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.

The economic burden of mental disorders: results from the Netherlands mental health survey and incidence study-2

OBJECTIVE

Currently, there is a paucity of up-to-date estimates of the economic burden caused by mental disorders. Such information could provide vital insight into one of the most serious and costly-yet to some extent preventable-health challenges facing the world today.

METHOD

Data from a national psychiatric-epidemiological cohort study (NEMESIS-2, N = 6506) were used to provide reliable, relevant, and up-to-date cost estimates (in 2019 Euro) regarding healthcare costs, productivity losses, and patient and family costs associated with DSM-IV mental disorders both at individual level, but also in the general population and in the workforce of the Netherlands (per 1 million population).

RESULTS

In the general population, the costs of mood disorders, specifically depression, are substantial and rank above those from the anxiety disorders, whilst costs of anxiety disorders are more substantial than those stemming from substance use disorders, even when the per-person costs of drug abuse appear highest of all. In the workforce, specific and social phobias are leading causes of excess costs. The workforce has lower healthcare costs but higher productivity costs than general population.

DISCUSSION

The findings suggest that (preventive) healthcare interventions targeting the workforce are likely to become cost-effective and underscore the importance for employers to create healthy work environments. Overall, the results highlight the need to strengthen the role of mental health promotion and prevention of mental disorders in the social domain before people require treatment to reduce the staggering and costly burden caused by mental disorders to individuals and society.

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.

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.

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.

(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.

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 modelling 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.

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.

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.

Mapping of the World Health Organization's Disability Assessment Schedule 2.0 to disability weights using the Multi-Country Survey Study on Health and Responsiveness

OBJECTIVES

To develop and test an internationally applicable mapping function for converting WHODAS-2.0 scores to disability weights, thereby enabling WHODAS-2.0 to be used in cost-utility analyses and sectoral decision-making.

METHODS

Data from 14 countries were used from the WHO Multi-Country Survey Study on Health and Responsiveness, administered among nationally representative samples of respondents aged 18+ years who were non-institutionalized and living in private households. For the combined total of 92,006 respondents, available WHODAS-2.0 items (for both 36-item and 12-item versions) were mapped onto disability weight estimates using a machine learning approach, whereby data were split into separate training and test sets; cross-validation was used to compare the performance of different regression and penalized regression models. Sensitivity analyses considered different imputation strategies and compared overall model performance with that of country-specific models.

RESULTS

Mapping functions converted WHODAS-2.0 scores into disability weights; R-squared values of 0.700-0.754 were obtained for the test data set. Penalized regression models reached comparable performance to standard regression models but with fewer predictors. Imputation had little impact on model performance. Model performance of the generic model on country-specific test sets was comparable to model performance of country-specific models.

CONCLUSIONS

Disability weights can be generated with good accuracy using WHODAS 2.0 scores, including in national settings where health state valuations are not directly available, which signifies the utility of WHODAS as an outcome measure in evaluative studies that express intervention benefits in terms of QALYs gained.

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.

Real-World Treatment Costs and Care Utilization in Patients with Major Depressive Disorder With and Without Psychiatric Comorbidities in Specialist Mental Healthcare

BACKGROUND

The majority of patients with major depressive disorder (MDD) have comorbid mental conditions.

OBJECTIVES

Since most cost-of-illness studies correct for comorbidity, this study focuses on mental healthcare utilization and treatment costs in patients with MDD including psychiatric comorbidities in specialist mental healthcare, particularly patients with a comorbid personality disorder (PD).

METHODS

The Psychiatric Case Register North Netherlands contains administrative data of specialist mental healthcare providers. Treatment episodes were identified from uninterrupted healthcare use. Costs were calculated by multiplying care utilization with unit prices (price level year: 2018). Using generalized linear models, cost drivers were investigated for the entire cohort.

RESULTS

A total of 34,713 patients had MDD as a primary diagnosis over the period 2000-2012. The number of patients with psychiatric comorbidities was 24,888 (71.7%), including 13,798 with PD. Costs were highly skewed, with an average ± standard deviation cost per treatment episode of €21,186 ± 74,192 (median €2320). Major cost drivers were inpatient days and daycare days (50 and 28% of total costs), occurring in 12.7 and 12.5% of episodes, respectively. Compared with patients with MDD only (€11,612), costs of patients with additional PD and with or without other comorbidities were, respectively, 2.71 (p < .001) and 2.06 (p < .001) times higher and were 1.36 (p < .001) times higher in patients with MDD and comorbidities other than PD. Other cost drivers were age, calendar year, and first episodes.

CONCLUSIONS

Psychiatric comorbidities (especially PD) in addition to age and first episodes drive costs in patients with MDD. Knowledge of cost drivers may help in the development of future stratified disease management programs.

Design of a health-economic Markov model to assess cost-effectiveness and budget impact of the prevention and treatment of depressive disorder

Background/objective: To describe the design of 'DepMod,' a health-economic Markov model for assessing cost-effectiveness and budget impact of user-defined preventive interventions and treatments in depressive disorders.Methods: DepMod has an epidemiological layer describing how a cohort of people can transition between health states (sub-threshold depression, first episode of mild, moderate or severe depression (partial) remission, recurrence, death). Superimposed on the epidemiological layer, DepMod has an intervention layer consisting of a reference scenario and alternative scenario comparing the effectiveness and cost-effectiveness of a user-defined package of preventive interventions and psychological and pharmacological treatments of depression. Results are presented in terms of quality-adjusted life years (QALYs) gained and healthcare expenditure. Costs and effects can be modeled over 5 years and are subjected to probabilistic sensitivity analysis.Results: DepMod was used to assess the cost-effectiveness of scaling up preventive interventions for treating people with subclinical depression, which showed that there is an 82% probability that scaling up prevention is cost-effective given a willingness-to-pay threshold of €20,000 per QALY.Conclusion: DepMod is a Markov model that assesses the cost-utility and budget impact of different healthcare packages aimed at preventing and treating depression and is freely available for academic purposes upon request at the authors.

Economic evaluations of non-pharmacological interventions and cost-of-illness studies in bipolar disorder: A systematic review

Background Bipolar disorder (BD) is associated with substantial societal burden. Therefore, economic studies in BD are becoming increasingly important. The goal of the current study is three-fold: (1) summarize the evidence regarding economic evaluations (EEs) of non-pharmacological interventions for BD, (2) summarize cost-of-illness studies (COIs) for BD published 2012 or later and (3) assess the quality of the identified studies. Methods A systematic search was conducted in MedLine, EMBASE and PsycINFO. For both EEs and COIs, quality assessments were conducted and general and methodological characteristics of the studies were extracted. Outcomes included incremental-cost-effectiveness ratios for EEs and direct and indirect costs for COIs. Results Eight EEs and ten COIs were identified. The included studies revealed high heterogeneity in general and methodological characteristics and study quality. All interventions resulted in improved clinical outcomes. Five studies additionally concluded decreased total costs. For COIs, we found a wide range of direct ($881-$27,617) and indirect cost estimates per capita per year ($1,568-$116,062). Limitations High heterogeneity in terms of interventions, study design and outcomes made it difficult to compare results across studies. Conclusions Interventions improved clinical outcomes in all studies and led to cost-savings in five studies. Findings suggest that non-pharmacological intervention for BD might be cost-effective. Studies on the costs of BD revealed that BD has a substantial economic burden. However, we also found that the number of EEs was relatively low and methodology was heterogenous and therefore encourage future research to widen the body of knowledge in this research field and use standardized methodology.

Applying machine learning on health record data from general practitioners to predict suicidality

BACKGROUND

Suicidal behaviour is difficult to detect in the general practice. Machine learning (ML) algorithms using routinely collected data might support General Practitioners (GPs) in the detection of suicidal behaviour. In this paper, we applied machine learning techniques to support GPs recognizing suicidal behaviour in primary care patients using routinely collected general practice data.

METHODS

This case-control study used data from a national representative primary care database including over 1.5 million patients (Nivel Primary Care Database). Patients with a suicide (attempt) in 2017 were selected as cases (N = 574) and an at risk control group (N = 207,308) was selected from patients with psychological vulnerability but without a suicide attempt in 2017. RandomForest was trained on a small subsample of the data (training set), and evaluated on unseen data (test set).

RESULTS

Almost two-third (65%) of the cases visited their GP within the last 30 days before the suicide (attempt). RandomForest showed a positive predictive value (PPV) of 0.05 (0.04-0.06), with a sensitivity of 0.39 (0.32-0.47) and area under the curve (AUC) of 0.85 (0.81-0.88). Almost all controls were accurately labeled as controls (specificity = 0.98 (0.97-0.98)). Among a sample of 650 at-risk primary care patients, the algorithm would label 20 patients as high-risk. Of those, one would be an actual case and additionally, one case would be missed.

CONCLUSION

In this study, we applied machine learning to predict suicidal behaviour using general practice data. Our results showed that these techniques can be used as a complementary step in the identification and stratification of patients at risk of suicidal behaviour. The results are encouraging and provide a first step to use automated screening directly in clinical practice. Additional data from different social domains, such as employment and education, might improve accuracy.

Predicting future suicidal behaviour in young adults, with different machine learning techniques: A population-based longitudinal study

BACKGROUND

The predictive accuracy of suicidal behaviour has not improved over the last decades. We aimed to explore the potential of machine learning to predict future suicidal behaviour using population-based longitudinal data.

METHOD

Baseline risk data assessed within the Scottish wellbeing study, in which 3508 young adults (18-34 years) completed a battery of psychological measures, were used to predict both suicide ideation and suicide attempts at one-year follow-up. The performance of the following algorithms was compared: regular logistic regression, K-nearest neighbors, classification tree, random forests, gradient boosting and support vector machine.

RESULTS

At one year follow up, 2428 respondents (71%) finished the second assessment. 336 respondents (14%) reported suicide ideation between baseline and follow up, and 50 (2%) reported a suicide attempt. All performance metrics were highly similar across methods. The random forest algorithm was the best algorithm to predict suicide ideation (AUC 0.83, PPV 0.52, BA 0.74) and the gradient boosting to predict suicide attempt (AUC 0.80, PPV 0.10, BA 0.69).

LIMITATIONS

The number of respondents with suicidal behaviour at follow up was small. We only had data on psychological risk factors, limiting the potential of the more complex machine learning algorithms to outperform regular logistical regression.

CONCLUSIONS

When applied to population-based longitudinal data containing multiple psychological measurements, machine learning techniques did not significantly improve the predictive accuracy of suicidal behaviour. Adding more detailed data on for example employment, education or previous health care uptake, might result in better performance of machine learning over regular logistical regression.

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

Social cost-benefit analysis of regulatory policies to reduce alcohol use in The Netherlands

Background

If all costs and all benefits of alcohol use are expressed in monetary terms, the net costs were 2,3 to 4,2 billion euro in 2013. Examples of the costs of alcohol are less productivity at work, costs of police and justice and traffic accidents.

Methods

In this study three regulatory policies have been modelled using the Social Cost-Benefit Analysis (SCBA) approach. Regulatory policies aimed at curbing alcohol consumption were (1) an increase in excise taxes, (2) a reduction of the number of sales venues, and (3) a total mediaban for advertising alcohol.

Results

In the long run, over a period of 50 years, an increase in excise taxes of 50% will result in societal benefits of 4.5 to 10.7 billion euro, an increase of excise taxes of 200% will result in societal benefits of 12.2 to 35.8 billion euro. The societal benefits of closure of 10% of sales venues are estimated at 1.8 to 4.3 billion euro after 50 years, and at 4.6 to 10.7 billion euro when 25% of sales venues would be closed. The societal benefits of a mediaban would amount to 3.5 to 7.8 billion euro after 50 years, but this estimate is surrounded by uncertainty.

Conclusions

Regulatory policies aimed at reducing the amount of alcohol consumed, such as a further increase of excise taxes, a reduction of the number of sales venues and a total mediaban, will result in savings for society at large. However, costs and benefits are spread unequally over the different stakeholders.

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

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.

A discrete-choice experiment to assess treatment modality preferences of patients with depression

AIMS

There is an increasing interest in understanding patients' preferences in the area of healthcare decision-making to better match treatment with patients' preferences and improve treatment uptake and adherence. The aim of this study was to elicit the preferences of patients with a depressive disorder regarding treatment modalities.

MATERIALS AND METHODS

In a discrete-choice experiment, patients chose repetitively between two hypothetical depression treatments that varied in four treatment attributes: waiting time until the start of treatment, treatment intensity, level of digitalization, and group size. A Bayesian-efficient design was used to develop 12 choice sets, and patients' preferences and preference variation was estimated using a random parameters logit model.

RESULTS

A total of 165 patients with depression completed the survey. Patients preferred short (over long) waiting times, face-to-face (over digital) treatment, individual (over group) treatment, and one session per week over two sessions per week or one session per 2 weeks. Patients disfavoured digital treatment and treatment in a large group. Waiting time and treatment intensity were substantially less important attributes to patients than face-to-face (vs digital) and group size. Significant variation in preferences was observed for each attribute, and sub-group analyses revealed that these differences were in part related to education.

LIMITATIONS

The convenience sample over-represented the female and younger population, limiting generalizability. Limited information on background characteristics limited the possibilities to explore preference heterogeneity.

CONCLUSION

This study demonstrated how different treatment components for depression affect patients' preferences for those treatments. There is significant variation in treatment preferences, even after accounting for education. Incorporating individual patients' preferences into treatment decisions could potentially lead to improved adherence of treatments for depressive disorders.

A discrete-choice experiment to assess treatment modality preferences of patients with anxiety disorder

AIMS

The aim of this study was to elicit the preference of patients with an anxiety disorder regarding treatment modalities. Understanding patients' preferences could help optimize treatment uptake and adherence to therapeutic interventions.

MATERIALS AND METHODS

A discrete-choice experiment was used to elicit patients' preferences with regard to four treatment characteristics: waiting time until first treatment, intensity of treatment, face-to-face vs digital treatment, and group size. In 12 choice sets, participants were asked to choose between two treatment alternatives. A random parameters logit model was used to analyse the data.

RESULTS

A total of 126 participants, aged 18 years and older, currently or in the previous year in treatment for an anxiety disorder, completed the survey. Respondents preferred short (over long) waiting times, face-to-face (over digital) treatment, individual (over group) treatment and a treatment intensity of one session per week rather than two sessions per week or one session every two weeks. Waiting time and treatment intensity were substantially less important to patients than level of digitalization and group size. Heterogeneity in preference was significant for each attribute, and sub-group analyses revealed this was partly related to education level and age.

LIMITATIONS

The convenience sample over-represented the female and younger population, limiting generalizability. Limited information on background characteristics limited the possibilities to explore preference heterogeneity.

CONCLUSIONS

This study demonstrated how different treatment components for anxiety disorders affect patients' preferences for those treatments. There is significant variation in treatment preferences, even after accounting for age and education. Incorporating patients' preferences into treatment decisions could potentially lead to improved adherence of treatments for anxiety disorders.

Cost-effectiveness, cost-utility and the budget impact of antidepressants versus preventive cognitive therapy with or without tapering of antidepressants

BACKGROUND

As depression has a recurrent course, relapse and recurrence prevention is essential.AimsIn our randomised controlled trial (registered with the Nederlands trial register, identifier: NTR1907), we found that adding preventive cognitive therapy (PCT) to maintenance antidepressants (PCT+AD) yielded substantial protective effects versus antidepressants only in individuals with recurrent depression. Antidepressants were not superior to PCT while tapering antidepressants (PCT/-AD). To inform decision-makers on treatment allocation, we present the corresponding cost-effectiveness, cost-utility and budget impact.

METHOD

Data were analysed (n = 289) using a societal perspective with 24-months of follow-up, with depression-free days and quality-adjusted life years (QALYs) as health outcomes. Incremental cost-effectiveness ratios were calculated and cost-effectiveness planes and cost-effectiveness acceptability curves were derived to provide information about cost-effectiveness. The budget impact was examined with a health economic simulation model.

RESULTS

Mean total costs over 24 months were €6814, €10 264 and €13 282 for AD+PCT, antidepressants only and PCT/-AD, respectively. Compared with antidepressants only, PCT+AD resulted in significant improvements in depression-free days but not QALYs. Health gains did not significantly favour antidepressants only versus PCT/-AD. High probabilities were found that PCT+AD versus antidepressants only and antidepressants only versus PCT/-AD were dominant with low willingness-to-pay thresholds. The budget impact analysis showed decreased societal costs for PCT+AD versus antidepressants only and for antidepressants only versus PCT/-AD.

CONCLUSIONS

Adding PCT to antidepressants is cost-effective over 24 months and PCT with guided tapering of antidepressants in long-term users might result in extra costs. Future studies examining costs and effects of antidepressants versus psychological interventions over a longer period may identify a break-even point where PCT/-AD will become cost-effective.Declaration of interestC.L.H.B. is co-editor of PLOS One and receives no honorarium for this role. She is also co-developer of the Dutch multidisciplinary clinical guideline for anxiety and depression, for which she receives no remuneration. She is a member of the scientific advisory board of the National Insure Institute, for which she receives an honorarium, although this role has no direct relation to this study. C.L.H.B. has presented keynote addresses at conferences, such as the European Psychiatry Association and the European Conference Association, for which she sometimes receives an honorarium. She has presented clinical training workshops, some including a fee. She receives royalties from her books and co-edited books and she developed preventive cognitive therapy on the basis of the cognitive model of A. T. Beck. W.A.N. has received grants from the Netherlands Organisation for Health Research and Development and the European Union and honoraria and speakers' fees from Lundbeck and Aristo Pharma, and has served as a consultant for Daleco Pharma.

Health-economic benefits of treating trauma in psychosis

Background: Co-occurrence of posttraumatic stress disorder (PTSD) in psychosis (estimated as 12%) raises personal suffering and societal costs. Health-economic studies on PTSD treatments in patients with a diagnosis of a psychotic disorder have not yet been conducted, but are needed for guideline development and implementation. This study aims to analyse the cost-effectiveness of guideline PTSD therapies in patients with a psychotic disorder. Methods: This health-economic evaluation alongside a randomized controlled trial included 155 patients with a psychotic disorder in care as usual (CAU), with comorbid PTSD. Participants received eye movement desensitization and reprocessing (EMDR) (n = 55), prolonged exposure (PE) (n = 53) or waiting list (WL) (n = 47) with masked assessments at baseline (T0) and at the two-month (post-treatment, T2) and six-month follow-up (T6). Costs were calculated using the TiC-P interview for assessing healthcare consumption and productivity losses. Incremental cost-effectiveness ratios and economic acceptability were calculated for quality-adjusted life years (EQ-5D-3L-based QALYs) and PTSD 'Loss of diagnosis' (LoD, CAPS). Results: Compared to WL, costs were lower in EMDR (-€1410) and PE (-€501) per patient per six months. In addition, EMDR (robust SE 0.024, t = 2.14, p = .035) and PE (robust SE 0.024, t = 2.14, p = .035) yielded a 0.052 and 0.051 incremental QALY gain, respectively, as well as 26% greater probability for LoD following EMDR (robust SE = 0.096, z = 2.66, p = .008) and 22% following PE (robust SE 0.098, z = 2.28, p = .023). Acceptability curves indicate high probabilities of PTSD treatments being the better economic choice. Sensitivity analyses corroborated these outcomes. Conclusion: Adding PTSD treatment to CAU for individuals with psychosis and PTSD seem to yield better health and less PTSD at lower costs, which argues for implementation.

Value of information analysis of an early intervention for subthreshold panic disorder: Healthcare versus societal perspective

Background

Panic disorder is associated with high productivity costs. These costs, which should be included in cost-effectiveness analyses (CEA) from a societal perspective, have a considerable impact on cost-effectiveness estimates. However, they are often omitted in published CEAs. It is therefore uncertain whether choosing a societal perspective changes priority setting in future research as compared to a healthcare perspective.

Objectives

To identify research priorities regarding the cost-effectiveness of an early intervention for subthreshold panic disorder using value of information (VOI) analysis and to investigate to what extent priority setting depends on the perspective.

Methods

We calculated the cost-effectiveness of an early intervention for panic disorder from a healthcare perspective and a societal perspective. We performed a VOI analysis, which estimates the expected value of eliminating the uncertainty surrounding cost-effectiveness estimates, for both perspectives.

Results

From a healthcare perspective the early intervention was more effective at higher costs compared to usual care (€17,144 per QALY), whereas it was cost-saving from a societal perspective. Additional research to eliminate parameter uncertainty was valued at €129.7 million from a healthcare perspective and €29.5 million from a societal perspective. Additional research on the early intervention utility gain was most valuable from a healthcare perspective, whereas from a societal perspective additional research would generate little added value.

Conclusions

Priority setting for future research differed substantially according to the perspective. Our study underlines that the health-economic perspective of CEAs on interventions for panic disorder must be chosen carefully in order to avoid inappropriate choices in research priorities.

Cost-effectiveness of interventions for medically unexplained symptoms: A systematic review

BACKGROUND

In primary and secondary care medically unexplained symptoms (MUS) or functional somatic syndromes (FSS) constitute a major burden for patients and society with high healthcare costs and societal costs. Objectives were to provide an overview of the evidence regarding the cost-effectiveness of interventions for MUS or FSS, and to assess the quality of these studies.

METHODS

We searched the databases PubMed, PsycINFO, the National Health Service Economic Evaluation Database (NHS-EED) and the CEA registry to conduct a systematic review. Articles with full economic evaluations on interventions focusing on adult patients with undifferentiated MUS or fibromyalgia (FM), irritable bowel syndrome (IBS) and chronic fatigue syndrome (CFS), with no restrictions on comparators, published until 15 June 2018, were included. We excluded preventive interventions. Two reviewers independently extracted study characteristics and cost-effectiveness data and used the Consensus on Health Economic Criteria Checklist to appraise the methodological quality.

RESULTS

A total of 39 studies out of 1,613 articles met the inclusion criteria. Twenty-two studies reported costs per quality-adjusted life year (QALY) gained and cost-utility analyses (CUAs). In 13 CUAs the intervention conditions dominated the control conditions or had an incremental cost-effectiveness ratio below the willingness-to-pay threshold of € 50,000 per QALY, meaning that the interventions were (on average) cost-effective in comparison with the control condition. Group interventions focusing on MUS (n = 3) or FM (n = 4) might be more cost-effective than individual interventions. The included studies were heterogeneous with regard to the included patients, interventions, study design, and outcomes.

CONCLUSION

This review provides an overview of 39 included studies of interventions for patients with MUS and FSS and the methodological quality of these studies. Considering the limited comparability due to the heterogeneity of the studies, group interventions might be more cost-effective than individual interventions.

REGISTRATION

Study methods were documented in an international prospective register of systematic reviews (PROSPERO) protocol, registration number: CRD42017060424.

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.

Cost-effectiveness of interventions for treating anxiety disorders: A systematic review

BACKGROUND

Anxiety disorders are highly prevalent mental disorders that constitute a major burden on patients and society. As a consequence, economic evaluations of the interventions have become increasingly important. However, no recent overview of these economic evaluations is currently available and the quality of the published economic evaluations has not yet been assessed. Therefore, the current study has two aims: to provide an overview of the evidence regarding the cost-effectiveness of interventions for anxiety disorders, and to assess the quality of the studies identified.

METHODS

A systematic review was conducted using PubMed, PsycINFO, NHS-EED, and the CEA registry. We included full economic evaluations on interventions for all anxiety disorders published before April 2016, with no restrictions on study populations and comparators. Preventive interventions were excluded. Study characteristics and cost-effectiveness data were collected. The quality of the studies was appraised using the Consensus on Health Economic Criteria.

RESULTS

Forty-two out of 826 identified studies met the inclusion criteria. The studies were heterogeneous and the quality was variable. Internet-delivered cognitive behavioural therapy (iCBT) appeared to be cost-effective in comparison with the control conditions. Four out of five studies comparing psychological interventions with pharmacological interventions showed that psychological interventions were more cost-effective than pharmacotherapy.

LIMITATIONS

Comparability was limited by heterogeneity in terms of interventions, study design, outcome and study quality.

CONCLUSIONS

Forty-two studies reporting cost-effectiveness of interventions for anxiety disorders were identified. iCBT was cost-effective in comparison with the control conditions. Psychological interventions for anxiety disorders might be more cost-effective than pharmacological interventions.

A Systematic Review and Critical Appraisal of Economic Evaluations of Pharmacological Interventions for People with Bipolar Disorder

BACKGROUND

Bipolar disorder (BD) is a chronic mood disorder that causes substantial psychological and financial burden. Various pharmacological treatments are effective in the management and prevention of acute episodes of BD. In an era of tighter healthcare budgets and a need for more efficient use of resources, several economic evaluations have evaluated the cost effectiveness of treatments for BD.

OBJECTIVE

The aim of this study was to systematically review and appraise published economic evaluations of pharmacological interventions for BD.

METHODS

A systematic search combining search terms specific to BD with a health economics search filter was conducted on six bibliographic databases (EMBASE, MEDLINE, PsycINFO, HTA, NHS EED, CENTRAL) in order to identify trial- or model-based full economic evaluations of pharmacological treatments of any phase of the disorder that were published between 1 January 1990 and 18 December 2015. Studies that met the inclusion criteria were critically appraised using the Quality of Health Economic Studies (QHES) checklist, and synthesised in a narrative way.

RESULTS

The review included 19 economic studies, which varied with regard to the type and number of interventions assessed, the study design, the phase of treatment (acute or maintenance), the source of efficacy data and the method for evidence synthesis, the outcome measures, the time horizon and the countries/settings in which the studies were conducted. The study quality was variable but the majority of studies were of high or fair quality.

CONCLUSION

Pharmacological interventions are cost effective, compared with no treatment, in the management of BD, both in the acute and maintenance phases. However, it is difficult to draw safe conclusions on the relative cost effectiveness between drugs due to differences across studies and limitations characterising many of them. Future economic evaluations need to consider the whole range of treatment options available for the management of BD and adopt appropriate methods for evidence synthesis and economic modelling, to explore more robustly the relative cost effectiveness of pharmacological interventions for people with BD.

Four-Year Cost-effectiveness of Cognitive Behavior Therapy for Preventing First-episode Psychosis: The Dutch Early Detection Intervention Evaluation (EDIE-NL) Trial

Background

This study aims to evaluate the long-term cost-effectiveness of add-on cognitive behavior therapy (CBT) for the prevention of psychosis for individuals at ultrahigh risk (UHR) of psychosis.

Method

The Dutch Early Detection and Intervention randomized controlled trial was used, comparing routine care (RC; n = 101) with routine care plus CBT for UHR (here called CBTuhr; n = 95). A cost-effectiveness analysis was conducted with treatment response (defined as proportion of averted transitions to psychosis) as an outcome and a cost-utility analysis with quality-adjusted life years (QALYs) gained as a secondary outcome.

Results

The proportion of averted transitions to psychosis was significantly higher in the CBTuhr condition (with a risk difference of 0.122; b = 1.324, SEb = 0.017, z = 7.99, P < 0.001). CBTuhr showed an 83% probability of being more effective and less costly than RC by -US$ 5777 (savings) per participant. In addition, over the 4-year follow-up period, cumulative QALY health gains were marginally (but not significantly) higher in CBTuhr than for RC (2.63 vs. 2.46) and the CBTuhr intervention had a 75% probability of being the superior treatment (more QALY gains at lower costs) and a 92% probability of being cost-effective compared with RC at the Dutch threshold value (US$ 24 560; €20 000 per QALY).

Conclusions

Add-on preventive CBTuhr had a high likelihood (83%) of resulting in more averted transitions to psychosis and lower costs as compared with RC. In addition, the intervention had a high likelihood (75%) of resulting in more QALY gains and lower costs as compared to RC.

[Prognostic modelling and proactive intervention in psychosis: efficacy and cost-effectiveness]

Diagnoses have heterogeneous outcomes, varying from good to extremely poor. There is a need to single out an ultra-high-risk group of individuals who have illnesses that might well end unfavourably or who might later develop serious psychopathology.<br/> AIM: To devise a screening instrument that can identify a group of individuals who run a very high risk of developing a first-episode psychosis, and to create a type of intervention that can modify the course of the illness.<br/> METHOD: We developed a short screening instrument (PQ-16) and were able to ascertain its predictive value. We also tested an intervention that could influence risk factors and deal with emerging symptoms thereby achieving a better outcome for the patient.<br/> RESULTS: We developed a two-step detection instrument with a positive predictive value of 44%. The intervention, involving cognitive behavioural therapy for ultra-high-risk patients, was effective and led to a risk reduction of about 50%. Using the ultra-high-risk group of patients, we were able to model three prognostic profiles, each carrying a 4%, 13%, and 70% risk of subsequently developing psychosis. The intervention was cost-effective, reducing the financial burden on the health care services and on society as a whole.<br/> CONCLUSION: Prognostic modelling and proactive intervention can achieve improvements in health at lower costs.

[Online self-help for persons with suicidal intentions: budget impact analysis]

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[Cost-effectiveness of psychotherapy for personality disorders. A systematic review on economic evaluation studies]

So far, there has not been a complete overview of the cost-effectiveness of psychotherapy for patients with a personality disorder.<br/> AIM: To provide an overview of scientific literature on the cost-effectiveness of psychotherapy for patients with a personality disorder.<br/> METHOD: We reviewed the literature systematically, searching the NHS EED, PubMed and PsycINFO databases. We concentrated solely on full economic evaluations of treatments in which all patients had a personality disorder.<br/> RESULTS: Most studies concluded that at least one of the psychotherapeutic treatments investigated was cost-effective. Dialectical behavior therapy was studied the most; schema therapy came next, followed by cognitive behavioural therapy.<br/> CONCLUSION: In general, scientific evidence indicates that psychotherapeutic treatments for patients with personality disorders are cost-effective relative to the comparator treatments. This is important information because it can influence decisions on whether the costs of psychotherapy should be reimbursed.

[A study of cost-effectiveness of treating serious mental illness: challenges and solutions]

BACKGROUND People with serious mental illness (SMI) often suffer high healthcare costs and enduring loss of quality of life. Increasing our understanding of the cost-effectiveness of people with SMI is important when striving for optimal health at affordable costs. AIM To describe aspects that can be important for cost-effectiveness research targeting people with SMI. METHOD These aspects are demonstrated by considering pro-active care, rehabilitation and involuntary treatment RESULTS The possible involvement of a large number of stakeholders outside of healthcare requires cost-effectiveness research to also map the costs and benefits outside of healthcare, preferably for each stakeholder specifically. Availability of data, the possibility to combine datasets, and ways to deal with dropouts require extra attention. CONCLUSION Cost-effectiveness research targeting people with SMI could be enhanced when solutions are found for the availability of data inside and outside of healthcare and when dropout can be compensated for by other sources of data, such that costs and benefits for each stakeholder can be estimated more reliably.

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.

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.

Modeling the cost-effectiveness of health care systems for alcohol use disorders: how implementation of eHealth interventions improves cost-effectiveness

Background

Informing policy decisions about the cost-effectiveness of health care systems (ie, packages of clinical interventions) is probably best done using a modeling approach. To this end, an alcohol model (ALCMOD) was developed.

Objective

The aim of ALCMOD is to estimate the cost-effectiveness of competing health care systems in curbing alcohol use at the national level. This is illustrated for scenarios where new eHealth technologies for alcohol use disorders are introduced in the Dutch health care system.

Method

ALCMOD assesses short-term (12-month) incremental cost-effectiveness in terms of reductions in disease burden, that is, disability adjusted life years (DALYs) and health care budget impacts.

Results

Introduction of new eHealth technologies would substantially increase the cost-effectiveness of the Dutch health care system for alcohol use disorders: every euro spent under the current system returns a value of about the same size (€ 1.08, ie, a “surplus” of 8 euro cents) while the new health care system offers much better returns on investment, that is, every euro spent generates € 1.62 in health-related value.

Conclusion

Based on the best available evidence, ALCMOD's computations suggest that implementation of new eHealth technologies would make the Dutch health care system more cost-effective. This type of information may help (1) to identify opportunities for system innovation, (2) to set agendas for further research, and (3) to inform policy decisions about resource allocation.