Service- and practitioner-level variation in non-consensual dropout from child mental health services.

Non-attendance of mental health service appointments is an international problem. In the UK, for example, the estimated cost of non-attendance in child mental health services is over £45 million (US dollar 60.94 million) per annum. The objective of this study was to examine whether there were service- and practitioner-level variation in non-consensual dropout in child mental health services. This was an analysis of routinely collected data. Service-level variation (as services covered different geographic areas) and practitioner-level variation were examined in N = 3622 children (mean age 12.70 years; SD 3.62, 57% female, 50% white or white British) seen by 896 practitioners across 39 services. Overall, 35% of the variation in non-consensual dropout was explained at the service level and 15% at the practitioner level. Children were almost four times more likely to drop out depending on which service they attended (median odds ratio = 3.92) and were two-and-a-half times more likely to drop out depending on which practitioner they saw (median odds ratio = 2.53). These levels of variation were not explained by levels of deprivation in areas covered by services or by children's demographic and case characteristics. The findings of the present research may suggest that, beyond service-level variation, there is also practitioner-level variation in non-consensual dropout in child mental health services.

Developing a case mix classification for child and adolescent mental health services: the influence of presenting problems, complexity factors and service providers on number of appointments.

Background: Case-mix classification is a focus of international attention in considering how best to manage and fund services, by providing a basis for fairer comparison of resource utilization. Yet there is little evidence of the best ways to establish case mix for child and adolescent mental health services (CAMHS).Aim: To develop a case mix classification for CAMHS that is clinically meaningful and predictive of number of appointments attended and to investigate the influence of presenting problems, context and complexity factors and provider variation.Method: We analysed 4573 completed episodes of outpatient care from 11 English CAMHS. Cluster analysis, regression trees and a conceptual classification based on clinical best practice guidelines were compared regarding their ability to predict number of appointments, using mixed effects negative binomial regression.Results: The conceptual classification is clinically meaningful and did as well as data-driven classifications in accounting for number of appointments. There was little evidence for effects of complexity or context factors, with the possible exception of school attendance problems. Substantial variation in resource provision between providers was not explained well by case mix.Conclusion: The conceptually-derived classification merits further testing and development in the context of collaborative decision making.