Mental ill-health has a major impact on young people, with pain often co-occurring. We estimated the prevalence and impact of pain in young people with mental ill-health.

Longitudinal data (baseline and three-month follow-up) of 1,107 Australian young people (aged 12–25 years) attending one of five youth mental health services. Multi-level linear mixed models estimated associations between pain characteristics (frequency, intensity, and limitations) and outcomes with false discovery rate (FDR) adjustment. Pain characteristics were baseline-centered to estimate if the baseline score (between-participant effect) and/or change from baseline (within-participant effect) was associated with outcomes.

At baseline, 16% reported serious pain more than 3 days, 51% reported at least moderate pain, and 25% reported pain-related activity limitations in the last week. Between participants, higher serious pain frequency was associated with greater anxiety symptoms (β[95%CI]: 0.90 [0.45, 1.35], FDR-p=0.001), higher pain intensity was associated with greater symptoms of depression (1.50 [0.71, 2.28], FDR-p=0.001), anxiety (1.22 [0.56, 1.89], FDR-p=0.002), and suicidal ideation (3.47 [0.98, 5.96], FDR-p=0.020), and higher pain limitations were associated with greater depressive symptoms (1.13 [0.63, 1.63], FDR-p<0.001). Within participants, increases in pain intensity were associated with increases in tobacco use risk (1.09 [0.48, 1.70], FDR-p=0.002), and increases in pain limitations were associated with increases in depressive symptoms (0.99 [0.54, 1.43], FDR-p<0.001) and decreases in social and occupational functioning (−1.08 [−1.78, −0.38], FDR-p=0.009).

One-in-two young people seeking support for mental ill-health report pain. Youth mental health services should consider integrating pain management.

The specific and multifaceted service needs of young people have driven the development of youth-specific integrated primary mental healthcare models, such as the internationally pioneering headspace services in Australia. Although these services were designed for early intervention, they often need to cater for young people with severe conditions and complex needs, creating challenges in service planning and resource allocation. There is, however, a lack of understanding and consensus on the definition of complexity in such clinical settings.

This retrospective study involved analysis of headspace’s clinical minimum data set from young people accessing services in Australia between 1 July 2018 and 30 June 2019. Based on consultations with experts, complexity factors were mapped from a range of demographic information, symptom severity, diagnoses, illness stage, primary presenting issues and service engagement patterns. Consensus clustering was used to identify complexity subgroups based on identified factors. Multinomial logistic regression was then used to evaluate whether these complexity subgroups were associated with other risk factors.

A total of 81,622 episodes of care from 76,021 young people across 113 services were analysed. Around 20% of young people clustered into a ‘high complexity’ group, presenting with a variety of complexity factors, including severe disorders, a trauma history and psychosocial impairments. Two moderate complexity groups were identified representing ‘distress complexity’ and ‘psychosocial complexity’ (about 20% each). Compared with the ‘distress complexity’ group, young people in the ‘psychosocial complexity’ group presented with a higher proportion of education, employment and housing issues in addition to psychological distress, and had lower levels of service engagement. The distribution of complexity profiles also varied across different headspace services.

The proposed data-driven complexity model offers valuable insights for clinical planning and resource allocation. The identified groups highlight the importance of adopting a holistic and multidisciplinary approach to address the diverse factors contributing to clinical complexity. The large number of young people presenting with moderate-to-high complexity to headspace early intervention services emphasises the need for systemic change in youth mental healthcare to ensure the availability of appropriate and timely support for all young people.

The utility of quality of life (QoL) as an outcome measure in youth-specific primary mental health care settings has yet to be determined. We aimed to determine: (i) whether heterogeneity on individual items of a QoL measure could be used to identify distinct groups of help-seeking young people; and (ii) the validity of these groups based on having clinically meaningful differences in demographic and clinical characteristics.

Young people, at their first presentation to one of five primary mental health services, completed a range of questionnaires, including the Assessment of Quality of Life–6 dimensions adolescent version (AQoL-6D). Latent class analysis (LCA) and multivariate multinomial logistic regression were used to define classes based on AQoL-6D and determine demographic and clinical characteristics associated with class membership.

1107 young people (12–25 years) participated. Four groups were identified: (i) no-to-mild impairment in QoL; (ii) moderate impairment across dimensions but especially mental health and coping; (iii) moderate impairment across dimensions but especially on the pain dimension; and (iv) poor QoL across all dimensions along with a greater likelihood of complex and severe clinical presentations. Differences between groups were observed with respect to demographic and clinical features.

Adding multi-attribute utility instruments such as the AQoL-6D to routine data collection in mental health services might generate insights into the care needs of young people beyond reducing psychological distress and promoting symptom recovery. In young people with impairments across all QoL dimensions, the need for a holistic and personalised approach to treatment and recovery is heightened.

Subjective cognitive difficulties are common in mental illness and have a negative impact on role functioning. Little is understood about subjective cognition and the longitudinal relationship with depression and anxiety symptoms in young people.

To examine the relationship between changes in levels of depression and anxiety and changes in subjective cognitive functioning over 3 months in help-seeking youth.

This was a cohort study of 656 youth aged 12–25 years attending Australian headspace primary mental health services. Subjective changes in cognitive functioning (rated as better, same, worse) reported after 3 months of treatment was assessed using the Neuropsychological Symptom Self-Report. Multivariate multinomial logistic regression analysis was conducted to evaluate the impact of baseline levels of and changes in depression (nine-item Patient Health Questionnaire; PHQ9) and anxiety symptoms (seven-item Generalised Anxiety Disorder scale; GAD7) on changes in subjective cognitive function at follow-up while controlling for covariates.

With a one-point reduction in PHQ9 at follow-up, there was an estimated 11–18% increase in ratings of better subjective cognitive functioning at follow-up, relative to stable cognitive functioning. A one-point increase in PHQ9 from baseline to follow-up was associated with 7–14% increase in ratings of worse subjective cognitive functioning over 3 months, relative to stable cognitive functioning. A similar attenuated pattern of findings was observed for the GAD7.

A clear association exists between subjective cognitive functioning outcomes and changes in self-reported severity of affective symptoms in young people over the first 3 months of treatment. Understanding the timing and mechanisms of these associations is needed to tailor treatment.