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Early Intervention in Psychiatry 2014; ••: ••–••

doi:10.1111/eip.12133

Original Article Demographic and clinical characteristics of young people seeking help at youth mental health services: baseline findings of the Transitions Study Rosemary Purcell,1 Anthony F. Jorm,2 Ian B. Hickie,3 Alison R. Yung,1,6 Christos Pantelis,4 G. Paul Amminger,1 Nick Glozier,3 Eoin Killackey,1 Lisa J. Phillips,5 Stephen J. Wood,4,7 Susy Harrigan,1 Andrew Mackinnon,1 Elizabeth Scott,3 Daniel F. Hermens,3 Adam J. Guastella,3 Amanda Kenyon,3 Laura Mundy,1 Alissa Nichles,3 Antoinette Scaffidi,1 Daniela Spiliotacopoulos,1 Lara Taylor,1 Janet P.Y. Tong,1 Suzanne Wiltink,1 Natalia Zmicerevska3 and Patrick D. McGorry1 Abstract

1 Orygen Youth Health Research Centre, Centre for Youth Mental Health, Schools of 2Population and Global Health and 5 Psychological Sciences, The University of Melbourne, 4Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Victoria, and 3Brain & Mind Research Institute, The University of Sydney, Sydney, New South Wales, Australia; and 6Institute of Brain, Behaviour and Mental Health, University of Manchester, Manchester, and 7School of Psychology, University of Birmingham, Edgbaston, UK

Aim: The Transitions Study was designed to establish a cohort of young people (12–25 years) seeking help for mental health problems, in order to longitudinally explore and refine a clinical staging model of the development and progression of mental disorders. This paper presents the baseline demographic and clinical characteristics of the cohort, particularly the nature and severity of psychopathology. Method: All eligible young people attending one of four headspace clinical services were invited to participate, and completed a battery of selfreport and interviewer-administered measures of psychopathology and functional impairment at baseline, which will be repeated at the annual follow up.

severity of mental health problems varied, with 51% meeting the criteria for probable caseness related to generalized anxiety, 45% presenting with moderate to severe depressive symptoms and over a third experiencing subthreshold psychotic symptomatology. Disordered eating (32%) and problematic tobacco (56%), cannabis (30%) and alcohol (38%) use also affected a significant proportion. Overall, 39% of the cohort were classed as being functionally impaired at baseline. Conclusion: The Transitions Study recruited a heterogeneous cohort at baseline in relation to the nature and severity of mental health problems and levels of functional impairment. The variation in clinical presentations within the cohort, from mild, through moderate to severe levels of psychopathology and impairment, increases the likelihood of the Transitions Study ultimately being able to achieve its aims of empirically testing a clinical staging model for mental disorders.

Corresponding author: A/Professor Rosemary Purcell, Centre for Forensic Behavioural Science, Swinburne University of Technology, 505 Hoddle St, Clifton Hill, Melbourne, Vic. 3068, Australia. Email: [email protected]

Results: Of 1615 eligible clients, 802 young people (66% women; mean age = 18.3 years) consented to participate and completed baseline assessments (participation rate = 50%). The

Received 25 November 2013; accepted 6 February 2014

Key words: anxiety, clinical staging, depression, early psychosis, youth mental health.

Over the past decade, the scale and significance of the problem of mental ill-health in adolescents and young adults has been increasingly recognized.1,2 This has been driven by epidemiological studies © 2014 Wiley Publishing Asia Pty Ltd

of the prevalence and patterns of onset of mental disorders, as well as by research on the relationship between illness prevalence and clinical service provision. The National Comorbidity Study Replication 1

Baseline transitions study results in the USA indicated that the majority of mental ill-health emerges during early life, with half of all lifetime cases commencing by 14 years of age, and 75% by 24 years.1 Mental disorders are now estimated to be the largest contributor to the burden of disease in young people.2–5 Despite this, access to, and use of, health services by young people to improve mental health outcomes is low. In Australia, data from the 2007 National Survey of Mental Health and Wellbeing indicated that the gap between the peak prevalence of disorders and service use is greatest in young people aged 16–24 years.6 While 26% of this age group met the International Statistical Classification of Diseases and Related Health Problems, 10th revision diagnostic criteria for a mental disorder in the previous 12 months, fewer than one in four such individuals received clinical services for these problems. This rate of mental health service utilization is lower than the 36.2% reported in the US National Comorbidity SurveyAdolescent Supplement study,7 although the US sample was confined to 13–18-year-olds, with high rates of utilization being observed for younger adolescents with attention deficit and other behavioural disorders. In response to the inverse relationship between the burden of mental ill-health in young people and access to and engagement with traditional health services, in 2006, the Australian Federal Government established headspace, the National Youth Mental Health Foundation, to promote and support early intervention in 12–25-year-olds.8 From this initiative, 55 headspace clinical services currently operate throughout Australia providing youthfocused mental health services (including alcohol and drug services), primary health-care services (including sexual health consultations) and vocational assistance. Funding has been allocated to increase the number of headspace centres to 100 by 2016. Similar programmes designed to target the needs of young people with mental ill-health have since been established in other countries, including Youthspace in the UK, Headstrong in Ireland9 and headspace in Denmark. As this new service stream of care for youth mental health is in its early phases of development,10 so too is research regarding the characteristics and clinical needs of help-seekers attending youth mental health services such as headspace. In a study of 1260 young people (mean age 18.1 years, 53% male) attending two headspace centres in Sydney, Scott and colleagues11 reported high levels of psychological distress (K1012), disability and vocational impairment. Over two-thirds (69.5%) of the sample scored in the high to very high range on the 2

K-10 and 63.2% reported having experienced at least 2 days in which they were ‘unable to function’ in the past month. Some 41.5% of clients were not currently engaged in any form of education and fewer than 10% were employed full-time, the latter result consistent with international findings.13 Overall, a quarter of Scott et al.’s11 sample were neither employed nor studying (24.8%). Clinicians rated half the sample (51.7%) as being socially and/or functionally impaired on the Social and Occupational Functioning Assessment Scale (SOFAS14). Scott et al.’s study presented the first comprehensive, systematic data regarding youth mental health service clients, highlighting the high levels of distress and disability observed even in the context of an early intervention service. We designed the Transitions Study15 in order to assemble, characterize and follow a cohort of young people accessing headspace, and to longitudinally explore and refine a clinical staging model of the development and progression of mental disorders16–18 within a large cohort of help-seeking young people. headspace centres were selected as the recruitment sites over tertiary Children and Adolescent Mental Health Services (CAMHS), as headspace provides services to a broader age range (12–25 years) and are more likely than CAMHS to manage a range of illness severity, particularly mild to moderate clinical presentations. Participants completed interviewer and self-rated measures of psychopathology (e.g. depression, anxiety, mania, psychosis, disordered eating and substance misuse), as well as a range of psychosocial measures (e.g. childhood trauma, parental bonding, social support) at baseline and annual follow-up time points (12 and 24 months). The purpose of this paper was to describe the baseline demographic and clinical characteristics of the Transitions Study cohort, particularly the nature and severity of psychopathology in this population, including their current stage of mental ill-health.

METHOD The study methodology has been described in detail elsewhere.15 Participants were young people aged 12–25 years who sought help from one of four headspace clinical services in Melbourne and Sydney, Australia, between January 2011 and August 2012. As headspace centres focus both on youth mental health and early intervention, young people may present for care with varying illness severity (e.g. sub-threshold through to severe disorder, and mild to severely impaired functioning) across a range of mental health problems. © 2014 Wiley Publishing Asia Pty Ltd

R. Purcell et al. Procedure All young people aged 12–25 who were receiving a clinical service at one of the four headspace centres involved in the study, were English-speaking and able to provide informed consent were approached for participation (see Fig. 1 for recruitment flowchart). Participants included both young people who had not yet received a clinical service at headspace (e.g. on the waitlist), as well as those who were currently receiving clinical interventions. The study headspace centres were located in major metropolitan regions of Melbourne (n = 2) and Sydney (n = 2) and were selected on the basis of their being affiliated with the investigators’ research centres as part of their governance models. Three of the centres are located in outer-metropolitan suburbs that are characterized by socio-economic disadvantage and minimal private sector mental health services, while the fourth centre is located in a relatively affluent inner-city suburb. It should be noted, however, that there are no defined geographical catchment areas for headspace centres and therefore, young people can attend headspace irrespective of their place of residence. Clinical services at headspace are delivered in a youth-friendly environment by general practitioners, psychologists, psychiatrists and other allied health professionals. The majority of consultations are funded under government health-care schemes, although full or co-payment is also accepted. The centres accept self-referrals, as well as referrals from families,

friends, school welfare coordinators and health practitioners. Young people who were acutely suicidal, as determined by their assessing or treating headspace clinician, were not approached for study inclusion until the degree of suicidal risk had resolved to safe levels. Of the 1615 eligible clients approached for study participation, 806 consented (of whom four subsequently withdrew), representing a participation rate of 49.9% (see Fig. 1). Research assistants (RAs) with a minimum 4-year graduate psychology degree implemented the study protocol. The RAs were trained in the use of the study measures and had achieved very good (i.e. kappa of at least 0.8) interrater reliability on the interviewer-rated clinical measures before commencing recruitment. The RAs conducted structured interviews with each participant using the clinical measures summarized later, before then providing an iPad or laptop for the completion of a range of self-report measures. Participants each received a $20 gift voucher for reimbursement. The study protocol was approved by the Human Research Ethics Committees at the University of Melbourne and the University of Sydney. Clinical measures A comprehensive description of the study measures, including their scoring, is available elsewhere.15 Briefly, the interviewer administered measures included the 16-item adolescent version of the

FIGURE 1. Transitions study recruitment flowchart.

Approached across all sites (N = 1685) Ineligible upon approach

Eligible upon approach

(N = 70)

(N = 1615) Recruited Not recruited

(N = 806)

(N = 809) Withdrew from study (N = 4) Declined (N = 375) RAs spoke to client once but unable to reach again (N = 132) Failed to aƩend 2+ research appointments (N = 113) Only interested in late appointments (N = 25) Final study sample (N = 802)

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Inappropriate to contact at the Ɵme (N = 23) Unable to speak to client aŌer mulƟple contact aƩempts (N = 141)

3

Baseline transitions study results Quick Inventory of Depressive Symptomatology (QIDS19), the Young Mania Rating Scale (YMRS20), the Comprehensive Assessment of At-Risk Mental States (CAARMS21), the World Health Organization Alcohol, Smoking and Substance Involvement Screening Test (WHO-ASSIST22), the Clinical Global Impressions Scale (CGI23)and the SOFAS.14 Selfreport measures included the Kessler 10 (K-1012), the 7-item Generalized Anxiety Disorder scale (GAD724), the Overall Anxiety Severity and Impairment Scale (OASIS25), the 5-item SCOFF,26 which screens for symptoms of eating disorders, and the WHO Quality of Life measure (WHOQOL-127). Demographic characteristics were also measured, along with a range of psychosocial measures that will be used as predictor variables when annual follow-up data are subsequently available (e.g. personality, adverse life events, ruminative style). Each participant was also assigned a clinical stage, based on the detailed descriptive criteria provided by Hickie et al.,18 which elaborates on McGorry et al.’s16 original clinical staging model for mental disorders. The staging model defines where a person lies along a continuum of the course of illness, which enables a prevention-oriented framework for understanding pathogenesis and the efficacy of interventions that are specifically targeted to the stage of illness. Briefly, the clinical stages indicate the following discrete categories: Stage 0, ‘no mental health problem’; Stage 1a, ‘help-seeking for psychological distress’; Stage 1b, ‘attenuated syndrome’; Stage 2, ‘discrete disorder’; Stage 3, ‘persistent or recurrent illness’; and Stage 4, ‘chronic debilitating illness’. The clinical stage is based on a composite picture of the participant’s presentation, including current major symptoms (severity, frequency, type), previous ‘worst ever’ symptoms and treatments (including hospital admissions), and current (as compared with premorbid) levels of functioning. For further information regarding how clinical stages are derived and the factors that differentiate stages (e.g. the severity or persistence of symptoms), see Hickie et al.18 The stage for each participant was formulated following discussion between the RA and their clinical supervisors (RP and ES, registered psychologist and psychiatrist, respectively). Statistical analysis Data were analysed using the Statistical Package for the Social Sciences (SPSS) Version 21 (SPSS, Inc., Chicago, IL, USA). Discrete variables were analysed using χ2 and continuous variables were compared among groups using independent t-tests or analysis 4

of variance. In those instances where the assumptions for parametric statistics were violated, non-parametric tests were employed (e.g. Mann– Whitney U-test: MWU). Post-hoc analyses of group main effects were conducted using Tukey’s honestly significant difference (HSD) test. In order to minimize type I error associated with multiple comparisons, the error rate required to demonstrate significance was set at 0.01. RESULTS Demographic characteristics A summary of the participant characteristics is provided in Table 1. The majority of participants were female (66.0%; n = 529) and the mean age of participants was 18.3 years, with 22.7% (182) aged 12–15 years, 41.5% (333) aged 16–19, and 35.8% (287) aged 20–25 years. There were no significant differences between men (18.4 years) and women (18.2 years) with respect to mean age at baseline assessment (t = 1.016; P = 0.310). The majority of participants were Australian born, of whom 4.8% identified as Aboriginal or Torres Strait Islander. Of the participants born overseas (8.4%), most were from English-speaking countries such as the UK and New Zealand. The majority of participants indicated that their parents were Australian born (71.4% of mothers and 68.4% of fathers). Of the non-Australian-born parents (28.6% TABLE 1. Summary of demographic characteristics of Transitions Study participants Variable Gender: % female Mean (SD) age Australian born† Engaged in education(total)‡ School University Vocational institution Engaged in employment (total)‡ Full-time Part-time Relationship status‡ Single/not partnered Partnered (>3 months) Married/living together Sexual orientation§ Heterosexual Bisexual Same-sex attracted

Value 66.0% (n = 529) 18.3 (3.2) 91.6% (n = 718) 67.4% (n = 528) 52.8% (n = 277) 26.9% (n = 141) 12.6% (n = 66) 39.1% (n = 306) 8.4% (n = 66) 30.7% (n = 240) 62.5% (n = 489) 25.3% (n = 198) 3.3% (n = 26) 75.6% (n = 589) 12.8% (n = 100) 4.9% (38)

†18 cases missing data; ‡19 cases missing data; §23 cases missing data. SD, standard deviation. © 2014 Wiley Publishing Asia Pty Ltd

R. Purcell et al. mothers; 31.6% fathers), a significant proportion again originated from the UK, followed by New Zealand, with a broad range of Asian, South and North American, European and African nationalities also represented. Virtually all participants (98.5%) indicated that English was the primary language spoken at home. The majority of participants reported currently residing at the family home (69.7%), or living independently in a rental property (22.2%). Few indicated living in boarding accommodation or hostels (1.8%) and only 1.1% reported owning their own residence. Two-thirds of the sample indicated being enrolled in some form of education, of whom half were attending at school, over a quarter at university, and 1 in 10 a technical/vocational institution. Not unexpectedly, education was associated with age, with 93.3% of 12–15-years-olds currently engaged in education, compared with 71.1% of 16–19-year-olds and 47.0% of 20–25-year-olds. Female participants were significantly more likely to be currently engaged in education (73.0%) than male participants (56.7%; χ2 = 21.3; degrees of freedom (d.f.) = 1; P < 0.001). The type of educational institution attended also differed according to gender (χ2 = 12.75; d.f. = 4; P = 0.013), with men more likely than women to be attending a technical/vocational institution (19.2% vs. 9.9%) and women more likely than men to be attending university (29.9% vs. 19.2%). Some 59.7% of those engaged in education reported having been absent from education for at least 2 days in the past month, with 22.4% missing 7 days or more. Days absent from education did not differ significantly according to either gender or age. Some 39% of the sample reported being engaged in part-time or full-time employment in the past month. Employment status and gender were significantly associated (χ2 = 19.4; d.f. = 3; P < .001), with men more likely not to have any job (43.3%) compared with women (35.3%). However, men were more likely than women to be in full-time work (12.3% vs. 6.4%), whereas women were more likely to be working part-time (35.0% vs. 22.4%). The majority of participants were either single/ never married, followed by being in a relationship with a duration of longer than 3 months (Table 1). Only 3.3% were married or living in a de facto relationship, 8.4% indicated that they were in a relationship with a duration of less than 3 months and 0.5% were separated but not divorced. There was a significant association between relationship status and gender (χ2 = 24.4; d.f. = 3; P < .001), with there being more men (72.2%) who were single compared with women (57.9%). Women (30.6%) were more likely to © 2014 Wiley Publishing Asia Pty Ltd

report being in a longer-term relationship (>3 months) compared with men (15.4%). Frequencies of men and women in other relationship groups were comparable. Not unexpectedly, relationship status also differed significantly according to age group (χ2 = 55.1; d.f. = 6; P < .001), with participants aged 12–15 years more likely to be single (77.0%) than the 20–25 year group. The sexual orientation of participants was predominantly heterosexual (Table 1). There were 4.4% of participants who were unsure of their sexual orientation and 2.3% indicated that they preferred not to disclose this information. There was a statistically significant association between gender and sexual orientation (χ2 = 35.5; d.f. = 4; P < .001), with fewer women reporting that they were heterosexual (70.5%) compared with men (85.4%) and more women indicating bisexuality (17.4% vs. 4.1% men). Differences between women and men with respect to same-sex attracted orientation were minor (4.1% women; 6.4% men). Sexual orientation also differed according to age group (χ2 = 20.7; d.f. = 8; P = 0.008), with participants aged 12–15 years less likely to report being same-sex attracted (0%) as compared with the 20–25-year age group (7.1%), and more likely to indicate not knowing their sexual orientation (7.4%). Other sexual orientation categories were comparable among the age groups.

Mental health characteristics Participant scores on interviewer and self-rated measures of psychopathology are presented in Table 2.

Psychological distress The K-1011 assessed psychological distress and negative emotional states experienced in the past 4 weeks. The total mean score of the cohort on the K-10 (see Table 3) was in the range indicative of the likelihood of having a ‘moderately severe mental disorder’ (total score between 25 and 29). Using established classifications, 18.6% of the cohort scored in the ‘likely to be well’ range; 13.3% scored in the range ‘likely to have a mild disorder’; 18.4% scored in the ‘likely to have a moderate mental disorder’ range and almost half (49.7%) scored in the ‘likely to have a severe mental disorder’ range. Women scored significantly higher than men on the K-10, both in terms of mean score (Table 3) and categorical ratings (χ2 = 27.0; d.f. = 3; P < .001). Neither continuous nor categorical scores on the K-10 differed significantly according to age group. 5

Baseline transitions study results TABLE 2. Mental health characteristics of young help-seekers Variable

Total (n = 802)

Male (n = 273)

Female (n = 529)

Depression: Mean (SD) QIDS† total score Mania: Mean (SD) YMRS‡ total score Median Generalized anxiety: Mean (SD) GAD-7§ total score OASIS¶ total score Subthreshold psychotic symptoms††: (% meeting CAARMS criteria) Eating disorder: Mean (SD) SCOFF‡‡ total score Median Psychological distress: Mean (SD) K-10§§ total score Clinical global impression¶¶ (CGI) Mean (SD) total score Functioning: Mean (SD) SOFASa total score

10.3 (5.4) 3.9 (4.7) 2.0 9.9 (6.0) 7.5 (5.1) 38.3% 1.1 (1.2) 1.0 29.1 (9.6) 3.4 (1.1) 65.3 (11.6)

9.1 (5.2) 3.7 (4.9) 2.0 8.5 (5.9) 6.5 (5.2) 37.5% 0.8 (1.0) 0.0 26.8 (9.7) 3.4 (1.1) 63.5 (12.2)

10.9 (5.4) 4.1 (4.6) 3.0 10.6 (5.9) 8.1 (5.1) 38.6% 1.3 (1.3) 1.0 30.2 (9.3) 3.4 (1.1) 66.2 (11.2)

Test

t = 4.5, P < 0.001 Z = 2.2, P = 0.026 t = 4.5, P < 0.001 t = 4.2, P < 0.001 χ2 = 0.09, P = 0.765 Z = 6.1, P < 0.001 t = 4.8, P < 0.001 t = 0.09, P = 0.927 t = 3.1, P = 0.002

The bold values in table indicates statistically significant results. Missing data: † = 1; ‡ = 2; § = 28; ¶ = 27; †† = 11; ‡‡ = 28; §§ = 26; ¶¶ = 2; a = 5. CAARMS, Comprehensive Assessment of At-Risk Mental States; GAD, Generalized Anxiety Disorder; OASIS, Overall Anxiety Severity and Impairment Scale; QIDS, Quick Inventory of Depressive Symptomatology; SD, standard deviation; SOFAS, Social and Occupational Functioning Assessment Scale YMRS, Young Mania Rating Scale.

Depressive symptoms Over half the cohort scored either in the ‘mild’ (32.3%; n = 259) or ‘no’ depression range (21.6%; n = 173) on the QIDS (symptoms experienced over the past week). However, a quarter were rated as experiencing ‘moderate’ levels of depression (28.2%; n = 226) and 14.1% (n = 113) scored in the severe range, with an additional 3.7% (n = 30) in the ‘very severe’ range. There was a significant association between gender and depression severity (χ2 = 17.2; d.f. = 4; P = 0.002), with women more likely than men to score in the severe range (16.1% vs. 10.3%) and less likely than men to be rated as experiencing ‘no’ depression (18.3% vs. 27.9%). Depression severity was also significantly associated with age group (χ2 = 20.7; d.f. = 8; P = 0.008). Participants aged 12–15 years (30.4%; n = 55) were more likely to experience ‘no’ depression, compared with 17.7% (n = 59) of the 16–19 year group. The latter group were more likely to experience ‘moderate’ depression levels (33.6%; n = 112) compared with the 20–25 year group (24.0%; n = 69); the older group tended to have a greater likelihood of experiencing ‘very severe’ depression (5.6%; n = 16). Anxiety symptoms Moderate to high levels of generalized anxiety symptoms in the past 2 weeks were reported (see Table 2), with women scoring significantly higher than men on the GAD-7. Overall, 51.9% of participants were categorized as a ‘probable case’ on the GAD-7 (determined by a total score of 10 or more), with the rates of caseness being significantly higher among women (57.2%) compared with men 6

(41.6%;χ2 = 16.9; d.f. = 1; P < .001). There were no significant differences in GAD-7 scores by age. Total scores on the OASIS, which focuses on avoidance behaviours and the extent to which anxiety interferes with functioning in the past week, also differed by gender (see Table 2) and age (F = 18.9, d.f. = 2, P < .001). Tukey’s HSD test indicated that the 12–15year-olds scored significantly lower on the OASIS than both the older age groups (mean differences = 2.2, 3.0 points, P < .001), although there was no significant difference in scores between the 16–19 and 20–25-year-olds. Manic symptoms Total scores on the YMRS, which measures the nature and severity of manic symptoms in the past 48 hours, were extremely skewed, with the modal score being 0 (median = 2; mean = 3.93; standard deviation = 4.76). Very few participants (n = 9) scored in the recognized clinical range (total score >20). There were no significant differences on YMRS scores according to gender or age. Psychotic symptoms Using the CAARMS criteria,21 we examined the proportion of participants experiencing positive psychotic symptoms in the past 12 months that do not meet the threshold for frank psychosis, as well as those meeting the criteria for psychotic disorder. Overall, 280 participants (38.3%) reported experiencing sub-threshold positive psychotic symptoms in the past year. Of these affected participants, the most common psychotic symptom reported was Non-Bizarre Ideation (68.6%; n = 190), such as © 2014 Wiley Publishing Asia Pty Ltd

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Notes: significance levels for each post hoc comparison are depicted by: ***P < 0.001; **P < 0.01; *P < 0.05. The bold values in table indicates statistically significant results. †Post hoc comparisons include Dunnett’s T3 test (for unequal variances) and Tukey’s honestly significant difference (equal variances); Kruskall–Wallis (K–W) tests applied where data were unsuited to analysis using parametric methods. CAARMS, Comprehensive Assessment of At-Risk Mental States; GAD, Generalized Anxiety Disorder; OASIS, Overall Anxiety Severity and Impairment Scale; QIDS, Quick Inventory of Depressive Symptomatology; SD, standard deviation; SOFAS, Social and Occupational Functioning Assessment Scale; YMRS, Young Mania Rating Scale.

*** *** *** *** *** *** *** *** F = 35.2; P < 0.001 F = 218.0; P < 0.001 F = 55.3; P < 0.001

*** *** * *** *** *** F = 38.6;P < 0.001 F = 43.0;P < 0.001 K-W χ2 = 16.5; P < 0.001

*** ***

12.8 (6.1) 5.5 (6.8) 3.0 10.9 (6.5 ) 9.3 (5.4) 1.3 (1.3) 1.0 30.5(10.4) 4.5 (1.0) 57.0 (11.6) 7.7 (4.2) 3.0 (3.0) 2.0 7.5 (5.0) 5.4 (4.5) 0.9 (1.1) 1.0 25.5 (8.5) 2.6 (0.8) 69.7 (10.4) Depression: QIDS total Mania: YMRS total Median Generalized Anxiety: GAD-7 total OASIS total Eating Disorder: SCOFF total Median Psychological Distress: K-10 total Clinical Global Impression (CGI) severity Functioning: SOFAS score

11.5 (5.2) 4.3 (5.0) 3.0 11.3 (6.0) 8.7 (5.1) 1.3 (1.3) 1.0 31.4 (9.2) 3.7 (0.9) 64.0 (10.9)

F = 61.3; P < 0.001 K-W χ2 = 7.4; P = 0.025

1a vs. 2+

Stage 2+ (n = 96) Stage 1b (n = 412) Stage 1a (n = 285) Variable

TABLE 3. Mean scores (SD) of mental health characteristics by clinical stage

Significance

1a vs. 1b

Post-hoc tests†

1b vs. 2+

R. Purcell et al. persecutory ideas. Perceptual abnormalities were reported by 40.4% (n = 112) and unusual thought content (highly improbable beliefs) by 35.6% (n = 98). The least common symptom was disorganized speech, observed in only 6.5% (n = 18). The types of psychotic symptoms reported by participants did not differ significantly according to gender or age group. An additional 59 participants (7.4%) met the criteria for frank psychosis as defined by the CAARMS (i.e. a global rating score of 6 on unusual thought content, non-bizarre ideas or disorganized speech; or a rating of 5–6 on perceptual abnormalities; and an associated frequency score of 4–6, and with symptoms lasting 1 week or longer). Frank psychosis was not linked with either gender or age category. Eating disorder symptoms The 5-item SCOFF26 screens for core symptoms of anorexia nervosa and bulimia nervosa. Each endorsed item receives a score of 1 and a total score of 2 or more indicates probable ‘caseness’ for an eating disorder (i.e. the likelihood of having a diagnosable disorder). Overall, the data for this measure was highly skewed, with the modal score being 0 (median = 1). Overall, 32.6% of the cohort met the threshold for probable caseness, the rates being significantly higher among women (39.3%) than men (19.4%; χ2 = 31.4; d.f. = 1; P < .001). However, there were no significant effects of age on either total mean score or caseness. Substance misuse The WHO-ASSIST22 was used to assess lifetime use of tobacco, alcohol and illicit substances. For those participants endorsing lifetime use of a substance(s), their problematic use (defined as weekly use or more) in the 3 months prior to interview was also examined. Lifetime rates were high for the use of alcohol (84.9%; n = 680), tobacco (66.3%; n = 531) and cannabis (50.9%; n = 408), and a substantial minority reported lifetime use of amphetamines and stimulants (26.9%; n = 215), hallucinogens (18.3%; n = 146), cocaine (15.8%; n = 126) and sedatives/sleeping pills (14.8%; n = 118). Fewer participants reported having ever used inhalants (9.1%; n = 73) or opioids (5.9%; n = 47). There were no significant differences in lifetime rates of substance use by gender, with the exception of cocaine use, which was more likely to be reported by men than women (21.0% vs. 13.0%;χ2 = 8.6, d.f. = 1; P = .003). Among the participants who indicated lifetime use of substances, the rates of problematic substance use in the past 3 months were also high. For 7

Baseline transitions study results example, over half of those who used tobacco smoked at least weekly (56.9%; n = 302), a third who used alcohol drank at least weekly (38.1%; n = 259) and 30.4% (n = 124) used cannabis. Few participants engaged in problematic use of cocaine (1.6%), hallucinogens (2.7%) or inhalants (4.1%). Problematic use of sedatives was 8.5%, and of those participants who indicated any lifetime use of opioids, some 19% reported weekly use in the past 3 months. Problematic substance use did not differ according to gender, except for alcohol use (46.4% vs. 34%;χ2 = 9.8, d.f. = 1; P = .002), which was higher for men than women. Not unexpectedly, there were highly significant effects of age group for lifetime substance use, with rates increasing with age. However there were no significant differences across age groups for problematic recent substance use, expect for alcohol, which again increased with age (16% vs. 34% vs. 50.5%; χ2 = 41.1, d.f. = 2; P < .001). CGI scale The CGI indicates the severity of illness, ranked from 1 ‘normal, not ill at all’ to 7 ‘among the most extremely ill patients’, derived from all available information in the assessment and the interviewer’s impression of the participant’s functioning, symptoms and behaviour. The mean score for the sample was in the ‘mildly ill’ range and did not differ according to gender (Table 3). However there was a significant effect of age (F = 15.2, d.f. = 2, P < .001), with Tukey’s HSD test indicating that the 20–25-yearolds scored significantly higher than both the younger age groups, and the 16–19-year-olds scored higher than the 12–15-year-olds. Quality of life (QoL) Self-reported QoL in the past 4 weeks was rated by participants as ‘very good’ (8.1%); ‘good’ (29.4%); ‘neither poor nor good’ (36.6%); ‘poor’ (18.2%) or ‘very poor’ (7.7%). These ratings did not differ according to gender; however, there was a significant effect of age (χ2 = 20.7; d.f. = 8; P = .008), with the youngest age group participants more likely to rate their QoL as good compared with the older age groups. Social and vocational functioning Overall, 39.4% of the sample was classed as functionally impaired on the SOFAS (total score of 60 or less). Men were significantly more likely than women to score in the functionally impaired range 8

(47.2% vs. 35.4%; χ2 = 10.6; d.f. = 1; P = .001). There were no significant effects of age on SOFAS ratings. Clinical stage Over a third of the cohort (35.8%; n = 285) were assigned to clinical stage 1a (‘help seeking for mild to moderate non-specific mental health symptoms), and half (51.7%; n = 412) were classed as clinical stage 1b (‘attenuated syndrome’). A further 9.0% (n = 72) were categorized as stage 2 (‘discrete disorder’). Only four participants (0.5%) were classed as stage 0 (‘no mental health disorder’); in each case, these participants attended headspace for a primary health-care consultation. Finally, 24 cases (3.0%) were classed as stages 3 or 4, indicating a recurrent or chronic mental disorder. Symptom scores, psychological distress and functional impairment where compared according to stages 1a (n = 285), 1b (n = 412), and stages 2, 3 and 4 combined (2+: n = 96). As Table 3 shows, participants classed as stage 1a were differentiated from each of 1b and 2+ on all measures. Conversely, stages 1b and 2+ were comparable on all measures with the exception of CGI-severity and SOFAS, where a step function was observed for all three groups (increasing severity on the CGI and decreased functioning on the SOFAS, respectively).

DISCUSSION The purpose of the Transitions Study was to assemble, characterize and follow a cohort of young people accessing headspace services and to explore and refine a clinical staging model of the development and progression of mental disorders. The 802 young people recruited at baseline represent a relatively heterogeneous cohort in relation to the nature of their mental health problems and levels of functional impairment. The majority of participants were classed in the early clinical stages of 1a (mild to moderate non-specific mental health symptoms) and 1b (symptoms indicative of an attenuated form of a distinguishable mental disorder). A further 1 in 10 participants, however, were classed in the more severe stages of an existing (or persistent) serious mental disorder (stages 2 and onward). The preponderance of early clinical stage presentations increases the likelihood of the Transitions Study ultimately being able to achieve its aims of elucidating the social, biological and personal risk and protective factors that influence movement across stages of illness (particularly worsening of mental ill-health). © 2014 Wiley Publishing Asia Pty Ltd

R. Purcell et al. Overall, half of all eligible clients attending the headspace centres over the recruitment period consented to participate. The majority of those who declined to participate (n = 375) were considered to be ‘passive’ refusers (on the basis that they were ‘too busy’ or ‘not interested in hearing about the study’), with only 15% considered ‘active’ refusers (e.g. they or their parent(s) did not want to participate after receiving information about the study). We were not permitted by the ethics committee to compare the characteristics of the participants versus nonparticipants and therefore cannot fully establish the representativeness of the cohort. However the gender, age and proportion of Australian-born participants in this study are consistent with that observed across headspace services nationally.28 Demographic characteristics The majority of the Transitions Study participants are female, with male participants constituting a third of the cohort. The mean age at baseline was 18.3 years, although all age groups (early and middle adolescence, and early adulthood) were well represented. The patterns of living arrangements, relationship status, country of birth and language(s) spoken observed in the cohort are consistent with the overall trends observed in young adults according to the most recent Australian Census data,29 suggesting that the recruited cohort is demographically comparable with their general population counterparts. The rates of participant enrolment in all forms of education (i.e. school, vocational and tertiary education, including both part-time and full-time enrolments) at baseline were relatively high (93%) for the younger participants (12–15-year-olds), but declined to 70% in the 16–19 years age group. The latter result is similar to that observed nationally, where 75% of Australian 15–19-year-olds are in fulltime education.30 Almost half of the 20–24-yearsolds in this cohort were enrolled in some form of education (47%; which includes both part- and fulltime study), although there are currently no comparable national figures with which to compare this result. Significantly more women were engaged in any form of education compared with their male counterparts, and specifically more young adult women were attending university than men, the latter trend also consistent with Australian census data.29 Female participants were also significantly less likely than men to be unemployed at baseline. These gender effects in educational and vocational engagement are also reflected to some extent in ratings on the SOFAS (a measure of social and occu© 2014 Wiley Publishing Asia Pty Ltd

pational functioning), with a higher proportion of male participants rated as functionally impaired (47%) compared with women (35%). Mental health characteristics As previously indicated, the majority of the cohort (over three-quarters) were assigned to clinical stages 1a and 1b, indicating the presence of subthreshold or attenuated mental health disorders, respectively. This accords with the results of the interviewer-rated measures, in which the majority of participants were assessed as experiencing mild to moderate forms of mental ill-health, with only a minority scoring in the severe range indicative of a discrete mental disorder. For example, on the QIDS, 60% of the cohort scored in the mild to moderate range of depression severity, although 17% reported symptoms assessed as being severe or very severe. Positive psychotic symptoms in the past year were reported by over a third of participants (38%), and a further 7% met the criteria for frank psychosis. The rates of positive symptoms in this cohort are largely consistent with the literature regarding the prevalence of psychotic-like experiences in the community and among help-seeking young people,31–34 being particularly elevated in young people presenting with comorbid depressive and anxiety disorders.35 Furthermore, while only a handful of participants (n = 9) scored in the clinical range for current (e.g. past 48 hours) mania on the YMRS, the reporting of discrete manic symptoms (e.g. increased activity, decreased need for sleep, etc) were more commonly endorsed. High rates of lifetime substance use were reported for both licit and illicit substances, and 37% of the cohort indicated that they were current smokers, a rate double the prevalence of tobacco use (16%) according to Census data. The rates of problematic substance use were also elevated for users of cannabis and alcohol, consistent with the findings of Hermens et al.36 in another youth mental health sample. Almost 1 in 5 participants who had indicated opioid use were using the substance at least weekly. Interestingly, participants self-reported higher rates of general mental ill-health and specific anxiety and eating disorders. For example, half the cohort (49%) scored in the ‘likely to have a severe mental disorder’ range on the basis of their K-10 scores, half met the caseness threshold for generalized anxiety, and a third met the threshold for probable caseness related to an eating disorder. On each of these self-report scales, scores were significantly elevated for female compared with male participants, which accords with the extant literature for 9

Baseline transitions study results these disorders. The high rates of self-reported symptomatology, relative to the clinician scored measures, is suggestive of significant subjective distress associated with the young people’s help seeking. The baseline results of the Transitions Study indicate that while the preponderance of participants are characterized as within the early stages of emergence and evolution of mental ill-health (on the basis of their clinical stage, and relatively intact social and occupational functioning, both of which are interviewer-assessed measures), they are nonetheless experiencing moderate to high levels of distress which has impelled them to seek and accept help at headspace services. As the Transitions Study consists of a clinical help-seeking cohort, with variation in terms of the nature and severity of mental ill-health, comparisons with other age-appropriate populations are problematic. For example, mean scores on the K-10, QIDS, OASIS and GAD-7 in our cohort are two to three times higher than those observed in general community samples (37–40, respectively), although in the case of the QIDS, are equivalent to those reported in a depressed adolescent outpatient sample.41 Despite an important difference in gender ratio (34% male here vs. 53% in Scott et al.’s sample) our findings are largely comparable with the results of Scott and colleagues,11 which also involved a large sample of young people attending headspace. Importantly, the proportion of young people reporting ‘high’ to ‘very high’ levels of psychological distress (on the K-10) are similar, and the rates of poor engagement with education and full-time employment are largely equivalent (approximately 35–40% and 8–10%, respectively). The gender difference, however, may underpin the rates of functional impairment (according to the SOFAS) being somewhat lower in this Transitions cohort (40% vs. 52%).

CONCLUSION The baseline data from the Transitions Study provides a broad indication of the demographic characteristics, and the nature and severity of psychopathology in the cohort. While the majority of help-seeking young people are characterized by moderate to high levels of subjective distress, their levels of functioning at this stage are relatively intact (with only 39% classed as socially and/or occupationally impaired). The extent to which this clinical profile alters, and correlates of any change in psychopathology or functioning (including the impact 10

of treatments received), will be examined in the forthcoming 12-month follow-up data. The variation observed in the cohort at baseline, particularly the preponderance of early stage clinical presentations, augers well for the objectives of the Transitions Study to ultimately elucidate factors that predict transitions between stages of mental illhealth, both in terms of illness progression and remission. ACKNOWLEDGEMENTS This work was supported by a NHMRC Program Grant (ID: 566529) to McGorry, Jorm, Hickie, Yung, Pantelis, Purcell, Glozier, Wood, Killackey, Amminger and Phillips. Prof Jorm was supported by a NHMRC Australia Fellowship, Prof Christos Pantelis was supported by a NHMRC Senior Principal Research Fellowship (ID: 628386), and NARSAD Distinguished Investigator Award. Prof Wood was supported by an NHMRC Clinical Career Developmental Award (ID: 359223). REFERENCES 1. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005; 62: 593–602. 2. Gibb SJ, Fergusson DM, Horwood LJ. Burden of psychiatric disorder in young adulthood and life outcomes at age 30. Br J Psychiatry 2010; 197: 122–7. 3. Murray CJL, Lopez AD. The Global Burden of Disease, Vol. 1. Boston: Harvard University Press, 1996. 4. Insel TR, Fenton WS. Psychiatric epidemiology: it’s not just about counting anymore. Arch Gen Psychiatry 2005; 62: 590–2. 5. Copeland W, Shanahan L, Costello EJ, Angold A. Cumulative prevalence of psychiatric disorders by young adulthood: a prospective cohort analysis from the Great Smoky Mountains Study. J Am Acad Child Adolesc Psychiatry 2011; 50: 252–61. 6. Australian Bureau of Statistics. National Survey of Mental Health and Wellbeing: Summary of Results. Canberra: Australian Bureau of Statistics, 2008. 7. Merikangas KR, He JP, Burstein M et al. Service utilization for lifetime mental disorders in US adolescents: results of the National Comorbidity Survey – Adolescent Supplement (NCSA). J Am Acad Child Adolesc Psychiatry 2011; 50: 32–45. 8. McGorry PD, Tanti C, Stokes R et al. headspace: Australia’s National Youth Mental Health Foundation – where young minds come first. Med J Aust 2007; 187 (Suppl.): S68–70. 9. Illback RJ, Bates T. Transforming youth mental health services and supports in Ireland. Early Interv Psychiatry 2011; 5: 22–7. 10. Purcell R, Goldstone S, Moran J et al. Towards a 21st century approach to youth mental health care: the Australian initiatives. Int J Ment Health 2011; 40: 72–87. 11. Scott EM, Hermens DF, Glozier N, Naismith SL, Guastella AJ, Hickie IB. Targeted primary care-based mental health services for young Australians. Med J Aust 2012; 196: 136–40. 12. Kessler RC, Andrews G, Colpe LJ et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med 2002; 32: 959–76. © 2014 Wiley Publishing Asia Pty Ltd

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28. Rickwood DJ, Telford NR, Parker AG, Tanti CJ, McGorry PD. headspace – Australia’s innovation in youth mental health: who are the clients and why are they presenting? Med J Aust 2014; 200 (2): 1–4. 29. Australian Bureau of Statistics. Young adults: then and now. 2013. [Cited 18 Sep 2013.] Available from URL: http:// www.abs.gov.au/AUSSTATS/[email protected]/Lookup/4102.0Main +Features40April+2013 30. Australian Bureau of Statistics. Labour Force, Australia, Detailed – Electronic Delivery (cat.no. 6291.0.55.001), August 2013. 31. Kelleher I, Cannon M. Psychotic-like experiences in the general population: characterizing a high-risk group for psychosis. Psychol Med 2011; 1: 1–6. 32. Verdoux H, van Os J. Psychotic symptoms in non-clinical populations and the continuum of psychosis. Schizophr Res 2002; 54: 59–65. 33. Armando M, Nelson B, Yung AR et al. Psychotic-like experiences and correlation with distress and depressive symptoms in a community sample of adolescents and young adults. Schizophr Res 2010; 119 (1–3): 258–65. 34. Yung AR, Nelson B, Baker K, Buckby J, Baksheev G, Cosgrave E. Psychotic-like experiences in a community sample of adolescents: implications for the continuum model of psychosis and prediction of schizophrenia. Aust N Z J Psychiatry 2009; 43: 118–28. 35. Varghese D, Scott J, Welham J et al. Psychotic-like experiences in major depression and anxiety disorders: a population-based survey in young adults. Schizophr Bull 2011; 37: 389–93. 36. Hermens DF, Scott EM, White D et al. Frequent alcohol, nicotine or cannabis use is common in young persons presenting for mental healthcare: a crosssectional study. BMJ Open 2013; 3: e002229. doi: 10.1136/bmjopen-2012-002229. 37. Slade T, Grove R, Burgess P. Kessler psychological distress Scale. Normative data from the 2007 Australian National Survey of Mental Health and Wellbeing. Aust N Z J Psychiatry 2011; 45: 308–16. 38. Gonzales AD, Boals A, Jenkins SR, Schuler ER, Taylor D. Psychometrics and latent structure of the IDS and QIDS with young adult students. J Affect Disord 2013; 149: 217–20. 39. Norman SB, Campbell-Sills L, Hitchcock CA et al. Psychometrics of a brief measure of anxiety to detect severity and impairment: the overall anxiety severity and impairment scale (OASIS). J Psychiatr Res 2011; 45: 262–8. 40. Löwe B, Decker O, Müller S et al. Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. Med Care 2008; 45: 266–74. 41. Bernstein IH, Rush AJ, Trivedi MH et al. Psychometric properties of the Quick Inventory of Depressive Symptomatology in adolescents. Int J Methods Psychiatr Res 2010; 19: 185–94.

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