Assessing the validity and responsiveness of a generic preference quality of life measure in the context of posttraumatic stress disorder

Quality of Life Research https://doi.org/10.1007/s11136-023-03432-y Assessing the validity and responsiveness of a generic preference quality of life measure in the context of posttraumatic stress disorder Sheradyn R. Matthews2 Reginald D. V. Nixon1,2 · Marja Elizabeth2 · Larissa N. Roberts2 · Billingsley Kaambwa3 · Tracey D. Wade1,2 · Accepted: 26 April 2023 © Crown 2023 Abstract Purpose There is limited research exploring the usefulness of generic preference-based quality of life (GPQoL) measures used to facilitate economic evaluation in the context of posttraumatic stress disorder (PTSD). The aim of the current study was to explore the validity and responsiveness of a common GPQoL measure (Assessment of Quality of Life 8 Dimension [AQoL-8D]) in relation to a PTSD condition-specific outcome measure (Posttraumatic Stress Disorder Checklist for the DSM-5 [PCL-5]). Method This aim was investigated in a sample of individuals (N = 147) who received trauma-focused cognitive-behavioural therapies for posttraumatic stress disorder. Convergent validity was investigated using spearman’s correlations, and the level of agreement was investigated using Bland–Altman plots. Responsiveness was investigated by exploring the standardised response means (SRM) from pre-post-treatment across the two measures, which allow the comparison of the magnitude of change between the measures over time. Results Correlations between the AQoL-8D (dimensions, utility and summary total scores) and the PCL-5 total score ranged from small to large and agreement between the measures was considered moderate to good. While SRMs were large for the AQoL-8D and PCL-5 total scores, the SRM for the PCL-5 was nearly double that of the AQoL-8D. Conclusion Our findings demonstrate that the AQoL-8D has good construct validity but present preliminary evidence that economic evaluations using only GPQoL measures may not fully capture the effectiveness of PTSD treatments. Keywords AQoL-8D · PTSD · PCL-5 · Responsiveness · Validity As demand for mental health services increases, effective allocation of health resources to maximise mental health outcomes is paramount [1]. Economic evaluations provide information on the cost-effectiveness of health treatments, therefore providing policymakers with evidence regarding where to strategically invest scarce resources to maximise health gains [2]. Although the use of economic * Reginald D. V. Nixon Sheradyn R. Matthews 1 Flinders University Institute for Mental Health and Wellbeing, P.O Box 2100, Adelaide, SA 5001, Australia 2 College of Education, Psychology and Social Work, Flinders University, P.O. Box 2100, Adelaide, SA 5001, Australia 3 College of Medicine and Public Health, Flinders University, P.O Box 2100, Adelaide, SA 5001, Australia evaluation in mental health conditions such as depression, anxiety and schizophrenia has increased in recent years [3], far less attention has been paid to the cost-effectiveness of posttraumatic stress disorder (PTSD) treatments, a mental health condition that impacts 1.5 million Australian adults annually [4]. Despite recommendations for the inclusion of economic analyses alongside randomised control trials, this rarely occurs and as such, there are substantial gaps in our understanding of the economic impact of particular mental health treatments [3]. Conservative estimates suggest effective and targeted resource allocation in the treatment of PTSD in adult survivors of childhood trauma would save the Australian government 9.1 billion dollars annually [5]. This estimate would inevitably grow if individuals who experienced trauma in their adulthood were included. Effective treatment of PTSD also results in substantial improvements in social functioning and quality of life for individuals [6, 7]. As such, promoting the use of economic evaluations in 13 Vol.:(0123456789) Quality of Life Research understudied areas such as PTSD is vital to ensure that there is evidence to guide effective resource allocation. Cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) are the most commonly used types of economic evaluations [2]. These two methods are identical in how costs are quantified; however, they differ in how health outcomes are measured. In CEAs, outcomes are measured in natural units relevant to the disorder/disease in question (e.g., PTSD symptoms, number of hospital admissions), whereas in CUA the outcome is measured in terms of a generic metric of health [8], most commonly expressed in Quality Adjusted Life Years (QALYs). A QALY represents the quantity and quality of an individual’s life where one QALY is equivalent to one year in perfect health [2]. Generic preference quality of life (GPQoL) instruments (i.e., a measure of health-related quality of life not specific to any particular health condition) or condition-specific preference quality of life (CPQoL) instruments allow the calculation of QALYS. The total scores of these instruments are converted into utility values, which are then multiplied by a specific timeframe to provide the number of QALYS lost or gained over time (see [8] for greater detail regarding how QALYS are derived from GPQoL or CPQoL instruments]. Given that QALYS are a generic measure, they can enable comparison of QALYS gained from different treatments across various condition/disease areas [2]. This contrasts with a CEA approach where comparisons between economic analyses are only possible for studies that use the same condition-specific outcome measure. For this reason, policy bodies such as the Medical Benefits Advisory Committee (MSAC) in Australia and the National Institute for Health and Clinical Excellence (NICE) in the United Kingdom recommend the use of preference-based QoL measures in economic evaluations to facilitate the calculation of QALYS [9, 10]. Policymakers can then make cross-health sector comparisons to better inform decisions regarding the allocation of resources across the entire health sector [2]. There have, however, been inconsistent findings surrounding the usefulness of GPQoL instruments in the field of mental health. A recent review of reviews by Finch et al. [11] found that three common GPQoL instruments—EQ-5D [12], Short Form-6 Dimension (SF-6D) [13], and the Health Utilities Index Mark 3 (HUI-3) [14]—generally performed well in terms of their convergent validity and responsiveness (i.e., a measures ability to detect health change over time) to symptom change following treatment when compared to depression and anxiety measures. However, the EQ-5D, a widely used GPQoL measure in the health and mental health field, performed poorly when compared to specific measures of schizophrenia, bipolar and personality disorders [11, 15]. The authors were unable to comment on the efficacy of the SF-6D and HUI-3 in relation to schizophrenia, bipolar and personality disorder due to the limited studies including 13 these measures. However, individual studies elsewhere (...truncated)