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Predictive risk modelling in the Spanish population: a cross-sectional study
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 objectiv...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3750562/ https://www.ncbi.nlm.nih.gov/pubmed/23837560 http://dx.doi.org/10.1186/1472-6963-13-269 |
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author | Orueta, Juan F Nuño-Solinis, Roberto Mateos, Maider Vergara, Itziar Grandes, Gonzalo Esnaola, Santiago |
author_facet | Orueta, Juan F Nuño-Solinis, Roberto Mateos, Maider Vergara, Itziar Grandes, Gonzalo Esnaola, Santiago |
author_sort | Orueta, Juan F |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3750562 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-37505622013-08-24 Predictive risk modelling in the Spanish population: a cross-sectional study Orueta, Juan F Nuño-Solinis, Roberto Mateos, Maider Vergara, Itziar Grandes, Gonzalo Esnaola, Santiago BMC Health Serv Res Research Article 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. BioMed Central 2013-07-09 /pmc/articles/PMC3750562/ /pubmed/23837560 http://dx.doi.org/10.1186/1472-6963-13-269 Text en Copyright © 2013 Orueta et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Orueta, Juan F Nuño-Solinis, Roberto Mateos, Maider Vergara, Itziar Grandes, Gonzalo Esnaola, Santiago Predictive risk modelling in the Spanish population: a cross-sectional study |
title | Predictive risk modelling in the Spanish population: a cross-sectional study |
title_full | Predictive risk modelling in the Spanish population: a cross-sectional study |
title_fullStr | Predictive risk modelling in the Spanish population: a cross-sectional study |
title_full_unstemmed | Predictive risk modelling in the Spanish population: a cross-sectional study |
title_short | Predictive risk modelling in the Spanish population: a cross-sectional study |
title_sort | predictive risk modelling in the spanish population: a cross-sectional study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3750562/ https://www.ncbi.nlm.nih.gov/pubmed/23837560 http://dx.doi.org/10.1186/1472-6963-13-269 |
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