Cost-Benefit Analysis of the KiVa Anti-Bullying Program in the Netherlands

This study performs a cost-benefit analysis of the implementation of the KiVa anti-bullying program in the Netherlands. Specifically, it addressed whether the expected benefits of KiVa for victims in terms of lifetime income are greater than the costs that are made for implementing the program. The KiVa intervention was examined in a randomized controlled trial in the Netherlands in 2012–2014 in 98 Dutch primary schools (target grades US-level 3–4, 8 to 9 years old). A model-based approach was applied to the effects for the expected income for prevented victims, which is a long-term outcome that can be quantified. The estimated costs and benefits of implementing KiVa were used to estimate the return-on-investment (ROI) that indicated the expected benefits per euro invested. Investing in KiVa in the Netherlands generated an ROI of €4.04–€6.72, indicating that it is good value for money to invest in KiVa. The chosen estimates in this study were deemed conservative; on the cost side, it was assumed that schools maximally implement KiVa (thus, maximum costs), and on the benefit side, only the expected income effect for victims was included to the model. Quantifying and incorporating other outcomes (i.e., depression, anxiety, psychiatric problems, not only for victims but also for bullies, bystanders, parents, teachers) may further increase the ROI for this intervention

Predicting future service use in Dutch mental healthcare:A machine learning approach

Item does not contain fulltextA 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.9 p

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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40273-021-01012-x

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

Background:It remains a challenge to predict which treatment will work for which patient in mental healthcare.Objective:The aims of this multi-site study were two-fold: 1) to predict patient’s response to treatment, during treatment, in Dutch basic mental healthcare using commonly available data from routine care; and 2) to compare the performance of these machine learning models across three different mental healthcare organizations in the Netherlands by using clinically interpretable models.Methods:Using anonymized datasets from three different mental healthcare organizations in the Netherlands (n = 6,452), we applied three times a lasso regression to predict 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 AUC of the internal validations as well as the corresponding external validations, were in the range of 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 outcome. Results of this study hold substantial implications for clinical practice by demonstrating that model performance of a model derived from one site is similar when applied to another site (i.e. good external validation)

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

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

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.</p