Does recognition of depression in primary care affect outcome? The PREDICT-NL study

Evaluating the Prevalence and Correlates of Major Depressive Disorder Among Residents of Fort McMurray, Canada, One Year After a Devastating Flood

OBJECTIVE

This study assessed the prevalence and correlates of depression following the April 2020 flooding in Fort McMurray.

METHODOLOGY

A cross-sectional study design. Questionnaires were self-administered through an anonymous, online survey. Data collected included sociodemographics, flooding-related variables, clinical information, and likely major depressive disorder (MDD) using PHQ-9 scoring. Data were analyzed using descriptive statistics, the chi-square test, and logistic regression at P = < 0.05.

RESULTS

Of the 186 respondents who completed the survey, 85.5% (159) of the respondents were females, 14.5% (27) were males, 52.7% (98) were above 40 years of age, and 94% (175) were employed. The prevalence of mild to severe depression among the respondents was 53.7% (75). Respondents who reported that they are unemployed are 12 times more likely to have a moderate to severe depression (OR = 12.16; 95% CI: 1.08-136.26). Respondents who had previously received a mental health diagnosis of MDD are five times more likely to have moderate to severe depression (OR = 5.306; 95% CI: 1.84-15.27).

CONCLUSION

This study suggests that flooding could impact the psychosocial and mental health of affected people. There is a need to reassess the existing guidelines on emergency planning for flooding to reduce its impacts on mental health and identify where research can support future evidence-based guidelines.

Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis

BACKGROUND

Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored.

OBJECTIVE

The aim of this study was to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings.

METHODS

We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effects models were used to explore the associations between daily-life gait features and depression symptom severity measured by the 15-item Geriatric Depression Scale (GDS-15) and 8-item Patient Health Questionnaire (PHQ-8) self-reported questionnaires. The likelihood-ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features.

RESULTS

Higher depression symptom severity was significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both data sets. The linear regression model with long-term daily-life gait features (R2=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R2=0.06).

CONCLUSIONS

This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings.

Under detection of depression in primary care settings in low and middle-income countries: a systematic review and meta-analysis

BACKGROUND

Depression is one of the commonest mental disorders in primary care but is poorly identified. The objective of this review was to determine the level of detection of depression by primary care clinicians and its determinants in studies from low- to middle-income countries (LMICs).

METHODS

A systematic review and meta-analysis was conducted using PubMed, PsycINFO, MEDLINE, EMBASE, LILAC, and AJOL with no restriction of year of publication. Risk of bias within studies was evaluated with the Effective Public Health Practice Project (EPHPP). "Gold standard" diagnosis for the purposes of this review was based on the 9-item Patient Health Questionnaire (PHQ-9; cutoff scores of 5 and 10), other standard questionnaires and interview scales or expert diagnosis. Meta-analysis was conducted excluding studies on special populations. Analyses of pooled data were stratified by diagnostic approaches.

RESULTS

A total of 3159 non-duplicate publications were screened. Nine publications, 2 multi-country studies, and 7 single-country studies, making 12 country-level reports, were included. Overall methodological quality of the studies was good. Depression detection was 0.0% in four of the twelve reports and < 12% in another five. PHQ-9 was the main tool used: the pooled detection in two reports that used PHQ-9 at a cutoff point of 5 (combined sample size = 1426) was 3.9% (95% CI = 2.3%, 5.5%); in four reports that used PHQ-9 cutoff score of 10 (combined sample size = 5481), the pooled detection was 7.0% (95% CI = 3.9%, 10.2%). Severity of depression and suicidality were significantly associated with detection.

CONCLUSIONS

While the use of screening tools is an important limitation, the extremely low detection of depression by primary care clinicians poses a serious threat to scaling up mental healthcare in LMICs. Interventions to improve detection should be prioritized.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42016039704 .

Stepped care for the treatment of depression: a systematic review and meta-analysis

BACKGROUND

The Stepped Care Model (SCM) proposes a sequential approach in the treatment of depression, applying interventions of increasing intensity according to the level of severity of the individual.

METHODS

A systematic review was carried out until January 2020 in eight electronic databases. We included randomized (RCT) and non-randomized controlled trials comparing the SCM to usual non-sequential care.

RESULTS

Eighteen RCT with patients with confirmed or probable depression diagnosis were included. Meta-analyses yielded high heterogeneity, and subgroup analyses showed significant effects of the SCM only in studies with baseline moderately severe symptoms on average, compared to samples with mild/moderate depression. In the former subgroup, effects at 3-6 and 9-12 months were small for symptoms' reduction (g = -0.33, 95%CI: -0.55, -0.17 and -0.34, 95%CI: -0.53, -0.16) and moderate-to-strong in response and remission (Risk Ratios between 1.70-1.90). Overall, a significant benefit on quality of life was also observed (6 months: g = 0.31, 95%CI: 0.12, 0.49; 12 months: g = 0.18, 95%CI: 0.06, 0.31). More patients in the SCM groups were prescribed antidepressants at 6 months (RR = 1.31, 95%CI: 1.09, 1.57; I² = 87%).

LIMITATIONS

The search does not guarantee the identification of all the relevant literature. Most included studies show uncertain or high risk of bias.

CONCLUSIONS

The SCM seems to outperform usual care in populations with at least moderately severe symptoms on average. Results show high heterogeneity and future research should explore its sources.

Detecting Depression Through Gait Data: Examining the Contribution of Gait Features in Recognizing Depression

While depression is one of the most common mental disorders affecting more than 300 million people across the world, it is often left undiagnosed. This paper investigated the association between depression and gait characteristics with the aim to assist in diagnosing depression. Our dataset consisted of 121 healthy people and 126 patients with depression who diagnosed by psychiatrists according to the Diagnostic and Statistical Manual of Mental Disorders. Spatiotemporal, temporal-domain, and frequency-domain features were extracted based on the walking data of 247 participants recorded by Microsoft Kinect (Version 2). Multiple logistic regression was used to analyze the variance of spatiotemporal (12.55%), time-domain (58.36%), and frequency-domain features (60.71%) on recognizing depression based on Nagelkerke's R 2 measure, respectively. The contributions of the different types of features were further explored by building machine learning models by using support vector machine algorithm. All the combinations of the three types of gait features were used as training data of machine learning models, respectively. The results showed that the model trained using only time- and frequency-domain features demonstrated the same best performance compared to the model trained using all the features (sensitivity = 0.94, specificity = 0.91, and AUC = 0.93). These results indicated that depression could be effectively recognized through gait analysis. This approach is a step forward toward developing low-cost, non-intrusive solutions for real-time depression recognition.

Individuals with depression express more distorted thinking on social media

Depression is a leading cause of disability worldwide, but is often underdiagnosed and undertreated. Cognitive behavioural therapy holds that individuals with depression exhibit distorted modes of thinking, that is, cognitive distortions, that can negatively affect their emotions and motivation. Here, we show that the language of individuals with a self-reported diagnosis of depression on social media is characterized by higher levels of distorted thinking compared with a random sample. This effect is specific to the distorted nature of the expression and cannot be explained by the presence of specific topics, sentiment or first-person pronouns. This study identifies online language patterns that are indicative of depression-related distorted thinking. We caution that any future applications of this research should carefully consider ethical and data privacy issues.

Depression screening using mobile phone usage metadata: a machine learning approach

OBJECTIVE

Depression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression remains undiagnosed in many cases. As mobile phones have now become an integral part of daily life, this study examines the possibility of screening for depressive symptoms continuously based on patients' mobile usage patterns.

MATERIALS AND METHODS

412 research participants reported a range of their mobile usage statistics. Beck Depression Inventory-2nd ed (BDI-II) was used to measure the severity of depression among participants. A wide array of machine learning classification algorithms was trained to detect participants with depression symptoms (ie, BDI-II score ≥ 14). The relative importance of individual variables was additionally quantified.

RESULTS

Participants with depression were found to have fewer saved contacts on their devices, spend more time on their mobile devices to make and receive fewer and shorter calls, and send more text messages than participants without depression. The best model was a random forest classifier, which had an out-of-sample balanced accuracy of 0.768. The balanced accuracy increased to 0.811 when participants' age and gender were included.

DISCUSSIONS/CONCLUSION

The significant predictive power of mobile usage attributes implies that, by collecting mobile usage statistics, mental health mobile applications can continuously screen for depressive symptoms for initial diagnosis or for monitoring the progress of ongoing treatments. Moreover, the input variables used in this study were aggregated mobile usage metadata attributes, which has low privacy sensitivity making it more likely for patients to grant required application permissions.

Examining the Correlation Between Depression and Social Behavior on Smartphones Through Usage Metadata: Empirical Study

BACKGROUND

As smartphone has been widely used, understanding how depression correlates with social behavior on smartphones can be beneficial for early diagnosis of depression. An enormous amount of research relied on self-report questionnaires, which is not objective. Only recently the increased availability of rich data about human behavior in digital space has provided new perspectives for the investigation of individual differences.

OBJECTIVE

The objective of this study was to explore depressed Chinese individuals' social behavior in digital space through metadata collected via smartphones.

METHODS

A total of 120 participants were recruited to carry a smartphone with a metadata collection app (MobileSens). At the end of metadata collection, they were instructed to complete the Center for Epidemiological Studies-Depression Scale (CES-D). We then separated participants into nondepressed and depressed groups based on their scores on CES-D. From the metadata of smartphone usage, we extracted 44 features, including traditional social behaviors such as making calls and sending SMS text messages, and the usage of social apps (eg, WeChat and Sina Weibo, 2 popular social apps in China). The 2-way ANOVA (nondepressed vs depressed × male vs female) and multiple logistic regression analysis were conducted to investigate differences in social behaviors on smartphones among users.

RESULTS

The results found depressed users received less calls from contacts (all day: F_1,116=3.995, P=.048, η₂=0.033; afternoon: F_1,116=5.278, P=.02, η₂=0.044), and used social apps more frequently (all day: F_1,116=6.801, P=.01, η₂=0.055; evening: F_1,116=6.902, P=.01, η₂=0.056) than nondepressed ones. In the depressed group, females used Weibo more frequently than males (all day: F_1,116=11.744, P=.001, η₂=0.092; morning: F_1,116=9.105, P=.003, η₂=0.073; afternoon: F_1,116=14.224, P<.001, η₂=0.109; evening: F_1,116=9.052, P=.003, η₂=0.072). Moreover, usage of social apps in the evening emerged as a predictor of depressive symptoms for all participants (odds ratio [OR] 1.007, 95% CI 1.001-1.013; P=.02) and male (OR 1.013, 95% CI 1.003-1.022; P=.01), and usage of Weibo in the morning emerged as a predictor for female (OR 1.183, 95% CI 1.015-1.378; P=.03).

CONCLUSIONS

This paper finds that there exists a certain correlation between depression and social behavior on smartphones. The result may be useful to improve social interaction for depressed individuals in the daily lives and may be insightful for early diagnosis of depression.

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

PURPOSE

Depression is a symptom commonly encountered in primary care; however, it is often not detected by doctors. Recently, disease diagnosis and treatment approaches have been attempted using smart devices. In this study, instrumental effectiveness was confirmed with the diagnostic meta-analysis of studies that demonstrated the diagnostic effectiveness of PHQ-9 for depression using mobile devices.

PATIENTS AND METHODS

We found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021. We performed a meta-analysis by including 1099 subjects in four studies. We performed a diagnostic meta-analysis according to the PHQ-9 cut-off score and machine learning algorithm techniques. Quality assessment was conducted using the QUADAS-2 tool. Data on the sensitivity and specificity of the studies included in the meta-analysis were extracted in a standardized format. Bivariate and summary receiver operating characteristic (SROC) curve were constructed using the metandi, midas, metabias, and metareg functions of the Stata algorithm meta-analysis words.

RESULTS

Using four studies out of the 5476 papers searched, a diagnostic meta-analysis of the PHQ-9 scores of 1099 people diagnosed with depression was performed. The pooled sensitivity and specificity were 0.797 (95% CI = 0.642-0.895) and 0.85 (95% CI = 0.780-0.900), respectively. The diagnostic odds ratio was 22.16 (95% CI = 7.273-67.499). Overall, a good balance was maintained, and no heterogeneity or publication bias was presented.

CONCLUSION

Through various machine learning algorithm techniques, it was possible to confirm that PHQ-9 depression screening in mobiles is an effective diagnostic tool when integrated into a diagnostic meta-analysis.

Depression alters the circadian pattern of online activity

Human sleep/wake cycles follow a stable circadian rhythm associated with hormonal, emotional, and cognitive changes. Changes of this cycle are implicated in many mental health concerns. In fact, the bidirectional relation between major depressive disorder and sleep has been well-documented. Despite a clear link between sleep disturbances and subsequent disturbances in mood, it is difficult to determine from self-reported data which specific changes of the sleep/wake cycle play the most important role in this association. Here we observe marked changes of activity cycles in millions of twitter posts of 688 subjects who explicitly stated in unequivocal terms that they had received a (clinical) diagnosis of depression as compared to the activity cycles of a large control group (n = 8791). Rather than a phase-shift, as reported in other work, we find significant changes of activity levels in the evening and before dawn. Compared to the control group, depressed subjects were significantly more active from 7 PM to midnight and less active from 3 to 6 AM. Content analysis of tweets revealed a steady rise in rumination and emotional content from midnight to dawn among depressed individuals. These results suggest that diagnosis and treatment of depression may focus on modifying the timing of activity, reducing rumination, and decreasing social media use at specific hours of the day.

Deficits in history taking skills among final year medical students in a family medicine course: A study from KSA

OBJECTIVES

History taking is considered an important diagnostic tool in medicine. Medical students should be competent in focused history-taking skills to reach initial diagnosis. The aim of this study was to identify deficits in history-taking skills among final year medical students in family medicine courses in Qassim University, KSA.

METHODS

All objective structured clinical examination (OSCE) sheets were collected and analysed to evaluate the history-taking component of the final examination from 2016 until January 2018.

RESULTS

A total of 94 OSCE sheets were evaluated. Achievement in some history taking skills of the students was low (differential diagnosis 31.9%, alarming symptoms of disease 39.4%, clarification of major complaint-associated symptoms 47.9%, and stress, anxiety, and depression screening 59.6%). However, the students' performances were better with respect to communication skills in general and exploration of the patients' ideas, concerns, and expectations. Significantly more male than female students had a better performance in some skills such as facilitating technique, appropriately exploring major complaint-associated symptoms, enquiring about differential diagnoses, and to rule out alarm symptoms.

CONCLUSIONS

In this study, the students' performance was generally better with respect to communication skills and psychosocial history. However, the students showed poor knowledge in other aspects of history-taking skills as they failed to formulate more than one hypothesis and to ask about alarm symptoms. Teaching communication and clinical reasoning skills and connecting physical and psychosocial aspects of patient care promotes understanding of the patient as a whole and should be taught in all courses of the clinical phase, with emphasis on bedside training.

Factors contributing to the recognition of anxiety and depression in general practice

Background

Adequate recognition of anxiety and depression by general practitioners (GPs) can be improved. Research on factors that are associated with recognition is limited and shows mixed results. The aim of this study was to explore which patient and GP characteristics are associated with recognition of anxiety and depression.

Methods

We performed a secondary analysis on data from 444 patients who were recruited for a randomized trial. Recognition of anxiety and depression was defined in terms of information in the medical records, in patients who screened positive on the extended Kessler 10 (EK-10). A total of 10 patient and GP characteristics, measured at baseline, were tested and included in a multilevel regression model to examine their impact on recognition.

Results

Patients who reported a perceived need for psychological care (OR = 2.54, 95% CI 1.60–4.03) and those with higher 4DSQ distress scores (OR = 1.03; 95% CI 1.00–1.07) were more likely to be recognized. In addition, patients’ anxiety or depression was less likely to be recognized when GPs were less confident in their abilities to identify depression (OR = 0.97; 95% CI 0.95–0.99). Patients’ age, chronic medical condition, somatisation, severity of anxiety and depression, and functional status were not associated with the recognition of anxiety and depression.

Conclusions

There is room for improvement of the recognition of anxiety and depression. Quality improvement activities that focus on increasing GPs’ confidence in the ability to identify symptoms of distress, anxiety and depression, as part of care according to guidelines, may improve recognition.

Electronic supplementary material

The online version of this article (10.1186/s12875-018-0784-8) contains supplementary material, which is available to authorized users.

Factor structure and measurement invariance across various demographic groups and over time for the PHQ-9 in primary care patients in Spain

The Patient Health Questionnaire (PHQ-9) is a widely-used screening tool for depression in primary care settings. The purpose of the present study is to identify the factor structure of the PHQ-9 and to examine the measurement invariance of this instrument across different sociodemographic groups and over time in a sample of primary care patients in Spain. Data came from 836 primary care patients enrolled in a randomized controlled trial (PsicAP study) and a subsample of 218 patients who participated in a follow-up assessment at 3 months. Confirmatory factor analysis (CFA) was used to test one- and two-factor structures identified in previous studies. Analyses of multiple-group invariance were conducted to determine the extent to which the factor structure is comparable across various demographic groups (i.e., gender, age, marital status, level of education, and employment situation) and over time. Both one-factor and two-factor re-specified models met all the pre-established fit criteria. However, because the factors identified in the two-factor model were highly correlated (r = .86), the one-factor model was preferred for its parsimony. Multi-group CFA indicated measurement invariance across different demographic groups and across time. The present findings suggest that physicians in Spain can use the PHQ-9 to obtain a global score for depression severity in different demographic groups and to reliably monitor changes over time in the primary care setting.

Multimorbidity and depression: A systematic review and meta-analysis

BACKGROUND

Multimorbidity, the presence of two or more chronic conditions, is increasingly common and complicates the assessment and management of depression. The aim was to investigate the relationship between multimorbidity and depression.

METHOD

A systematic literature search was conducted using the databases; PsychINFO, Medline, Embase, CINAHL and Cochrane Central. Results were meta-analysed to determine risk for a depressive disorder or depressive symptoms in people with multimorbidity.

RESULTS

Forty articles were identified as eligible (n = 381527). The risk for depressive disorder was twice as great for people with multimorbidity compared to those without multimorbidity [RR: 2.13 (95% CI 1.62-2.80) p<0.001] and three times greater for people with multimorbidity compared to those without any chronic physical condition [RR: 2.97 (95% CI 2.06-4.27) p<0.001]. There was a 45% greater odds of having a depressive disorder with each additional chronic condition compared to the odds of having a depressive disorder with no chronic physical condition [OR: 1.45 (95% CI 1.28-1.64) p<0.001]. A significant but weak association was found between the number of chronic conditions and depressive symptoms [r = 0.26 (95% CI 0.18-0.33) p <0.001].

LIMITATIONS

Although valid measures of depression were used in these studies, the majority assessed the presence or absence of multimorbidity by self-report measures.

CONCLUSIONS

Depression is two to three times more likely in people with multimorbidity compared to people without multimorbidity or those who have no chronic physical condition. Greater knowledge of this risk supports identification and management of depression.

Health Service Utilisation, Detection Rates by Family Practitioners, and Management of Patients with Common Mental Disorders in French Family Practice

OBJECTIVE

Provide up-to-date detection rates for common mental disorders (CMD) and examine patient service-use since the Preferred Doctor scheme was introduced to France in 2005, with patients encouraged to register with and consult a family practitioner (FP) of their choice.

METHODS

Study of 1133 consecutive patients consulting 38 FPs in the Montpellier region, replicating a study performed before the scheme. Patients in the waiting room completed the self-report Patient Health Questionnaire (PHQ) and Client Service-Receipt Inventory with questions on registration with a Preferred Doctor and doctor-shopping. CMD was defined as reaching PHQ criteria for depression, somatoform, panic or anxiety disorder. For each patient, FPs completed a questionnaire capturing psychiatric caseness.

RESULTS

81.2% of patients were seeing their Preferred Doctor on the survey-day. Of those with a CMD, 52.6% were detected by the FP. This increased with CMD severity and comorbidity. Detected cases were more likely to be consulting their Preferred Doctor (84.7% versus 79.4% for non-detected cases, p = 0.05) rather than another FP. They declared more visits to psychiatrists (17.2% versus 6.7%, p = 0.002). There was no association with consultation frequency or doctor-shopping, which both declined between the two studies.

CONCLUSION

The CMD detection rate is relatively high, with no increase compared to our previous study, despite a decline in doctor-shopping. An explanation is the same high proportion of patients visiting their usual FP on the survey-day at both periods, suggesting a limited impact of the scheme on care continuity. FP action taken highlights the importance of improving detection for providing care to patients with CMDs.

Depression, Help-Seeking and Self-Recognition of Depression among Dominican, Ecuadorian and Colombian Immigrant Primary Care Patients in the Northeastern United States

Latinos, the largest minority group in the United States, experience mental health disparities, which include decreased access to care, lower quality of care and diminished treatment engagement. The purpose of this cross-sectional study of 177 Latino immigrants in primary care is to identify demographic factors, attitudes and beliefs, such as stigma, perceived stress, and ethnic identity that are associated with depression, help-seeking and self-recognition of depression. Results indicated that 45 participants (25%) had depression by Patient Health Questionnaire (PHQ-9) criteria. Factors most likely to be associated with depression were: poverty; difficulty in functioning; greater somatic symptoms, perceived stress and stigma; number of chronic illnesses; and poor or fair self-rated mental health. Fifty-four people endorsed help-seeking. Factors associated with help-seeking were: female gender, difficulty in functioning, greater somatic symptoms, severity of depression, having someone else tell you that you have an emotional problem, and poor or fair self-rated mental health. Factors most likely to be associated with self-recognition were the same, but also included greater perceived stress. This manuscript contributes to the literature by examining attitudinal factors that may be associated with depression, help-seeking and self-recognition among subethnic groups of Latinos that are underrepresented in research studies.

The influence of comorbid chronic physical conditions on depression recognition in primary care: a systematic review

OBJECTIVE

People with depression often suffer from comorbid chronic physical conditions and such conditions are widely believed to interfere with primary care providers' ability to recognize their depression. We aimed to examine the evidence related to the influence of chronic physical comorbidity burden on depression recognition in routine, community-based primary care settings.

METHODS

We conducted a systematic review of the literature on depression recognition in primary care that featured comparisons between patient groups with higher and lower burdens of chronic physical comorbidity. Medline, Embase, PsycINFO, CINAHL and Cochrane Central Register of Controlled Trials were searched from inception to July 2013. Reference list and reverse citation searches were also performed. A narrative synthesis was conducted given clinical and methodological heterogeneity between studies.

RESULTS

Our search identified 5817 unique citations, out of which we identified 13 studies reporting data on the relationship between chronic physical comorbidity burden and depression recognition in primary care. Four studies provided some evidence that higher chronic physical comorbidity burden negatively affected primary care providers' ability to recognize depression. In contrast, two studies reported higher rates of recognition in patients with higher comorbidity burden and seven studies reported no differences in recognition between comorbidity groups.

CONCLUSION

Chronic physical comorbidity burden does not consistently affect depression recognition negatively in primary care. Instead, recognition seems to vary depending on the specific conditions or combination of conditions examined. Methodological choices of authors, such as approaches to measuring recognition and chronic medical comorbidity, also likely explain some divergent results across studies.

Diagnostic accuracy and adequacy of treatment of depressive and anxiety disorders: A comparison of primary care and specialized care patients

BACKGROUND

Clinical diagnosis of depressive and anxiety disorders has poor sensitivity, and treatment is often not guideline-concordant. This longitudinal study aims to compare diagnostic validity and treatment adequacy in primary care (PC) and specialized care (SC), to assess associated risk factors, and to evaluate their impact on clinical outcome at one-month and three-month follow-ups.

METHODS

Two hundred twelve patients with depressive and anxious symptoms were recruited from 3 PC and 1 SC centers in Barcelona, Spain. Sensitivity and specificity were calculated comparing medical records׳ diagnoses with a reference (MINI interview). Adequate treatment was defined according to clinical guidelines. Logistic regression was used to estimate associations with risk factors. Impact on outcome was assessed with MANOVA models.

RESULTS

Valid diagnosis of depression was more frequent in patients attending SC. Sensitivity for depression was 0.75 in SC and 0.49 in PC (adjusted OR=17.34, 95% CI=4.73-63.61). Detection of anxious comorbidity in depressed patients was low (50%) in SC. Treatment adequacy of depressive disorders was higher in SC than in PC (94.4% vs. 80.6%, adjusted OR=8.11, 95% CI=1.39-47.34). Depression severity was associated with valid diagnosis.

LIMITATIONS

Only four disorders (major depression, dysthymia, panic disorder and generalized anxiety disorder) were evaluated with the MINI interview in a convenience clinical sample. Treatment dosage was unavailable.

CONCLUSIONS

Our results suggest that GPs need tools to improve detection of depression and its severity. Psychiatrists should enhance recognition of anxious comorbidity. Evaluation of the impact on outcome deserves further research.

Toward personalizing treatment for depression: predicting diagnosis and severity

Objective

Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment. Toward the goal of personalizing treatment for depression, we develop and evaluate computational models that use electronic health record (EHR) data for predicting the diagnosis and severity of depression, and response to treatment.

Materials and methods

We develop regression-based models for predicting depression, its severity, and response to treatment from EHR data, using structured diagnosis and medication codes as well as free-text clinical reports. We used two datasets: 35 000 patients (5000 depressed) from the Palo Alto Medical Foundation and 5651 patients treated for depression from the Group Health Research Institute.

Results

Our models are able to predict a future diagnosis of depression up to 12 months in advance (area under the receiver operating characteristic curve (AUC) 0.70–0.80). We can differentiate patients with severe baseline depression from those with minimal or mild baseline depression (AUC 0.72). Baseline depression severity was the strongest predictor of treatment response for medication and psychotherapy.

Conclusions

It is possible to use EHR data to predict a diagnosis of depression up to 12 months in advance and to differentiate between extreme baseline levels of depression. The models use commonly available data on diagnosis, medication, and clinical progress notes, making them easily portable. The ability to automatically determine severity can facilitate assembly of large patient cohorts with similar severity from multiple sites, which may enable elucidation of the moderators of treatment response in the future.

Depression diagnosis and treatment amongst multimorbid patients: a thematic analysis

BACKGROUND

We explored experiences of depression diagnosis and treatment amongst multimorbid patients referred to a metropolitan multidisciplinary outpatient clinic to identify commonalities across this patient group.

METHODS

Patients with two or more chronic conditions and a diagnosis of depression participated in semi-structured interviews that were digitally recorded and transcribed. Thematic analysis was performed on the transcriptions.

RESULTS

Multimorbid patients attributed depressive symptoms to the loss of 'normal' roles and functionality and struggled to reconcile the depression diagnosis with their sense of identity. Beliefs about themselves and depression affected their receptivity to diagnosis and intervention strategies. These included prescribed interventions, such as psychotherapy or pharmacotherapy, and patient-developed strategies.

CONCLUSIONS

Functional and social role losses present a clear context in which GPs should raise the subject of mood, with the situational attribution of depression suggesting that psychotherapy, which is rarely offered, should be prioritised in these circumstances.

Factors affecting the presence of depression, anxiety disorders, and suicidal ideation in patients attending primary health care service in Lithuania

OBJECTIVE

The aim of this study was to establish prevalence, recognition, and risk factors for mental disorders and suicidal ideation in PC patients.

DESIGN

A cross-sectional survey based on standard mental health evaluation.

SETTING

Lithuanian primary care.

SUBJECTS

998 patients from four urban PC clinics.

MAIN OUTCOME MEASURES

Current mental disorders and suicidal ideation assessed using the Mini International Neuropsychiatric Interview (MINI).

RESULTS

According to the MINI, 27% of patients were diagnosed with at least one current mental disorder. The most common mental disorders were generalized anxiety disorder (18%) and major depressive episode (MDE) (15%), followed by social phobia (3%), panic disorder (3%), and post-traumatic stress disorder (2%). Some 6% of patients reported suicidal ideation. About 70% of patients with current mental disorder had no documented psychiatric diagnosis and about 60% received no psychiatric treatment. Greater adjusted odds for current MDE were associated with being widowed or divorced patients (odds ratio, OR = 1.8, 95% CI 1.2-2.8) and with lower education (OR = 1.6, 95% CI 1.1-2.3), while greater adjusted odds for any current anxiety disorder were found for women (OR = 1.9, 95% CI 1.3-2.8) and for patients with documented insomnia (OR = 2.2, 95% CI 1.2-4.2). Suicidal ideation was independently associated with use of antidepressants (OR = 5.4, 95% CI 1.7-16.9), with current MDE (OR = 2.9, 95% CI 1.5-5.8), and with excessive alcohol consumption (OR = 2.0, 95% CI 1.1-3.8).

CONCLUSIONS

Depression, anxiety disorders, and suicidal ideation are prevalent but poorly recognized among PC patients. The presence of current MDE is independently associated with marital status and with lower education, while current anxiety disorder is associated with female gender and insomnia. Suicidal ideation is associated with current MDE, and with antidepressants and alcohol use.

Prevalence and clinical characteristics of the DSM IV major depression among general internal medicine patients

OBJECTIVE

The aim of this study was to investigate the prevalence and clinical characteristics of the DSM IV major depressive disorder (MDD) among patients admitted to the General Internal Medicine Service of the Geneva University Hospital.

METHOD

557 patients admitted to the IM of the Geneva University Hospital aged 18 to 70 were investigated. Each subject was assessed by a clinical psychologist using the SCID (Structured Clinical Interview Depression for DSM-IV) questionnaire.

RESULTS

69 patients (12.4%) met diagnostic criteria for MDD (men: 8.8%, women: 16.9%, p=.004). Among subjects with major depression, depressed mood (97%), fatigue (91%), and diminished interest and pleasure (81%) were the most prevalent symptoms. Recurrent thoughts of death were present in 48% of depressed patients.

CONCLUSIONS

This study raises further evidence that an elevated proportion of patients admitted to an acute care general internal medicine facility meet DSM IV criteria for MDD with nearly half of depressed patients suffering from recurrent thoughts of death. It emphasizes the necessity of a targeted, continuous, and active support given by the psychiatry liaison service in the internal medicine setting.

The role of somatic health problems in the recognition of depressive and anxiety disorders by general practitioners

BACKGROUND

Recognition of depression and anxiety by general practitioners (GPs) is suboptimal and there is uncertainty as to whether particular somatic health problems hinder or facilitate GP recognition. The objective of this study was to investigate the associations between somatic health problems and GP recognition of depression and anxiety.

METHODS

We studied primary care patients with a DSM-IV based psychiatric diagnosis of depressive or anxiety disorder during a face-to-face interview (n=778). GPs' registrations of depression and anxiety diagnoses, based on medical file extractions, were compared with the DSM-IV based psychiatric diagnoses as reference standard. Somatic health problems were based on self-report of several chronic somatic diseases and pain symptoms, using the Chronic Pain Grade (CPG), during the interview.

RESULTS

Depression and anxiety was recognized in sixty percent of the patients. None of the health problems were negatively associated with recognition. Greater severity of pain symptoms (OR=1.18, p=.02), and chest pain (OR=1.56, p=.02), in particular, were associated with more GP recognition of depression and anxiety. Mediation analyses showed that depression and anxiety in these patients were better recognized through the presence of more severe psychiatric symptoms.

LIMITATIONS

Some specific chronic diseases had low prevalence.

CONCLUSIONS

This study shows that the presence of particular chronic diseases does not influence GP recognition of depression and anxiety. GPs tend to recognize depression and anxiety better in patients with pain symptoms, partly due to more severe psychiatric symptoms among those with pain.

Two-year stability and change of neuroticism and extraversion in treated and untreated persons with depression: findings from the Netherlands Study of Depression and Anxiety (NESDA)

BACKGROUND

The personality dimensions neuroticism and extraversion likely represent part of the vulnerability to depression. The stability over longer time periods of these personality dimensions in depressed patients treated with psychological treatment or medication and in untreated persons with depression in the general population remains unclear. Stability of neuroticism and extraversion in treated and untreated depressed persons would suggest that part of the vulnerability to depression remains stable over time. The current study addressed the question whether treatment in depressed patients is related to changes in neuroticism and extraversion.

METHODS

Data are from 709 patients with major depressive disorder participating in a cohort study (Netherlands Study of Depression and Anxiety; NESDA). We determined the 2-year stability of extraversion and neuroticism in treated and untreated persons and related change in depression severity to change in personality over time.

RESULTS

Neuroticism decreased from baseline to 2-year follow-up (d=0.73) in both treated and untreated persons. Extraversion did not change significantly after controlling for neuroticism and depression severity at baseline and follow-up. Decreased depressive symptoms over time were related to decreased neuroticism (d=1.91) whereas increased depressive symptoms over time were unrelated to neuroticism (d=0.06).

LIMITATIONS

Patients were not randomized to treatment conditions and the groups are therefore not directly comparable.

CONCLUSIONS

Treated patients with depression in the general population improve just as much on depression severity and neuroticism as untreated persons with depression. This suggests that changes in neuroticism in the context of treatment likely represent mood-state effects rather than direct effects of treatment.

Invisible emotional expressions influence social judgments and pupillary responses of both depressed and non-depressed individuals

We used filtered low spatial frequency images of facial emotional expressions (angry, fearful, happy, sad, or neutral faces) that were blended with a high-frequency image of the same face but with a neutral facial expression, so as to obtain a “hybrid” face image that “masked” the subjective perception of its emotional expression. Participants were categorized in three groups of participants: healthy control participants (N = 49), recovered previously depressed (N = 79), and currently depressed individuals (N = 36), All participants were asked to rate how friendly the person in the picture looked. Simultaneously we recorded, by use of an infrared eye-tracker, their pupillary responses. We expected that depressed individuals (either currently or previously depressed) would show a negative bias and therefore rate the negative emotional faces, albeit the emotions being invisible, as more negative (i.e., less friendly) than the healthy controls would. Similarly, we expected that depressed individuals would overreact to the negative emotions and that this would result in greater dilations of the pupil's diameter than those shown by controls for the same emotions. Although we observed the expected pattern of effects of the hidden emotions on both ratings and pupillary changes, both responses did not differ significantly among the three groups of participants. The implications of this finding are discussed.

A qualitative investigation of the impact of multimorbidity on GP diagnosis and treatment of depression in Australia

Primary care providers often struggle to identify depression, with patients with multiple chronic conditions presenting additional unique challenges. Whilst the diagnosis and treatment of depression has been explored in a range of contexts in the literature, there is a paucity of information on the impact of multimorbidity on general practitioners (GPs) attempting to diagnose and manage depression in primary care. Eight GPs with multiple referrals to a multidisciplinary clinic engaged in a semi-structured interview to discuss the impact of multimorbidity on the diagnosis and detection of depression. Interviews were transcribed and thematic analysis was used to identify key themes. Grounded theory was generated from data relating to the role of multimorbidity. Participants described multimorbidity as obscuring symptom causation, but also creating time to investigate causation and negotiate the depression diagnosis with the patient, and generating relationship through frequent presentations. Knowledge of the patient impacted on intervention recommendations, and trust facilitated patient receptivity. Treatment was affected by a range of variables, and included medical and social interventions. GP process for multimorbid patients is similar to that of patients with chronic illness. Further research is needed to know whether different processes or diagnostic categories are warranted where multiple chronic illnesses are present. Also, GPs recommend social interventions where medical interventions are perceived as inappropriate. Research into the efficacy of social interventions in multimorbid patients is needed.

Cognitive function in unipolar major depression: a comparison of currently depressed, previously depressed, and never depressed individuals

There is a lack of consensus upon a conclusive cognitive profile characterizing unipolar major depression. Currently depressed (n = 37), recovered previously depressed (n = 81), and never depressed controls (n = 50) underwent assessment of executive functions, working memory, attention, and psychomotor speed. Currently depressed yielded significantly lower test scores than previously and never depressed subjects on a measure of working memory. Both currently depressed and previously depressed scored significantly lower than never depressed subjects on measures of processing speed. Recurrent depressed performed similarly to subjects with a single depressive episode. These findings indicate a mild and limited cognitive impairment during the course of a mild to moderate major depressive disorder among relatively young adults. Impaired processing speed should be considered in further studies as a potential irreversible marker for recurrent depression.