Faculty Bibliography

Early life adversity and prenatal stress are consistently associated with an increased risk for schizophrenia, although the exact pathogenic mechanisms linking the exposures with the disease remain elusive. Our previous view of the HPA stress axis as an elegant but simple negative feedback loop, orchestrating adaptation to stressors among the hypothalamus, pituitary, and adrenal glands, needs to be updated. Research in the last two decades shows that important bidirectional signaling between the HPA axis and intestinal mucosa modulates brain function and neurochemistry, including effects on glucocorticoid hormones and brain-derived neurotrophic factor (BDNF). The intestinal microbiome in earliest life, which is seeded by the vaginal microbiome during delivery, programs the development of the HPA axis in a critical developmental window, determining stress sensitivity and HPA function as well as immune system development. The crosstalk between the HPA and the Microbiome Gut Brain Axis (MGBA) is particularly high in the hippocampus, the most consistently disrupted neural region in persons with schizophrenia. Animal models suggest that the MGBA remains influential on behavior and physiology across developmental stages, including the perinatal window, early childhood, adolescence, and young adulthood. Understanding the role of the microbiome on critical risk related stressors may enhance or transform of understanding of the origins of schizophrenia and offer new approaches to increase resilience against stress effects for preventing and treating schizophrenia.

This introductory article describes the genesis of the Festschrift for Leonard Bickman and of this Festschrift special issue entitled, The Future of Children's Mental Health Services. The special issue includes a collection of 11 original children's mental health services research articles, broadly organized in accordance with three themes (i.e., Improving Precision and Use of Service Data to Guide Policy and Practice, Implementation and Dissemination, and Preparing for Innovation), followed by an interview-style article with Bickman. Then follows a featured manuscript by Bickman himself, three invited commentaries, and a compilation of letters and notes in which colleagues reflect on his career and on their experiences of him. The introduction concludes with a few thoughts about the future of children's mental health services portended by the extraordinary scholarly contributions of Bickman and those who have been inspired by him.

BACKGROUND:Despite significant income-related disparities in pediatric sleep, few early childhood sleep interventions have been tailored for or tested with families of lower socio-economic status (SES). This qualitative study assessed caregiver and clinician perspectives to inform adaptation and implementation of evidence-based behavioral sleep interventions in urban primary care with families who are predominantly of lower SES. METHODS:Semi-structured interviews were conducted with (a) 23 caregivers (96% mothers; 83% Black; 65% ≤125% U.S. poverty level) of toddlers and preschoolers with insomnia or insufficient sleep and (b) 22 urban primary care clinicians (physicians, nurse practitioners, social workers, and psychologists; 87% female; 73% White). Guided by the Consolidated Framework for Implementation Research, the interview guide assessed multilevel factors across five domains related to intervention implementation. Qualitative data were analyzed using an integrated approach to identify thematic patterns across participants and domains. RESULTS:Patterns of convergence and divergence in stakeholder perspectives emerged across themes. Participants agreed upon the importance of child sleep and intervention barriers (family work schedules; household and neighborhood factors). Perspectives aligned on intervention (flexibility; collaborative and empowering care) and implementation (caregiver-to-caregiver support and use of technology) facilitators. Clinicians identified many family barriers to treatment engagement, but caregivers perceived few barriers. Clinicians also raised healthcare setting factors that could support (integrated care) or hinder (space and resources) implementation. CONCLUSIONS:Findings point to adaptations to evidence-based early childhood sleep intervention that may be necessary for effective implementation in urban primary care. Such adaptations could potentially reduce significant pediatric sleep-related health disparities.

INTRODUCTION/BACKGROUND:Symptoms of attention-deficit hyperactivity disorder (ADHD) and conduct problems have been associated with heightened temporal discounting of reward value resulting in a preference for immediate over delayed outcomes. We examined the cross-sectional relationship between future preference (including intertemporal choice) and prospection (the ability to bring to mind and imagine the experience of future personally-relevant events and outcomes) in adolescents with a range of ADHD symptoms and aggressive behaviour. METHODS:A combination of behavioural tasks and self-reports measured intertemporal decision making, individual differences in preference for future outcomes and experience of prospection in a convenience sample of English adolescents aged 11-17 (n = 64, 43.8% males). Parents rated symptoms of ADHD and aggression. RESULTS:& Conclusions: Factor analysis identified two factors: "Future Preference" and "Prospection". Significant negative bivariate correlations were found between ADHD and the scores of both factors and between aggression and Future Preference. A path model confirmed the independent significant association of ADHD with both factors but not with aggression. There was no evidence that Prospection was associated with Future Preference or that it reduced the associations between ADHD symptoms and Future Preference. These results provide further evidence that ADHD is associated with a tendency to prefer immediate over future outcomes. The same association with aggression seemed to be driven by the overlap with ADHD symptoms. We provide some of the first evidence that individuals with high ADHD symptoms have difficulty in prospecting about future episodes. However, this is unrelated to their preference for future outcomes.

OBJECTIVE/UNASSIGNED:To conduct a systematic review of studies assessing the relationship between exposure to pesticides and ADHD or Autism Spectrum Disorder (ASD). METHODS/UNASSIGNED:Based on a pre-registered protocol in PROPSERO (CRD42018107847), we searched PubMed, Ovid databases, and ISI Web of Knowledge with no date/language/document type restrictions, up to May 2019. The Newcastle Ottawa Scale was used to assess study quality. RESULTS/UNASSIGNED:Among the 29 retained studies, 13 focused on ADHD, 14 on ASD, and two on both disorders. Ten studies reported a significant association between exposure to pesticides and ADHD/ADHD symptoms and 12 studies found a significant association with ASD/ASD traits. The strengths of the association and the possible confounders controlled for varied substantially across studies. CONCLUSION/UNASSIGNED:Whilst there is some evidence suggesting a possible link between pesticides and ADHD/ASD, heterogeneity across studies prevents firm conclusions. We provide methodological indications for future studies.

BACKGROUND:The use of antidepressants in children and adolescents remains controversial. We examined trends over time and variation in antidepressant prescribing in children and young people in England and whether the drugs prescribed reflected UK licensing and guidelines. METHODS AND FINDINGS/RESULTS:QResearch is a primary care database containing anonymised healthcare records of over 32 million patients from more than 1,500 general practices across the UK. All eligible children and young people aged 5-17 years in 1998-2017 from QResearch were included. Incidence and prevalence rates of antidepressant prescriptions in each year were calculated overall, for 4 antidepressant classes (selective serotonin reuptake inhibitors [SSRIs], tricyclic and related antidepressants [TCAs], serotonin and norepinephrine reuptake inhibitors [SNRIs], and other antidepressants), and for individual drugs. Adjusted trends over time and differences by social deprivation, region, and ethnicity were examined using Poisson regression, taking clustering within general practitioner (GP) practices into account using multilevel modelling. Of the 4.3 million children and young people in the cohort, 49,434 (1.1%) were prescribed antidepressants for the first time during 20 million years of follow-up. Males made up 52.0% of the cohorts, but only 34.1% of those who were first prescribed an antidepressant in the study period. The largest proportion of the cohort was from London (24.4%), and whilst ethnicity information was missing for 39.5% of the cohort, of those with known ethnicity, 75.3% were White. Overall, SSRIs (62.6%) were the most commonly prescribed first antidepressant, followed by TCAs (35.7%). Incident antidepressant prescribing decreased in 5- to 11-year-olds from a peak of 0.9 in females and 1.6 in males in 1999 to less than 0.2 per 1,000 for both sexes in 2017, but incidence rates more than doubled in 12- to 17-year-olds between 2005 and 2017 to 9.7 (females) and 4.2 (males) per 1,000 person-years. The lowest prescription incidence rates were in London, and the highest were in the South East of England (excluding London) for all sex and age groups. Those living in more deprived areas were more likely to be prescribed antidepressants after adjusting for region. The strongest trend was seen in 12- to 17-year-old females (adjusted incidence rate ratio [aIRR] 1.12, 95% confidence interval [95% CI] 1.11-1.13, p < 0.001, per deprivation quintile increase). Prescribing rates were highest in White and lowest in Black adolescents (aIRR 0.32, 95% CI 0.29-0.36, p < 0.001 [females]; aIRR 0.32, 95% CI 0.27-0.38, p < 0.001 [males]). The 5 most commonly prescribed antidepressants were either licensed in the UK for use in children and young people (CYP) or included in national guidelines. Limitations of the study are that, because we did not have access to secondary care prescribing information, we may be underestimating the prevalence and misidentifying the first antidepressant prescription. We could not assess whether antidepressants were dispensed or taken. CONCLUSIONS:Our analysis provides evidence of a continuing rise of antidepressant prescribing in adolescents aged 12-17 years since 2005, driven by SSRI prescriptions, but a decrease in children aged 5-11 years. The variation in prescribing by deprivation, region, and ethnicity could represent inequities. Future research should examine whether prescribing trends and variation are due to true differences in need and risk factors, access to diagnosis or treatment, prescribing behaviour, or young people's help-seeking behaviour.

BACKGROUND:Suicide is one of the leading causes of death among young and middle-aged people. However, little is understood about the behaviors leading up to actual suicide attempts and whether these behaviors are specific to the nature of suicide attempts. OBJECTIVE:The goal of this study was to examine the clusters of behaviors antecedent to suicide attempts to determine if they could be used to assess the potential lethality of the attempt. To accomplish this goal, we developed a deep learning model using the relationships among behaviors antecedent to suicide attempts and the attempts themselves. METHODS:This study used data from the Korea National Suicide Survey. We identified 1112 individuals who attempted suicide and completed a psychiatric evaluation in the emergency room. The 15-item Beck Suicide Intent Scale (SIS) was used for assessing antecedent behaviors, and the medical outcomes of the suicide attempts were measured by assessing lethality with the Columbia Suicide Severity Rating Scale (C-SSRS; lethal suicide attempt >3 and nonlethal attempt ≤3). RESULTS:Using scores from the SIS, individuals who had lethal and nonlethal attempts comprised two different network nodes with the edges representing the relationships among nodes. Among the antecedent behaviors, the conception of a method's lethality predicted suicidal behaviors with severe medical outcomes. The vectorized relationship values among the elements of antecedent behaviors in our deep learning model (E-GONet) increased performances, such as F1 and area under the precision-recall gain curve (AUPRG), for identifying lethal attempts (up to 3% for F1 and 32% for AUPRG), as compared with other models (mean F1: 0.81 for E-GONet, 0.78 for linear regression, and 0.80 for random forest; mean AUPRG: 0.73 for E-GONet, 0.41 for linear regression, and 0.69 for random forest). CONCLUSIONS:The relationships among behaviors antecedent to suicide attempts can be used to understand the suicidal intent of individuals and help identify the lethality of potential suicide attempts. Such a model may be useful in prioritizing cases for preventive intervention.