Clinical diagnosis of depression in primary care: a meta-analysis

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data

BACKGROUND

The absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presentations. Despite extensive machine learning studies in psychiatric diagnosis, a reliable tool integrating multi-modality data is still lacking.

METHODS

In this study, blood samples from 100 MDD and 100 HC were analyzed, along with MRI images from 46 MDD and 49 HC. Here, we devised a novel algorithm, integrating graph neural networks and attention modules, for MDD diagnosis based on inflammatory cytokines, neurotrophic factors, and Orexin A levels in the blood samples. Model performance was assessed via accuracy and F1 value in 3-fold cross-validation, comparing with 9 traditional algorithms. We then applied our algorithm to a dataset containing both the aforementioned protein quantifications and neuroimages, evaluating if integrating neuroimages into the model improves performance.

RESULTS

Compared to HC, MDD showed significant alterations in plasma protein levels and gray matter volume revealed by MRI. Our new algorithm exhibited superior performance, achieving an F1 value and accuracy of 0.9436 and 94.08 %, respectively. Integration of neuroimaging data enhanced our novel algorithm's performance, resulting in an improved F1 value and accuracy, reaching 0.9543 and 95.06 %.

LIMITATIONS

This single-center study with a small sample size requires future evaluations on a larger test set for improved reliability.

CONCLUSIONS

In comparison to traditional machine learning models, our newly developed MDD diagnostic model exhibited superior performance and showed promising potential for inclusion in routine clinical diagnosis for MDD.

Sleep disturbances and incident risk of major depressive disorder in a population-based cohort

Sleep disturbances are well-known symptoms of major depressive disorder (MDD). However, the prospective risk of MDD in the presence of sleep disturbances in a general population-based cohort is not well known. This study investigated associations between both polysomnography (PSG)-based or subjective sleep features and incident MDD. Participants representative of the general population who had never had MDD completed sleep questionnaires (n = 2000) and/or underwent PSG (n = 717). Over 8 years' follow-up, participants completed psychiatric interviews enabling the diagnosis of MDD. Survival Cox models were used to analyze associations between sleep features and MDD incidence. A higher Epworth Sleepiness Scale and presence of insomnia symptoms were significantly associated with a higher incidence of MDD (hazard ratio [HR] [95 % confidence interval (CI)]: 1.062 [1.022-1.103], p = 0.002 and 1.437 [1.064-1.940], p = 0.018, respectively). Higher density of rapid eye movements in rapid eye movement (REM) sleep was associated with a higher incidence of MDD in men (HR 1.270 [95 % CI 1.064-1.516], p = 0.008). In women, higher delta power spectral density was associated with a lower MDD incidence (HR 0.674 [95 % CI 0.463-0.981], p = 0.039). This study confirmed the associations between subjective and objective sleep features and the incidence of MDD in a large community dwelling cohort.

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB)

BACKGROUND

The diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it is particularly important to find more objective biomarkers to aid in diagnosis and further treatment. Alpha-band activity (7-13 Hz) is the most prominent component in resting electroencephalogram (EEG), which is also thought to be a potential biomarker. Recent studies have shown the existence of multiple sub-oscillations within the alpha band, with distinct neural underpinnings. However, the specific contribution of these alpha sub-oscillations to the diagnosis and treatment of MDD remains unclear.

METHODS

In this study, we recorded the resting-state EEG from MDD and HC populations in both open and closed-eye state conditions. We also assessed cognitive processing using the MATRICS Consensus Cognitive Battery (MCCB).

RESULTS

We found that the MDD group showed significantly higher power in the high alpha range (10.5-11.5 Hz) and lower power in the low alpha range (7-8.5 Hz) compared to the HC group. Notably, high alpha power in the MDD group is negatively correlated with working memory performance in MCCB, whereas no such correlation was found in the HC group. Furthermore, using five established classification algorithms, we discovered that combining alpha oscillations with MCCB scores as features yielded the highest classification accuracy compared to using EEG or MCCB scores alone.

CONCLUSIONS

Our results demonstrate the potential of sub-oscillations within the alpha frequency band as a potential distinct biomarker. When combined with psychological scales, they may provide guidance relevant for the diagnosis and treatment of MDD.

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals

Objective.Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge.Approach.Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance.Main results. The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls, in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks.Significance. The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.

The burden of major depressive disorder in the Middle East and North Africa region, 1990-2019

BACKGROUND

Major depressive disorder (MDD) is one of the leading causes of disability. We aimed to report the MDD-attributable prevalence, incidence and years lived with disability (YLDs) in the Middle East and North Africa (MENA) region from 1990 to 2019 by age, sex and socio-demographic index (SDI).

METHODS

Publicly available data on the burden of MDD were retrieved from the Global Burden of Disease (GBD) study 2019 for the 21 countries in MENA. The counts and age-standardised rates (per 100,000) were presented, along with their corresponding 95% uncertainty intervals.

RESULTS

In 2019, MDD had an age-standardised point prevalence of 3322.1 and an incidence rate of 4921.7 per 100,000 population in MENA. Furthermore, there were 4.1 million YLDs in 2019. However, there were no substantial changes in the MDD burden over the period 1990-2019. In 2019, Palestine had the highest burden of MDD. The highest prevalence, incidence and YLDs attributable to MDD were found in the 35-39 age group. In 2019, the YLD rate in MENA was higher than the global rate for almost all age groups. Furthermore, there was a broadly negative association between the YLD rate and SDI.

CONCLUSION

The study highlights the need to prevent the disorder using a multidisciplinary approach and for the provision of cost-effective treatments for those affected, in order to increase their quality of life.

Norwegian general practitioners' perceptions of their depression care - a national survey

BACKGROUND

The General Practitioner (GP) is often the first professional contact for patients with depression. Depression care constitutes a substantial part of GPs' workload.

OBJECTIVE

To assess how GPs experience their patients' expectations and their own provision of depression care; further, how their depression care was associated with doctor- and practice-characteristics.

METHODS

A cross-sectional questionnaire study about depression care in general practice among the GPs in the Norwegian Physician Survey of 2021.

RESULTS

Of the 221 responding GPs, 50% were female and 70% agreed to have constant time pressure due to workload. The GPs believed that patients with depression were interested in their professional assessment (87.2%) and saw them as providers of talking therapy (76,9%). Still, 77,8% of the GPs thought the patients expected a referral. Talking therapy was commonly provided (79.6%) along with consultations of more than 30 min (80.4%). The youngest age group and GPs with shorter patient lists spent more time. Most GPs (92.3%) considered their help to be of great benefit for depressed patients. However, one-fourth of the GPs did not feel competent in providing talking therapy, less frequently reported by the GPs aged 40-54 years.

CONCLUSIONS

Talking therapy is commonly provided by GPs. However, there is a need to investigate what GP talking therapy implies, and to strengthen GP skills in this regard. Overall, the GPs experience their depression care to be useful for their patients, and do not de-prioritize this although they experience workload pressure.

Economic Evaluation of a Web Application Implemented in Primary Care for the Treatment of Depression in Patients With Type 2 Diabetes Mellitus: Multicenter Randomized Controlled Trial

BACKGROUND

Depressive disorder and type 2 diabetes mellitus (T2DM) are prevalent in primary care (PC). Pharmacological treatment, despite controversy, is commonly chosen due to resource limitations and difficulties in accessing face-to-face interventions. Depression significantly impacts various aspects of a person's life, affecting adherence to medical prescriptions and glycemic control and leading to future complications and increased health care costs. To address these challenges, information and communication technologies (eg, eHealth) have been introduced, showing promise in improving treatment continuity and accessibility. However, while eHealth programs have demonstrated effectiveness in alleviating depressive symptoms, evidence regarding glycemic control remains inconclusive. This randomized controlled trial aimed to test the efficacy of a low-intensity psychological intervention via a web app for mild-moderate depressive symptoms in individuals with T2DM compared with treatment as usual (TAU) in PC.

OBJECTIVE

This study aimed to analyze the cost-effectiveness and cost-utility of a web-based psychological intervention to treat depressive symptomatology in people with T2DM compared with TAU in a PC setting.

METHODS

A multicenter randomized controlled trial was conducted with 49 patients with T2DM, depressive symptoms of moderate severity, and glycosylated hemoglobin (HbA1c) of 7.47% in PC settings. Patients were randomized to TAU (n=27) or a web-based psychological treatment group (n=22). This web-based treatment consisted of cognitive behavioral therapy, improvement of diabetes self-care behaviors, and mindfulness. Cost-effectiveness analysis for the improvement of depressive symptomatology was conducted based on reductions in 3, 5, or 50 points on the Patient Health Questionnaire-9 (PHQ-9). The efficacy of diabetes control was estimated based on a 0.5% reduction in HbA1c levels. Follow-up was performed at 3 and 6 months. The cost-utility analysis was performed based on quality-adjusted life years.

RESULTS

Efficacy analysis showed that the web-based treatment program was more effective in improving depressive symptoms than TAU but showed only a slight improvement in HbA1c. Incremental cost-effectiveness ratios of 186.76 for a 3-point reduction in PHQ-9 and 206.31 for reductions of 5 and 50 percentage points were obtained. In contrast, the incremental cost-effectiveness ratio for improving HbA1c levels amounted to €1510.90 (€1=US $1.18 in 2018) per participant. The incremental cost-utility ratio resulted in €4119.33 per quality-adjusted life year gained.

CONCLUSIONS

The intervention, using web-based modules incorporating cognitive behavioral therapy tools, diabetes self-care promotion, and mindfulness, effectively reduced depressive symptoms and enhanced glycemic control in patients with T2DM. Notably, it demonstrated clinical efficacy and economic efficiency. This supports the idea that eHealth interventions not only benefit patients clinically but also offer cost-effectiveness for health care systems. The study emphasizes the importance of including specific modules to enhance diabetes self-care behaviors in future web-based psychological interventions, emphasizing personalization and adaptation for this population.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03426709; https://clinicaltrials.gov/study/NCT03426709.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/S12888-019-2037-3.

Prevalence of depression and associated symptoms among patients attending primary healthcare facilities: a cross-sectional study in Nepal

BACKGROUND

Depression is a prevalent mental health condition worldwide but there is limited data on its presentation and associated symptoms in primary care settings in low- and middle-income countries like Nepal. This study aims to assess the prevalence of depression, its hallmark and other associated symptoms that meet the Diagnostic and Statistical Manual (DSM-5) criteria in primary healthcare facilities in Nepal. The collected information will be used to determine the content of a mobile app-based clinical guidelines for better detection and management of depression in primary care.

METHODS

A total of 1,897 adult patients aged 18-91 (63.1% women) attending ten primary healthcare facilities in Jhapa, a district in eastern Nepal, were recruited for the study between August 2, 2021, and March 25, 2022. Trained research assistants conducted face-to-face interviews in private spaces before the consultation with healthcare providers. Depression symptoms, including hallmark symptoms, was assessed using the validated Nepali version of the Patient Health Questionnaire (PHQ-9).

RESULTS

One in seven (14.5%) individuals attending primary health care facilities in Jhapa met the threshold for depression based on a validated cut-off score ( > = 10) on the PHQ-9. The most commonly reported depressive symptoms were loss of energy and sleep difficulties. Approximately 25.4% of women and 18.9% of men endorsed at least one of the two hallmark symptoms on the PHQ-9. Using a DSM-5 algorithm (at least one hallmark symptom and five or more total symptoms) to score the PHQ-9, 6.3% of women and 4.3% of men met the criteria for depression. The intra-class correlation coefficient for PHQ-9 total scores by health facility as the unit of clustering was 0.01 (95% confidence interval, 0.00-0.04).

CONCLUSION

Depression symptoms are common among people attending primary healthcare facilities in Nepal. However, the most common symptoms are not the two hallmark criteria. Use of total scores on a screening tool such as the PHQ-9 risks overestimating the prevalence and generating false positive diagnoses. Compared to using cut off scores on screening tools, training health workers to first screen for hallmark criteria may increase the accuracy of identification and lead to better allocation of treatment resources.

The association of symptomatic vitreous floaters with depression and anxiety

PURPOSE

To evaluate the levels of anxiety and depression in patients with symptomatic vitreous floaters and to determine the possible correlations of psychological implications with the symptoms duration and possible improvement, the degree of posterior vitreous detachment, and the discomfort severity.

METHODS

Ninety patients complaining for floaters and fifty-seven age- and gender-matched healthy-control subjects were recruited. Every participant underwent a complete ophthalmological examination, including funduscopy and optical coherence tomography scans, while clinical and demographic data were also gathered. The Patient Health Questionnaire-9 (PHQ-9), the Zung Depression Inventory-Self-Rating Depression Scale (Zung SDS), and the Hospital Anxiety and Depression Scale (HADS) were completed by everyone.

RESULTS

Between the studied groups, no significant differences were detected regarding the clinical and demographic data (p > 0.05). The patients with floaters had significantly higher scores of PHQ-9, Zung SDS, HADS Anxiety, and HADS Depression (p < 0.001). After adjustment for several confounders, PHQ-9 (p = 0.041), Zung SDS (p = 0.003), and HADS Anxiety (p = 0.036) values remained significantly impaired. Among the patients, PHQ-9 and Zung SDS scores were significantly elevated in the patients with floaters duration less than 4 weeks (p < 0.05). Finally, anxiety and depression were significantly correlated with the symptoms duration and intensity, with the floater-associated discomfort, and with the stage of posterior vitreous detachment.

CONCLUSION

Vitreous floaters have a negative impact on patients' psychological status, by the terms of enhanced depressive and anxiety levels. To the best of our knowledge, our study is the first in the literature to elaborate the aforementioned association, by assessing three different questionnaires simultaneously.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from behavior should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Asthma medication adherence, control, and psychological symptoms: a cross-sectional study

BACKGROUND

Nonadherence to therapies and psychological disorders are associated with poor asthma control. This study aims to assess the prevalence of anxiety and depressive symptoms, asthma control, and adherence to inhalers and to investigate whether there is an association of anxiety and depressive symptoms with adherence to inhalers and asthma control.

METHODS

We measured anxiety and depressive symptoms using the Hospital Anxiety and Depression Scale in patients with asthma. Asthma Control Test and the 10-Item Test of Adherence to Inhalers Scale were used to assess levels of asthma control adherence to inhalers, respectively. Univariate and multivariate regression models assessed the associations of anxiety and depressive symptoms with adherence to inhalers and asthma control.

RESULTS

A total of 287 patients completed the study, of whom 72% were female. The mean ± SD age and body mass index of our study population were 44 ± 13 years and 29 ± 7.2 kg/m2, respectively. Poor adherence to inhaler use was highly prevalent (49.8%; 95% CI: 43.8 to 55.7). The prevalence of anxiety, depression and poor asthma control was 27.2% (95% CI: 22.1 to 32.7), 20.9% (95% CI: 16.3 to 26.1), and 22.7% (95% CI: 17.9 to 27.9), respectively. We found a negative relationship between asthma control and anxiety, and depressive symptoms (adjusted β: -0.25; 95% CI: -0.36 to -0.14; p < 0.001 and adjusted β: -0.29; 95% CI: -0.40 to -0.18; p < 0.001, respectively). A negative relationship was also observed between adherence to inhalers and anxiety and depressive symptoms (adjusted β: -0.34; 95% CI: -0.46 to -0.22; p < 0.001 and adjusted β: -0.36; 95% CI: - 0.48 to - 0.24; p < 0.001, respectively).

CONCLUSIONS

The high prevalence of uncontrolled asthma symptoms and poor adherence to inhalers and their impact on anxiety and depression levels among patients with asthma point to the need for early screening for psychological symptoms and recognition of nonadherence as part of asthma assessment and management plan in primary care in Saudi Arabia to avoid further worsening of asthma symptoms. Further studies are needed to explore the effectiveness of specific psychoeducational interventions and investigate the long-term impact of early psychological symptom detection on asthma outcomes.

Psychometric network analysis of the Patient Health Questionnaire-4 (PHQ-4) in Paraguayan general population

BACKGROUND

Depression and anxiety are two of the most prevalent and disabling mental disorders worldwide, both in the general population and in outpatient clinical settings.

OBJECTIVE

This study aimed to analyze the psychometric properties of the Patient Health Questionnaire-4 (PHQ-4) based on network analysis metrics.

METHODS

A total of 911 Paraguayans (23.71% women and 76.29% men; mean age 31.25 years, SD = 10.63), selected by non-probabilistic convenience sampling, participated in the study. Network analysis was used to evaluate the internal structure, reliability, and measurement invariance between men and women.

RESULTS

The results revealed that the PHQ-4 is a unidimensional measure through Exploratory Graph Analysis (EGA). Reliability, through structural consistency, identified that 100% of the time, only a single dimension was obtained, and all items remained stable, as they were always replicated within the empirical dimension. The unidimensional structure has shown evidence of configural invariance; therefore, the network structure functioned equally among the different sex groups.

CONCLUSION

The PHQ-4 presented optimal preliminary evidence of validity based on its internal structure, reliability, and invariance between sexes. Therefore, it may be useful as an accurate and brief measure of anxiety and depressive symptoms in the Paraguayan context.

Evaluating the effect of exercise-based interventions on functioning in people with transdiagnostic depressive symptoms: A systematic review of randomised controlled trials

BACKGROUND

Depressive symptoms are associated with various conditions and can exacerbate the outcome of somatic diseases. Transdiagnostic symptom-based approaches provide treatment flexibility, and exercise has demonstrated benefits beyond clinical symptoms. This work aimed to synthesise and establish the effects of exercise-based interventions on global functioning and quality of life in adults with transdiagnostic depressive symptoms, as well as their impact on clinical symptoms.

METHODS

A systematic review was conducted following PRISMA guidelines. PubMed, Scopus and PsycINFO databases were searched from inception to April 2023. Eligibility criteria included randomised controlled trials involving adults with transdiagnostic depressive symptoms who received exercise-based interventions and provided details of the interventions. Comparators included treatment as usual or other active control groups. The Cochrane quality assessment tool was used for quality assessment.

RESULTS

Fifteen articles involving 2064 participants were included. Data on study design, sample, intervention characteristics, and outcomes were extracted. Several trials demonstrated the expected positive effects of exercise on functioning (7/15). Most results supported the benefits of adjunctive exercise interventions on illness outcomes.

LIMITATIONS

The studies had methodological limitations, including small sample sizes and an underrepresentation of somatic diseases.

CONCLUSIONS

The functional consequences of exercise-based interventions targeting depressive symptoms are often understudied. Incorporating exercise routinely as an add-on treatment for transdiagnostic depressive symptoms could improve overall functioning, quality of life, and symptom severity. There is a need to expand the focus of exercise-based interventions to incorporate functional outcomes. Future research should address the methodological limitations and include a wider range of participants, including those with somatic diseases.

Epigenetic mechanisms in depression: Implications for pathogenesis and treatment

The risk of depression is influenced by both genetic and environmental factors. It has been suggested that epigenetic mechanisms may mediate the risk of depression following exposure to adverse life events. Epigenetics encompasses stable alterations in gene expression that are controlled through transcriptional, post-transcriptional, translational, or post-translational processes, including DNA modifications, chromatin remodeling, histone modifications, RNA modifications, and non-coding RNA (ncRNA) regulation, without any changes in the DNA sequence. In this review, we explore recent research advancements in the realm of epigenetics concerning depression. Furthermore, we evaluate the potential of epigenetic changes as diagnostic and therapeutic biomarkers for depression.

Clinical effectiveness of patient-targeted feedback following depression screening in general practice (GET.FEEDBACK.GP): an investigator-initiated, prospective, multicentre, three-arm, observer-blinded, randomised controlled trial in Germany

BACKGROUND

Screening for depression in primary care alone is not sufficient to improve clinical outcomes. However, targeted feedback of the screening results to patients might result in beneficial effects. The GET.FEEDBACK.GP trial investigated whether targeted feedback of the depression screening result to patients, in addition to feedback to general practitioners (GPs), leads to greater reductions in depression severity than GP feedback alone or no feedback.

METHODS

The GET.FEEDBACK.GP trial was an investigator-initiated, multicentre, three-arm, observer-blinded, randomised controlled trial. Depression screening was conducted electronically using the Patient Health Questionnaire-9 (PHQ-9) in 64 GP practices across five regions in Germany while patients were waiting to see their GP. Currently undiagnosed patients (aged ≥18 years) who screened positive for depression (PHQ-9 score ≥10), were proficient in the German language, and had a personal consultation with a GP were randomly assigned (1:1:1) into a group that received no feedback on their depression screening result, a group in which only the GP received feedback, or a group in which both GP and patient received feedback. Randomisation was stratified by treating GP and PHQ-9 depression severity. Trial staff were masked to patient enrolment and study group allocation and GPs were masked to the feedback recieved by the patient. Written feedback, including the screening result and information on depression, was provided to the relevant groups before the consultation. The primary outcome was PHQ-9-measured depression severity at 6 months after randomisation. An intention-to-treat analysis was conducted for patients who had at least one follow-up visit. This study is registered at ClinicalTrials.gov (NCT03988985) and is complete.

FINDINGS

Between July 17, 2019, and Jan 31, 2022, 25 279 patients were approached for eligibility screening, 17 150 were excluded, and 8129 patients completed screening, of whom 1030 (12·7%) screened positive for depression. 344 patients were randomly assigned to receive no feedback, 344 were assigned to receive GP-targeted feedback, and 339 were assigned to receive GP-targeted plus patient-targeted feedback. 252 (73%) patients in the no feedback group, 252 (73%) in the GP-targeted feedback group, and 256 (76%) in the GP-targeted and patient-targeted feedback group were included in the analysis of the primary outcome at 6 months, which reflected a follow-up rate of 74%. Gender was reported as female by 637 (62·1%) of 1025 participants, male by 384 (37·5%), and diverse by four (0·4%). 169 (16%) of 1026 patients with available migration data had a migration background. Mean age was 39·5 years (SD 15·2). PHQ-9 scores improved for each group between baseline and 6 months by -4·15 (95% CI -4·99 to -3·30) in the no feedback group, -4·19 (-5·04 to -3·33) in the GP feedback group, and -4·91 (-5·76 to -4·07) in the GP plus patient feedback group, with no significant difference between the three groups (global p=0·13). The difference in PHQ-9 scores when comparing the GP plus patient feedback group with the no feedback group was -0·77 (-1·60 to 0·07, d=-0·16) and when comparing with the GP-only feedback group was -0·73 (-1·56 to 0·11, d=-0·15). No increase in suicidality was observed as an adverse event in either group.

INTERPRETATION

Providing targeted feedback to patients and GPs after depression screening does not significantly reduce depression severity compared with GP feedback alone or no feedback. Further research is required to investigate the potential specific effectiveness of depression screening with systematic feedback for selected subgroups.

FUNDING

German Innovation Fund.

TRANSLATION

For the German translation of the abstract see Supplementary Materials section.

Should Physician-Assisted Suicide or Euthanasia be Legalized in the United States? A Medically Informed Perspective

There is a pressing debate in the United States concerning the implied physicians' obligation to do no harm and the status of legalizing physician-assisted suicide (PAS). Key issues that underpin the debate are important to consider. These include: (1) foundational medical beginnings; (2) euthanasia's historical and legal background context; and (3) the key arguments held by those for and against legalization of PAS. This paper reviews the major claims made by proponents for the legalization of PAS and the associated complexities and concerns that help underscore the importance of conscience freedoms. Relief of suffering, respect for patient autonomy, and public policy arguments are discussed in these contexts. We argue here that the emphasis by healthcare providers should be on high quality and compassionate care for those at the end of life's journey who are questioning whether to prematurely end their lives. If medicine loses its chief focus on the quality of caring-even when a cure is not possible-it betrays its objective and purpose. In this backdrop, legalization of PAS harms not only healthcare professionals, but also the medical profession's mission itself. Medicine's foundation is grounded in the concept of never intentionally to inflict harm. Inflicting death by any means is not professional or proper, and is not trustworthy medicine.

Access to Mental Health and Substance Use Treatment in Comprehensive Primary Care Plus

Importance

To meet increasing demand for mental health and substance use services, the Centers for Medicare & Medicaid Services launched the 5-year Comprehensive Primary Care Plus (CPC+) demonstration in 2017, requiring primary care practices to integrate behavioral health services.

Objective

To examine the association of CPC+ with access to mental health and substance use treatment before and during the COVID-19 pandemic.

Design, Setting, and Participants

Using difference-in-differences analyses, this retrospective cohort study compared adults attributed to CPC+ and non-CPC+ practices, from January 1, 2018, to June 30, 2022. The study included adults aged 19 to 64 years who had depression, anxiety, or opioid use disorder (OUD) and were enrolled with a private health insurer in Pennsylvania. Data were analyzed from January to June 2023.

Exposure

Receipt of care at a practice participating in CPC+.

Main Outcomes and Measures

Total cost of care and the number of primary care visits for evaluation and management, community mental health center visits, psychiatric hospitalizations, substance use treatment visits (residential and nonresidential), and prescriptions filled for antidepressants, anxiolytics, buprenorphine, naltrexone, or methadone.

Results

The 188 770 individuals in the sample included 102 733 adults (mean [SD] age, 49.5 [5.6] years; 57 531 women [56.4%]) attributed to 152 CPC+ practices and 86 037 adults (mean [SD] age, 51.6 [6.6] years; 47 321 women [54.9%]) attributed to 317 non-CPC+ practices. Among patients diagnosed with OUD, compared with patients attributed to non-CPC+ practices, attribution to a CPC+ practice was associated with filling more prescriptions for buprenorphine (0.117 [95% CI, 0.037 to 0.196] prescriptions per patient per quarter) and anxiolytics (0.162 [95% CI, 0.005 to 0.319] prescriptions per patient per quarter). Among patients diagnosed with depression or anxiety, attribution to a CPC+ practice was associated with more prescriptions for buprenorphine (0.024 [95% CI, 0.006 to 0.041] prescriptions per patient per quarter).

Conclusions and Relevance

Findings of this cohort study suggest that individuals with an OUD who received care at a CPC+ practice filled more buprenorphine and anxiolytics prescriptions compared with patients who received care at a non-CPC+ practice. As the Centers for Medicare & Medicaid Innovation invests in advanced primary care demonstrations, it is critical to understand whether these models are associated with indicators of high-quality primary care.

Association between sociodemographic factors, clinic characteristics and mental health screening rates in primary care

BACKGROUND

Screening for mental health problems has been shown to be effective to detect depression and initiate treatment in primary care. Current guidelines recommend periodic screening for depression and anxiety. This study examines the association of patient sociodemographic factors and clinic characteristics on mental health screening in primary care.

DESIGN

In this retrospective cohort study, electronic medical record (EMR) data from a 14-month period from 10/15/2021 to 12/14/2022 were analyzed. Data were retrieved from 18 primary care clinics from the Corewell Health healthcare system in West Michigan. The main outcome was documentation of any Patient Health Questionnaire (PHQ-4/PHQ-9/GAD-7) screening in the EMR within the 14-month period at patient level. General linear regression models with logit link function were used to assess adjusted odds ratio (aOR) of having a documented screening.

RESULTS

In total, 126,306 unique patients aged 16 years or older with a total of 291,789 encounters were included. The prevalence of 14-month screening was 79.8% (95% CI, 79.6-80.0). Regression analyses revealed higher screening odds for patients of smaller clinics (<5,000 patients, aOR 1.88; 95% CI 1.80-1.98 vs. clinics >10.000 patients), clinics in areas with mental health provider shortages (aOR 1.69; 95% CI 1.62-1.77), frequent visits (aOR 1.80; 95% CI, 1.78-1.83), and having an annual physical / well child visit encounter (aOR 1.52; 95% CI, 1.47-1.57). Smaller positive effect sizes were also found for male sex, Black or African American race, Asian race, Latinx ethnicity (ref. White/Caucasians), and having insurance through Medicaid (ref. other private insurance).

DISCUSSION

The 14-month mental health screening rates have been shown to be significantly lower among patients with infrequent visits seeking care in larger clinics and available mental health resources in the community. Introducing and incentivizing mandatory mental health screening protocols in annual well visits, are viable options to increase screening rates.

The Agreement Between Diagnoses as Stated by Patients and Those Contained in Routine Health Insurance Data

BACKGROUND

The frequency of medical diagnoses is a figure of central importance in epidemiology and health services research. Prevalence estimates vary depending on the underlying data. For a better understanding of such discrepancies, we compared patients' diagnoses as reported by themselves in response to our questioning with their diagnoses as stated in the routine data of their health insurance carrier.

METHODS

For 6558 adults insured by BARMER, one of the statutory health insurance carriers in Germany, we compared the diagnoses of various illnesses over a twelve-month period, as reported by the patients themselves in response to our questioning (October to December 2021), with their ICD-10-based diagnosis codes (Q4/2020-Q3/2021). The degree of agreement was assessed with two kappa values, sensitivity, and specificity.

RESULTS

The patients' stated diagnoses of diabetes and hypertension agreed well or very well with their diagnosis codes, with kappa and PABAK values near 0.8, as well as very high sensitivity and specificity. Moderately good agreement with respect to kappa was seen for the diagnoses of heart failure (0.4), obesity, anxiety disorder, depression, and coronary heart disease (0.5 each). The poorest agreement (kappa ≤ 0.3) was seen for posttraumatic stress disorder, alcohol-related disorder, and mental and somatoform disorder. Agreement was worse with increasing age.

CONCLUSION

Diagnoses as stated by patients often differ from those found in routine health insurance data. Discrepancies that can be considered negligible were found for only two of the 11 diseases that we studied. Our investigation confirms that these two sources of data yield different estimates of prevalence. Age is a key factor; further reasons for the discrepancies should be investigated, and avoidable causes should be addressed.

Palliative care and euthanasia: A Nepalese scenario

Palliative care, namely the relief of pain, is a priority for cases of incurable diseases in Nepal. However, since oral morphine is not available, pain control is often inadequate. Euthanasia is not permissible by law but could be a better solution in some cases and should be made understandable to patients and physicians as in developed countries. Should euthanasia be legal? If cheap and effective palliative care were easily accessible, most terminally ill Nepalese would avoid pressure to practise euthanasia because it is a cheaper option.

Antidepressants use in Italy: an ecological study of national and regional trends and associated factors

The present study aimed to (1) provide an update on trends in AD consumption both at the national and regional unit of analysis for the period 2000-2020 in Italy and (2) analyze sociodemographic and healthcare system-related factors associated with AD prescribing at the regional-population level between 2000 and 2019. Data were extracted from reports of the Italian Medicines Agency and databases of the Italian National Institute of Statistics. Linear regression and mixed models were applied to analyze trends in AD use (DDD/1000/day) and ecological factors associated with AD prescribing. Between 2000 and 2010 AD prescription rates constantly increased. Thereafter they stabilized until 2017 when a positive trend began again. There was a positive ecological association between AD prescribing and rates of hospital discharge due to affective disorders, antibiotics prescribing, public non-drug healthcare spending per capita, and Northern regions compared to Southern regions. AD consumption increased massively during the 2000s, flattened during the 2010s but thereafter increased again until 2020. The ecological correlation between healthcare provision/spending and AD consumption suggests that health-economic factors may play an important role.

Patient-reported outcome measures for monitoring primary care patients with depression: the PROMDEP cluster RCT and economic evaluation

Background

Guidelines on the management of depression recommend that practitioners use patient-reported outcome measures for the follow-up monitoring of symptoms, but there is a lack of evidence of benefit in terms of patient outcomes.

Objective

To test using the Patient Health Questionnaire-9 questionnaire as a patient-reported outcome measure for monitoring depression, training practitioners in interpreting scores and giving patients feedback.

Design

Parallel-group, cluster-randomised superiority trial; 1 : 1 allocation to intervention and control.

Setting

UK primary care (141 group general practices in England and Wales).

Inclusion criteria

Patients aged ≥ 18 years with a new episode of depressive disorder or symptoms, recruited mainly through medical record searches, plus opportunistically in consultations.

Exclusions

Current depression treatment, dementia, psychosis, substance misuse and risk of suicide.

Intervention

Administration of the Patient Health Questionnaire-9 questionnaire with patient feedback soon after diagnosis, and at follow-up 10-35 days later, compared with usual care.

Primary outcome

Beck Depression Inventory, 2nd edition, symptom scores at 12 weeks.

Secondary outcomes

Beck Depression Inventory, 2nd edition, scores at 26 weeks; antidepressant drug treatment and mental health service contacts; social functioning (Work and Social Adjustment Scale) and quality of life (EuroQol 5-Dimension, five-level) at 12 and 26 weeks; service use over 26 weeks to calculate NHS costs; patient satisfaction at 26 weeks (Medical Informant Satisfaction Scale); and adverse events.

Sample size

The original target sample of 676 patients recruited was reduced to 554 due to finding a significant correlation between baseline and follow-up values for the primary outcome measure.

Randomisation

Remote computerised randomisation with minimisation by recruiting university, small/large practice and urban/rural location.

Blinding

Blinding of participants was impossible given the open cluster design, but self-report outcome measures prevented observer bias. Analysis was blind to allocation.

Analysis

Linear mixed models were used, adjusted for baseline depression, baseline anxiety, sociodemographic factors, and clustering including practice as random effect. Quality of life and costs were analysed over 26 weeks.

Qualitative interviews

Practitioner and patient interviews were conducted to reflect on trial processes and use of the Patient Health Questionnaire-9 using the Normalization Process Theory framework.

Results

Three hundred and two patients were recruited in intervention arm practices and 227 patients were recruited in control practices. Primary outcome data were collected for 252 (83.4%) and 195 (85.9%), respectively. No significant difference in Beck Depression Inventory, 2nd edition, score was found at 12 weeks (adjusted mean difference -0.46, 95% confidence interval -2.16 to 1.26). Nor were significant differences found in Beck Depression Inventory, 2nd Edition, score at 26 weeks, social functioning, patient satisfaction or adverse events. EuroQol-5 Dimensions, five-level version, quality-of-life scores favoured the intervention arm at 26 weeks (adjusted mean difference 0.053, 95% confidence interval 0.013 to 0.093). However, quality-adjusted life-years over 26 weeks were not significantly greater (difference 0.0013, 95% confidence interval -0.0157 to 0.0182). Costs were lower in the intervention arm but, again, not significantly (-£163, 95% confidence interval -£349 to £28). Cost-effectiveness and cost-utility analyses, therefore, suggested that the intervention was dominant over usual care, but with considerable uncertainty around the point estimates. Patients valued using the Patient Health Questionnaire-9 to compare scores at baseline and follow-up, whereas practitioner views were more mixed, with some considering it too time-consuming.

Conclusions

We found no evidence of improved depression management or outcome at 12 weeks from using the Patient Health Questionnaire-9, but patients' quality of life was better at 26 weeks, perhaps because feedback of Patient Health Questionnaire-9 scores increased their awareness of improvement in their depression and reduced their anxiety. Further research in primary care should evaluate patient-reported outcome measures including anxiety symptoms, administered remotely, with algorithms delivering clear recommendations for changes in treatment.

Study registration

This study is registered as IRAS250225 and ISRCTN17299295.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/42/02) and is published in full in Health Technology Assessment; Vol. 28, No. 17. See the NIHR Funding and Awards website for further award information.

The effect of exercise on mental health and health-related quality of life in individuals with multiple sclerosis: A Systematic review and meta-analysis

BACKGROUND

A large body of evidence has tested the effect of exercise interventions on mental health and health-related quality of life (HRQoL) in individuals with multiple sclerosis (PwMS).

OBJECTIVE

To determine the effect of exercise interventions on mental health and HRQoL in PwMS.

METHODS

We searched four databases up to April 2023, and included randomized controlled trials that: 1) involved PwMS ≥18 years old; 2) delivered an exercise intervention; 3) measured subjective well-being, psychological well-being, social well-being, or HRQoL as outcomes. We reported standardized differences in means (d) with a 95 % confidence interval (CI), for continuous outcomes and an incidence rate ratio (IRR) with a 95 % CI for dichotomous outcomes.

RESULTS

Forty-nine studies (n = 2,057 participants) were included. Exercise improved overall well-being (d = 0.78; 95 % CI 0.483, 1.077; moderate certainty evidence), subjective well-being (d = 0.666; 95 % CI 0.405, 0.928; moderate certainty evidence), social well-being (d = 1.046; 95 % CI 0.569, 1.523; low certainty evidence), and HRQoL (d = 0.568; 95 % CI 0.396, 0.74; moderate certainty evidence).

CONCLUSION

Exercise interventions can improve well-being and HRQoL in PwMS. Future studies should focus on PwMS ≥ 65 years or with higher level of impairments.

A Deep Learning Driven Simulation Analysis of the Emotional Profiles of Depression Based on Facial Expression Dynamics

Objective

: Diagnosis and assessment of depression rely on scoring systems based on questionnaires, either self-reported by patients or administered by clinicians, and observation of patient facial expressions during the interviews plays a crucial role in making impressions in clinical settings. Deep learning driven approaches can assist clinicians in the course of diagnosis of depression by recognizing subtle facial expressions and emotions in depression patients.

Methods

: Seventeen simulated patients who acted as depressed patients participated in this study. A trained psychiatrist structurally interviewed each participant with moderate depression in accordance with a prepared scenario and without depressive features. Interviews were video-recorded, and a facial emotion recognition algorithm was used to classify emotions of each frame.

Results

: Among seven emotions (anger, disgust, fear, happiness, neutral, sadness, and surprise), sadness was expressed in a higher proportion on average in the depression-simulated group compared to the normal group. Neutral and fear were expressed in higher proportions on average in the normal group compared to the normal group. The overall distribution of emotions between the two groups was significantly different (p ＜ 0.001). Variance in emotion was significantly less in the depression-simulated group (p ＜ 0.05).

Conclusion

: This study suggests a novel and practical approach to understand the emotional expression of depression patients based on deep learning techniques. Further research would allow us to obtain more perspectives on the emotional profiles of clinical patients, potentially providing helpful insights in making diagnosis of depression patients.

Developing interventions to improve detection of depression in primary healthcare settings in rural Ethiopia

BACKGROUND

The poor detection of depression in primary healthcare (PHC) in low- and middle-income countries continues to threaten the plan to scale up mental healthcare coverage.

AIMS

To describe the process followed to develop an intervention package to improve detection of depression in PHC settings in rural Ethiopia.

METHOD

The study was conducted in Sodo, a rural district in south Ethiopia. The Medical Research Council's framework for the development of complex interventions was followed. Qualitative interviews, observations of provider-patient communication, intervention development workshops and pre-testing of the screening component of the intervention were conducted to develop the intervention.

RESULTS

A multicomponent intervention package was developed, which included (a) manual-based training of PHC workers for 10 days, adapted from the World Health Organization's Mental Health Gap Action Programme Intervention Guide, with emphasis on depression, locally identified depressive symptoms, communication skills, training by people with lived experience and active learning methods; (b) screening for culturally salient manifestations of depression, using a four-item tool; (c) raising awareness among people attending out-patient clinics about depression, using information leaflets and health education; and (d) system-level interventions, such as supportive supervision, use of posters at health facilities and a decision support mobile app.

CONCLUSIONS

This contextualised, multicomponent intervention package may lead to meaningful impact on the detection of depression in PHC in rural Ethiopia and similar settings. The intervention will be pilot tested for feasibility, acceptability and effectiveness before its wider implementation.

The longitudinal association of stressful life events with depression remission among SHARP trial participants with depression and hypertension or diabetes in Malawi

Depressive disorders are leading contributors to morbidity in low- and middle-income countries and are particularly prevalent among people with non-communicable diseases (NCD). Stressful life events (SLEs) are risk factors for, and can help identify those at risk of, severe depressive illness requiring more aggressive treatment. Yet, research on the impact of SLEs on the trajectory of depressive symptoms among NCD patients indicated for depression treatment is lacking, especially in low resource settings. This study aims to estimate the longitudinal association of SLEs at baseline with depression remission achievement at three, six, and 12 months among adults with either hypertension or diabetes and comorbid depression identified as being eligible for depression treatment. Participants were recruited from 10 NCD clinics in Malawi from May 2019-December 2021. SLEs were measured by the Life Events Survey and depression remission was defined as achieving a Patient Health Questionaire-9 (PHQ-9) score <5 at follow-up. The study population (n = 737) consisted predominately of females aged 50 or higher with primary education and current employment. At baseline, participants reported a mean of 3.5 SLEs in the prior three months with 90% reporting ≥1 SLE. After adjustment, each additional SLE was associated with a lower probability of achieving depression remission at three months (cumulative incidence ratio (CIR) 0.94; 95% confidence interval: 0.90, 0.98, p = 0.002), six months (0.95; 0.92, 0.98, p = 0.002) and 12 months (0.96; 0.94, 0.99, p = 0.011). Re-expressed per 3-unit change, the probability of achieving depression remission at three, six, and 12 months was 0.82, 0.86, and 0.89 times lower per 3 SLEs (the median number of SLEs). Among NCD patients identified as eligible for depression treatment, recent SLEs at baseline were associated with lower probability of achieving depression remission at three, six, and 12 months. Findings suggest that interventions addressing SLEs during integrated NCD and depression care interventions (e.g., teaching and practicing SLE coping strategies) may improve success of depression treatment among adult patient populations in low-resource settings and may help identify those at risk of severe and treatment resistant depression.

Perception of Apps for Mental Health Assessment With Recommendations for Future Design: United Kingdom Semistructured Interview Study

BACKGROUND

Mental health care provision in the United Kingdom is overwhelmed by a high demand for services. There are high rates of under-, over-, and misdiagnosis of common mental health disorders in primary care and delays in accessing secondary care. This negatively affects patient functioning and outcomes. Digital tools may offer a time-efficient avenue for the remote assessment and triage of mental health disorders that can be integrated directly into existing care pathways to support clinicians. However, despite the potential of digital tools in the field of mental health, there remain gaps in our understanding of how the intended user base, people with lived experiences of mental health concerns, perceive these technologies.

OBJECTIVE

This study explores the perspectives and attitudes of individuals with lived experiences of mental health concerns on mental health apps that are designed to support self-assessment and triage.

METHODS

A semistructured interview approach was used to explore the perspectives of the interviewees using 5 open-ended questions. Interviews were transcribed verbatim from audio data recordings. The average interview lasted 46 minutes (rounded to the nearest min; SD 12.93 min). A thematic analysis was conducted.

RESULTS

Overall, 16 individuals were interviewed in this study. The average age was 42.25 (SD 15.18) years, half of the interviewees identified as women (8/16, 50%), and all were White (16/16, 100%). The thematic analysis revealed six major themes: (1) availability and accessibility, (2) quality, (3) attitudes, (4) safety, (5) impact, and (6) functionality.

CONCLUSIONS

Engaging in clear communication regarding data security and privacy policies, adopting a consent-driven approach to data sharing, and identifying gaps in the app marketplace to foster the inclusion of a range of mental health conditions and avoid oversaturation of apps for common mental health disorders (eg, depression and anxiety) were identified as priorities from interviewees' comments. Furthermore, reputation was identified as a driver of uptake and engagement, with endorsement from a respected source (ie, health care provider, academic institution) or direct recommendation from a trusted health care professional associated with increased interest and trust. Furthermore, there was an interest in the role that co-designed digital self-assessments could play in existing care pathways, particularly in terms of facilitating informed discussions with health care professionals during appointments and by signposting individuals to the most appropriate services. In addition, interviewees discussed the potential of mental health apps to provide waiting list support to individuals awaiting treatment by providing personalized psychoeducation, self-help tips, and sources of help. However, concerns regarding the quality of care being affected because of digital delivery have been reported; therefore, frequent monitoring of patient acceptability and care outcomes is warranted. In addition, communicating the rationale and benefits of digitizing services will likely be important for securing interest and uptake from health care service users.

Task-state skin potential abnormalities can distinguish major depressive disorder and bipolar depression from healthy controls

Early detection of bipolar depression (BPD) and major depressive disorder (MDD) has been challenging due to the lack of reliable and easily measurable biological markers. This study aimed to investigate the accuracy of discriminating patients with mood disorders from healthy controls based on task state skin potential characteristics and their correlation with individual indicators of oxidative stress. A total of 77 patients with BPD, 53 patients with MDD, and 79 healthy controls were recruited. A custom-made device, previously shown to be sufficiently accurate, was used to collect skin potential data during six emotion-inducing tasks involving video, pictorial, or textual stimuli. Blood indicators reflecting individual levels of oxidative stress were collected. A discriminant model based on the support vector machine (SVM) algorithm was constructed for discriminant analysis. MDD and BPD patients were found to have abnormal skin potential characteristics on most tasks. The accuracy of the SVM model built with SP features to discriminate MDD patients from healthy controls was 78% (sensitivity 78%, specificity 82%). The SVM model gave an accuracy of 59% (sensitivity 59%, specificity 79%) in classifying BPD patients, MDD patients, and healthy controls into three groups. Significant correlations were also found between oxidative stress indicators in the blood of patients and certain SP features. Patients with depression and bipolar depression have abnormalities in task-state skin potential that partially reflect the pathological mechanism of the illness, and the abnormalities are potential biological markers of affective disorders.

Development of depression assessment tools using humanoid robots -Can tele-operated robots talk with depressive persons like humans?

BACKGROUND

Depression is a common mental disorder and causes significant social loss. Early intervention for depression is important. Nonetheless, depressed patients tend to conceal their symptoms from others based on shame and stigma, thus hesitate to visit psychiatrists especially during early phase. We hypothesize that application of humanoid robots would be a novel solution. Depressed patients may feel more comfortable talking with such robots than humans.

METHODS

We recruited 13 patients with major depressive disorder (MDD) and 27 healthy volunteers as controls. Participants took both tele-operated humanoid robot and human interviews to evaluate severity of depression using the Hamilton Depression Rating Scale (HDRS). In addition, participants completed a self-administered questionnaire asking about their impressions of the robot interview.

RESULTS

Confidence interval and t-test analysis have revealed that the HDRS scores are equally reliable between robot and human interviews. No significant differences were observed between the two interviews regarding "nervousness about the interview" and "hesitancy to talk about depressed moods and suicidal ideation." Compared to human interviews, robot interviews yielded significantly lower scores on shame-related factors especially among patients with MDD.

LIMITATION

Small sample size, and the evaluator is male only.

CONCLUSIONS

This is the first report to show the reliability of tele-operated humanoid robot interviews for assessment of depression. Robot interviews are potentially equally reliable as human interviews. Robot interviews are suggested to be more appropriate in assessing shame-related suppressed emotions and hidden thoughts of depressed patients in clinical practice, which may reduce the stigma associated with depression.

Association between depressive symptoms and dynamic balance among young adults in the community

Background

Detecting and addressing depression symptoms at their outset can reduce the burden on individuals and society; however, it has a limitation in that such evaluations mainly rely on self-reports. Several studies have demonstrated a strong association between motor symptoms and early depression. We aimed to associate body balance measured by the Lower Quarter Y Balance Test (YBT-LQ) with depressive symptoms among young adults in the community, to confirm the current evidence that depression negatively influences body balance.

Research question

Is the YBT-LQ an objective tool for measuring and evaluating young adults' depression risks, as well as assessing whether depression negatively influences body balance?

Methods

Our participants comprised 36 young adults. We assessed their depressive symptoms using the Center for Epidemiological Studies Depression Scale (CES-D) via a Google survey, measured their body balance with the YBT-LQ, and analyzed data with Spearman's rank-order correlation coefficient test, using SPSS version 27.0.

Results

We found that the right leg's anterior, posteromedial, and posterolateral scores- Z = -2.129, p = .033; Z = -2.181, p = .029; and Z = -2.250, p = .024, respectively-and composite scores-Z = 73.00, p = .027 -were significantly lower in the group with risk for clinical depression compared to the normal group. The CES-D total score had a negative association with all YBT-LQ scores, except for the anterior score of the left leg. Among the CES-D sub-factors, somatic and retarded activity showed negative correlations with all the YBT-LQ scores.

Significance

Our findings revealed that depressive symptoms have a negative association with balance, and that the YBT-LQ can be a reliable tool for measuring motor symptoms of depression, specifically among young adults.

Outpatient depression current care expenditure changes in Liaoning Province from 2015 to 2020: a study based on the "system of health accounts 2011"

Introduction: Depression is the leading cause of disability worldwide and has become a health issue of global concern. Based on the "System of Health Accounts 2011" (SHA 2011) for patients with depression, this paper studies the changes in the current curative expenditure (CCE) of outpatient depression in Liaoning Province, China, and provides policy recommendations. Method: A stratified multistage random sample of 56,994 patients with depression included from 1,227 healthcare facilities in Liaoning Province were included. The significance of differences in variables within groups was analyzed by univariate analysis (including descriptive statistics analysis, Mann-Whitney U test and Kruskal-Wallis H test), and factors influencing depression outpatient CCE were analyzed by multiple linear regression analysis and constructing structural equation models (SEM). Results: The CCE of outpatient depression was ranging from CNY 75.57 million to CNY 100.53 million in 2015-2020, with the highest of CNY 100.53 million in 2018, CNY 103.28 million in 2019. Medical expenditures are mainly concentrated in general hospitals and provincial healthcare institutions, accounting for about 90% of all provincial scope expenditures. The multiple regression results show that provincial healthcare institutions, purchase of drug, select medical treatment for depression, general hospitals and urban employees' health insurance are the main influencing factors for depression outpatient CCE. The results of SEM show that insurance status negative impact outpatient expenditure. Conclusion: Health insurance is an important factor in equitable access to healthcare resources for patients, and medication expenditure is the influential factor affecting the high expenditure of outpatient clinics. It is of great importance to reduce the medical burden of patients by increasing the coverage of medical insurance, increasing the proportion of bills that are eligible for reimbursement, and improving the system by guaranteeing the supply of psychotropic medication.

Physical Exercise as an Intervention for Depression: Evidence for Efficacy and Mu-Opioid Receptors as a Mechanism of Action

Physical exercise is often cited as an important part of an intervention for depression, and there is empirical evidence to support this. However, the mechanism of action through which any potential antidepressant effects are produced is not widely understood. Recent evidence points toward the involvement of endogenous opioids, and especially the mu-opioid system, as a partial mediator of these effects. In this chapter, we discuss the current level of empirical support for physical exercise as either an adjunctive or standalone intervention for depression. We then review the extant evidence for involvement of endogenous opioids in the proposed antidepressant effects of exercise, with a focus specifically on evidence for mu-opioid system involvement.

Evaluation of deep learning-based depression detection using medical claims data

Human accuracy in diagnosing psychiatric disorders is still low. Even though digitizing health care leads to more and more data, the successful adoption of AI-based digital decision support (DDSS) is rare. One reason is that AI algorithms are often not evaluated based on large, real-world data. This research shows the potential of using deep learning on the medical claims data of 812,853 people between 2018 and 2022, with 26,973,943 ICD-10-coded diseases, to predict depression (F32 and F33 ICD-10 codes). The dataset used represents almost the entire adult population of Estonia. Based on these data, to show the critical importance of the underlying temporal properties of the data for the detection of depression, we evaluate the performance of non-sequential models (LR, FNN), sequential models (LSTM, CNN-LSTM) and the sequential model with a decay factor (GRU-Δt, GRU-decay). Furthermore, since explainability is necessary for the medical domain, we combine a self-attention model with the GRU decay and evaluate its performance. We named this combination Att-GRU-decay. After extensive empirical experimentation, our model (Att-GRU-decay), with an AUC score of 0.990, an AUPRC score of 0.974, a specificity of 0.999 and a sensitivity of 0.944, proved to be the most accurate. The results of our novel Att-GRU-decay model outperform the current state of the art, demonstrating the potential usefulness of deep learning algorithms for DDSS development. We further expand this by describing a possible application scenario of the proposed algorithm for depression screening in a general practitioner (GP) setting-not only to decrease healthcare costs, but also to improve the quality of care and ultimately decrease people's suffering.

Experience of primary healthcare workers in using the mobile app-based WHO mhGAP intervention guide in detection and treatment of people with mental disorders: A qualitative study in Nepal

This study assessed perception and experience of trained primary health care workers in using a mobile app-based version of the WHO mental health gap action programme (mhGAP) Intervention Guide in the assessment and management of people with mental health conditions in primary care in Nepal. A qualitative study was conducted with primary healthcare workers who were trained in using the WHO mhGAP mobile application in Jhapa, a district in eastern Nepal. Semi-structured interviews were carried out with 15 healthcare workers (3 females and 12 males) nine months after the training. Interviews were audio recorded, transcribed and translated into English for the thematic qualitative data analysis. Health care workers (HCWs) found the mobile app helpful to verify and confirm their assessment and diagnosis. The other benefits reported by the participants were: bringing uniformity in diagnosis and management of mental health conditions across primary healthcare facilities; reminding the HCWs specific assessment questions; creating awareness on the importance of psychosocial intervention; improving the interaction between patients and service providers; and the importance of follow-up care. Despite these advantages, lack of clarity about report submission modality; lack of electricity or internet connectivity; and low technology literacy among older HCWs were some of the barriers in using the app. The major recommendation made by the HCWs for the guide included revision of the assessment process and system; inclusion of a chat function with supervisors; minimizing the amount of patients' data to be collected in the app; and addition of conversion and anxiety disorders to the app. This study concludes that the mobile decision support application could be acceptable and feasible in primary care settings if the suggested modifications are incorporated, in addition to addressing other systemic issues facing the primary care-based delivery of mental health services.

Emotion-dependent language featuring depression

BACKGROUND AND OBJECTIVES

Understanding language features of depression contributes to the detection of the disorder. Considering that depression is characterized by dysfunctions in emotion and individuals with depression often show emotion-dependent cognition, the present study investigated the speech features and word use of emotion-dependent narrations in patients with depression.

METHODS

Forty depression patients and forty controls were required to narrate self-relevant memories under five basic human emotions (i.e., sad, angry, fearful, neutral, and happy). Recorded speech and transcribed texts were analyzed.

RESULTS

Patients with depression, as compared to non-depressed individuals, talked slower and less. They also performed differently in using negative emotion, work, family, sex, biology, health, and assent words regardless of emotion manipulation. Moreover, the use of words such as first person singular pronoun, past tense, causation, achievement, family, death, psychology, impersonal pronoun, quantifier and preposition words displayed emotion-dependent differences between groups. With the involvement of emotion, linguistic indicators associated with depressive symptoms were identified and explained 71.6% variances of depression severity.

LIMITATIONS

Word use was analyzed based on the dictionary which does not cover all the words spoken in the memory task, resulting in text data loss. Besides, a relatively small number of depression patients were included in the present study and therefore the results need confirmation in future research using big emotion-dependent data of speech and texts.

CONCLUSIONS

Our findings suggest that consideration of different emotional contexts is an effective means to improve the accuracy of depression detection via the analysis of word use and speech features.

How adults with suspected depressive disorder experience online depression screening: A qualitative interview study

Background

While evidence on the effects and mechanisms of online depression screening is inconclusive, publicly available 'online depression tests' are already frequently used. To further a comprehensive understanding of online depression screening and evince the perspectives of those affected, we aimed to qualitatively explore how adults with undiagnosed but suspected depressive disorder experience the screening process.

Methods

This study is a qualitative follow-up of a German-wide, 3-arm, randomised controlled trial on feedback after online depression screening conducted between Jan 2021 and Sep 2022. A subsample of 26 participants with undiagnosed but suspected depressive disorder (Patient Health Questionnaire-9 ≥ 10; no depression diagnosis/treatment within the last year) were purposefully selected based on maximum variation in gender, age, and study arm. In-depth semi-structured telephone interviews (mean = 37 min) were conducted approximately six months after screening. Data were analysed within a contextualist theoretical framework using inductive reflexive thematic analysis.

Results

Participants were balanced in terms of gender (female/male, n = 15/11), age (range = 22 to 61 years), and study arm (no feedback/standard feedback/tailored feedback, n = 7/11/8). Reported experiences of online depression screening can be described as a two-step process: Step 1 is the initial reaction to the screening procedure and comprises the theme recognition of depressive symptoms: from denial to awareness. Step 2 describes a subsequent self-explorative process encompassing the themes cognitive positioning: rejection vs. acceptance, emotional reaction: between overload and empowerment, and personal activation: from reflection to action.

Conclusions

Findings indicate that online depression screening with and without feedback of results is experienced as a two-step process promoting symptom recognition and subsequent self-exploration. While few participants reported negative effects, the majority described the screening process as insightful, empowering, and activating. Future research should determine to what extent online depression screening may pose a standalone form of low-threshold support for individuals with undiagnosed depressive disorder, while focusing as well on potential negative effects.

Genome-Wide Association Study of Chronic Dizziness in the Elderly Identifies Loci Implicating MLLT10, BPTF, LINC01224, and ROS1

PURPOSE

Chronic age-related imbalance is a common cause of falls and subsequent death in the elderly and can arise from dysfunction of the vestibular system, an elegant neuroanatomical group of pathways that mediates human perception of acceleration, gravity, and angular head motion. Studies indicate that 27-46% of the risk of age-related chronic imbalance is genetic; nevertheless, the underlying genes remain unknown.

METHODS

The cohort consisted of 50,339 cases and 366,900 controls in the Million Veteran Program. The phenotype comprised cases with two ICD diagnoses of vertigo or dizziness at least 6 months apart, excluding acute or recurrent vertiginous syndromes and other non-vestibular disorders. Genome-wide association studies were performed as individual logistic regressions on European, African American, and Hispanic ancestries followed by trans-ancestry meta-analysis. Downstream analysis included case-case-GWAS, fine mapping, probabilistic colocalization of significant variants and genes with eQTLs, and functional analysis of significant hits.

RESULTS

Two significant loci were identified in Europeans, another in the Hispanic population, and two additional in trans-ancestry meta-analysis, including three novel loci. Fine mapping revealed credible sets of intronic single nucleotide polymorphisms (SNPs) in MLLT10 - a histone methyl transferase cofactor, BPTF - a subunit of a nucleosome remodeling complex implicated in neurodevelopment, and LINC01224 - a proto-oncogene receptor tyrosine kinase.

CONCLUSION

Despite the difficulties of phenotyping the nature of chronic imbalance, we replicated two loci from previous vertigo GWAS studies and identified three novel loci. Findings suggest candidates for further study and ultimate treatment of this common elderly disorder.

The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization

For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have emerged. However, previous research has virtually neglected practical application scenarios, as most studies have focused on analyzing and modeling EEG data. Furthermore, EEG data is typically obtained from specialized devices that are large, complex to operate, and poorly ubiquitous. To address these challenges, a wearable three-lead EEG sensor with flexible electrodes was developed to obtain prefrontal-lobe EEG data. Experimental measurements show that the EEG sensor achieves promising performance (background noise of no more than 0.91 μVpp, Signal-to-Noise Ratio (SNR) of 26--48 dB, and electrode-skin contact impedance of less than 1 K Ω). In addition, EEG data from 70 depressed patients and 108 healthy controls were collected using the EEG sensor, and the linear and nonlinear features were extracted. The features were then weighted and selected using the Ant Lion Optimization (ALO) algorithm to improve classification performance. The experimental results show that the k-NN classifier achieves a classification accuracy of 90.70%, specificity of 96.53%, and sensitivity of 81.79%, indicating the promising potential of the three-lead EEG sensor combined with the ALO algorithm and the k-NN classifier for EEG-assisted depression diagnosis.

Enhancing Provider Mental Health Screening in Primary Care: A Quality Improvement Project

Background: Depression and anxiety occur in 7.8% and 19.1% of the US population, respectively. About half of those patients are diagnosed in primary care. Objective: The purpose of this quality improvement project was to improve the screening and diagnosing of anxiety and/or depression among adult patients at a primary care clinic by implementing an evidence-based mental health screening interview technique. Methods: The team implemented a mental health screening interview technique that incorporates background, affect, trouble, handling, and empathy and motivational interviewing techniques in addition to self-report surveys. Pre- and post-intervention surveys were conducted to assess providers' perceptions of the new interview technique. ICD-10 code data were gathered to assess the effectiveness of the new mental health screening interview technique. Results: The number of documented diagnoses of single-episode major depressive disorder decreased by 18%, recurrent major depressive disorder increased by 34%, and anxiety disorders increased by 3%. There were more favorable provider perceptions of the new screening interview technique versus the traditional screening method. Conclusions: The results show an improvement in provider comfort and providers preferred the new mental health screening when using an evidence-based mental health screening interview technique. Implications for nursing: The results show the potential benefits of using a structured mental health interview with self-report screening tools when diagnosing anxiety and depression in primary care.

Development and functioning of the mobile app-based mh-GAP intervention guide in detection and treatment of people with mental health conditions in primary healthcare settings in Nepal

This paper describes the development process of a mobile app-based version of the World Health Organization mental health Gap Action Programme Intervention Guide, testing of the app prototypes, and its functionality in the assessment and management of people with mental health conditions in Nepal. Health workers' perception of feasibility and acceptability of using mobile technology in mental health care was assessed during the inspiration phase (N = 43); the ideation phase involved the creation of prototypes; and prototype testing was conducted over multiple rounds with 15 healthcare providers. The app provides provisional diagnoses and treatment options based on reported symptoms. Participants found the app prototype useful in reminding them of the process of assessment and management of mental disorders. Some challenges were noted, these included a slow app prototype with multiple technical problems, including difficulty in navigating 'yes'/'no' options, and there were challenges reviewing detailed symptoms of a particular disorder using a "more information" icon. The initial feasibility work suggests that if the technical issues are addressed, the e-mhGAP warrants further research to understand if it is a useful method in improving the detection of people with mental health conditions and initiation of evidence-based treatment in primary healthcare facilities.

Prevalence of common mental health disorders in military veterans: using primary healthcare data

INTRODUCTION

Serving military personnel and military veterans have been identified as having a high prevalence of mental disorders. Since 1985, UK patients' primary healthcare (PHC) medical records contain Read Codes (now being replaced by Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) codes) that mark characteristics such as diagnosis, ethnicity and therapeutic interventions. This English study accesses a cohort profile of British Armed Forces veterans to examine the diagnosed common mental disorders by using PHC records.

METHODS

This analysis has been drawn from initiatives with PHC practices in the Northwest of England to increase veteran registration in general practice. Demographic data were collected including gender, age and marital status. Data were also collected on common mental health disorders associated with the Armed Forces.

RESULT

2449 veteran PHC records were analysed. 38% (N=938) of veterans in this cohort had a code on their medical record for common mental health disorders. The highest disorder prevalence was depression (17.8%, N=437), followed by alcohol misuse (17.3%, N=423) and anxiety (15.0%, N=367). Lower disorder prevalence was seen across post-traumatic stress disorder (PTSD) (3.4%, N=83), dementia (1.8%, N=45) and substance misuse (0.8%, N=19). Female veterans had a higher prevalence of mental disorders than their male counterparts, while men a higher prevalence of PTSD; however, the gender difference in the latter was not significant (p>0.05).

CONCLUSION

The SNOMED searches do not detail why certain groups had higher recordings of certain disorders. A future study that accesses the PHC written medical notes would prove enlightening to specifically identify what situational factors are having the most impact on the veteran population. The results from a sizeable English veteran population provide information that should be considered in developing veteran-specific clinical provision, educational syllabus and policy.

Strengthening integrated depression services within routine primary health care using the RE-AIM framework in South Africa

Integration of mental health into routine primary health care (PHC) services in low-and middle-income countries is globally accepted to improve health outcomes of other conditions and narrow the mental health treatment gap. Yet implementation remains a challenge. The aim of this study was to identify implementation strategies that improve implementation outcomes of an evidence-based depression care collaborative implementation model integrated with routine PHC clinic services in South Africa. An iterative, quasi-experimental, observational implementation research design, incorporating the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework, was applied to evaluate implementation outcomes of a strengthened package of implementation strategies (stage two) compared with an initial evaluation of the model (stage one). The first stage package was implemented and evaluated in 10 PHC clinics and the second stage strengthened package in 19 PHC clinics (inclusive of the initial 10 clinics) in one resource-scarce district in the province of KwaZulu-Natal, South Africa. Diagnosed service users were more likely to be referred for counselling treatment in the second stage compared with stage one (OR 23.15, SE = 18.03, z = 4.04, 95%CI [5.03-106.49], p < .001). Training in and use of a validated, mandated mental health screening tool, including on-site educational outreach and technical support visits, was an important promoter of nurse-level diagnosis rates (OR 3.75, 95% CI [1.19, 11.80], p = 0.02). Nurses who perceived the integrated care model as acceptable were also more likely to successfully diagnose patients (OR 2.57, 95% CI [1.03-6.40], p = 0.043). Consistent availability of a clinic counsellor was associated with a greater probability of referral (OR 5.9, 95%CI [1.29-27.75], p = 0.022). Treatment uptake among referred service users remained a concern across both stages, with inconsistent co-located counselling services associated with poor uptake. The importance of implementation research for strengthening implementation strategies along the cascade of care for integrating depression care within routine PHC services is highlighted.

Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England

BACKGROUND

Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions.

METHODS AND FINDINGS

We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p < 0.001) for those with no preexisting conditions and decreases to 0.95 ([0.75, 1.21]; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 ([1.95, 2.12], p < 0.001), compared to transitions from CVD 1.50 ([1.43, 1.58], p < 0.001), CKD 1.37 ([1.30, 1.44], p < 0.001), and HF 1.55 ([1.34, 1.79], p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results.

CONCLUSIONS

Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.

Beyond the Asylum Walls: Tracing the Tapestry of Mental Health Interventions Across Eras and Cultures

This article offers an extensive review of the changing field of mental health therapies, charting a transformational path from traditional methods to modern breakthroughs and speculating on potential future developments. The story develops by investigating historical viewpoints while reflecting on the present and highlighting the lessons learned and their impact on contemporary practices. We have advanced from the stigmatized constraints of asylums to a paradigm that puts human rights, dignity, and individualized, culturally sensitive treatment first. Modern methods are much more varied and evidence-based, from cutting-edge technical advancements to evidence-based psychotherapies. The ethical considerations arising from the delicate balance of pharmacological therapies underline the responsibility of administering drugs that significantly affect mental health. Cultural factors become a pillar, highlighting how crucial cultural sensitivity is to promoting tolerance. By acknowledging how many facets of the human experience are interrelated, holistic methods help close the gap between the mind and body. Integrative medicine and alternative therapies represent a shift away from reductionist approaches and toward a holistic viewpoint. The delivery of mental health treatment is being reimagined by technological advancements, with virtual and digital environments opening up new access and support channels. These developments cut beyond regional boundaries, reinventing conventional therapy dynamics and paving the way for individualized therapies. Cultural concerns highlight the significance of cultural competency in navigating the complex mental health treatment system and adapting interventions to fit the particular requirements of various cultural contexts. With telepsychiatry, virtual reality, and artificial intelligence among the new technologies that promise to further revolutionize mental health therapies, the essay looks to the future. This review concludes by imagining a day when mental health is prioritized, therapies are available, and the diversity of human experience is valued. The path to a society that values, nurtures, and celebrates mental health continues.

Air pollution, social engagement, and depression in older adults: Results from a Swedish population-based cohort study

Although emerging research has investigated the relationship between outdoor air pollution and depression risk in older adults, the results remain inconclusive. We aimed to determine the relationship between long-term exposure to ambient air pollution and depression among older adults and explore whether active social engagement may modify this association. At baseline (2001-2004), 2812 depression-free older adults from Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) were included. SNAC-K is a longitudinal population-based cohort in Stockholm, Sweden. Incident depression cases occurred during 2004-2013 were ascertained using the Diagnostic and Statistical Manual of Mental Disorders 4th Edition. Air pollution [particulate matter (PM) and nitrogen oxides (NOx)] at the residency were estimated using dispersion models. Social engagement was measured as active participation in social activities (at least twice/week) or inactive (less than twice/week) in the last 12 months. The hazard ratios (HR) and 95% confidence intervals of depression from air pollution exposure of 3-year moving average before diagnosis (1-μg/m3 difference in PM2.5 and PM10, and 10-μg/m3 difference in NOx) were obtained from Cox models considering greenspace and noise. A product term of air pollutant and social activity was added to test the multiplicative interaction and attributable proportion due to interaction was calculated for assessing additive interaction. We identified 137 (4.9%) incident depression cases. Participants exposed to higher concentrations of PM2.5, NOx, and PM10 had 53% (HR:1.53 [1.22, 1.93]), 26% (HR:1.26 [1.01, 1.58]), and 7% (HR:1.07 [0.98, 1.18]) increased hazard of depression, respectively. These associations were largely attenuated in people with active social engagement (HR for PM2.5: 1.04 [0.70, 1.55]; HR for PM10: 0.98 [0.81, 1.18]; and HR for NOx: 1.09 [0.71, 1.66]). Our findings suggest long-term exposure to air pollution may be a risk factor for depression among older adults. An active social engagement might however decrease this risk.

An economic evaluation of universal and targeted case-finding strategies for identifying antenatal depression: a model-based analysis comparing common case-finding instruments

Half of women with depression in the perinatal period are not identified in routine care, and missed cases reflect inequalities in other areas of maternity care. Case finding (screening) for depression in pregnant women may be a cost-effective strategy to improve identification, and targeted case finding directs finite resources towards the greatest need. We compared the cost-effectiveness of three case-finding strategies: no case finding, universal (all pregnant women), and targeted (only pregnant women with risk factors for antenatal depression, i.e. history of anxiety/depression, age < 20 years, and adverse life events). A decision tree model was developed to represent case finding (at around 20 weeks gestation) and subsequent treatment for antenatal depression (up to 40 weeks gestation). Costs include case finding and treatment. Health benefits are measured as quality-adjusted life years (QALYs). The sensitivity and specificity of case-finding instruments and prevalence and severity of antenatal depression were estimated from a cohort study of pregnant women. Other model parameters were derived from published literature and expert consultation. The most cost-effective case-finding strategy was a two-stage strategy comprising the Whooley questions followed by the PHQ-9. The mean costs were £52 (universal), £61 (no case finding), and £62 (targeted case finding). Both case-finding strategies improve health compared with no case finding. Universal case finding is cost-saving. Costs associated with targeted case finding are similar to no case finding, with greater health gains, although targeted case finding is not cost-effective compared with universal case finding. Universal case finding for antenatal depression is cost-saving compared to no case finding and more cost-effective than targeted case finding.

Patient characteristics, validity of clinical diagnoses and Outcomes Associated with Suicidality in Inpatients with Symptoms of Depression (OASIS-D): design, procedures and outcomes

BACKGROUND

Suicidality, ranging from passive suicidal thoughts to suicide attempt, is common in major depressive disorder (MDD). However, relatively little is known about patient, illness and treatment characteristics in those with co-occurring MDD and suicidality, including the timing of and factors associated with the offset, continuation or reemergence of suicidality. Here, we present the background, rationale, design and hypotheses of the Patient Characteristics, Validity of Clinical Diagnoses and Outcomes Associated with Suicidality in Inpatients with Symptoms of Depression (OASIS-D) study, an investigator-initiated, observational study, funded by Janssen-Cilag GmbH.

METHODS/RESULTS

OASIS-D is an eight-site, six-month, cohort study of patients aged 18-75 hospitalized with MDD. Divided into three sub-studies and patient populations (PPs), OASIS-D will (i) systematically characterize approximately 4500 consecutively hospitalized patients with any form of unipolar depressive episode (PP1), (ii) evaluate the validity of the clinical diagnosis of moderate or severe unipolar depressive episode with the Mini-International Neuropsychiatric Interview (M.I.N.I.) and present suicidality (at least passive suicidal thoughts) present ≥ 48 h after admission with the Sheehan-Suicide Tracking Scale (S-STS), assessing also predictors of the diagnostic concordance/discordance of MDD in around 500 inpatients (PP2), and (iii) characterize and prospectively follow for 6 months 315 inpatients with a research-verified moderate or severe unipolar depressive episode and at least passive suicidal thoughts ≥ 48 h after admission, evaluating treatment and illness/response patterns at baseline, hospital discharge, 3 and 6 months. Exploratory objectives will describe the association between the number of days with suicidality and utilization of outpatient and inpatient care services, and structured assessments of factors influencing the risk of self-injurious behavior without suicidal intent, and of continuous, intermittent or remitted suicidality during the 6-month observation period.

CONCLUSION

Despite their frequency and clinical relevance, relatively little is known about patient and treatment characteristics of individuals with MDD and suicidality, including factors moderating and mediating the outcome of both MDD and suicidality. Results of the OASIS-D study are hoped to improve the understanding of the frequency, correlates and 6-month naturalistic treatment and outcome trajectories of different levels of suicidality in hospitalized adults with MDD and suicidality.

TRIAL REGISTRATION

NCT04404309 [ClinicalTrials.gov].

Cognitive Dysfunction and Affective Mood Disorder Screening in Patients With Chronic Inflammatory Bowel Disease: Protocol for a Prospective Case-Control Study

BACKGROUND

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) might be more frequent in patients with inflammatory bowel disease (IBD), but the relationship between these 2 entities is yet to be entirely established. Certain blood biomarkers (eg, serum amyloid A [SAA] and serum homocysteine [Hcy], which increase in IBD and MCI; brain-derived neurotrophic factor [BDNF], which decreases in MCI and AD but is not clearly modified in IBD; and S100 calcium-binding protein B [S100B], which increases in the blood-brain barrier and neuronal lesions) might predict the stage of MCI or dementia or progression to a further state. The gut-brain axis (GBA) might be the key to the development of MCI in patients with IBD, along with systemic inflammation and the possible and unknown adverse effects of disease-modifying medication.

OBJECTIVE

The aim of this study is to investigate whether GBA interactions play a role in MCI development in patients with IBD.

METHODS

A case-control study will be conducted on at least 100 patients diagnosed with IBD, matched with 100 healthy individual controls. The matching will include sex, age, and education. Patients will be fully examined, and a full interview and a neurological and cognitive examination will be performed. The primary clinical outcomes will be cognitive test scores (Montreal Cognitive Assessment, Trail Making Test, Digit Symbol Substitution Test, forward and backward digit span testing). Depression, stress, and anxiety screening will also be performed. Blood samples from all participants will be collected, and aliquots will be immediately stored in a biobank. Primary laboratory outcomes will include serum levels of presumed cognitive dysfunction blood biomarkers SAA, Hcy, S100B, and BDNF. Follow-up will be performed at 12, 24, 36, and 48 months.

RESULTS

Data collection started in December 2021 and is ongoing. So far, 53 patients with IBD have been recruited and 50 HC matched. Data collection should end in January 2030. Intermediary analysis will be performed in April 2024. We expect patients with IBD to have lower scores on cognitive testing and a positive correlation between disease length and cognitive impairment level. In addition, the levels of stress, anxiety, and depression should be higher in the IBD group. The serum levels of the 4 biomarkers could correlate or anticorrelate with cognitive scores and serve as predictive factors for MCI or dementia development. A higher level of education, a younger age, the absence of malabsorption, and good disease control might serve as protectors against MCI.

CONCLUSIONS

GBA interactions, along with systemic inflammation and the adverse effects of medication, might be a cause of MCI and AD development in patients with IBD. Serum biomarkers could prove cheap and useful predictors of MCI development.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05760729; https://clinicaltrials.gov/study/NCT05760729.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50546.