Open AccessAnalysing the Costs of Integrated Care: A Case on Model Selection for Chronic Care Purposes
Author(s) -
Marc Carreras,
Inma Sánchez-Pérez,
Pere Ibern,
Jordi Coderch,
José María Inoriza
Publication year - 2016
Publication title -
international journal of integrated care
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.083
H-Index - 32
ISSN - 1568-4156
DOI - 10.5334/ijic.2422
Subject(s) - context (archaeology) , activity based costing , health care , heteroscedasticity , health economics , set (abstract data type) , population , medicine , computer science , linear model , actuarial science , econometrics , mathematics , public health , economics , machine learning , nursing , business , environmental health , marketing , geography , economic growth , archaeology , programming language
Background: The objective of this study is to investigate whether the algorithm proposed by Manning and Mullahy, a consolidated health economics procedure, can also be used to estimate individual costs for different groups of healthcare services in the context of integrated care. Methods: A cross-sectional study focused on the population of the Baix Empordà (Catalonia-Spain) for the year 2012 (N = 92,498 individuals). A set of individual cost models as a function of sex, age and morbidity burden were adjusted and individual healthcare costs were calculated using a retrospective full-costing system. The individual morbidity burden was inferred using the Clinical Risk Groups (CRG) patient classification system. Results: Depending on the characteristics of the data, and according to the algorithm criteria, the choice of model was a linear model on the log of costs or a generalized linear model with a log link. We checked for goodness of fit, accuracy, linear structure and heteroscedasticity for the models obtained. Conclusion: The proposed algorithm identified a set of suitable cost models for the distinct groups of services integrated care entails. The individual morbidity burden was found to be indispensable when allocating appropriate resources to targeted individuals.