Purpose The purpose of this study was to develop and psychometrically evaluate a new quality of life measure for use in people with mental health problems—the Mental Health Quality of Life questionnaire (MHQoL). Methods The MHQoL dimensions were based on prior research by Connell and colleagues, highlighting the seven most important quality of life dimensions in the context of mental health. Items were generated following a systematic review we performed and through inviting expert opinion. A focus group and an online qualitative study (N = 120) were carried out to assess the face and content validity of the MHQoL. The MHQoL was further tested for its internal consistency, convergent validity, known-group validity and test–retest reliability among mental healthcare service users (N = 479) and members of the general population (N = 110). Results The MHQoL consists of a descriptive system (MHQoL-7D), including s items covering seven dimensions (self-image, independence, mood, relationships, daily activities, physical health, future) and a visual analogue scale of general psychological well-being (MHQoL-VAS). Internal consistency was high (Cronbach's ∝ = 0.85) and correlations between MHQoL-7D scores and related measures (EQ-5D-5L, MANSA, ICECAP-A, and BSI) supported convergent validity. The intraclass correlation coefficient of the MHQoL-7D sum score for test–retest reliability was 0.85. Known-group validity was supported by the ability to detect significant differences in MHQoL-7D levels between service users and the general population, and between groups with different levels of psychological distress. Conclusion The MHQoL demonstrated favourable psychometric properties and showed promise as a simple and effective measure to assess quality of life in people with mental health problems.

Objectives The importance of economic evaluations of mental healthcare interventions is increasingly recognized. Despite the multitude of available quality of life instruments, concerns have been raised regarding the content validity of these instruments, and hence suitability for use in mental health. The aim of this paper, therefore, was to assess the content validity and the suitability of existing quality of life instruments for use in economic evaluations in mental health problems. Methods In order to identify available quality of life instruments used in people with mental health problems, a systematic review was performed using the Embase, Medline and PsycINFO databases (time period January 2012 to January 2018). Two reviewers independently assessed study eligibility and executed data extraction. The evaluation framework of Connell and colleagues was used to assess whether the identified quality of life instruments cover the dimensions valued highly by people with mental health problems. Two reviewers independently mapped the content of each identified instrument onto the evaluation framework and indicated the extent to which the instrument covered each of the dimensions of the evaluation framework. Results Searches of databases yielded a total of 5727 references. Following duplicate removal and double-independent screening, 949 studies were included in the qualitative synthesis. A total of 44 unique quality of life instruments were identified, of which 12 were adapted versions of original instruments. The best coverage of the dimensions of the evaluation framework of Connell and colleagues was by the WHOQOL-100, S-QoL, SQLS, EDQoL, QLI and the IMHQOL, but none fully covered all dimensions of the evaluation framework. Conclusions The results of this study highlight the multitude of available quality of life instruments used in people with mental health problems and indicate that none of the available quality of life instruments fully cover the dimensions previously found to be important in people with mental health problems. Future research should explore the possibilities of refining or expanding existing instruments as well as the development and testing of new quality of life instruments to ensure that all relevant quality of life dimensions for people with mental health problems are covered in evaluations.

ObjectivesEarly identification of patients with major depressive disorder (MDD) that cannot be managed by secondary mental health services and who require highly specialized mental healthcare could enhance need-based patient stratification. This, in turn, may reduce the number of treatment steps needed to achieve and sustain an adequate treatment response. The development of a valid tool to identify patients with MDD in need of highly specialized care is hampered by the lack of a comprehensive understanding of indicators that distinguish patients with and without a need for highly specialized MDD care. The aim of this study, therefore, was to systematically review studies on indicators of patients with MDD likely in need of highly specialized care.MethodsA structured literature search was performed on the PubMed and PsycINFO databases following PRISMA guidelines. Two reviewers independently assessed study eligibility and determined the quality of the identified studies. Three reviewers independently executed data extraction by using a pre-piloted, standardized extraction form. The resulting indicators were grouped by topical similarity, creating a concise summary of the findings.ResultsThe systematic search of all databases yielded a total of 7,360 references, of which sixteen were eligible for inclusion. The sixteen papers yielded a total of 48 unique indicators. Overall, a more pronounced depression severity, a younger age of onset, a history of prior poor treatment response, psychiatric comorbidity, somatic comorbidity, childhood trauma, psychosocial impairment, older age, and a socioeconomically disadvantaged status were found to be associated with proxies of need for highly specialized MDD care.ConclusionsSeveral indicators are associated with the need for highly specialized MDD care. These indicators provide easily measurable factors that may serve as a starting point for the development of a valid tool to identify patients with MDD in need of highly specialized care.

BackgroundEarly identification of the subgroup of patients with major depressive disorder (MDD) in need of highly specialized care could enhance personalized intervention. This, in turn, may reduce the number of treatment steps needed to achieve and sustain an adequate treatment response. The aim of this study was to identify patient‐related indicators that could facilitate the early identification of the subgroup of patients with MDD in need of highly specialized care.MethodsInitial patient indicators were derived from a systematic review. Subsequently, a structured conceptualization methodology known as concept mapping was employed to complement the initial list of indicators by clinical expertise and develop a consensus‐based conceptual framework. Subject‐matter experts were invited to participate in the subsequent steps (brainstorming, sorting, and rating) of the concept mapping process. A final concept map solution was generated using nonmetric multidimensional scaling and agglomerative hierarchical cluster analyses.ResultsIn total, 67 subject‐matter experts participated in the concept mapping process. The final concept map revealed the following 10 major clusters of indicators: 1‐depression severity, 2‐onset and (treatment) course, 3‐comorbid personality disorder, 4‐comorbid substance use disorder, 5‐other psychiatric comorbidity, 6‐somatic comorbidity, 7‐maladaptive coping, 8‐childhood trauma, 9‐social factors, and 10‐psychosocial dysfunction.ConclusionsThe study findings highlight the need for a comprehensive assessment of patient indicators in determining the need for highly specialized care, and suggest that the treatment allocation of patients with MDD to highly specialized mental healthcare settings should be guided by the assessment of clinical and nonclinical patient factors.

Background Selection of the optimal initial treatment in patients with major depressive disorder (MDD) in need of highly specialized care has the potential to benefit treatment outcomes and cost-effectiveness of treatment strategies. However, to date, there is a paucity of measures that could guide the selection of the initial treatment, in particular to indicate which patients with MDD are in need of highly specialized care. Recognizing this gap, this paper reports on the development and psychometric evaluation of the Decision Tool Unipolar Depression (DTUD), aimed to facilitate the early identification of patients with MDD in need of highly specialized care. Methods The DTUD was developed using a mixed-methods approach, consisting of a systematic review and a concept mapping study. To evaluate the psychometric features of the DTUD, a cross-sectional multicenter study was conducted. A total of 243 patients with MDD were evaluated with the DTUD. Feasibility was operationalized as the time required to complete the DTUD and the content clarity of the DTUD. Inter-rater reliability was evaluated using Krippendorf’s alpha. The Maudsley Staging Method (MSM) and the Dutch Measure for quantification of Treatment Resistance in Depression (DM-TRD) were administered to assess the convergent validity. A receiver operator characteristic curve was generated to evaluate the criterion validity and establish the optimal cut-off value. Results The mean administration time was 4.49 min (SD = 2.71), and the content of the total DTUD was judged as clear in 94.7% of the evaluations. Inter-rater reliability values ranged from 0.69 to 0.91. Higher scores on the DTUD were associated with higher scores on the MSM (r s = 0.47) and DM-TRD (r s = 0.53). Based on the maximum Youden index (0.494), maximum discrimination was reached at a cut-off score of ≥5 (sensitivity 67%, specificity 83%). Conclusion The DTUD demonstrated to be a tool with solid psychometric properties and, therefore, is a promising measure for the early identification of patients with MDD in need of highly specialized care. Use of the DTUD has the potential to facilitate the selection and initiation of the optimal initial treatment in patients with MDD, which in turn may improve the clinical effectiveness and cost-effectiveness of treatment strategies. Electronic supplementary material The online version of this article (10.1186/s12888-019-2165-9) contains supplementary material, which is available to authorized users.