Hybrid representation learning for cognitive diagnosis in late-life depression over 5 years with structural MRI.

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks.

Accelerated Brain Atrophy, Microstructural Decline and Connectopathy in Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) has recently been linked to cognitive impairment. We hypothesized that AMD modifies the brain aging trajectory, and we conducted a longitudinal diffusion MRI study on 40 participants (20 with AMD and 20 controls) to reveal the location, extent, and dynamics of AMD-related brain changes. Voxel-based analyses at the first visit identified reduced volume in AMD participants in the cuneate gyrus, associated with vision, and the temporal and bilateral cingulate gyrus, linked to higher cognition and memory. The second visit occurred 2 years after the first and revealed that AMD participants had reduced cingulate and superior frontal gyrus volumes, as well as lower fractional anisotropy (FA) for the bilateral occipital lobe, including the visual and the superior frontal cortex. We detected faster rates of volume and FA reduction in AMD participants in the left temporal cortex. We identified inter-lingual and lingual-cerebellar connections as important differentiators in AMD participants. Bundle analyses revealed that the lingual gyrus had a lower streamline length in the AMD participants at the first visit, indicating a connection between retinal and brain health. FA differences in select inter-lingual and lingual cerebellar bundles at the second visit showed downstream effects of vision loss. Our analyses revealed widespread changes in AMD participants, beyond brain networks directly involved in vision processing.

Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model.

Objectives: Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD). Participants: Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study. Design: Cross-sectional. Measurements: Structural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a priori regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes. Results: Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex. Conclusions: We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information.

Addressing multi-site functional MRI heterogeneity through dual-expert collaborative learning for brain disease identification.

Several studies employ multi-site rs-fMRI data for major depressive disorder (MDD) identification, with a specific site as the to-be-analyzed target domain and other site(s) as the source domain. But they usually suffer from significant inter-site heterogeneity caused by the use of different scanners and/or scanning protocols and fail to build generalizable models that can well adapt to multiple target domains. In this article, we propose a dual-expert fMRI harmonization (DFH) framework for automated MDD diagnosis. Our DFH is designed to simultaneously exploit data from a single labeled source domain/site and two unlabeled target domains for mitigating data distribution differences across domains. Specifically, the DFH consists of a domain-generic student model and two domain-specific teacher/expert models that are jointly trained to perform knowledge distillation through a deep collaborative learning module. A student model with strong generalizability is finally derived, which can be well adapted to unseen target domains and analysis of other brain diseases. To the best of our knowledge, this is among the first attempts to investigate multi-target fMRI harmonization for MDD diagnosis. Comprehensive experiments on 836 subjects with rs-fMRI data from 3 different sites show the superiority of our method. The discriminative brain functional connectivities identified by our method could be regarded as potential biomarkers for fMRI-related MDD diagnosis.

Reliable Cognitive Decline in Late-Life Major Depression.

OBJECTIVE: Major depression in older adults increases the statistical likelihood of dementia. It is challenging to translate statistical evidence of cognitive decline at the group level into knowledge of individual cognitive outcomes. The objective of the current study is to investigate 2-year reliable cognitive change in late-life depression (LLD), which will enhance understanding of cognitive changes in LLD and provide a means to assess individual change. METHODS: In a sample of non-depressed cognitively normal older adults or NDCN (n = 113), we used linear regression to predict tests of global cognition, processing speed-executive functioning, and memory administered 1 and 2 years later. Stepwise regression was used to select covariates among demographics and raw test scores (either baseline or year 1) and we cross-validated the final models using the predicted residual error sum of squares (PRESS). We then derived a z-change score from the difference between actual and predicted follow-up scores and investigated the proportion of LLD patients (n = 199) and NDCN adults who experienced reliable "decline" (a z-score < -1.645), "stability" (z-scores between + - 1.645), and "improvement" (z scores > +1.645). RESULTS: A greater proportion LLD compared with NDCN experienced cognitive decline in processing speed/executive functioning and global cognition over 2 years. When compared to NDCN, a greater proportion of LLD also significantly improved on one test of processing speed over 2 years. CONCLUSIONS: Older adults with LLD are at risk of meaningful cognitive decline over a relatively short period, particularly in the domain of executive functioning and processing speed. This study provides a series of reliable change equations for common neuropsychological tests that can be applied clinically.

Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification.

Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used for automated diagnosis of brain disorders such as major depressive disorder (MDD) to assist in timely intervention. Multi-site fMRI data have been increasingly employed to augment sample size and improve statistical power for investigating MDD. However, previous studies usually suffer from significant inter-site heterogeneity caused for instance by differences in scanners and/or scanning protocols. To address this issue, we develop a novel discrepancy-based unsupervised cross-domain fMRI adaptation framework (called UFA-Net) for automated MDD identification. The proposed UFA-Net is designed to model spatio-temporal fMRI patterns of labeled source and unlabeled target samples via an attention-guided graph convolution module, and also leverage a maximum mean discrepancy constrained module for unsupervised cross-site feature alignment between two domains. To the best of our knowledge, this is one of the first attempts to explore unsupervised rs-fMRI adaptation for cross-site MDD identification. Extensive evaluation on 681 subjects from two imaging sites shows that the proposed method outperforms several state-of-the-art methods. Our method helps localize disease-associated functional connectivity abnormalities and is therefore well interpretable and can facilitate fMRI-based analysis of MDD in clinical practice.

Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI.

Brain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specific label information, brain anatomical structures provided by these MRIs can be used to boost learning performance intuitively. Unfortunately, existing research seldom takes advantage of such brain anatomy prior. To this end, this paper proposes a brain anatomy-guided representation (BAR) learning framework for assessing the clinical progression of cognitive impairment with T1-weighted MRIs. The BAR consists of a pretext model and a downstream model, with a shared brain anatomy-guided encoder for MRI feature extraction. The pretext model also contains a decoder for brain tissue segmentation, while the downstream model relies on a predictor for classification. We first train the pretext model through a brain tissue segmentation task on 9,544 auxiliary T1-weighted MRIs, yielding a generalizable encoder. The downstream model with the learned encoder is further fine-tuned on target MRIs for prediction tasks. We validate the proposed BAR on two CI-related studies with a total of 391 subjects with T1-weighted MRIs. Experimental results suggest that the BAR outperforms several state-of-the-art (SOTA) methods. The source code and pre-trained models are available at https://github.com/goodaycoder/BAR.

The Preventing Alzheimer's with Cognitive Training (PACT) randomized clinical trial.

BACKGROUND: To address the rising prevalence of Alzheimer's disease and related dementias, effective interventions that can be widely disseminated are warranted. The Preventing Alzheimer's with Cognitive Training study (PACT) investigates a commercially available computerized cognitive training program targeting improved Useful Field of View Training (UFOVT) performance. The primary goal is to test the effectiveness of UFOVT to reduce incidence of clinically defined mild cognitive impairment (MCI) or dementia with a secondary objective to examine if effects are moderated by plasma ß-amyloid level or apolipoprotein E e4 (APOE e4) allele status. METHODS/DESIGN: This multisite study utilizes a randomized, controlled experimental design with blinded assessors and investigators. Individuals who are 65 years of age and older are recruited from the community. Eligible participants who demonstrate intact cognitive status (Montreal Cognitive Assessment score > 25) are randomized and asked to complete 45 sessions of either a commercially available computerized-cognitive training program (UFOVT) or computerized games across 2.5 years. After three years, participants are screened for cognitive decline. For those demonstrating decline or who are part of a random subsample, a comprehensive neuropsychological assessment is completed. Those who perform below a pre-specified level are asked to complete a clinical evaluation, including an MRI, to ascertain clinical diagnosis of normal cognition, MCI, or dementia. Participants are asked to provide blood samples for analyses of Alzheimer's disease related biomarkers. DISCUSSION: The PACT study addresses the rapidly increasing prevalence of dementia. Computerized cognitive training may provide a non-pharmaceutical option for reducing incidence of MCI or dementia to improve public health. REGISTRATION: The PACT study is registered at http://Clinicaltrials.govNCT03848312.

Perceived Adequacy of Infection Control Practices and Symptoms of Anxiety Among In-Person Elementary School Educators in Ontario.

OBJECTIVE: The aim of this study was to examine the association between the perceived adequacy of infection control practices (ICPs) and symptoms of anxiety among educators in Ontario, Canada. METHODS: Data from 4947 educators were collected in December 2020. Modified Poisson models assessed the association between adequacy of ICPs and moderate or severe anxiety symptoms, adjusting for a range of covariates. RESULTS: Approximately 60% of respondents reported moderate or severe anxiety symptoms. Two-thirds (66.5%) of the sample had less than half of their ICP needs met. Respondents with less than half their ICP needs met were more than three times more likely to have moderate or severe anxiety, compared with respondents with their ICP needs met. CONCLUSION: Findings highlight the importance of adequate administrative and engineering controls in schools, not only to minimize risk of infection, but also for educator's mental health.

Understanding Clinical Progression of Late-Life Depression to Alzheimer's Disease Over 5 Years with Structural MRI.

Previous studies have shown that late-life depression (LLD) may be a precursor of neurodegenerative diseases and may increase the risk of dementia. At present, the pathological relationship between LLD and dementia, in particularly Alzheimer's disease (AD) is unclear. Structural MRI (sMRI) can provide objective biomarkers for the computer-aided diagnosis of LLD and AD, providing a promising solution to understand the clinical progression of brain disorders. But few studies have focused on sMRI-based predictive analysis of clinical progression from LLD to AD. In this paper, we develop a deep learning method to predict the clinical progression of LLD to AD up to 5 years after baseline time using T1-weighted structural MRIs. We also analyze several important factors that limit the diagnostic performance of learning-based methods, including data imbalance, small-sample-size, and multi-site data heterogeneity, by leveraging a relatively large-scale database to aid model training. Experimental results on 308 subjects with sMRIs acquired from 2 imaging sites and the publicly available ADNI database demonstrate the potential of deep learning in predicting the clinical progression of LLD to AD. To the best of our knowledge, this is among the first attempts to explore the complex pathophysiological relationship between LLD and AD based on structural MRI using a deep learning method.

The Demand-Control Model as a Predictor of Depressive Symptoms-Interaction and Differential Subscale Effects: Prospective Analyses of 2212 German Employees.

Testing assumptions of the widely used demand-control (DC) model in occupational psychosocial epidemiology, we investigated (a) interaction, i.e., whether the combined effect of low job control and high psychological demands on depressive symptoms was stronger than the sum of their single effects (i.e., superadditivity) and (b) whether subscales of psychological demands and job control had similar associations with depressive symptoms. Logistic longitudinal regression analyses of the 5-year cohort of the German Study of Mental Health at Work (S-MGA) 2011/12-2017 of 2212 employees were conducted. The observed combined effect of low job control and high psychological demands on depressive symptoms did not indicate interaction (RERI = -0.26, 95% CI = -0.91; 0.40). When dichotomizing subscales at the median, differential effects of subscales were not found. When dividing subscales into categories based on value ranges, differential effects for job control subscales (namely, decision authority and skill discretion) were found (p = 0.04). This study does not support all assumptions of the DC model: (1) it corroborates previous studies not finding an interaction of psychological demands and job control; and (2) signs of differential subscale effects were found regarding job control. Too few prospective studies have been carried out regarding differential subscale effects.

The impact of the COVID-19 pandemic on mental health of nurses in British Columbia, Canada using trends analysis across three time points.

PURPOSE: This study examined trends over time in the prevalence of anxiety and depression among Canadian nurses: 6 months before, 1-month after, and 3 months after COVID-19 was declared a pandemic. METHODS: This study adopted a repeated cross-sectional design and surveyed unionized nurses in British Columbia (BC), Canada on three occasions: September 2019 (Time 1, prepandemic), April 2020 (Time 2, early-pandemic) and June 2020 (Time 3). RESULTS: A total of 10,117 responses were collected across three timepoints. This study found a significant increase of 10% to 15% in anxiety and depression between Time 1 and 2, and relative stability between Time 2 and 3, with Time 3 levels still higher than Time 1 levels. Cross-sector analyses showed similar patterns of findings for acute care and community nurses. Long-term care nurses showed a two-fold increase in the prevalence of anxiety early pandemic, followed by a sharper decline mid pandemic. CONCLUSIONS: COVID-19 has had short- and mid-term mental health implications for BC nurses particularly among those in the long-term care sector. Future research should evaluate the impact of COVID-19 on the mental health of health workers in different contexts, such as jurisdictional analyses, and better understand the long-term health and labor market consequences of elevated mental health symptoms over an extended time period.

Cerebral white matter connectivity, cognition, and age-related macular degeneration.

Age-related macular degeneration (AMD) is a common retina disease associated with cognitive impairment in older adults. The mechanism(s) that account for the link between AMD and cognitive decline remain unclear. Here we aim to shed light on this issue by investigating whether relationships between cognition and white matter in the brain differ by AMD status. In a direct group comparison of brain connectometry maps from diffusion weighted images, AMD patients showed significantly weaker quantitative anisotropy (QA) than healthy controls, predominantly in the splenium and left optic radiation. The QA of these tracts, however, did not correlate with the visual acuity measure, indicating that this group effect is not directly driven by visual loss. The AMD and control groups did not differ significantly in cognitive performance.Across all participants, better cognitive performance (e.g. verbal fluency) is associated with stronger connectivity strength in white matter tracts including the splenium and the left inferior fronto-occipital fasciculus/inferior longitudinal fasciculus. However, there were significant interactions between group and cognitive performance (verbal fluency, memory), suggesting that the relation between QA and cognitive performance was weaker in AMD patients than in controls.This may be explained by unmeasured determinants of performance that are more common or impactful in AMD or by a recruitment bias whereby the AMD group had higher cognitive reserve. In general, our findings suggest that neural degeneration in the brain might occur in parallel to AMD in the eyes, although the participants studied here do not (yet) exhibit overt cognitive declines per standard assessments.

Psychosocial considerations in the assessment of hand transplant candidates: A single-center experience and brief literature review.

Vascularized composite allograft, including hand transplantation (HT), has gained wider usage as a reconstructive treatment over the past 30 years. HT recipients face unique psychosocial challenges compared to their solid organ and/or bone marrow transplant counterparts. Accordingly, the psychosocial evaluation among HT candidates continues to evolve, leaving a lack of consensus as to the critical psychosocial domains and psychometric testing instruments to help evaluate individuals considering HT. The present manuscript describes the psychosocial evaluation process within the Duke HT program, which been contacted by 80 potential candidates since 2014. The Duke HT evaluation process incorporates a comprehensive psychosocial assessment within domains including personality, cognitive function, mood, behavioral adherence, social support, and substance use history, among others. Our experience underscores the potential utility of collecting thorough psychosocial evaluations, supplemented by psychometric test data, to comprehensively assess potential HT candidates.

Slower information processing speed is associated with persistent burnout symptoms but not depression symptoms in nursing workers.

Introduction: Burnout and depression both occur with chronic work-related stress, and cognitive deficits have been found when symptom severity results in work disability. Less is known about cognitive deficits associated with milder symptoms among active workers, and few studies have examined whether cognitive deficits predict persistent burnout and depression symptoms. The goal of this study was to examine the association of information processing speed and executive function performance to burnout and depression symptoms at baseline and 12-month follow-up in a sample of actively working individuals (N = 372).Method: The design was prospective with laboratory cognitive data at baseline, and burnout and depressive symptoms assessed at baseline and monthly follow-ups. Information processing speed and executive functions were assessed in a task-switching paradigm, including single-task reaction time (RT), switching costs, and mixing costs. Burnout was assessed with the Exhaustion subscale of the Oldenburg Burnout Inventory and depression with the Patient Health Questionnaire-9.Results: Slower RT was modestly associated with higher levels of burnout symptoms both cross-sectionally and prospectively, but switching costs and mixing costs were not associated with burnout symptoms. None of the cognitive measures were associated with depression symptoms cross-sectionally or prospectively.Conclusions: Despite statistically significant findings of slowed RT in acute exhaustion-related burnout, the proportion of variance accounted for in the models was small and did not predict clinically significant levels of distress. The absence of statistically significant findings for depression symptoms suggests the cognitive profile associated with the exhaustion dimension of burnout may be distinct from that of depression, which reflects a more heterogeneous symptomatology. Our data suggest the clinical impact of burnout symptoms on actively working individuals is marginal; nonetheless, it is important to screen and intervene on burnout and depression symptoms in the workplace because they can lead to other forms of work impairment.

Video-Enhanced Care Management for Medically Complex Older Adults with Cognitive Impairment.

OBJECTIVE: This pilot study assessed feasibility of video-enhanced care management for complex older veterans with suspected mild cognitive impairment (CI) and their care partners, compared with telephone delivery. DESIGN: Pilot randomized controlled trial. SETTING: Durham Veterans Affairs Health Care System. PARTICIPANTS: Participants were enrolled as dyads, consisting of veterans aged 65 years or older with complex medical conditions (Care Assessment Need score ≥90) and suspected mild CI (education-adjusted Modified Telephone Interview for Cognitive Status score 20-31) and their care partners. INTERVENTION: The 12-week care management intervention consisted of monthly calls from a study nurse covering medication management, cardiovascular disease risk reduction, physical activity, and sleep behaviors, delivered via video compared with telephone. MEASUREMENTS: Dyads completed baseline and follow-up assessments to assess feasibility, acceptability, and usability. RESULTS: Forty veterans (mean (standard deviation (SD)) age = 72.4 (6.1) years; 100% male; 37.5% Black) and their care partners (mean (SD) age = 64.7 (10.8) years) were enrolled and randomized to telephone or video-enhanced care management. About a third of veteran participants indicated familiarity with relevant technology (regular tablet use and/or experience with videoconferencing); 53.6% of internet users were comfortable or very comfortable using the internet. Overall, 43 (71.7%) care management calls were completed in the video arm and 52 (86.7%) were completed in the telephone arm. Usability of the video telehealth platform was rated higher for participants already familiar with technology used to deliver the intervention (mean (SD) System Usability Scale scores: 65.0 (17.0) vs 55.6 (19.6)). Veterans, care partners, and study nurses reported greater engagement, communication, and interaction in the video arm. CONCLUSION: Video-delivered care management calls were feasible and preferred over telephone for some complex older adults with mild CI and their care partners. Future research should focus on understanding how to assess and incorporate patient and family preferences related to uptake and maintenance of video telehealth interventions.

The Association between the Perceived Adequacy of Workplace Infection Control Procedures and Personal Protective Equipment with Mental Health Symptoms: A Cross-sectional Survey of Canadian Health-care Workers during the COVID-19 Pandemic: L'association entre le caractère adéquat perçu des procédures de contrôle des infections au travail et de l'équipement de protection personnel pour les symptômes de santé mentale. Un sondage transversal des travailleurs de la santé canadiens durant la pandémie COVID-19.

OBJECTIVES: To examine the relationship between perceived adequacy of personal protective equipment (PPE) and workplace-based infection control procedures (ICP) and mental health symptoms among a sample of health-care workers in Canada within the context of the current COVID-19 pandemic. METHODS: A convenience-based internet survey of health-care workers in Canada was facilitated through various labor organizations between April 7 and May 13, 2020. A total of 7,298 respondents started the survey, of which 5,988 reported information on the main exposures and outcomes. Anxiety symptoms were assessed using the Generalized Anxiety Disorder (GAD-2) screener, and depression symptoms using the Patient Health Questionnaire (PHQ-2) screener. We assessed the perceived need and adequacy of 8 types of PPE and 10 different ICP. Regression analyses examined the proportion of GAD-2 and PHQ-2 scores of 3 and higher across levels of PPE and ICP, adjusted for a range of demographic, occupation, workplace, and COVID-19-specific measures. RESULTS: A total of 54.8% (95% confidence interval [CI], 53.5% to 56.1%) of the sample had GAD-2 scores of 3 and higher, and 42.3% (95% CI, 41.0% to 43.6%) of the sample had PHQ-2 scores of 3 and higher. Absolute differences of 18% (95% CI, 12% to 23%) and 17% (95% CI, 12% to 22%) were observed in the prevalence of GAD-2 scores of 3 and higher between workers whose perceived PPE needs and ICP needs were met compared to those who needs were not met. Differences of between 11% (95% CI, 6% to 17%) and 19% (95% CI, 14% to 24%) were observed in PHQ-2 scores of 3 and higher across these same PPE and ICP categories. CONCLUSIONS: Our results suggest strengthening employer-based infection control strategies likely has important implications for the mental health symptoms among health-care workers in Canada.

Labour Market Attachment, Workplace Infection Control Procedures and Mental Health: A Cross-Sectional Survey of Canadian Non-healthcare Workers during the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic has led to large proportions of the labour market moving to remote work, while others have become unemployed. Those still at their physical workplace likely face increased risk of infection, compared to other workers. The objective of this paper is to understand the relationship between working arrangements, infection control programs (ICP), and symptoms of anxiety and depression among Canadian workers, not specifically working in healthcare. METHODS: A convenience-based internet survey of Canadian non-healthcare workers was facilitated through various labour organizations between April 26 and June 6, 2020. A total of 5180 respondents started the survey, of which 3779 were assessed as employed in a full-time or part-time capacity on 2 March 2020 (prior to large-scale COVID-19 pandemic responses in Canada). Of this sample, 3305 (87.5%) had complete information on main exposures and outcomes. Anxiety symptoms were measured using the Generalised Anxiety Disorder screener (GAD-2), and depressive symptoms using the Patient Health Questionnaire screener (PHQ-2). For workers at their physical workplace (site-based workers) we asked questions about the adequacy and implementation of 11 different types of ICP, and the adequacy and supply of eight different types of personal protective equipment (PPE). Respondents were classified as either: working remotely; site-based workers with 100% of their ICP/PPE needs met; site-based workers with 50-99% of ICP/PPE needs met; site-based workers with 1-49% of ICP/PPE needs met; site-based workers with none of ICP/PPE needs met; or no longer employed. Regression analyses examined the association between working arrangements and ICP/PPE adequacy and having GAD-2 and PHQ-2 scores of three and higher (a common screening point in both scales). Models were adjusted for a range of demographic, occupation, workplace, and COVID-19-specific factors. RESULTS: A total of 42.3% (95% CI: 40.6-44.0%) of the sample had GAD-2 scores of 3 and higher, and 34.6% (95% CI: 32.-36.2%) had PHQ-2 scores of 3 and higher. In initial analyses, symptoms of anxiety and depression were lowest among those working remotely (35.4 and 27.5%), compared to site-based workers (43.5 and 34.7%) and those who had lost their jobs (44.1 and 35.9%). When adequacy of ICP and PPE was taken into account, the lowest prevalence of anxiety and depressive symptoms was observed among site-based workers with all of their ICP needs being met (29.8% prevalence for GAD-2 scores of 3 and higher, and 23.0% prevalence for PHQ-2 scores of 3 and higher), while the highest prevalence was observed among site-based workers with none of their ICP needs being met (52.3% for GAD-2 scores of 3 and higher, and 45.8% for PHQ-2 scores of 3 and higher). CONCLUSION: Our results suggest that the adequate design and implementation of employer-based ICP have implications for the mental health of site-based workers. As economies re-open the ongoing assessment of ICP and associated mental health outcomes among the workforce is warranted.

The Interaction of Personality and Social Support on Prospective Suicidal Ideation in Men and Women With Late-Life Depression.

OBJECTIVE: Evidence suggests a cross-sectional association between personality traits and suicidal ideation in LLD. Yet, it is unclear how personality may influence suicidal ideation over time in LLD, or whether such an association would be moderated by psychosocial and biological individual differences. The present study had three aims: 1) to examine whether personality traits increase suicidal ideation in LLD over time, 2) to understand whether this relationship is influenced by subjective social support, and 3) to determine whether the potential relationship between social support, personality, and suicidal ideation is different for men and women. DESIGN: Participants were enrolled in the Duke University Neurocognitive Outcomes of Depression in the Elderly (NCODE), a longitudinal investigation of the predictors of poor illness course in LLD. Patients were initially enrolled in the NCODE study between December 1994 and June 2000 and were followed for an average of six years. SETTING: NCODE operates in a naturalistic treatment milieu. PARTICIPANTS: One hundred twelve participants aged 60 and older with a current diagnosis of major depressive disorder. MEASUREMENTS: Annual assessments of depression, suicidal ideation, and social support (measured with the Duke Social Support Index). Participants also completed the NEO Personality Inventory-Revised (NEO-PI-R) providing measures of the five major personality dimensions (neuroticism, extraversion, openness, conscientiousness, and agreeableness). RESULTS: Univariate logistic generalized linear mixed modeling (GLMM) analyses revealed that higher levels of depression at baseline, less subjective social support, higher neuroticism, and lower extraversion were significantly associated with an increased likelihood of suicidal ideation over time. While the relationship between these dimensions and suicidal ideation were no longer significant in multivariate analyses, there was a significant moderating effect of social support on the association between suicidal ideation and certain neuroticism and extraversion personality facets. Decreased subjective social support was associated with an increased likelihood of suicidal ideation in LLD patients with high (but not low) impulsiveness and low (but not high) gregariousness and positive emotions. Across all models, social support was beneficial to women, but not men, in decreasing the likelihood of future suicidal ideation. CONCLUSION: Changes in social support may contribute to suicidal ideation in older depressed adults with certain personality traits. Irrespective of personality traits, changes in social support had a significant effect on the suicidal ideation of women but not men. These relationships were apparent even when controlling for depression severity, age, and history of suicide attempt.