The burden of anxiety, depression, and stress, along with the prevalence of symptoms of PTSD, and perceptions of the drivers of psychological harms, as perceived by doctors and nurses working in ICUs in Nepal during the COVID-19 pandemic; a mixed method evaluation.

BACKGROUND: The COVID-19 pandemic resulted in significant physical and psychological impacts for survivors, and for the healthcare professionals caring for patients. Nurses and doctors in critical care faced longer working hours, increased burden of patients, and limited resources, all in the context of personal social isolation and uncertainties regarding cross-infection. We evaluated the burden of anxiety, depression, stress, post-traumatic stress disorder (PTSD), and alcohol dependence among doctors and nurses working in intensive care units (ICUs) in Nepal and explored the individual and social drivers for these impacts. METHODS: We conducted a mixed-methods study in Nepal, using an online survey to assess psychological well-being and semi-structured interviews to explore perceptions as to the drivers of anxiety, stress, and depression. Participants were recruited from existing national critical care professional organisations in Nepal and using a snowball technique. The online survey comprised of validated assessment tools for anxiety, depression, stress, PTSD, and alcohol dependence; all tools were analysed using published guidelines. Interviews were analysed using rapid appraisal techniques, and themes regarding the drivers for psychological distress were explored. RESULTS: 134 respondents (113 nurses, 21 doctors) completed the online survey. Twenty-eight (21%) participants experienced moderate to severe symptoms of depression; 67 (50%) experienced moderate or severe symptoms of anxiety; 114 (85%) had scores indicative of moderate to high levels of stress; 46 out of 100 reported symptoms of PTSD. Compared to doctors, nurses experienced more severe symptoms of depression, anxiety, and PTSD, whereas doctors experienced higher levels of stress than nurses. Most (95%) participants had scores indicative of low risk of alcohol dependence. Twenty participants were followed up in interviews. Social stigmatism, physical and emotional safety, enforced role change and the absence of organisational support were perceived drivers for poor psychological well-being. CONCLUSION: Nurses and doctors working in ICU during the COVID-19 pandemic sustained psychological impacts, manifesting as stress, anxiety, and for some, symptoms of PTSD. Nurses were more vulnerable. Individual characteristics and professional inequalities in healthcare may be potential modifiable factors for policy makers seeking to mitigate risks for healthcare providers.

Anxiety and depression symptoms in adolescents and young adults with juvenile idiopathic arthritis: results of an outpatient screening.

BACKGROUND: Previous studies have shown that growing up with rheumatic conditions can fuel dissatisfaction and psychological distress, which in turn affects disease self-management and treatment adherence. Primary objective of this study was to estimate the prevalence of anxiety and depression symptoms in adolescents and young adults (AYA) with juvenile idiopathic arthritis (JIA) and to identify correlates of conspicuous screening results. METHODS: Initiated as part of the COACH multicenter observational study, outpatients aged 12 to 21 years participating in the National Pediatric Rheumatological Database (NPRD) were prospectively screened for mental health using the Patient Health Questionnaire-9 (PHQ-9) and the Generalised Anxiety Disorder Scale-7 (GAD-7). RESULTS: Data from 1,150 adolescents with JIA (mean age 15.6 ± 2.2 years; mean disease duration 7.2 ± 4.9 years, 69% female, 43% oligoarthritis, 26% polyarthritis) were analysed. Overall, 32.7% (n = 316) of AYA showed conspicuous screening results, of whom 30.4% reported clinically relevant suicidal or self-harm thoughts. About 19% of screened patients showed moderate to severe depressive or anxious symptoms. AYA with conspicuous screening results were older (15.8 vs. 15.2 years; p < 0.0001), more often female (81% vs. 64%; p < 0.0001) and more often overweight (25% vs. 17%; p = 0.006). They had higher disease activity (physician global assessment on NRS 0-10; 1.7 vs. 1.2; p < 0.0001), more functional limitations (CHAQ; 0.44 vs. 0.14; <0.0001) and rated their health status worse (NRS 0-10; 3.5 vs. 1.8; p < 0.0001) than AYA with inconspicuous screening results. Females (OR 2.33 [CI 1.53-3.56]; p < 0.0001), older age (OR 1.09 [CI 1.01-1.18]; p = 0.026), patients with more functional limitations (OR 3.36 [CI 1.98-5.72]; p < 0.0001), and patients with worse subjective health status (OR 1.17 [CI 1.07-1.27]; p < 0.0001) were more likely to have a conspicuous screening result. Regular sports participation was associated with a lower likelihood of conspicuous screening result (OR 0.69 [CI 0.49-0.98]; p = 0.039). CONCLUSIONS: A large-scale outpatient screening of AYA with JIA in Germany shows a high prevalence of anxiety and depression symptoms. The need for routine screening for early detection of mental health problems became apparent.

Improving speech depression detection using transfer learning with wav2vec 2.0 in low-resource environments.

Depression, a pervasive global mental disorder, profoundly impacts daily lives. Despite numerous deep learning studies focused on depression detection through speech analysis, the shortage of annotated bulk samples hampers the development of effective models. In response to this challenge, our research introduces a transfer learning approach for detecting depression in speech, aiming to overcome constraints imposed by limited resources. In the context of feature representation, we obtain depression-related features by fine-tuning wav2vec 2.0. By integrating 1D-CNN and attention pooling structures, we generate advanced features at the segment level, thereby enhancing the model's capability to capture temporal relationships within audio frames. In the realm of prediction results, we integrate LSTM and self-attention mechanisms. This incorporation assigns greater weights to segments associated with depression, thereby augmenting the model's discernment of depression-related information. The experimental results indicate that our model has achieved impressive F1 scores, reaching 79% on the DAIC-WOZ dataset and 90.53% on the CMDC dataset. It outperforms recent baseline models in the field of speech-based depression detection. This provides a promising solution for effective depression detection in low-resource environments.

Predictive modeling of initiation and delayed mental health contact for depression.

BACKGROUND: Depression is prevalent among Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veterans, yet rates of Veteran mental health care utilization remain modest. The current study examined: factors in electronic health records (EHR) associated with lack of treatment initiation and treatment delay; the accuracy of regression and machine learning models to predict initiation of treatment. METHODS: We obtained data from the VA Corporate Data Warehouse (CDW). EHR data were extracted for 127,423 Veterans who deployed to Iraq/Afghanistan after 9/11 with a positive depression screen and a first depression diagnosis between 2001 and 2021. We also obtained 12-month pre-diagnosis and post-diagnosis patient data. Retrospective cohort analysis was employed to test if predictors can reliably differentiate patients who initiated, delayed, or received no mental health treatment associated with their depression diagnosis. RESULTS: 108,457 Veterans with depression, initiated depression-related care (55,492 Veterans delayed treatment beyond one month). Those who were male, without VA disability benefits, with a mild depression diagnosis, and had a history of psychotherapy were less likely to initiate treatment. Among those who initiated care, those with single and mild depression episodes at baseline, with either PTSD or who lacked comorbidities were more likely to delay treatment for depression. A history of mental health treatment, of an anxiety disorder, and a positive depression screen were each related to faster treatment initiation. Classification of patients was modest (ROC AUC = 0.59 95%CI = 0.586-0.602; machine learning F-measure = 0.46). CONCLUSIONS: Having VA disability benefits was the strongest predictor of treatment initiation after a depression diagnosis and a history of mental health treatment was the strongest predictor of delayed initiation of treatment. The complexity of the relationship between VA benefits and history of mental health care with treatment initiation after a depression diagnosis is further discussed. Modest classification accuracy with currently known predictors suggests the need to identify additional predictors of successful depression management.

The revision and factor analytic evaluation of the German version of the depression literacy scale (D-Lit-R German).

BACKGROUND: Depression is a common mental health disorder and the second leading cause of disability worldwide. In people with depression, low depression literacy, which could be characterized by a poor recognition of depressive symptoms and less knowledge about the availability of treatment options, can hinder adequate therapy for depression. Nevertheless, questionnaires measuring depression literacy in Germany are rare. Consequently, for the present study, the German Depression Literacy Scale (D-Lit) has been revised and evaluated. METHODS: First, a team of clinical psychologists revised the D-Lit German scale. Next, cognitive interviews were conducted with patients with depression to improve the comprehensibility of the scale items. Our revision of the D-Lit-R German scale was then subjected to an anonymous online study. Finally, the data went through an exploratory factor analysis, and sociodemographic subgroup analyses were performed. RESULTS: N = 524 individuals (age 18-80) completed the D-Lit-R German scale and a questionnaire on their sociodemographic data. Cronbach´s alpha was estimated as α = .72, and McDonald's Omega (categorical) was estimated as ω = .77. The mean Item difficulty was M = .75 (SD = .15). An EFA was performed for a unidimensional model, a 5-factor-model and at last a 3-factor-model. The 5-factorial model showed a good model fit (χ2emp,WLSMV(131) = 92.424, p > .05; CFI = 1, RMSEA = 0, SRMR = .07) but was rejected since the content of the potential 5 factors could not be determined. The 3-factor model showed an arguable model fit. The Chi2 test was significant (χ2emp,WLSMV(168) = 199.912, p < .05), but the CFI and the RMSEA met an acceptable model fit (CFI = .990, RMSEA of .019, 90% CI[.003, .029]). Substantively, the three factors were defined as (1) Distractors and other symptoms, (2) Depressive symptoms, and (3) Pharmacological and psychotherapeutic depression treatment. Furthermore, there were significant differences in sum scores regarding the subgroup's gender, treatment for mental health problems, depression treatment, experience with depression, and different career fields. CONCLUSIONS: The D-Lit-R German scale is a time-efficient scale to assess some aspects of the depression literacy construct that can be easily applied. Since there was no perfect model fit, it is recommended to continue to revise the scale. Further evaluation studies could ask for knowledge of the etiological factors of depression. Future studies could then use this instrument to convey depression literacy. This instrument could assess the growth of knowledge after psychoeducational interventions in different settings. TRIAL REGISTRATION: This trial was preregistered at the platform osf.io ( https://osf.io/49xdh ). REGISTRATION NUMBER: https://doi.org/10.17605/OSF.IO/49XDH Date of registration: 28 April 2022.

The comorbid network characteristics of anxiety and depressive symptoms among Chinese college freshmen.

BACKGROUND: This study aimed to investigate the interplay between anxiety and depressive symptoms in Chinese college freshmen using the causal system perspective (CSP), which differs from the traditional common cause perspective (CCP) by providing an alternative explanation by attributing comorbidity to direct interactions among symptoms. METHODS: A convenience sample of 2,082 Chinese college freshmen (39.51% male, Mage = 18.61) from a normal university completed the Generalized Anxiety Disorder 7-Item Scale (GAD-7) and the Patient Health Questionnaire (PHQ-9). Network analysis was conducted and evaluated as to centrality, stability, node predictability, and bridging features. Moreover, the moderated network model (MNM) was utilized to detect the moderation effects of gender in the comorbidity network. RESULTS: The network of anxiety and depressive symptoms exhibited stability, characterized by the core symptoms of "restlessness", "lack of energy", and "excessive worry about control", as well as the bridging symptoms of "fearfulness", "sad mood", and "irritability". Notably, the nodes representing "uncontrollable worry" and "difficulty in relaxation" demonstrated the highest predictive power. Gender did not exert any moderating effects on the anxiety and depressive symptom network. CONCLUSION: These results reinforce that certain anxiety or depressive symptoms are more central than others, and thus play a more vital role in the comorbid network. These findings highlight underlying potential targeting symptoms to consider in future interventions.

Predictors of quality of life, functional status, depression and fatigue in early arthritis: comparison between clinically suspect arthralgia, unclassified arthritis and rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is often preceded by symptomatic phases during which classification criteria are not fulfilled. The health burden of these "at-risk" stages is not well described. This study assessed health-related quality of life (HRQoL), function, fatigue and depression in newly presenting patients with clinically suspect arthralgia (CSA), unclassified arthritis (UA) or RA. METHODS: Cross-sectional analysis of baseline Patient-Reported Outcome Measures (PROMs) was conducted in patients from the Birmingham Early Arthritis Cohort. HRQoL, function, depression and fatigue at presentation were assessed using EQ-5D, HAQ-DI, PHQ-9 and FACIT-F. PROMs were compared across CSA, UA and RA and with population averages from the HSE with descriptive statistics. Multivariate linear regression assessed associations between PROMs and clinical and sociodemographic variables. RESULTS: Of 838 patients included in the analysis, 484 had RA, 200 had CSA and 154 had UA. Patients with RA reported worse outcomes for all PROMs than those with CSA or UA. However, "mean EQ-5D utilities were 0.65 (95%CI: 0.61 to 0.69) in CSA, 0.61 (0.56 to 0.66) in UA and 0.47 (0.44 to 0.50) in RA, which was lower than in general and older (≥ 65 years) background populations." In patients with CSA or UA, HRQoL was comparable to chronic conditions such as heart failure, severe COPD or mild angina. Higher BMI and older age (≥ 60 years) predicted worse depression (PHQ-9: -2.47 (-3.85 to -1.09), P < 0.001) and fatigue (FACIT-F: 5.05 (2.37 to 7.73), P < 0.001). Women were more likely to report worse function (HAQ-DI: 0.13 (0.03 to 0.21), P = 0.01) and fatigue (FACIT-F: -3.64 (-5.59 to -1.70), P < 0.001), and residents of more deprived areas experienced decreased function (HAQ-DI: 0.23 (0.10 to 0.36), P = 0.001), greater depression (PHQ-9: 1.89 (0.59 to 3.18), P = 0.004) and fatigue (FACIT-F: -2.60 (-5.11 to 0.09), P = 0.04). After adjustments for confounding factors, diagnostic category was not associated with PROMs, but disease activity and polypharmacy were associated with poorer performance across all PROMs. CONCLUSIONS: Patient-reported outcomes were associated with disease activity and sociodemographic characteristics. Patients presenting with RA reported a higher health burden than those with CSA or UA, however HRQoL in the pre-RA groups was significantly lower than population averages.

Paternal depression in the postpartum year and children's behaviors at age 5 in an urban U.S. birth cohort.

OBJECTIVE: To investigate associations between postpartum depression in fathers and children's behaviors at age 5 in a national high-risk U.S. sample. STUDY DESIGN: A secondary data analysis of 1,796 children in a national birth cohort study that oversampled non-marital births was conducted. Paternal depression was assessed 1 year after the child was born and children's behaviors were assessed by their primary caregivers when the children were 5 years old. Unadjusted and adjusted negative binomial regression models of associations between paternal depression and child behavior scores and logistic regression models of associations between paternal depression and high scores (at least 1.5 or 2.0 standard deviations above the mean) were estimated. RESULTS: In negative binomial regression models that adjusted for child, paternal, and family characteristics and maternal depression, paternal depression was associated with a 17% higher total externalizing behavior score (Incidence Rate Ratio (IRR): 1.17; 95% Confidence Interval (CI): 1.07-1.27), a 17% higher aggressive subscale score (IRR: 1.17; 95% CI: 1.08-1.27), and an 18% higher delinquent subscale score (IRR: 1.18; 95% CI: 1.03-1.35). In adjusted logistic regression models for scores ≥2.0 standard deviations above the mean, paternal depression was associated with high total externalizing scores (e.g., Odds Ratio (OR): 3.09; 95% CI: 1.77-5.41), high aggressive behavior scores (OR: 2.40; 95% CI: 1.30-4.43), and high delinquent behavior scores (OR: 2.08; 95% CI: 1.01-4.27). There were suggestive but non-robust associations between paternal depression and attention problems and no associations between paternal depression and internalizing behaviors or social problems. CONCLUSION: Fathers' depression at age 1 was associated with children's externalizing behaviors at age 5, an important developmental stage when children transition to school. These findings suggest a need to identify and support fathers with depressive symptoms to promote optimal child development.

Using machine learning to develop a five-item short form of the children's depression inventory.

BACKGROUND: Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children's Depression Inventory (CDI) is widely utilized in China, it lacks a localized revision or simplified version. With its 27 items requiring professional administration, the original CDI proves to be a time-consuming method for predicting children and adolescents with high depression risk. Hence, this study aimed to develop a shortened version of the CDI to predict high depression risk, thereby enhancing the efficiency of prediction and intervention. METHODS: Initially, backward elimination is conducted to identify various version of the short-form scales (e.g., three-item and five-item versions). Subsequently, the performance of five machine learning (ML) algorithms on these versions is evaluated using the area under the ROC curve (AUC) to determine the best algorithm. The chosen algorithm is then utilized to model the short-form scales, facilitating the identification of the optimal short-form scale based on predefined evaluation metrics. Following this, evaluation metrics are computed for all potential decision thresholds of the optimal short-form scale, and the threshold value is determined. Finally, the reliability and validity of the optimal short-form scale are assessed using a new sample. RESULTS: The study identified a five-item short-form CDI with a decision threshold of 4 as the most appropriate scale considering all assessment indicators. The scale had 81.48% fewer items than the original version, indicating good predictive performance (AUC = 0.81, Accuracy = 0.83, Recall = 0.76, Precision = 0.71). Based on the test of 315 middle school students, the results showed that the five-item CDI had good measurement indexes (Cronbach's alpha = 0.72, criterion-related validity = 0.77). CONCLUSIONS: This five-item short-form CDI is the first shortened and revised version of the CDI in China based on large local data samples.

Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model.

OBJECTIVES: COVID-19 epidemics often lead to elevated levels of depression. To accurately identify and predict depression levels in home-quarantined individuals during a COVID-19 epidemic, this study constructed a depression prediction model based on multiple machine learning algorithms and validated its effectiveness. METHODS: A cross-sectional method was used to examine the depression status of individuals quarantined at home during the epidemic via the network. Characteristics included variables on sociodemographics, COVID-19 and its prevention and control measures, impact on life, work, health and economy after the city was sealed off, and PHQ-9 scale scores. The home-quarantined subjects were randomly divided into training set and validation set according to the ratio of 7:3, and the performance of different machine learning models were compared by 10-fold cross-validation, and the model algorithm with the best performance was selected from 15 models to construct and validate the depression prediction model for home-quarantined subjects. The validity of different models was compared based on accuracy, precision, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC), and the best model suitable for the data framework of this study was identified. RESULTS: The prevalence of depression among home-quarantined individuals during the epidemic was 31.66% (202/638), and the constructed Adaboost depression prediction model had an ACC of 0.7917, an accuracy of 0.7180, and an AUC of 0.7803, which was better than the other 15 models on the combination of various performance measures. In the validation sets, the AUC was greater than 0.83. CONCLUSIONS: The Adaboost machine learning algorithm developed in this study can be used to construct a depression prediction model for home-quarantined individuals that has better machine learning performance, as well as high effectiveness, robustness, and generalizability.

Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis.

Background: During the height of the COVID-19 pandemic, the Korean government temporarily allowed full scale telehealth care for safety and usability. However, limited studies have evaluated the impact of telehealth by analyzing the physical and/or mental health data of patients with COVID-19 diagnosis collected through telehealth targeting Korean population. Objective: This study aimed to identify subgroup of depressive symptom trajectories in patients with clinically mild COVID-19 using collected longitudinal data from a telehealth-based contactless clinical trial. Methods: A total of 199 patients with COVID-19 were accrued for contactless clinical trial using telehealth from March 23 to July 20, 2022. Depressive symptoms were measured using the patient health questionnaire-9 on the start day of quarantine, on the final day of quarantine, and 1 month after release from quarantine. Additionally, acute COVID-19 symptoms were assessed every day during quarantine. This study used a latent class mixed model to differentiate subgroups of depressive symptom trajectories and a logistic regression model with Firth's correction to identify associations between acute COVID-19 symptoms and the subgroups. Results: Two latent classes were identified: class 1 with declining linearity at a slow rate and class 2 with increasing linearity. Among COVID-19 symptoms, fever, chest pain, and brain fog 1 month after release from quarantine showed strong associations with class 2 (fever: OR, 19.43, 95% CI, 2.30-165.42; chest pain: OR, 6.55, 95% CI, 1.15-34.61; brain fog: OR, 7.03, 95% CI 2.57-20.95). Sleeping difficulty and gastrointestinal symptoms were also associated with class 2 (gastrointestinal symptoms: OR, 4.76, 95% CI, 1.71-14.21; sleeping difficulty: OR, 3.12, 95% CI, 1.71-14.21). Conclusion: These findings emphasize the need for the early detection of depressive symptoms in patients in the acute phase of COVID-19 using telemedicine. Active intervention, including digital therapeutics, may help patients with aggravated depressive symptoms.

Post-stroke arrhythmia could be a potential predictor for post-stroke depression.

Post-stroke depression (PSD) is regarded as the consequence of multiple contributors involving the process of cognition, mood and autonomic system, with the specific mechanism unclear yet. As a common type of stroke-heart syndromes, post-stroke arrhythmia shared some common pathogenesis with PSD. We presumed that post-stroke arrhythmia might be an early distinguishable marker for the presence of PSD and aimed to verity their association in this study. Patients with first-ever ischemic stroke were enrolled. The presence of post-stroke ectopic arrhythmia and the symptoms of arrhythmia were recorded with anti-arrhythmia drugs prescribed when necessary. Patients were followed up 3 months later to identify their presence and severity of PSD using Hamilton Depression Scale (HAMD) and also presence and severity of arrhythmia. Characteristics including the prevalence of various types of arrhythmias were compared between PSD and non-PSD groups. The HAMD scores were compared between patients with and without arrhythmia in PSD group. Logistic regression was used to identify the independent predictor of PSD. Patients with PSD had higher prevalence of post-stroke arrhythmia especially newly-detected arrhythmia, symptomatic arrhythmia and poor-controlled arrhythmia. In PSD group, patients of post-stroke arrhythmia had higher scores of HAMD than those without arrhythmia. Presence of newly-detected, symptomatic and poor-controlled arrhythmias were independent predictor of PSD. post-stroke arrhythmia especially newly-detected arrhythmia and symptomatic arrhythmia could be an early predictor of PSD. Successful control of arrhythmia was associated with reduced prevalence and severity of PSD.

Interaction between anxiety symptoms and decreased meaning in life: One possible pathway linking childhood trauma and depression- evidence from the network analysis.

BACKGROUND: Robust evidence suggests that individuals exposed to childhood trauma are more vulnerable to suffering from later depression. However, the pathway connecting the experience of childhood trauma and depression remains unclear. PARTICIPANTS AND SETTINGS: A total of 3663 participants from six colleges in China completed the Childhood Trauma Questionnaire-Short Form, Patient Health Questionnaire-9, Generalized Anxiety Disorder Scale-7, and Multidimensional Existential Meaning Scale. Among all participants, 3115 (Mage = 19.20, SDage = 1.38, males = 1384) participants met the selective standard of suffering from childhood trauma and were divided into the traumatized depressed group (the DT group) (n = 1432, Mage = 19.26, males = 700) and traumatized non-depressed group (the UDT group) (n = 1683, Mage = 19.15, males = 684). METHODS: In the present study, we examined the comorbidity of anxiety and the facets of meaning in the life network model. We then calculated the bridge symptoms and compared the networks of the DT group and the UDT group. RESULTS: The results of the t-test showed that the DT group scored significantly higher on all symptoms of anxiety and significantly lower on all dimensions of meaning in life compared to the UDT group. Meanwhile, the strongest bridge exists between "Mattering" and "Restlessness" in the symptom network of the DT group, while there is no bridge in the symptom network of the UDT group. The result of NCT indicates that the global strength and the EI value of "Mattering" are significantly higher in the symptom network of the DT group than in the UDT group. CONCLUSION: Intervention targeting improving the self-esteem of individuals suffering from childhood trauma may help to alleviate their depression and anxiety symptoms.

The factors influencing the occurrence of post-ischemic stroke depression and its relationship with the burden score of cerebral small vessel disease.

OBJECTIVE: This study explored the determinants of post-stroke depression (PSD) in ischemic stroke (AIS) patients and its association with the burden score of cerebral small vessel disease (CSVD). PATIENTS AND METHODS: We analyzed 374 AIS patients treated between January 2020 and January 2022. Patients were categorized into 90 with PSD and 284 without PSD, enabling an investigation into PSD risk factors and the CSVD-PSD relationship. RESULTS: There was no significant difference in health factors between PSD and non-PSD patients (p>0.05). However, significant disparities were noted in age, gender, initial Barthel Index (BI), Mini-Mental State Examination (MMSE) score, plasma fibrinogen, homocysteine, red cell distribution width, National Institutes of Health Stroke Scale (NIHSS) score, and CSVD burden score (p<0.05). Regression analysis indicated that these variables were pivotal PSD predictors (OR>1, p<0.05). Surprisingly, a positive correlation with PSD occurrence was found for age, NIHSS score, plasma fibrinogen, homocysteine levels, red cell distribution width, CSVD burden score (r=0.565, 0.615, 0.482, 0.514, 0.572, 0.608, respectively; p<0.05). Meanwhile, the MMSE score and BI index were inversely related to PSD onset (r=-0.604, -0.590; p<0.05). The ROC curve analysis of the combination model based on MMSE, NIHSS and CSVD score revealed an AUC of 0.926 and Youden's index of 0.744. CONCLUSIONS: Age, MMSE score, BI index, NIHSS score, plasma fibrinogen concentration, homocysteine level, red blood cell distribution width, and CSVD burden score are all major influencing factors in the occurrence of PSD. The combination model based on MMSE, NIHSS, and CSVD scores presented a valuable approach to predicting PSD.

Perinatal Depression Associated With Increased Pediatric Emergency Department Use And Charges In The First Year Of Life.

In New Jersey, universal screening for perinatal depression at the time of delivery has resulted in a 95 percent screening rate. The widespread availability of screening data allowed me to investigate the association between perinatal depression severity and infant emergency department (ED) use and charges in the first year of life. I used birth records linked to hospital discharge records for the period 2016-19. Compared with infants who had mothers with no symptoms, infants with mothers with mild or moderate/severe depressive symptoms had significantly higher overall and nonemergent ED use, but not significantly higher emergent ED use. The positive associations between depressive symptoms and ED charges were particularly striking for infants with Medicaid, which pays for a disproportionate share of pediatric ED care in the United States. This study contributes to the evidence base linking perinatal depression screening and pediatric ED use. Opportunities may exist within Medicaid to optimize screening and referrals for perinatal depression, with potential cost-saving benefits for reducing nonemergent pediatric ED visits.

Social Determinants Among Pregnant Clients With Perinatal Depression, Anxiety, Or Serious Mental Illness.

To characterize co-occurring social determinants of health for clients experiencing perinatal anxiety and depression (perinatal mood and anxiety disorders) or serious mental illness (SMI) in a diverse population receiving prenatal care in a safety-net health system, we conducted a latent class analysis, using data from a social determinants screener in pregnancy for the health system's clients during 2017-20. The sample included clients with positive screens for depression or anxiety or SMI diagnoses. Prenatal clients with a positive screen for perinatal mood and anxiety disorders or SMI comprised 13-30 percent of classes, characterized by more than two co-occurring social determinants (for example, co-occurring socioeconomic and interpersonal factors). The study findings highlight the salience of social determinants among prenatal patients experiencing perinatal mood and anxiety disorder and SMI and suggest the necessity of consistent screening for both social determinants and perinatal mental health. Policies to address social determinants within and beyond health care settings are critical.

Effectiveness of binaural beat music combined with rhythmical photic stimulation on older people with depressive symptoms in long-term care institution: a quasi-experimental pilot study.

BACKGROUND: Many older adults residing in long-term care often face issues like poor sleep, reduced vitality, and depression. Non-pharmacological approaches, specifically Binaural Beat Music (BBM) and Rhythmic Photic Stimulation (RPS), may alleviate these symptoms, yet their efficacy in this demographic has not been extensively explored. AIMS: This study investigated the effects of combined BBM and RPS interventions on sleep quality, vitality, and depression among older residents with depressive symptoms in long-term care facilities. METHODS: Using a quasi-experimental design, a total of 88 older adults with depressive symptoms from Taiwanese daytime care centers were divided into the BBM with RPS, and Sham groups (44 each). They underwent 20-minute daily sessions of their assigned treatment for two weeks. The BBM with RPS group listened to 10 Hz binaural beat music with 10 Hz photic stimulation glasses, and the Sham group received non-stimulating music and glasses. RESULTS: After the intervention, participants in the BBM with RPS groups showed significant improvements in vitality and depressive mood, with a notable increase in sympathetic nervous system activity. Conversely, the Sham group exhibited significant deterioration in vitality and mental health, with a significant increase in parasympathetic activity. Additionally, compared with the Sham group, the BBM and RPS groups showed significant improvements in vitality, mental health, and depression, with a significant increase in sympathetic nervous activity. CONCLUSION: The two-week intervention suggests that the combination of BBM and RPS, as a non-invasive intervention, can potentially improve vitality, mental health, and depressive mood among older adults in long-term care institutions.

Comparison of lifestyle, cognitive function, mental health, and quality of life between hospitalized older adults with COVID-19 and non-COVID-19 in South Korea: a cross-sectional study.

BACKGROUND: The coronavirus disease (COVID-19) pandemic has greatly impacted older adults, resulting in many deaths. The impact of lifestyle and mental health on vulnerable groups, such as older adults, can be large and long lasting. Therefore, this study aimed to investigate the effects of COVID-19 confirmation on cognition, lifestyle, mental health, and quality of life in adults aged 55 years. METHODS: The sample consisted of 111 people in the COVID group and 189 people in the non-COVID group aged over 55 years in South Korea. An online survey was conducted between January and May 2022. Participants responded to the following assessment tools: Yonsei Lifestyle Profile, Prospective and Retrospective Memory (PRMQ), Subjective Memory Complaints Questionnaire (SMCQ), Visual Analogue Scale, Patient Health Questionnaire-9 (PHQ-9), Insomnia Severity Index (ISI), Fear of COVID-19 Scale (FCV-19 S), and the World Health Organization Quality of Life Scale abbreviated version (WHOQOL-BREF). Differences in lifestyle, cognition, depression, anxiety, and quality of life were compared between the two groups. RESULTS: There were significant differences in physical activity, diet, the total score of the PRMQ, PM (a sub-score of the PRMQ), PHQ-9, Korean version of the ISI (ISI-K), and WHOQOL-BREF scores between the COVID and non-COVID groups. However, there were no significant differences in activity participation, Self-Rating Anxiety Scale (SAS), or FCV-19 S between groups. CONCLUSIONS: The study confirms that COVID-19 negatively affects memory, physical activity, diet, quality of life, depression, and insomnia in the older adults. Therefore, this study implicated that prevention and intervention strategies required improving the memory, lifestyle, and mental health of older adults with COVID-19. TRIAL REGISTRATION: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Yonsei university in Korea (Registration number: 1041849-202112-SB-226-03, Date of registration: 01042022).

Fairness and bias correction in machine learning for depression prediction across four study populations.

A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches regularly present biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. There is no one best ML model for depression prediction that provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we also identify positive habits and open challenges that practitioners could follow to enhance fairness in their models.