Measurement Invariance in Longitudinal Bifactor Models: Review and Application Based on the p Factor.

Bifactor models are increasingly being utilized to study latent constructs such as psychopathology and cognition, which change over the lifespan. Although longitudinal measurement invariance (MI) testing helps ensure valid interpretation of change in a construct over time, this is rarely and inconsistently performed in bifactor models. Our review of MI simulation literature revealed that only one study assessed MI in bifactor models under limited conditions. Recommendations for how to assess MI in bifactor models are suggested based on existing simulation studies of related models. Estimator choice and influence of missing data on MI are also discussed. An empirical example based on a model of the general psychopathology factor (p) elucidates our recommendations, with the present model of p being the first to exhibit residual MI across gender and time. Thus, changes in the ordered-categorical indicators can be attributed to changes in the latent factors. However, further work is needed to clarify MI guidelines for bifactor models, including considering the impact of model complexity and number of indicators. Nonetheless, using the guidelines justified herein to establish MI allows findings from bifactor models to be more confidently interpreted, increasing their comparability and utility

How do the prevalence and relative risk of non-suicidal self-injury and suicidal thoughts vary across the population distribution of common mental distress (the p factor)? Observational analyses replicated in two independent UK cohorts of young people

Funder: National Institute for Health Research; FundRef: http://dx.doi.org/10.13039/501100000272Objectives: To inform suicide prevention policies and responses to youths at risk by investigating whether suicide risk is predicted by a summary measure of common mental distress (CMD (the p factor)) as well as by conventional psychopathological domains; to define the distribution of suicide risks over the population range of CMD; to test whether such distress mediates the medium-term persistence of suicide risks. Design: Two independent population-based cohorts. Setting: Population based in two UK centres. Participants: Volunteers aged 14–24 years recruited from primary healthcare registers, schools and colleges, with advertisements to complete quotas in age-sex-strata. Cohort 1 is the Neuroscience in Psychiatry Network (n=2403); cohort 2 is the ROOTS sample (n=1074). Primary outcome measures: Suicidal thoughts (ST) and non-suicidal self-injury (NSSI). Results: We calculated a CMD score using confirmatory bifactor analysis and then used logistic regressions to determine adjusted associations between risks and CMD; curve fitting was used to examine the relative prevalence of STs and NSSI over the population distribution of CMD. We found a dose–response relationship between levels of CMD and risk of suicide. The majority of all subjects experiencing ST and NSSI (78% and 76% in cohort 1, and 66% and 71% in cohort 2) had CMD scores no more than 2 SDs above the population mean; higher scores indicated the highest risk but were, by definition, infrequent. Pathway mediation models showed that CMD mediated the longitudinal course of both ST and NSSI. Conclusions: NSSI and ST in youths reflect CMD that also mediates their persistence. Universal prevention strategies reducing levels of CMD in the whole population without recourse to screening or measurement may prevent more suicides than approaches targeting youths with the most severe distress or with psychiatric disorders

Using fish models to investigate the links between microbiome and social behaviour: the next step for translational microbiome research?

Recent research has revealed surprisingly important connections between animals’ microbiome and social behaviour. Social interactions can affect the composition and function of the microbiome; conversely, the microbiome affects social communication by influencing the hosts’ central nervous system and peripheral chemical communication. These discoveries set the stage for novel research focusing on the evolution and physiology of animal social behaviour in relation to microbial transmission strategies. Here, we discuss the emerging roles of teleost fish models and their potential for advancing research fields, linked to sociality and microbial regulation. We argue that fish models, such as the zebrafish (Danio rerio, Cyprinidae), sticklebacks (‎Gasterosteidae), guppies (Poeciliidae) and cleaner–client dyads (e.g., obligate cleaner fish from the Labridae and Gobiidae families and their visiting clientele), will provide valuable insights into the roles of microbiome in shaping social behaviour and vice versa, while also being of direct relevance to the food and ornamental fish trades. The diversity of fish behaviour warrants more interdisciplinary research, including microbiome studies, which should have a strong ecological (field‐derived) approach, together with laboratory‐based cognitive and neurobiological experimentation. The implications of such integrated approaches may be of translational relevance, opening new avenues for future investigation using fish models

The Delphi Delirium Management Algorithms. A practical tool for clinicians, the result of a modified Delphi expert consensus approach

Delirium is common in hospitalised patients, and there is currently no specific treatment. Identifying and treating underlying somatic causes of delirium is the first priority once delirium is diagnosed. Several international guidelines provide clinicians with an evidence-based approach to screening, diagnosis and symptomatic treatment. However, current guidelines do not offer a structured approach to identification of underlying causes. A panel of 37 internationally recognised delirium experts from diverse medical backgrounds worked together in a modified Delphi approach via an online platform. Consensus was reached after five voting rounds. The final product of this project is a set of three delirium management algorithms (the Delirium Delphi Algorithms), one for ward patients, one for patients after cardiac surgery and one for patients in the intensive care unit.</p

Brain-behaviour modes of covariation in healthy and clinically depressed young people

Abstract: Understanding how variations in dimensions of psychometrics, IQ and demographics relate to changes in brain connectivity during the critical developmental period of adolescence and early adulthood is a major challenge. This has particular relevance for mental health disorders where a failure to understand these links might hinder the development of better diagnostic approaches and therapeutics. Here, we investigated this question in 306 adolescents and young adults (14–24 y, 25 clinically depressed) using a multivariate statistical framework, based on canonical correlation analysis (CCA). By linking individual functional brain connectivity profiles to self-report questionnaires, IQ and demographic data we identified two distinct modes of covariation. The first mode mapped onto an externalization/internalization axis and showed a strong association with sex. The second mode mapped onto a well-being/distress axis independent of sex. Interestingly, both modes showed an association with age. Crucially, the changes in functional brain connectivity associated with changes in these phenotypes showed marked developmental effects. The findings point to a role for the default mode, frontoparietal and limbic networks in psychopathology and depression

The Delphi Delirium Management Algorithms: A practical tool for clinicians, the result of a modified Delphi expert consensus approach

Delirium is common in hospitalised patients, and there is currently no specific treatment. Identifying and treating underlying somatic causes of delirium is the first priority once delirium is diagnosed. Several international guidelines provide clinicians with an evidence-based approach to screening, diagnosis and symptomatic treatment. However, current guidelines do not offer a structured approach to identification of underlying causes. A panel of 37 internationally recognised delirium experts from diverse medical backgrounds worked together in a modified Delphi approach via an online platform. Consensus was reached after five voting rounds. The final product of this project is a set of three delirium management algorithms (the Delirium Delphi Algorithms), one for ward patients, one for patients after cardiac surgery and one for patients in the intensive care unit

Microbiome to Brain:Unravelling the Multidirectional Axes of Communication

The gut microbiome plays a crucial role in host physiology. Disruption of its community structure and function can have wide-ranging effects making it critical to understand exactly how the interactive dialogue between the host and its microbiota is regulated to maintain homeostasis. An array of multidirectional signalling molecules is clearly involved in the host-microbiome communication. This interactive signalling not only impacts the gastrointestinal tract, where the majority of microbiota resides, but also extends to affect other host systems including the brain and liver as well as the microbiome itself. Understanding the mechanistic principles of this inter-kingdom signalling is fundamental to unravelling how our supraorganism function to maintain wellbeing, subsequently opening up new avenues for microbiome manipulation to favour desirable mental health outcome

sj-docx-1-asm-10.1177_10731911241229573 – Supplemental material for Longitudinal and Gender Measurement Invariance of the General Health Questionnaire-12 (GHQ-12) From Adolescence to Emerging Adulthood

Supplemental material, sj-docx-1-asm-10.1177_10731911241229573 for Longitudinal and Gender Measurement Invariance of the General Health Questionnaire-12 (GHQ-12) From Adolescence to Emerging Adulthood by Pascal Schlechter and Sharon A. S. Neufeld in Assessment</p

Depressive symptom networks in the UK general adolescent population and in those looked after by local authorities

Peer reviewed: TrueBackground: Despite the importance of understanding depressive symptom constellations during adolescence and specifically in looked-after children, studies often only apply sum score models to understand depression in these populations, neglecting associations among single symptoms that can be elucidated in network analysis. The few network analyses in adolescents have relied on different measures to assess depressive symptoms, contributing to inconsistent cross-study results. Objective: In three population-based studies using the Short Mood and Feelings Questionnaire, we used network analyses to study depressive symptoms during adolescence and specifically in looked-after children. Method: We computed cross-sectional networks (Gaussian Graphical Model) in three separate datasets: the Mental Health of Children and Young People in Great Britain 1999 survey (n=4235, age 10–15 years), the mental health of young people looked after by local authorities in Great Britain 2002 survey (n=643, age 11–17 years) and the Millennium Cohort Study in the UK 2015 (n=11 176, age 14 years). Findings: In all three networks, self-hate emerged as a key symptom, which aligns with former network studies. I was no good anymore was also among the most central symptoms. Among looked-after children, I was a bad person constituted a central symptom, while this was among the least central symptom in the other two datasets. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition symptom I did not enjoy anything was not central. Conclusions: Findings indicate that looked-after children’s depressive symptoms may be more affected by negative self-evaluation compared with the general population. Clinical implications: Intervention efforts may benefit from being tailored to negative self-evaluations

The development of depressive symptoms in older adults from a network perspective in the English Longitudinal Study of Ageing

Abstract An increased understanding of the interrelations between depressive symptoms among older populations could help improve interventions. However, studies often use sum scores to understand depression in older populations, neglecting important symptom dynamics that can be elucidated in evolving depressive symptom networks. We computed Cross-Lagged Panel Network Models (CLPN) of depression symptoms in 11,391 adults from the English Longitudinal Study of Ageing. Adults aged 50 and above (mean age 65) were followed over 16 years throughout this nine-wave representative population study. Using the eight-item Center for Epidemiological Studies Depression Scale, we computed eight CLPNs covering each consecutive wave. Across waves, networks were consistent with respect to the strength of lagged associations (edge weights) and the degree of interrelationships among symptoms (centrality indices). Everything was an effort and could not get going displayed the strongest reciprocal cross-lagged associations across waves. These two symptoms and loneliness were core symptoms as reflected in strong incoming and outgoing connections. Feeling depressed was strongly predicted by other symptoms only (incoming but not strong outgoing connections were observed) and thus was not related to new symptom onset. Restless sleep had outgoing connections only and thus was a precursor to other depression symptoms. Being happy and enjoying life were the least central symptoms. This research underscores the relevance of somatic symptoms in evolving depression networks among older populations. Findings suggest the central symptoms from the present study (everything was an effort, could not get going, loneliness) may be potential key intervention targets to mitigate depression in older adults