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Objective Youth with early-onset mood or psychotic disorders are occasionally prescribed metformin to manage cardiometabolic risk. This retrospective study explores the demographic, clinical and metabolic factors associated with metformin prescription youth with mood or psychotic disorders. Method Participants included 72 youth with mood or psychotic disorders from a young adult mental health inpatient unit, of which 18 (33%) were newly prescribed metformin, and 54 (66%) were not prescribed metformin. Demographic and clinical information were extracted from the patients’ medical files along with body mass index (BMI), fasting serum bloods and calculated updated homeostatic model of insulin resistance assessment (HOMA2-IR) scores to compare profiles between groups. Results Of those prescribed metformin, 83% were overweight or obese and 72% had elevated HOMA2-IR scores. Of those not receiving metformin treatment, 41% were overweight or obese and 22% had elevated HOMA2-IR scores. Youth prescribed metformin had significantly higher BMI, and elevated markers of insulin resistance, but did not differ to those not prescribed metformin on other demographic, clinical or metabolic factors. Conclusions While metformin is prescribed to some youth with mood or psychotic disorders displaying markers of cardiometabolic disturbance, there is a need to develop clearer treatment guidelines for metformin in these youth.

Patients with depression and bipolar disorder have previously been shown to have impaired white matter (WM) integrity compared with healthy controls. This study aimed to investigate potential sex differences that may provide further insight into the pathophysiology of these highly debilitating mood disorders.Participants aged 17 to 30 years (168 with depression [60% females], 107 with bipolar disorder [74% females], and 61 controls [64% females]) completed clinical assessment, self-report measures, and a neuropsychological assessment battery. Participants also underwent magnetic resonance imaging from which diffusion tensor imaging data were collected among five fronto-limbic WM tracts: cingulum bundle (cingulate gyrus and hippocampus subsections), fornix, stria terminalis, and the uncinate fasciculus. Mean fractional anisotropy (FA) scores were compared between groups using analyses of variance with sex and diagnosis as fixed factors.Among the nine WM tracts analyzed, one revealed a significant interaction between sex and diagnosis, controlling for age. Male patients with bipolar disorder had significantly lower FA scores in the fornix compared with the other groups. Furthermore, partial correlations revealed a significant positive association between FA scores for the fornix and psychomotor speed.Our findings suggest that males with bipolar disorder may be at increased risk of disruptions in WM integrity, especially in the fornix, which is thought to be responsible for a range of cognitive functions. More broadly, our findings suggest that sex differences may exist in WM integrity and thereby alter our understanding of the pathophysiology of mood disorders.

There is significant interest in the possible influence of chronotype on clinical states in young people with emerging mental disorders. We apply a dynamic approach (bivariate latent change score modelling) to examine the possible prospective influence of chronotype on depressive and hypo/manic symptoms in a youth cohort with predominantly depressive, bipolar, and psychotic disorders (N = 118; 14–30-years), who completed a baseline and follow-up assessment of these constructs (mean interval = 1.8-years). Our primary hypotheses were that greater baseline eveningness would predict increases in depressive but not hypo/manic symptoms. We found moderate to strong autoregressive effects for chronotype (β = -0.447 to −0.448, p < 0.001), depressive (β = -0.650, p < 0.001) and hypo/manic symptoms (β = -0.819, p < 0.001). Against our predictions, baseline chronotypes did not predict change in depressive (β = -0.016, p = 0.810) or hypo/manic symptoms (β = 0.077, p = 0.104). Similarly, the change in chronotype did not correlate with the change in depressive symptoms (β = -0.096, p = 0.295) nor did the change in chronotype and the change in hypo/manic symptoms (β = -0.166, p = 0.070). These data suggest that chronotypes may have low utility for predicting future hypo/manic and depressive symptoms in the short term, or that more frequent assessments over longer periods are needed to observe these associations. Future studies should test whether other circadian phenotypes (e.g. sleep-wake variability) are better indicators of illness course.

AIM Young people with common mood disorders face the prospect of shortened life expectancy largely due to premature cardiovascular disease. Metabolic dysfunction is a risk factor for premature cardiovascular disease. There is an ongoing debate whether metabolic dysfunction can be simply explained by weight gain secondary to psychotropic medications or whether shared genetic vulnerability, intrinsic immune-metabolic disturbances or other system perturbations (e.g. dysregulated sympathetic nervous system, circadian dysfunction) are more relevant determinants of premature cardiovascular disease. Thus, we aimed to investigate underlying drivers of metabolic dysfunction and premature cardiovascular disease in young people in the early phases of common mood disorders. METHODS We evaluated the relationships between insulin resistance (assessed by HOMA2-IR) and body mass index (BMI), sex, diagnosis, medication, inflammatory markers and hormonal factors in 327 inpatients with emerging affective and major mood disorders admitted to the Young Adult Mental Health Unit, St Vincent's Private Hospital, Sydney. RESULTS While HOMA2-IR scores were positively associated with BMI (rs = 0.465, p

BACKGROUND As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to an individual’s risk of illness progression. The application of staging has been traditionally limited to trained clinicians, yet if digital technologies could be leveraged to apply clinical staging, then this could increase the scalability and utility of this model in services. OBJECTIVE The aim of this study is to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders. METHODS The cohort comprised 131 young people, aged between 16 to 25 years, who presented to youth mental health services in Australia for the first time between November 2018 to March 2021. Clinical stages (either stage 1a or stage 1b+) were allocated independently by expert psychiatrists and compared to the digital algorithm based on a multidimensional self-report questionnaire. RESULTS Of the 131 participants, the mean (SD) age was 20.3 (2.4) years and 94 (71.8%) were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the expert ratings with a substantial interrater agreement (κ=0.67, P CONCLUSIONS This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in early stages of common anxiety and depressive disorders. Between 11% and 27% of young people presenting for care may be suitable for low intensity online or brief interventions, creating additional clinical capacity to be directed towards those who are in stage 1b+ for further assessment and intervention.

BACKGROUND As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to an individual’s risk of illness progression. The application of staging has been traditionally limited to trained clinicians, yet if digital technologies could be leveraged to apply clinical staging, then this could increase the scalability and utility of this model in services. OBJECTIVE The aim of this proof-of-concept study is to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders. METHODS The cohort comprised 131 young people, aged between 16 to 25 years, who presented to youth mental health services in Australia for the first time between November 2018 to March 2021. Clinical stages (either stage 1a or stage 1b+) were allocated independently by expert psychiatrists and compared to the digital algorithm based on a multidimensional self-report questionnaire. RESULTS Of the 131 participants, the mean (SD) age was 20.3 (2.4) years and 94 (71.8%) were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the expert ratings with a substantial interrater agreement (κ=0.67, P CONCLUSIONS This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in early stages of common anxiety and depressive disorders. Between 11% and 27% of young people presenting for care may be suitable for low intensity online or brief interventions, creating additional clinical capacity to be directed towards those who are stage 1b+ for further assessment and intervention.

Objective To identify trajectories of social and occupational functioning in young people during the two years after presenting for early intervention mental health care; to identify demographic and clinical factors that influence these trajectories. Design Longitudinal, observational study of young people presenting for mental health care. Setting Two primary care-based early intervention mental health services at the Brain and Mind Centre (University of Sydney), 1 June 2008 - 31 July 2018. Participants 1510 people aged 12-25 years who had presented with anxiety, mood, or psychotic disorders, for whom two years' follow-up data were available for analysis. Main outcome measures Latent class trajectories of social and occupational functioning based on growth mixture modelling of Social and Occupational Assessment Scale (SOFAS) scores. Results We identified four trajectories of functioning during the first two years of care: deteriorating and volatile (733 participants, 49%); persistent impairment (237, 16%); stable good functioning (291, 19%); and improving, but late recurrence (249, 16%). The less favourable trajectories (deteriorating and volatile; persistent impairment) were associated with physical comorbidity, not being in education, employment, or training, having substance-related disorders, having been hospitalised, and having a childhood onset mental disorder, psychosis-like experiences, or a history of self-harm or suicidality. Conclusions Two in three young people with emerging mental disorders did not experience meaningful improvement in social and occupational functioning during two years of early intervention care. Most functional trajectories were also quite volatile, indicating the need for dynamic service models that emphasise multidisciplinary interventions and measurement-based care.

A rate-limiting step in the prevention and early intervention of depressive disorders in young people is our insufficient understanding of causal mechanisms. One plausible pathophysiological pathway is disturbance in the 24 h sleep-wake cycle and the underlying circadian system. Abnormalities in circadian rhythms are well documented in adults with various depressive disorders and have been linked to core clinical features, including unstable mood, daytime fatigue, non-restorative sleep, reduced motor activity, somatic symptoms, and appetite and weight change. In this Review, we summarise four areas of research: basic circadian biology and animal models of circadian disturbances; developmental changes in circadian rhythms during adolescence and implications for the emergence of adolescent-onset depressive syndromes; community and clinical studies linking 24 h sleep-wake cycle disturbances and depressive disorders; and clinical trials of circadian-based treatments. We present recommendations based on a highly personalised, early intervention model for circadian-linked depression in young people.

Major mood syndromes are among the most common and disabling mental disorders. However, a lack of clear delineation of their underlying pathophysiological mechanisms is a major barrier to prevention and optimised treatments. Dysfunction of the 24-h circadian system is a candidate mechanism that has genetic, behavioural, and neurobiological links to mood syndromes. Here, we outline evidence for a new clinical phenotype, which we have called ‘circadian depression’. We propose that key clinical characteristics of circadian depression include disrupted 24-h sleep-wake cycles, reduced motor activity, low subjective energy, and weight gain. The illness course includes early age-of-onset, phenomena suggestive of bipolarity (defined by bidirectional associations between objective motor and subjective energy/mood states), poor response to conventional antidepressant medications, and concurrent cardiometabolic and inflammatory disturbances. Identifying this phenotype could be clinically valuable, as circadian-targeted strategies show promise for reducing depressive symptoms and stabilising illness course. Further investigation of underlying circadian disturbances in mood syndromes is needed to evaluate the clinical utility of this phenotype and guide the optimal use of circadian-targeted interventions.

This paper presents a case study of an innovative direct-to-consumer preclinic triage system designed to reduce predicted peak demand for Australian mental health services as a result of COVID-19 and its associated socioeconomic consequences by guiding Australians to the right mental health care first time. Our innovative, digital health solution comprises two components: (1) a highly personalised and measurement-based model of care (Brain and Mind Centre model of care) that considers both the heterogeneity of mental disorders and other underlying comorbidities, as well as clinical staging; and (2) a health information technology (i.e. the InnoWell Platform). This digital health solution has been embedded as part of standard service delivery into a community-based intake service, thus resulting in a redesigned service model. The service model is currently being implemented as part of a pilot feasibility study, the marker of acceptability at the health professional and service level, and is now under active evaluation to determine its effect on outcomes for consumers, health professionals and the service. For the purposes of this paper, this model served as a prototype for the preclinic triage system that was conceptualised for national scalability at the primary health network level. When implemented at a national level, our direct-to-consumer preclinic triage system is expected to be an effective population health demand management strategy to address the rapidly emerging mental health demand crisis in Australia, and is aligned with the recent recommendation from the Productivity Commission to develop a sustainable national digital platform to facilitate the assessment and referral process to ensure access to mental health care matched to an individual’s level of need. What is known about the topic? Although there is increased recognition of the mental health demand crisis in Australia as a result of the COVID-19 pandemic, little has been done to ‘flatten’ the curve. The Australian Government committed additional funding to support the Better Access Pandemic Support measure; however, this approach to care fails to appreciate both the disparities in service availability across Australia and the gap fees that are prohibitive to some of those seeking help. Furthermore, the expansion of this program may only result in those in care remaining in care, thus further delaying access to those in need. What does this paper add? This paper describes a digital health solution, comprised of a highly personalised and measurement-based model of care coupled with a health information technology, that has been embedded as part of standard service delivery. Consumers seeking mental health care complete a multidimensional self-report assessment via the technology, the results of which are available in real-time and used to facilitate triage to pathways of care as indicated by the severity of the consumer’s illness and level of need to more effectively and efficiently allocate consumers to care. The redesigned service model is now under active evaluation to determine its effects on outcomes at consumer, health professional and service levels. What are the implications for practitioners? The redesigned local service model served as a prototype for our innovative direct-to-consumer preclinic triage system specifically designed to allocate consumers to self-management, ambulatory care or acute care based on clinical stage and level of need. It is our hypothesis that the preclinic triage system will be an effective population health demand management strategy. Importantly, the proposed preclinic triage system aligns with the recent recommendation from the Productivity Commission for the Australian Government to fund the development and sustained implementation of a digital platform to facilitate assessment and referral to evidence-based interventions matched to a consumer’s level of need.

An ongoing need exists for innovation in service delivery to ensure that mental health services deliver high-quality treatment and prevention in the population. This Special Article proposes the adoption of “staged care” as a population health–oriented service delivery model for packages of specialized services delivered largely in ambulatory care settings for individuals with common affective disorders. Staged care integrates measures of clinical need alongside clinical stage and personal choice to select hierarchically arranged service packages for individuals. Packages then vary according to the intensity, duration, and mix of treatment options. This Special Article describes five levels of care in staged care: self- or family-directed monitoring and management, low-intensity services, moderate-intensity services, high-intensity services, and acute and specialist community mental health services. The care environment, treatment team, and length of treatment are also described, and provisional criteria are specified for assigning individuals to different care levels on the basis of current clinical need and clinical stage. Staged care is presented as a model that guides treatment selection and health service delivery to ensure that the high-quality care aims of “right care first time” and prevention are achieved and optimal use of available resources is considered.

ObjectivesThe aim of this study was to determine the prevalence of disordered eating in young people attending a headspace centre, an enhanced primary care centre providing early intervention services for mental health disorders for young people aged 12–25 years, in metropolitan Sydney.DesignCross-sectional assessment of disordered eating symptoms and behaviours.SettingAn enhanced primary care youth mental health service in inner urban Sydney, Australia.ParticipantsA sequential cohort of 530 young people aged 14–26 years presenting to headspace Camperdown for support with mental health concerns.Outcome measuresParticipants completed a series of questionnaires online which included items assessing the presence of eating disorder symptoms and behaviours.ResultsOver one-third of young people aged 14–26 years presenting to headspace Camperdown in a 22-month period reported symptoms of disordered eating. Of these, 32% endorsed overeating behaviours, 25% endorsed dietary restriction and 8% reported purging behaviours. In total, 44% reported engaging in one of more of these behaviours on a regular basis. Almost half reported experiencing significant shape and weight concerns. Eating disorder behaviours were particularly prevalent among female and gender-diverse participants (48% of females and 46% of gender-diverse participants compared with 35% of males) and overall scores across all of the eating disorder and body image items assessed were significantly higher for female participants compared with males.ConclusionsDisordered eating behaviours and symptoms are common among those presenting to youth mental health primary care services. Proactive screening for these behaviours presents opportunities for early detection and specific interventions.Trial registration numberACTRN12618001676202; Results.

Background The World Economic Forum has recently highlighted substantial problems in mental health service provision and called for the rapid deployment of smarter, digitally-enhanced health services as a means to facilitate effective care coordination and address issues of demand. In mental health, the biggest enabler of digital solutions is the implementation of an effective model of care that is facilitated by integrated health information technologies (HITs); the latter ensuring the solution is easily accessible, scalable and sustainable. The University of Sydney’s Brain and Mind Centre (BMC) has developed an innovative digital health solution – delivered through the Youth Mental Health and Technology Program – which incorporates two components: 1) a highly personalised and measurement-based (data-driven) model of youth mental health care; and 2) an industrial grade HIT registered on the Australian Register of Therapeutic Goods. This paper describes a research protocol to evaluate the impact of implementing the BMC’s digital health solution into youth mental health services (i.e. headspace - a highly accessible, youth-friendly integrated service that responds to the mental health, physical health, alcohol or other substance use, and vocational concerns of young people aged 12 to 25 years) within urban and regional areas of Australia. Methods The digital health solution will be implemented into participating headspace centres using a naturalistic research design. Quantitative and qualitative data will be collected from headspace health professionals, service managers and administrators, as well as from lead agency and local Primary Health Network (PHN) staff, via service audits, Implementation Officer logs, online surveys, and semi-structured interviews, at baseline and then three-monthly intervals over the course of 12 months. Discussion At the time of publication, six headspace centres had been recruited to this study and had commenced implementation and impact evaluation. The first results are expected to be submitted for publication in 2021. This study will focus on the impact of implementing a digital health solution at both a service and staff level, and will evaluate digital readiness of service and staff adoption; quality, usability and acceptability of the solution by staff; staff self-reported clinical competency; overall impact on headspace centres as well as their lead agencies and local PHNs; and social return on investment.

Objective This study utilised digital technology to assess the clinical needs of young people presenting for care at headspace centres across Australia. Method 1490 young people (12–25 years) who presented to one of 11 headspace services from four geographical locations (urban New South Wales, urban South Australia, regional New South Wales, and regional Queensland) completed a digital multidimensional assessment at initial presentation. Characteristics were compared between services and geographical locations. Results We identified major variation in the demographics, and the type and severity of needs across different services. Individuals from regional services were more likely to be younger, of Aboriginal and Torres Strait Islander origin, and present with psychotic-like symptoms and suicidality, while those in urban areas were more likely to have previously sought help and have problematic alcohol use. Further differences in age, distress, depressive symptoms, psychotic-like experiences, trauma, family history, alcohol use, education/employment engagement, and days out of role were identified between different urban sites. Conclusions The variability between services provides insight into the heterogeneity of youth mental health populations which has implications for appropriate early intervention and prevention service provisions. We propose that integrating digital technologies has the potential to provide insights for smarter service planning and evaluation.

Objectives The Holmes County Amish have low vaccination rates, an increasingly diverse population, and have an increased incidence of certain inherited diseases. The objectives were to evaluate; the rate and influences of vaccine hesitancy compared to a decade ago, vaccination patterns between Amish affiliations, vaccine practices of Amish special needs children, and the Amish’s acceptance of a COVID-19 vaccine. Study design. In April of 2020, a survey assessing vaccination patterns and beliefs were mailed to 1000 Amish families, including ultra-conservative Amish sects and special needs families. Results The response rate was 39%. Among 391 respondents, 59% did not vaccinate their children, compared to only 14% that refused all vaccinations reported by Wenger et al in the same community only a decade ago. The ultra-conservative Amish rejected vaccines more often. Amish special needs children were more likely to receive vaccines than healthy Amish children. 75% responded they would reject a COVID-19 vaccine. Fear of adverse effects was the most common reason to reject vaccines. Families that accepted vaccines were more likely to cite a healthcare worker as the primary influence to vaccinate. Wives were more likely to cite their spouse as the primary influence to vaccinate. Families that rejected vaccines were more likely to state their bishop was the most influential person on vaccination. Conclusion The Holmes County Amish have decreasing vaccine acceptance. Efforts to improve vaccination will require a targeted focus on the primary influences and beliefs of sub-populations within the Amish. Physician advocacy, peer mentorship, father-directed education, and close partnership with Church leadership will be needed to limit vaccine-preventable disease. The Amish may be at risk for low uptake of a COVID-19 vaccine.

UNSTRUCTURED This is a corrigendum.

BackgroundPredictors of new-onset bipolar disorder (BD) or psychotic disorder (PD) have been proposed on the basis of retrospective or prospective studies of ‘at-risk’ cohorts. Few studies have compared concurrently or longitudinally factors associated with the onset of BD or PDs in youth presenting to early intervention services. We aimed to identify clinical predictors of the onset of full-threshold (FT) BD or PD in this population.MethodMulti-state Markov modelling was used to assess the relationships between baseline characteristics and the likelihood of the onset of FT BD or PD in youth (aged 12–30) presenting to mental health services.ResultsOf 2330 individuals assessed longitudinally, 4.3% (n = 100) met criteria for new-onset FT BD and 2.2% (n = 51) met criteria for a new-onset FT PD. The emergence of FT BD was associated with older age, lower social and occupational functioning, mania-like experiences (MLE), suicide attempts, reduced incidence of physical illness, childhood-onset depression, and childhood-onset anxiety. The emergence of a PD was associated with older age, male sex, psychosis-like experiences (PLE), suicide attempts, stimulant use, and childhood-onset depression.ConclusionsIdentifying risk factors for the onset of either BD or PDs in young people presenting to early intervention services is assisted not only by the increased focus on MLE and PLE, but also by recognising the predictive significance of poorer social function, childhood-onset anxiety and mood disorders, and suicide attempts prior to the time of entry to services. Secondary prevention may be enhanced by greater attention to those risk factors that are modifiable or shared by both illness trajectories.

Service-learning has become an established pedagogy in higher education classrooms. With a push in recent years to offer classroom experiences that offer more than static lectures, service-learning incorporates three main elements into its model; experiential learning, contribution to the community, and reflection which merges theory with practice for a unique learning experience. Service-learning can also be found in early models of library science education. This article will highlight service-learning projects in both libraries and archives that used innovative methods to create partnerships in the community. It will also argue that both libraries and archives should not be overlooked when considering these projects and can be valuable resources for successful service-learning partnerships.