Insufficient Sleep is Associated With Increasing Trends in Adolescent Suicidal Behaviors.

PURPOSE: Youth suicide has been increasing and became a public health concern worldwide. Identifying insufficient sleep as the potential risk factor is critical to reducing suicide risk and increasing trends. This study aimed to determine whether insufficient sleep is associated with increasing trends in suicidal behaviors and disparities by sex, age, and race/ethnicity among school adolescents. METHODS: The present study used biennial data from the US nationally representative Youth Risk Behavior Survey from 2007 to 2019. Joinpoint regression models were used to estimate biennial percent changes (BPCs) and average BPCs (ABPCs) of suicidal behaviors by sleep duration. Logistic regression models were used to examine the association between insufficient sleep and suicidal behaviors. RESULTS: Of 73,356 adolescent students included (mean [standard deviation] age, 16.11 [1.23] years), 50.03% were female. Suicidal ideation and suicide plan among insufficient sleep group increased from 2007 to 2019 (BPC = 2.88% [95% confidence interval {CI}: 1.65%, 4.13%]; BPC = 3.42% [95% CI: 2.09%, 4.77%]), but were nonsignificant among sufficient sleep group. Trends in suicidal ideation (ABPC = 3.03% [95% CI: 1.35%, 4.73%]) and suicide plan (ABPC = 4.03% [95% CI: 2.47%, 5.62%]) among female adolescents with insufficient sleep increased, but nonsignificant among male adolescents with insufficient sleep. Suicidal ideation (ABPC = 1.73% [95% CI: 0.51%, 2.97%]) and suicide plan (ABPC = 2.31% [95% CI: 0.70%, 3.95%]) increased among younger adolescents only with insufficient sleep, whereas suicide trends by sleep duration were similar among older adolescents. Suicide plan among insufficient sleep group increased across the four racial groups, with BPC highest for the White (BPC = 3.48% [95% CI: 1.31%, 5.69%]), and lowest for the Hispanic/Latino (BPC = 1.18% [95% CI: 0.15%, 2.23%]), but were nonsignificant among sufficient sleep group except for the White (BPC = 2.83% [95% CI: 0.62%, 5.09%]). DISCUSSION: Insufficient sleep was disproportionately associated with increasing trends in suicidal behaviors among female, younger, and non-White adolescent students. Ensuring sufficient sleep can potentially reduce suicide among school adolescents.

The New Orientation of Postoperative Analgesia: Remote Ischemic Preconditioning.

Purpose of Review: Postoperative analgesia is currently a significant topic in anesthesiology. Currently, the predominant approach for achieving multimodal analgesia involves the utilization of pharmacotherapy and regional anesthesia procedures. The primary objectives of this approach are to mitigate postoperative pain, enhance patient satisfaction, and diminish overall opioid usage. Nevertheless, there is a scarcity of research on the use of remote ischemia preconditioning aimed at mitigating postoperative pain. Recent Findings: Transient stoppage of blood flow to an organ has been found to elicit remote ischemia preconditioning (RIPC), which serves as a potent intrinsic mechanism for protecting numerous organs. In addition to its established role in protecting against reperfusion injury, RIPC has recently been identified as having potential benefits in the context of postoperative analgesia. Summary: In addition to traditional perioperative analgesia, RIPC provides perioperative analgesia and organ protection.

Decoding Suicide Decedent Profiles and Signs of Suicidal Intent Using Latent Class Analysis.

Importance: Suicide rates in the US increased by 35.6% from 2001 to 2021. Given that most individuals die on their first attempt, earlier detection and intervention are crucial. Understanding modifiable risk factors is key to effective prevention strategies. Objective: To identify distinct suicide profiles or classes, associated signs of suicidal intent, and patterns of modifiable risks for targeted prevention efforts. Design, Setting, and Participants: This cross-sectional study used data from the 2003-2020 National Violent Death Reporting System Restricted Access Database for 306 800 suicide decedents. Statistical analysis was performed from July 2022 to June 2023. Exposures: Suicide decedent profiles were determined using latent class analyses of available data on suicide circumstances, toxicology, and methods. Main Outcomes and Measures: Disclosure of recent intent, suicide note presence, and known psychotropic usage. Results: Among 306 800 suicide decedents (mean [SD] age, 46.3 [18.4] years; 239 627 males [78.1%] and 67 108 females [21.9%]), 5 profiles or classes were identified. The largest class, class 4 (97 175 [31.7%]), predominantly faced physical health challenges, followed by polysubstance problems in class 5 (58 803 [19.2%]), and crisis, alcohol-related, and intimate partner problems in class 3 (55 367 [18.0%]), mental health problems (class 2, 53 928 [17.6%]), and comorbid mental health and substance use disorders (class 1, 41 527 [13.5%]). Class 4 had the lowest rates of disclosing suicidal intent (13 952 [14.4%]) and leaving a suicide note (24 351 [25.1%]). Adjusting for covariates, compared with class 1, class 4 had the highest odds of not disclosing suicide intent (odds ratio [OR], 2.58; 95% CI, 2.51-2.66) and not leaving a suicide note (OR, 1.45; 95% CI, 1.41-1.49). Class 4 also had the lowest rates of all known psychiatric illnesses and psychotropic medications among all suicide profiles. Class 4 had more older adults (23 794 were aged 55-70 years [24.5%]; 20 100 aged ≥71 years [20.7%]), veterans (22 220 [22.9%]), widows (8633 [8.9%]), individuals with less than high school education (15 690 [16.1%]), and rural residents (23 966 [24.7%]). Conclusions and Relevance: This study identified 5 distinct suicide profiles, highlighting a need for tailored prevention strategies. Improving the detection and treatment of coexisting mental health conditions, substance and alcohol use disorders, and physical illnesses is paramount. The implementation of means restriction strategies plays a vital role in reducing suicide risks across most of the profiles, reinforcing the need for a multifaceted approach to suicide prevention.

Are there reciprocal interplays among Chinese adolescents', fathers', and mothers' depression at the within-family level? A family systems perspective.

Controversy surrounds the reciprocity between adolescent and parental depression. Limited studies rigorously tested the transactional model of depression from a family systems perspective considering the involvement of all family members, particularly in non-Western nations, using advanced modeling approaches that disentangle between- and within-unit (i.e., family) variances (e.g., random intercept cross-lagged panel model [RI-CLPM]). This population-based multi-informant longitudinal study applied RI-CLPM to evaluate the temporal dynamics of the interrelations among adolescent, maternal, and paternal depression in 1,733 Chinese families assessed biannually. Findings from two large independent samples (primary sample [N = 1,733]; replication sample [N = 989]) converged to suggest, in macro timescales: (a) more depressed parents-especially mothers-generally have more depressed adolescents (between-family associations); (b) a family member becoming more depressed than usual co-occurred with other members becoming more depressed than usual in the same wave (within-family cofluctuations), with the mother-adolescent dyads exhibiting greater concordance than the father-adolescent dyads; and (c) a family member becoming more depressed than usual did not prospectively predict other members becoming more depressed than usual (i.e., no within-family reciprocal effects). While patterns of cross-lagged effects were consistently null across contexts, cofluctuations were stronger in rural than urban families and stronger in families with older adolescents. Overall, findings suggest that in macro timescales, the previously identified associations between adolescent and parental depression likely occurred at the trait-like between-family level and state-like within-family cofluctuations. Future studies employing micro timescales (e.g., daily) can complement macro-timescale analysis to provide greater temporal resolution of the within-family interplays of affective symptoms between family members. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records.

Objective: To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression. Method: This study examined 808 patients with depression at a New York City-based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either "Recovering" or "Worsening" (i.e., non-Recovering), measured by the slope of individual-level Patient Health Questionnaire-9 (PHQ-9) score trajectory spanning 6 months following treatment initiation. A patient was designated as "Recovering" if the slope is less than 0 and as "Worsening" if the slope was no less than 0. Multiple machine learning (ML) models including L2 norm regularized Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting Decision Tree (GBDT) were used to predict treatment outcome based on additional data from EHRs, including demographics and diagnoses. Shapley Additive Explanations were applied to identify the most important predictors. Results: The GBDT achieved the best results of predicting "Recovering" (AUC: 0.7654 ± 0.0227; precision: 0.6002 ± 0.0215; recall: 0.5131 ± 0.0336). When excluding patients with low PHQ-9 scores (<10) at baseline, the results of predicting "Recovering" (AUC: 0.7254 ± 0.0218; precision: 0.5392 ± 0.0437; recall: 0.4431 ± 0.0513) were obtained. Prior diagnosis of anxiety, psychotherapy, recurrent depression, and baseline depression symptom severity were strong predictors. Conclusions: The results demonstrate the potential utility of using ML in longitudinal EHRs to predict antidepressant treatment outcome. Our predictive tool holds the promise to accelerate personalized medical management in patients with psychiatric illnesses.

Effects of Geography on Risk for Future Suicidal Ideation and Attempts Among Children and Youth.

Objective: Geography may influence the relationships of predictors for suicidal ideation (SI) and suicide attempts (SA) in children and youth. Method: This is a nationwide retrospective cohort study of 124,424 individuals less than 25 years of age using commercial claims data (2011-2015) from the Health Care Cost Institute. Outcomes were time to SI or SA within 3 months after the indexed mental health or substance use disorder (MH/SUD) outpatient visit. Predictors included sociodemographic and clinical characteristics up to 3 years before the index event. Results: At each follow-up time period, rates of SI and SA varied by the US geographic division (p < .001), and the Mountain Division consistently had the highest rates for both SI and SA (5.44%-10.26% for SI; 0.70%-2.82% for SA). Having MH emergency department (ED) visits in the past year increased the risk of SI by 28% to 65% for individuals residing in the New England, Mid-Atlantic, East North Central, West North Central, and East South Central Divisions. The main effects of geographic divisions were significant for SA (p<0.001). Risk of SA was lower in New England, Mid-Atlantic, South Atlantic, and Pacific (hazard ratios = 0.57, 0.51, 0.67, and 0.79, respectively) and higher in the Mountain Division (hazard ratio = 1.46). Conclusion: To understand the underlying mechanisms driving the high prevalence of SI and SA in the Mountain Division and the elevated risk of SI after having MH ED visits, future research examining regional differences in risks for SI and SA should include indicators of access to MH ED care and other social determinants of health.

Patterns of Social Determinants of Health and Child Mental Health, Cognition, and Physical Health.

Importance: Social determinants of health (SDOH) influence child health. However, most previous studies have used individual, small-set, or cherry-picked SDOH variables without examining unbiased computed SDOH patterns from high-dimensional SDOH factors to investigate associations with child mental health, cognition, and physical health. Objective: To identify SDOH patterns and estimate their associations with children's mental, cognitive, and physical developmental outcomes. Design, Setting, and Participants: This population-based cohort study included children aged 9 to 10 years at baseline and their caregivers enrolled in the Adolescent Brain Cognitive Development (ABCD) Study between 2016 and 2021. The ABCD Study includes 21 sites across 17 states. Exposures: Eighty-four neighborhood-level, geocoded variables spanning 7 domains of SDOH, including bias, education, physical and health infrastructure, natural environment, socioeconomic status, social context, and crime and drugs, were studied. Hierarchical agglomerative clustering was used to identify SDOH patterns. Main Outcomes and Measures: Associations of SDOH and child mental health (internalizing and externalizing behaviors) and suicidal behaviors, cognitive function (performance, reading skills), and physical health (body mass index, exercise, sleep disorder) were estimated using mixed-effects linear and logistic regression models. Results: Among 10 504 children (baseline median [SD] age, 9.9 [0.6] years; 5510 boys [52.5%] and 4994 girls [47.5%]; 229 Asian [2.2%], 1468 Black [14.0%], 2128 Hispanic [20.3%], 5565 White [53.0%], and 1108 multiracial [10.5%]), 4 SDOH patterns were identified: pattern 1, affluence (4078 children [38.8%]); pattern 2, high-stigma environment (2661 children [25.3%]); pattern 3, high socioeconomic deprivation (2653 children [25.3%]); and pattern 4, high crime and drug sales, low education, and high population density (1112 children [10.6%]). The SDOH patterns were distinctly associated with child health outcomes. Children exposed to socioeconomic deprivation (SDOH pattern 3) showed the worst health profiles, manifesting more internalizing (ß = 0.75; 95% CI, 0.14-1.37) and externalizing (ß = 1.43; 95% CI, 0.83-2.02) mental health problems, lower cognitive performance, and adverse physical health. Conclusions: This study shows that an unbiased quantitative analysis of multidimensional SDOH can permit the determination of how SDOH patterns are associated with child developmental outcomes. Children exposed to socioeconomic deprivation showed the worst outcomes relative to other SDOH categories. These findings suggest the need to determine whether improvement in socioeconomic conditions can enhance child developmental outcomes.

Trends in Healthcare Service Disruptions and Associations with COVID-19 Outcomes among Patients with SMI vs. Non-SMI during COVID-19.

Objective: The study aims to quantify differential changes in outpatient mental health service utilization among 3,724,348 individuals, contrasting those with Severe Mental Illness (SMI) to those without, in the context of the COVID-19 pandemic. Design & Setting: A retrospective cohort study was conducted, utilizing data from Healthix, the second-largest health information exchange in the U.S. Participants: The study population included 3,134,959 Non-SMI patients (84.2%), 355,397 SMI patients (9.5%), and 149,345 Recurrent SMI Patients (4.0%). Exposures: The primary exposure was the COVID-19 pandemic, with a focus on its impact on outpatient mental health services. Main Outcomes and Measures: The primary outcome was the rate of utilization of outpatient mental health services. Secondary outcomes included COVID-19 infection rates and vaccination rates among the study cohorts. Results: Among the non-SMI patients, there was a 30% decline in emergency visits from 650,000 pre-COVID to 455,000 post-COVID (OR=0.70, p < 0.001), and outpatient visits decreased by 50% from 1.2 million to 600,000 (OR=0.50, p = 0.002). In contrast, the SMI group witnessed a 20% reduction in outpatient visits from 120,000 to 96,000 (OR=0.80, p = 0.015) and a 40% decrease in inpatient visits from 50,000 to 30,000 (OR=0.60, p = 0.008). Recurrent SMI patients exhibited a 25% decline in emergency visits from 32,000 to 24,000 (OR=0.75, p = 0.03) and a 35% drop in outpatient visits from 40,000 to 26,000 (OR=0.65, p = 0.009).The pandemic influenced the type of disorders diagnosed. Non-SMI patients experienced a 23% rise in anxiety-related disorders (n=80,000, OR=1.23, p = 0.01) and an 18% increase in stress-related disorders (n=70,000, OR=1.18, p = 0.04). SMI patients had a 15% surge in severe anxiety disorders (n=9,000, OR=1.15, p = 0.02) and a 12% uptick in substance-related disorders (n=7,200, OR=1.12, p = 0.05). Recurrent SMI patients showed a 20% increase in anxiety and adjustment disorders (n=6,400, OR=1.20, p = 0.03).SMI patients were more adversely affected by COVID-19, with a higher infection rate of 7.8% (n=45,972) compared to 4.2% (n=131,669) in non-SMI patients (OR=1.88, p < 0.001). Hospitalization rates also followed this trend, with 5.2% (n=30,648) of SMI patients being hospitalized compared to 3.7% (n=115,995) among non-SMI patients (OR=1.41, p = 0.007). Moreover, SMI patients had lower vaccination rates of 45.6% (n=268,888) versus 58.9% (n=1,844,261) among non-SMI patients (OR=0.77, p = 0.019). Conclusions: In conclusion, our findings reveal significant disparities in healthcare service utilization between individuals with Serious Mental Illness (SMI) and those without. Notably, the SMI cohort experienced greater disruptions in service continuity, with a more pronounced decline in both outpatient and inpatient visits. Furthermore, the types of disorders diagnosed among this group also saw a shift, emphasizing the need for specialized care and attention during times of crisis. The higher rates of COVID-19 infection and hospitalization among SMI patients compared to non-SMI patients underscore the urgency of targeted public health interventions for this vulnerable group. The lower vaccination rates in the SMI cohort highlight another layer of healthcare disparity that needs to be urgently addressed. These findings suggest that the pandemic has amplified pre-existing inequalities in healthcare access and outcomes for individuals with SMI, calling for immediate, evidence-based interventions to mitigate these effects and ensure equitable healthcare service provision.

Students' COVID-19 vaccine behaviors, intentions, and beliefs at a US Native American-Serving Nontribal Institution (NASNTI).

OBJECTIVE: Multiple national and international studies of college student COVID-19 vaccination have been recently published, providing important descriptive information and a conceptual basis to inform future decisions about infectious disease prevention in higher education settings. Yet almost no research has examined Native American-Serving Nontribal Institutions (NASNTIs), which occupy a unique space in US higher education in terms of structure and students served. To address that gap, this report describes results from a two-wave cross-sectional survey administered at a NASNTI in Durango, Colorado, as part of a larger study of COVID-19 campus response. Surveys were administered prior to (wave one) and following (wave two) statewide availability of the COVID-19 vaccine for ages 16+. Comparisons between waves used Cramer's V and Mann-Whitney U tests. RESULTS: A total of 283 students responded to wave one, and 186 responded to wave two. Notable results included a self-reported COVID-19 vaccination rate (40.1%) at wave one that far exceeded parallel national rates. Injunctive and disjunctive normative beliefs were also less supportive of vaccination among the unvaccinated at wave two compared to wave one. Findings from this study should be considered in the context of all available evidence and not used to make inferences in isolation.

Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022.

BACKGROUND: Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people's expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics. OBJECTIVE: This study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics. METHODS: This study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated. RESULTS: Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5% (n=196) of cities overlapped. In addition, Twitter-identified suicide risks were at a relatively lower level after midnight compared to a higher level in the afternoon, as well as a higher level on Sundays and Saturdays compared to weekdays. CONCLUSIONS: Social media platforms provide an anonymous space where people express their suicidal thoughts, ideation, and acts. Such expressions can serve as an alternative source to estimating and predicting suicide in countries without reliable suicide statistics. It can also provide real-time tracking of suicide risks, serving as an early warning for suicide. The identification of areas where suicide risks are highly concentrated is crucial for location-based mental health planning, enabling suicide prevention and intervention through social media in a spatially and temporally explicit manner.

Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served populations.

In the United States, primary open-angle glaucoma (POAG) is the leading cause of blindness, especially among African American and Hispanic individuals. Deep learning has been widely used to detect POAG using fundus images as its performance is comparable to or even surpasses diagnosis by clinicians. However, human bias in clinical diagnosis may be reflected and amplified in the widely-used deep learning models, thus impacting their performance. Biases may cause (1) underdiagnosis, increasing the risks of delayed or inadequate treatment, and (2) overdiagnosis, which may increase individuals' stress, fear, well-being, and unnecessary/costly treatment. In this study, we examined the underdiagnosis and overdiagnosis when applying deep learning in POAG detection based on the Ocular Hypertension Treatment Study (OHTS) from 22 centers across 16 states in the United States. Our results show that the widely-used deep learning model can underdiagnose or overdiagnose under-served populations. The most underdiagnosed group is female younger (< 60 yrs) group, and the most overdiagnosed group is Black older (≥ 60 yrs) group. Biased diagnosis through traditional deep learning methods may delay disease detection, treatment and create burdens among under-served populations, thereby, raising ethical concerns about using deep learning models in ophthalmology clinics.

Sexual Assault Victimization in Premature Female Mortalities: Evidence from the National Longitudinal Study of Adolescent to Adult Health.

Previous research has documented many behavioral problems associated with being a female victim of sexual assault, but little attention has been devoted to whether this experience might be related to premature mortalities. We investigated this utilizing the National Longitudinal Study of Adolescent to Adult Health survey, collected from over 10,000 adolescent females in 1995, whose premature deaths (n = 65) were noted in 2007 in National Death Index records. Significant associations were found between females with a substance misuse history and their premature deaths, but not with being a sexual assault victim. The subset of respondents (n = 208) evincing both these characteristics showed significantly higher risks of dying prematurely, as did those females with early histories of drug misuse alone. Yet, adolescent females with histories of drug misuse who also attempted suicide (n = 214) did not show similar elevated risks of dying prematurely compared to others without these experiences. This exploratory evidence points to an affinity between both being a female sexual assault victim and having an early history of misusing drugs, putting such people at heightened risks for dying prematurely, suggesting the potential benefits of counseling and supportive services for those so affected.

An NLP approach to identify SDoH-related circumstance and suicide crisis from death investigation narratives.

OBJECTIVES: Suicide presents a major public health challenge worldwide, affecting people across the lifespan. While previous studies revealed strong associations between Social Determinants of Health (SDoH) and suicide deaths, existing evidence is limited by the reliance on structured data. To resolve this, we aim to adapt a suicide-specific SDoH ontology (Suicide-SDoHO) and use natural language processing (NLP) to effectively identify individual-level SDoH-related social risks from death investigation narratives. MATERIALS AND METHODS: We used the latest National Violent Death Report System (NVDRS), which contains 267 804 victim suicide data from 2003 to 2019. After adapting the Suicide-SDoHO, we developed a transformer-based model to identify SDoH-related circumstances and crises in death investigation narratives. We applied our model retrospectively to annotate narratives whose crisis variables were not coded in NVDRS. The crisis rates were calculated as the percentage of the group's total suicide population with the crisis present. RESULTS: The Suicide-SDoHO contains 57 fine-grained circumstances in a hierarchical structure. Our classifier achieves AUCs of 0.966 and 0.942 for classifying circumstances and crises, respectively. Through the crisis trend analysis, we observed that not everyone is equally affected by SDoH-related social risks. For the economic stability crisis, our result showed a significant increase in crisis rate in 2007-2009, parallel with the Great Recession. CONCLUSIONS: This is the first study curating a Suicide-SDoHO using death investigation narratives. We showcased that our model can effectively classify SDoH-related social risks through NLP approaches. We hope our study will facilitate the understanding of suicide crises and inform effective prevention strategies.

Using Normative Language When Describing Scientific Findings: Randomized Controlled Trial of Effects on Trust and Credibility.

BACKGROUND: Scientists often make cognitive claims (eg, the results of their work) and normative claims (eg, what should be done based on those results). Yet, these types of statements contain very different information and implications. This randomized controlled trial sought to characterize the granular effects of using normative language in science communication. OBJECTIVE: Our study examined whether viewing a social media post containing scientific claims about face masks for COVID-19 using both normative and cognitive language (intervention arm) would reduce perceptions of trust and credibility in science and scientists compared with an identical post using only cognitive language (control arm). We also examined whether effects were mediated by political orientation. METHODS: This was a 2-arm, parallel group, randomized controlled trial. We aimed to recruit 1500 US adults (age 18+) from the Prolific platform who were representative of the US population census by cross sections of age, race/ethnicity, and gender. Participants were randomly assigned to view 1 of 2 images of a social media post about face masks to prevent COVID-19. The control image described the results of a real study (cognitive language), and the intervention image was identical, but also included recommendations from the same study about what people should do based on the results (normative language). Primary outcomes were trust in science and scientists (21-item scale) and 4 individual items related to trust and credibility; 9 additional covariates (eg, sociodemographics, political orientation) were measured and included in analyses. RESULTS: From September 4, 2022, to September 6, 2022, 1526 individuals completed the study. For the sample as a whole (eg, without interaction terms), there was no evidence that a single exposure to normative language affected perceptions of trust or credibility in science or scientists. When including the interaction term (study arm × political orientation), there was some evidence of differential effects, such that individuals with liberal political orientation were more likely to trust scientific information from the social media post's author if the post included normative language, and political conservatives were more likely to trust scientific information from the post's author if the post included only cognitive language (ß=0.05, 95% CI 0.00 to 0.10; P=.04). CONCLUSIONS: This study does not support the authors' original hypotheses that single exposures to normative language can reduce perceptions of trust or credibility in science or scientists for all people. However, the secondary preregistered analyses indicate the possibility that political orientation may differentially mediate the effect of normative and cognitive language from scientists on people's perceptions. We do not submit this paper as definitive evidence thereof but do believe that there is sufficient evidence to support additional research into this topic, which may have implications for effective scientific communication. TRIAL REGISTRATION: OSF Registries osf.io/kb3yh; https://osf.io/kb3yh. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/41747.

Effects of social deprivation on risk factors for suicidal ideation and suicide attempts in commercially insured US youth and adults.

We used US nationwide commercial insurance claims data (2011-2015) to study the effect of social deprivation on clinical and demographic risk factors for suicidal ideation (SI) and suicide attempts (SA) among US youth and adults < 65 years, after having a mental health or substance use disorder-related outpatient encounter. Neighborhood social deprivation level was summarized by the quintile of social deprivation index (SDI) at individuals' zip code level. Cox proportional hazard models were used to evaluate the effect of social deprivation on demographic and clinical risk factors for SI and SA. The study cohort consisted of 317,383 individuals < 65 years, with 124,424 aged < 25 (youth) and 192,959 aged between 25 and 64 (adults). Neighborhood social deprivation impacted risk factors for SI and SA differently for youth and adults. Among youth, SDI interacted with multiple risk factors for both SI and SA. The effects of the risk factors were larger on youth from middle socioeconomic neighborhoods. Among adults, risk of SI was the strongest in the most deprived neighborhoods, but risk of SA did not vary by neighborhood deprivation level. Our findings suggest community-based suicide prevention initiatives should be tailored according to neighborhood deprivation level and the targeted individual's age to maximize the impact.

COVID-19 Policies, Pandemic Disruptions, and Changes in Child Mental Health and Sleep in the United States.

Importance: The adverse effects of COVID-19 containment policies disrupting child mental health and sleep have been debated. However, few current estimates correct biases of these potential effects. Objectives: To determine whether financial and school disruptions related to COVID-19 containment policies and unemployment rates were separately associated with perceived stress, sadness, positive affect, COVID-19-related worry, and sleep. Design, Setting, and Participants: This cohort study was based on the Adolescent Brain Cognitive Development Study COVID-19 Rapid Response Release and used data collected 5 times between May and December 2020. Indexes of state-level COVID-19 policies (restrictive, supportive) and county-level unemployment rates were used to plausibly address confounding biases through 2-stage limited information maximum likelihood instrumental variables analyses. Data from 6030 US children aged 10 to 13 years were included. Data analysis was conducted from May 2021 to January 2023. Exposures: Policy-induced financial disruptions (lost wages or work due to COVID-19 economic impact); policy-induced school disruptions (switches to online or partial in-person schooling). Main Outcomes and Measures: Perceived stress scale, National Institutes of Health (NIH)-Toolbox sadness, NIH-Toolbox positive affect, COVID-19-related worry, and sleep (latency, inertia, duration). Results: In this study, 6030 children were included in the mental health sample (weighted median [IQR] age, 13 [12-13] years; 2947 [48.9%] females, 273 [4.5%] Asian children, 461 [7.6%] Black children, 1167 [19.4%] Hispanic children, 3783 [62.7%] White children, 347 [5.7%] children of other or multiracial ethnicity). After imputing missing data, experiencing financial disruption was associated with a 205.2% [95% CI, 52.9%-509.0%] increase in stress, a 112.1% [95% CI, 22.2%-268.1%] increase in sadness, 32.9% [95% CI, 3.5%-53.4%] decrease in positive affect, and a 73.9 [95% CI, 13.2-134.7] percentage-point increase in moderate-to-extreme COVID-19-related worry. There was no association between school disruption and mental health. Neither school disruption nor financial disruption were associated with sleep. Conclusions and Relevance: To our knowledge, this study presents the first bias-corrected estimates linking COVID-19 policy-related financial disruptions with child mental health outcomes. School disruptions did not affect indices of children's mental health. These findings suggest public policy should consider the economic impact on families due to pandemic containment measures, in part to protect child mental health until vaccines and antiviral drugs become available.

The Indirect Effect of Sleep on the Association Between Protracted Social Stressors and Psychological Distress Among Hong Kong Young People.

PURPOSE: Hong Kong youth and young adults experienced unprecedented stress amid social unrest and the COVID-19 pandemic. Few studies have examined how these stressors were related to psychological distress among youth and young adults. This study assessed how psychological distress is associated with stress from social unrest, financial circumstances, and the COVID-19 pandemic, and whether poor sleep quality may explain these associations. METHODS: Participants of a representative phone survey included 1,501 Hong Kong youth and young adults (Mage = 26.1 (4.0); 48.2% female). We examined the associations between psychological distress and three types of stress (social unrest, financial, and COVID-19 stress), and the indirect effect of poor sleep. RESULTS: Eleven point nine percent, 4.1%, and 9.7% of respondents reported feeling very seriously distressed by social unrest, financial circumstances, and the COVID-19 pandemic, respectively. All three forms of stress were associated with poor sleep. The indirect effects of poor sleep on the association between all three forms of stress and psychological distress were identified. Moderated indirect effect analysis indicated that being female intensified the effect of COVID-19-related stress on psychological distress and that younger female youth and older male youth were more vulnerable to financial stress and social unrest stress (vs. older female youth and younger male youth). DISCUSSION: Sleep may be one mechanism that accounts for the association between psychological distress and protracted stressors among Hong Kong youth and young adults. These results suggest the importance of prioritizing sleep improvement in mental health interventions during times of societal change.

Moving suicide prevention upstream by understanding the effect of flourishing on suicidal ideation in midlife: an instrumental variable approach.

Prior research has examined the association between flourishing and suicidal ideation, but it is unknown whether this association is causal. Understanding the causality between flourishing and suicidal ideation is important for clinicians and policymakers to determine the value of innovative suicide prevention programs by improving flourishing in at-risk groups. Using a linked nationwide longitudinal sample of 1619 middle-aged adults (mean age 53, 53% female, 88% White) from the National Survey of Midlife Development in the United States (MIDUS), this retrospective cohort study aims to assess the causal relationship between flourishing and suicidal ideation among middle-aged adults in the US. Flourishing is a theory-informed 13-scale index covering three domains: emotional, psychological, and social well-being. Suicidal ideation was self-reported in a follow-up interview conducted after measuring flourishing. We estimated instrumental variable models to examine the potential causal relationship between flourishing and suicidal ideation. High-level flourishing (binary) was reported by 486 (30.0%) individuals, and was associated with an 18.6% reduction in any suicidal ideation (binary) (95% CI, - 29.3- - 8.0). Using alternative measures, a one standard deviation increase in flourishing (z-score) was associated with a 0.518 (95% CI, 0.069, 0.968) standard deviation decrease in suicidal ideation (z-score). Our results suggest that prevention programs that increase flourishing in midlife should result in meaningful reductions in suicide risk. Strengthening population-level collaboration between policymakers, clinical practitioners, and non-medical partners to promote flourishing can support our collective ability to reduce suicide risks across social, economic, and other structural circumstances.