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IssuesConsideration of an individual's quality of life (QoL) can benefit assessment and treatment of addictive disorders, however, uncertainty remains over operationalisation of the construct as an outcome and the appropriateness of existing measures for these populations. This systematic review aimed to identify and evaluate QoL and health-related QoL outcome instruments used in addiction-related risk and harm research and map their conceptualised domains.ApproachThree electronic databases and a specialised assessment library were searched on 1 February 2022 for QoL or health-related QoL outcome instruments used with addiction-related risk and harm populations. PRISMA reporting guidance was followed and included outcome instruments were appraised using mixed methods. Psychometric evidence supporting their use was summarised. The COSMIN risk of bias tool was used to assess validation studies.Key FindingsA total of 298 articles (330 studies) used 53 outcome instruments and 41 unique domains of QoL. Eleven instruments' psychometric properties were evaluated. No instrument was assessed for any parameter in at least five studies for meta-analytic pooling. Cronbach's alpha (α) internal consistency was the most widely assessed parameter with the AQoLS, WHOQOL-BREF, ALQoL-9, Q-LES-Q-SF, SF-12, DUQoL, QLI and SF-36 displaying promising statistics (α > 0.70).Implications and ConclusionMany instruments have been utilised. However, a significant proportion of studies applied a small number of instruments with minimal high-quality validation evidence supporting their use within addiction-related risk and harm. Promising instruments are recommended, however, the paucity of supporting evidence limits confidence in the reliability and validity of QoL measurement in these populations.

Purpose The extant response shift definitions and theoretical response shift models, while helpful, also introduce predicaments and theoretical debates continue. To address these predicaments and stimulate empirical research, we propose a more specific formal definition of response shift and a revised theoretical model. Methods This work is an international collaborative effort and involved a critical assessment of the literature. Results Three main predicaments were identified. First, the formal definitions of response shift need further specification and clarification. Second, previous models were focused on explaining change in the construct intended to be measured rather than explaining the construct at multiple time points and neglected the importance of using at least two time points to investigate response shift. Third, extant models do not explicitly distinguish the measure from the construct. Here we define response shift as an effect occurring whenever observed change (e.g., change in patient-reported outcome measures (PROM) scores) is not fully explained by target change (i.e., change in the construct intended to be measured). The revised model distinguishes the measure (e.g., PROM) from the underlying target construct (e.g., quality of life) at two time points. The major plausible paths are delineated, and the underlying assumptions of this model are explicated. Conclusion It is our hope that this refined definition and model are useful in the further development of response shift theory. The model with its explicit list of assumptions and hypothesized relationships lends itself for critical, empirical examination. Future studies are needed to empirically test the assumptions and hypothesized relationships. Supplementary Information The online version contains supplementary material available at 10.1007/s11136-021-02846-w.

The article “Response shift in patient‑reported outcomes: definition, theory, and a revised model”, written by Antoine Vanier, Frans J. Oort, Leah McClimans, Nikki Ow, Bernice G. Gulek, Jan R. Böhnke, Mirjam Sprangers, Véronique Sébille, Nancy Mayo and the Response Shift - in Sync Working Group, was originally published electronically on the publisher’s internet portal on 28 April 2021 without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 17 May 2021 to

Reimbursement of English mental health hospitals is moving away from block contracts and towards activity and outcome‐based payments. Under the new model, patients are categorised into 20 groups with similar levels of need, called clusters, to which prices may be assigned prospectively. Clinicians, who make clustering decisions, have substantial discretion and can, in principle, directly influence the level of reimbursement the hospital receives. This may create incentives for upcoding. Clinicians are supported in their allocation decision by a clinical clustering algorithm, the Mental Health Clustering Tool, which provides an external reference against which clustering behaviour can be benchmarked. The aims of this study are to investigate the degree of mismatch between predicted and actual clustering and to test whether there are systematic differences amongst providers in their clustering behaviour. We use administrative data for all mental health patients in England who were clustered for the first time during the financial year 2014/15 and estimate multinomial multilevel models of over, under, or matching clustering. Results suggest that hospitals vary systematically in their probability of mismatch but this variation is not consistently associated with observed hospital characteristics.

Background The presence of mental health conditions in cardiac rehabilitation (CR) patients such as anxiety and depression can lead to reduced programme adherence, increased mortality and increased re-occurrence of cardiovascular events undermining the aims and benefit of CR. Earlier research has identified a relationship between delayed commencement of CR and poorer physical activity outcomes. This study wished to explore whether a similar relationship between CR wait time and mental health outcomes can be found and to what degree participation in CR varies by mental health status. Methods Data from the UK National Audit of Cardiac Rehabilitation, a dataset that captures information on routine CR practice and patient outcomes, was extracted between 2012 and 2016. Logistic and multinomial regression models were used to explore the relationship between timing of CR and mental health outcomes measured on the hospital anxiety and depression scale. Results The results of this study showed participation in CR varied by mental health status, particularly in relation to completion of CR, with a higher proportion of non-completers with symptoms of anxiety (5% higher) and symptoms of depression (8% higher). Regression analyses also revealed that delays to CR commencement significantly impact mental health outcomes post-CR. Conclusion In these analyses CR wait time has been shown to predict the outcome of anxiety and depression status to the extent that delays in starting CR are detrimental. Programmes falling outside the 4-week window for commencement of CR following referral must strive to reduce wait times to avoid negative impacts to patient outcome.

Background: Recent developments in psychometric modeling and technology allow pooling well-validated items from existing instruments into larger item banks and their deployment through methods of computerized adaptive testing (CAT). Use of item response theory-based bifactor methods and integrative data analysis overcomes barriers in cross-instrument comparison. This paper presents the joint calibration of an item bank for researchers keen to investigate population variations in general psychological distress (GPD). Methods: Multidimensional item response theory was used on existing health survey data from the Scottish Health Education Population Survey (n = 766) to calibrate an item bank consisting of pooled items from the short common mental disorder screen (GHQ-12) and the Affectometer-2 (a measure of “general happiness”). Computer simulation was used to evaluate usefulness and efficacy of its adaptive administration. Results: A bifactor model capturing variation across a continuum of population distress (while controlling for artefacts due to item wording) was supported. The numbers of items for different required reliabilities in adaptive administration demonstrated promising efficacy of the proposed item bank. Conclusions: Psychometric modeling of the common dimension captured by more than one instrument offers the potential of adaptive testing for GPD using individually sequenced combinations of existing survey items. The potential for linking other item sets with alternative candidate measures of positive mental health is discussed since an optimal item bank may require even more items than these., Charles University PRVOUK programme nr. P38, This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s12874-016-0158-7