Detecting suicide risk among U.S. servicemembers and veterans: a deep learning approach using social media data.

BACKGROUND: Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of suicidal thoughts. We developed an algorithm to identify posts containing suicide-related content on a military-specific social media platform. METHODS: Publicly-shared social media posts (n = 8449) from a military-specific social media platform were reviewed and labeled by our team for the presence/absence of suicidal thoughts and behaviors and used to train several machine learning models to identify such posts. RESULTS: The best performing model was a deep learning (RoBERTa) model that incorporated post text and metadata and detected the presence of suicidal posts with relatively high sensitivity (0.85), specificity (0.96), precision (0.64), F1 score (0.73), and an area under the precision-recall curve of 0.84. Compared to non-suicidal posts, suicidal posts were more likely to contain explicit mentions of suicide, descriptions of risk factors (e.g. depression, PTSD) and help-seeking, and first-person singular pronouns. CONCLUSIONS: Our results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.

Trauma Center Hospitals Charged Higher Prices For Some Nontrauma Care Than Non-Trauma Center Hospitals, 2012-18.

Rising prices are a major cause of increased health care spending and health insurance premiums in the US. Hospital prices, specifically-for both inpatient and outpatient care-are the largest driver of rising health care spending in the commercial insurance market. As a result, policy makers and employers are increasingly interested in understanding the determinants of hospital prices. Hospitals serving as trauma centers are often endowed by regulators with monopoly power over trauma services in their geographic areas, and this monopoly power may spill over to nontrauma services. This study focused on the growing number of designated trauma centers and how trauma center status affects hospital prices for other, nontrauma services. We found that hospitals designated as trauma centers charged higher prices for nontrauma inpatient admissions and nontrauma emergency department visits when compared with hospitals that were not designated as trauma centers, even after controlling for potential confounders.

The evolutionary loss of the Eh1 motif in FoxE1 in the lineage of placental mammals.

Forkhead box E1 (FoxE1) protein is a transcriptional regulator known to play a major role in the development of the thyroid gland. By performing sequence alignments, we detected a deletion in FoxE1, which occurred in the evolution of mammals, near the point of divergence of placental mammals. This deletion led to the loss of the majority of the Eh1 motif, which was important for interactions with transcriptional corepressors. To investigate a potential mechanism for this deletion, we analyzed replication through the deletion area in mammalian cells with two-dimensional gel electrophoresis, and in vitro, using a primer extension reaction. We demonstrated that the area of the deletion presented an obstacle for replication in both assays. The exact position of polymerization arrest in primer extension indicated that it was most likely caused by a quadruplex DNA structure. The quadruplex structure hypothesis is also consistent with the exact borders of the deletion. The exact roles of these evolutionary changes in FoxE1 family proteins are still to be determined.

Digital bibliotherapy as a scalable intervention for suicidal thoughts: A randomized controlled trial.

OBJECTIVE: Suicide is a major public health concern in the United States, but few effective and scalable interventions exist to help those with suicidal thoughts. We hypothesized that reading first-person narratives about working through suicidal thoughts would reduce the desire to die among adults and that this effect would be mediated by increased perceived shared experience and optimism. METHOD: Using a randomized waitlist-controlled trial, we tested the effect of digital narrative-based bibliotherapy among 528 adults visiting a social media platform dedicated to providing mental health support. Participants were randomized to either a treatment condition (n = 266), in which they read one suicide narrative per day for 14 days or to a waitlist control condition (n = 262). The primary outcome was a measure of desire to die assessed daily for the 14-day trial period and at 2-week follow-up. RESULTS: Participants in the treatment condition reported lower desire to die than participants in the control condition during the 14-day trial period (ß = -0.26, p = .001) and at 2-week follow-up (t = -2.82, p = .005). Increased perceived shared experience (indirect effect b = -0.55, p < .001) and optimism (indirect effect b = -0.85, p < .001) mediated the effect of treatment on desire to die. CONCLUSIONS: Digital narrative-based bibliotherapy may be an effective intervention for those at risk for suicide, and may work in part by increasing feelings of perceived shared experience and optimism. Future research is needed to test the generalizability of these results to other platforms, groups, and conditions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The relationship between provider age and opioid prescribing behavior.

OBJECTIVES: The relationship between provider age and quality of care is theoretically indeterminate. Older providers are more experienced, which could lead to a positive relationship between age and quality, but providers' practice patterns could become outdated as technology and scientific knowledge change, which could lead to a negative relationship between age and quality. However, little work has investigated the provider age/quality relationship, and no work has investigated the relationship between provider age and opioid prescribing behavior. STUDY DESIGN: We analyze Medicare Part D data to investigate how opioid prescribing differs by provider age. METHODS: We use regression analysis to estimate the effect of provider age, holding other factors constant. RESULTS: We find that older providers prescribe significantly more opioids, with the gap between older and younger providers increasing from 2010 to 2015. CONCLUSIONS: Assuming that older physicians follow patterns of previous generations, anticipated retirement of older providers and entry by younger providers will tend to reduce opioid volumes, undoing at least in part the rapid increase since 2000.

Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers.

BACKGROUND: Interest in developing machine learning models that use electronic health record data to predict patients' risk of suicidal behavior has recently proliferated. However, whether and how such models might be implemented and useful in clinical practice remain unknown. To ultimately make automated suicide risk-prediction models useful in practice, and thus better prevent patient suicides, it is critical to partner with key stakeholders, including the frontline providers who will be using such tools, at each stage of the implementation process. OBJECTIVE: The aim of this focus group study is to inform ongoing and future efforts to deploy suicide risk-prediction models in clinical practice. The specific goals are to better understand hospital providers' current practices for assessing and managing suicide risk; determine providers' perspectives on using automated suicide risk-prediction models in practice; and identify barriers, facilitators, recommendations, and factors to consider. METHODS: We conducted 10 two-hour focus groups with a total of 40 providers from psychiatry, internal medicine and primary care, emergency medicine, and obstetrics and gynecology departments within an urban academic medical center. Audio recordings of open-ended group discussions were transcribed and coded for relevant and recurrent themes by 2 independent study staff members. All coded text was reviewed and discrepancies were resolved in consensus meetings with doctoral-level staff. RESULTS: Although most providers reported using standardized suicide risk assessment tools in their clinical practices, existing tools were commonly described as unhelpful and providers indicated dissatisfaction with current suicide risk assessment methods. Overall, providers' general attitudes toward the practical use of automated suicide risk-prediction models and corresponding clinical decision support tools were positive. Providers were especially interested in the potential to identify high-risk patients who might be missed by traditional screening methods. Some expressed skepticism about the potential usefulness of these models in routine care; specific barriers included concerns about liability, alert fatigue, and increased demand on the health care system. Key facilitators included presenting specific patient-level features contributing to risk scores, emphasizing changes in risk over time, and developing systematic clinical workflows and provider training. Participants also recommended considering risk-prediction windows, timing of alerts, who will have access to model predictions, and variability across treatment settings. CONCLUSIONS: Providers were dissatisfied with current suicide risk assessment methods and were open to the use of a machine learning-based risk-prediction system to inform clinical decision-making. They also raised multiple concerns about potential barriers to the usefulness of this approach and suggested several possible facilitators. Future efforts in this area will benefit from incorporating systematic qualitative feedback from providers, patients, administrators, and payers on the use of these new approaches in routine care, especially given the complex, sensitive, and unfortunately still stigmatized nature of suicide risk.

Widespread attenuating changes in brain connectivity associated with the general factor of psychopathology in 9- and 10-year olds.

Convergent research identifies a general factor ("P factor") that confers transdiagnostic risk for psychopathology. Large-scale networks are key organizational units of the human brain. However, studies of altered network connectivity patterns associated with the P factor are limited, especially in early adolescence when most mental disorders are first emerging. We studied 11,875 9- and 10-year olds from the Adolescent Brain and Cognitive Development (ABCD) study, of whom 6593 had high-quality resting-state scans. Network contingency analysis was used to identify altered interconnections associated with the P factor among 16 large-scale networks. These connectivity changes were then further characterized with quadrant analysis that quantified the directionality of P factor effects in relation to neurotypical patterns of positive versus negative connectivity across connections. The results showed that the P factor was associated with altered connectivity across 28 network cells (i.e., sets of connections linking pairs of networks); pPERMUTATION values < 0.05 FDR-corrected for multiple comparisons. Higher P factor scores were associated with hypoconnectivity within default network and hyperconnectivity between default network and multiple control networks. Among connections within these 28 significant cells, the P factor was predominantly associated with "attenuating" effects (67%; pPERMUTATION < 0.0002), i.e., reduced connectivity at neurotypically positive connections and increased connectivity at neurotypically negative connections. These results demonstrate that the general factor of psychopathology produces attenuating changes across multiple networks including default network, involved in spontaneous responses, and control networks involved in cognitive control. Moreover, they clarify mechanisms of transdiagnostic risk for psychopathology and invite further research into developmental causes of distributed attenuated connectivity.

It's quality and quantity: the effect of the amount of comments on online suicidal posts.

Every day, individuals post suicide notes on social media asking for support, resources, and reasons to live. Some posts receive few comments while others receive many. While prior studies have analyzed whether specific responses are more or less helpful, it is not clear if the quantity of comments received is beneficial in reducing symptoms or in keeping the user engaged with the platform and hence with life. In the present study, we create a large dataset of users' first r/SuicideWatch (SW) posts from Reddit (N=21,274), collect the comments as well as the user's subsequent posts (N=1,615,699) to determine whether they post in SW again in the future. We use propensity score stratification, a causal inference method for observational data, and estimate whether the amount of comments -as a measure of social support- increases or decreases the likelihood of posting again on SW. One hypothesis is that receiving more comments may decrease the likelihood of the user posting in SW in the future, either by reducing symptoms or because comments from untrained peers may be harmful. On the contrary, we find that receiving more comments increases the likelihood a user will post in SW again. We discuss how receiving more comments is helpful, not by permanently relieving symptoms since users make another SW post and their second posts have similar mentions of suicidal ideation, but rather by reinforcing users to seek support and remain engaged with the platform. Furthermore, since receiving only 1 comment -the most common case- decreases the likelihood of posting again by 14% on average depending on the time window, it is important to develop systems that encourage more commenting.

Increased Functional Segregation Related to the Salience Network in Unaffected Siblings of Youths With Attention-Deficit/Hyperactivity Disorder.

OBJECTIVE: Although there are frequent reports of shared neurofunctional and neurostructural alterations among probands with attention-deficit/hyperactivity disorder (ADHD) and their unaffected siblings, there is little knowledge regarding whether abnormalities in the resting-state functional connectivity of ADHD probands is also expressed in unaffected siblings, or whether this unaffected (but at-risk) cohort manifests distinct patterns. METHOD: We used a multivariate connectome-wide association study examining intrinsic functional connectivity with resting-state functional magnetic resonance imaging (MRI) in a sample (aged 8-17 years) of medication-naive ADHD probands (n = 56), their unaffected siblings (n = 55), and typically developing (TD) youths (n = 106). RESULTS: ADHD probands showed, relative to TD youths, increased connectivity between the default-mode network (DMN) and task-positive networks. Relative to ADHD and TD groups, respectively, unaffected siblings showed increased connectivity within the salience network and reduced connectivity between the DMN and salience network. No shared alterations in functional connectivity among ADHD probands and their unaffected siblings were identified. These findings were largely confirmed by complementary pairwise connectomic comparisons. However, the main connectivity differences between ADHD and unaffected siblings were not replicated in a tightly age- and sex-matched subsample (20 proband-sibling pairs and 60 TD youths). CONCLUSION: Our findings suggest that increased functional segregation related to the attention networks, especially the salience (ventral attention) system, may be a potential feature of at-risk siblings who remain unaffected by ADHD expression. Further replications are needed in other larger and sex-matched samples. CLINICAL TRIAL REGISTRATION INFORMATION: Structural and Functional Connectivity of Frontostriatal and Frontoparietal Networks as Endophenotypes of ADHD; https://clinicaltrials.gov/; NCT01682915.

Emotion expression and regulation in three cultures: Chinese, Japanese, and American preschoolers' reactions to disappointment.

There are strong cultural norms for how emotions are expressed, yet little is known about cultural variations in preschoolers' outward displays and regulation of disappointment. Chinese, Japanese, and American preschoolers' (N = 150) displays of emotion to an undesired gift were coded across both social and nonsocial contexts in a "disappointing gift" paradigm. Generalized estimating equations revealed that, regardless of culture, when children received a disappointing gift, they showed more positive expressions of emotion ("fake smile") in social contexts (in the presence of unfamiliar and familiar examiners) relative to when they were alone, suggesting that preschool-aged children are able to mask their disappointment with positive displays. However, children's emotion expressions varied across both cultures and contexts. American children were more positively and negatively expressive than Japanese children and were more negatively expressive than Chinese children. Chinese and Japanese preschoolers verbally reported more negative emotions but showed more neutral expressions than American preschoolers when receiving the disappointing gift. In addition, across different contexts of the task, there were subtle differences in how Chinese and Japanese children regulated their emotional expressions, with Chinese children showing similar levels of neutral expressions (e.g., "poker face") across different contexts in the task. Thus, our findings highlight the importance of understanding cultural meanings and practices underlying emotion development during early childhood.

Altered resting-state functional connectivity in adolescents is associated with PTSD symptoms and trauma exposure.

Alterations in resting-state functional connectivity (rsFC) have been demonstrated in Posttraumatic Stress Disorder (PTSD). However, such reports have primarily focused on adult participants, whereas findings in adolescents with PTSD are mixed and not entirely consistent with the adult literature. Here, we examined rsFC in a non-treatment seeking adolescent sample with posttraumatic stress symptoms (PTSS; n = 59) relative to asymptomatic controls (n = 226). We also examined differences between trauma-exposed and non-exposed control subgroups (TEC n = 73 and Non-TEC n = 153) to examine alterations associated with more general trauma exposure. Finally, we compared the PTSS and TEC groups, to confirm that the reported alterations in PTSS were not driven by trauma exposure. Using a seed-based approach, we examined connectivity of default-mode (DMN) and salience (SN) networks, where alterations have been previously reported. Results suggest that PTSS are associated with less within-DMN connectivity and greater SN-DMN connectivity, as well as altered connectivity with attention regions. Trauma exposure is associated with greater within-SN connectivity. Additionally, we report findings from exploratory connectome-based analysis, which demonstrate a number of topological alterations within DMN in the PTSS group. Overall, our findings replicate prior reports of altered rsFC in PTSD and extend them to non-treatment seeking, trauma-exposed adolescents, who did or did not report PTSS. They specifically highlight SN-DMN desegregation, lower within-DMN and greater within-SN connectivity, as well as altered connectivity with attention regions, in trauma-exposed adolescents. Future research is required to confirm that adolescents with diagnosed PTSD have similar/exacerbated connectivity patterns.

Questions and controversies in the study of time-varying functional connectivity in resting fMRI.

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

The effects of medicare advantage on opioid use.

Despite a vast literature on the determinants of prescription opioid use, the role of health insurance plans has received little attention. We study how the form of Medicare beneficiaries' drug coverage affects the volume of opioids they consume. We find that enrollment in Medicare Advantage, which integrates drug coverage with other medical benefits, significantly reduces beneficiaries' likelihood of filling an opioid prescription, as compared to enrollment in a stand-alone drug plan. Approximately half of this effect was due to fewer fills from prescribers who write a very large number of opioid prescriptions.

Does Multispecialty Practice Enhance Physician Market Power?

In health care, vertical integration - common ownership of producers of complementary services - may have both pro- and anti-competitive effects. We use data on 40 million commercially-insured individuals from the Health Care Cost Institute to construct price indices for office visits to general-practice and specialist physicians for the years 2008-2012. Controlling for generalist market concentration, we find that generalists charge higher prices when they are integrated with specialists, and that the effect of integration is larger in more concentrated specialist markets. Conversely, controlling for specialist market concentration, specialists charge higher prices when integrated with generalists, with larger effects in more concentrated generalist markets. Our results suggest that multispecialty practice enhances physician market power.

Resting state network modularity along the prodromal late onset Alzheimer's disease continuum.

Alzheimer's disease is considered a disconnection syndrome, motivating the use of brain network measures to detect changes in whole-brain resting state functional connectivity (FC). We investigated changes in FC within and among resting state networks (RSN) across four different stages in the Alzheimer's disease continuum. FC changes were examined in two independent cohorts of individuals (84 and 58 individuals, respectively) each comprising control, subjective cognitive decline, mild cognitive impairment and Alzheimer's dementia groups. For each participant, FC was computed as a matrix of Pearson correlations between pairs of time series from 278 gray matter brain regions. We determined significant differences in FC modular organization with two distinct approaches, network contingency analysis and multiresolution consensus clustering. Network contingency analysis identified RSN sub-blocks that differed significantly across clinical groups. Multiresolution consensus clustering identified differences in the stability of modules across multiple spatial scales. Significant modules were further tested for statistical association with memory and executive function cognitive domain scores. Across both analytic approaches and in both participant cohorts, the findings converged on a pattern of FC that varied systematically with diagnosis within the frontoparietal network (FP) and between the FP network and default mode network (DMN). Disturbances of modular organization were manifest as greater internal coherence of the FP network and stronger coupling between FP and DMN, resulting in less segregation of these two networks. Our findings suggest that the pattern of interactions within and between specific RSNs offers new insight into the functional disruption that occurs across the Alzheimer's disease spectrum.

Basic Units of Inter-Individual Variation in Resting State Connectomes.

Resting state functional connectomes are massive and complex. It is an open question, however, whether connectomes differ across individuals in a correspondingly massive number of ways, or whether most differences take a small number of characteristic forms. We systematically investigated this question and found clear evidence of low-rank structure in which a modest number of connectomic components, around 50-150, account for a sizable portion of inter-individual connectomic variation. This number was convergently arrived at with multiple methods including estimation of intrinsic dimensionality and assessment of reconstruction of out-of-sample data. In addition, we show that these connectomic components enable prediction of a broad array of neurocognitive and clinical symptom variables at levels comparable to a leading method that is trained on the whole connectome. Qualitative observation reveals that these connectomic components exhibit extensive community structure reflecting interrelationships between intrinsic connectivity networks. We provide quantitative validation of this observation using novel stochastic block model-based methods. We propose that these connectivity components form an effective basis set for quantifying and interpreting inter-individual connectomic differences, and for predicting behavioral/clinical phenotypes.

Competition in Outpatient Procedure Markets.

BACKGROUND: More than half of all medical procedures performed in the United States occur in an outpatient setting, yet few studies have explored how competition among ambulatory surgery centers (ASCs) and hospitals affects prices for commercially insured outpatient services. OBJECTIVES: We examined the association between prices for commercially insured outpatient procedures and competition among ASCs and hospitals. RESEARCH DESIGN: Using claims from the Health Care Cost Institute for 2008-2012, we constructed county-level price indices for outpatient procedures in hospital outpatient departments and ASCs. Using regression analysis, we estimated the association between prices and ASC availability, outpatient and inpatient hospital competition, hospital/physician integration, and several other hospital market characteristics. Our estimates were identified from changes within counties over time. RESULTS: First, ASC availability was associated with decreases in overall outpatient procedure prices, mostly due to reductions in the prices paid to hospital outpatient departments. Second, competition among hospitals was also associated with decreases in outpatient procedure prices-and had an effect more than twice as large as the effect of ASC availability. Third, competition among ASCs was also associated with reductions in the prices paid to other ASCs. CONCLUSIONS: Our results suggest that competition from ASCs benefits consumers through lower prices for outpatient procedures. Any conclusions about the broader welfare implications of the rise in ASCs, however, must balance the price reductions that we found with the volume increases found in previous work, particularly the volume increases at physician-owned ASCs.

Pediatric Feeding Disorder: Consensus Definition and Conceptual Framework.

Pediatric feeding disorders (PFDs) lack a universally accepted definition. Feeding disorders require comprehensive assessment and treatment of 4 closely related, complementary domains (medical, psychosocial, and feeding skill-based systems and associated nutritional complications). Previous diagnostic paradigms have, however, typically defined feeding disorders using the lens of a single professional discipline and fail to characterize associated functional limitations that are critical to plan appropriate interventions and improve quality of life. Using the framework of the World Health Organization International Classification of Functioning, Disability, and Health, a unifying diagnostic term is proposed: "Pediatric Feeding Disorder" (PFD), defined as impaired oral intake that is not age-appropriate, and is associated with medical, nutritional, feeding skill, and/or psychosocial dysfunction. By incorporating associated functional limitations, the proposed diagnostic criteria for PFD should enable practitioners and researchers to better characterize the needs of heterogeneous patient populations, facilitate inclusion of all relevant disciplines in treatment planning, and promote the use of common, precise, terminology necessary to advance clinical practice, research, and health-care policy.

NETWORK CLASSIFICATION WITH APPLICATIONS TO BRAIN CONNECTOMICS.

While statistical analysis of a single network has received a lot of attention in recent years, with a focus on social networks, analysis of a sample of networks presents its own challenges which require a different set of analytic tools. Here we study the problem of classification of networks with labeled nodes, motivated by applications in neuroimaging. Brain networks are constructed from imaging data to represent functional connectivity between regions of the brain, and previous work has shown the potential of such networks to distinguish between various brain disorders, giving rise to a network classification problem. Existing approaches tend to either treat all edge weights as a long vector, ignoring the network structure, or focus on graph topology as represented by summary measures while ignoring the edge weights. Our goal is to design a classification method that uses both the individual edge information and the network structure of the data in a computationally efficient way, and that can produce a parsimonious and interpretable representation of differences in brain connectivity patterns between classes. We propose a graph classification method that uses edge weights as predictors but incorporates the network nature of the data via penalties that promote sparsity in the number of nodes, in addition to the usual sparsity penalties that encourage selection of edges. We implement the method via efficient convex optimization and provide a detailed analysis of data from two fMRI studies of schizophrenia.