0, p  = 0.07). This implies that RI differences are due to symptom nature, and not symptom severity.

Impact of symptoms across impairment domains

Constraining regression weights of symptoms to be equal across the five domains of impairment in model II significantly reduced model fit compared to model I in which symptom contributions were freely estimated ( χ 2  = 299.8, df  = 56, p <0.001). This means that symptoms have differential impacts across impairment domains; these differences between the symptoms-impairment associations across domains are visualized in Figure 2 . Of the diverse findings, three are especially noteworthy:

The arrows represent standardized regression coefficients of the 14 QIDS-16 depression symptoms (s1–s14) on the five WSAS impairment domains (D1–D5). Thickness of arrows indicates strength of regression weights.

https://doi.org/10.1371/journal.pone.0090311.g002

(1) sad mood and concentration were among the four most debilitating symptoms in all domains;

(2) early insomnia had comparably strong effects on work impairment, self-blame on close relationships, interest loss on social activities, and fatigue on home management;

(3) compared to other domains, interest loss was less impairing for the domain work, fatigue for close relationships, sad mood for home management, and concentration for social activities as well as close relationships.

Overall, individual depressive symptoms have differential effects on impairment, confirming our main hypothesis. Depressed mood, poor concentration, fatigue and loss of interest explained a large proportion of variance in impairment, whereas weight problems, mid-nocturnal insomnia and hypersomnia made few unique contributions to impairment.

Subsymptoms within symptom domains had differential effects as well. For instance, psychomotor retardation explained roughly four times as much variance of impairment as psychomotor agitation. These findings highlight not only the importance of considering the nine DSM symptoms individually, but also the importance of considering sub-symptoms within the symptom domains. The three most debilitating symptoms include one affective, one cognitive and one somatic symptom, suggesting the need to monitor all kinds of depressive symptoms instead of focusing on only one domain or factor score. Furthermore, the two DSM MDD core symptoms, depressed mood and interest loss, made high contributions to explaining impairment, ranking 1 (20.7%) and 4 (13.1%) in general RI estimates. Lastly, although some symptoms were roughly equally debilitating across different domains of impairment, the majority of symptoms varied in their influence across domains.

Implications

While prior research has established that symptoms are differentially associated with demographic variables and personality traits [15] , risk factors [21] , stressful life events [22] – [25] , and gene polymorphisms [26] – [28] , our report reveals yet another dimension of covert heterogeneity: symptoms have variable associations with impairment of psychosocial functioning. The broad depression diagnosis not only obscures important differences between patients and lumps individuals suffering from diverse symptoms into the same category – two patients with the same number of depressive symptoms may differ drastically in their functioning levels. This concealed variability within MDD potentially explains some of the most prominent “disappointing” findings portrayed in recent literature: (1) the DSM-V field trials [45] reported a “questionable” inter-rater reliability of 0.28 (CI 0.20–0.35) for MDD diagnosis, lower than the majority of other disorders (e.g., borderline personality disorder 0.54 (CI 0.43–0.66)); (2) antidepressants are only marginally efficacious compared to placebos, in spite of substantial publication and reporting bias inflating apparent antidepressant efficacy [46] ; (3) there are few consistencies between studies investigating which brain regions are involved in the pathophysiology of MDD [47] ; (4) none of more than half a million common genetic markers were associated with antidepressant response in a study with 1,790 individuals [48] ; (5) lastly, no single locus reached genome-wide significance in a genome-wide association study of 17 population-based samples containing 34,549 subjects [49] .

The dependent variable in all studies is either a symptom sum-score, or the categorical distinction between depressed and healthy. In both cases, potentially important information about symptoms is lost, and a closer examination of these symptoms is likely to reveal important insights hidden by analyses of sum-scores. In the present study, sleep onset insomnia had comparably strong impact on functioning in the domain of work. It has also been established that MDD treatment is less effective in patients suffering from sleep problems [50] , that patients with persistent sleep problems are more than twice as likely to remain depressed [51] , and that targeting sleep problems in patients diagnosed with MDD increases overall depression improvement [52] , [53] . This example elucidates how clinically useful symptom-based approaches can be: they provide detailed information about the nature of problems individuals suffer from, and thus offer the opportunity to improving MDD prevention and treatment.

In addition to studying individual MDD criterion symptoms of depression, it is important to acknowledge that the current DSM symptoms are but a small subset of possible depression symptoms, and were determined largely by clinical consensus instead of empirical evidence [15] , [54] . Several non-DSM MDD symptoms merit closer examination and should be assessed in future studies of depressive symptoms, because they are highly prevalent and associated with worse clinical outcomes. For example, studies found anxiety and anger/irritability to be present in more than half of the patients diagnosed with MDD [55] , [56] , and while remission of MDD was less likely and took longer in patients reporting anxiety [56] , anger/irritability was a clinical marker of a more severe, chronic, and complex depressive illness [55] .

Limitations

The results have to be interpreted in the light of five limitations. First, although the impairment scale used in the STAR*D study specifically instructed participants to rate the effects of their depression on functioning, both depressive symptoms and functional impairment were assessed at the same measurement point, so caution about causal interpretations is warranted. Symptoms and impairment potentially reinforce each other and are thus likely to blur, especially in individuals suffering from chronic depression. Second, while subjects at baseline of STAR*D were not taking antidepressant medication, many participants reported other medical conditions for which prescribed medications might have affected symptom reports. Third, the bootstrapped CIs for the RI estimates are fairly large for a sample of 3,703 subjects, implying a moderate amount of model uncertainty due to the high number of regressors as well as substantial covariation between them. Fourth, item wording may have biased the associations of individual symptoms with impairment; in particular, because subjects were asked to rate the impact of their depression on impairment, sadness may be artificially inflated. To explore this further would require alternative question wording. Lastly, differential variability in depressive symptoms is a potential source of biased RI estimates, because heavily skewed symptoms with means close to the minimum and maximum are less likely to demonstrate pronounced statistical relationships. However, symptom means that ranged from 0.44 (insomnia) to 2.35 (mid-nocturnal insomnia) did not significantly predict RI estimates, and even the symptom with the lowest mean of 0.44 (insomnia) showed substantial variability ( sd  = 0.83; sd range of all other symptoms excluding insomnia: 0.83 to 1.21).

Acknowledgments

We would like to thank all patients who participated in STAR*D for their kind cooperation.

Author Contributions

Analyzed the data: EF RN. Wrote the paper: EF RN.

• 1. American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Washington, DC: American Psychiatric Association.
• View Article
• Google Scholar
• 5. Murray CJL, Lopez A (1996) Global burden of disease: A comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. Cambridge MA: Harvard School of Public Health.
• 10. Katschnig H, Pakesch G, Egger-Zeidner E (1986) Life stress and depressive subtypes: A review of present diagnostic criteria and recent research results. In: Katschnig H, editor. Life events and psychiatric disorders: Controversial issues.Cambridge MA: Cambridge University Press.
• 16. Bollen KA (1989) Structural Equations with Latent Variables. New York: Wiley.
• 21. Fried EI, Nesse RM, Zivin K, Guille C, Sen S (2013) Depression is more than the sum score of its parts: individual DSM symptoms have different risk factors. Psychological medicine: 1–10. doi: https://doi.org/10.1017/S0033291713002900 .
• 24. Keller MC, Neale MC, Kendler KS (2007) Association of different adverse life events with distinct patterns of depressive symptoms. The American journal of psychiatry 164: 1521–9; quiz 1622. doi: https://doi.org/10.1176/appi.ajp.2007.06091564 .
• 30. American Psychiatric Association (1980) Diagnostic and Statistical Manual of Mental Disorders, Third Edition. Washington, DC: American Psychiatric Association.
• 36. Marks IM (1986) Behavioural Psychotherapy. Bristol: John Wright.
• 38. Heiberger RM, Holland B (2004) Statistical Analysis and Data Display: An Intermediate Course with Examples in S-PLUS, R, and SAS. Springer.
• 43. Muthén BO, Muthén L (2012) Mplus User's Guide, seventh edition. Los Angeles: Muthén & Muthén.
• 44. R Development Core Team (2008) R: A language and environment for statistical computing.
• 55. Judd LL, Schettler PJ, Coryell W, Akiskal HS, Fiedorowicz JG (2013) Overt Irritability/Anger in Unipolar Major Depressive Episodes: Past and Current Characteristics and Implications for Long-term Course. JAMA psychiatry 92093. doi: https://doi.org/10.1001/jamapsychiatry.2013.1957 .