The influence of economic policies on social environments and mental health.

Despite increased advocacy and investments in mental health systems globally, there has been limited progress in reducing mental disorder prevalence. In this paper, we argue that meaningful advancements in population mental health necessitate addressing the fundamental sources of shared distress. Using a systems perspective, economic structures and policies are identified as the potential cause of causes of mental ill-health. Neoliberal ideologies, prioritizing economic optimization and continuous growth, contribute to the promotion of individualism, job insecurity, increasing demands on workers, parental stress, social disconnection and a broad range of manifestations well-recognized to erode mental health. We emphasize the need for mental health researchers and advocates to increasingly engage with the economic policy discourse to draw attention to mental health and well-being implications. We call for a shift towards a well-being economy to better align commercial interests with collective well-being and social prosperity. The involvement of individuals with lived mental ill-health experiences, practitioners and researchers is needed to mobilize communities for change and influence economic policies to safeguard well-being. Additionally, we call for the establishment of national mental wealth observatories to inform coordinated health, social and economic policies and realize the transition to a more sustainable well-being economy that offers promise for progress on population mental health outcomes.

Malgré une meilleure sensibilisation et des investissements accrus dans les systèmes de santé mentale à travers le monde, les progrès en matière de réduction du degré de prévalence des troubles mentaux demeurent très limités. Dans le présent document, nous estimons que, pour réaliser des avancées au niveau de la santé mentale des populations, il est impératif de s'attaquer aux sources de cette détresse collective. En adoptant une perspective systémique, force est de constater que les politiques et structures économiques constituent les causes potentielles d'une mauvaise santé mentale. Les idéologies néolibérales, qui privilégient l'optimisation économique et la croissance ininterrompue, contribuent à promouvoir l'individualisme, l'insécurité professionnelle, la pression pesant sur les travailleurs, le stress parental, l'isolement social et un large éventail de facteurs associés à une dégradation de la santé mentale. Nous insistons sur la nécessité de faire appel à des chercheurs et défenseurs actifs dans ce domaine, afin de jouer un rôle dans la politique économique en attirant l'attention sur les implications pour le bien-être et la santé mentale. Nous plaidons pour une transition vers une économie du bien-être visant à rapprocher les intérêts commerciaux de la prospérité sociale et collective. L'intervention de personnes ayant été confrontées à des troubles mentaux, de praticiens et de chercheurs est nécessaire pour mobiliser les communautés en faveur d'un changement et influencer les politiques économiques pour préserver le bien-être. Par ailleurs, nous militons pour la création d'observatoires nationaux de la santé mentale qui serviront à orienter des politiques économiques, sociales et sanitaires coordonnées, mais aussi à favoriser l'évolution vers une économie du bien-être plus durable, laissant entrevoir une amélioration de la santé mentale au sein de la population.

A pesar del aumento de la promoción y las inversiones en sistemas de salud mental en todo el mundo, los avances en la reducción de la prevalencia de los trastornos mentales han sido limitados. En este documento, sostenemos que para lograr avances significativos en la salud mental de la población es necesario abordar las fuentes fundamentales de la angustia compartida. Mediante una perspectiva sistémica, las estructuras y políticas económicas se identifican como la posible causa de los problemas de salud mental. Las ideologías neoliberales, que priorizan la optimización económica y el crecimiento continuo, contribuyen al fomento del individualismo, la inseguridad laboral, el aumento de las exigencias a los trabajadores, el estrés parental, la desconexión social y una gran variedad de manifestaciones bien reconocidas que perjudican la salud mental. Insistimos en la necesidad de que los investigadores y los defensores de la salud mental se impliquen cada vez más en el discurso de la política económica para atraer la atención sobre las implicaciones para la salud mental y el bienestar. Pedimos un cambio hacia una economía del bienestar para alinear mejor los intereses comerciales con el bienestar colectivo y la prosperidad social. Para movilizar a las comunidades en favor del cambio e influir en las políticas económicas con el fin de salvaguardar el bienestar, es necesaria la participación de personas que han padecido enfermedades mentales, profesionales e investigadores. Además, pedimos la creación de observatorios nacionales de bienestar mental que sirvan de base a las políticas sanitarias, sociales y económicas coordinadas y permitan la transición a una economía del bienestar más sostenible, que ofrezca perspectivas de progreso en los resultados de salud mental de la población.

Sex-specific blood biomarkers linked to memory changes in middle-aged adults: The Framingham Heart Study.

The relationship between sex-specific blood biomarkers and memory changes in middle-aged adults remains unclear. We aimed to investigate this relationship using the data from the Framingham Heart Study (FHS). We conducted association analysis, partial correlation analysis, and causal dose-response curves using blood biomarkers and other data from 793 middle-aged participants (≤ 60 years) from the FHS Offspring Cohort. The results revealed associations of adiponectin and fasting blood glucose with midlife memory change, along with a U-shaped relationship of high-density lipoprotein cholesterol with memory change. No significant associations were found for the other blood biomarkers (e.g., amyloid beta protein 42) with memory change. To our knowledge, this is the first sex-specific network analysis of blood biomarkers related to midlife memory change in a prospective cohort study. Our findings highlight the importance of targeting cardiometabolic risks and the need to validate midlife-specific biomarkers that can accelerate the development of primary preventive strategies.

Deep learning-aided decision support for diagnosis of skin disease across skin tones.

Although advances in deep learning systems for image-based medical diagnosis demonstrate their potential to augment clinical decision-making, the effectiveness of physician-machine partnerships remains an open question, in part because physicians and algorithms are both susceptible to systematic errors, especially for diagnosis of underrepresented populations. Here we present results from a large-scale digital experiment involving board-certified dermatologists (n = 389) and primary-care physicians (n = 459) from 39 countries to evaluate the accuracy of diagnoses submitted by physicians in a store-and-forward teledermatology simulation. In this experiment, physicians were presented with 364 images spanning 46 skin diseases and asked to submit up to four differential diagnoses. Specialists and generalists achieved diagnostic accuracies of 38% and 19%, respectively, but both specialists and generalists were four percentage points less accurate for the diagnosis of images of dark skin as compared to light skin. Fair deep learning system decision support improved the diagnostic accuracy of both specialists and generalists by more than 33%, but exacerbated the gap in the diagnostic accuracy of generalists across skin tones. These results demonstrate that well-designed physician-machine partnerships can enhance the diagnostic accuracy of physicians, illustrating that success in improving overall diagnostic accuracy does not necessarily address bias.

Individual bioenergetic capacity as a potential source of resilience to Alzheimer's disease.

Impaired glucose uptake in the brain is one of the earliest presymptomatic manifestations of Alzheimer's disease (AD). The absence of symptoms for extended periods of time suggests that compensatory metabolic mechanisms can provide resilience. Here, we introduce the concept of a systemic 'bioenergetic capacity' as the innate ability to maintain energy homeostasis under pathological conditions, potentially serving as such a compensatory mechanism. We argue that fasting blood acylcarnitine profiles provide an approximate peripheral measure for this capacity that mirrors bioenergetic dysregulation in the brain. Using unsupervised subgroup identification, we show that fasting serum acylcarnitine profiles of participants from the AD Neuroimaging Initiative yields bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. To assess the potential clinical relevance of this finding, we examined factors that may offer diagnostic and therapeutic opportunities. First, we identified a genotype affecting the bioenergetic capacity which was linked to succinylcarnitine metabolism and significantly modulated the rate of future cognitive decline. Second, a potentially modifiable influence of beta-oxidation efficiency seemed to decelerate bioenergetic aging and disease progression. Our findings, which are supported by data from more than 9,000 individuals, suggest that interventions tailored to enhance energetic health and to slow bioenergetic aging could mitigate the risk of symptomatic AD, especially in individuals with specific mitochondrial genotypes.

Identifying a stable and generalizable factor structure of major depressive disorder across three large longitudinal cohorts.

The Patient Health Questionnaire 9 (PHQ-9) is the current standard outpatient screening tool for measuring and tracking the nine symptoms of major depressive disorder (MDD). While the PHQ-9 was originally conceptualized as a unidimensional measure, it has become clear that MDD is not a monolithic construct, as evidenced by high comorbidities with other theoretically distinct diagnoses and common symptom overlap between depression and other diagnoses. Therefore, identifying reliable and temporally stable subfactors of depressive symptoms could allow research and care to be tailored to different depression phenotypes. This study improved on previous factor analysis studies of the PHQ-9 by leveraging samples that were clinical (participants with depression only), large (N = 1483 depressed individuals in total), longitudinal (up to 5 years), and from three diverse (matching racial distribution of the United States) datasets. By refraining from assuming the number of factors or item loadings a priori, and thus utilizing a solely data-driven approach, we identified a ranked list of best-fitting models, with the parsimonious one achieving good model fit across studies at most timepoints (average TLI >= 0.90). This model categorizes the PHQ-9 items into four factors: (1) Affective (Anhedonia + Depressed Mood), (2) Somatic (Sleep + Fatigue + Appetite), (3) Internalizing (Worth/Guilt + Suicidality), (4) Sensorimotor (Concentration + Psychomotor), which may be used to further precision psychiatry by testing factor-specific interventions in research and clinical settings.

MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum.

Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques can detect complex patterns in MRI data and have potential for noninvasive characterization of ATN status. Purpose To use deep learning to predict PET-determined ATN biomarker status using MRI and readily available diagnostic data. Materials and Methods MRI and PET data were retrospectively collected from the Alzheimer's Disease Imaging Initiative. PET scans were paired with MRI scans acquired within 30 days, from August 2005 to September 2020. Pairs were randomly split into subsets as follows: 70% for training, 10% for validation, and 20% for final testing. A bimodal Gaussian mixture model was used to threshold PET scans into positive and negative labels. MRI data were fed into a convolutional neural network to generate imaging features. These features were combined in a logistic regression model with patient demographics, APOE gene status, cognitive scores, hippocampal volumes, and clinical diagnoses to classify each ATN biomarker component as positive or negative. Area under the receiver operating characteristic curve (AUC) analysis was used for model evaluation. Feature importance was derived from model coefficients and gradients. Results There were 2099 amyloid (mean patient age, 75 years ± 10 [SD]; 1110 male), 557 tau (mean patient age, 75 years ± 7; 280 male), and 2768 FDG PET (mean patient age, 75 years ± 7; 1645 male) and MRI pairs. Model AUCs for the test set were as follows: amyloid, 0.79 (95% CI: 0.74, 0.83); tau, 0.73 (95% CI: 0.58, 0.86); and neurodegeneration, 0.86 (95% CI: 0.83, 0.89). Within the networks, high gradients were present in key temporal, parietal, frontal, and occipital cortical regions. Model coefficients for cognitive scores, hippocampal volumes, and APOE status were highest. Conclusion A deep learning algorithm predicted each component of PET-determined ATN status with acceptable to excellent efficacy using MRI and other available diagnostic data. © RSNA, 2023 Supplemental material is available for this article.

Enhancing social connectedness with companion robots using AI.

Companion robots with AI may usher a new science of social connectedness that requires the development of ethical frameworks.

Expanding the scope of health disparities research in Alzheimer's disease and related dementias: Recommendations from the "Leveraging Existing Data and Analytic Methods for Health Disparities Research Related to Aging and Alzheimer's Disease and Related Dementias" Workshop Series.

Topics discussed at the "Leveraging Existing Data and Analytic Methods for Health Disparities Research Related to Aging and Alzheimer's Disease and Related Dementias" workshop, held by Duke University and the Alzheimer's Association with support from the National Institute on Aging, are summarized.  Ways in which existing data resources paired with innovative applications of both novel and well-known methodologies can be used to identify the effects of multi-level societal, community, and individual determinants of race/ethnicity, sex, and geography-related health disparities in Alzheimer's disease and related dementia are proposed.  Current literature on the population analyses of these health disparities is summarized with a focus on identifying existing gaps in knowledge, and ways to mitigate these gaps using data/method combinations are discussed at the workshop.  Substantive and methodological directions of future research capable of advancing health disparities research related to aging are formulated.

Impact of Computerized Cognitive Training on Default Mode Network Connectivity in Subjects at Risk for Alzheimer's Disease: A 78-week Randomized Controlled Trial.

BACKGROUND: Mild cognitive impairment (MCI) represents a high risk group for Alzheimer's disease (AD). Computerized Cognitive Games Training (CCT) is an investigational strategy to improve targeted functions in MCI through the modulation of cognitive networks. OBJECTIVE: The goal of this study was to examine the effect of CCT versus a non-targeted active brain exercise on functional cognitive networks. METHODS: 107 patients with MCI were randomized to CCT or web-based crossword puzzles. Resting-state functional MRI (fMRI) was obtained at baseline and 18 months to evaluate differences in fMRI measured within- and between-network functional connectivity (FC) of the default mode network (DMN) and other large-scale brain networks: the executive control, salience, and sensorimotor networks. RESULTS: There were no differences between crosswords and games in the primary outcome, within-network DMN FC across all subjects. However, secondary analyses suggest differential effects on between-network connectivity involving the DMN and SLN, and within-network connectivity of the DMN in subjects with late MCI. Paradoxically, in both cases, there was a decrease in FC for games and an increase for the crosswords control (p < 0.05), accompanied by lesser cognitive decline in the crosswords group. CONCLUSION: Results do not support a differential impact on within-network DMN FC between games and crossword puzzle interventions. However, crossword puzzles might result in cognitively beneficial remodeling between the DMN and other networks in more severely impaired MCI subjects, parallel to the observed clinical benefits.

Cerebral venous biomarkers and veno-arterial gradients: untapped resources in Alzheimer's disease.

Blood based biomarkers (BBB) derived from forearm veins for estimating brain changes is becoming ubiquitous in Alzheimer's Disease (AD) research and could soon become standard in routine clinical diagnosis. However, there are many peripheral sources of contamination through which concentrations of these metabolites can be raised or lowered after leaving the brain and entering the central venous pool. This raises the issue of potential false conclusions that could lead to erroneous diagnosis or research findings. We propose the use of simultaneous sampling of internal jugular venous and arterial blood to calculate veno-arterial gradient, which can reveal either a surplus or a deficit of metabolites exiting the brain. Methods for sampling internal jugular venous and arterial blood are described along with examples of the use of the veno-arterial gradient in non-AD brain research. Such methods in turn could help better establish the accuracy of forearm venous biomarkers.

Models of depressive pseudoamnestic disorder.

Objective: Little effort has been made in the past to validate depressive pseudodementia based on hypothesis-driven approaches. We extended this concept to individuals with amnestic Mild Cognitive Impairment and Major Depression, that is, pseudodepressive amnestic disorder. We tested two hypotheses consistent with the presentations and mechanisms associated with this potential syndrome: improvements in cognition would be significantly correlated with improvements in depression after treatment (Hypothesis 1), and if not confirmed, the presence of such an association could be identified once moderator variables were taken into account (Hypothesis 2). Methods: Within a clinical trial, 61 individuals received open label serotonin reuptake inhibitor (citalopram or venlafaxine) treatment over a 16-week period. Selective Reminding Test and Hamilton Depression scale were conducted serially to measure change in memory and depression, respectively. Magnetic resonance imaging, other cognitive measures (Alzheimer's Disease Assessment Scale-Cognitive and speed of processing tests), and additional depression measure (Beck Depression Inventory [BDI]) were also administered. Results: No significant associations between improvement in depression and improvement in cognition were observed. Sensitivity analyses with other cognitive measures, the BDI, and exclusion of possible "placebo" responders were negative as well. There were no significant moderation effects for baseline Hamilton Rating Scale for Depression as a measure of symptom severity or age. APOE ε4 genotype and white matter hyperintensity burden yielded counter-intuitive, albeit marginally significant results. Conclusions: Negative findings cast doubt on the frequency of depressive pseudoamnestic disorder in older populations with documented depression and memory impairments.

Pre-exposure cognitive performance variability is associated with severity of respiratory infection.

Using data from a longitudinal viral challenge study, we find that the post-exposure viral shedding and symptom severity are associated with a novel measure of pre-exposure cognitive performance variability (CPV), defined before viral exposure occurs. Each individual's CPV score is computed from data collected from a repeated NeuroCognitive Performance Test (NCPT) over a 3 day pre-exposure period. Of the 18 NCPT measures reported by the tests, 6 contribute materially to the CPV score, prospectively differentiating the high from the low shedders. Among these 6 are the 4 clinical measures digSym-time, digSym-correct, trail-time, and reaction-time, commonly used for assessing cognitive executive functioning. CPV is found to be correlated with stress and also with several genes previously reported to be associated with cognitive development and dysfunction. A perturbation study over the number and timing of NCPT sessions indicates that as few as 5 sessions is sufficient to maintain high association between the CPV score and viral shedding, as long as the timing of these sessions is balanced over the three pre-exposure days. Our results suggest that variations in cognitive function are closely related to immunity and susceptibility to severe infection. Further studying these relationships may help us better understand the links between neurocognitive and neuroimmune systems which is timely in this COVID-19 pandemic era.

Sex-specific biomarkers in Alzheimer's disease progression: Framingham Heart Study.

Background: Sex differences in Alzheimer's disease (AD) are not well understood. Methods: We performed sex-specific analyses of AD and annualized cognitive decline with clinical and blood biomarker data in participants 60+ years old in the community-based longitudinal Framingham Heart Study Offspring Cohort (n = 1398, mean age 68 years, 55% women). Results: During 11 years of follow-up, women were 96% more likely than men to be diagnosed with clinical AD dementia after adjusting for age and education in the younger age group 60 to 70 years (n = 946; 95% confidence interval [CI], 1.08 to 3.56) although not in the older age group (70+) (n = 452; hazard ratio = 0.98; 95% CI, 0.68 to 1.53). Sex-differences in incident AD rates decreased with increasing levels of education. The total contribution of the biomarkers to AD risk variance was 7.6% in women and 11.7% in men. One unit (pg/ml) lower plasma Aß42 was associated with 0.0095 unit faster memory decline in women (p = 0.0002) but not in men (p = 0.55) after adjusting for age and education. Discussion: Our study suggests that both early life and later-life pathological factors may contribute to potential sex differences in incident AD.

Physician perceptions of surveillance: Wearables, Apps, and Chatbots for COVID-19.

Background and Purpose: To characterize the global physician community's opinions on the use of digital tools for COVID-19 public health surveillance and self-surveillance. Materials and Methods: Cross-sectional, random, stratified survey done on Sermo, a physician networking platform, between September 9 and 15, 2020. We aimed to sample 1000 physicians divided among the USA, EU, and rest of the world. The survey questioned physicians on the risk-benefit ratio of digital tools, as well as matters of data privacy and trust. Statistical Analysis Used: Descriptive statistics examined physicians' characteristics and opinions by age group, gender, frontline status, and geographic region. ANOVA, t-test, and Chi-square tests with P < 0.05 were viewed as qualitatively different. As this was an exploratory study, we did not adjust for small cell sizes or multiplicity. We used JMP Pro 15 (SAS), as well as Protobi. Results: The survey was completed by 1004 physicians with a mean (standard deviation) age of 49.14 (12) years. Enthusiasm was highest for self-monitoring smartwatches (66%) and contact tracing apps (66%) and slightly lower (48-56%) for other tools. Trust was highest for health providers (68%) and lowest for technology companies (30%). Most respondents (69.8%) felt that loosening privacy standards to fight the pandemic would lead to misuse of privacy in the future. Conclusion: The survey provides foundational insights into how physicians think of surveillance.

Multi-Omic analyses characterize the ceramide/sphingomyelin pathway as a therapeutic target in Alzheimer's disease.

Dysregulation of sphingomyelin and ceramide metabolism have been implicated in Alzheimer's disease. Genome-wide and transcriptome-wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with Alzheimer's disease. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. We focus on the sphingolipid pathway and carry out multi-omics analyses to identify central and peripheral metabolic changes in Alzheimer's patients, correlating them to imaging features. Our multi-omics approach is based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes; (b) in silico metabolic flux analysis on context-specific metabolic networks identified differential reaction fluxes; (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in sphingomyelin pathway with Alzheimer's disease pathogenesis; (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in Alzheimer's; and (e) metabolite genome-wide association studies to define receptors within the pathway as a potential drug target. We validate our hypothesis in amyloidogenic APP/PS1 mice and show prolonged exposure to fingolimod alleviated synaptic plasticity and cognitive impairment in mice. Our integrative multi-omics approach identifies potential targets in the sphingomyelin pathway and suggests modulators of S1P metabolism as possible candidates for Alzheimer's disease treatment.

Data-driven causal model discovery and personalized prediction in Alzheimer's disease.

With the explosive growth of biomarker data in Alzheimer's disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model's parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer's Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.

Measuring, Modeling, and Forecasting the Mental Wealth of Nations.

The COVID-19 pandemic has exposed the deep links and fragility of economic, health and social systems. Discussions of reconstruction include renewed interest in moving beyond GDP and recognizing "human capital", "brain capital", "mental capital", and "wellbeing" as assets fundamental to economic reimagining, productivity, and prosperity. This paper describes how the conceptualization of Mental Wealth provides an important framing for measuring and shaping social and economic renewal to underpin healthy, productive, resilient, and thriving communities. We propose a transdisciplinary application of systems modeling to forecast a nation's Mental Wealth and understand the extent to which policy-mediated changes in economic, social, and health sectors could enhance collective mental health and wellbeing, social cohesion, and national prosperity. Specifically, simulation will allow comparison of the projected impacts of a range of cross-sector strategies (education sector, mental health system, labor market, and macroeconomic reforms) on GDP and national Mental Wealth, and provide decision support capability for future investments and actions to foster Mental Wealth. Finally, this paper introduces the Mental Wealth Initiative that is harnessing complex systems science to examine the interrelationships between social, commercial, and structural determinants of mental health and wellbeing, and working to empirically challenge the notion that fostering universal social prosperity is at odds with economic and commercial interests.

General Anxiety Disorder-7 Questionnaire as a marker of low socioeconomic status and inequity.

BACKGROUND: The General Anxiety Disorder-7 (GAD-7) questionnaire is a standard tool used for screening and follow-up of patients with Generalized Anxiety Disorder (GAD). Although it is generally accepted that anxiety correlates with clinical and psychosocial stressors, precise quantitative data is limited on the relations among GAD-7, traditional biomarkers, and other measures of health. Further research is needed about how GAD-7 relates to race, ethnicity, and socioeconomic status (SES) as an assembly. We determined how multiple demographic and socioeconomic data correlate with the participants' GAD-7 results when compared with laboratory, physical function, clinical, and other biological markers. METHODS: The Project Baseline Health Study (BHS) is a prospective cohort of adults representing several populations in the USA. We analyzed a deeply phenotyped group of 2502 participants from that study. Measures of interest included: clinical markers or history of medical diagnoses; physical function markers including gait, grip strength, balance time, daily steps, and echocardiographic parameters; psychometric measurements; activities of daily living; socioeconomic characteristics; and laboratory results. RESULTS: Higher GAD-7 scores were associated with female sex, younger age, and Hispanic ethnicity. Measures of low SES were also associated with higher scores, including unemployment, income ≤$25,000, and ≤12 years of education. After adjustment for 158 demographic, clinical, laboratory, and symptom characteristics, unemployment and overall higher SES risk scores were highly correlated with anxiety scores. Protective factors included Black race and older age. LIMITATIONS: Correlations identified in this cross-sectional study cannot be used to infer causal relationships; further, we were not able to account for possible use of anxiety treatments by study participants. CONCLUSIONS: These findings highlight the importance of understanding anxiety as a biopsychosocial entity. Clinicians and provider organizations need to consider both the physical manifestations of the disorder and their patients' social determinants of health when considering treatment pathways and designing interventions.