Predicting healthcare expenditure by multimorbidity groups
[EN] Objectives: This article has two main purposes. Firstly, to model the integrated healthcare expenditure for the entire population of a health district in Spain, according to multimorbidity, using Clinical Risk Groups (CRG). Secondly, to show how the predictive model is applied to the allocation...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2019 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositório: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglês |
| OAI Identifier: | oai:riunet.upv.es:10251/157298 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/157298 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Budget Case-mix system Health econometrics Healthcare expenditure Multimorbidity Risk adjustment Two-part models ECONOMIA APLICADA ECONOMIA, SOCIOLOGIA Y POLITICA AGRARIA |
| Resumo: | [EN] Objectives: This article has two main purposes. Firstly, to model the integrated healthcare expenditure for the entire population of a health district in Spain, according to multimorbidity, using Clinical Risk Groups (CRG). Secondly, to show how the predictive model is applied to the allocation of health budgets. Methods: The database used contains the information of 156,811 inhabitants in a Valencian Community health district in 2013. The variables were: age, sex, CRG's main health statuses, severity level, and healthcare expenditure. The two-part models were used for predicting healthcare expenditure. From the coefficients of the selected model, the relative weights of each group were calculated to set a case-mix in each health district. Results: Models based on multimorbidity-related variables better explained integrated healthcare expenditure. In the first part of the two-part models, a logit model was used, while the positive costs were modelled with a log-linear OLS regression. An adjusted R-2 of 46-49% between actual and predicted values was obtained. With the weights obtained by CRG, the differences found with the case-mix of each health district proved most useful for budgetary purposes. Conclusions: The expenditure models allowed improved budget allocations between health districts by taking into account morbidity, as opposed to budgeting based solely on population size. |
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