Longitudinal clinical and functional outcome in distinct cognitive subgroups of first-episode psychosis: a cluster analysis.

BACKGROUND: Cognitive deficits may be characteristic for only a subgroup of first-episode psychosis (FEP) and the link with clinical and functional outcomes is less profound than previously thought. This study aimed to identify cognitive subgroups in a large sample of FEP using a clustering approach with healthy controls as a reference group, subsequently linking cognitive subgroups to clinical and functional outcomes. METHODS: 204 FEP patients were included. Hierarchical cluster analysis was performed using baseline brief assessment of cognition in schizophrenia (BACS). Cognitive subgroups were compared to 40 controls and linked to longitudinal clinical and functional outcomes (PANSS, GAF, self-reported WHODAS 2.0) up to 12-month follow-up. RESULTS: Three distinct cognitive clusters emerged: relative to controls, we found one cluster with preserved cognition (n = 76), one moderately impaired cluster (n = 74) and one severely impaired cluster (n = 54). Patients with severely impaired cognition had more severe clinical symptoms at baseline, 6- and 12-month follow-up as compared to patients with preserved cognition. General functioning (GAF) in the severely impaired cluster was significantly lower than in those with preserved cognition at baseline and showed trend-level effects at 6- and 12-month follow-up. No significant differences in self-reported functional outcome (WHODAS 2.0) were present. CONCLUSIONS: Current results demonstrate the existence of three distinct cognitive subgroups, corresponding with clinical outcome at baseline, 6- and 12-month follow-up. Importantly, the cognitively preserved subgroup was larger than the severely impaired group. Early identification of discrete cognitive profiles can offer valuable information about the clinical outcome but may not be relevant in predicting self-reported functional outcomes.

Dealing with confounding in observational studies: A scoping review of methods evaluated in simulation studies with single-point exposure.

The aim of this article was to perform a scoping review of methods available for dealing with confounding when analyzing the effect of health care treatments with single-point exposure in observational data. We aim to provide an overview of methods and their performance assessed by simulation studies indexed in PubMed. We searched PubMed for simulation studies published until January 2021. Our search was restricted to studies evaluating binary treatments and binary and/or continuous outcomes. Information was extracted on the methods' assumptions, performance, and technical properties. Of 28,548 identified references, 127 studies were eligible for inclusion. Of them, 84 assessed 14 different methods (ie, groups of estimators that share assumptions and implementation) for dealing with measured confounding, and 43 assessed 10 different methods for dealing with unmeasured confounding. Results suggest that there are large differences in performance between methods and that the performance of a specific method is highly dependent on the estimator. Furthermore, the methods' assumptions regarding the specific data features also substantially influence the methods' performance. Finally, the methods result in different estimands (ie, target of inference), which can even vary within methods. In conclusion, when choosing a method to adjust for measured or unmeasured confounding it is important to choose the most appropriate estimand, while considering the population of interest, data structure, and whether the plausibility of the methods' required assumptions hold.

Closing the gap between screening and depression prevention: a qualitative study on barriers and facilitators from the perspective of public health professionals in a school-based prevention approach.

BACKGROUND: The prevalence of depression has increased among adolescents in western countries. Prevention is needed to reduce the number of adolescents who experience depression and to avoid negative consequences, including suicide. Several preventive interventions are found to be promising, especially multi-modal approaches, for example combining screening and preventive intervention. However, an important bottleneck arises during the implementation of preventive intervention. Only a small percentage of adolescents who are eligible for participation actually participate in the intervention. To ensure that more adolescents can benefit from prevention, we need to close the gap between detection and preventive intervention. We investigated the barriers and facilitators from the perspective of public health professionals in screening for depressive and suicidal symptoms and depression prevention referral in a school-based setting. METHODS: We conducted 13 semi-structured interviews with public health professionals, who execute screening and depression prevention referral within the Strong Teens and Resilient Minds (STORM) approach. The interviews were recorded, transcribed verbatim, and coded in several cycles using ATLAS.ti Web. RESULTS: Three main themes of barriers and facilitators emerged from the interviews, namely "professional capabilities," "organization and collaboration," and "beliefs about depressive and suicidal symptoms and participation in prevention". The interviews revealed that professionals do not always feel sufficiently equipped in terms of knowledge, skills and supporting networks. Consequently, they do not always feel well able to execute the process of screening and prevention referral. In addition, a lack of knowledge and support in schools and other cooperating organizationorganizations was seen to hinder the process. Last, the beliefs of public health professionals, school staff, adolescents, and parents -especially stigma and taboo-were found to make the screening and prevention referral process more challenging. CONCLUSIONS: To further improve the process of screening and prevention referral in a school-based setting, enhancing professional competence and a holding work environment for professionals, a strong collaboration and a joint approach with schools and other cooperating organizations and society wide education about depressive and suicidal symptoms and preventive intervention are suggested. Future research should determine whether these recommendations actually lead to closing the gap between detection and prevention.

(Cost)-effectiveness and implementation of integrated community-based care for patients with severe mental illness: a study protocol.

BACKGROUND: As severe mental illness (SMI) is associated with a high disease burden and persistent nature, patients with SMI are often subjected to long-term mental healthcare and are in need of additional social support services. Community-based care and support services are organized via different providers and institutions, which are often lacking structural communication, resulting in a fragmented approach. To improve the efficiency of care provision and optimize patient wellbeing, an integrated multi-agency approach to community-based mental health and social services has been developed and implemented. AIM: To present a research protocol describing the evaluation of flexible assertive community teams integrated with social services in terms of effectiveness, cost-effectiveness, and implementation. METHODS/DESIGN: A quasi-experimental study will be conducted using prospective and retrospective observational data in patients with severe mental illness. Patients receiving care from three teams, consisting of flexible assertive community treatment and separately provided social support services (care as usual), will be compared to patients receiving care from two teams integrating these mental and social services into a single team. The study will consist of three parts: 1) an effectiveness evaluation, 2) a health-economic evaluation, and 3) a process implementation evaluation. To assess (cost-)effectiveness, both real-world aggregated and individual patient data will be collected using informed consent, and analysed using a longitudinal mixed model. The economic evaluation will consist of a cost-utility analysis and a cost-effectiveness analysis. For the process and implementation evaluation a mixed method design will be used to describe if the integrated teams have been implemented as planned, if its predefined goals are achieved, and what the experiences are of its team members. DISCUSSION: The integration of health and social services is expected to allow for a more holistic and recovery oriented treatment approach, whilst improving the allocation of scarce resources. This study aims to identify and describe these effects using a mixed-method approach, and support decision-making in the structural implementation of integrating mental and social services.

Predicting Future Service Use in Dutch Mental Healthcare: A Machine Learning Approach.

A mental healthcare system in which the scarce resources are equitably and efficiently allocated, benefits from a predictive model about expected service use. The skewness in service use is a challenge for such models. In this study, we applied a machine learning approach to forecast expected service use, as a starting point for agreements between financiers and suppliers of mental healthcare. This study used administrative data from a large mental healthcare organization in the Netherlands. A training set was selected using records from 2017 (N = 10,911), and a test set was selected using records from 2018 (N = 10,201). A baseline model and three random forest models were created from different types of input data to predict (the remainder of) numeric individual treatment hours. A visual analysis was performed on the individual predictions. Patients consumed 62 h of mental healthcare on average in 2018. The model that best predicted service use had a mean error of 21 min at the insurance group level and an average absolute error of 28 h at the patient level. There was a systematic under prediction of service use for high service use patients. The application of machine learning techniques on mental healthcare data is useful for predicting expected service on group level. The results indicate that these models could support financiers and suppliers of healthcare in the planning and allocation of resources. Nevertheless, uncertainty in the prediction of high-cost patients remains a challenge.

Patients' and Psychologists' Preferences for Feedback Reports on Expected Mental Health Treatment Outcomes: A Discrete-Choice Experiment.

In recent years, there has been an increasing focus on routine outcome monitoring (ROM) to provide feedback on patient progress during mental health treatment, with some systems also predicting the expected treatment outcome. The aim of this study was to elicit patients' and psychologists' preferences regarding how ROM system-generated feedback reports should display predicted treatment outcomes. In a discrete-choice experiment, participants were asked 12-13 times to choose between two ways of displaying an expected treatment outcome. The choices varied in four different attributes: representation, outcome, predictors, and advice. A conditional logistic regression was used to estimate participants' preferences. A total of 104 participants (68 patients and 36 psychologists) completed the questionnaire. Participants preferred feedback reports on expected treatment outcome that included: (a) both text and images, (b) a continuous outcome or an outcome that is expressed in terms of a probability, (c) specific predictors, and (d) specific advice. For both patients and psychologists, specific predictors appeared to be most important, specific advice was second most important, a continuous outcome or a probability was third most important, and feedback that includes both text and images was fourth in importance. The ranking in importance of both the attributes and the attribute levels was identical for patients and psychologists. This suggests that, as long as the report is understandable to the patient, psychologists and patients can use the same ROM feedback report, eliminating the need for ROM administrators to develop different versions.

More than cost-effectiveness? Applying a second-stage filter to improve policy decision making.

BACKGROUND: Apart from cost-effectiveness, considerations like equity and acceptability may affect health-care priority setting. Preferably, priority setting combines evidence evaluation with an appraisal procedure, to elicit and weigh these considerations. OBJECTIVE: To demonstrate a structured approach for eliciting and evaluating a broad range of assessment criteria, including key stakeholders' values, aiming to support decision makers in priority setting. METHODS: For a set of cost-effective substitute interventions for depression care, the appraisal criteria were adopted from the Australian Assessing Cost-Effectiveness initiative. All substitute interventions were assessed in an appraisal, using focus group discussions and semi-structured interviews conducted among key stakeholders. RESULTS: Appraisal of the substitute cost-effective interventions yielded an overview of considerations and an overall recommendation for decision makers. Two out of the thirteen pairs were deemed acceptable and realistic, that is investment in therapist-guided and Internet-based cognitive behavioural therapy instead of cognitive behavioural therapy in mild depression, and investment in combination therapy rather than individual psychotherapy in severe depression. In the remaining substitution pairs, substantive issues affected acceptability. The key issues identified were as follows: workforce capacity, lack of stakeholder support and the need for change in clinicians' attitude. CONCLUSIONS: Systematic identification of stakeholders' considerations allows decision makers to prioritize among cost-effective policy options. Moreover, this approach entails an explicit and transparent priority-setting procedure and provides insights into the intended and unintended consequences of using a certain health technology. PATIENT CONTRIBUTION: Patients were involved in the conduct of the study for instance, by sharing their values regarding considerations relevant for priority setting.

Implementing interventions to reduce work-related stress among health-care workers: an investment appraisal from the employer's perspective.

PURPOSE: The Stress-Prevention@Work implementation strategy has been demonstrated to be successful in reducing stress in employees. Now, we assess the economic return-on-investment to see if it would make for a favourable business case for employers. METHODS: Data were collected from 303 health-care workers assigned to either a waitlisted control condition (142 employees in 15 teams) or to Stress-Prevention@Work (161 employees in 15 teams). Main outcome was productivity losses measured using the Trimbos and iMTA Cost questionnaire in Psychiatry. Measurements were taken at baseline, 6, and 12 months post-baseline. RESULTS: The per-employee costs of the strategy were €50. Net monetary benefits were the benefits (i.e., improved productivity) minus the costs (i.e., intervention costs) and were the main outcome of this investment appraisal. Per-employee net benefits amounted to €2981 on average, which was an almost 60-fold payout of the initial investment of €50. There was a 96.7% likelihood for the modest investment of €50 to be offset by cost savings within 1 year. Moreover, a net benefit of at least €1000 still has a likelihood of 88.2%. CONCLUSIONS: In general, there was a high likelihood that Stress-Prevention@Work offers an appealing business case from the perspective of employers, but the employer should factor in the additional per-employee costs of the stress-reducing interventions. Still, if these additional costs were as high as €2981, then costs and benefits would break even. This study was registered in the Netherlands National Trial Register, trial code: NTR5527.

Cost-Effectiveness of Virtual Reality Cognitive Behavioral Therapy for Psychosis: Health-Economic Evaluation Within a Randomized Controlled Trial.

BACKGROUND: Evidence was found for the effectiveness of virtual reality-based cognitive behavioral therapy (VR-CBT) for treating paranoia in psychosis, but health-economic evaluations are lacking. OBJECTIVE: This study aimed to determine the short-term cost-effectiveness of VR-CBT. METHODS: The health-economic evaluation was embedded in a randomized controlled trial evaluating VR-CBT in 116 patients with a psychotic disorder suffering from paranoid ideation. The control group (n=58) received treatment as usual (TAU) for psychotic disorders in accordance with the clinical guidelines. The experimental group (n=58) received TAU complemented with add-on VR-CBT to reduce paranoid ideation and social avoidance. Data were collected at baseline and at 3 and 6 months postbaseline. Treatment response was defined as a pre-post improvement of symptoms of at least 20% in social participation measures. Change in quality-adjusted life years (QALYs) was estimated by using Sanderson et al's conversion factor to map a change in the standardized mean difference of Green's Paranoid Thoughts Scale score on a corresponding change in utility. The incremental cost-effectiveness ratios were calculated using 5000 bootstraps of seemingly unrelated regression equations of costs and effects. The cost-effectiveness acceptability curves were graphed for the costs per treatment responder gained and per QALY gained. RESULTS: The average mean incremental costs for a treatment responder on social participation ranged between €8079 and €19,525, with 90.74%-99.74% showing improvement. The average incremental cost per QALY was €48,868 over the 6 months of follow-up, with 99.98% showing improved QALYs. Sensitivity analyses show costs to be lower when relevant baseline differences were included in the analysis. Average costs per treatment responder now ranged between €6800 and €16,597, while the average cost per QALY gained was €42,030. CONCLUSIONS: This study demonstrates that offering VR-CBT to patients with paranoid delusions is an economically viable approach toward improving patients' health in a cost-effective manner. Long-term effects need further research. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Number (ISRCTN) 12929657; http://www.isrctn.com/ISRCTN12929657.

Cost and Effectiveness of Blended Versus Standard Cognitive Behavioral Therapy for Outpatients With Depression in Routine Specialized Mental Health Care: Pilot Randomized Controlled Trial.

BACKGROUND: Cognitive behavioral therapy (CBT) is an effective treatment, but access is often restricted due to costs and limited availability of trained therapists. Blending online and face-to-face CBT for depression might improve cost-effectiveness and treatment availability. OBJECTIVE: This pilot study aimed to examine the costs and effectiveness of blended CBT compared with standard CBT for depressed patients in specialized mental health care to guide further research and development of blended CBT. METHODS: Patients were randomly allocated to blended CBT (n=53) or standard CBT (n=49). Blended CBT consisted of 10 weekly face-to-face sessions and 9 Web-based sessions. Standard CBT consisted of 15 to 20 weekly face-to-face sessions. At baseline and 10, 20, and 30 weeks after start of treatment, self-assessed depression severity, quality-adjusted life-years (QALYs), and costs were measured. Clinicians, blinded to treatment allocation, assessed psychopathology at all time points. Data were analyzed using linear mixed models. Uncertainty intervals around cost and effect estimates were estimated with 5000 Monte Carlo simulations. RESULTS: Blended CBT treatment duration was mean 19.0 (SD 12.6) weeks versus mean 33.2 (SD 23.0) weeks in standard CBT (P<.001). No significant differences were found between groups for depressive episodes (risk difference [RD] 0.06, 95% CI -0.05 to 0.19), response to treatment (RD 0.03, 95% CI -0.10 to 0.15), and QALYs (mean difference 0.01, 95% CI -0.03 to 0.04). Mean societal costs for blended CBT were €1183 higher than standard CBT. This difference was not significant (95% CI -399 to 2765). Blended CBT had a probability of being cost-effective compared with standard CBT of 0.02 per extra QALY and 0.37 for an additional treatment response, at a ceiling ratio of €25,000. For health care providers, mean costs for blended CBT were €176 lower than standard CBT. This difference was not significant (95% CI -659 to 343). At €0 per additional unit of effect, the probability of blended CBT being cost-effective compared with standard CBT was 0.75. The probability increased to 0.88 at a ceiling ratio of €5000 for an added treatment response, and to 0.85 at €10,000 per QALY gained. For avoiding new depressive episodes, blended CBT was deemed not cost-effective compared with standard CBT because the increase in costs was associated with negative effects. CONCLUSIONS: This pilot study shows that blended CBT might be a promising way to engage depressed patients in specialized mental health care. Compared with standard CBT, blended CBT was not considered cost-effective from a societal perspective but had an acceptable probability of being cost-effective from the health care provider perspective. Results should be carefully interpreted due to the small sample size. Further research in larger replication studies focused on optimizing the clinical effects of blended CBT and its budget impact is warranted. TRIAL REGISTRATION: Netherlands Trial Register NTR4650; https://www.trialregister.nl/trial/4408. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12888-014-0290-z.

A Cost-Effectiveness Analysis to Evaluate a System Change in Mental Healthcare in the Netherlands for Patients with Depression or Anxiety.

Over the last decade, the Dutch mental healthcare system has been subject to profound policy reforms, in order to achieve affordable, accessible, and high quality care. One of the adjustments was to substitute part of the specialized care for general mental healthcare. Using a quasi-experimental design, we compared the cost-effectiveness of patients in the new setting with comparable patients from specialized mental healthcare in the old setting. Results showed that for this group of patients the average cost of treatment was significantly reduced by, on average, €2132 (p < 0.001), with similar health outcomes as in the old system.

Improving the cost-effectiveness of a healthcare system for depressive disorders by implementing telemedicine: a health economic modeling study.

OBJECTIVES: Depressive disorders are significant causes of disease burden and are associated with substantial economic costs. It is therefore important to design a healthcare system that can effectively manage depression at sustainable costs. This article computes the benefit-to-cost ratio of the current Dutch healthcare system for depression, and investigates whether offering more online preventive interventions improves the cost-effectiveness overall. METHODS: A health economic (Markov) model was used to synthesize clinical and economic evidence and to compute population-level costs and effects of interventions. The model compared a base case scenario without preventive telemedicine and alternative scenarios with preventive telemedicine. The central outcome was the benefit-to-cost ratio, also known as return-on-investment (ROI). RESULTS: In terms of ROI, a healthcare system with preventive telemedicine for depressive disorders offers better value for money than a healthcare system without Internet-based prevention. Overall, the ROI increases from €1.45 ($1.72) in the base case scenario to €1.76 ($2.09) in the alternative scenario in which preventive telemedicine is offered. In a scenario in which the costs of offering preventive telemedicine are balanced by reducing the expenditures for curative interventions, ROI increases to €1.77 ($2.10), while keeping the healthcare budget constant. CONCLUSIONS: For a healthcare system for depressive disorders to remain economically sustainable, its cost-benefit ratio needs to be improved. Offering preventive telemedicine at a large scale is likely to introduce such an improvement.

Blended vs. face-to-face cognitive behavioural treatment for major depression in specialized mental health care: study protocol of a randomized controlled cost-effectiveness trial.

BACKGROUND: Depression is a prevalent disorder, associated with a high disease burden and substantial societal, economic and personal costs. Cognitive behavioural treatment has been shown to provide adequate treatment for depression. By offering this treatment in a blended format, in which online and face-to-face treatment are combined, it might be possible to reduce the number of costly face-to-face sessions required to deliver the treatment protocol. This could improve the cost-effectiveness of treatment, while maintaining clinical effects. This protocol describes the design of a pilot study for the evaluation of the feasibility, acceptability and cost-effectiveness of blended cognitive behavioural therapy for patients with major depressive disorder in specialized outpatient mental health care. METHODS/DESIGN: In a randomized controlled trial design, adult patients with major depressive disorder are allocated to either blended cognitive behavioural treatment or traditional face-to-face cognitive behavioural treatment (treatment as usual). We aim to recruit one hundred and fifty patients. Blended treatment will consist of ten face-to-face and nine online sessions provided alternately on a weekly basis. Traditional cognitive behavioural treatment will consist of twenty weekly sessions. Costs and effects are measured at baseline and after 10, 20 and 30 weeks. Evaluations are directed at cost-effectiveness (with depression severity and diagnostic status as outcomes), and cost-utility (with costs per quality adjusted life year, QALY, as outcome). Costs will encompass health care uptake costs and productivity losses due to absence from work and lower levels of efficiency while at work. Other measures of interest are mastery, working alliance, treatment preference at baseline, depressive cognitions, treatment satisfaction and system usability. DISCUSSION: The results of this pilot study will provide an initial insight into the feasibility and acceptability of blended cognitive behavioural treatment in terms of clinical and economic outcomes (proof of concept) in routine specialized mental health care settings, and an indication as to whether a well-powered clinical trial of blended cognitive behavioural treatment for depression in routine practice would be advisable. This will be determined based on the perspective of various stakeholders including patients, mental health service providers and health insurers. Strengths and limitations of the study are discussed. TRIAL REGISTRATION: Netherlands Trial Register NTR4650 . Registered 18 June 2014.

Mental health care system optimization from a health-economics perspective: where to sow and where to reap?

BACKGROUND: Health care expenditure (as % of GDP) has been rising in all OECD countries over the last decades. Now, in the context of the economic downturn, there is an even more pressing need to better guarantee the sustainability of health care systems. This requires that policy makers are informed about optimal allocation of budgets. We take the Dutch mental health system in the primary care setting as an example of new ways to approach optimal allocation. AIMS OF THE STUDY: To demonstrate how health economic modelling can help in identifying opportunities to improve the Dutch mental health care system for patients presenting at their GP with symptoms of anxiety, stress, symptoms of depression, alcohol abuse/dependence, anxiety disorder or depressive disorder such that changes in the health care system have the biggest leverage in terms of improved cost-effectiveness. Investigating such scenarios may serve as a starting point for setting an agenda for innovative and sustainable health care policies. METHODS: A health economic simulation model was used to synthesize clinical and economic evidence. The model was populated with data from GPs' national register on the diagnosis, treatment, referral and prescription of their patients in the year 2009. A series of `what-if' analyses was conducted to see what parameters (uptake, adherence, effectiveness and the costs of the interventions) are associated with the most substantial impact on the cost-effectiveness of the health care system overall. RESULTS: In terms of improving the overall cost-effectiveness of the primary mental health care system, substantial benefits could be derived from increasing uptake of psycho-education by GPs for patients presenting with stress and when low cost interventions are made available that help to increase the patients' compliance with pharmaceutical interventions, particularly in patients presenting with symptoms of anxiety. In terms of intervention costs, decreasing the costs of antidepressants is expected to yield the biggest impact on the cost-effectiveness of the primary mental health care system as a whole. These "target group -- intervention" combinations are the most appealing candidates for system innovation from a cost-effectiveness point of view, but need to be carefully aligned with other considerations such as equity, acceptability, appropriateness, feasibility and strength of evidence. DISCUSSION AND LIMITATIONS: The study has some strengths and limitations. Cost-effectiveness analysis is performed using a health economic model that is based on registration data from a sample of GPs, but assumptions had to be made on how these data could be extrapolated to all GPs. Parameters on compliance rates were obtained from a focus group or were based on mere assumptions, while the clinical effectiveness of interventions were taken from meta-analyses or randomised trials. Effectiveness is expressed in terms of years lived with disability (YLD) averted; indirect benefits such as reduction of lost productivity or lesser pressure on informal caregivers are not taken into account. Whenever assumptions had to be made, we opted for conservative estimates that are unlikely to have resulted in an overly optimistic portrayal of the cost-effectiveness ratios. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: The model can be used to guide health care system innovation, by identifying those parameters where changes in the uptake, compliance, effectiveness and costs of interventions have the largest impact on the cost-effectiveness of a mental health care system overall. In this sense, the model could assist policy makers during the first stage of decision making on where to make improvements in the health care system, or assist the process of guideline development. However, the improvement candidates need to be assessed during a second-stage 'normative filter', to address considerations other than cost-effectiveness.

Health-Economic Evaluation of Psychological Interventions for Anxiety Prevention: A Systematic Review.

OBJECTIVE: Although evidence supports the effectiveness of psychological interventions for prevention of anxiety, little is known about their cost-effectiveness. The aim of this study was to conduct a systematic review of health-economic evaluations of psychological interventions for anxiety prevention. METHODS: PubMed, PsycInfo, Web of Science, Embase, Cochrane Central Register of Controlled Trials, EconLit, National Health Service (NHS) Economic Evaluations Database, NHS Health Technology Assessment, and OpenGrey databases were searched electronically on December 23, 2022. Included studies focused on economic evaluations based on randomized controlled trials of psychological interventions to prevent anxiety. Study data were extracted, and the quality of the selected studies was assessed by using the Consensus on Health Economic Criteria and the Cochrane risk-of-bias tool. RESULTS: All included studies (N=5) had economic evaluations that were considered to be of good quality. In two studies, the interventions showed favorable cost-effectiveness compared with usual care groups. In one study, the intervention was not cost-effective. Findings from another study cast doubt on the cost-effectiveness of the intervention, and the cost-effectiveness of the intervention in the remaining study could not be established. CONCLUSIONS: Although the findings suggest some preliminary evidence of cost-effectiveness of psychological interventions for preventing anxiety, they were limited by the small number of included studies. Additional research on the cost-effectiveness of psychological interventions for anxiety in different countries and populations is required.

Non-fatal burden of disease due to mental disorders in the Netherlands.

PURPOSE: To estimate the disease burden due to 15 mental disorders at both individual and population level. METHODS: Using a population-based survey (NEMESIS, N = 7,056) the number of years lived with disability per one million population were assessed. This was done with and without adjustment for comorbidity. RESULTS: At individual level, major depression, dysthymia, bipolar disorder, panic disorder, social phobia, eating disorder and schizophrenia are the disorders most markedly associated with health-related quality of life decrement. However, at population level, the number of affected people and the amount of time spent in an adverse health state become strong drivers of population ill-health. Simple phobia, social phobia, depression, dysthymia and alcohol dependence emerged as public health priorities. CONCLUSIONS: From a clinical perspective, we tend to give priority to the disorders that exact a heavy toll on individuals. This puts the spotlight on disorders such as bipolar disorder and schizophrenia. However, from a public health perspective, disorders such as simple phobia, social phobia and dysthymia--which are highly prevalent and tend to run a chronic course--are identified as leading causes of population ill-health, and thus, emerge as public health priorities.

Predicting Undesired Treatment Outcomes With Machine Learning in Mental Health Care: Multisite Study.

Background: Predicting which treatment will work for which patient in mental health care remains a challenge. Objective: The aim of this multisite study was 2-fold: (1) to predict patients' response to treatment in Dutch basic mental health care using commonly available data from routine care and (2) to compare the performance of these machine learning models across three different mental health care organizations in the Netherlands by using clinically interpretable models. Methods: Using anonymized data sets from three different mental health care organizations in the Netherlands (n=6452), we applied a least absolute shrinkage and selection operator regression 3 times to predict the treatment outcome. The algorithms were internally validated with cross-validation within each site and externally validated on the data from the other sites. Results: The performance of the algorithms, measured by the area under the curve of the internal validations as well as the corresponding external validations, ranged from 0.77 to 0.80. Conclusions: Machine learning models provide a robust and generalizable approach in automated risk signaling technology to identify cases at risk of poor treatment outcomes. The results of this study hold substantial implications for clinical practice by demonstrating that the performance of a model derived from one site is similar when applied to another site (ie, good external validation).

A roadmap for applying machine learning when working with privacy-sensitive data: predicting non-response to treatment for eating disorders.

OBJECTIVES: Applying machine-learning methodology to clinical data could present a promising avenue for predicting outcomes in patients receiving treatment for psychiatric disorders. However, preserving privacy when working with patient data remains a critical concern. METHODS: In showcasing how machine-learning can be used to build a clinically relevant prediction model on clinical data, we apply two commonly used machine-learning algorithms (Random Forest and least absolute shrinkage and selection operator) to routine outcome monitoring data collected from 593 patients with eating disorders to predict absence of reliable improvement 12 months after entering outpatient treatment. RESULTS: An RF model trained on data collected at baseline and after three months made 31.3% fewer errors in predicting lack of reliable improvement at 12 months, in comparison with chance. Adding data from a six-month follow-up resulted in only marginal improvements to accuracy. CONCLUSION: We were able to build and validate a model that could aid clinicians and researchers in more accurately predicting treatment response in patients with EDs. We also demonstrated how this could be done without compromising privacy. ML presents a promising approach to developing accurate prediction models for psychiatric disorders such as ED.

Design and testing of a health economic Markov model for treatment of anorexia nervosa.

OBJECTIVES: Anorexia Nervosa (AN) is a severe psychiatric disorder and knowledge about the cost-effectiveness of potential interventions is limited. The aim of this paper is to introduce the Trimbos Institute health economic cost-effectiveness model for Anorexia Nervosa (AnoMod-TI), a flexible modeling tool for assessing the long-term cost-effectiveness of interventions for AN in late adolescent and adult patients, which could support clinical decision making. METHODS: AnoMod-TI is a state-transition cohort simulation (Markov) model developed from a Dutch societal perspective, which consists of four health states - namely full remission (FR), partial remission (PR), AN and death. Results are expressed as total healthcare costs, QALYs and incremental cost-effectiveness ratio. RESULTS: For the purpose of demonstrating AnoMod-TI and how it could be used to estimate cost-effectiveness over a 20-year time horizon, it was applied to a hypothetical treatment scenario. Results illustrate how a relatively costly intervention with only modest effects can still be cost-effective in the long term. CONCLUSIONS: AnoMod-TI can be used to examine long-term cost-effectiveness of various interventions aimed at either treating AN or preventing relapse from a state of partial or full remission. AnoMod-TI is freely available upon request to the authors.

Designing and testing of a health-economic Markov model to assess the cost-effectiveness of treatments for Bipolar disorder: TiBipoMod.

Background: Bipolar disorder is an often recurrent mood disorder that is associated with a significant economic and health-related burden. Increasing the availability of health-economic evidence may aid in reducing this burden. The aim of this study is to describe the design of an open-source health-economic Markov model for assessing the cost-effectiveness of interventions in the treatment of Bipolar Disorders type I and II, TiBipoMod. Methods: TiBipoMod is a decision-analytic Markov model that allows for user-defined incorporation of both pharmacological and non-pharmacological interventions for the treatment of BD. TiBipoMod includes the health states remission, depression, (hypo)mania and death. Costs and effects are modeled over a lifetime horizon from a societal and healthcare perspective, and results are presented as the total costs, Quality-Adjusted Life Years (QALY), Life Years (LY), and incremental costs per QALYs and LYs gained. Results: Functionalities of TiBipoMod are demonstrated by performing a cost-utility analysis of mindfulness-based cognitive therapy (MBCT) compared to the standard of care. Treatment with MBCT resulted in an increase of 0.18 QALYs per patient, and a dominant incremental cost-effectiveness ratio per QALY gained for MBCT at a probability of being cost-effective of 71% when assuming a €50,000 willingness-to-pay threshold. Conclusion: TiBipoMod can easily be adapted and used to determine the cost-effectiveness of interventions in the treatment in Bipolar Disorder type I and II, and is freely available for academic purposes upon request at the authors.