Heterogeneity of quality of life in young people attending primary mental health services

Abstract

Aims 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. Methods 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. Results 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. Conclusions 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.

Mapping psychological distress, depression and anxiety measures to adolescent AQoL-6D utility using data from a sample of young people presenting to primary mental health services

“…php/aqolinstruments?id=92), using Australian population preference weights. The distribution of AQoL-6D instrument item specific scores within our sample has been described in a previous study [17].…”

Accelerating youth mental health services research

“…Via latent class analysis (LCA), we differentiated by demographic and clinical features four subgroups of young people based on their responses to items on the Assessment of Quality of Life – 6 dimensions (AQoL-6D) scale. 28 We concluded that including 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 specific subgroups of young people beyond reducing psychological distress and promoting symptom recovery. 28 Another study identified young people vulnerable to social exclusion across domains of housing, work and study, social functioning, and lifestyle factors.…”

“…28 We concluded that including 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 specific subgroups of young people beyond reducing psychological distress and promoting symptom recovery. 28 Another study identified young people vulnerable to social exclusion across domains of housing, work and study, social functioning, and lifestyle factors. 29 Those experiencing multiple domain vulnerabilities reported greater mental health needs; for example, those not in education, employment and/or training (NEET) and with unstable housing had higher levels of distress, substance use, poor functioning, and lower social support.…”

Ensemble clustering: A practical tutorial

“…To demonstrate the need for ensemble clustering in psychology, we conducted simulations using datasets from three published studies (Amiet et al, 2020;Cotton et al, 2022;Jakobson & Rigby, 2021) (see details of these studies in Table S3 in Supplementary Material I). The primary aim of these simulations was to assess the stability of different clustering algorithms with perturbation introduced into the data.…”