Keep it simple? Predicting primary health care costs with clinical morbidity measures

Published: March 2, 2014
Category: Bibliography > Papers
Authors: Brilleman SL, Gravelle H, Hollinghurst S, Purdy S, Salisbury C, Windmeijer F
Countries: United Kingdom
Language: null
Types: Care Management
Settings: Health Plan, PCP

J Health Econ 35C:109-122.

University of Bristol, Bristol, UK; University of York, York, UK

Models of the determinants of individuals’ primary care costs can be used to set capitation payments to providers and to test for horizontal equity. We compare the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs and examine capitation payments based on them. The measures were derived from four morbidity descriptive systems: 17 chronic diseases in the Quality and Outcomes Framework (QOF); 17 chronic diseases in the Charlson scheme; 114 Expanded Diagnosis Clusters (EDCs); and 68 Adjusted Clinical Groups (ACGs). These were applied to patient records of 86,100 individuals in 174 English practices. For a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power. The EDC measures performed best followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Comparisons of predictive power for different morbidity measures were similar for linear and exponential models, but the relative predictive power of the models varied with the morbidity measure. Capitation payments for an individual patient vary considerably with the different morbidity measures included in the cost model. Even for the best fitting model large differences between expected cost and capitation for some types of patient suggest incentives for patient selection. Models with any of the morbidity measures show higher cost for more deprived patients but the positive effect of deprivation on cost was smaller in better fitting models.

PMID: 24657375

Capitation,Payment,High-Impact Chronic Conditions,Morbidity Patterns,Adult,Age Distribution,80 and over,Co-morbidity,Diagnosis-Related Groups/classification,United Kingdom,Gender,Forecasting,Middle Aged,Patient Selection,Regression Analysis,Sex Distribution,Socioeconomic Factors,Young Adult

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