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Recent Publications List

July 2022, Predicting Homelessness Among US Army Soldiers No Longer on Active Duty


Introduction: The ability to predict and prevent homelessness has been an elusive goal. The purpose of this study was to develop a prediction model that identified U.S. Army soldiers at high risk of becoming homeless after transitioning to civilian life based on information available before the time of this transition.

Methods: The prospective cohort study consisted of observations from 16,589 soldiers who were separated or deactivated from service and who had previously participated in 1 of 3 baseline surveys of the Army Study to Assess Risk and Resilience in Servicemembers in 2011-2014. A machine learning model was developed in a 70% training sample and evaluated in the remaining 30% test sample to predict self-reported homelessness in 1 of 2 Longitudinal Study surveys administered in 2016-2018 and 2018-2019. Predictors included survey, administrative, and geospatial variables available before separation/deactivation. Analysis was conducted in November 2020-May 2021.

Results: The 12-month prevalence of homelessness was 2.9% (SE=0.2%) in the total Longitudinal Study sample. The area under the receiver operating characteristic curve in the test sample was 0.78 (SE=0.02) for homelessness. The 4 highest ventiles (top 20%) of predicted risk included 61% of respondents with homelessness. Self-reported lifetime histories of depression, trauma of having a loved one murdered, and post-traumatic stress disorder were the 3 strongest predictors of homelessness.

Conclusions: A prediction model for homelessness can accurately target soldiers for preventive intervention before transition to civilian life.

Koh, K. A., Montgomery, A. E., O'Brien, R. W., Kennedy, C. J., Luedtke, A., Sampson, N. A., ... & Kessler, R. C. (2022). Predicting Homelessness Among US Army Soldiers No Longer on Active Duty. American Journal of Preventive Medicine, in press.

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June 2022, Assessing Annotator Identity Sensitivity via Item Response Theory: A Care Study in a Hate Speech Corpus


Content Warning: This paper contains content considered profane, hateful, and offensive.

Annotators, by labeling data samples, play an essential role in the production of machine learning datasets. Their role is increasingly prevalent for more complex tasks such as hate speech or disinformation classification, where labels may be particularly subjective, as evidenced by low inter-annotator agreement statistics. Annotators may exhibit observable differences in their labeling patterns when grouped by their self-reported demographic identities, such as race, gender, etc. We frame these patterns as annotator identity sensitivities, referring to an annotator’s increased likelihood of assigning a particular label on a data sample, conditional on a self-reported identity group. We purposefully refrain from using the term annotator bias, which we argue is problematic terminology in such subjective scenarios. Since annotator identity sensitivities can play a role in the patterns learned by machine learning algorithms, quantifying and characterizing them is of paramount importance for fairness and accountability in machine learning. In this work, we utilize item response theory (IRT), a methodological approach developed for measurement theory, to quantify annotator identity sensitivity. IRT models can be constructed to incorporate diverse factors that influence a label on a specific data sample, such as the data sample itself, the annotator, and the labeling instrument’s wording and response options. An IRT model captures the contributions of these facets to the label via a latent-variable probabilistic model, thereby allowing the direct quantification of annotator sensitivity. As a case study, we examine a hate speech corpus containing over 50,000 social media comments from Reddit, YouTube, and Twitter, rated by 10,000 annotators on 10 components of hate speech (e.g., sentiment, respect, violence, dehumanization, etc.). We leverage three different IRT techniques which are complementary in that they quantify sensitivity from different perspectives: separated measurements, annotator-level interactions, and group-level interactions. We use these techniques to assess whether an annotator’s racial identity is associated with their ratings on comments that target different racial identities. We find that, after controlling for the estimated hatefulness of social media comments, annotators tended to be more sensitive when rating comments targeting a group they identify with. Specifically, annotators were more likely to rate comments targeting their own racial identity as possessing elements of hate speech. Our results identify a correspondence between annotator identity and the target identity of hate speech comments, and provide a set of tools that can assess annotator identity sensitivity in machine learning datasets at large.

Sachdeva, P. S., Barreto, R., von Vacano, C., & Kennedy, C. J. (2022). Assessing Annotator Identity Sensitivity via Item Response Theory: A Case Study in a Hate Speech Corpus. In 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1585-1603).

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June 2022, Machine learning nonresponse adjustment of patient-reported opioid consumption data to enable consumption-informed postoperative opioid prescribing guidelines



Post-discharge opioid consumption is a crucial patient-reported outcome informing opioid prescribing guidelines, but its collection is resource-intensive and vulnerable to inaccuracy due to nonresponse bias.


We developed a post-discharge text message-to-web survey system for efficient collection of patient-reported pain outcomes. We prospectively recruited surgical patients at Beth Israel Deaconess Medical Center in Boston, Massachusetts from March 2019 through October 2020, sending an SMS link to a secure web survey to quantify opioids consumed after discharge from hospitalization. Patient factors extracted from the electronic health record were tested for nonresponse bias and observable confounding. Following targeted learning-based nonresponse adjustment, procedure-specific opioid consumption quantiles (medians and 75th percentiles) were estimated and compared to a previous telephone-based reference survey.


6553 patients were included. Opioid consumption was measured in 44% of patients (2868), including 21% (1342) through survey response. Characteristics associated with inability to measure opioid consumption included age, tobacco use, and prescribed opioid dose. Among the 10 most common procedures, median consumption was only 36% of the median prescription size; 64% of prescribed opioids were not consumed. Among those procedures, nonresponse adjustment corrected the median opioid consumption by an average of 37% (IQR: 7, 65%) compared to unadjusted estimates, and corrected the 75th percentile by an average of 5% (IQR: 0, 12%). This brought median estimates for 5/10 procedures closer to telephone survey-based consumption estimates, and 75th percentile estimates for 2/10 procedures closer to telephone survey-based estimates.


SMS-recruited online surveying can generate reliable opioid consumption estimates after nonresponse adjustment using patient factors recorded in the electronic health record, protecting patients from the risk of inaccurate prescription guidelines.

Kennedy, C. J., Marwaha, J. S., Beaulieu-Jones, B. R., Scalise, P. N., Robinson, K. A., Booth, B., ... & Brat, G. A. (2022). Machine learning nonresponse adjustment of patient-reported opioid consumption data to enable consumption-informed postoperative opioid prescribing guidelines. Surgery in Practice and Science, 100098.

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June 2022, Sleep irregularity and nonsuicidal self-injurious urges and behaviors


Study objectives: The objectives of this study were to examine the relationships between sleep regularity and nonsuicidal self-injury (NSSI), including lifetime NSSI history and daily NSSI urges.

Methods: Undergraduate students (N = 119; 18-26 years), approximately half of whom endorsed a lifetime history of repetitive NSSI, completed a 10-day actigraphy and ecological momentary assessment (EMA) protocol. A Sleep Regularity Index was calculated for all participants using scored epoch by epoch data to capture rapid changes in sleep schedules. Participants responded to EMA prompts assessing NSSI urge severity and negative affect three times daily over the 10-day assessment period.

Results: Results indicate that individuals with a repetitive NSSI history were more likely to experience sleep irregularity than those without a history of NSSI. Findings also suggest that sleep irregularity was associated with more intense urges to engage in NSSI on a daily basis, even after accounting for average daily sleep duration, sleep timing, negative affect, and NSSI history. Neither sleep duration nor sleep timing was associated with NSSI history nor daily NSSI urge intensity.

Conclusions: Findings suggest that sleep irregularity is linked with NSSI, including NSSI history and intensity of urges to engage in NSSI. The present study not only supports the growing evidence linking sleep disturbance with the risk for self-injury but also demonstrates this relationship using actigraphy and real-time assessments of NSSI urge severity. Findings highlight the importance of delineating the nuances in sleep irregularity that are proximally associated with NSSI risk and identifying targets for intervention.

Keywords: actigraphy; ecological momentary assessment; nonsuicidal self-injury; nonsuicidal self-injury urges; self-harm; sleep disturbance; sleep dysregulation; sleep problems; sleep regularity.

Burke TA, Hamilton JL, Seigel D, Kautz M, Liu RT, Alloy LB, Barker DH. Sleep irregularity and nonsuicidal self-injurious urges and behaviors. Sleep. 2022 Jun 13;45(6):zsac084. doi: 10.1093/sleep/zsac084. PMID: 35397476; PMCID: PMC9189944.

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May 2022, Prevalence and Correlates of Suicide and Nonsuicidal Self-injury in Children: A Systematic Review and Meta-analysis


Importance: Considerably less is known about self-injurious thoughts and behaviors (SITBs) in preadolescence than older age groups, owing partly to the common view that young children are incapable of suicidal thoughts. Yet, preadolescent suicide has increased in recent years and is now the fifth leading cause of death in this age group, leading the National Institute of Mental Health to identify it as a priority for research and intervention.

Objective: To assess prevalence estimates of preadolescent SITBs, identify correlates of these outcomes, and conduct head-to-head comparisons of preadolescent and adolescent SITBs in terms of associated characteristics.

Data sources: MEDLINE, PsycINFO, and Embase were systematically searched from inception through December 23, 2021, for studies on the prevalence and correlates of preadolescent SITBs. The search was restricted to English language publications and peer-reviewed journals.

Study selection: Two reviewers independently identified studies providing data on prevalence and correlates of preadolescent SITBs.

Data extraction and synthesis: Two reviewers independently extracted data from each study, and the Joanna Briggs Institute Checklist for Prevalence Studies was used to assess study quality. Pooled prevalence and Cohen d were derived from random-effects meta-analyses. Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline was followed.

Main outcomes and measures: Prevalence and correlates of suicidal ideation, suicide attempts, suicide deaths, and nonsuicidal self-injury among preadolescents.

Results: Fifty-eight studies with 626 486 590 individuals were included. Lifetime prevalence of suicide in the general population was 0.79 per 1 million children. Prevalence for lifetime suicidal thoughts, suicide attempts, and nonsuicidal self-injury among preadolescents were 15.1%, 2.6%, and 6.2%, respectively, in community samples. These data suggest that approximately 17.0% of preadolescents with suicidal ideation transition to attempting suicide. Across several analyses, male individuals appear more likely to have SITBs in preadolescence than adolescence. Correlate data were modest for SITBs other than suicidal ideation, but among specific disorders, attention-deficit/hyperactivity disorder (suicidal ideation: d = 0.54 [95% CI, 0.34-0.75]) and depression (suicidal ideation: d = 0.90 [95% CI, 0.71-1.09]; suicide attempts: d = 0.47 [95% CI, 0.26-0.68]) emerged as the strongest correlates. Among interpersonal factors, child maltreatment (suicidal ideation: d = 2.62 [95% CI, 1.56-3.67]) and parental support (suicidal ideation: d = -0.34 [95% CI, -0.46 to -0.22]) yielded the largest effect sizes.

Conclusions and relevance: In this systematic review anda meta-analysis, although preadolescent suicide deaths were rare, other SITB types occur with concerning frequency. Male individuals were at greater risk for SITBs in preadolescence relative to adolescence. Attention-deficit/hyperactivity disorder, child maltreatment, and parental support were especially relevant to suicidal ideation, as well as depression for suicidal thoughts and behaviors, in this age group. Further study, especially of SITBs other than suicidal ideation, is needed.

Liu RT, Walsh RFL, Sheehan AE, Cheek SM, Sanzari CM. Prevalence and Correlates of Suicide and Nonsuicidal Self-injury in Children: A Systematic Review and Meta-analysis. JAMA Psychiatry. 2022 May 25:e221256. doi: 10.1001/jamapsychiatry.2022.1256. Epub ahead of print. PMID: 35612875; PMCID: PMC9134039.

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May 2022, Effects of social support on depression risk during the COVID-19 pandemic: What support types and for whom? 


Background: Rates of depression have increased worldwide during the COVID-19 pandemic. One known protective factor for depression is social support, but more work is needed to quantify the extent to which social support could reduce depression risk during a global crisis, and specifically to identify which types of support are most helpful, and who might benefit most.

Methods: Data were obtained from participants in the All of Us Research Program who responded to the CO VID-19 P articipant E xperience (COPE) survey administered monthly from May 2020 to July 2020 (N=69,066, 66% female). Social support was assessed using 10 items measuring emotional/informational support (e.g., someone to confide in or talk to about yourself or your problems), positive social interaction support (e.g., someone to do things with to help you get your mind off things), and tangible support (e.g., someone to help with daily chores if sick). Elevated depression symptoms were defined based on having a moderate-to-severe (≥10) score on the Patient Health Questionnaire (PHQ-9). Mixed-effects logistic regression models were used to test associations across time between overall social support and its subtypes with depression, adjusting for age, sex, race, ethnicity, and socioeconomic factors. We then assessed interactions between social support and potential effect modifiers: age, sex, pre-pandemic mood disorder, and pandemic-related stressors (e.g., financial insecurity).

Results: Approximately 16% of the sample experienced elevated depressive symptoms. Overall social support was associated with significantly reduced odds of depression (adjusted odds ratio, aOR [95% CI]=0.44 [0.42-0.45]). Among subtypes, emotional/informational support (aOR=0.42 [0.41-0.43]) and positive social interactions (aOR=0.43 [0.41-0.44]) showed the largest protective associations with depression, followed by tangible support (aOR=0.63 [0.61-0.65]). Sex, age, and pandemic-related financial stressors were statistically significant modifiers of the association between social support and depression.

Conclusions: Individuals reporting higher levels of social support were at reduced risk of depression during the early COVID-19 pandemic. The perceived availability of emotional support and positive social interactions, more so than tangible support, was key. Individuals more vulnerable to depression (e.g., women, younger individuals, and those experiencing financial stressors) may particularly benefit from enhanced social support, supporting a precision prevention approach.

Choi KW, Lee YH, Liu Z, Fatori D, Bauermeister JR, Luh RA, Clark CR, Brunoni AR, Bauermeister S, Smoller JW. Effects of social support on depression risk during the COVID-19 pandemic: What support types and for whom? medRxiv [Preprint]. 2022 May 16:2022.05.15.22274976. doi: 10.1101/2022.05.15.22274976. PMID: 35611337; PMCID: PMC9128784.

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May 2022, Improving polygenic prediction in ancestrally diverse populations


Polygenic risk scores (PRS) have attenuated cross-population predictive performance. As existing genome-wide association studies (GWAS) have been conducted predominantly in individuals of European descent, the limited transferability of PRS reduces their clinical value in non-European populations, and may exacerbate healthcare disparities. Recent efforts to level ancestry imbalance in genomic research have expanded the scale of non-European GWAS, although most remain underpowered. Here, we present a new PRS construction method, PRS-CSx, which improves cross-population polygenic prediction by integrating GWAS summary statistics from multiple populations. PRS-CSx couples genetic effects across populations via a shared continuous shrinkage (CS) prior, enabling more accurate effect size estimation by sharing information between summary statistics and leveraging linkage disequilibrium diversity across discovery samples, while inheriting computational efficiency and robustness from PRS-CS. We show that PRS-CSx outperforms alternative methods across traits with a wide range of genetic architectures, cross-population genetic overlaps and discovery GWAS sample sizes in simulations, and improves the prediction of quantitative traits and schizophrenia risk in non-European populations.

Ruan, Y., Lin, YF., Feng, YC.A. et al. Improving polygenic prediction in ancestrally diverse populations. Nat Genet 54, 573–580 (2022).

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May 2022, Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis


We interrogate the joint genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. We identify four broad factors (neurodevelopmental, compulsive, psychotic and internalizing) that underlie genetic correlations among the disorders and test whether these factors adequately explain their genetic correlations with biobehavioral traits. We introduce stratified genomic structural equation modeling, which we use to identify gene sets that disproportionately contribute to genetic risk sharing. This includes protein-truncating variant-intolerant genes expressed in excitatory and GABAergic brain cells that are enriched for genetic overlap across disorders with psychotic features. Multivariate association analyses detect 152 (20 new) independent loci that act on the individual factors and identify nine loci that act heterogeneously across disorders within a factor. Despite moderate-to-high genetic correlations across all 11 disorders, we find little utility of a single dimension of genetic risk across psychiatric disorders either at the level of biobehavioral correlates or at the level of individual variants.

Grotzinger AD, Mallard TT, Akingbuwa WA, Ip HF, Adams MJ, Lewis CM, McIntosh AM, Grove J, Dalsgaard S, Lesch KP, Strom N, Meier SM, Mattheisen M, Børglum AD, Mors O, Breen G; iPSYCH; Tourette Syndrome and Obsessive Compulsive Disorder Working Group of the Psychiatric Genetics Consortium; Bipolar Disorder Working Group of the Psychiatric Genetics Consortium; Major Depressive Disorder Working Group of the Psychiatric Genetics Consortium; Schizophrenia Working Group of the Psychiatric Genetics Consortium, Lee PH, Kendler KS, Smoller JW, Tucker-Drob EM, Nivard MG. Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. Nat Genet. 2022 May;54(5):548-559. doi: 10.1038/s41588-022-01057-4. Epub 2022 May 5. PMID: 35513722; PMCID: PMC9117465.

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April 2022, What can genetics tell us about the schizophrenia construct?

Smoller JW. What can genetics tell us about the schizophrenia construct? Schizophr Res. 2022 Apr;242:126-129. doi: 10.1016/j.schres.2021.12.008. Epub 2022 Feb 4. PMID: 35125284.

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March 2022, The schizophrenia-associated variant in SLC39A8 alters protein glycosylation in the mouse brain


A missense mutation (A391T) in SLC39A8 is strongly associated with schizophrenia in genomic studies, though the molecular connection to the brain is unknown. Human carriers of A391T have reduced serum manganese, altered plasma glycosylation, and brain MRI changes consistent with altered metal transport. Here, using a knock-in mouse model homozygous for A391T, we show that the schizophrenia-associated variant changes protein glycosylation in the brain. Glycosylation of Asn residues in glycoproteins (N-glycosylation) was most significantly impaired, with effects differing between regions. RNAseq analysis showed negligible regional variation, consistent with changes in the activity of glycosylation enzymes rather than gene expression. Finally, nearly one-third of detected glycoproteins were differentially N-glycosylated in the cortex, including members of several pathways previously implicated in schizophrenia, such as cell adhesion molecules and neurotransmitter receptors that are expressed across all cell types. These findings provide a mechanistic link between a risk allele and potentially reversible biochemical changes in the brain, furthering our molecular understanding of the pathophysiology of schizophrenia and a novel opportunity for therapeutic development.

Mealer RG
, Williams SE, Noel M, Yang B, D'Souza AK, Nakata T, Graham DB, Creasey EA, Cetinbas M, Sadreyev RI, Scolnick EM, Woo CM, Smoller JW, Xavier RJ, Cummings RD. The schizophrenia-associated variant in SLC39A8 alters protein glycosylation in the mouse brain. Mol Psychiatry. 2022 Mar;27(3):1405-1415. doi: 10.1038/s41380-022-01490-1. Epub 2022 Mar 8. PMID: 35260802; PMCID: PMC9106890.

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March 2022, 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.

Keywords: implementation; machine learning; mobile phone; suicide.

Bentley KH
, Zuromski KL, Fortgang RG, Madsen EM, Kessler D, Lee H, Nock MK, Reis BY, Castro VM, Smoller JW. Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers. JMIR Form Res. 2022 Mar 11;6(3):e30946. doi: 10.2196/30946. PMID: 35275075; PMCID: PMC8956996.

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January 2022, Mammalian brain glycoproteins exhibit diminished glycan complexity compared to other tissues


Glycosylation is essential to brain development and function, but prior studies have often been limited to a single analytical technique and excluded region- and sex-specific analyses. Here, using several methodologies, we analyze Asn-linked and Ser/Thr/Tyr-linked protein glycosylation between brain regions and sexes in mice. Brain N-glycans are less complex in sequence and variety compared to other tissues, consisting predominantly of high-mannose and fucosylated/bisected structures. Most brain O-glycans are unbranched, sialylated O-GalNAc and O-mannose structures. A consistent pattern is observed between regions, and sex differences are minimal compared to those in plasma. Brain glycans correlate with RNA expression of their synthetic enzymes, and analysis of glycosylation genes in humans show a global downregulation in the brain compared to other tissues. We hypothesize that this restricted repertoire of protein glycans arises from their tight regulation in the brain. These results provide a roadmap for future studies of glycosylation in neurodevelopment and disease.

Williams SE, Noel M, Lehoux S, Cetinbas M, Xavier RJ, Sadreyev RI, Scolnick EM, Smoller JW, Cummings RD, Mealer RG. Mammalian brain glycoproteins exhibit diminished glycan complexity compared to other tissues. Nat Commun. 2022 Jan 12;13(1):275. doi: 10.1038/s41467-021-27781-9. PMID: 35022400; PMCID: PMC8755730.

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October 2021, Disclosure of Self-Injurious Thoughts and Behaviors Across Sexual and Gender Identities


Objectives: Evidence suggests that sexual minority (SM) and gender minority (GM) youth are more likely to experience self-injurious thoughts and behaviors (SITBs) than heterosexual and cisgender youth. A major barrier to identifying and treating SITBs is nondisclosure. In this study, we explored differences in SITB disclosure patterns between SM and GM youth and their heterosexual and cisgender peers. In this study, we further examined the association between discrimination experiences and SITB disclosure.

Methods: Adolescents (N = 931) completed questionnaires assessing demographics, SITBs, disclosure history, disclosure barriers, future intentions to disclose SITBs, and discrimination history.

Results: Few differences in SITB disclosure patterns emerged between SM and GM youth and heterosexual and cisgender youth (P > .05). SM and GM youth endorsed greater rates of fear of disclosure to and worrying parents, two parent-related barriers ([Formula: see text] = 8.11, P = .017; [Formula: see text] = 7.25, P = .027). GM youth reported greater discrimination experiences than SM youth (F = 6.17, P = .002); discrimination experiences impacted their willingness to disclose future SITBs more so than their SM and heterosexual and cisgender peers (F = 11.58, P < .001). Among the full sample, more discrimination experiences were associated with lower previous disclosure honesty to therapists and pediatricians (r = -0.09 to -0.10, P < .05). Among SM and GM youth, discrimination experiences were associated with lesser odds of disclosing suicide attempts in the future (r = -0.12, P < .05).

Conclusions: Minority stress experiences may interfere with SITB disclosure, particularly among GM youth. Targeted interventions should be considered to reduce minority stress and support disclosure.

Burke TA
, Bettis AH, Barnicle SC, Wang SB, Fox KR. Disclosure of Self-Injurious Thoughts and Behaviors Across Sexual and Gender Identities. Pediatrics. 2021 Oct;148(4):e2021050255. doi: 10.1542/peds.2021-050255. Epub 2021 Sep 14. PMID: 34521728; PMCID: PMC9115868.

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September 2021, Prevalence and correlates of suicidal ideation and suicide attempts in preadolescent children: A US population-based study


The present study evaluated sociodemographic and diagnostic predictors of suicidal ideation and attempts in a nationally representative sample of preadolescent youth enrolled in the Adolescent Brain Cognitive Development Study. Rates and predictors of psychiatric treatment utilization among suicidal youth also were examined. Eleven thousand eight hundred and seventy-five 9- and 10-year-old children residing in the United States were assessed. Children and their parents/guardians provided reports of children's lifetime history of suicidal ideation, suicide attempts, and psychiatric disorders. Parents also reported on sociodemographic characteristics and mental health service utilization. Multivariate logistic regression analyses were employed to evaluate sociodemographic and diagnostic correlates of suicidal ideation, suicide attempts among youth with suicidal ideation, and treatment utilization among youth with suicidal ideation and suicide attempts. Lifetime prevalence rates were 14.33% for suicidal ideation and 1.26% for suicide attempts. Youth who identified as male, a sexual minority, or multiracial had greater odds of suicidal ideation, and sexual minority youth and youth with a low family income had greater odds of suicide attempts. Comorbid psychopathology was associated with higher odds of both suicidal ideation and suicide attempts. In youth, 34.59% who have suicidal ideation and 54.82% who had attempted suicide received psychiatric treatment. Treatment utilization among suicidal youth was lower among those who identified as female, Black, and Hispanic. Suicidal ideation and attempts among preadolescent children are concerningly high and targeted assessment and preventative efforts are needed, especially for males, racial, ethnic, and sexual minority youth, and those youth experiencing comorbidity.

Lawrence HR, Burke TA, Sheehan AE, Pastro B, Levin RY, Walsh RFL, Bettis AH, Liu RT. Prevalence and correlates of suicidal ideation and suicide attempts in preadolescent children: A US population-based study. Transl Psychiatry. 2021 Sep 22;11(1):489. doi: 10.1038/s41398-021-01593-3. PMID: 34552053; PMCID: PMC8458398.

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July 2021, Emotional response inhibition to self-harm stimuli interacts with momentary negative affect to predict nonsuicidal self-injury urges


The current study investigated whether impaired emotional response inhibition to self-harm stimuli is a risk factor for real-time nonsuicidal self-injury (NSSI) urges. Participants were 60 university students with a history of repetitive NSSI. At baseline, participants completed an emotional stop-signal task assessing response inhibition to self-harm stimuli. Participants subsequently completed an ecological momentary assessment protocol in which they reported negative affect, urgency, and NSSI urge intensity three times daily over a ten-day period. Impaired emotional response inhibition to self-harm stimuli did not evidence a main effect on the strength of momentary NSSI urges. However, emotional response inhibition to self-harm images interacted with momentary negative affect to predict the strength of real-time NSSI urges, after adjusting for emotional response inhibition to neutral images. Our findings suggest that emotional response inhibition deficits specifically to self-harm stimuli may pose vulnerability for increased NSSI urge intensity during real-time, state-level negative affect.

Keywords: Ecological momentary assessment; Emotional stop-signal task; Inhibitory control; Negative affect; Nonsuicidal self-injury; Urgency.

Burke TA
, Allen KJD, Carpenter RW, Siegel DM, Kautz MM, Liu RT, Alloy LB. Emotional response inhibition to self-harm stimuli interacts with momentary negative affect to predict nonsuicidal self-injury urges. Behav Res Ther. 2021 Jul;142:103865. doi: 10.1016/j.brat.2021.103865. Epub 2021 Apr 18. PMID: 33940222; PMCID: PMC8523023.

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May 2021, The epidemiology of non-suicidal self-injury: lifetime prevalence, sociodemographic and clinical correlates, and treatment use in a nationally representative sample of adults in England


Background: Although the clinical importance of non-suicidal self-injury (NSSI) has received increasing recognition, relatively little is known about its epidemiology. The objective of this study was to estimate the lifetime prevalence of NSSI in adults and its association with sociodemographic characteristics, psychiatric disorders, and lifetime treatment for NSSI.

Methods: A nationally representative face-to-face survey was conducted with 7192 adults aged ≥18 years in England. Respondents were interviewed about engagement in NSSI, psychiatric illness, suicidal thoughts and behavior, and treatment history for this behavior.

Results: The estimated lifetime prevalence rate of NSSI was 4.86%. Younger age, growing up without biological parents in the household, being unmarried, and impoverished backgrounds were associated with NSSI. The majority of respondents with lifetime NSSI (63.82%) had at least one current psychiatric disorder. Most psychiatric conditions were associated with greater odds of lifetime NSSI in multivariate models. NSSI was strongly associated with suicidal ideation and suicide attempts, respectively, even after accounting for psychiatric disorders and sociodemographic covariates. A substantial proportion of respondents with NSSI history (30.92%) have engaged in medically severe self-harm, as indexed by requiring medical attention for this behavior. The majority of respondents with NSSI (56.20%) had not received psychiatric care for this behavior.

Conclusions: NSSI is prevalent in the general population and associated with considerable psychiatric comorbidity. A high rate of unmet treatment needs is evident among those with this behavior. Those at the greatest lifetime risk for NSSI may also be particularly limited in their resources to cope with this behavior.

Keywords: Epidemiology; non-suicidal self-injury; treatment.

Liu RT
. The epidemiology of non-suicidal self-injury: lifetime prevalence, sociodemographic and clinical correlates, and treatment use in a nationally representative sample of adults in England. Psychol Med. 2021 May 7:1-9. doi: 10.1017/S003329172100146X. Epub ahead of print. PMID: 33960286.

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