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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.
There are no published estimates of the health state utility values (HSUVs) for a broad range of eating disorders (EDs). HSUVs are used in economic evaluations to determine quality-adjusted life years or as a measure of disorder burden. The main objective of the current study is to present HSUVs for a broad range of EDs based on DSM-5 diagnoses.
We used pooled data of two Health Omnibus Surveys (2015 and 2016) including representative samples of individuals aged 15 + years living in South Australia. HSUVs were derived from the SF-6D (based on the SF-12 health-related quality of life questionnaire) and analysed by ED classification, ED symptoms (frequency of binge-eating or distress associated to binge eating) and weight status. Multiple linear regression models, adjusted for socio-demographics, were used to test the differences of HSUVs across ED groups.
Overall, 18% of the 5609 individuals met criteria for ED threshold and subthreshold. EDs were associated with HSUV decrements, especially if they were severe disorders (compared to non-ED), binge ED: −0.16 (95% CI −0.19 to −0.13), bulimia nervosa: −0.12, (95% CI −0.16 to −0.08). There was an inverse relationship between distress related binge eating and HSUVs. HSUVs were lower among people with overweight/obese compared to those with healthy weight regardless of ED diagnosis.
EDs were significantly associated with lower HSUVs compared to people without such disorders. This study, therefore, provides new insights into the burden of EDs. The derived HSUVs can also be used to populate future economic models.
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