BackgroundFew cost-utility studies of child and adolescent mental health services (CAMHS) use quality adjusted life years (a combination of utility weights and time in health state) as the outcome to enable comparison across disparate programs and modalities. Part of the solution to this problem involves embedding preference-based health-related quality of life (PBHRQOL) utility instruments, which generate utility weights, in clinical practice and research. The Child Health Utility (CHU9D) is a generic PBHRQOL instrument developed specifically for use in young people. The purpose of this study was to assess the suitability of the CHU9D as a routine outcome measure in CAMHS clinical practice.MethodsTwo hundred caregivers of children receiving community mental health services completed the CHU9D alongside a standardised child and adolescent mental health measure (the Strengths and Difficulties Questionnaire – SDQ) during a telephone interview. We investigated face validity, practicality, internal consistency, and convergent validity of the CHU9D. In addition, we compared the utility weights obtained in this group with utility weights from other studies of child and adolescent mental health populations.ResultsParticipants found the CHU9D easy and quick to complete. It demonstrated acceptable internal consistency, and correlated moderately with the SDQ. It was able to discriminate between children in the abnormal range and those in the non-clinical/borderline range as measured by the SDQ. Three CHU9D items without corollaries in the SDQ (sleep, schoolwork, daily routine) were found to be significant predictors of the SDQ total score and may be useful clinical metrics. The mean utility weight of this sample was comparable with clinical subsamples from other CHU9D studies, but was significantly higher than mean utility weights noted in other child and adolescent mental health samples.ConclusionsInitial validation suggests further investigation of the CHU9D as a routine outcome measure in CAMHS is warranted. Further investigation should explore test-retest reliability, sensitivity to change, concordance between caregiver and child-completed forms, and the calibration of the utility weights. Differences between utility weights generated by the CHU9D and other utility instruments in this population should be further examined by administering a range of PBHRQOL instruments concurrently in a mental health group.

Treatment options are limited for families in which the child has severe and intractable disturbances of emotion and behavior, in which there is suspected or confirmed maltreatment by the mother, and in which the mother has her own history of childhood neglect and abuse. This paper proposes a model for understanding maltreatment in mother-child dyads, drawing upon the developmental psychopathology, behavior, and trauma literatures. At the core of this model is the hypothesis that a mother's maltreating behavior arises from unconscious attempts to experientially avoid the reemergence of an attachment-related dissociative part of the personality that contains the distress arising from her own early experiences of attachment relationships. The implications of this model for therapy are considered.

This research generated algorithms for translating SDQ scores to utility values and providing researchers with an additional tool for conducting health economic evaluations with child and adolescent mental health data.

While the importance of looking at the entire family system in the context of child and adolescent mental health is well recognised, siblings of children with mental health problems (MHPs) are often overlooked. The existing literature on the mental health of these siblings needs to be reviewed. A systematic search located publications from 1990 to 2011 in four electronic databases. Thirty-nine relevant studies reported data on the prevalence of psychopathology in siblings of target children with MHPs. Siblings of target children had higher rates of at least one type of psychopathology than comparison children. Risk of psychopathology varied across the type of MHP in the target child. Other covariates included sibling age and gender and parental psychopathology. Significant variations and limitations in methodology were found in the existing literature. Methodological guidelines for future studies are outlined. Implications for clinicians, parents, and for future research are discussed.

National Health and Medical Research Council (Australia), Department of Health SA.

Background During COVID-19, the psychological distress and well-being of the general population has been precarious, increasing the need to determine the impact of complementary internet-based psychological interventions on both positive mental health as well as distress states. Psychological distress and mental well-being represent distinct dimensions of our mental health, and congruent changes in outcomes of distress and well-being do not necessarily co-occur within individuals. When testing intervention impact, it is therefore important to assess change in both outcomes at the individual level, rather than solely testing group differences in average scores at the group level. Objective This study set out to investigate the differential impact of an internet-based group mental health intervention on outcomes of positive mental health (ie, well-being, life satisfaction, resilience) and indicators of psychological distress (ie, depression, anxiety, stress). Methods A 5-week mental health intervention was delivered to 89 participants using the Zoom platform during 2020. Impact on outcomes of distress, well-being, and resilience was assessed at the start and end of the program with multiple analysis of variance (MANOVA) and reliable change indices (RCIs) being used to determine program impact at the group and individual levels, respectively. Results The intervention significantly improved all mental health outcomes measured, (F6,83=5.60, P<.001; Wilks Λ=.71; partial η2=.29) showing small to moderate effect sizes on individual outcomes. The largest effect sizes were observed for life satisfaction and overall well-being (η2=.22 and η2=.2, respectively). Larger effect sizes were noted for those with problematic mental health scores at baseline. A total of 92% (82/89) of participants demonstrated reliable change in at least one mental health outcome. Differential response patterns using RCI revealed that more than one-half of the participants showed improvement in both mental well-being and psychological distress, over one-quarter in outcomes of well-being only, and almost one-fifth in distress only. Conclusions The results provide evidence for the significant impact of an internet-based mental health intervention during COVID-19 and indicate the importance of assessing dimensions of both well-being and distress when determining mental health intervention effectiveness.

BackgroundMental illness is prevalent across the globe and affects multiple aspects of life. Despite advances in treatment, there is little evidence that prevalence rates of mental illness are falling. While the prevention of cardiovascular disease and cancers are common in the policy dialogue and in service delivery, the prevention of mental illness remains a neglected area. There is accumulating evidence that mental illness is at least partially preventable, with increasing recognition that its antecedents are often found in infancy, childhood, adolescence and youth, creating multiple opportunities into young adulthood for prevention. Developing valid and reproducible methods for translating the evidence base in mental illness prevention into actionable policy recommendations is a crucial step in taking the prevention agenda forward.MethodBuilding on an aetiological model of adult mental illness that emphasizes the importance of intervening during infancy, childhood, adolescence and youth, we adapted a workforce and service planning framework, originally applied to diabetes care, to the analysis of the workforce and service structures required for best-practice prevention of mental illness.ResultsThe resulting framework consists of 6 steps that include identifying priority risk factors, profiling the population in terms of these risk factors to identify at-risk groups, matching these at-risk groups to best-practice interventions, translation of these interventions to competencies, translation of competencies to workforce and service estimates, and finally, exploring the policy implications of these workforce and services estimates. The framework outlines the specific tasks involved in translating the evidence-base in prevention, to clearly actionable workforce, service delivery and funding recommendations.ConclusionsThe framework describes the means to deliver mental illness prevention that the literature indicates is achievable, and is the basis of an ongoing project to model the workforce and service structures required for mental illness prevention.

The categorisation scheme developed in this paper is a step towards a more detailed taxonomy of risk factors for mental illness; this will be most useful in guiding clinicians, researchers and policy-makers in driving the prevention agenda forward.