Beware of blotting paper hallucinogens: severe toxicity with NBOMes

Clinical record

16-year-old male presented to the emergency department after ingesting what he believed to be LSD (lysergic acid diethylamide) on red blotting paper while camping with friends in rural New South Wales in late 2014. He had no past medical or mental health history, and was taking no regular medications. He had three seizures before arriving in the ED, where his Glasgow coma scale score was 9. He had a fourth seizure about 1 hour after presenting, and was given 5 mg midazolam intravenously. His initial venous blood gas parameters were: pH 6.93 (reference range [RR], 7.35–7.45); PCO₂, 120 mmHg (RR, 35–48 mmHg); and base excess, −7 (RR, 0.5–1.6). He was then intubated, ventilated, paralysed with rocuronium, and sedated with morphine/midazolam for transfer to a tertiary intensive care unit. His heart rate was 70 bpm, his blood pressure 130/60 mmHg, and he was afebrile after intubation. Over the next 3 hours and before medical retrieval, his blood gases normalised with improved ventilation (pH 7.4; PCO₂, 29.6 mmHg).

He had no further seizures after his transfer to the tertiary intensive care unit. His overnight urine output was initially reduced; this improved with increased fluid replacement. On arrival at the intensive care unit, his blood parameters were: white cell count, 16.3 × 10⁹/L (RR, 4–11 × 10⁹/L); neutrophils 12.1 × 10⁹/L (RR, 1.7–8.8 × 10⁹/L); haemoglobin, 136 g/L (RR, 130–180 g/L); platelets, 198 × 10⁹/L (RR, 150–400 × 10⁹/L); sodium, 142 mM (RR, 134–145 mM); potassium, 3.9 mM (RR, 3.5–5.0 mM); and creatinine, 108 mM (64–104 mM). He remained haemodynamically stable and was extubated the following day. He was transferred to the paediatric ward, and on Day 2 his creatinine and creatine kinase levels were rising, with normal urine output (Figure). Except for some initial nausea that lasted for 24 hours after extubation, he had no other symptoms over the next 3 days, and experienced no hallucinations or agitation. His creatinine levels peaked at 246 mM [RR, 64–104 mM] 37.5 hours after ingestion, and his creatine kinase levels peaked at 34 778 U/L (RR, 1–370 U/L) 90 hours after ingestion. He was discharged well on Day 5 without complications.

NBOMe assays are not currently part of routine emergency toxicology testing; worldwide, only a few forensic and commercial laboratories offer qualitative NBOMe testing in blood or urine. Blood specimens from the patient were sent to the Department of Pathology at Virginia Commonwealth University (USA) for NBOMe detection and quantification. The specimens were tested by previously validated high-performance liquid chromatography/mass spectrometry assays.1,2 25B-NBOMe was detected in the blood specimen at a concentration of 0.089 μg/L, 22 hours after ingestion.

Dimethoxyphenyl-N-[(2-methoxyphenyl)methyl]ethanamine derivatives (NBOMes) are a novel class of potent synthetic hallucinogens originally developed as 5-HT₂ receptor agonists for research purposes, but which have become available as recreational drugs in the past few years.3 They are available under a number of street names, including “N-bombs”, and are often sold as “acid” or “LSD” on blotting paper, as a powder, or as blue tablets (“blue batman”). They have been increasingly associated over the past 2 years with deaths and severe toxicity in North America and Europe.3–5 Most reports have concerned 25I-NBOMe intoxication, and there is much less information on the 25B- and 25C-NBOMe derivatives.2,6,7 While difficult to assess because of the sparse number of reports, 25B-NBOMe may be more toxic than the more commonly reported 25I-NBOMe.3,4 Our case is consistent with previous reports of severe NBOMe toxicity, with agitation, tachycardia and mild hypertension, seizures, rhabdomyolysis and acute kidney injury.3

There have been few reports of NBOMe poisoning in Australia, and only one report of a fatality.8 Most reports in Australia have been in the popular media, describing the presence of NBOMes in this country. There is limited information available to health care professionals about their potential toxicity. An international online survey in 2012 found that NBOMes were being used in Australia, although not as commonly as in the United States.5 NBOMes are reported to be relatively inexpensive, and are usually purchased over the internet. For this reason, as in our case, intoxicated NBOMe users may present to rural and smaller regional hospitals. As in other reports, our patient believed he had taken “acid” or LSD. The one reported death in Western Australia involved a woman who had inhaled a white powder she thought to be “synthetic LSD”; she began behaving oddly, before collapsing and dying.8 In comparison with the dramatic systemic effects seen in our case and those described in the literature, LSD is not associated with such severe medical complications.9

NBOMe toxicity is characterised by hallucinations and acute behavioural disturbance, with seizures, rhabdomyolysis and acute kidney injury in more severe cases.3,4,6,9,10 Our patient was postictal when he presented, and required immediate sedation and intubation, after which he was reported to have a normal heart rate and blood pressure. Rising creatinine and creatine kinase levels were recognised on the medical ward after the patient had been extubated.

Previous reviews3,4 suggested that there are two different presentation types of NBOMe toxicity: one form dominated by hallucinations and agitation, and another involving more severe medical complications. Patients presenting with the first type should be managed in a similar manner to other patients with acute behavioural disturbance, including verbal de-escalation and oral or parenteral sedation as required.11 In many cases, these patients will present with undifferentiated behavioural disturbance, and only the persistence of hallucinations or agitation and the history given by the patient will suggest the diagnosis. In patients with more severe medical complications, directed supportive care is appropriate, including intubation and ventilation for coma, and fluid replacement for rhabdomyolysis and acute renal impairment. Serial electrolyte, creatinine and creatine kinase measurements should be made in all cases to identify these complications and to monitor the progress of the patient. Further, such investigations may potentially play a role in identifying NBOMe as a cause in patients who present with undifferentiated agitation and hallucinations lasting 24 hours or more.

Lessons from practice

Dimethoxyphenyl-N-[(2-methoxyphenyl)methyl]ethanamine derivatives (NBOMes) are hallucinogenic substances that have become available as drugs of misuse in the past few years.
NBOMe toxicity can cause acute behavioural disturbance, and in severe cases can cause seizures, rhabdomyolysis and acute kidney injury.
NBOMes may be distributed as lysergic acid diethylamide (LSD) or “acid” on blotting paper.
Treatment is supportive, including sedation for agitation and intravenous fluid therapy for rhabdomyolysis and acute renal failure.

Clinicians need to be aware that newer synthetic hallucinogens, such as NBOMes, are available in Australia, and that patients may believe them to be “acid” or LSD. NBOMes cause prolonged agitation and hallucinations and, in more severe cases, seizures, rhabdomyolysis and acute kidney injury.

Figure

Serial measurements of creatinine and creatine kinase levels in our patient after ingesting NBOMe.

The delicate balance between quantity and quality – a view on the increase in prevalence of mental illness in children and adolescents

Dr Helen Schultz is a psychiatrist and author of How Shrinks Think, her story of her journey through psychiatry training, and life beyond. If you work in healthcare and have a blog topic you would like to write for doctorportal, please get in touch.

It’s official. What we at the coal face see has been confirmed by a recent study, “Young Minds Matter” that concludes thousands of children and teenagers suffer from mental illness. And, as we know at the coal face, they largely suffer in silence.

What a sad state of affairs in a time when we know so much more about prevention and mental illness, what a tragedy for the next generation, what a social disaster. Continual erosion to the basics must play a part. Financial distress, the epidemic of drug and alcohol misuse, and the loss of the family structure due to poverty and violence. Incalculable factors, specific to some families but generalised as a whole.

In essence, we have lost our way when it comes to remembering that in fact the family is an integral protective structure for children’s mental health and resilience, and attempts to threaten this will inevitably be felt by the next generation.

As a psychiatrist, I have seen a large number of adolescents over the years, and I know in many cases the problems expressed within the child generate from their environment. When that environment consists of those whom the children fear they will suffer in silence rather than speak up. Children learn from a young age whether or not their parents or other adults can cope with their ‘stuff’.

In many cases children become parentified and learn to conceal their angst and be available for adult’s problems. They present later in life struggling to understand how to relate to others, unable to show kindness to themselves, or identify their purpose in life. And so the cycle continues.

This new study reports that 7% of Australia’s children and adolescents have anxiety to the point where it is a recognisable mental illness. One in 20 children have chosen a place to commit suicide. I am sure the rates are higher and there would be large spikes in incidence in sectors of society. I can’t imagine how prevalent anxiety disorders are for those children held in immigration detention centres. Or children with marked social disadvantage. Of course they don’t tell parents. Their parents are often emotionally and physically unavailable.

When I underwent child psychiatry training I learnt all about the child within a system. I still operate within this approach when I see my patients; that is to recognise the ‘big picture’ and try and provide interventions that address these other crucial factors, such as parental conflict or school place bullying. I learnt that this work takes time and takes a team. Often the child that presents is not the patient. They can be the harbinger for a family in crisis.

So why then the gross dismantling of multidisciplinary services? Why at a time when family structure is crumbling under the weight of societal forces are we allowing mental health services to crumble too? The federal health minister, Ms Sussan Ley stated that she sees the results from this recent study as positive in that children are coming forward to ask for help. Her press release stated

“It’s also a credit to young Australians, and society as a whole, that so many are not only bravely opening up about their emotions and behaviours, they’re actively seeking out help and taking positive actions to manage them”

A credit? A tragedy that they have to ask at all. And more importantly, who are they telling and what happens when they do so?

At the same time as this news is breaking, so are the warnings about an alarming rise in the use of antipsychotic and antidepressant medications in this same age group. In particular is the distressing trend for young adolescents and children to be commenced on major antipsychotic medications such as quetiapine for off-label indications such as insomnia.

Doctors are exposing children and adolescents to the harmful short and long term effects of antipsychotic medications, including weight gain, diabetes and potential cardiovascular disease without any evidence. Numerous reports identify this rise in prescribing of these agents has nothing to do with a rise in psychosis, but simply that such medications are seen as a benign broad brush stoke approach to any emotional distress and child could present with.

It is not just happening in psychiatry but in primary care. We will regret exposing our children and adolescents to these medications in the future, I am sure, but right now, it seems to be the only approach to mental illness and emotional distress in a society where most psychosocial services are no longer funded or regarded as valuable.

So yes, the quantity is there – we do need to remember that our children and adolescents are vulnerable and experience distress borne from a modern society and new stresses and strains – but we fall short from providing quality care. And I am sure we will reflect on this time as a failed opportunity to use our knowledge and wisdom, advocate for a better mental health system based on evidence, where children and adolescents can feel safe, and go on to lead the lives they deserve.

This blog was previously published on DrHelenSchultz.com and has been republished with permission. Dr Schultz is appearing at “The Power of Story” on Friday 4th September 2015 in Melbourne, alongside other health care radicals who are passionate about storytelling in health.

Other doctorportal blogs

Impact of grief delivered via media technology

Advances in media technology have had a significant impact on our daily lives, in terms of how we conduct business and leisure. Recent events have also had significant emotional impact and we wish to highlight the potential health impact. The grief experienced following the death of a loved one can be one of the greatest stressors for those surviving — spouse and/or relatives. It is not only the emotional strain but also physiological changes in the immediate period after bereavement1,2 that affect health.

Bereavement may also affect non-relatives, which was only captured by these two patients referred independently for ambulatory blood pressure (ABP) monitoring as part of their clinical management. Both watched the televised broadcast of funeral proceedings for Australian cricketer Phillip Hughes. Although neither was a relative of Phillip Hughes and neither had met him, they both reported an emotional response to the broadcast.

Panel A in the Box shows the ABP recording for a woman who was taking an angiotensin II receptor antagonist. The recording shows that she had an increase in systolic blood pressure (25 mmHg) while she was sitting watching the funeral that was comparable with her responses to physical activity.

The ABP recording for the second woman is shown in Panel B in the Box; her antihypertensive therapy included β-blockade. There was a 25 mmHg increase in systolic blood pressure while she sat watching the broadcast of the funeral. The increase was comparable with that for people with mild hypertension receiving mental stressor tests3 and may have been greater in the absence of β-blockade.

Such increases in blood pressure reflect overall changes in the cardiovascular system at the time of emotional stress and are associated with heightened cardiovascular risk.4,5 We therefore need to be aware of the significant impact that media technology may have on our health and consider preventive treatment strategies.

1
Ambulatory blood pressure recordings for two patients who watched a televised funeral*

*Neither patient was related to the deceased.

[Review] The science of early adversity: is there a role for large institutions in the care of vulnerable children?

It has been more than 80 years since researchers in child psychiatry first documented developmental delays among children separated from family environments and placed in orphanages or other institutions. Informed by such findings, global conventions, including the 1989 UN Convention on the Rights of the Child, assert a child’s right to care within a family-like environment that offers individualised support. Nevertheless, an estimated 8 million children are presently growing up in congregate care institutions.

New GP guide for prescribing benzodiazepines to help prevent ‘doctor shopping’

The Royal Australian College of General Practitioners (RACGP) has launched a new guide for GPs to follow when prescribing benzodiazepines.

Nearly 7 million prescriptions for these drugs are issued every year in Australia, mostly to treat anxiety and insomnia.

The most common varieties are Valium and temazepam.

RACGP President Dr Frank R Jones says their use has led to concerns about the harms associated with both authorised and unauthorised use of the drugs.

“There is significant debate in the medical community about the appropriate role and use of these drugs and this has been exacerbated by a lack of clinical guidelines in the area. The RACGP’s new guide, Prescribing drugs of dependence in general practice, Part B: Benzodiazepines is the first in Australia to comprehensively address these issues,” Dr Jones said.

The guide explains that prescribing benzodiazepines shouldn’t be the first treatment option and should be regarded as a short-term therapeutic option.

Problems associated with the use of these drugs in the short term are rare, however some patients are more vulnerable to harm than others.

“As GPs we need to be vigilant in identifying patients who may be misusing or abusing benzodiazepines because this can become a long-term and distressing problem.”

Use of these types of drugs beyond four weeks should be uncommon and should be made with a full risk-benefit analysis. There should also be careful monitoring.

The RACGP hopes the guide will help reduce patient harm associated with benzodiazepine misuse.

“Patients who have a substance use disorder may ‘doctor shop’ to gain prescriptions and increase their use and dosage. When taken in combination with other substances such as opioid medications, illicit drugs and alcohol, this can result in death,” Dr Jones warned.

Australian clinical trial activity and burden of disease: an analysis of registered trials in National Health Priority Areas

To improve Australia’s health, clinical research programs should devote substantial activity to advancing practice in areas of high clinical need. Clinical trials are designed to provide high-quality evidence of the effectiveness of new interventions to establish best clinical practice. However, few studies have examined the extent to which Australian clinical trials address priority areas of clinical need.

The Australian Institute of Health and Welfare (AIHW) National Health Priority Areas (NHPAs) were introduced to encourage appropriate targeting of health services and clinical research to improve health. Currently, there are nine NHPAs: cancer control, cardiovascular health, mental health, injury prevention and control, diabetes mellitus, obesity, arthritis and musculoskeletal conditions, dementia and asthma. These NHPAs account for approximately three-quarters of the total estimated burden of disease in Australia (1 915 600 of 2 632 800 disability-adjusted life-years [DALYs]).1

Previous studies have reported a disparity between the level of National Health and Medical Research Council (NHMRC) grant funding for studies investigating NHPA conditions relative to their disease burden.2,3 The founding of clinical trial registries, including the Australian New Zealand Clinical Trial Registry (ANZCTR) in 2005, provides the first opportunity to examine how well clinical trial activity in Australia is targeted to NHPAs.

Methods

We conducted a retrospective analysis using ANZCTR and ClinicalTrials.gov (CT.gov) data to report on Australian trial activity and characteristics for NHPAs; and to compare the level of trial activity to the relative burden of disease for each NHPA.

Ethics approval was not required for this analysis of publicly available trial data.

Data sources

Trial registration is voluntary in Australia.4

The ANZCTR is an online public registry of clinical trials maintained by the NHMRC Clinical Trials Centre, the University of Sydney. It collects information about trial interventions, investigated health conditions, planned recruitment, outcomes, funding and sponsorship using the World Health Organization-defined 20-item minimum dataset.5 Health conditions are coded using the United Kingdom Clinical Research Collaboration Health Research Classification System (http://www.hrcsonline.net). Additional data are collected about trial design, including randomisation and blinding. The ANZCTR 2011 Data Quality and Completeness Audit reported that, on average, at least 93 of 94 data fields for 148 trials were complete.6

CT.gov is an online public registry of clinical trials maintained by the United States National Library of Medicine (https://clinicaltrials.gov). It records similar data items to the ANZCTR.

Trial sample and characteristics

The trial sample included all trials of health-related interventions registered on the ANZCTR or CT.gov between 1 January 2008 and 31 December 2012 that included Australia as a country of recruitment. To avoid entering duplicate trial data, trials that listed a CT.gov or ANZCTR registration number as a secondary identifier were only included in the ANZCTR trial list.

Condition categories and codes were used to classify individual trials as addressing one or more NHPA conditions, or other, non-NHPA conditions. For each trial, we extracted information for: purpose of intervention (treatment, prevention, diagnosis, education/counselling/training, other/missing); allocation of intervention (randomised, non-randomised); trial phase (I–IV, not applicable, missing), blinding (blinded, open, other/missing), planned recruitment (reported as target sample size, and classified as < 100, 100–1000, > 1000 participants); participant age range (< 18 years, 18–69 years, ≥ 70 years); and countries of recruitment (Australia only, Australia and overseas).

Analysis

To measure trial activity, we recorded the total number and planned recruitment of registered trials investigating NHPA conditions. To assess whether trial activity reflected the burden of disease for each NHPA, we compared the relative trial activity targeted to each NHPA, measured as a proportion of the total trial activity, with the “expected” distribution of trial activity estimated from the relative burden of disease for that NHPA. Burden of disease was estimated from published estimates of DALYs for each NHPA expressed as a percentage of the total burden of disease and injury in Australia (%DALY).1

To describe disparities in relative trial activity by NHPA, we identified NHPAs where the observed trial activity was less than 50% or more than 200% of expected values. The χ2 goodness-of-fit test was also used to test for statistically significant differences between observed and expected trial activity for each NHPA. For these analyses, a two-sided P < 0.006 was regarded as statistically significant using the Bonferroni adjustment for multiple comparisons (nine comparisons).

For assessment of trial recruitment across NHPA, we also conducted a sensitivity analysis to examine trial recruitment to NHPA from Australian sites, where Australian recruitment was estimated from the planned recruitment from all ANZCTR trials plus 10% of the planned recruitment from CT.gov trials that included at least one Australian site. The figure of 10% was estimated from a randomly selected sample of 100 CT.gov registered trials that included at least one Australian site and represents the number of Australian sites as a proportion of all sites for each trial.

We also calculated the frequency distribution of trial characteristics for each NHPA. SAS, version 9.3 (SAS Institute) was used for data analyses.

Results

There were 5143 intervention trials registered during 2008–2012 that planned to recruit in Australia (ANZCTR, 3379; CT.gov, 1764). Of these, 3032 (59%) related to NHPA conditions (ANZCTR, 1908; CT.gov, 1124). Total planned recruitment for the trial sample was 2 404 609 participants, including 1 532 064 (64%) for NHPA trials (ANZCTR, 670 832; CT.gov, 861 232).

Trial activity in NHPA

The three disease areas that contribute the largest %DALY — cancer, cardiovascular diseases and mental disorders — also attracted the largest number of trial registrations and the largest planned recruitment (Box 1; Box 2).

The proportions of registered trials that investigated dementia or injury interventions were less than half those expected from their %DALYs (65/185 [35%] and 137/360 [38%], respectively; Box 1). The proportions of obesity and asthma trials were also lower than expected (195/386 [51%] and 68/123 [55%], respectively). In contrast, the proportion of registered arthritis and musculoskeletal diseases trials was about twice as high as expected on the basis of the %DALY (Box 1).

The proportions of planned recruitment to trials investigating obesity and dementia were also substantially lower than expected from their %DALYs (33 948/180 346 [19%] and 24 248/86 566 [28%], respectively), and was also low for asthma (29 468/57 711 [51%]) (Box 1).

When this analysis was repeated using estimated recruitment from Australian sites only, a similar pattern was observed, with the exception of recruitment to diabetes trials. For diabetes trials, total trial planned recruitment was relatively high (185 929/132 253 [141%]) compared with Australian sites (44 201/66 607 [66%]).

Trial characteristics

Overall, 2335 of 3032 (77%) NHPA trials used a randomised design and 1509 (50%) planned recruitment of ≤ 100 participants (Box 3). Of the 2931 NHPA trials that reported information about blinding, 1504 (51%) reported using it (Box 3).

About three-quarters of NHPA intervention trials investigated treatments (2321 [76%]) and 397 (13%) investigated prevention interventions (Box 3). The ratio of treatment to prevention trials ranged from less than 2 : 1 for obesity trials to 14 : 1 for cancer trials.

Most NHPA trials excluded children, whereas 2252 (75%) specified a maximum participant age of ≥ 70 years, or did not specify a maximum age (Box 3). International recruitment sites were reported in 1081 (36%) of NHPA trials (169 ANZCTR trials, 912 CT.gov trials) and varied by condition (Box 3).

Discussion

This study provides the first overview of clinical trial activity in Australia. We found that more than half of Australian registered intervention trials and planned trial recruitment are targeted to NHPA conditions.

Trial activity for cancer, cardiovascular diseases and mental disorders was high relative to other NHPA conditions, consistent with their position as the three major contributors to disability and premature death in Australia. In contrast, trial activity for obesity and dementia interventions was substantially less than the level expected from their contribution to the total DALY.

To interpret these results, the number of trials can be considered to provide a proxy measure for the number of active research questions being investigated to identify more effective interventions in each area. Planned trial recruitment provides a measure of the number of patients actively participating in research to determine best practice in each area.

These findings suggest there is a need to further examine research activity for obesity, dementia and asthma to determine if and how clinical trials research in these areas should be increased. However, this study does not allow us to define the optimum level of trial activity for each condition. Clearly, not all important research questions for NHPAs are amenable to investigation through clinical trials. For conditions where trial activity is already high relative to other disease areas, further increases may still represent good value for money by improving health care. For example, if promising new interventions are available; or practice variations or controversies exist with gaps in evidence to guide best practice. Conversely, for some conditions where trial activity is currently low, research priorities may warrant other study designs, such as those used in translational research or behavioural science, to develop new interventions.

This study also provides the first opportunity to assess the extent to which Australian trials are designed to provide robust, high-quality evidence for guiding practice. The use of randomisation and blinding provides a measure of trial quality; trial size provides an indicator of study power. Trials enrolling more than 100 participants are generally required to assess clinically meaningful health outcomes and to weigh up the benefits and harms of the new strategy, whereas smaller trials are generally designed to assess surrogate outcomes. About three-quarters of Australian trials used a randomised design; however, only around half reported blinding, or planned recruitment of more than 100 participants. These findings are slightly more favourable than those of a recent analysis of 79 413 intervention trials registered on CT.gov between 2000 and 2010, which reported that 70% used a randomised design, 44% used a blinded design and 38% enrolled 100 or more participants.7

One commonly raised concern about clinical trials research is the applicability of trial data to routine clinical practice populations and settings. Our finding that more than two-thirds of trials in NHPA areas did not exclude participants aged 70 years or older is encouraging.

The main strength of our study is that it provides a unique, timely overview of Australian clinical trials to inform current debate on the achievements, limitations and future directions for clinical trials research in Australia. Clinical researchers can use the same methods to further explore gaps for conditions within specific disease areas, as has been performed for cancer trials.8

There are two main limitations to our study that could affect our estimates of trial activity in different directions. First, we relied on trial registrations to estimate trial activity. As trial registration is not compulsory in Australia, we may have underestimated trial activity. Additionally, we only included international trials registered on the ANZCTR or CT.gov. A search using the WHO International Clinical Trials Registry Platform Search Portal (http://www.who.int/ictrp/search/en) showed that 11 096 of 11 412 (97%) trials with Australian sites are registered on these two registries. The total number of registered trials may therefore be 3% higher than our study estimate.

Second, our estimates of trial participation may overestimate the number of Australians participating in clinical trials, because 1622 of 5143 trials (32%) included sites outside Australia. Nevertheless, by including Australian sites, these trial recruitment figures capture participation in trials that can be expected to provide evidence relevant to Australian practice.

Despite these limitations, we believe our findings are valuable in informing initiatives to increase clinical trial activity.9,10 It is well documented that trial research is often not available to guide many routine clinical decisions about selecting interventions.11 To guide practice, large trials with adequate long-term follow-up are needed to identify small incremental improvements in health outcomes and/or adverse events. Our findings on trial size suggest that further efforts are needed to promote and support the conduct of large trials, or support the conduct of small high-quality trials that can later contribute data to meta-analyses.

Overall, we demonstrate the feasibility and value of using publicly available trial registry data to examine the profile of trials research for particular conditions and identify gaps in trial activity to inform trial initiatives. The ANZCTR provides a valuable resource for researchers to ensure new studies build on, or contribute to, existing trials.

1 Number of registered Australian intervention trials and total planned recruitment in National Health Priority Areas, as a percentage of total trial activity, and comparison to the expected number based on %DALY, Australian New Zealand Clinical Trials Registry and ClinicalTrials.gov, 2008–2012

	DALY		Trials					Planned recruitment
National Health Priority Area	Rank	%	Rank	Observed no. (%)	Expected no.	Observed/ expected %	P*	Rank	Observed no. (%)	Expected no.	Observed/ expected %	P*

Cancer control	1	19.0%	1	871 (16.9%)	977	89%	0.007	2	427 188 (17.8%)	456 876	94%	< 0.001
Cardiovascular health	2	18.0%	3	646 (12.6%)	926	70%	< 0.001	1	577 178 (24.0%)	432 830	133%	< 0.001
Mental health	3	13.3%	2	693 (13.5%)	684	101%	0.82	3	196 826 (8.2%)	319 813	62%	< 0.001
Obesity	4	7.5%	6	195 (3.8%)	386	51%	< 0.001	7	33 948 (1.4%)	180 346	19%	< 0.001
Injury prevention and control	5	7.0%	7	137 (2.7%)	360	38%	< 0.001	5	125 256 (5.2%)	168 323	74%	< 0.001
Diabetes mellitus	6	5.5%	5	282 (5.5%)	283	100%	1.00	4	185 929 (7.7%)	132 253	141%	< 0.001
Arthritis and musculoskeletal conditions	7	4.0%	4	410 (8.0%)	206	199%	< 0.001	6	109 107 (4.5%)	96 184	113%	< 0.001
Dementia	8	3.6%	9	65 (1.3%)	185	35%	< 0.001	9	24 248 (1.0%)	86 566	28%	< 0.001
Asthma	9	2.4%	8	68 (1.3%)	123	55%	< 0.001	8	29 468 (1.2%)	57 711	51%	< 0.001

DALY = disability-adjusted life-years. %DALY = DALYs expressed as a proportion of the total burden of disease in Australia.1 Observed number of trials is expressed as a percentage of total 5143 registered intervention trials. Observed planned recruitment is expressed as a % of total 2 404 609 planned recruitment. Expected number of trials is calculated by applying %DALY to total 5143 registered intervention trials. Expected planned recruitment is calculated by applying %DALY to total 2 404 609 planned recruitment. * χ2 goodness-of-fit test for comparison of observed versus expected values.

2 Relationship between trial characteristics and %DALY for each NHPA, Australian New Zealand Clinical Trials Registry and ClinicalTrials.gov, 2008–2012

The diagonal line represents the line of equality where %DALY is equal to trial number as a percentage of total registered trials (A) or planned trial participation as % of total planned trial participation (B). Dots below the line show NHPAs where the variable falls below the %DALY. The size of dots corresponds to the size of planned trial participation (A) or number of trials (B) for the NHPA.

%DALY = disability-adjusted life-years expressed as a proportion of the total burden of disease in Australia.1 NHPA = National Health Priority Area.

3 Australian intervention trial characteristics, overall and by National Health Priority Area (NHPA),* Australian New Zealand Clinical Trials Registry and ClinicalTrials.gov, 2008–2012

Characteristic	All trials	NHPA trials	Cancer	Cardio- vascular	Mental health	Obesity	Injury	Diabetes	Arthritis/ musculoskeletal	Dementia	Asthma

Total	5143	3032	871	646	693	195	137	282	410	65	68
Randomisation
Yes	3990 (78%)	2335 (77%)	564 (65%)	494 (77%)	579 (84%)	163 (84%)	125 (91%)	253 (90%)	321 (78%)	53 (82%)	59 (87%)
No	1137 (22%)	691 (23%)	304 (35%)	150 (23%)	113 (16%)	31 (16%)	12 (9%)	28 (10%)	89 (22%)	12 (18%)	9 (13%)
Missing	16	6	3	2	1	1		1
Intervention type
Treatment	3834 (75%)	2321 (76%)	732 (84%)	444 (69%)	494 (71%)	108 (55%)	103 (75%)	210 (75%)	357 (87%)	50 (77%)	46 (68%)
Prevention	781 (15%)	397 (13%)	52 (6%)	131 (20%)	98 (14%)	67 (34%)	25 (18%)	46 (16%)	34 (8%)	5 (8%)	10 (15%)
Diagnosis	152 (3%)	78 (3%)	29 (3%)	26 (4%)	11 (2%)	3 (2%)	2 (2%)	8 (3%)	4 (1%)	4 (6%)	0
Educational/ counselling/training	263 (5%)	171 (6%)	39 (5%)	26 (4%)	73 (11%)	10 (5%)	4 (3%)	15 (5%)	9 (2%)	5 (8%)	7 (10%)
Other/missing	113 (2%)	65 (2%)	19 (2%)	19 (3%)	17 (2%)	7 (4%)	3 (2%)	3 (1%)	6 (2%)	1 (2%)	5 (7%)
Age group (years)
Minimum age < 18†	987 (19%)	490 (16%)	122 (14%)	60 (9%)	156 (23%)	29 (15%)	42 (31%)	28 (10%)	57 (14%)	7(11%)	26 (38%)
Missing	5	2	1							1
Maximum age ≥ 70†	3652 (71%)	2252 (75%)	774 (89%)	558 (87%)	397 (57%)	69 (36%)	98 (72%)	199 (71%)	316 (77%)	59 (94%)	41 (60%)
Missing	18	10	2	2		1			2	2
Blinding
Blinded	2639 (53%)	1504 (51%)	270 (31%)	347 (55%)	405 (61%)	93 (51%)	89 (67%)	141 (52%)	249 (64%)	47 (72%)	48 (72%)
Open	2322 (47%)	1427 (49%)	589 (69%)	281 (45%)	260 (39%)	91 (49%)	43 (33%)	129 (48%)	139 (36%)	18 (28%)	19 (28%)
Missing	182	101	12	18	28	11	5	12	22	0	1
Planned recruitment
1–100	2689 (52%)	1509 (50%)	361 (41%)	325 (50%)	361 (52%)	132 (68%)	66 (48%)	133 (47%)	228 (56%)	22 (35%)	33 (49%)
101–1000	2066 (40%)	1274 (42%)	427 (49%)	244 (38%)	300 (43%)	58 (30%)	61 (45%)	119 (42%)	161 (39%)	35 (55%)	31 (46%)
> 1000	383 (7%)	246 (8%)	83 (10%)	77 (12%)	30 (4%)	5 (2%)	10 (7%)	30 (11%)	21 (5%)	6 (10%)	3 (5%)
Missing	5	3	1		2					2	1
Country of recruitment
Australia only	3521 (68%)	1951 (64%)	349 (40%)	401 (62%)	578 (83%)	184 (94%)	113 (82%)	192 (68%)	286 (70%)	37 (57%)	47 (69%)
Australia and overseas	1622 (32%)	1081 (36%)	522 (60%)	245 (38%)	115 (17%)	11 (6%)	24 (18%)	90 (32%)	124 (30%)	28 (43%)	21 (31%)

Data are no. (%) unless otherwise specified. * Trials may be classified under more than one NHPA (eg, obesity and diabetes). † Includes trials that did not specify age limits.

Changes in psychological distress and psychosocial functioning in young people visiting headspace centres for mental health problems

Improving the mental health and wellbeing of adolescents and young adults is receiving increasing attention throughout the world.1 The Australian Government was the first to invest significant funds in a practical and systematic response to this challenge, initiating a national reform process that created new service platforms for young people through its founding of headspace, the National Youth Mental Health Foundation.2

The initiative commenced in 2006, establishing an initial 10 centres and is set to increase to a network of 100 centres across Australia by 2016. headspace centres are one-stop entry points offering a mix of the services that young people need most. Centres provide early intervention by responding to early presentations of mental health problems and by assisting young people at greater risk of developing mental disorders. Being youth-friendly and non-stigmatising are priorities, and centre activities are founded on youth participation and engagement at all levels.3

From the beginning, the headspace initiative has evaluated its activities, despite the significant challenges inherent in determining the outcomes of such a complex, long-term, real-world, system-wide intervention. A preliminary external evaluation in 2009 showed that young people approved the approach used by the initial centres.4 At that time, however, it was still too early, in terms of implementation of the headspace initiative, to assess outcomes for the clients.

To facilitate investigation of the impact of the headspace centres, an innovative routine data capture system was introduced in 2013. This system collects information each time a young person accesses a headspace centre for service, and attempts to follow them up after they have finished engaging with the centre. Analysis of the dataset has shown that young people presenting to headspace centres have a wide range of mental health concerns, and are typically in the early stages of the development of a mental disorder.5 Further analyses have explored the types of service young people receive at the centres. In the companion paper to this article, we report that most of the young people seeking help at headspace centres present with mental health concerns, that they generally receive a timely response, and receive assessment and mental health care services. We also found that the initiative is primarily supported by funding from the headspace grant and by the Australian Government Medical Benefits Schedule.6

The current study reports the main clinical outcomes for young people who had presented to headspace centres for mental health concerns. The primary aim was to determine the extent to which psychological distress was reduced and psychosocial functioning improved in headspace clients.

Methods

Participants and procedure

Participants were all clients who had commenced an episode of care at a headspace centre for mental health reasons between 1 April 2013 and 31 March 2014. Young people who initially visited headspace for other reasons (situational, physical or sexual health, alcohol or other drug, or vocational reasons) were excluded from analyses. This selection was made because young people presenting with mental health concerns comprise the vast majority of those who seek help at headspace centres and definitely use their mental health care services; young people primarily presenting for other reasons may not have used mental health care services (see the companion paper to this article6). Analyses were limited to a young person’s first episode of care during the 12-month data collection period.

The procedure for the routine collection of data provided by the young people and service providers to the headspace Minimum Data Set is described elsewhere.5 Data related to psychological distress were collected from young people immediately before their first, third, sixth, 10th and 15th visits, as well as at follow-up. Measures of psychosocial functioning were recorded by service providers at each occasion of service.

Young people were invited to consent to being followed up when they first attended headspace. They provided an email address, and data were solicited after a 90-day pause in service provision by sending an email with a link to the follow-up questions. Young people could choose to answer these questions electronically, and responses were uploaded into the headspace data warehouse. Ethics approval for the follow-up was obtained from Melbourne Health Quality Assurance Review.

Measures

The primary presenting concern was categorised according to the clinical presentation features as determined by clinicians. These did not comprise diagnoses, but were rather the main symptoms evident at the initial presentation that were indicative of mental health problems.
Treatment services were recorded by clinicians, and were categorised as: cognitive behaviour therapy (CBT), interpersonal therapy, acceptance and commitment therapy, psychoeducation (including skills training and relaxation strategies), general and supportive counselling, mindfulness-based therapies, motivational interviewing, problem-solving therapy, and other interventions.
Client outcomes that were assessed were:
- the level of psychological distress, based on self-reports according to the 10-item Kessler Psychological Distress Scale (K10);7 and
- overall psychosocial functioning, assessed by service providers using the Social and Occupational Functioning Assessment Scale (SOFAS).8

Appendix 1 presents the number of clients for whom data were available at key time points.

Statistical analyses

IBM SPSS Statistics 21 was used for statistical analyses. Frequencies of each primary presenting concern were calculated, and age group and sex differences were assessed by χ2 analyses with Bonferroni correction for multiple comparisons.

Changes in each of the outcome measures over time were analysed in two ways.9 First, mixed-model repeated measures analysis of variance (ANOVA) was used to assess aggregate changes over time in K10 and SOFAS scores according to time point, number of service sessions, age group and sex. The statistical relationship between K10 and SOFAS scores was expressed as a Pearson product-moment correlation coefficient (r). Differences between the characteristics of clients who provided follow-up data and those who did not were analysed by logistic regression.

Second, significant change, reliable change and clinically significant change scores were calculated for the K10 and SOFAS data, as increasingly conditional indicators of change. The criterion for significant change was a moderate effect size (0.5) or greater for the degree of change.10 The reliable change index (RCI) (indicating reliable improvement or decline) and clinically significant change index (CSI) (cut-off point at which the person is more likely to belong to a non-clinical rather than a clinical population) were determined using Jacobson and Truax’s method.11

For the K10 scores, the RCI was estimated as a 6.73-point change (rounded to 7 points) using reliability coefficients reported for an Australian normative group (age group, 16–24 years) in the 2007 National Survey of Mental Health and Wellbeing.12 Using the same norms, the CSI cut-off was estimated to be 22.56 points (rounded to 23 points). For the SOFAS data, an RCI score of 10 was used; this was based on comparable outpatient psychiatric services data using the Global Assessment of Functioning scale as an equivalent. The CSI for the same comparison group was a score of 69 (Söderberg and Tungström [2006], cited by Falkenström13).

Results

The participants were 24 034 clients from the 55 headspace centres fully operational during the study period. Almost two-thirds of clients were female (62.7%), 36.9% were male and 0.4% were intersex or transgender. The mean age was 17.8 years (SD, 3.3), with 16.7% aged 12–14 years, 35.0% aged 15–17 years, 25.7% aged 18–20 years, and 22.6% aged 21–25 years.

Follow-up data were collected between June 2013 and August 2014. Of the total sample, 20 903 clients (87.0%) were eligible to provide follow-up data; the remaining 13.0% were still receiving headspace services or had not yet had a 90-day service-free period. Only 3.1% of eligible young people (651 clients) responded to the follow-up survey.

Presenting concern and treatment services

The most common mental health problems at initial presentation were depressive symptoms and anxiety, which together accounted for more than two-thirds of presentations. These were the most common presenting reasons for all age/sex groups, with the exception of 12–14-year-old boys, who presented most frequently with anxiety and anger problems and less frequently for depressive symptoms (Appendix 2).

Age and sex differences among those presenting with mental health concerns were indicated by χ2 analysis (χ2 [70] = 3300.57, P < 0.001). The proportions of younger males (12–14 years of age) presenting for anger or behavioural problems was greater than for other age/sex groups. Younger females (12–14 years of age) had higher presentation rates for deliberate self-harm than other groups (Appendix 2).

The most common treatment provided for all primary presenting concerns was CBT; for example, 43.6% of service provided to clients presenting with depressive symptoms involved CBT. A similar pattern of treatments was evident for all primary presenting concerns, with the second most common treatment being supportive counselling (except for borderline personality trait presentations). Psychoeducation was ranked third for most mental health problems (Box 1).

Mean changes in outcomes over time

Changes in the two outcome scores over time are depicted in Box 2 and Box 3. These plot the mean scores at each session that they were recorded, according to the total number of sessions attended. The sample sizes for each point declined as the number of sessions attended increased (Appendix 3). The follow-up data analyses were based on a particularly small sample size; further, no clinician-rated measures were available at this point, as the follow-up was based solely on self-report.

For the change in K10 between initial presentation and last recorded assessment, the factor with the greatest effect size was time, which explained 10.8% of the variance (Appendix 4, ANOVA 1; Box 2). Including the 3-month follow-up in the analysis showed that the time effect remained significant and explained 12.5% of the variance (Appendix 4, ANOVA 2). On average, there was a 3-point improvement in K10 scores from first to last assessment, and a further 3-point improvement from last service to follow-up for the small proportion of young people who provided follow-up data.

It is, however, important to note that the group of clients who provided follow-up data was significantly different from the much larger group of those who did not (χ2 [17] = 153.43, P < 0.001, Nagelkerke R2 = 0.062). Those who provided follow-up data were more likely to be female (odds ratio [OR], 1.63; 95% CI, 1.27–2.11), older (OR, 1.07; 95% CI, 1.04–1.11), have attended a greater number of service sessions (OR, 1.59; 95% CI, 1.39–1.82) and had better psychosocial functioning at exit (OR, 1.03; 95% CI, 1.02–1.05).

For change in SOFAS scores, time was again the strongest factor, but explained only 4.5% of the variance in this outcome measure Appendix 3, ANOVA 3; Box 3).

Significant, reliable and clinically significant change

The percentages of young people showing significant, reliable and clinically significant change between their first and last recorded assessments (not including follow-up) are presented in Box 4. Of the young people for whom data were available, psychological distress was significantly reduced in 36%, was reliably improved in 26%, and clinically significantly improved (by crossing the threshold distinguishing a clinical from a non-clinical population) in 21%. In 13% of clients, K10 scores significantly worsened, and in 8% they reliably deteriorated. According to clinician ratings of psychosocial functioning, significant and clinically significant improvement were each evident for 37% of the assessed clients, while 31% reliably improved. In contrast, function significantly declined in almost a fifth of clients, and reliably declined in 15%.

For 9957 clients, both K10 and SOFAS change data were available. Of these, 59.9% significantly improved and 49.2% reliably improved on at least one of the two scales, while 40.4% of those in the clinical group showed clinically significantly improvement on one or both of the scales.

It is important to note that the K10 and SOFAS scales measure different aspects of mental health, and that psychological distress (K10) was self-reported by young people, while social and occupational functioning (SOFAS) was assessed by a clinician. K10 and SOFAS scores were weakly correlated at presentation (r = − 0.19, P < 0.001) and at final assessment (r = − 0.23, P < 0.001).

There were statistically significant differences between those who improved and those who did not (significant improvement on at least one measure: χ2 [15] = 1168.48, P < 0.001, Nagelkerke R2 = 0.153). Improvement was predicted by greater distress (OR, 1.03; 95% CI, 1.02–1.04) and lower psychosocial functioning (OR, 0.94; 95% CI, 0.94–0.95) at service entry, and by attending a greater number of service sessions (OR, 1.16; 95% CI, 1.10–1.22). Age, sex and primary presenting concern did not predict improvement.

Discussion

This article reports the first outcome data for young people who have accessed the national headspace centre network for mental health problems. The analyses focused on the two key clinical outcomes, psychological distress and psychosocial functioning. The results show that psychological distress was significantly reduced in more than one-third of clients for whom data were available, and psychosocial functioning improved in a similar proportion. If improvement in either measure is considered, 60% of clients experienced significant change. Improvements in young people with greater distress and poorer functioning at service entry were noted in those who engaged well with the service (ie, attended more health care sessions). The findings are consistent with those reported from a single Sydney-based headspace service that found both symptomatic and functional improvements in its clients.14

Comparative data that would help determine whether these outcomes are acceptable are difficult to find. headspace clients present for a wide range of reasons and attend for varying numbers of sessions; although only outcomes for mental health clients were examined here, these young people still constitute a diverse group.6 Comparisons with outcomes from highly controlled clinical studies are therefore inappropriate. A study of psychotherapeutic outcomes in similarly aged young people attending a mental health clinic in the Netherlands, where the clients also presented with a variety of mental health concerns and received varying amounts of service, found that psychosocial functioning reliably improved in 19% of clients.13 This compares with the considerably higher rate of 31% that we have reported.

Comparative Australian data are scarce. Public tertiary mental health services use age bands of 0–17 and 18–64 years in their outcomes reports, and these are not comparable with either the age range of clients in these analyses or with the enhanced primary care service model of headspace. The most recent report from the National Outcomes and Casemix Collection (NOCC), which used the Health of the Nation Outcome Scales (HoNOS) family of outcome measures, showed that 37% of those aged 0–17 years and 24% of those aged 18–64 years using community-based public mental health services reported a significant improvement between the first and last occasions of service.15 The outcomes in young people reported here are similar to the child and adolescent results of the NOCC report, but much better than its findings for adults. However, the degree to which HoNOS outcomes are comparable with K10 and SOFAS scores is unclear, and the lack of directly comparable age groups makes interpretation difficult.

Drawing conclusions from the current study is restricted by several limitations. Primarily, the absence of a control group and other limitations inherent to observational studies means that the changes in scores reported cannot be attributed to headspace care.16 Further, most of the outcome data were derived from the last recorded assessment point for each client, but for many young people this was not at the completion of treatment. Our results are therefore likely to underestimate psychological and psychosocial gains in the course of treatment.

The follow-up rate was disappointing, although wholly expected, and highlights the considerable challenges in persuading young people to provide follow-up information after they have stopped attending for service. Without committing substantial resources to maintaining contact with people after leaving a health service, obtaining longer-term outcomes from real-world interventions will always be a major hurdle. Nevertheless, the headspace initiative has developed a process that attempts to routinely follow up young people after the end of service, and this may be unique in service delivery outside a well resourced prospective clinical trial. Over time, this follow-up database will grow and yield a rich source of information, even though there will be inevitable bias in those who provide follow-up data.

Another limitation is that the data cannot clearly determine the extent to which headspace clients received sufficient and appropriately matched “doses” of evidence-based therapies for different presenting problems and diagnoses, although it is evident that most clients did receive evidence-based therapies. headspace centres differ considerably in both their priorities and their capacity as a result of the diverse community and workforce contexts in which they are embedded,17 although all centres pursue a common vision of youth-focused, evidence-based, early intervention.3 The complexity and severity of young people’s presenting concerns also varies, with a substantial subset of young people who need, but are unable to gain, access to specialised tertiary services,18 which may have an impact on average improvement scores for the total client group.

Nevertheless, this article demonstrates that headspace is committed to examining and reporting outcomes for young people using its services, and that the headspace centre initiative is associated with improved mental health outcomes for a large number of young people assisted by this network across Australia.

1 Most common types of mental health care service received by headspace clients, according to the primary presenting problem*

Total sessions

Treatment services type rank

Presenting concern

Depressive symptoms

25 708

CBT
(43.6%)

Supportive counselling
(18.6%)

Psycho-education
(8.2%)

IPT
(7.5%)

ACT
(4.8%)

Anxiety symptoms

21 516

CBT
(47.0%)

Supportive counselling
(14.6%)

Psycho-education
(9.7%)

ACT
(7.5%)

IPT
(4.9%)

Anger problems

3859

CBT
(36.7%)

Supportive counselling
(21.3%)

Psycho-education
(16.6%)

IPT
(6.8%)

Motivational interviewing
(3.3%)

Stress related

3521

CBT
(34.0%)

Supportive counselling
(21.9%)

Psycho-education
(12.1%)

IPT
(7.2%)

ACT
(5.5%)

Suicidal thoughts or behaviour

2355

CBT
(36.9%)

Supportive counselling
(19.5%)

IPT
(9.6%)

Psycho-education
(9.2%)

ACT
(5.1%)

Behavioural problems

1389

CBT
(32.1%)

Supportive counselling
(23.3%)

Psycho-education
(18.8%)

IPT
(4.7%)

ACT
(3.6%)

Deliberate self-harm

1479

CBT
(36.3%)

Supportive counselling
(22.4%)

Psycho-education
(11.8%)

IPT
(6.6%)

ACT
(5.8%)

Eating disorder related

1159

CBT
(47.9%)

Supportive counselling
(12.9%)

Psycho-education
(8.4%)

IPT
(7.1%)

ACT
(6.0%)

Psychotic symptoms

531

CBT
(33.5%)

Supportive counselling
(23.0%)

Other
(18.8%)

Psycho-education
(12.2%)

IPT
(7.9%)

Borderline personality traits

523

CBT
(31.4%)

Other
(18.2%)

Supportive counselling
(17.8%)

Psycho-education
(11.1%)

IPT
(7.6%)

All presenting concerns

63 221

CBT
(42.8%)

Supportive counselling
(17.9%)

Psycho-education
(9.9%)

IPT
(6.5%)

ACT
(5.6%)

CBT = cognitive behaviour therapy. IPT = interpersonal therapy. ACT = acceptance and commitment therapy.

* Percentages refer to proportion of total mental health care sessions received by clients presenting with the respective concern. Percentages in rows do not add to 100% as other treatment modes were possible.

2 Mean psychological distress scores (K10) at different time points

3 Mean psychosocial functioning scores (SOFAS) at different time points

4 Proportion of young people showing significant, reliable and clinical change in psychological distress and psychosocial functioning between first and last service ratings

Measure	Method	Number of clients	Change category
Measure	Method	Number of clients	Improvement	No change	Deterioration
K10	Significant change (effect size ≥ 0.5)	10 228	36.1%	50.9%	13.0%
	Reliable change	10 228	26.2%	65.9%	8.0%
	Clinically significant change*	8205	21.1%	78.9%	NA
SOFAS	Significant change (effect size ≥ 0.5)	15 496	37.1%	43.4%	19.5%
	Reliable change	15 496	30.9%	53.6%	15.5%
	Clinically significant change*	9556	37.0%	63.0%	NA

K10 = Kessler Psychological Distress Scale. SOFAS = Social and Occupational Functioning Assessment Scale. NA = not applicable: young people in the clinical population are, by definition, not able to deteriorate, but rather remain in the clinical population. * It was not possible to assess the clinical improvement of young people who were in the non-clinical population at the first time point (19.8% of total sample for K10 and 38.3% of total sample for SOFAS); they were therefore excluded from this analysis.

The services provided to young people through the headspace centres across Australia

headspace, the National Youth Mental Health Foundation, was initiated by the Australian Government in 2006 because it was recognised that the prevalence of mental disorders and the burden of disease associated with mental health problems was greater for those in their adolescent and early adult years than in older adults, but that young people were less likely to access professional help.1 headspace centres aim to be highly accessible, youth-friendly integrated service hubs that respond to the mental health, general health, alcohol and other drug, and vocational concerns of young people aged 12 to 25 years.2 The main goal is to improve mental health outcomes by reducing help-seeking barriers and facilitating early access to services that meet the holistic needs of young people. Recent data indicate that the initiative is largely achieving its aim to improve access to services early in the development of mental illness.3

As the headspace network has grown, the key components of the model have become clearer.4 At the heart of all headspace services is a youth-friendly, non-stigmatising, inclusive “no wrong door” approach, essential for engaging young people in mental health care.5 This is both a challenge and a major point of difference from other mental health services, which are often highly targeted, with clear exclusion criteria. Consequently, there has been a high level of demand for the services offered by headspace.3Centres have been set up across Australia in highly diverse community settings with a flexible local capacity for service delivery. The variation in focus between centres and in the types of services they offer has been noted as both a strength and a concern.6 Workforce problems are an ongoing challenge for many centres, particularly in rural and remote locations.7

headspace aims to provide a timely and appropriate response to the various problems presented by young people, and to provide a soft entry point to mental health care. In this study we set out to investigate what services headspace centres are providing to young people and how they are being delivered. The proportions of young people who initially presented in each of the main service streams — mental health, situational, physical health, alcohol and other drugs, and vocational health — were determined, as were the numbers of clients who received mental health care at headspace centres after initially presenting to the service for other reasons. We examined the waiting time for services, patterns of service use (number of sessions of each service type attended, types of service mix), as well as the major providers and the funding streams that support service delivery.

Methods

Participants and procedures

All participants had commenced an episode of care at a headspace centre between 1 April 2013 and 31 March 2014.

Data were drawn from the headspace Minimum Data Set,3 which includes the routine data collected from all clients who provide consent, producing a near-complete census of headspace clients. Young people enter data into an electronic form before each service visit, and service providers also submit relevant information about each visit. Data were de-identified by encryption and extracted to the headspace national office data warehouse.

Ethics approval was obtained through internal quality assurance processes; these consent processes were reviewed and endorsed by an independent body, Australasian Human Research Ethics Consultancy Services. Follow-up data collection was approved by Melbourne Health Quality Assurance.

Measures

The main presenting problem or concern was categorised by the service provider as: mental health or behavioural (symptoms of a mental health problem); situational (eg, bullying at school, difficulty with personal relationships, grief); physical or sexual health; alcohol or other drugs (AOD); vocational; or other.
The service type was categorised as one of the following on each occasion of service: mental health; physical or sexual health; AOD; vocational; or engagement and assessment. The number of sessions of each main service type attended by a young person during the data collection period was calculated.
The wait time was measured by asking clients how long they had waited after requesting an appointment for their first service appointment, and whether they thought they had been required to wait too long.
Service providers were categorised by profession and role. This included intake and youth workers, psychologists, allied mental health workers (social workers, mental health nurses and occupational therapists), general practitioners, nurses, psychiatrists, AOD workers, vocational workers, clinical leads and administrative staff (including reception staff, managers and practice managers).
The funding stream was categorised as: the headspace grant (each centre is funded through a headspace grant); the Medicare Benefits Schedule (MBS); Access to Allied Psychological Services (ATAPS); the Mental Health Nurse Initiative (MHNI); Rural Primary Health Services (RPHS); in-kind contributions by partner organisations; or other.

Results

Data were assessed for 33 038 young people who had commenced an episode of care at one of 55 established headspace centres during the study period; 16.8% were aged 12–14 years, 34.4% aged 15–17 years, 25.8% aged 18–20 years, and 23.0% were 21–25 years of age. Most were female (61.9%); 37.5% were male.

Main presenting problems or concerns

The proportions of young people who attended headspace centres for each category of main presenting problem or concern and the number of service sessions they attended are shown in Box 1. Almost three-quarters of presentations specifically involved mental health and behavioural problems; 13.4% were for situational problems and 7.1% for physical or sexual health concerns. Only a small proportion (3.1%) presented primarily for AOD problems, and very few (1.8%) for vocational reasons.

The vast majority of clients, regardless of their initial problem or concern, attended mental health sessions; this included almost all who presented with situational or AOD problems, and almost 85% of those who presented with a vocational problem. The exception was that less than half of those who presented with physical or sexual health concerns also used mental health services.

Clients who first presented for mental health reasons attended the most service sessions, with an average of 4.4 and a median of 3.0 sessions per person. More than a quarter of these young people attended six or more sessions, and more than 10% attended 10 or more. Less than a third attended only once for mental health consultations.

Those who first presented for a physical or sexual health problem attended the fewest service sessions.

Wait time

Most of the young people reported that they did not wait too long for their first appointment (Box 1).

According to their detailed responses, 38.9% of clients had waited less than one week for their first appointment, 41.2% for 1–2 weeks, 14.6% for 3–4 weeks, and only 5.3% had waited more than 4 weeks. Unsurprisingly, almost half of those who had to wait more than 4 weeks reported that they had waited too long.

Service mix

headspace clients typically attend at least one session of engagement and assessment, except those who present primarily for physical or sexual health problems. The time used for engagement and assessment increased with the total number of sessions attended, regardless of the initial presenting problem (see Appendix).

Box 2 shows the proportions of each type of service provision for each of the core streams accessed by clients with different initial reasons for presenting. These data show the strong similarity in service patterns for those who presented with mental health and situational problems. Young people who first presented with situational concerns received slightly more engagement and assessment, but were otherwise similar to those who presented with mental health problems.

Young people presenting with physical or sexual health problems had quite a different pattern to those presenting with other concerns, although there was still a large component of engagement and assessment and mental health treatment. Young people who presented for AOD problems tended to have a greater need for engagement and assessment.

Service providers and funding streams

The service providers that delivered most of each service type are shown in Box 3A. In line with the headspace service model — young people usually have an engagement and assessment session with an intake or youth worker during their initial appointment to gather information and to determine their needs — intake and youth workers provided almost half of the engagement and assessment service, followed by psychologists, who delivered almost 20%. Other allied mental health workers, including social workers and occupational therapists, provided just over 12%.

Mental health services were mostly delivered by allied mental health professionals (81%), with over half provided by psychologists; only 1.2% was provided by psychiatrists, and just over 10% by general practitioners. Almost all physical or sexual health service was provided by GPs or nurses. Specialist AOD workers undertook a third of AOD service, complemented by contributions from allied mental health workers. The small amount of vocational service was largely provided by specialised vocational workers, although a quarter was undertaken by intake and youth workers.

The provision of headspace services relies on a number of funding streams. The major sources for each service type are compiled in Box 3B. Engagement and assessment services were mostly funded by the headspace grant (71%) or through the MBS (21%). Nearly two-thirds of mental health services were funded by the MBS and a smaller contribution by the ATAPS program, with just under a third funded by the headspace grant. Physical and sexual health services were primarily funded through MBS items, but 22% was supported by headspace grant funds. In contrast, the main funding source for AOD and vocational services was in-kind support by co-located services or consortium partners.

It should be noted that there was variation between headspace centres in each of the parameters discussed here, but space precludes the presentation of detailed analyses. Generally, however, no major differences were associated with the size, age or geographical location of centres. The one exception was waiting too long; significantly fewer young people reported waiting too long at the most recently established centres than at centres established during the first three rounds of the headspace program (7.0% v. 10.6%; P < 0.001). The longest wait times were experienced in one large centre in a major city, where 27% of young people reported they had waited too long, compared with only 2% at each of a small inner regional and a medium-sized outer regional centre. Waiting too long was significantly more common at large centres (12.0%) than at medium (9.6%) and small (9.4%) centres (P < 0.001). It was also significantly more frequent in major cities (11.9%) than at inner regional, outer regional and remote centres (8.3%, 9.2% and 8.1%, respectively; P < 0.001).

Discussion

There is considerable interest in the headspace initiative because it comprises a significant investment by the Australian Government in an innovative approach to youth mental health. The results presented here show that the vast majority of young people specifically attend headspace centres for mental health problems, and that the next most common reason for attendance involves situational problems that affect the wellbeing of the young person, such as bullying at school, difficulty with personal relationships or grief. This is consistent with the general early intervention aim of the headspace initiative, and with the recognition that mental health problems and related risk factors are the primary health concerns for adolescents and young adults.8

A sizeable minority of young people initially attended headspace for physical or sexual health problems. For almost half of these clients, this led to a mental health consultation, supporting the contention that physical and sexual health care can and should be a pathway to mental health care (and vice versa).

The headspace initiative engages young people with a range of health and wellbeing concerns, not just those with mental health problems. Few clients, however, presented primarily for AOD problems and vocational difficulties, suggesting that these are more often accompanying problems than primary concerns for those attending headspace centres, although half of the headspace clients aged 17–25 years are looking for work (compared with less than 10% for this age group in the general population).9 Funding for these two core streams relied primarily on in-kind contributions by headspace service partners, emphasising the value of the local partnership model that underpins service delivery, but also revealing vulnerability in terms of stable funding. Building the capacity of the headspace model to better support young people with vocational needs and secondary AOD problems should be a priority.

As young people are often reluctant to attend mental health services, receiving an appointment promptly after a young person has decided to seek help is crucial. The vast majority of headspace clients waited 2 weeks or less for initial service, a notable achievement. Wait times are a major barrier in traditional mental health services,10 and minimising waiting is a distinguishing focus of headspace. Nevertheless, some clients waited longer, and wait times were longer in more established centres. Minimising wait times must remain a constant focus for headspace services, while continuing to respond to the growing demands of young people with a range of presenting problems. Engagement and assessment are also critical elements.

Australia claims to lead the world in innovative approaches to youth mental health care. Our results confirm patterns that diverge from traditional mental health service delivery, and we argue that these patterns are more appropriate for meeting the social and mental health needs of young people.5

1 Number of headspace service sessions attended (all types) and initial wait time for young people presenting with different categories of problem or concern

		Main reason for presenting to headspace
	All clients	Mental health and behaviour	Situational	Physical or sexual health	Alcohol or other drugs	Vocational

Number of presentations (% of all clients)	33 038†	24 034 (72.7%)	4440 (13.4%)	2332 (7.1%)	1030 (3.1%)	583 (1.8%)
Number who received mental health service (% of presentations for respective reason)*	31 134 (94.2%)	23 738 (98.8%)	4331 (97.5%)	1134 (48.6%)	951 (92.3%)	493 (84.6%)

Mean number of sessions attended (SD)	4.1 (4.2)	4.4 (4.4)	3.6 (3.7)	2.5 (2.9)	3.0 (3.4)	3.2 (3.6)
Median number of sessions attended	3.0	3.0	2.0	1.0	2.0	2.0
Number of sessions attended
1 session	35.4%	32.1%	39.0%	50.8%	45.2%	45.5%
2 sessions	14.0%	12.8%	14.8%	22.2%	19.3%	16.1%
3–5 sessions	25.6%	26.7%	25.7%	18.7%	21.0%	22.3%
6–9 sessions	15.1%	16.9%	13.3%	4.6%	10.0%	9.8%
10 or more sessions	10.0%	11.5%	7.2%	3.6%	4.6%	6.3%

Client did not wait too long for first service	89.4%	88.7%	91.5%	90.9%	91.4%	92.1%

* Includes engagement and assessment services. † Includes 619 young people (1.9% of sample) who had presented for “other” primary reasons not included in the five major categories, such as attention deficit and developmental disorders.

2 Service mix according to initial presenting problem or concern

3 Main service providers (A) and main funding sources (B) for each headspace service type*

(A)

Main types of service providers (rank)

Service type

Engagement and assessment

Intake/youth worker
(46.4%)

Psychologist
(18.6%)

Allied mental health
(12.2%)

GP
(7.4%)

Mental health

Psychologist
(50.6%)

Allied mental health
(17.2%)

Intake/youth worker
(13.2%)

GP
(11.5%)

Physical or sexual health

GP
(76.1%)

Nurse
(11.7%)

Alcohol or drugs

AOD worker
(31.4%)

Allied mental health
(31.4%)

Intake/youth worker
(13.2%)

Psychologist
(10.3%)

Vocational

Vocational
(38.4%)

Intake/youth worker
(24.7%)

Miscellaneous†
(16.5%)

Psychologist
(8.2%)

(B)

Main funding sources (rank)

Service type

Engagement and assessment

headspace
(70.8%)

MBS
(20.9%)

Mental health

MBS
(57.4%)

headspace
(29.5%)

ATAPS
(7.8%)

Physical or sexual health

MBS
(69.3%)

headspace
(21.8%)

In-kind
(6.7%)

Alcohol or drugs

In-kind
(50.3%)

headspace
(28.6%)

MBS
(17.8%)

Vocational

In-kind
(46.8%)

headspace
(37.2%)

MBS
(11.7%)

AOD = alcohol or drugs; ATAPS = Access to Allied Psychological Services; GP = general practitioner; MBS = Medical Benefits Scheme.

* A maximum of four service providers and three funding sources are reported here; contributions under 5% are not included. For these reasons, rows do not add to 100%.

† Consisting of various types of provider, mainly interns and placement, community engagement and education officers.

The gap remains: NHMRC research funding for suicide and self-harm, 2000–2014

To the Editor: In an article on National Health and Medical Research Council (NHMRC) funding for the National Health Priority Areas, Christensen and colleagues concluded that there was no narrowing of the gap in the proportion of funding provided to mental health research from 2001 to 2010.1 In particular, NHMRC funding per disability-adjusted life-year was the lowest for suicide and self-harm in comparison with other mental health categories and did not increase over 2001–2009.

Analysis of NHMRC research funding for suicide and self-harm in comparison with other causes of high morbidity and mortality in Australia from 2000 to 2014 does not indicate that much progress has been made, and the gap still remains. Intentional self-injury in Australia ranked higher as the leading cause of death in 2013 than did skin cancers and accidental falls (14th, 16th and 18th leading cause, respectively).2 In 2013, suicide claimed more lives (2520) than either skin cancers (2209), accidental falls (1920) or transport accidents (1428); and the standardised suicide rate considerably exceeded mortality rates for these causes of death (10.7 compared with 8.3, 6.7 and 6.0 per 100 000 population, respectively).2 Yet NHMRC research funding for suicide and self-harm from 2000 to 2014 was lower than that for all skin cancers, falls and vehicle accidents (Box).3–5

Over this period, the NHMRC invested $17 407 912 in suicide research in the mental health category, funding 41 projects, including 21 project grants, four research fellowships and three early career fellowships.3 We have observed an evolution and a shift in the types of funding available and granted. NHMRC project grants to create new knowledge, which dominated in the early 2000s, have received less and less funding over time, suggesting that the complexity of suicide research may be out of step with NHMRC competitive criteria. On the other hand, funding opportunities for grants to build Australia’s future capability (such as research, early career and career development fellowships) have increased, including fellowships to the NHMRC Centre of Research Excellence in Suicide Prevention (CRESP). Although only in operation for 3 years, funding for this new centre ($2 564 924 for 2012–2017) accounts for almost 10% of the total NHMRC actual expenditure. Three of five NHMRC project grants over 2013–2014 were awarded to CRESP researchers.

However, this investment is simply not enough. One centre of research excellence is not sufficient to make a serious dent in the problem of suicide and self-harm in Australia. All the while, we are losing seven people a day to suicide, a fact that requires an immediate response. The outrage associated with deaths from HIV/AIDS led to social action and subsequent science that has reduced the impact of this disease, allowing people to live meaningful lives. We need to do the same for suicide, which takes the lives of our productive and young people every day.

National Health and Medical Research Council funding in selected research areas, 2000–20143–5

[Editorial] Adolescents with diabetes

Diabetes has long been known to influence and be influenced by comorbidity. A collection of papers on diabetes and mental health, published jointly by The Lancet Diabetes & Endocrinology and The Lancet Psychiatry on May 18, shows that the diversity of interactions is far broader than once was imagined. From the family to the environment, diabetes control is subject to a myriad of stimuli. When these factors combine in adolescence, amidst the psychosocial and physiological transitions of puberty, the confluence can be problematic for glycaemic control and for relationships with health professionals.