Measuring recovery in deaf, hard-of-hearing, and tinnitus patients in a mental health care setting:validation of the I.ROC

This study was aimed at validating the Individual Recovery Outcomes Counter (I.ROC) for deaf, hard-of-hearing, and tinnitus patients in a mental health care setting. There is a need for an accessible instrument to monitor treatment effects in this population. The I.ROC measures recovery, seeing recovery as a process of experiencing a meaningful life, despite the limitations caused by illness or disability. A total of 84 adults referred to 2 specialist mental health centers for deaf, hard-of-hearing, and tinnitus adults in the Netherlands completed the Dutch version of I.ROC and 3 other instruments. A total of 25 patients refused or did not complete the instruments: 50% of patients using sign language and 18% of patients using spoken language. Participants completed the measures at intake and then every 3 months. In this sample I.ROC demonstrated good internal consistency and convergent validity. Sensitivity to change was good, especially over a period of 6 or 9 months. This study provides preliminary evidence that the I.ROC is a valid instrument measuring recovery for hard-of-hearing and tinnitus patients using spoken language. For deaf patients using sign language, specifically those with limited language skills in spoken and written Dutch, more research is needed.</p

Effects and side-effects of integrating care: the case of mental health care in the Netherlands

Contains fulltext : 56200.pdf ( ) (Open Access)Purpose: Description and analysis of the effects and side-effects of integrated mental health care in the Netherlands. Context of case: Due to a number of large-scale mergers, Dutch mental health care has become an illustration of integration and coherence of care services. This process of integration, however, has not only brought a better organisation of care but apparently has also resulted in a number of serious side-effects. This has raised the question whether integration is still the best way of reorganising mental health care. Data sources: Literature, data books, patients and professionals, the advice of the Dutch Commission for Mental Health Care, and policy papers. Case description: Despite its organisational and patient-centred integration, the problems in the Dutch mental health care system have not diminished: long waiting lists, insufficient fine tuning of care, public order problems with chronic psychiatric patients, etc. These problems are related to a sharp rise in the number of mental health care registrations in contrast with a decrease of registered patients in first-level services. This indicates that care for people with mental health problems has become solely a task for the mental health care services (monopolisation). At the same time, integrated institutions have developed in the direction of specialised medical care (homogenisation). Monopolisation and homogenisation together have put the integrated institutions into an impossible divided position. Conclusions and discussion: Integration of care within the institutions in the Netherlands has resulted in withdrawal of other care providers. These side-effects lead to a new discussion on the real nature and benefits of an integrated mental health care system. Integration requires also a broadly shared vision on good care for the various target groups. This would require a radicalisation of the distinction between care providers as well as a recognition of the different goals of mental health care.11 p

Measuring personal recovery in a low-intensity community mental healthcare setting:validation of the Dutch version of the individual recovery outcomes counter (I.ROC)

BackgroundMeasuring progress in treatment is essential for systematic evaluation by service users and their care providers. In low-intensity community mental healthcare, a questionnaire to measure progress in treatment should be aimed at personal recovery and should require little effort to complete.MethodsThe Individual Recovery Outcome Counter (I.ROC) was translated from English into Dutch, and psychometric evaluations were performed. Data were collected on personal recovery (Recovery Assessment Scale), quality of life (Manchester Short Assessment of Quality of Life), and symptoms of mental illness and social functioning (Outcome Questionnaire, OQ-45) for assessing the validity of the I.ROC. Test–retest reliability was evaluated by calculating the Intraclass Correlation Coefficient and internal consistency was evaluated by calculating Cronbach’s alpha. Exploratory factor analysis was performed to determine construct validity. To assess convergent validity, the I.ROC was compared to relevant questionnaires by calculating Pearson correlation coefficients. To evaluate discriminant validity, I.ROC scores of certain subgroups were compared using either a t-test or analysis of variance.ResultsThere were 764 participants in this study who mostly completed more than one I.ROC (total n = 2,863). The I.ROC aimed to measure the concept of personal recovery as a whole, which was confirmed by a factor analysis. The test–retest reliability was satisfactory (Intraclass Correlation Coefficient is 0.856), as were the internal consistency (Cronbachs Alpha is 0.921) and the convergent validity. Sensitivity to change was small, but comparable to that of the OQ-45.ConclusionsThe Dutch version of the I.ROC appears to have satisfactory psychometric properties to warrant its use in daily practice. Discriminant validity and sensitivity to change need further research

Psychometric properties of the Dutch version of the Evidence-Based Practice Attitude Scale (EBPAS).

BackgroundThe Evidence-Based Practice Attitude Scale (EBPAS) was developed in the United States to assess attitudes of mental health and welfare professionals toward evidence-based interventions. Although the EBPAS has been translated in different languages and is being used in several countries, all research on the psychometric properties of the EBPAS within youth care has been carried out in the United States. The purpose of this study was to investigate the psychometric properties of the Dutch version of the EBPAS.MethodsAfter translation into Dutch, the Dutch version of the EBPAS was examined in a diverse sample of 270 youth care professionals working in five institutions in the Netherlands. We examined the factor structure with both exploratory and confirmatory factor analyses and the internal consistency reliability. We also conducted multiple linear regression analyses to examine the association of EBPAS scores with professionals' characteristics. It was hypothesized that responses to the EBPAS items could be explained by one general factor plus four specific factors, good to excellent internal consistency reliability would be found, and EBPAS scores would vary by age, sex, and educational level.ResultsThe exploratory factor analysis suggested a four-factor solution according to the hypothesized dimensions: Requirements, Appeal, Openness, and Divergence. Cronbach's alphas ranged from 0.67 to 0.89, and the overall scale alpha was 0.72. The confirmatory factor analyses confirmed the factor structure and suggested that the lower order EBPAS factors are indicators of a higher order construct. However, Divergence was not significantly correlated with any of the subscales or the total score. The confirmatory bifactor analysis endorsed that variance was explained both by a general attitude towards evidence-based interventions and by four specific factors. The regression analyses showed an association between EBPAS scores and youth care professionals' age, sex, and educational level.ConclusionsThe present study provides strong support for a structure with a general factor plus four specific factors and internal consistency reliability of the Dutch version of the EBPAS in a diverse sample of youth care professionals. Hence, the factor structure and reliability of the original version of the EBPAS seem generalizable to the Dutch version of the EBPAS

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

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)

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

Treatment results for severe psychiatric illness: Which method is best suited to denote the outcome of mental health care?

Background: The present study investigates the suitability of various treatment outcome indicators to evaluate performance of mental health institutions that provide care to patients with severe mental illness. Several categorical approaches are compared to a reference indicator (continuous outcome) using pretest-posttest data of the Health of Nation Outcome Sc

Predicting Return to Work in Employees Sick-Listed Due to Minor Mental Disorders

Objective To investigate which factors predict return to work (RTW) after 3 and 6 months in employees sick-listed due to minor mental disorders. Methods Seventy GPs recruited 194 subjects at the start of sick leave due to minor mental disorders. At baseline (T0), 3 and 6 months later (T1 and T2, respectively), subjects received a questionnaire and were interviewed by telephone. Using multivariate logistic regression analyses, we developed three prediction models to predict RTW at T1 and T2. Results The RTW rates were 38% after 3 months (T1) and 61% after 6 months (T2). The main negative predictors of RTW at T1 were: (a) a duration of the problems of more than 3 months before sick leave; and (b) somatisation. The main negative predictors of RTW at T2 were: (a) a duration of the problems of more than 3 months before sick leave; (b) more than 3 weeks of sick leave before inclusion in the study; and (c) anxiety. The main negative predictors of RTW at T2 for those who had not resumed work at T1 were: (a) more than 3 weeks of sick leave before inclusion in the study; and (b) depression at T1. The predictive power of the models was moderate with AUC-values between 0.695 and 0.763. Conclusions The main predictors of RTW were associated with the severity of the problems. A long duration of the problems before the occurrence of sick leave and a long duration of sick leave before seeking help predict a relatively small probability to RTW within 3–6 months. High baseline somatisation and anxiety, and high depression after 3 months make the prospect even worse. Since these predictors are readily assessable with just a few questions and a symptom questionnaire, this opens the opportunity to select high-risk employees for a targeted intervention to prevent long-term absenteeism