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*

References

• Andersen RM. (1995). Revisiting the behavioural model and access to medical care: Does it matter? J Health Soc Behav, 36, 1–10
   PubMed Web of Science ®Google Scholar
• Beecham J, Green J, Jacobs B, Dunn G. (2009). Cost variation in child and adolescent psychiatric inpatient treatment. Eur Child Adolesc Psychiatry, 18, 535–42
   PubMed Web of Science ®Google Scholar
• Buckingham B, Burgess P, Solomon S, et al. (1998). Developing a casemix classification for mental health services. Volume 1: Main Report. Commonwealth Department of Health and Family Services: Canberra
   Google Scholar
• Care Pathways and Packages Project. (2015). The legacy. Bradford, Leeds, Rotherham & Sheffield: Care Pathways and Packages Project: Developing Currencies for Mental Health
   Google Scholar
• Costa A, Poss JW, McKillop I. (2015). Contemplating case mix: A primer on case mix classification and management. Health Manage Forum, 28, 12–15
   Google Scholar
• CYP IAPT. (2013). Children and Young People's Improving Access to Psychological Therapies. Dataset Specification V. 3.1.2, 15.07.2013
   Google Scholar
• Denham-Vaughan S, Clark M. (2012). Transformation of payment by results in mental health: Ensuring a recovery focused and socially inclusive system. Mental Health Social Inclusion, 16, 155–8
   Google Scholar
• Fetter RB, Shin Y, Freeman JL, et al. (1980). Case mix definition by diagnosis-related groups. Med Care, 18,1–53
   Google Scholar
• Fournier DA, Skaug HJ, Ancheta J, et al. (2012). AD Model Builder: Using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim. Methods Softw, 27, 233–49
   Web of Science ®Google Scholar
• Gaines P, Bower A, Buckingham B, et al. (2003). New Zealand mental health classification outcomes study: Final report. Auckland: Health Research Council of New Zealand
   Google Scholar
• Gower JC. (1971). A general coefficient of similarity and some of its properties. Biometrics, 27, 857–71
   Web of Science ®Google Scholar
• Halsteinli V, Kittelsen SA, Magnussen J. (2010). Productivity growth in outpatient child and adolescent mental health services: The impact of case-mix adjustment. Soc Sci Med, 70, 439–46
   PubMed Web of Science ®Google Scholar
• Harris M, Legge N, Diminic S, et al. (2013). Mental health service cost drivers - an international literature review. Final report for Stage B of the Definition and Cost Drivers for Mental Health Services project: The University of Queensland
   Google Scholar
• Health and Social Care Information Centre. (2006). End Stage Report - Mental Health Casemix Classification Development. Leeds
   Google Scholar
• Hornbrook MC. (1980). Hospital case mix: Its definition, measurement and use: Part I. The conceptual framework. Med Care Rev, 39, 1–43
   Google Scholar
• Hothorn T, Hornik K, Zeileis A. (2006). Unbiased recursive partitioning: A conditional inference framework. J Comp Graph Stat, 15, 651–74
   Web of Science ®Google Scholar
• Independent Hospital Pricing Authority. (2015a). Development of the Australian mental health care classification . Public consultation paper 2. Sydney: Independent Hospital Pricing Authority
   Google Scholar
• Independent Hospital Pricing Authority. (2015b). Development of the Australian mental health care classification. Public consultation paper 1. Sydney: Independent Hospital Pricing Authority
   Google Scholar
• Jones M, Hopkins K, Kyrke-Smith R, et al. (2013). Current view tool completion guide. London: CAMHS Press
   Google Scholar
• Kaufman L, Rousseeuw PJ. (2009). Finding groups in data: An introduction to cluster analysis. Hoboken: Wiley
   Google Scholar
• Kuhn M, Johnson K. (2013). Applied predictive modeling. New York: Springer
   Google Scholar
• Kvalseth T. (1985). Cautionary Note About R2. Am Stat, 39, 279–85
   Google Scholar
• Mason A, Goddard M, Myers L, Verzulli R. (2011). Navigating uncharted waters? How international experience can inform the funding of mental health care in England. JMH, 20, 234–48
   Google Scholar
• NHS England. (2015). Developing a new approach to palliative care funding. Leeds: Palliative Care Team
   Google Scholar
• Ogles BM, Carlson B, Hatfield D, Karpenko V. (2008). Models of case mix adjustment for Ohio mental health consumer outcomes among children and adolescents. Adm Policy Ment Health, 35, 295–304
   PubMed Web of Science ®Google Scholar
• Phillips SD, Kramer TL, Compton SN, et al. (2003). Case-mix adjustment of adolescent mental health treatment outcomes. J Behav Health Servand Res, 30, 125–36
   PubMed Web of Science ®Google Scholar
• R Core Team. (2014). R: A language and environment for statistical computing. Austria: R Foundation for Statistical Computing
   Google Scholar
• Rousseeuw PJ. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math, 20, 53–65
   Web of Science ®Google Scholar
• Royal College of Psychiatrists. (2014). Statement on mental health payment systems (formerly payment by results). Position statement PS01/2014. London: Royal College of Psychiatrists
   Google Scholar
• Self R, Painter J. (2009). Study: To improve and demonstrate the structural properties of the care clusters that form the basis of the PbR currency development programme. Wakefield
   Google Scholar
• Self R, Rigby A, Leggett C, Paxton R. (2008). Clinical decision support tool: A rational needs-based approach to making clinical decisions. JMH, 17, 33–48
   Google Scholar
• Vostanis P, Martin P, Davies R, et al. (2015). Development of a framework for prospective payment for child mental health services. J Health Serv Res Policy, 20, 202–9
   PubMed Web of Science ®Google Scholar
• Wolpert M, Vostanis P, Young S, et al. (2015). Child and adolescent mental health services payment system project: final report. London: CAMHS Press
   Google Scholar
• Yeomans D. (2014). Clustering in mental health payment by results: A critical summary for the clinician. BJPsych Adv, 20, 227–34
   Google Scholar