Multilevel latent class casemix modelling: a novel approach to accommodate patient casemix

• Published in: BMC health services research Vol. 11; no. 1; p. 53
• Main Authors: Gilthorpe, Mark S, Harrison, Wendy J, Downing, Amy, Forman, David, West, Robert M
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01.03.2011; BioMed Central; BMC
• Subjects: Age; Aged; Bias; Care and treatment; Clinical outcomes; Colorectal cancer; Decision trees; Diagnosis-Related Groups - classification; England - epidemiology; Epidemiology; Female; Health sciences; Health services; Hospitals, Public - standards; Humans; Information services; Male; Medical care; Medical prognosis; Models, Statistical; Models, Theoretical; Neoplasms - classification; Neoplasms - mortality; Patient outcomes; Patients; Quality Assurance, Health Care - methods; Quality management; Registries; Regression analysis; Reproducibility of Results; Risk Factors; Statistical methods; Studies; Survival Analysis; Trusts; Utilization; Variables; England; United Kingdom
• ISSN: 1472-6963; 1472-6963
• DOI: 10.1186/1472-6963-11-53

Using routinely collected patient data we explore the utility of multilevel latent class (MLLC) models to adjust for patient casemix and rank Trust performance. We contrast this with ranks derived from Trust standardised mortality ratios (SMRs). Patients with colorectal cancer diagnosed between 1998 and 2004 and resident in Northern and Yorkshire regions were identified from the cancer registry database (n = 24,640). Patient age, sex, stage-at-diagnosis (Dukes), and Trust of diagnosis/treatment were extracted. Socioeconomic background was derived using the Townsend Index. Outcome was survival at 3 years after diagnosis. MLLC-modelled and SMR-generated Trust ranks were compared. Patients were assigned to two classes of similar size: one with reasonable prognosis (63.0% died within 3 years), and one with better prognosis (39.3% died within 3 years). In patient class one, all patients diagnosed at stage B or C died within 3 years; in patient class two, all patients diagnosed at stage A, B or C survived. Trusts were assigned two classes with 51.3% and 53.2% of patients respectively dying within 3 years. Differences in the ranked Trust performance between the MLLC model and SMRs were all within estimated 95% CIs. A novel approach to casemix adjustment is illustrated, ranking Trust performance whilst facilitating the evaluation of factors associated with the patient journey (e.g. treatments) and factors associated with the processes of healthcare delivery (e.g. delays). Further research can demonstrate the value of modelling patient pathways and evaluating healthcare processes across provider institutions.