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Open Access Research article

Predictive risk modelling in the Spanish population: a cross-sectional study

Juan F Orueta1*, Roberto Nuño-Solinis2, Maider Mateos2, Itziar Vergara1, Gonzalo Grandes1 and Santiago Esnaola3

Author Affiliations

1 Osakidetza, Basque Health Service, Bilbao, Spain

2 O + berri, Basque Institute for Healthcare Innovation, Plaza Asua 1, 48150, Sondika, Spain

3 Department of Health and Consumer Affairs, Basque Government, Vitoria, Spain

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BMC Health Services Research 2013, 13:269  doi:10.1186/1472-6963-13-269

Published: 9 July 2013

Abstract

Background

An increase in chronic conditions is currently the greatest threat to human health and to the sustainability of health systems. Risk adjustment systems may enable population stratification programmes to be developed and become instrumental in implementing new models of care.

The objectives of this study are to evaluate the capability of ACG-PM, DCG-HCC and CRG-based models to predict healthcare costs and identify patients that will be high consumers and to analyse changes to predictive capacity when socio-economic variables are added.

Methods

This cross-sectional study used data of all Basque Country citizens over 14 years of age (n = 1,964,337) collected in a period of 2 years. Data from the first 12 months (age, sex, area deprivation index, diagnoses, procedures, prescriptions and previous cost) were used to construct the explanatory variables. The ability of models to predict healthcare costs in the following 12 months was assessed using the coefficient of determination and to identify the patients with highest costs by means of receiver operating characteristic (ROC) curve analysis.

Results

The coefficients of determination ranged from 0.18 to 0.21 for diagnosis-based models, 0.17-0.18 for prescription-based and 0.21-0.24 for the combination of both. The observed area under the ROC curve was 0.78-0.86 (identifying patients with a cost higher than P-95) and 0.83-0.90 (P-99). The values of the DCG-HCC models are slightly higher and those of the CRG models are lower, although prescription information could not be used in the latter. On adding previous cost data, differences between the three systems decrease appreciably. Inclusion of the deprivation index led to only marginal improvements in explanatory power.

Conclusion

The case-mix systems developed in the USA can be useful in a publicly financed healthcare system with universal coverage to identify people at risk of high health resource consumption and whose situation is potentially preventable through proactive interventions.

Keywords:
Risk-adjustment; Burden of illness; Actuarial prediction; Health risk stratification