Commentary: Holy grails, personalized medicine, and the public health burden of psychopathology - a reflection on Ahuvia et al. (2023).

Clinical psychology and psychiatry have many 'holy grails' or research findings that are widely sought after but remain elusive. The use of machine learning (ML) models for treatment selection is one of these holy grails. Ahuvia et al. (Journal of Child Psychology and Psychiatry, 2023) recently analyzed a large trial (n = 996) of two distinct single-session interventions (SSIs) for internalizing distress and found little evidence that an ML model could predict differential treatment response. I discuss potential avenues for advancing SSI research. One avenue is the dissemination and implementation of SSIs, including how they interact with other treatments in routine care. Quantifying and critically questioning the promises of holy grails like ML models is sorely needed. Using simulation modeling to evaluate the relative merits of using ML models for treatment selection or using SSIs versus other treatment strategies may be another path forward.

Historical language records reveal a surge of cognitive distortions in recent decades.

Individuals with depression are prone to maladaptive patterns of thinking, known as cognitive distortions, whereby they think about themselves, the world, and the future in overly negative and inaccurate ways. These distortions are associated with marked changes in an individual's mood, behavior, and language. We hypothesize that societies can undergo similar changes in their collective psychology that are reflected in historical records of language use. Here, we investigate the prevalence of textual markers of cognitive distortions in over 14 million books for the past 125 y and observe a surge of their prevalence since the 1980s, to levels exceeding those of the Great Depression and both World Wars. This pattern does not seem to be driven by changes in word meaning, publishing and writing standards, or the Google Books sample. Our results suggest a recent societal shift toward language associated with cognitive distortions and internalizing disorders.

Race, Ethnicity, and Other Cultural Background Factors in Trials of Internet-Based Cognitive Behavioral Therapy for Depression: Systematic Review.

BACKGROUND: There is a growing interest in developing scalable interventions, including internet-based cognitive behavioral therapy (iCBT), to meet the increasing demand for mental health services. Given the growth in diversity worldwide, it is essential that the clinical trials of iCBT for depression include diverse samples or, at least, report information on the race, ethnicity, or other background indicators of their samples. Unfortunately, the field lacks data on how well diversity is currently reported and represented in the iCBT literature. OBJECTIVE: Thus, the main objective of this systematic review was to examine the overall reporting of racial and ethnic identities in published clinical trials of iCBT for depression. We also aimed to review the representation of specific racial and ethnic minoritized groups and the inclusion of alternative background indicators such as migration status or country of residence. METHODS: Studies were included if they were randomized controlled trials in which iCBT was compared to a waiting list, care-as-usual, active control, or another iCBT. The included papers also had to have a focus on acute treatment (eg, 4 weeks to 6 months) of depression, be delivered via the internet on a website or a smartphone app and use guided or unguided self-help. Studies were initially identified from the METAPSY database (n=59) and then extended to include papers up to 2022, with papers retrieved from Embase, PubMed, PsycINFO, and Cochrane (n=3). Risk of bias assessment suggested that reported studies had at least some risk of bias due to use of self-report outcome measures. RESULTS: A total of 62 iCBT randomized controlled trials representing 17,210 participants are summarized in this study. Out of those 62 papers, only 17 (27%) of the trials reported race, and only 12 (19%) reported ethnicity. Reporting outside of the United States was very poor, with the United States accounting for 15 (88%) out of 17 of studies that reported race and 9 (75%) out of 12 for ethnicity. Out of 3,623 participants whose race was reported in the systematic review, the racial category reported the most was White (n=2716, 74.9%), followed by Asian (n=209, 5.8%) and Black (n=274, 7.6%). Furthermore, only 25 (54%) out of the 46 papers conducted outside of the United States reported other background demographics. CONCLUSIONS: It is important to note that the underreporting observed in this study does not necessarily indicate an underrepresentation in the actual study population. However, these findings highlight the poor reporting of race and ethnicity in iCBT trials for depression found in the literature. This lack of diversity reporting may have significant implications for the scalability of these interventions.

"I would not want the mechanic to direct me to an engine repair manual": a qualitative analysis of provider perspectives on low-intensity treatments for patients on waiting lists.

BACKGROUND: Low-intensity treatments (LITs), such as bibliotherapy or online self-help, have the potential to reach more individuals than traditional face-to-face care by circumventing many of the common barriers to mental health treatment. Despite substantial research evidence supporting their usability and efficacy across several clinical presentations, prior work suggests that mental health providers rarely recommend LITs for patients waiting for treatment. METHODS: The present study analyzed provider open responses to a prompt asking about perceived barriers, thoughts, and comments related to additional treatment resources for patients on treatment waiting lists. We surveyed 141 practicing mental health providers, 65 of whom responded to an open text box with additional thoughts on using LITs for patients on treatment waiting lists. Responses were qualitatively coded using a thematic coding process. RESULTS: Qualitative outcomes yielded 11 codes: patient appropriateness, research evidence, feasibility, patient barriers, liability, patient personal contact, additional resources, positive attitudes, trust in programs, systemic problems, and downplaying distress. CONCLUSIONS: Results suggest providers are predominantly concerned about the potential of suggesting a LIT that would be ultimately inappropriate for their patient due to a lack of assessment of the patient's needs. Furthermore, providers noted ambiguity around the legal and ethical liability of recommending a LIT to someone who may not yet be a patient. Guidelines and standards for recommending LITs to patients on treatment waiting lists may help address ambiguity regarding their use in routine care.

Methods for quantifying the heterogeneity of psychopathology.

OBJECTIVES: Specifiers for a major depressive disorder (MDE) are supposed to reduce diagnostic heterogeneity. However, recent literature challenges the idea that the atypical and melancholic specifiers identify more homogenous or coherent subgroups. We introduce the usage of distance metrics to characterize symptom heterogeneity. We attempt to replicate prior findings and explore whether symptom heterogeneity is reduced using specifier subgroups. METHODS: We used data derived from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC Wave I; N = 5,749) and the Sequenced Treatment Alternatives to Relieve Depression study (STAR*D; N = 2,498). We computed Hamming and Manhattan distances from study participants' unique symptom profiles. Distances were standardized from 0-1 and compared by their within- and between-group similarities to their non-specifier counterparts for the melancholic and atypical specifiers. RESULTS: There was no evidence of statistically significant differences in heterogeneity for specifier (i.e., melancholic or atypical) vs. non-specifier designations (i.e., non-melancholic vs. non-atypical). CONCLUSION: Replicating prior work, melancholic and atypical depression specifiers appear to have limited utility in reducing heterogeneity. The current study does not support the claim that specifiers create more coherent subgroups as operationalized by similarity in the number of symptoms and their severity. Distance metrics are useful for quantifying symptom heterogeneity.

Efficacy of an Unguided, Digital Single-Session Intervention for Internalizing Symptoms in Web-Based Workers: Randomized Controlled Trial.

BACKGROUND: The Common Elements Toolbox (COMET) is an unguided digital single-session intervention (SSI) based on principles of cognitive behavioral therapy and positive psychology. Although unguided digital SSIs have shown promise in the treatment of youth psychopathology, the data are more mixed regarding their efficacy in adults. OBJECTIVE: This study aimed to investigate the efficacy of COMET-SSI versus a waiting list control in depression and other transdiagnostic mental health outcomes for Prolific participants with a history of psychopathology. METHODS: We conducted an investigator-blinded, preregistered randomized controlled trial comparing COMET-SSI (n=409) with an 8-week waiting list control (n=419). Participants were recruited from the web-based workspace Prolific and assessed for depression, anxiety, work and social functioning, psychological well-being, and emotion regulation at baseline and at 2, 4, and 8 weeks after the intervention. The main outcomes were short-term (2 weeks) and long-term (8 weeks) changes in depression and anxiety. The secondary outcomes were the 8-week changes in work and social functioning, well-being, and emotion regulation. Analyses were conducted according to the intent-to-treat principle with imputation, without imputation, and using a per-protocol sample. In addition, we conducted sensitivity analyses to identify inattentive responders. RESULTS: The sample comprised 61.9% (513/828) of women, with a mean age of 35.75 (SD 11.93) years. Most participants (732/828, 88.3%) met the criteria for screening for depression or anxiety using at least one validated screening scale. A review of the text data suggested that adherence to the COMET-SSI was near perfect, there were very few inattentive respondents, and satisfaction with the intervention was high. However, despite being powered to detect small effects, there were negligible differences between the conditions in the various outcomes at the various time points, even when focusing on subsets of individuals with more severe symptoms. CONCLUSIONS: Our results do not support the use of the COMET-SSI in adult Prolific participants. Future work should explore alternate ways of intervening with paid web-based participants, including matching individuals to SSIs they may be most responsive to. TRIAL REGISTRATION: ClinicalTrials.gov NCT05379881, https://clinicaltrials.gov/ct2/show/NCT05379881.

Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning.

BACKGROUND: Shortly after the worst of the COVID-19 pandemic, an outbreak of mpox introduced another critical public health emergency. Like the COVID-19 pandemic, the mpox outbreak was characterized by a rising prevalence of public health misinformation on social media, through which many US adults receive and engage with news. Digital misinformation continues to challenge the efforts of public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the mpox outbreak to map the tension between rapidly diffusing misinformation and public health communication. OBJECTIVE: This study aims to observe topical themes occurring in a large-scale collection of tweets about mpox using deep learning. METHODS: We leveraged a data set comprised of all mpox-related tweets that were posted between May 7, 2022, and July 23, 2022. We then applied Sentence Bidirectional Encoder Representations From Transformers (S-BERT) to the content of each tweet to generate a representation of its content in high-dimensional vector space, where semantically similar tweets will be located closely together. We projected the set of tweet embeddings to a 2D map by applying principal component analysis and Uniform Manifold Approximation Projection (UMAP). Finally, we group these data points into 7 topical clusters using k-means clustering and analyze each cluster to determine its dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal thematic changes. RESULTS: Our deep-learning pipeline revealed 7 distinct clusters of content: (1) cynicism, (2) exasperation, (3) COVID-19, (4) men who have sex with men, (5) case reports, (6) vaccination, and (7) World Health Organization (WHO). Clusters that largely communicated erroneous or irrelevant information began earlier and grew faster, reaching a wider audience than later communications by official instances and health officials. CONCLUSIONS: Within a few weeks of the first reported mpox cases, an avalanche of mostly false, misleading, irrelevant, or damaging information started to circulate on social media. Official institutions, including the WHO, acted promptly, providing case reports and accurate information within weeks, but were overshadowed by rapidly spreading social media chatter. Our results point to the need for real-time monitoring of social media content to optimize responses to public health emergencies.

The Road to Cognitive Skill Acquisition: Psychometric Evaluation of the Competencies of Cognitive Therapy Scale.

OBJECTIVE: Cognitive therapy (CT) skills are an index of treatment progress. They predict changes in patients' acute depressive symptoms and symptom relapses. However, the psychometric properties of the various measures of CT skills are poorly understood. This study aimed to investigate the factor structure of the Competencies of Cognitive Therapy Scale-Self Report (CCTS-SR) and assess its concurrent validity. METHODS: The psychometric properties of the CCTS-SR were explored by using data from a panel of online respondents (N=410). The fit of a one-factor solution was explored by using a confirmatory factor analysis. Exploratory bifactor analyses (EBFA) were then conducted to determine other possible factor structures. RESULTS: The one-factor solution did not fit the data well. Results of the EBFA suggested that the factor structure of the CCTS-SR may be characterized by a single underlying dimension capturing the general use of CT skills as well as by more specific factors the authors labeled "behavioral activation" and "CT comprehension." The variance captured by the factor initially labeled as CT comprehension was correlated with measures of depression and emotional dysregulation, suggesting that these items do not capture CT comprehension and should be removed from the scale. CONCLUSIONS: The CCTS-SR seems to be characterized by more than a single factor, and items that seemingly compose CT comprehension (i.e., items 13 and 14) may need to be removed. Although the CCTS-SR may be a valid index of therapy progress, more attention needs to be paid to its psychometric properties.

Heterogeneity in major depression and its melancholic and atypical specifiers: a secondary analysis of STAR*D.

OBJECTIVES: The melancholic and atypical specifiers for a major depressive episode (MDE) are supposed to reduce heterogeneity in symptom presentation by requiring additional, specific features. Fried et al. (2020) recently showed that the melancholic specifier may increase the potential heterogeneity in presenting symptoms. In a large sample of outpatients with depression, our objective was to explore whether the melancholic and atypical specifiers reduced observed heterogeneity in symptoms. METHODS: We used baseline data from the Inventory of Depression Symptoms (IDS), which was available for 3,717 patients, from the Sequenced Alternatives to Relieve Depression (STAR*D) trial. A subsample met criteria for MDE on the IDS ("IDS-MDE"; N =2,496). For patients with IDS-MDE, we differentiated between those with melancholic, non-melancholic, non-melancholic, atypical, and non-atypical depression. We quantified the observed heterogeneity between groups by counting the number of unique symptom combinations pertaining to their given diagnostic group (e.g., counting the melancholic symptoms for melancholic and non-melancholic groups), as well as the profiles of DSM-MDE symptoms (i.e., ignoring the specifier symptoms). RESULTS: When considering the specifier and depressive symptoms, there was more observed heterogeneity within the melancholic and atypical subgroups than in the IDS-MDE sample (i.e., ignoring the specifier subgroups). The differences in number of profiles between the melancholic and non-melancholic groups were not statistically significant, irrespective of whether focusing on the specifier symptoms or only the DSM-MDE symptoms. The differences between the atypical and non-atypical subgroups were smaller than what would be expected by chance. We found no evidence that the specifier groups reduce heterogeneity, as can be quantified by unique symptom profiles. Most symptom profiles, even in the specifier subgroups, had five or fewer individuals. CONCLUSION: We found no evidence that the atypical and melancholic specifiers create more symptomatically homogeneous groups. Indeed, the melancholic and atypical specifiers introduce heterogeneity by adding symptoms to the DSM diagnosis of MDE.

A placebo prognostic index (PI) as a moderator of outcomes in the treatment of adolescent depression: Could it inform risk-stratification in treatment with cognitive-behavioral therapy, fluoxetine, or their combination?

Introduction: Researchers have proposed that predicting who is a likely placebo responder may help guide treatment allocations to treatment regimens that differ in intensity. Methods: We used data from the Treatment of Adolescent Depression Study (TADS) in which adolescents (n = 439) were randomized 1:1:1:1 to placebo, cognitive-behavioral therapy (CBT), medications (MEDs), or their combination (COMB). We developed a prognostic index (PI) in the placebo group to predict self-reported (RADS) and observer-rated (CDRS) depression outcomes using elastic net regularization. We explored whether the PIs moderated outcomes in the treatment conditions. Results: PI-CDRS was predicted by multiple variables but it did not moderate outcomes. PI-RADS was predicted by baseline severity, age, sleep problems, expectations, maternal depression, and the action stage of change. It moderated outcomes such that there were treatment differences for less placebo-responsive patients. For participants prone to placebo response, type of treatment had no statistically significant impact on outcomes. Baseline depression severity accounted for this effect: treatment differences were small and non-significant for patients with milder depression but larger in more severely depressed patients. Discussion: Future work should investigate whether multiple variable explain outcomes beyond severity as well as complex interactions between severity and other variables.

Cross-trial prediction in psychotherapy: External validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression.

Objective: Optimizing treatment selection may improve treatment outcomes in depression. A promising approach is the Personalized Advantage Index (PAI), which predicts the optimal treatment for a given individual. To determine the generalizability of the PAI, models needs to be externally validated, which has rarely been done. Method: PAI models were developed within each of two independent trials, with substantial between-study differences, that both compared CBT and IPT for depression (STEPd: n = 151 and FreqMech: n = 200). Subsequently, both PAI models were tested in the other dataset. Results: In the STEPd study, post-treatment depression was significantly different between individuals assigned to their PAI-indicated treatment versus those assigned to their non-indicated treatment (d = .57). In the FreqMech study, post-treatment depression was not significantly different between patients receiving their indicated treatment versus those receiving their non-indicated treatment (d = .20). Cross-trial predictions indicated that post-treatment depression was not significantly different between those receiving their indicated treatment and those receiving their non-indicated treatment (d = .16 and d = .27). Sensitivity analyses indicated that cross-trial prediction based on only overlapping variables didn't improve the results. Conclusion: External validation of the PAI has modest results and emphasizes between-study differences and many other challenges.

The Generalizability of Randomized Controlled Trials of Self-Guided Internet-Based Cognitive Behavioral Therapy for Depressive Symptoms: Systematic Review and Meta-Regression Analysis.

BACKGROUND: Self-guided internet-based cognitive behavioral therapies (iCBTs) for depressive symptoms may substantially increase accessibility to mental health treatment. Despite this, questions remain as to the generalizability of the research on self-guided iCBT. OBJECTIVE: We sought to describe the clinical entry criteria used in studies of self-guided iCBT, explore the criteria's effects on study outcomes, and compare the frequency of use of these criteria with their use in studies of face-to-face psychotherapy and antidepressant medications. We hypothesized that self-guided iCBT studies would use more stringent criteria that would bias the sample toward those with a less complex clinical profile, thus inflating treatment outcomes. METHODS: We updated a recently published meta-analysis by conducting a systematic literature search in PubMed, MEDLINE, PsycINFO, and EMBASE. We conducted a meta-regression analysis to test the effect of the different commonly used psychiatric entry criteria on the treatment-control differences. We also compared the frequency with which exclusion criteria were used in the self-guided iCBT studies versus studies of face-to-face psychotherapy and antidepressants from a recently published review. RESULTS: Our search yielded 5 additional studies, which we added to the 16 studies identified by Karyotaki and colleagues in 2017. Few self-guided iCBT studies excluded patients with severe depressive symptoms (6/21, 29%), but self-guided iCBT studies were more likely than antidepressant (14/170, 8.2%) studies to use this criterion. However, self-guided iCBT studies did not use this criterion more frequently than face-to-face psychotherapy studies (6/16, 38%). Beyond this, we found no evidence that self-guided iCBTs used more stringent entry criteria. Strong evidence suggested that they were actually less likely to use most entry criteria, especially exclusions on the basis of substance use or personality pathology. None of the entry criteria used had an effect on outcomes. CONCLUSIONS: A conservative interpretation of our findings is that the patient population sampled in the literature on self-guided iCBT is relatively comparable with that of studies of antidepressants or face-to-face psychotherapy. Alternatively, studies of unguided cognitive behavioral therapy may sample from a more heterogeneous and representative patient population. Until evidence emerges to suggest otherwise, the patient population sampled in self-guided iCBT studies cannot be considered as less complex than the patient population from face-to-face psychotherapy or antidepressant studies.

Comparison of treatment outcome using two definitions of rapid cycling in subjects with bipolar II disorder.

OBJECTIVES: We examined differences in treatment outcome between Diagnostic and Statistical Manual Fourth Edition (DSM-IV)-defined rapid cycling and average lifetime-defined rapid cycling in subjects with bipolar II disorder. We hypothesized that, compared with the DSM-IV definition, the average lifetime definition of rapid cycling may better identify subjects with a history of more mood lability and a greater likelihood of hypomanic symptom induction during long-term treatment. METHODS: Subjects ≥18 years old with a bipolar II major depressive episode (n=129) were categorized into DSM-IV- and average lifetime-defined rapid cycling and prospectively treated with either venlafaxine or lithium monotherapy for 12 weeks. Responders (n=59) received continuation monotherapy for six additional months. RESULTS: These exploratory analyses found moderate agreement between the two rapid-cycling definitions (κ=0.56). The lifetime definition captured subjects with more chronic courses of bipolar II depression, whereas the DSM-IV definition captured subjects with more acute symptoms of hypomania. There was no difference between rapid-cycling definitions with respect to the response to acute venlafaxine or lithium monotherapy. However, the lifetime definition was slightly superior to the DSM-IV definition in identifying subjects who went on to experience hypomanic symptoms during continuation therapy. CONCLUSIONS: Although sample sizes were limited, the findings suggest that the lifetime definition of rapid cycling may identify individuals with a chronic rapid-cycling course and may also be slightly superior to the DSM-IV definition in identifying individuals with hypomania during relapse-prevention therapy. These findings are preliminary in nature and need replication in larger, prospective, bipolar II studies.

Short-term venlafaxine v. lithium monotherapy for bipolar type II major depressive episodes: effectiveness and mood conversion rate.

BACKGROUND: Controversy exists over antidepressant use in bipolar II depression. AIMS: To compare the safety and effectiveness of antidepressantv.mood stabiliser monotherapy for bipolar type II major depressive episodes. METHOD: Randomised, double-blind, parallel-group, 12-week comparison of venlafaxine (n= 65)v.lithium (n= 64) monotherapy in adult out-patients (trial registration numberNCT00602537). RESULTS: Primary outcome - venlafaxine produced a greater response rate (67.7%)v lithium (34.4%,P<0.001). Secondary outcomes - venlafaxine produced a greater remission rate (58.5%v 28.1%,P<0.001); greater decline in depression symptom scores over time (ß = -5.32, s.e. = 1.16, χ(2)= 21.19,P<0.001); greater reduction in global severity scores over time (ß = -1.05, s.e. = 0.22, w(2)= 22.33,P<0.001); and greater improvement in global change scores (ß = -1.31, s.e. = 0.32, χ(2)= 16.95,P<0.001) relative to lithium. No statistically significant or clinically meaningful differences in hypomanic symptoms were observed between treatments. CONCLUSIONS: These findings suggest that short-term venlafaxine monotherapy may provide effective antidepressant treatment for bipolar II depression without a statistically significant increase in hypomanic symptoms relative to lithium.

Step-wise loss of antidepressant effectiveness with repeated antidepressant trials in bipolar II depression.

OBJECTIVE: This study examined the relationship between the number of prior antidepressant treatment trials and step-wise increase in pharmacodynamic tolerance (or progressive loss of effectiveness) in subjects with bipolar II depression. METHODS: Subjects ≥18 years old with bipolar II depression (n=129) were randomized to double-blind venlafaxine or lithium carbonate monotherapy for 12 weeks. Responders (n=59) received continuation monotherapy for six additional months. RESULTS: After controlling for baseline covariates of prior medications, there was a 25% reduction in the likelihood of response to treatment with each increase in the number of prior antidepressant trials (odds ratio [OR]=0.75, unstandardized coefficient [B]=-0.29, standard error (SE)=0.12; χ2 =5.70, P<.02], as well as a 32% reduction in the likelihood of remission with each prior antidepressant trial (OR=0.68, B=-0.39, SE=0.13; χ2 =9.71, P=.002). This step-wise increase in pharmacodynamic tolerance occurred in both treatment conditions. Prior selective serotonin reuptake inhibitor (SSRI) therapy was specifically associated with a step-wise increase in tolerance, whereas other prior antidepressants or mood stabilizers were not associated with pharmacodynamic tolerance. Neither the number of prior antidepressants, nor the number of prior SSRIs, or mood stabilizers, were associated with an increase in relapse during continuation therapy. CONCLUSIONS: The odds of responding or remitting during venlafaxine or lithium monotherapy were reduced by 25% and 32%, respectively, with each increase in the number of prior antidepressant treatment trials. There was no relationship between prior antidepressant exposure and depressive relapse during continuation therapy of bipolar II disorder.

Racial and ethnic differences in risk factors associated with suicidal behavior among young adults in the USA.

OBJECTIVE: The objectives of this study are to examine racial and ethnic differences in suicidal behaviour, its main risk factors, and the effect of the risk factors on suicidal behaviour in young adults in the United States. DESIGN: Using nationally representative data (n=10,585) from Add Health, we calculate the prevalence of suicidal behavior and associated risk factors for non-Hispanic White, non-Hispanic Black, and Hispanic youth (aged 18-26) using logistic regression models of suicidal ideation stratified by race. RESULTS: Non-Hispanic White and Hispanic young adults have higher rates of suicidal ideation than their non-Hispanic Black counterparts, but racial/ethnic differences in attempts are not statistically significant. Non-Hispanic Whites and Hispanic young adults are more likely to possess key risk factors for suicide. With the exception of substance use variables (i.e. alcohol and marijuana use) which appear to be more conducive to suicidal ideation in non-Hispanic Black than in non-Hispanic White young adults, the effects of risk factors appear to be similar across race/ethnicity. CONCLUSION: The higher prevalence of suicidal ideation in non-Hispanic White and Hispanic young adults may be driven by their greater exposure to risk factors, as opposed to differences in the effects of these risk factors. More research is needed to uncover why non-Hispanic White and Hispanic young adults have higher rates of suicidal ideation than their non-Hispanic Black counterparts; yet, rates of suicide attempts are comparable and non-Hispanic White young adults have the highest rate of completed suicides.

Mental health misinformation on social media: Review and future directions.

Social media use for health information is extremely common in the United States. Unfortunately, this use may expose users to misinformation. The prevalence and harms of misinformation are well documented in many health domains (e.g., infectious diseases). However, research on mental health misinformation is limited. Our review suggests that mental health misinformation is common, although its prevalence varies across disorders and treatment types. Individual differences in susceptibility to misinformation have been documented for health misinformation generally but less so for mental health specifically. We discuss conceptual issues in defining mental health misinformation versus other classifications such as overgeneralizations from personal experience. Although there is clear evidence for false and actively misleading content, future research should also explore the role of negative healthcare experiences and health disparities on mental health misinformation on social media.

Data-driven personalized medicine approaches to cognitive-behavioral therapy allocation in a large sample: A reanalysis of the ENRICHED study.

BACKGROUND: Although effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy could be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized controlled trials on psychological interventions for common mental health problems. METHODS: This is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample (n = 1203). RESULTS: Treatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects (d = 0.15), that were more pronounced for individuals matched to CBT (d = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU. LIMITATIONS: Limitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables. CONCLUSIONS: Small matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.