Ten years of integrated care for mental disorders in the Netherlands

Background and problem statement: Integrated care for mental disorders aims to encompass forms of collaboration between different health care settings for the treatment of mental disorders. To this end, it requires integration at several levels, i.e. integration of psychiatry in medicine, of the psychiatric discourse in the medical discourse; of localization of mental health care and general health care facilities; and of reimbursement systems.  Description of policy practice: Steps have been taken in the last decade to meet these requirements, enabling psychiatry to move on towards integrated treatment of mental disorder as such, by development of a collaborative care model that includes structural psychiatric consultation that was found to be applicable and effective in several Dutch health care settings. This collaborative care model is a feasible and effective model for integrated care in several health care settings. The Bio Psycho Social System has been developed as a feasible instrument for assessment in integrated care as well.Discussion: The discipline of Psychiatry has moved from anti-psychiatry in the last century, towards an emancipated medical discipline. This enabled big advances towards integrated care for mental disorder, in collaboration with other medical disciplines, in the last decade.Conclusion: Now is the time to further expand this concept of care towards other mental disorders, and towards integrated care for medical and mental co-morbidity. Integrated care for mental disorder should be readily available to the patient, according to his/her preference, taking somatic co-morbidity into account, and with a focus on rehabilitation of the patient in his or her social roles.</p

A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well

The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings

Learning from long COVID: integrated care for multiple long-term conditions

Long COVID is a multisystem condition requiring a range of medical, therapeutic, and psychological inputs. Given the complexity of the illness affecting multiple organ systems, often impacting physical and mental health, individuals can be heavy healthcare users across primary, secondary, and emergency services. The long COVID clinics commissioned in England1 have provided an opportunity to innovate within a complex care pathway, bringing multiple providers together to meet needs broader than has been historically possible for many other complex conditions. Designing these new services from a blank page has enabled teams to co-create services with patient groups and work more effectively in an integrated way. Significant benefits have been seen, including skills transference between professions

Treatment of mental disorder in the primary care setting in the Netherlands in the light of the new reimbursement system: a challenge?

Introduction: Different professionals provide health care for mental disorder in the primary care setting. In view of the changing reimbursement system in the Netherlands, information is needed on their specific expertise. <br><br> Method: This study attempts to describe this by literature study, by assessment of expert opinions, and by consulting Associations of the relevant professions. <br><br> Results: There is no clear differentiation of expertise and tasks amongst these professionals in primary care. Notably, distinction between different psychotherapeutic treatment modes provided by psychologists is unclear. <br><br> Discussion: Research is needed to assess actual treatment modules in correlation with patient diagnostic classification for the different professions in primary care. An alternative way of classifying patients, that takes into account not only mental disorder or problems but especially the level of functioning, is proposed to discern which patients can be treated in primary care, and which patients should not. Integrated care models are promising, because many professionals can be involved in treatment of mental disorder in the primary care setting; especially for collaborative care models, evidence favours the treatment of common mental disorders in this setting. <br><br> Conclusion: Integrated care models, such as collaborative care, provide a basis for multidisciplinary care for mental disorder in the primary care setting. Professional responsibilities should be clearly differentiated in order to facilitate integrated care. The level of functioning of patients with mental disorder can be used as indication criterion for treatment in the primary care setting or in Mental Health Institutions. Research to establish the feasibility of this model is needed