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.

     

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

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

     

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.     

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.     

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.     

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

ObjectivesTo 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.MethodsData 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.ResultsMapping 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.ConclusionsDisability 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

BackgroundApart 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.ObjectiveTo 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.MethodsFor 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.ResultsAppraisal 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.ConclusionsSystematic 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 contributionPatients were involved in the conduct of the study for instance, by sharing their values regarding considerations relevant for priority setting.     

Radiological and scintigraphic findings in patients with a clinical history of chronic inflammatory back pain

The prevalence of radiological abnormalities of the sacroiliac joints, the manubriosternal joint, and the lumbar spine were assessed, and quantitative sacroiliac scintigraphy was performed in 151 patients with a history of chronic inflammatory back pain and in 31 controls with non-inflammatory back pain. Sacroiliitis was found in 124 patients (82%), manubriosternal lesions in 84 patients (56%), and lesions of the lumbar spine in 58 patients (38%). In 19 patients (13%), manubriosternal lesions provided the sole radiological abnormality and in five patients (3%) no radiological abnormality could be demonstrated at any of these sites. Quantitative sacroiliac scintigraphy showed increased values in 69 of 137 patients examined (50%), but also in 10 out of 12 control patients with disc degeneration (83%) and is, therefore, nonspecific for inflammatory lesions. Radiological examination of the manubriosternal joint is recommended in patients with inflammatory back pain without radiographic evidence of sacroiliitis.